From db8e4ffc2ccd984c44befb7e936d778a2339ae6b Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 17:22:20 +0100
Subject: [PATCH 01/56] add Cache and test on Mamba

---
 src/transformers/cache_utils.py               | 196 +++++++++++++++++-
 .../models/jamba/configuration_jamba.py       |   6 +
 .../models/mamba/modeling_mamba.py            | 168 ++-------------
 3 files changed, 216 insertions(+), 154 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 7dede60a7b27..6f171ac0c5c9 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -669,6 +669,101 @@ def _dequantize(self, qtensor):
         return tensor
 
 
+class MambaCacheLayerMixin(ABC):
+    """Base, abstract class for a mamba single layer's cache."""
+
+    is_compileable = False
+
+    def __init__(self):
+        self.conv_states: torch.Tensor | None = None
+        self.ssm_states: torch.Tensor | None = None
+        self.is_initialized = False
+        self.has_previous_state = False
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}"
+
+    @abstractmethod
+    def lazy_initialization(self, conv_states: torch.Tensor) -> None: ...
+
+    @abstractmethod
+    def update_conv_state(self, conv_states: torch.Tensor) -> torch.Tensor: ...
+
+    @abstractmethod
+    def update_ssm_state(self, ssm_states: torch.Tensor) -> torch.Tensor: ...
+
+    def offload(self):
+        """Offload this layer's data to CPU device."""
+        if self.is_initialized:
+            self.conv_states = self.conv_states.to("cpu", non_blocking=True)
+            self.ssm_states = self.ssm_states.to("cpu", non_blocking=True)
+
+    def prefetch(self):
+        """In case of layer offloading, this allows to move the data back to the layer's device ahead of time."""
+        if self.is_initialized and self.conv_states.device != self.device:
+            self.conv_states = self.conv_states.to(self.device, non_blocking=True)
+            self.ssm_states = self.ssm_states.to(self.device, non_blocking=True)
+
+    def reset(self) -> None:
+        """Resets the cache values while preserving the objects"""
+        if self.is_initialized:
+            self.conv_states.zero_()
+            self.ssm_states.zero_()
+        self.has_previous_state = False
+
+
+class MambaLayer(MambaCacheLayerMixin):
+    def lazy_initialization(self, conv_states: torch.Tensor, ssm_states: torch.Tensor) -> None:
+        self.dtype, self.device = conv_states.dtype, conv_states.device
+        self.conv_states = torch.tensor([], dtype=self.dtype, device=self.device)
+        self.is_initialized = True
+
+    def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
+        """
+        Update the mamba cache in-place, and return the necessary conv states.
+
+        Args:
+            conv_states (`torch.Tensor`): The new conv states to cache.
+
+        Returns:
+            `torch.Tensor`: The updated conv states.
+        """
+        # Lazy initialization
+        if not self.is_initialized:
+            self.lazy_initialization(conv_states, conv_states)
+
+        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
+        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
+        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
+        if not self.has_previous_state:
+            self.conv_states = conv_states
+            self.has_previous_state = True
+        else:
+            new_conv_states = self.conv_states.roll(shifts=-1, dims=-1)
+            new_conv_states[:, :, -1:] = conv_states
+            self.conv_states = new_conv_states
+
+        return self.conv_states
+
+    def update_ssm_state(self, ssm_states: torch.Tensor, **kwargs) -> torch.Tensor:
+        """
+        Update the mamba cache in-place, and return the necessary ssm states.
+
+        Args:
+            smm_states (`torch.Tensor`): The new ssm states to cache.
+
+        Returns:
+            `torch.Tensor`: The updated ssm states.
+        """
+        # Lazy initialization
+        if not self.is_initialized:
+            self.lazy_initialization(ssm_states, ssm_states)
+
+        self.ssm_states = ssm_states
+
+        return self.ssm_states
+
+
 class Cache:
     """
     A `Cache` is mostly a list of `CacheLayerMixin` objects, one per model layer. It serves as a container for
@@ -676,9 +771,9 @@ class Cache:
 
     Args:
         layers (`Optional`, *optional*):
-            A list of pre-created `CacheLayerMixin`. If omitted (`None`), then `layer_class_to_replicate` will
-            be used.
-        layer_class_to_replicate (`type[CacheLayerMixin]`, *optional*):
+            A list of pre-created `CacheLayerMixin` or `MambaCacheLayerMixin`. If omitted (`None`), then `layer_class_to_replicate`
+            will be used.
+        layer_class_to_replicate (`type[CacheLayerMixin | MambaCacheLayerMixin]`, *optional*):
             Only used if `layers` is omitted (`None`), in which case it will be used as the base class for each layer,
             and the layers will be added lazily as soon as `update` is called with a `layer_idx` greater than the current
             list of layers.
@@ -691,8 +786,8 @@ class Cache:
 
     def __init__(
         self,
-        layers: list[CacheLayerMixin] | None = None,
-        layer_class_to_replicate: type[CacheLayerMixin] | None = None,
+        layers: list[CacheLayerMixin | MambaCacheLayerMixin] | None = None,
+        layer_class_to_replicate: type[CacheLayerMixin | MambaCacheLayerMixin] | None = None,
         offloading: bool = False,
         offload_only_non_sliding: bool = True,
     ):
@@ -779,6 +874,46 @@ def update(
 
         return keys, values
 
+    def update_conv_state(self, conv_states: torch.Tensor, layer_idx: int, **kwargs) -> torch.Tensor:
+        """
+        Updates the cache with the new `conv_states` for the layer `layer_idx`.
+
+        Parameters:
+            conv_states (`torch.Tensor`):
+                The new conv states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+
+        Return:
+            `torch.Tensor`: The updated conv states.
+        """
+        # NOTE: if we slightly break `update` arg order, we could combine this with it, and allow offloading support
+        # out of the box
+        if not isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+            raise ValueError("Cannot call `update_conv_state` on a non-Mamba layer!")
+        conv_states = self.layers[layer_idx].update_conv_state(conv_states, **kwargs)
+        return conv_states
+
+    def update_ssm_state(self, ssm_states: torch.Tensor, layer_idx: int, **kwargs) -> torch.Tensor:
+        """
+        Updates the cache with the new `ssm_states` for the layer `layer_idx`.
+
+        Parameters:
+            smm_states (`torch.Tensor`):
+                The new ssm states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+
+        Return:
+            `torch.Tensor`: The updated ssm states.
+        """
+        # NOTE: if we slightly break `update` arg order, we could combine this with it, and allow offloading support
+        # out of the box
+        if not isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+            raise ValueError("Cannot call `update_conv_state` on a non-Mamba layer!")
+        ssm_states = self.layers[layer_idx].update_ssm_state(ssm_states, **kwargs)
+        return ssm_states
+
     def early_initialization(
         self, batch_size: int, num_heads: int, head_dim: int, dtype: torch.dtype, device: torch.device
     ):
@@ -798,6 +933,24 @@ def get_seq_length(self, layer_idx: int = 0) -> int:
         """Returns the sequence length of the cache for the given layer."""
         if layer_idx >= len(self.layers):
             return 0
+
+        # For Hybrid attention-mamba caches, `get_seq_length` needs to use attention layer idx when called with default layer_idx
+        if isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+            # If this is called with non-default arg, raise
+            if layer_idx != 0:
+                raise ValueError(
+                    f"You called `get_seq_length` on layer index {layer_idx}, but this layer is a Mamba layer, which "
+                    "does not track sequence length."
+                )
+            try:
+                # Use the first attention layer
+                layer_idx = next(idx for idx in range(len(self)) if isinstance(self.layers[idx], CacheLayerMixin))
+            except StopIteration:
+                raise ValueError(
+                    "`get_seq_length` can only be called on Attention layers, and the current Cache seem to only contain "
+                    "Mamba layers."
+                )
+
         return self.layers[layer_idx].get_seq_length()
 
     def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
@@ -810,6 +963,24 @@ def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
         # simply the query_length
         if layer_idx >= len(self.layers):
             return query_length, 0
+
+        # For Hybrid attention-mamba caches, `get_seq_length` needs to use attention layer idx when called with default layer_idx
+        if isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+            # If this is called with non-default arg, raise
+            if layer_idx != 0:
+                raise ValueError(
+                    f"You called `get_mask_sizes` on layer index {layer_idx}, but this layer is a Mamba layer, which "
+                    "does not track sequence length."
+                )
+            try:
+                # Use the first attention layer
+                layer_idx = next(idx for idx in range(len(self)) if isinstance(self.layers[idx], CacheLayerMixin))
+            except StopIteration:
+                raise ValueError(
+                    "`get_mask_sizes` can only be called on Attention layers, and the current Cache seem to only contain "
+                    "Mamba layers."
+                )
+
         return self.layers[layer_idx].get_mask_sizes(query_length)
 
     def get_max_cache_shape(self, layer_idx: int = 0) -> int:
@@ -943,12 +1114,17 @@ def __init__(
             sliding_window = getattr(decoder_config, "sliding_window", None) or getattr(
                 decoder_config, "attention_chunk_size", None
             )
+            conv_kernel = getattr(decoder_config, "conv_kernel", None)
             layer_types = getattr(decoder_config, "layer_types", None)
             if layer_types is None:
-                layer_types = [
-                    "sliding_attention" if sliding_window is not None else "full_attention"
-                    for _ in range(decoder_config.num_hidden_layers)
-                ]
+                layer_types = []
+                for _ in range(decoder_config.num_hidden_layers):
+                    if sliding_window is not None:
+                        layer_types.append("sliding_attention")
+                    elif conv_kernel is not None:
+                        layer_types.append("mamba")
+                    else:
+                        layer_types.append("full_attention")
             # Some models have shared layers thus no cache is needed for them (e.g. Gemma3n)
             if hasattr(decoder_config, "num_kv_shared_layers"):
                 layer_types = layer_types[: -decoder_config.num_kv_shared_layers]
@@ -958,6 +1134,8 @@ def __init__(
                 # states they should return - only the mask changes to make them different at the end!
                 if layer_type in ("sliding_attention", "chunked_attention"):
                     layers.append(DynamicSlidingWindowLayer(sliding_window=sliding_window))
+                elif layer_type in ("mamba", "conv"):
+                    layers.append(MambaLayer())
                 else:
                     layers.append(DynamicLayer())
 
diff --git a/src/transformers/models/jamba/configuration_jamba.py b/src/transformers/models/jamba/configuration_jamba.py
index 8fdb63d4af5b..4c940aa6a3fb 100644
--- a/src/transformers/models/jamba/configuration_jamba.py
+++ b/src/transformers/models/jamba/configuration_jamba.py
@@ -93,6 +93,12 @@ def layers_block_type(self):
             for i in range(self.num_hidden_layers)
         ]
 
+    @property
+    def layer_types(self):
+        # Follow the `layer_types` conventions
+        layer_types = self.layers_block_type
+        return ["full_attention" if x == "attention" else x for x in layer_types]
+
     @property
     def layers_num_experts(self):
         return [
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index 69002f50ab78..cfb5a19be930 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -22,7 +22,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
-from ...configuration_utils import PreTrainedConfig
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import lazy_load_kernel
 from ...modeling_layers import GradientCheckpointingLayer
@@ -55,117 +55,6 @@
     pscan = None
 
 
-class MambaCache:
-    """
-    Cache for mamba model which does not have attention mechanism and key value states.
-
-    Arguments:
-        config (`PreTrainedConfig):
-            The configuration file defining the shape-related attributes required to initialize the static cache.
-        max_batch_size (`int`):
-            The maximum batch size with which the model will be used. Note that a new instance must be instantiated if
-            a smaller batch size is used.
-        dtype (`torch.dtype`, *optional*, defaults to `torch.float16`):
-            The default `dtype` to use when initializing the layer.
-        device (`torch.device` or `str`, *optional*):
-            The device on which the cache should be initialized. Should be the same as the layer.
-
-    Example:
-
-        ```python
-        >>> import torch
-        >>> from transformers import AutoTokenizer, MambaForCausalLM, MambaCache
-
-        >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
-        >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
-
-        >>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt")
-
-        >>> # Prepare a cache class and pass it to model's forward
-        >>> cache_params = MambaCache(config=model.config, max_batch_size=1, device=model.device, dtype=model.dtype)
-        >>> outputs = model(**inputs, cache_params=cache_params, use_cache=True)
-        >>> outputs.cache_params
-        ```
-    """
-
-    is_compileable = True
-
-    # TODO (joao): add layer_device_map arg and update code in `generate` accordingly
-    def __init__(
-        self,
-        config: PreTrainedConfig,
-        max_batch_size: int,
-        dtype: torch.dtype = torch.float16,
-        device: torch.device | str | None = None,
-    ):
-        self.max_batch_size = max_batch_size
-        self._dtype = dtype
-        self.intermediate_size = config.intermediate_size
-        self.ssm_state_size = config.state_size
-        self.conv_kernel_size = config.conv_kernel
-        self.has_previous_state = False
-
-        self.conv_states: list[torch.Tensor] = []
-        self.ssm_states: list[torch.Tensor] = []
-        device = torch.device(device) if device is not None else None
-        for _ in range(config.num_hidden_layers):
-            conv_state: torch.Tensor = torch.zeros(
-                self.max_batch_size,
-                self.intermediate_size,
-                self.conv_kernel_size,
-                device=device,
-                dtype=self._dtype,
-            )
-            ssm_state: torch.Tensor = torch.zeros(
-                self.max_batch_size,
-                self.intermediate_size,
-                self.ssm_state_size,
-                device=device,
-                dtype=self._dtype,
-            )
-
-            torch._dynamo.mark_static_address(conv_state)
-            torch._dynamo.mark_static_address(ssm_state)
-            self.conv_states.append(conv_state)
-            self.ssm_states.append(ssm_state)
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # This `if` blocks is only reached in multigpu and if `layer_device_map` is not passed. It is used
-        # when the cache is initialized in the forward pass (e.g. Mamba)
-        if self.conv_states[layer_idx].device != new_conv_state.device:
-            self.conv_states[layer_idx] = self.conv_states[layer_idx].to(new_conv_state.device)
-
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_states[layer_idx].copy_(new_conv_state)
-        else:
-            conv_state = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
-            conv_state[:, :, -1:] = new_conv_state
-            self.conv_states[layer_idx].copy_(conv_state)
-
-        # If last layer is updated, set the flag
-        if layer_idx == len(self.conv_states) - 1:
-            self.has_previous_state = True
-
-        return self.conv_states[layer_idx]
-
-    def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
-        self.ssm_states[layer_idx].zero_()
-        self.ssm_states[layer_idx] += new_ssm_state.to(self.ssm_states[layer_idx].device)
-        return self.ssm_states[layer_idx]
-
-    def reset(self):
-        self.has_previous_state = False
-        for layer_idx in range(len(self.conv_states)):
-            # In-place ops prevent breaking the static address
-            self.conv_states[layer_idx].zero_()
-            self.ssm_states[layer_idx].zero_()
-
-
 class MambaMixer(nn.Module):
     """
     Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
@@ -278,7 +167,7 @@ def warn_slow_implementation(self):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: MambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         # 1. Gated MLP's linear projection
@@ -325,7 +214,7 @@ def cuda_kernels_forward(
                     conv_states = nn.functional.pad(
                         hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)
                     )
-                    cache_params.update_conv_state(self.layer_idx, conv_states, cache_init=True)
+                    cache_params.update_conv_state(conv_states, self.layer_idx)
                 hidden_states = causal_conv1d_fn(
                     hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
                 )
@@ -371,14 +260,14 @@ def cuda_kernels_forward(
                     return_last_state=True,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(self.layer_idx, ssm_state)
+                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
             # 4. Final linear projection
             contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
         return contextualized_states
 
     # fmt: off
-    def slow_forward(self, input_states, cache_params: MambaCache | None=None, attention_mask: torch.LongTensor | None = None):
+    def slow_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.LongTensor | None = None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # 1. Gated MLP's linear projection
@@ -390,7 +279,7 @@ def slow_forward(self, input_states, cache_params: MambaCache | None=None, atten
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state:
                 conv_state = nn.functional.pad(
@@ -398,10 +287,10 @@ def slow_forward(self, input_states, cache_params: MambaCache | None=None, atten
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
 
-                cache_params.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])     # [batch, intermediate_size, seq_len]
             else:
-                conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_init=False)
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 conv_state = conv_state.to(self.conv1d.weight.device)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
@@ -465,7 +354,7 @@ def combine_fn(left, right):
             scan_output = (scan_output * self.act(gate))
 
             if cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                cache_params.update_ssm_states(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))  # [batch, seq_len, hidden_size]
@@ -475,7 +364,7 @@ def combine_fn(left, right):
     def forward(
         self,
         hidden_states,
-        cache_params: MambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
         **kwargs,
     ):
@@ -519,7 +408,7 @@ def __init__(self, config, layer_idx):
     def forward(
         self,
         hidden_states,
-        cache_params: MambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
         **kwargs,
     ):
@@ -587,7 +476,7 @@ def _init_weights(self, module):
 )
 class MambaOutput(ModelOutput):
     r"""
-    cache_params (`MambaCache`):
+    cache_params (`Cache`):
         The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
         avoid providing the old `input_ids`.
 
@@ -595,7 +484,7 @@ class MambaOutput(ModelOutput):
     """
 
     last_hidden_state: torch.FloatTensor | None = None
-    cache_params: MambaCache | None = None
+    cache_params: Cache | None = None
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
@@ -611,7 +500,7 @@ class MambaCausalLMOutput(ModelOutput):
         Language modeling loss (for next-token prediction).
     logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
         Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-    cache_params (`MambaCache`):
+    cache_params (`Cache`):
         The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
         avoid providing the old `input_ids`.
 
@@ -620,7 +509,7 @@ class MambaCausalLMOutput(ModelOutput):
 
     loss: torch.FloatTensor | None = None
     logits: torch.FloatTensor | None = None
-    cache_params: MambaCache | None = None
+    cache_params: Cache | None = None
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
@@ -655,7 +544,7 @@ def forward(
         self,
         input_ids: torch.LongTensor | None = None,
         inputs_embeds: torch.LongTensor | None = None,
-        cache_params: MambaCache | None = None,
+        cache_params: Cache | None = None,
         use_cache: bool | None = None,
         output_hidden_states: bool | None = None,
         return_dict: bool | None = None,
@@ -663,7 +552,7 @@ def forward(
         **kwargs,
     ) -> tuple | MambaOutput:
         r"""
-        cache_params (`MambaCache`, *optional*):
+        cache_params (`Cache`, *optional*):
             If passed along, the model uses the previous state in all the blocks (which will give the output for the
             `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
         use_cache (`bool`, *optional*):
@@ -685,9 +574,7 @@ def forward(
             use_cache = False
 
         if use_cache and cache_params is None:
-            cache_params = MambaCache(
-                self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype
-            )
+            cache_params = DynamicCache(self.config)
 
         hidden_states = inputs_embeds
         all_hidden_states = () if output_hidden_states else None
@@ -743,12 +630,11 @@ def prepare_inputs_for_generation(
         input_ids,
         inputs_embeds=None,
         use_cache=None,
-        cache_params: MambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
         is_first_iteration: bool | None = False,
         **kwargs,
     ):
-        # Overwritten -- has custom cache class `MambaCache`
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
             inputs_embeds=inputs_embeds,
@@ -759,15 +645,7 @@ def prepare_inputs_for_generation(
             **kwargs,
         )
 
-        if use_cache and cache_params is None:
-            if inputs_embeds is not None:
-                max_batch_size = inputs_embeds.size(0)
-            else:
-                max_batch_size = input_ids.size(0)
-            model_inputs["cache_params"] = MambaCache(
-                self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype
-            )
-        elif use_cache and not is_first_iteration:
+        if use_cache and not is_first_iteration:
             model_inputs["attention_mask"] = None
 
         return model_inputs
@@ -778,7 +656,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.LongTensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
-        cache_params: MambaCache | None = None,
+        cache_params: Cache | None = None,
         labels: torch.LongTensor | None = None,
         output_hidden_states: bool | None = None,
         return_dict: bool | None = None,
@@ -787,7 +665,7 @@ def forward(
         **kwargs,  # for now we need this for generation
     ) -> tuple | MambaCausalLMOutput:
         r"""
-        cache_params (`MambaCache`, *optional*):
+        cache_params (`Cache`, *optional*):
             If passed along, the model uses the previous state in all the blocks (which will give the output for the
             `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -837,4 +715,4 @@ def forward(
         )
 
 
-__all__ = ["MambaForCausalLM", "MambaModel", "MambaPreTrainedModel", "MambaCache"]
+__all__ = ["MambaForCausalLM", "MambaModel", "MambaPreTrainedModel"]

From 9d5259826631cc8ef37708c09470ef1ab20ef7fd Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 17:37:43 +0100
Subject: [PATCH 02/56] fix

---
 docs/source/en/model_doc/falcon_mamba.md      |  7 ---
 docs/source/en/model_doc/mamba.md             |  7 ---
 src/transformers/__init__.py                  |  1 -
 .../falcon_mamba/modular_falcon_mamba.py      | 59 ++++---------------
 .../models/mamba/modeling_mamba.py            |  6 +-
 .../test_modeling_falcon_mamba.py             | 21 ++++---
 tests/models/mamba/test_modeling_mamba.py     | 46 ++++-----------
 7 files changed, 35 insertions(+), 112 deletions(-)

diff --git a/docs/source/en/model_doc/falcon_mamba.md b/docs/source/en/model_doc/falcon_mamba.md
index 78b6e23a8127..bba9e95b63a2 100644
--- a/docs/source/en/model_doc/falcon_mamba.md
+++ b/docs/source/en/model_doc/falcon_mamba.md
@@ -111,13 +111,6 @@ outputs = model.generate(**inputs, max_new_tokens=100)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
-## FalconMambaCache
-
-[[autodoc]] FalconMambaCache
-    - update_conv_state
-    - update_ssm_state
-    - reset
-
 ## FalconMambaConfig
 
 [[autodoc]] FalconMambaConfig
diff --git a/docs/source/en/model_doc/mamba.md b/docs/source/en/model_doc/mamba.md
index 7add263ab4fd..dd2bb2580a1a 100644
--- a/docs/source/en/model_doc/mamba.md
+++ b/docs/source/en/model_doc/mamba.md
@@ -110,13 +110,6 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
   trainer.train()
    ```
 
-## MambaCache
-
-[[autodoc]] MambaCache
-    - update_conv_state
-    - update_ssm_state
-    - reset
-
 ## MambaConfig
 
 [[autodoc]] MambaConfig
diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py
index b6ba58e2e301..1f084cf413e3 100755
--- a/src/transformers/__init__.py
+++ b/src/transformers/__init__.py
@@ -632,7 +632,6 @@
     from .modeling_utils import AttentionInterface as AttentionInterface
     from .modeling_utils import PreTrainedModel as PreTrainedModel
     from .models import *
-    from .models.mamba.modeling_mamba import MambaCache as MambaCache
     from .models.timm_wrapper import TimmWrapperImageProcessor as TimmWrapperImageProcessor
 
     # Optimization
diff --git a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
index 8aec2f95ccb0..b5bda5b31dde 100644
--- a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
@@ -18,12 +18,12 @@
 from torch import nn
 
 from ... import initialization as init
+from ...cache_utils import Cache
 from ...utils import auto_docstring, logging
 from ...utils.import_utils import is_mambapy_available, is_torch_greater_or_equal, is_torchdynamo_compiling, is_tracing
 from ..mamba.configuration_mamba import MambaConfig
 from ..mamba.modeling_mamba import (
     MambaBlock,
-    MambaCache,
     MambaCausalLMOutput,
     MambaForCausalLM,
     MambaMixer,
@@ -102,40 +102,6 @@ class FalconMambaConfig(MambaConfig):
     mixer_rms_eps: float = 1e-6
 
 
-class FalconMambaCache(MambaCache):
-    """
-    Cache for falcon_mamba model which does not have attention mechanism and key value states.
-
-    Arguments:
-        config (`PreTrainedConfig):
-            The configuration file defining the shape-related attributes required to initialize the static cache.
-        max_batch_size (`int`):
-            The maximum batch size with which the model will be used. Note that a new instance must be instantiated if
-            a smaller batch size is used.
-        dtype (`torch.dtype`, *optional*, defaults to `torch.float16`):
-            The default `dtype` to use when initializing the layer.
-        device (`torch.device` or `str`, *optional*):
-            The device on which the cache should be initialized. Should be the same as the layer.
-
-    Example:
-
-        ```python
-        >>> import torch
-        >>> from transformers import AutoTokenizer, FalconMambaForCausalLM, FalconMambaCache
-
-        >>> model = FalconMambaForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b")
-        >>> tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
-
-        >>> inputs = tokenizer(text="My name is FalconMamba", return_tensors="pt")
-
-        >>> # Prepare a cache class and pass it to model's forward
-        >>> cache_params = FalconMambaCache(config=model.config, max_batch_size=1, device=model.device, dtype=model.dtype)
-        >>> outputs = model(**inputs, cache_params=cache_params, use_cache=True)
-        >>> outputs.cache_params
-        ```
-    """
-
-
 def rms_forward(hidden_states, variance_epsilon=1e-6):
     """
     Calculates simple RMSNorm with no learnable weights. `MambaRMSNorm` will
@@ -194,7 +160,7 @@ def __init__(self, config: FalconMambaConfig, layer_idx: int, initialize_mixer_w
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         # 1. Gated MLP's linear projection
@@ -233,7 +199,7 @@ def cuda_kernels_forward(
             if is_decoding:
                 hidden_states = causal_conv1d_update(
                     hidden_states.squeeze(-1),
-                    cache_params.conv_states[self.layer_idx],
+                    cache_params[self.layer_idx].conv_states,
                     conv_weights,
                     self.conv1d.bias,
                     self.activation,
@@ -244,7 +210,7 @@ def cuda_kernels_forward(
                     conv_states = nn.functional.pad(
                         hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)
                     )
-                    cache_params.update_conv_state(self.layer_idx, conv_states, cache_init=True)
+                    cache_params.update_conv_state(conv_states, self.layer_idx)
                 hidden_states = causal_conv1d_fn(
                     hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
                 )
@@ -275,7 +241,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params.ssm_states[self.layer_idx],
+                    cache_params[self.layer_idx].ssm_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -300,7 +266,7 @@ def cuda_kernels_forward(
                     return_last_state=True,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(self.layer_idx, ssm_state)
+                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
             # 4. Final linear projection
             contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -309,7 +275,7 @@ def cuda_kernels_forward(
     def slow_forward(
         self,
         input_states,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         batch_size, seq_len, _ = input_states.shape
@@ -323,17 +289,17 @@ def slow_forward(
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state:
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
-                cache_params.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(
                     self.conv1d(hidden_states)[..., :seq_len]
                 )  # [batch, intermediate_size, seq_len]
             else:
-                conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_init=False)
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 conv_state = conv_state.to(self.conv1d.weight.device)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
@@ -422,7 +388,7 @@ def combine_fn(left, right):
             scan_output = scan_output * self.act(gate)
 
             if cache_params is not None:
-                cache_params.update_ssm_state(self.layer_idx, ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))  # [batch, seq_len, hidden_size]
@@ -431,7 +397,7 @@ def combine_fn(left, right):
     def forward(
         self,
         hidden_states,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
         **kwargs,
     ):
@@ -499,6 +465,5 @@ class FalconMambaForCausalLM(MambaForCausalLM):
     "FalconMambaForCausalLM",
     "FalconMambaModel",
     "FalconMambaPreTrainedModel",
-    "FalconMambaCache",
     "FalconMambaConfig",
 ]
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index cfb5a19be930..b74fb8419d10 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -203,7 +203,7 @@ def cuda_kernels_forward(
             if is_decoding:
                 hidden_states = causal_conv1d_update(
                     hidden_states.squeeze(-1),
-                    cache_params.conv_states[self.layer_idx],
+                    cache_params[self.layer_idx].conv_states,
                     conv_weights,
                     self.conv1d.bias,
                     self.activation,
@@ -235,7 +235,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params.ssm_states[self.layer_idx],
+                    cache_params[self.layer_idx].ssm_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -574,7 +574,7 @@ def forward(
             use_cache = False
 
         if use_cache and cache_params is None:
-            cache_params = DynamicCache(self.config)
+            cache_params = DynamicCache(config=self.config)
 
         hidden_states = inputs_embeds
         all_hidden_states = () if output_hidden_states else None
diff --git a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
index 9e4ce3e5cb65..42cca6f8d447 100644
--- a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
+++ b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
@@ -41,8 +41,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import FalconMambaForCausalLM, FalconMambaModel
-    from transformers.models.falcon_mamba.modeling_falcon_mamba import FalconMambaCache
+    from transformers import DynamicCache, FalconMambaForCausalLM, FalconMambaModel
 
 
 # Copied from transformers.tests.models.mamba.MambaModelTester with Mamba->FalconMamba,mamba->falcon_mamba
@@ -255,7 +254,6 @@ def prepare_config_and_inputs_for_common(self):
 
 
 @require_torch
-# Copied from transformers.tests.models.mamba.MambaModelTest with Mamba->Falcon,mamba->falcon_mamba,FalconMambaCache->MambaCache
 class FalconMambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (FalconMambaModel, FalconMambaForCausalLM) if is_torch_available() else ()
     has_attentions = False  # FalconMamba does not support attentions
@@ -277,16 +275,16 @@ def setUp(self):
         )
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, FalconMambaCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         conv_shape = (batch_size, config.intermediate_size, config.conv_kernel)
         ssm_shape = (batch_size, config.intermediate_size, config.state_size)
 
-        self.assertTrue(config.num_hidden_layers, len(past_key_values.conv_states))
+        self.assertTrue(config.num_hidden_layers, len(past_key_values))
 
-        for idx in range(len(past_key_values.conv_states)):
-            self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-            self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
+        for idx in range(len(past_key_values)):
+            self.assertEqual(past_key_values[idx].conv_states.shape, conv_shape)
+            self.assertEqual(past_key_values[idx].ssm_states.shape, ssm_shape)
 
     def assertInterval(self, member, container, msg=None):
         r"""
@@ -348,9 +346,10 @@ def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
                 dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
 
                 def recursive_check(tuple_object, dict_object):
-                    if isinstance(tuple_object, FalconMambaCache):  # MODIFIED PART START
-                        recursive_check(tuple_object.conv_states, dict_object.conv_states)
-                        recursive_check(tuple_object.ssm_states, dict_object.ssm_states)
+                    if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
+                        for idx in len(tuple_object):
+                            recursive_check(tuple_object[idx].conv_states, dict_object[idx].conv_states)
+                            recursive_check(tuple_object[idx].ssm_states, dict_object[idx].ssm_states)
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)
diff --git a/tests/models/mamba/test_modeling_mamba.py b/tests/models/mamba/test_modeling_mamba.py
index 6430a014ec4f..88367447fddb 100644
--- a/tests/models/mamba/test_modeling_mamba.py
+++ b/tests/models/mamba/test_modeling_mamba.py
@@ -31,11 +31,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        MambaForCausalLM,
-        MambaModel,
-    )
-    from transformers.models.mamba.modeling_mamba import MambaCache
+    from transformers import DynamicCache, MambaForCausalLM, MambaModel
 
 
 class MambaModelTester:
@@ -247,16 +243,16 @@ def test_enable_input_require_grads(self):
         self.skipTest("Mamba currently requires CUDA/Metal/XPU to run enable_input_require_grads.")
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, MambaCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         conv_shape = (batch_size, config.intermediate_size, config.conv_kernel)
         ssm_shape = (batch_size, config.intermediate_size, config.state_size)
 
-        self.assertTrue(config.num_hidden_layers, len(past_key_values.conv_states))
+        self.assertTrue(config.num_hidden_layers, len(past_key_values))
 
-        for idx in range(len(past_key_values.conv_states)):
-            self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-            self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
+        for idx in range(len(past_key_values)):
+            self.assertEqual(past_key_values[idx].conv_states.shape, conv_shape)
+            self.assertEqual(past_key_values[idx].ssm_states.shape, ssm_shape)
 
     def assertInterval(self, member, container, msg=None):
         r"""
@@ -317,9 +313,10 @@ def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
                 dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
 
                 def recursive_check(tuple_object, dict_object):
-                    if isinstance(tuple_object, MambaCache):  # MODIFIED PART START
-                        recursive_check(tuple_object.conv_states, dict_object.conv_states)
-                        recursive_check(tuple_object.ssm_states, dict_object.ssm_states)
+                    if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
+                        for idx in len(tuple_object):
+                            recursive_check(tuple_object[idx].conv_states, dict_object[idx].conv_states)
+                            recursive_check(tuple_object[idx].ssm_states, dict_object[idx].ssm_states)
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)
@@ -368,29 +365,6 @@ def recursive_check(tuple_object, dict_object):
     def test_beam_sample_generate(self):
         pass
 
-    def test_dtype_mismatch_handled_in_cache(self):
-        config, input_ids, *args = self.model_tester.prepare_config_and_inputs()
-        model = MambaModel(config)
-        model.to(torch_device).to(torch.float16)
-        model.eval()
-
-        # Create cache with float32 dtype
-        cache_params = MambaCache(config, max_batch_size=input_ids.size(0), dtype=torch.float32, device=torch_device)
-
-        # If code is correct, no error occurs and test passes
-        outputs = model(
-            input_ids,
-            cache_params=cache_params,
-            use_cache=True,
-        )
-
-        self.assertIsNotNone(outputs)
-        self.assertIsNotNone(outputs.last_hidden_state)
-        self.assertEqual(
-            outputs.last_hidden_state.shape,
-            (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.hidden_size),
-        )
-
     @unittest.skip("Mamba models do not support DDP.")
     def test_multi_gpu_data_parallel_forward(self):
         pass

From 659beee9ba3a7af02abad67aab6bd373d1620528 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 17:52:23 +0100
Subject: [PATCH 03/56] fix

---
 .../falcon_mamba/modeling_falcon_mamba.py     | 172 +++---------------
 .../models/mamba2/modeling_mamba2.py          | 163 ++++-------------
 tests/models/mamba2/test_modeling_mamba2.py   |  24 ++-
 3 files changed, 67 insertions(+), 292 deletions(-)

diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index f80d0f7ca06f..cb98905ea795 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -27,7 +27,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
-from ...configuration_utils import PreTrainedConfig
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import lazy_load_kernel
 from ...modeling_layers import GradientCheckpointingLayer
@@ -57,117 +57,6 @@
 logger = logging.get_logger(__name__)
 
 
-class FalconMambaCache:
-    """
-    Cache for falcon_mamba model which does not have attention mechanism and key value states.
-
-    Arguments:
-        config (`PreTrainedConfig):
-            The configuration file defining the shape-related attributes required to initialize the static cache.
-        max_batch_size (`int`):
-            The maximum batch size with which the model will be used. Note that a new instance must be instantiated if
-            a smaller batch size is used.
-        dtype (`torch.dtype`, *optional*, defaults to `torch.float16`):
-            The default `dtype` to use when initializing the layer.
-        device (`torch.device` or `str`, *optional*):
-            The device on which the cache should be initialized. Should be the same as the layer.
-
-    Example:
-
-        ```python
-        >>> import torch
-        >>> from transformers import AutoTokenizer, FalconMambaForCausalLM, FalconMambaCache
-
-        >>> model = FalconMambaForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b")
-        >>> tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
-
-        >>> inputs = tokenizer(text="My name is FalconMamba", return_tensors="pt")
-
-        >>> # Prepare a cache class and pass it to model's forward
-        >>> cache_params = FalconMambaCache(config=model.config, max_batch_size=1, device=model.device, dtype=model.dtype)
-        >>> outputs = model(**inputs, cache_params=cache_params, use_cache=True)
-        >>> outputs.cache_params
-        ```
-    """
-
-    is_compileable = True
-
-    # TODO (joao): add layer_device_map arg and update code in `generate` accordingly
-    def __init__(
-        self,
-        config: PreTrainedConfig,
-        max_batch_size: int,
-        dtype: torch.dtype = torch.float16,
-        device: torch.device | str | None = None,
-    ):
-        self.max_batch_size = max_batch_size
-        self._dtype = dtype
-        self.intermediate_size = config.intermediate_size
-        self.ssm_state_size = config.state_size
-        self.conv_kernel_size = config.conv_kernel
-        self.has_previous_state = False
-
-        self.conv_states: list[torch.Tensor] = []
-        self.ssm_states: list[torch.Tensor] = []
-        device = torch.device(device) if device is not None else None
-        for _ in range(config.num_hidden_layers):
-            conv_state: torch.Tensor = torch.zeros(
-                self.max_batch_size,
-                self.intermediate_size,
-                self.conv_kernel_size,
-                device=device,
-                dtype=self._dtype,
-            )
-            ssm_state: torch.Tensor = torch.zeros(
-                self.max_batch_size,
-                self.intermediate_size,
-                self.ssm_state_size,
-                device=device,
-                dtype=self._dtype,
-            )
-
-            torch._dynamo.mark_static_address(conv_state)
-            torch._dynamo.mark_static_address(ssm_state)
-            self.conv_states.append(conv_state)
-            self.ssm_states.append(ssm_state)
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # This `if` blocks is only reached in multigpu and if `layer_device_map` is not passed. It is used
-        # when the cache is initialized in the forward pass (e.g. FalconMamba)
-        if self.conv_states[layer_idx].device != new_conv_state.device:
-            self.conv_states[layer_idx] = self.conv_states[layer_idx].to(new_conv_state.device)
-
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_states[layer_idx].copy_(new_conv_state)
-        else:
-            conv_state = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
-            conv_state[:, :, -1:] = new_conv_state
-            self.conv_states[layer_idx].copy_(conv_state)
-
-        # If last layer is updated, set the flag
-        if layer_idx == len(self.conv_states) - 1:
-            self.has_previous_state = True
-
-        return self.conv_states[layer_idx]
-
-    def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
-        self.ssm_states[layer_idx].zero_()
-        self.ssm_states[layer_idx] += new_ssm_state.to(self.ssm_states[layer_idx].device)
-        return self.ssm_states[layer_idx]
-
-    def reset(self):
-        self.has_previous_state = False
-        for layer_idx in range(len(self.conv_states)):
-            # In-place ops prevent breaking the static address
-            self.conv_states[layer_idx].zero_()
-            self.ssm_states[layer_idx].zero_()
-
-
 def rms_forward(hidden_states, variance_epsilon=1e-6):
     """
     Calculates simple RMSNorm with no learnable weights. `MambaRMSNorm` will
@@ -310,7 +199,7 @@ def warn_slow_implementation(self):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         # 1. Gated MLP's linear projection
@@ -349,7 +238,7 @@ def cuda_kernels_forward(
             if is_decoding:
                 hidden_states = causal_conv1d_update(
                     hidden_states.squeeze(-1),
-                    cache_params.conv_states[self.layer_idx],
+                    cache_params[self.layer_idx].conv_states,
                     conv_weights,
                     self.conv1d.bias,
                     self.activation,
@@ -360,7 +249,7 @@ def cuda_kernels_forward(
                     conv_states = nn.functional.pad(
                         hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)
                     )
-                    cache_params.update_conv_state(self.layer_idx, conv_states, cache_init=True)
+                    cache_params.update_conv_state(conv_states, self.layer_idx)
                 hidden_states = causal_conv1d_fn(
                     hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
                 )
@@ -391,7 +280,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params.ssm_states[self.layer_idx],
+                    cache_params[self.layer_idx].ssm_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -416,7 +305,7 @@ def cuda_kernels_forward(
                     return_last_state=True,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(self.layer_idx, ssm_state)
+                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
             # 4. Final linear projection
             contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -425,7 +314,7 @@ def cuda_kernels_forward(
     # fmt: off
     def slow_forward(self,
         input_states,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         batch_size, seq_len, _ = input_states.shape
@@ -439,17 +328,17 @@ def slow_forward(self,
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state:
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
-                cache_params.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(
                     self.conv1d(hidden_states)[..., :seq_len]
                 )  # [batch, intermediate_size, seq_len]
             else:
-                conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_init=False)
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 conv_state = conv_state.to(self.conv1d.weight.device)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
@@ -538,7 +427,7 @@ def combine_fn(left, right):
             scan_output = scan_output * self.act(gate)
 
             if cache_params is not None:
-                cache_params.update_ssm_state(self.layer_idx, ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))  # [batch, seq_len, hidden_size]
@@ -548,7 +437,7 @@ def combine_fn(left, right):
     def forward(
         self,
         hidden_states,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
         **kwargs,
     ):
@@ -590,7 +479,7 @@ def __init__(self, config, layer_idx):
     def forward(
         self,
         hidden_states,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
         **kwargs,
     ):
@@ -658,7 +547,7 @@ def _init_weights(self, module):
 )
 class FalconMambaOutput(ModelOutput):
     r"""
-    cache_params (`FalconMambaCache`):
+    cache_params (`Cache`):
         The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
         avoid providing the old `input_ids`.
 
@@ -666,7 +555,7 @@ class FalconMambaOutput(ModelOutput):
     """
 
     last_hidden_state: torch.FloatTensor | None = None
-    cache_params: FalconMambaCache | None = None
+    cache_params: Cache | None = None
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
@@ -682,7 +571,7 @@ class FalconMambaCausalLMOutput(ModelOutput):
         Language modeling loss (for next-token prediction).
     logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
         Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-    cache_params (`FalconMambaCache`):
+    cache_params (`Cache`):
         The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
         avoid providing the old `input_ids`.
 
@@ -691,7 +580,7 @@ class FalconMambaCausalLMOutput(ModelOutput):
 
     loss: torch.FloatTensor | None = None
     logits: torch.FloatTensor | None = None
-    cache_params: FalconMambaCache | None = None
+    cache_params: Cache | None = None
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
@@ -721,7 +610,7 @@ def forward(
         self,
         input_ids: torch.LongTensor | None = None,
         inputs_embeds: torch.LongTensor | None = None,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         use_cache: bool | None = None,
         output_hidden_states: bool | None = None,
         return_dict: bool | None = None,
@@ -729,7 +618,7 @@ def forward(
         **kwargs,
     ) -> tuple | FalconMambaOutput:
         r"""
-        cache_params (`FalconMambaCache`, *optional*):
+        cache_params (`Cache`, *optional*):
             If passed along, the model uses the previous state in all the blocks (which will give the output for the
             `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
         use_cache (`bool`, *optional*):
@@ -751,9 +640,7 @@ def forward(
             use_cache = False
 
         if use_cache and cache_params is None:
-            cache_params = FalconMambaCache(
-                self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype
-            )
+            cache_params = DynamicCache(config=self.config)
 
         hidden_states = inputs_embeds
         all_hidden_states = () if output_hidden_states else None
@@ -809,12 +696,11 @@ def prepare_inputs_for_generation(
         input_ids,
         inputs_embeds=None,
         use_cache=None,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
         is_first_iteration: bool | None = False,
         **kwargs,
     ):
-        # Overwritten -- has custom cache class `FalconMambaCache`
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
             inputs_embeds=inputs_embeds,
@@ -825,15 +711,7 @@ def prepare_inputs_for_generation(
             **kwargs,
         )
 
-        if use_cache and cache_params is None:
-            if inputs_embeds is not None:
-                max_batch_size = inputs_embeds.size(0)
-            else:
-                max_batch_size = input_ids.size(0)
-            model_inputs["cache_params"] = FalconMambaCache(
-                self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype
-            )
-        elif use_cache and not is_first_iteration:
+        if use_cache and not is_first_iteration:
             model_inputs["attention_mask"] = None
 
         return model_inputs
@@ -844,7 +722,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.LongTensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
-        cache_params: FalconMambaCache | None = None,
+        cache_params: Cache | None = None,
         labels: torch.LongTensor | None = None,
         output_hidden_states: bool | None = None,
         return_dict: bool | None = None,
@@ -853,7 +731,7 @@ def forward(
         **kwargs,  # for now we need this for generation
     ) -> tuple | FalconMambaCausalLMOutput:
         r"""
-        cache_params (`FalconMambaCache`, *optional*):
+        cache_params (`Cache`, *optional*):
             If passed along, the model uses the previous state in all the blocks (which will give the output for the
             `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -903,4 +781,4 @@ def forward(
         )
 
 
-__all__ = ["FalconMambaForCausalLM", "FalconMambaModel", "FalconMambaPreTrainedModel", "FalconMambaCache"]
+__all__ = ["FalconMambaForCausalLM", "FalconMambaModel", "FalconMambaPreTrainedModel"]
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 3a70ec35dfd9..0c3a1d14d12e 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -21,6 +21,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import lazy_load_kernel
 from ...modeling_layers import GradientCheckpointingLayer
@@ -99,94 +100,6 @@ def apply_mask_to_padding_states(hidden_states, attention_mask):
     return hidden_states
 
 
-class Mamba2Cache:
-    """
-    Arguments:
-        config: Mamba2Config
-        batch_size: int
-        dtype: torch.dtype
-        device: torch.device
-
-    Attributes:
-        dtype: (`torch.dtype`):
-            The default `dtype` used to initializing the cache.
-        conv_kernel_size: (`int`):
-            Model's convolution kernel size taken from config.
-        n_groups: (`int`):
-            Model's number of groups taken from the config - similar to tensor parallel in Transformer.
-        state_size: (`int`):
-            Model's SSM state size taken from config.
-        num_heads: (`int`):
-            The number of heads used in the linear attention / SSM.
-        head_dim: (`int`):
-            The respective dimension of the heads used in the linear attention / SSM.
-        intermediate_size: (`int`):
-            Model's intermediate_size based on (expand * hidden_dim) from config.
-        conv_states: (`torch.Tensor`):
-            A tensor of shape `[num_layers, batch_size, conv_kernel_size, intermediate_size + 2 * n_groups * state_size]` that holds convolutional states.
-        ssm_states: (`torch.Tensor`):
-            A tensor of shape `[num_layers, batch_size, num_heads, head_dim, state_size]` that holds ssm states.
-    """
-
-    def __init__(
-        self, config: Mamba2Config, batch_size: int, dtype: torch.dtype = torch.float16, device: str | None = None
-    ):
-        self.dtype = dtype
-        self.conv_kernel_size = config.conv_kernel
-        self.n_groups = config.n_groups
-        self.state_size = config.state_size
-        self.num_heads = config.num_heads
-        self.head_dim = config.head_dim
-        self.intermediate_size = int(config.expand * config.hidden_size)
-
-        self.conv_states = torch.zeros(
-            config.num_hidden_layers,
-            batch_size,
-            self.intermediate_size + 2 * self.n_groups * self.state_size,
-            self.conv_kernel_size,
-            device=device,
-            dtype=dtype,
-        )
-        self.ssm_states = torch.zeros(
-            config.num_hidden_layers,
-            batch_size,
-            self.num_heads,
-            self.head_dim,
-            self.state_size,
-            device=device,
-            dtype=dtype,
-        )
-        self.has_previous_state = False
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_states[layer_idx] = new_conv_state.to(self.conv_states.device)
-        else:
-            self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
-            self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states.device)
-
-        return self.conv_states[layer_idx]
-
-    def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
-        self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device)
-
-        # If last layer is updated, set the flag
-        if layer_idx == self.conv_states.shape[0] - 1:
-            self.has_previous_state = True
-
-        return self.ssm_states[layer_idx]
-
-    def reset(self):
-        self.has_previous_state = False
-        self.conv_states.zero_()
-        self.ssm_states.zero_()
-
-
 class MambaRMSNormGated(torch.nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         super().__init__()
@@ -325,7 +238,7 @@ def init_mamba2_weights(self):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Mamba2Cache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         # 1. Gated MLP's linear projection
@@ -351,7 +264,7 @@ def cuda_kernels_forward(
             # 2. Convolution sequence transformation
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -373,7 +286,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -429,11 +342,9 @@ def cuda_kernels_forward(
                     hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                     conv_states = nn.functional.pad(
                         hidden_states_B_C_transposed,
-                        (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
-                    )
-                    cache_params.update_conv_state(
-                        layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True
+                        (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
                     )
+                    conv_states = cache_params.update_conv_state(conv_states, layer_idx=self.layer_idx)
 
                 if self.activation not in ["silu", "swish"]:
                     hidden_states_B_C = self.act(
@@ -473,7 +384,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
+                    cache_params.update_ssm_state(ssm_state, layer_idx=self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -487,8 +398,8 @@ def cuda_kernels_forward(
     def torch_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Mamba2Cache | None=None,
-        attention_mask: torch.Tensor | None=None
+        cache_params: Cache | None = None,
+        attention_mask: torch.Tensor | None = None
     ):
         batch_size, seq_len, _ = hidden_states.shape
         dtype = hidden_states.dtype
@@ -503,10 +414,10 @@ def torch_forward(
 
         # 2. Convolution sequence transformation
         if cache_params is not None and cache_params.has_previous_state:
-            cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=hidden_states_B_C, cache_init=False)
+            cache_params.update_conv_state(hidden_states_B_C[:, 0:1, :], layer_idx=self.layer_idx)
 
             # We need to guarantee that anything regarding the cache is on the same device
-            conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
+            conv_states = cache_params[self.layer_idx].conv_states.to(device=self.conv1d.weight.device)
 
             hidden_states_B_C = torch.sum(
                 conv_states * self.conv1d.weight.squeeze(1), dim=-1
@@ -519,9 +430,9 @@ def torch_forward(
             if cache_params is not None:
                 hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                 conv_states = nn.functional.pad(
-                    hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+                    hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
                 )
-                cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True)
+                cache_params.update_conv_state(conv_states, layer_idx=self.layer_idx)
 
             hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
 
@@ -536,7 +447,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if cache_params is not None and cache_params.has_previous_state:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.ssm_states.device
+            cache_device = cache_params[self.layer_idx].device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -567,8 +478,8 @@ def torch_forward(
 
             # State calculation
             cache_params.update_ssm_state(
+                cache_params[self.layer_idx].ssm_states * dA + dBx,
                 layer_idx=self.layer_idx,
-                new_ssm_state=cache_params.ssm_states[self.layer_idx] * dA + dBx
             )
 
             # Subsequent output
@@ -578,7 +489,7 @@ def torch_forward(
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = cache_params[self.layer_idx].ssm_states.to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -640,7 +551,7 @@ def torch_forward(
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
             if cache_params is not None and cache_params.has_previous_state:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
+                previous_states = cache_params[self.layer_idx].ssm_states[:, None, ...].to(device=states.device)
             else:
                 previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
@@ -669,7 +580,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
+                cache_params.update_ssm_state(ssm_state, layer_idx=self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
@@ -683,7 +594,7 @@ def torch_forward(
     def forward(
         self,
         hidden_states,
-        cache_params: Mamba2Cache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -721,7 +632,7 @@ def __init__(self, config, layer_idx):
     def forward(
         self,
         hidden_states,
-        cache_params: Mamba2Cache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -790,7 +701,7 @@ def _init_weights(self, module):
 # Copied from transformers.models.mamba.modeling_mamba.MambaOutput with MAMBA->MAMBA2,Mamba->Mamba2
 class Mamba2Output(ModelOutput):
     r"""
-    cache_params (`Mamba2Cache`):
+    cache_params (`Cache`):
         The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
         avoid providing the old `input_ids`.
 
@@ -798,7 +709,7 @@ class Mamba2Output(ModelOutput):
     """
 
     last_hidden_state: torch.FloatTensor | None = None
-    cache_params: Mamba2Cache | None = None
+    cache_params: Cache | None = None
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
@@ -815,7 +726,7 @@ class Mamba2CausalLMOutput(ModelOutput):
         Language modeling loss (for next-token prediction).
     logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
         Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-    cache_params (`Mamba2Cache`):
+    cache_params (`Cache`):
         The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
         avoid providing the old `input_ids`.
 
@@ -824,7 +735,7 @@ class Mamba2CausalLMOutput(ModelOutput):
 
     loss: torch.FloatTensor | None = None
     logits: torch.FloatTensor | None = None
-    cache_params: Mamba2Cache | None = None
+    cache_params: Cache | None = None
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
@@ -859,7 +770,7 @@ def forward(
         self,
         input_ids: torch.LongTensor | None = None,
         inputs_embeds: torch.LongTensor | None = None,
-        cache_params: Mamba2Cache | None = None,
+        cache_params: Cache | None = None,
         use_cache: bool | None = None,
         output_hidden_states: bool | None = None,
         return_dict: bool | None = None,
@@ -867,7 +778,7 @@ def forward(
         **kwargs,
     ) -> tuple | Mamba2Output:
         r"""
-        cache_params (`Mamba2Cache`, *optional*):
+        cache_params (`Cache`, *optional*):
             If passed along, the model uses the previous state in all the blocks (which will give the output for the
             `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
         use_cache (`bool`, *optional*):
@@ -889,9 +800,7 @@ def forward(
             use_cache = False
 
         if use_cache and cache_params is None:
-            cache_params = Mamba2Cache(
-                self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype
-            )
+            cache_params = DynamicCache(config=self.config)
 
         hidden_states = inputs_embeds
         all_hidden_states = () if output_hidden_states else None
@@ -947,13 +856,11 @@ def prepare_inputs_for_generation(
         input_ids,
         inputs_embeds=None,
         use_cache=None,
-        cache_params: Mamba2Cache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         is_first_iteration: bool | None = False,
         **kwargs,
     ):
-        # Overwritten -- has custom cache class `Mamba2Cache`
-
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
             inputs_embeds=inputs_embeds,
@@ -964,15 +871,7 @@ def prepare_inputs_for_generation(
             **kwargs,
         )
 
-        if use_cache and cache_params is None:
-            if inputs_embeds is not None:
-                max_batch_size = inputs_embeds.size(0)
-            else:
-                max_batch_size = input_ids.size(0)
-            model_inputs["cache_params"] = Mamba2Cache(
-                self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype
-            )
-        elif use_cache and not is_first_iteration:
+        if use_cache and not is_first_iteration:
             model_inputs["attention_mask"] = None
 
         return model_inputs
@@ -982,7 +881,7 @@ def forward(
         self,
         input_ids: torch.LongTensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
-        cache_params: Mamba2Cache | None = None,
+        cache_params: Cache | None = None,
         labels: torch.LongTensor | None = None,
         output_hidden_states: bool | None = None,
         return_dict: bool | None = None,
@@ -992,7 +891,7 @@ def forward(
         **kwargs,  # for now we need this for generation and loss_function
     ) -> tuple | Mamba2CausalLMOutput:
         r"""
-        cache_params (`Mamba2Cache`, *optional*):
+        cache_params (`Cache`, *optional*):
             If passed along, the model uses the previous state in all the blocks (which will give the output for the
             `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
diff --git a/tests/models/mamba2/test_modeling_mamba2.py b/tests/models/mamba2/test_modeling_mamba2.py
index aef487d46351..bfbfa178f8d1 100644
--- a/tests/models/mamba2/test_modeling_mamba2.py
+++ b/tests/models/mamba2/test_modeling_mamba2.py
@@ -34,11 +34,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        Mamba2ForCausalLM,
-        Mamba2Model,
-    )
-    from transformers.models.mamba2.modeling_mamba2 import Mamba2Cache, Mamba2Mixer
+    from transformers import DynamicCache, Mamba2ForCausalLM, Mamba2Model
+    from transformers.models.mamba2.modeling_mamba2 import Mamba2Mixer
 
 
 class Mamba2ConfigTester(ConfigTester):
@@ -248,19 +245,19 @@ def setUp(self):
         )
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, Mamba2Cache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         intermediate_size = config.expand * config.hidden_size
         conv_shape = (
-            config.num_hidden_layers,
             batch_size,
             intermediate_size + 2 * config.n_groups * config.state_size,
             config.conv_kernel,
         )
-        ssm_shape = (config.num_hidden_layers, batch_size, config.num_heads, config.head_dim, config.state_size)
+        ssm_shape = (batch_size, config.num_heads, config.head_dim, config.state_size)
 
-        self.assertEqual(past_key_values.conv_states.shape, conv_shape)
-        self.assertEqual(past_key_values.ssm_states.shape, ssm_shape)
+        for idx in range(len(past_key_values)):
+            self.assertEqual(past_key_values[idx].conv_states.shape, conv_shape)
+            self.assertEqual(past_key_values[idx].ssm_states.shape, ssm_shape)
 
     def test_mamba2_caching(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -291,9 +288,10 @@ def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
                 dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
 
                 def recursive_check(tuple_object, dict_object):
-                    if isinstance(tuple_object, Mamba2Cache):  # MODIFIED PART START
-                        recursive_check(tuple_object.conv_states, dict_object.conv_states)
-                        recursive_check(tuple_object.ssm_states, dict_object.ssm_states)
+                    if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
+                        for idx in len(tuple_object):
+                            recursive_check(tuple_object[idx].conv_states, dict_object[idx].conv_states)
+                            recursive_check(tuple_object[idx].ssm_states, dict_object[idx].ssm_states)
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)

From 29b91abf0a8d802d076facfef900476fb181eecf Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 18:08:54 +0100
Subject: [PATCH 04/56] fix

---
 .../models/falcon_mamba/modeling_falcon_mamba.py    |  4 ++--
 .../models/falcon_mamba/modular_falcon_mamba.py     |  4 ++--
 src/transformers/models/mamba/modeling_mamba.py     |  4 ++--
 src/transformers/models/mamba2/modeling_mamba2.py   | 13 ++++++-------
 4 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index cb98905ea795..6bb8c9725721 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -231,7 +231,7 @@ def cuda_kernels_forward(
             if attention_mask is not None:
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-            is_decoding = cache_params is not None and cache_params.has_previous_state
+            is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
 
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
@@ -330,7 +330,7 @@ def slow_forward(self,
         if cache_params is not None:
             ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if not cache_params.has_previous_state:
+            if not cache_params[self.layer_idx].has_previous_state:
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
                 cache_params.update_conv_state(conv_state, self.layer_idx)
diff --git a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
index b5bda5b31dde..0c2dc6ab5541 100644
--- a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
@@ -192,7 +192,7 @@ def cuda_kernels_forward(
             if attention_mask is not None:
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-            is_decoding = cache_params is not None and cache_params.has_previous_state
+            is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
 
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
@@ -291,7 +291,7 @@ def slow_forward(
         if cache_params is not None:
             ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if not cache_params.has_previous_state:
+            if not cache_params[self.layer_idx].has_previous_state:
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
                 cache_params.update_conv_state(conv_state, self.layer_idx)
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index b74fb8419d10..a8ad08aba889 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -196,7 +196,7 @@ def cuda_kernels_forward(
             if attention_mask is not None:
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-            is_decoding = cache_params is not None and cache_params.has_previous_state
+            is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
 
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
@@ -281,7 +281,7 @@ def slow_forward(self, input_states, cache_params: Cache | None=None, attention_
         if cache_params is not None:
             ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if not cache_params.has_previous_state:
+            if not cache_params[self.layer_idx].has_previous_state:
                 conv_state = nn.functional.pad(
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 0c3a1d14d12e..4684bf7f06f9 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -256,7 +256,7 @@ def cuda_kernels_forward(
         ) // 2
 
         # Single step calculations via cache
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and cache_params[self.layer_idx].has_previous_state:
             _, _, gate, hidden_states_B_C, dt = projected_states.squeeze(1).split(
                 [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
             )
@@ -412,8 +412,10 @@ def torch_forward(
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
 
+        is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
+
         # 2. Convolution sequence transformation
-        if cache_params is not None and cache_params.has_previous_state:
+        if is_decoding:
             cache_params.update_conv_state(hidden_states_B_C[:, 0:1, :], layer_idx=self.layer_idx)
 
             # We need to guarantee that anything regarding the cache is on the same device
@@ -445,7 +447,7 @@ def torch_forward(
 
         # 3. SSM transformation
         A = -torch.exp(self.A_log.float())                            # [num_heads]
-        if cache_params is not None and cache_params.has_previous_state:
+        if is_decoding:
             # We need to guarantee that anything regarding the cache is on the same device
             cache_device = cache_params[self.layer_idx].device
 
@@ -550,10 +552,7 @@ def torch_forward(
 
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
-            if cache_params is not None and cache_params.has_previous_state:
-                previous_states = cache_params[self.layer_idx].ssm_states[:, None, ...].to(device=states.device)
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
             decay_chunk = decay_chunk.transpose(1, 3)

From 3e02650b77e50ad300ae843890f0a968a979f21f Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 18:17:42 +0100
Subject: [PATCH 05/56] fix

---
 src/transformers/cache_utils.py               | 19 +++++++++++++++++++
 .../falcon_mamba/modeling_falcon_mamba.py     |  4 ++--
 .../falcon_mamba/modular_falcon_mamba.py      |  4 ++--
 .../models/mamba/modeling_mamba.py            |  4 ++--
 .../models/mamba2/modeling_mamba2.py          |  4 ++--
 5 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 6f171ac0c5c9..aa4380a8c330 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -953,6 +953,25 @@ def get_seq_length(self, layer_idx: int = 0) -> int:
 
         return self.layers[layer_idx].get_seq_length()
 
+    def has_previous_state(self, layer_idx: int = -1) -> bool:
+        """Returns whether the Mamba layer at index `layer_idx` has previous state or not."""
+        if layer_idx >= len(self.layers):
+            return False
+
+        # In this case, use last Mamba layer
+        if layer_idx == -1:
+            try:
+                layer_idx = next(
+                    idx for idx in range(len(self) - 1, -1, -1) if isinstance(self.layers[idx], CacheLayerMixin)
+                )
+            except StopIteration:
+                raise ValueError(
+                    "`has_previous_state` can only be called on Mamba layers, and the current Cache seem to only contain "
+                    "Attention layers."
+                )
+
+        return self.layers[layer_idx].has_previous_state
+
     def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
         """
         Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index 6bb8c9725721..6dbf3017b6ff 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -231,7 +231,7 @@ def cuda_kernels_forward(
             if attention_mask is not None:
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-            is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
+            is_decoding = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
@@ -330,7 +330,7 @@ def slow_forward(self,
         if cache_params is not None:
             ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if not cache_params[self.layer_idx].has_previous_state:
+            if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
                 cache_params.update_conv_state(conv_state, self.layer_idx)
diff --git a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
index 0c2dc6ab5541..6dc29117a521 100644
--- a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
@@ -192,7 +192,7 @@ def cuda_kernels_forward(
             if attention_mask is not None:
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-            is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
+            is_decoding = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
@@ -291,7 +291,7 @@ def slow_forward(
         if cache_params is not None:
             ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if not cache_params[self.layer_idx].has_previous_state:
+            if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
                 cache_params.update_conv_state(conv_state, self.layer_idx)
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index a8ad08aba889..9791066b79ea 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -196,7 +196,7 @@ def cuda_kernels_forward(
             if attention_mask is not None:
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-            is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
+            is_decoding = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
@@ -281,7 +281,7 @@ def slow_forward(self, input_states, cache_params: Cache | None=None, attention_
         if cache_params is not None:
             ssm_state = cache_params[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if not cache_params[self.layer_idx].has_previous_state:
+            if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 4684bf7f06f9..77a83b5aa89a 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -256,7 +256,7 @@ def cuda_kernels_forward(
         ) // 2
 
         # Single step calculations via cache
-        if cache_params is not None and cache_params[self.layer_idx].has_previous_state:
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             _, _, gate, hidden_states_B_C, dt = projected_states.squeeze(1).split(
                 [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
             )
@@ -412,7 +412,7 @@ def torch_forward(
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
 
-        is_decoding = cache_params is not None and cache_params[self.layer_idx].has_previous_state
+        is_decoding = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
         # 2. Convolution sequence transformation
         if is_decoding:

From fb883452ff6453955e23490276e4ddb9c6a649f7 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 18:25:26 +0100
Subject: [PATCH 06/56] fix

---
 src/transformers/cache_utils.py                |  4 ++--
 .../falcon_mamba/modeling_falcon_mamba.py      |  6 +++---
 .../falcon_mamba/modular_falcon_mamba.py       |  6 +++---
 .../models/mamba/modeling_mamba.py             |  6 +++---
 .../models/mamba2/modeling_mamba2.py           | 18 ++++++------------
 5 files changed, 17 insertions(+), 23 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index aa4380a8c330..5079f81117a0 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -953,13 +953,13 @@ def get_seq_length(self, layer_idx: int = 0) -> int:
 
         return self.layers[layer_idx].get_seq_length()
 
-    def has_previous_state(self, layer_idx: int = -1) -> bool:
+    def has_previous_state(self, layer_idx: int | None = None) -> bool:
         """Returns whether the Mamba layer at index `layer_idx` has previous state or not."""
         if layer_idx >= len(self.layers):
             return False
 
         # In this case, use last Mamba layer
-        if layer_idx == -1:
+        if layer_idx is None:
             try:
                 layer_idx = next(
                     idx for idx in range(len(self) - 1, -1, -1) if isinstance(self.layers[idx], CacheLayerMixin)
diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index 6dbf3017b6ff..1886c1393960 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -238,7 +238,7 @@ def cuda_kernels_forward(
             if is_decoding:
                 hidden_states = causal_conv1d_update(
                     hidden_states.squeeze(-1),
-                    cache_params[self.layer_idx].conv_states,
+                    cache_params.layers[self.layer_idx].conv_states,
                     conv_weights,
                     self.conv1d.bias,
                     self.activation,
@@ -280,7 +280,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params[self.layer_idx].ssm_states,
+                    cache_params.layers[self.layer_idx].ssm_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -328,7 +328,7 @@ def slow_forward(self,
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
diff --git a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
index 6dc29117a521..e937112f2988 100644
--- a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
@@ -199,7 +199,7 @@ def cuda_kernels_forward(
             if is_decoding:
                 hidden_states = causal_conv1d_update(
                     hidden_states.squeeze(-1),
-                    cache_params[self.layer_idx].conv_states,
+                    cache_params.layers[self.layer_idx].conv_states,
                     conv_weights,
                     self.conv1d.bias,
                     self.activation,
@@ -241,7 +241,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params[self.layer_idx].ssm_states,
+                    cache_params.layers[self.layer_idx].ssm_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -289,7 +289,7 @@ def slow_forward(
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index 9791066b79ea..0f5a45ca3d57 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -203,7 +203,7 @@ def cuda_kernels_forward(
             if is_decoding:
                 hidden_states = causal_conv1d_update(
                     hidden_states.squeeze(-1),
-                    cache_params[self.layer_idx].conv_states,
+                    cache_params.layers[self.layer_idx].conv_states,
                     conv_weights,
                     self.conv1d.bias,
                     self.activation,
@@ -235,7 +235,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params[self.layer_idx].ssm_states,
+                    cache_params.layers[self.layer_idx].ssm_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -279,7 +279,7 @@ def slow_forward(self, input_states, cache_params: Cache | None=None, attention_
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 77a83b5aa89a..41d97923c120 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -264,7 +264,7 @@ def cuda_kernels_forward(
             # 2. Convolution sequence transformation
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params[self.layer_idx].conv_states,
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -286,7 +286,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -416,10 +416,7 @@ def torch_forward(
 
         # 2. Convolution sequence transformation
         if is_decoding:
-            cache_params.update_conv_state(hidden_states_B_C[:, 0:1, :], layer_idx=self.layer_idx)
-
-            # We need to guarantee that anything regarding the cache is on the same device
-            conv_states = cache_params[self.layer_idx].conv_states.to(device=self.conv1d.weight.device)
+            conv_states = cache_params.update_conv_state(hidden_states_B_C[:, 0:1, :], layer_idx=self.layer_idx)
 
             hidden_states_B_C = torch.sum(
                 conv_states * self.conv1d.weight.squeeze(1), dim=-1
@@ -449,7 +446,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if is_decoding:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params[self.layer_idx].device
+            cache_device = cache_params.layers[self.layer_idx].device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -479,10 +476,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            cache_params.update_ssm_state(
-                cache_params[self.layer_idx].ssm_states * dA + dBx,
-                layer_idx=self.layer_idx,
-            )
+            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
+            ssm_states = cache_params.update_ssm_state(ssm_states, layer_idx=self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -491,7 +486,6 @@ def torch_forward(
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params[self.layer_idx].ssm_states.to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]

From 1aeddfaefab66ade4a28dcf0f2554262e5ba3ab7 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 18:44:19 +0100
Subject: [PATCH 07/56] final fix

---
 .../models/falcon_mamba/modeling_falcon_mamba.py | 12 +++++++-----
 .../models/falcon_mamba/modular_falcon_mamba.py  | 12 +++++++-----
 src/transformers/models/mamba/modeling_mamba.py  | 16 +++++++++-------
 .../falcon_mamba/test_modeling_falcon_mamba.py   | 10 +++++-----
 tests/models/mamba/test_modeling_mamba.py        | 10 +++++-----
 tests/models/mamba2/test_modeling_mamba2.py      | 10 +++++-----
 6 files changed, 38 insertions(+), 32 deletions(-)

diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index 1886c1393960..b2fc8afdae75 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -326,10 +326,15 @@ def slow_forward(self,
         if attention_mask is not None:
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
+            )
+
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
@@ -347,9 +352,6 @@ def slow_forward(self,
                     self.act(hidden_states).to(dtype).unsqueeze(-1)
                 )  # [batch, intermediate_size, 1] : decoding
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
-            )
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])  # [batch, intermediate_size, seq_len]
 
         if attention_mask is not None:
diff --git a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
index e937112f2988..b53b85465cc2 100644
--- a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
@@ -287,10 +287,15 @@ def slow_forward(
         if attention_mask is not None:
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
+            )
+
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
 
@@ -308,9 +313,6 @@ def slow_forward(
                     self.act(hidden_states).to(dtype).unsqueeze(-1)
                 )  # [batch, intermediate_size, 1] : decoding
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
-            )
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])  # [batch, intermediate_size, seq_len]
 
         if attention_mask is not None:
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index 0f5a45ca3d57..1591583112dc 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -277,10 +277,16 @@ def slow_forward(self, input_states, cache_params: Cache | None=None, attention_
         if attention_mask is not None:
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.intermediate_size, self.ssm_state_size),
+                device=hidden_states.device, dtype=dtype
+            )
+
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state.to(hidden_states.device)
             if not cache_params.has_previous_state(self.layer_idx):
                 conv_state = nn.functional.pad(
                     hidden_states,
@@ -297,10 +303,6 @@ def slow_forward(self, input_states, cache_params: Cache | None=None, attention_
                     hidden_states += self.conv1d.bias
                 hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1)         # [batch, intermediate_size, 1] : decoding
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.intermediate_size, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])         # [batch, intermediate_size, seq_len]
 
         if attention_mask is not None:
@@ -354,7 +356,7 @@ def combine_fn(left, right):
             scan_output = (scan_output * self.act(gate))
 
             if cache_params is not None:
-                cache_params.update_ssm_states(ssm_state, self.layer_idx)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))  # [batch, seq_len, hidden_size]
diff --git a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
index 42cca6f8d447..8aea217bfbc1 100644
--- a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
+++ b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
@@ -283,8 +283,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         self.assertTrue(config.num_hidden_layers, len(past_key_values))
 
         for idx in range(len(past_key_values)):
-            self.assertEqual(past_key_values[idx].conv_states.shape, conv_shape)
-            self.assertEqual(past_key_values[idx].ssm_states.shape, ssm_shape)
+            self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+            self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
 
     def assertInterval(self, member, container, msg=None):
         r"""
@@ -347,9 +347,9 @@ def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
 
                 def recursive_check(tuple_object, dict_object):
                     if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
-                        for idx in len(tuple_object):
-                            recursive_check(tuple_object[idx].conv_states, dict_object[idx].conv_states)
-                            recursive_check(tuple_object[idx].ssm_states, dict_object[idx].ssm_states)
+                        for idx in range(len(tuple_object)):
+                            recursive_check(tuple_object.layers[idx].conv_states, dict_object.layers[idx].conv_states)
+                            recursive_check(tuple_object.layers[idx].ssm_states, dict_object.layers[idx].ssm_states)
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)
diff --git a/tests/models/mamba/test_modeling_mamba.py b/tests/models/mamba/test_modeling_mamba.py
index 88367447fddb..f68a17491590 100644
--- a/tests/models/mamba/test_modeling_mamba.py
+++ b/tests/models/mamba/test_modeling_mamba.py
@@ -251,8 +251,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         self.assertTrue(config.num_hidden_layers, len(past_key_values))
 
         for idx in range(len(past_key_values)):
-            self.assertEqual(past_key_values[idx].conv_states.shape, conv_shape)
-            self.assertEqual(past_key_values[idx].ssm_states.shape, ssm_shape)
+            self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+            self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
 
     def assertInterval(self, member, container, msg=None):
         r"""
@@ -314,9 +314,9 @@ def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
 
                 def recursive_check(tuple_object, dict_object):
                     if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
-                        for idx in len(tuple_object):
-                            recursive_check(tuple_object[idx].conv_states, dict_object[idx].conv_states)
-                            recursive_check(tuple_object[idx].ssm_states, dict_object[idx].ssm_states)
+                        for idx in range(len(tuple_object)):
+                            recursive_check(tuple_object.layers[idx].conv_states, dict_object.layers[idx].conv_states)
+                            recursive_check(tuple_object.layers[idx].ssm_states, dict_object.layers[idx].ssm_states)
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)
diff --git a/tests/models/mamba2/test_modeling_mamba2.py b/tests/models/mamba2/test_modeling_mamba2.py
index bfbfa178f8d1..6aa16ddeecd2 100644
--- a/tests/models/mamba2/test_modeling_mamba2.py
+++ b/tests/models/mamba2/test_modeling_mamba2.py
@@ -256,8 +256,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         ssm_shape = (batch_size, config.num_heads, config.head_dim, config.state_size)
 
         for idx in range(len(past_key_values)):
-            self.assertEqual(past_key_values[idx].conv_states.shape, conv_shape)
-            self.assertEqual(past_key_values[idx].ssm_states.shape, ssm_shape)
+            self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+            self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
 
     def test_mamba2_caching(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -289,9 +289,9 @@ def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
 
                 def recursive_check(tuple_object, dict_object):
                     if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
-                        for idx in len(tuple_object):
-                            recursive_check(tuple_object[idx].conv_states, dict_object[idx].conv_states)
-                            recursive_check(tuple_object[idx].ssm_states, dict_object[idx].ssm_states)
+                        for idx in range(len(tuple_object)):
+                            recursive_check(tuple_object.layers[idx].conv_states, dict_object.layers[idx].conv_states)
+                            recursive_check(tuple_object.layers[idx].ssm_states, dict_object.layers[idx].ssm_states)
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)

From 35db152bde81c10efa978430fa2b971e5720d62d Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 18:59:51 +0100
Subject: [PATCH 08/56] test hybrid with jamba

---
 .../models/jamba/modeling_jamba.py            | 174 ++++--------------
 .../models/jamba/modular_jamba.py             | 174 ++++--------------
 2 files changed, 62 insertions(+), 286 deletions(-)

diff --git a/src/transformers/models/jamba/modeling_jamba.py b/src/transformers/models/jamba/modeling_jamba.py
index 3d7986933395..ef0b15b6856b 100755
--- a/src/transformers/models/jamba/modeling_jamba.py
+++ b/src/transformers/models/jamba/modeling_jamba.py
@@ -23,13 +23,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections.abc import Callable
-from typing import Any
 
 import torch
 from torch import nn
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import (
     lazy_load_kernel,
@@ -74,100 +74,6 @@ def extra_repr(self):
         return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
 
 
-class HybridMambaAttentionDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config, batch_size, dtype=torch.float16, device=None):
-        self.dtype = dtype
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False  # only used by mamba
-        intermediate_size = config.mamba_expand * config.hidden_size
-        ssm_state_size = config.mamba_d_state
-        conv_kernel_size = config.mamba_d_conv
-        self.conv_states = []
-        self.ssm_states = []
-        self.transformer_layers = []
-        for i in range(config.num_hidden_layers):
-            if self.layers_block_type[i] == "mamba":
-                self.conv_states += [
-                    torch.zeros(batch_size, intermediate_size, conv_kernel_size, device=device, dtype=dtype)
-                ]
-                self.ssm_states += [
-                    torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype)
-                ]
-            else:
-                self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.transformer_layers.append(i)
-
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    def __getitem__(self, layer_idx):
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Update the cache
-        if self.key_cache[layer_idx].shape[-1] == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -260,7 +166,7 @@ def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         input_shape = hidden_states.shape[:-1]
@@ -371,17 +277,12 @@ def __init__(self, config: JambaConfig, layer_idx):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         batch_size, seq_len, _ = hidden_states.shape
         use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
         # 1. Gated MLP's linear projection
         projected_states = self.in_proj(hidden_states).transpose(1, 2)
@@ -398,7 +299,7 @@ def cuda_kernels_forward(
         if use_precomputed_states:
             hidden_states = causal_conv1d_update(
                 hidden_states.squeeze(-1),
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 conv_weights,
                 self.conv1d.bias,
                 self.activation,
@@ -407,7 +308,7 @@ def cuda_kernels_forward(
         else:
             if cache_params is not None:
                 conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                cache_params.update_conv_state(conv_states, self.layer_idx)
             hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation)
 
         if attention_mask is not None:
@@ -442,7 +343,7 @@ def cuda_kernels_forward(
         time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
         if use_precomputed_states:
             scan_outputs = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states[..., 0],
                 discrete_time_step[..., 0],
                 A,
@@ -467,7 +368,7 @@ def cuda_kernels_forward(
                 return_last_state=True,
             )
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -475,7 +376,7 @@ def cuda_kernels_forward(
         return contextualized_states
 
     # fmt: off
-    def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCache | None = None, attention_mask: torch.LongTensor | None = None):
+    def slow_forward(self, input_states, cache_params: Cache | None = None, attention_mask: torch.LongTensor | None = None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # 1. Gated MLP's linear projection
@@ -485,23 +386,19 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
         if attention_mask is not None:
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-        use_cache = isinstance(cache_params, HybridMambaAttentionDynamicCache)
-        # 2. Convolution sequence transformation
-        if use_cache and cache_params.ssm_states[self.layer_idx].shape[0] == batch_size:
-            if self.training:
-                # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-                ssm_state = cache_params.ssm_states[self.layer_idx].clone()
-            else:
-                ssm_state = cache_params.ssm_states[self.layer_idx]
-
-            ssm_state = ssm_state.to(hidden_states.device)
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
+            # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
+            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.intermediate_size, self.ssm_state_size),
+                device=hidden_states.device, dtype=dtype
+            )
 
-            if cache_params.has_previous_state and seq_len == 1 and \
-                    cache_params.conv_states[self.layer_idx].shape[0] == batch_size:
-                conv_state = cache_params.conv_states[self.layer_idx]
-                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
-                conv_state[:, :, -1] = hidden_states[:, :, 0]
-                cache_params.conv_states[self.layer_idx] = conv_state
+        # 2. Convolution sequence transformation
+        if cache_params is not None:
+            if cache_params.has_previous_state and seq_len == 1:
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
                     hidden_states += self.conv1d.bias
@@ -511,13 +408,9 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.intermediate_size, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
 
         if attention_mask is not None:
@@ -552,8 +445,8 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
         scan_output = scan_output + (hidden_states * self.D[None, :, None])
         scan_output = (scan_output * self.act(gate))
 
-        if use_cache:
-            cache_params.ssm_states[self.layer_idx] = ssm_state
+        if cache_params is not None:
+            cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))
@@ -563,7 +456,7 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
     def forward(
         self,
         hidden_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         if self.config.use_mamba_kernels and (
@@ -690,7 +583,7 @@ def forward(
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.FloatTensor:
@@ -727,7 +620,7 @@ def forward(
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.FloatTensor:
         residual = hidden_states
@@ -804,7 +697,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -816,12 +709,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = HybridMambaAttentionDynamicCache(
-                config=self.config,
-                batch_size=inputs_embeds.shape[0],
-                dtype=inputs_embeds.dtype,
-                device=inputs_embeds.device,
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -866,7 +754,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
@@ -980,7 +868,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
diff --git a/src/transformers/models/jamba/modular_jamba.py b/src/transformers/models/jamba/modular_jamba.py
index da80fbd7187e..45d529d4ff01 100644
--- a/src/transformers/models/jamba/modular_jamba.py
+++ b/src/transformers/models/jamba/modular_jamba.py
@@ -17,13 +17,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections.abc import Callable
-from typing import Any
 
 import torch
 from torch import nn
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...integrations import lazy_load_kernel
 from ...masking_utils import create_causal_mask
 from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
@@ -47,100 +47,6 @@ class JambaRMSNorm(LlamaRMSNorm):
     pass
 
 
-class HybridMambaAttentionDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config, batch_size, dtype=torch.float16, device=None):
-        self.dtype = dtype
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False  # only used by mamba
-        intermediate_size = config.mamba_expand * config.hidden_size
-        ssm_state_size = config.mamba_d_state
-        conv_kernel_size = config.mamba_d_conv
-        self.conv_states = []
-        self.ssm_states = []
-        self.transformer_layers = []
-        for i in range(config.num_hidden_layers):
-            if self.layers_block_type[i] == "mamba":
-                self.conv_states += [
-                    torch.zeros(batch_size, intermediate_size, conv_kernel_size, device=device, dtype=dtype)
-                ]
-                self.ssm_states += [
-                    torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype)
-                ]
-            else:
-                self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.transformer_layers.append(i)
-
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    def __getitem__(self, layer_idx):
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Update the cache
-        if self.key_cache[layer_idx].shape[-1] == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-
 class JambaAttention(LlamaAttention):
     def __init__(self, config: JambaConfig, layer_idx: int):
         super().__init__(config, layer_idx)
@@ -153,7 +59,7 @@ def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         input_shape = hidden_states.shape[:-1]
@@ -264,17 +170,12 @@ def __init__(self, config: JambaConfig, layer_idx):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         batch_size, seq_len, _ = hidden_states.shape
         use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
         # 1. Gated MLP's linear projection
         projected_states = self.in_proj(hidden_states).transpose(1, 2)
@@ -291,7 +192,7 @@ def cuda_kernels_forward(
         if use_precomputed_states:
             hidden_states = causal_conv1d_update(
                 hidden_states.squeeze(-1),
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 conv_weights,
                 self.conv1d.bias,
                 self.activation,
@@ -300,7 +201,7 @@ def cuda_kernels_forward(
         else:
             if cache_params is not None:
                 conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                cache_params.update_conv_state(conv_states, self.layer_idx)
             hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation)
 
         if attention_mask is not None:
@@ -335,7 +236,7 @@ def cuda_kernels_forward(
         time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
         if use_precomputed_states:
             scan_outputs = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states[..., 0],
                 discrete_time_step[..., 0],
                 A,
@@ -360,7 +261,7 @@ def cuda_kernels_forward(
                 return_last_state=True,
             )
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -368,7 +269,7 @@ def cuda_kernels_forward(
         return contextualized_states
 
     # fmt: off
-    def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCache | None = None, attention_mask: torch.LongTensor | None = None):
+    def slow_forward(self, input_states, cache_params: Cache | None = None, attention_mask: torch.LongTensor | None = None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # 1. Gated MLP's linear projection
@@ -378,23 +279,19 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
         if attention_mask is not None:
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
-        use_cache = isinstance(cache_params, HybridMambaAttentionDynamicCache)
-        # 2. Convolution sequence transformation
-        if use_cache and cache_params.ssm_states[self.layer_idx].shape[0] == batch_size:
-            if self.training:
-                # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-                ssm_state = cache_params.ssm_states[self.layer_idx].clone()
-            else:
-                ssm_state = cache_params.ssm_states[self.layer_idx]
-
-            ssm_state = ssm_state.to(hidden_states.device)
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
+            # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
+            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.intermediate_size, self.ssm_state_size),
+                device=hidden_states.device, dtype=dtype
+            )
 
-            if cache_params.has_previous_state and seq_len == 1 and \
-                    cache_params.conv_states[self.layer_idx].shape[0] == batch_size:
-                conv_state = cache_params.conv_states[self.layer_idx]
-                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
-                conv_state[:, :, -1] = hidden_states[:, :, 0]
-                cache_params.conv_states[self.layer_idx] = conv_state
+        # 2. Convolution sequence transformation
+        if cache_params is not None:
+            if cache_params.has_previous_state and seq_len == 1:
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
                     hidden_states += self.conv1d.bias
@@ -404,13 +301,9 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.intermediate_size, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
 
         if attention_mask is not None:
@@ -445,8 +338,8 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
         scan_output = scan_output + (hidden_states * self.D[None, :, None])
         scan_output = (scan_output * self.act(gate))
 
-        if use_cache:
-            cache_params.ssm_states[self.layer_idx] = ssm_state
+        if cache_params is not None:
+            cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))
@@ -456,7 +349,7 @@ def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCa
     def forward(
         self,
         hidden_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.LongTensor | None = None,
     ):
         if self.config.use_mamba_kernels and (
@@ -535,7 +428,7 @@ def forward(
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.FloatTensor:
@@ -572,7 +465,7 @@ def forward(
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.FloatTensor:
         residual = hidden_states
@@ -649,7 +542,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -661,12 +554,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = HybridMambaAttentionDynamicCache(
-                config=self.config,
-                batch_size=inputs_embeds.shape[0],
-                dtype=inputs_embeds.dtype,
-                device=inputs_embeds.device,
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -711,7 +599,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
@@ -728,7 +616,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,

From a50293ca0498dc0e79663773f429ac5461a807d7 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 19:06:55 +0100
Subject: [PATCH 09/56] fix tests

---
 tests/models/jamba/test_modeling_jamba.py | 49 +++++++----------------
 1 file changed, 15 insertions(+), 34 deletions(-)

diff --git a/tests/models/jamba/test_modeling_jamba.py b/tests/models/jamba/test_modeling_jamba.py
index a71271dd3cbe..b562fe477ab9 100644
--- a/tests/models/jamba/test_modeling_jamba.py
+++ b/tests/models/jamba/test_modeling_jamba.py
@@ -43,14 +43,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        JambaForCausalLM,
-        JambaForSequenceClassification,
-        JambaModel,
-    )
-    from transformers.models.jamba.modeling_jamba import (
-        HybridMambaAttentionDynamicCache,
-    )
+    from transformers import JambaForCausalLM, JambaForSequenceClassification, JambaModel, DynamicCache
 
 
 class JambaConfigTester(ConfigTester):
@@ -250,17 +243,7 @@ def create_and_check_decoder_model_past_large_inputs(
         model.to(torch_device)
         model.eval()
 
-        # first forward pass
-        # Attention: Jamba needs the cache to be initialized to return a cache!
-        past_key_values = HybridMambaAttentionDynamicCache(
-            config, input_ids.shape[0], model.dtype, device=model.device
-        )
-        outputs = model(
-            input_ids,
-            attention_mask=input_mask,
-            past_key_values=past_key_values,
-            use_cache=True,
-        )
+        outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
         past_key_values = outputs.past_key_values
 
         # create hypothetical multiple next token and extent to next_input_ids
@@ -340,7 +323,7 @@ class JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     )
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, HybridMambaAttentionDynamicCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -353,18 +336,14 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         for idx in range(len(past_key_values)):
             if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-                self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
             else:
-                self.assertEqual(past_key_values.key_cache[idx].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[idx].shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(
-        self, cache1: HybridMambaAttentionDynamicCache, cache2: HybridMambaAttentionDynamicCache
-    ):
-        if not isinstance(cache1, HybridMambaAttentionDynamicCache) or not isinstance(
-            cache2, HybridMambaAttentionDynamicCache
-        ):
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -372,10 +351,12 @@ def _check_caches_are_equal(
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx])
-            torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx])
+            if config.layers_block_type[idx] == "mamba":
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
     def setUp(self):
         self.model_tester = JambaModelTester(self)

From 1607fe29487ffad3e389612d523dd6b2a10ccada Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 19:16:40 +0100
Subject: [PATCH 10/56] fixes

---
 src/transformers/cache_utils.py                 | 4 ++--
 src/transformers/models/jamba/modeling_jamba.py | 2 +-
 src/transformers/models/jamba/modular_jamba.py  | 2 +-
 tests/models/jamba/test_modeling_jamba.py       | 5 +++--
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 5079f81117a0..2b5d921b2a7e 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -955,14 +955,14 @@ def get_seq_length(self, layer_idx: int = 0) -> int:
 
     def has_previous_state(self, layer_idx: int | None = None) -> bool:
         """Returns whether the Mamba layer at index `layer_idx` has previous state or not."""
-        if layer_idx >= len(self.layers):
+        if layer_idx is not None and layer_idx >= len(self.layers):
             return False
 
         # In this case, use last Mamba layer
         if layer_idx is None:
             try:
                 layer_idx = next(
-                    idx for idx in range(len(self) - 1, -1, -1) if isinstance(self.layers[idx], CacheLayerMixin)
+                    idx for idx in range(len(self) - 1, -1, -1) if isinstance(self.layers[idx], MambaCacheLayerMixin)
                 )
             except StopIteration:
                 raise ValueError(
diff --git a/src/transformers/models/jamba/modeling_jamba.py b/src/transformers/models/jamba/modeling_jamba.py
index ef0b15b6856b..7a12072e6bdd 100755
--- a/src/transformers/models/jamba/modeling_jamba.py
+++ b/src/transformers/models/jamba/modeling_jamba.py
@@ -388,7 +388,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.intermediate_size, self.ssm_state_size),
diff --git a/src/transformers/models/jamba/modular_jamba.py b/src/transformers/models/jamba/modular_jamba.py
index 45d529d4ff01..40e2188175a8 100644
--- a/src/transformers/models/jamba/modular_jamba.py
+++ b/src/transformers/models/jamba/modular_jamba.py
@@ -281,7 +281,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.intermediate_size, self.ssm_state_size),
diff --git a/tests/models/jamba/test_modeling_jamba.py b/tests/models/jamba/test_modeling_jamba.py
index b562fe477ab9..499941fcbe81 100644
--- a/tests/models/jamba/test_modeling_jamba.py
+++ b/tests/models/jamba/test_modeling_jamba.py
@@ -43,7 +43,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import JambaForCausalLM, JambaForSequenceClassification, JambaModel, DynamicCache
+    from transformers import DynamicCache, JambaForCausalLM, JambaForSequenceClassification, JambaModel
+    from transformers.cache_utils import MambaLayer
 
 
 class JambaConfigTester(ConfigTester):
@@ -351,7 +352,7 @@ def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            if config.layers_block_type[idx] == "mamba":
+            if isinstance(cache1.layers[idx], MambaLayer):
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
                 torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
             else:

From ddc198af68e4836537ec654ff1a7a32b8d670966 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Mon, 23 Mar 2026 19:27:43 +0100
Subject: [PATCH 11/56] fix

---
 src/transformers/cache_utils.py                 | 7 +++++++
 src/transformers/models/jamba/modeling_jamba.py | 4 ++--
 src/transformers/models/jamba/modular_jamba.py  | 4 ++--
 3 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 2b5d921b2a7e..3229141f67c8 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -711,11 +711,18 @@ def reset(self) -> None:
             self.ssm_states.zero_()
         self.has_previous_state = False
 
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        if self.has_previous_state:
+            self.conv_states = self.conv_states.index_select(0, beam_idx.to(self.device))
+            self.ssm_states = self.ssm_states.index_select(0, beam_idx.to(self.device))
+
 
 class MambaLayer(MambaCacheLayerMixin):
     def lazy_initialization(self, conv_states: torch.Tensor, ssm_states: torch.Tensor) -> None:
         self.dtype, self.device = conv_states.dtype, conv_states.device
         self.conv_states = torch.tensor([], dtype=self.dtype, device=self.device)
+        self.ssm_states = torch.tensor([], dtype=self.dtype, device=self.device)
         self.is_initialized = True
 
     def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
diff --git a/src/transformers/models/jamba/modeling_jamba.py b/src/transformers/models/jamba/modeling_jamba.py
index 7a12072e6bdd..753e703ff715 100755
--- a/src/transformers/models/jamba/modeling_jamba.py
+++ b/src/transformers/models/jamba/modeling_jamba.py
@@ -397,7 +397,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            if cache_params.has_previous_state and seq_len == 1:
+            if cache_params.has_previous_state(self.layer_idx) and seq_len == 1:
                 conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
@@ -408,7 +408,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
-                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
         else:
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
diff --git a/src/transformers/models/jamba/modular_jamba.py b/src/transformers/models/jamba/modular_jamba.py
index 40e2188175a8..2a5b21aad005 100644
--- a/src/transformers/models/jamba/modular_jamba.py
+++ b/src/transformers/models/jamba/modular_jamba.py
@@ -290,7 +290,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
 
         # 2. Convolution sequence transformation
         if cache_params is not None:
-            if cache_params.has_previous_state and seq_len == 1:
+            if cache_params.has_previous_state(self.layer_idx) and seq_len == 1:
                 conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
@@ -301,7 +301,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
-                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
         else:
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])

From bae4a78d64d2e8a3b953c58ea83c81b045906f71 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 11:16:48 +0100
Subject: [PATCH 12/56] fix

---
 src/transformers/cache_utils.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 3229141f67c8..dfb03d4eb311 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -719,8 +719,10 @@ def reorder_cache(self, beam_idx: torch.LongTensor):
 
 
 class MambaLayer(MambaCacheLayerMixin):
-    def lazy_initialization(self, conv_states: torch.Tensor, ssm_states: torch.Tensor) -> None:
+    def lazy_initialization(self, conv_states: torch.Tensor) -> None:
         self.dtype, self.device = conv_states.dtype, conv_states.device
+        # Even if prefill is larfer/shorter than the conv_size, the tensor is always either padded or truncated
+        self.conv_kernel_size = conv_states.shape[-1]
         self.conv_states = torch.tensor([], dtype=self.dtype, device=self.device)
         self.ssm_states = torch.tensor([], dtype=self.dtype, device=self.device)
         self.is_initialized = True
@@ -737,7 +739,7 @@ def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor
         """
         # Lazy initialization
         if not self.is_initialized:
-            self.lazy_initialization(conv_states, conv_states)
+            self.lazy_initialization(conv_states)
 
         # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
         # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
@@ -746,9 +748,13 @@ def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor
             self.conv_states = conv_states
             self.has_previous_state = True
         else:
-            new_conv_states = self.conv_states.roll(shifts=-1, dims=-1)
-            new_conv_states[:, :, -1:] = conv_states
-            self.conv_states = new_conv_states
+            num_new_tokens = conv_states.shape[-1]
+            if num_new_tokens >= self.conv_kernel_size:
+                self.conv_states = conv_states[..., -self.conv_kernel_size :]
+            else:
+                new_conv_states = self.conv_states.roll(shifts=-num_new_tokens, dims=-1)
+                new_conv_states[:, :, -num_new_tokens:] = conv_states
+                self.conv_states = new_conv_states
 
         return self.conv_states
 
@@ -762,12 +768,7 @@ def update_ssm_state(self, ssm_states: torch.Tensor, **kwargs) -> torch.Tensor:
         Returns:
             `torch.Tensor`: The updated ssm states.
         """
-        # Lazy initialization
-        if not self.is_initialized:
-            self.lazy_initialization(ssm_states, ssm_states)
-
         self.ssm_states = ssm_states
-
         return self.ssm_states
 
 

From 984b578a8e807e7e2dae077d118c7f5d446e56c6 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 11:54:57 +0100
Subject: [PATCH 13/56] fix

---
 src/transformers/models/mamba2/modeling_mamba2.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 41d97923c120..43b70dab9185 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -411,12 +411,13 @@ def torch_forward(
         _, _, gate, hidden_states_B_C, dt = projected_states.split(
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
+        hidden_states_B_C = hidden_states_B_C.transpose(1,2)
 
         is_decoding = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
         # 2. Convolution sequence transformation
         if is_decoding:
-            conv_states = cache_params.update_conv_state(hidden_states_B_C[:, 0:1, :], layer_idx=self.layer_idx)
+            conv_states = cache_params.update_conv_state(hidden_states_B_C, layer_idx=self.layer_idx)
 
             hidden_states_B_C = torch.sum(
                 conv_states * self.conv1d.weight.squeeze(1), dim=-1
@@ -427,13 +428,12 @@ def torch_forward(
         else:
             # Init cache
             if cache_params is not None:
-                hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                 conv_states = nn.functional.pad(
-                    hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+                    hidden_states_B_C, (self.conv_kernel_size - hidden_states_B_C.shape[-1], 0)
                 )
                 cache_params.update_conv_state(conv_states, layer_idx=self.layer_idx)
 
-            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C)[..., :seq_len].transpose(1, 2))
 
         hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
         hidden_states, B, C = torch.split(

From cac5d17c0b03c0bb68aa7f57fad204b99df63587 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 13:00:14 +0100
Subject: [PATCH 14/56] combine both types + zambas

---
 src/transformers/cache_utils.py               |  35 +++-
 .../models/zamba/modeling_zamba.py            | 190 +++---------------
 .../models/zamba2/modeling_zamba2.py          | 182 ++++-------------
 .../models/zamba2/modular_zamba2.py           | 132 +++---------
 4 files changed, 127 insertions(+), 412 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index dfb03d4eb311..42c1d8acf849 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -772,6 +772,26 @@ def update_ssm_state(self, ssm_states: torch.Tensor, **kwargs) -> torch.Tensor:
         return self.ssm_states
 
 
+class MambaAndAttentionLayer(MambaLayer, DynamicLayer):
+    def __init__(self):
+        DynamicLayer.__init__(self)
+        MambaLayer.__init__(self)
+
+    def lazy_initialization(self, states_1: torch.Tensor, states_2: torch.Tensor | None = None) -> None:
+        MambaLayer.lazy_initialization(self, states_1)
+        self.keys = torch.tensor([], dtype=self.dtype, device=self.device)
+        self.values = torch.tensor([], dtype=self.dtype, device=self.device)
+
+    def reset(self) -> None:
+        MambaLayer.reset(self)
+        DynamicLayer.reset(self)
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        MambaLayer.reorder_cache(self, beam_idx)
+        DynamicLayer.reorder_cache(self, beam_idx)
+
+
 class Cache:
     """
     A `Cache` is mostly a list of `CacheLayerMixin` objects, one per model layer. It serves as a container for
@@ -942,8 +962,8 @@ def get_seq_length(self, layer_idx: int = 0) -> int:
         if layer_idx >= len(self.layers):
             return 0
 
-        # For Hybrid attention-mamba caches, `get_seq_length` needs to use attention layer idx when called with default layer_idx
-        if isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+        # For alternating attention-mamba caches, `get_seq_length` needs to use attention layer idx when called with default layer_idx
+        if not isinstance(self.layers[layer_idx], CacheLayerMixin):
             # If this is called with non-default arg, raise
             if layer_idx != 0:
                 raise ValueError(
@@ -977,6 +997,11 @@ def has_previous_state(self, layer_idx: int | None = None) -> bool:
                     "`has_previous_state` can only be called on Mamba layers, and the current Cache seem to only contain "
                     "Attention layers."
                 )
+        elif not isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+            raise ValueError(
+                f"You called `has_previous_state` on layer index {layer_idx}, but this layer is an Attention layer, which "
+                "does not support calling it."
+            )
 
         return self.layers[layer_idx].has_previous_state
 
@@ -991,8 +1016,8 @@ def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
         if layer_idx >= len(self.layers):
             return query_length, 0
 
-        # For Hybrid attention-mamba caches, `get_seq_length` needs to use attention layer idx when called with default layer_idx
-        if isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+        # For alternating attention-mamba caches, `get_mask_sizes` needs to use attention layer idx when called with default layer_idx
+        if not isinstance(self.layers[layer_idx], CacheLayerMixin):
             # If this is called with non-default arg, raise
             if layer_idx != 0:
                 raise ValueError(
@@ -1163,6 +1188,8 @@ def __init__(
                     layers.append(DynamicSlidingWindowLayer(sliding_window=sliding_window))
                 elif layer_type in ("mamba", "conv"):
                     layers.append(MambaLayer())
+                elif layer_type == "hybrid":
+                    layers.append(MambaAndAttentionLayer())
                 else:
                     layers.append(DynamicLayer())
 
diff --git a/src/transformers/models/zamba/modeling_zamba.py b/src/transformers/models/zamba/modeling_zamba.py
index c985236ff0f7..dd6bf0ed5414 100644
--- a/src/transformers/models/zamba/modeling_zamba.py
+++ b/src/transformers/models/zamba/modeling_zamba.py
@@ -20,7 +20,6 @@
 
 import math
 from collections.abc import Callable
-from typing import Any
 
 import torch
 from torch import nn
@@ -28,7 +27,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations.hub_kernels import lazy_load_kernel
 from ...masking_utils import create_causal_mask
@@ -80,107 +79,6 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
     return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
 
 
-class ZambaHybridDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config, batch_size, dtype=torch.float16, device=None):
-        self.dtype = dtype
-        self.is_compileable = False
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False  # only used by mamba
-        self.intermediate_size = config.mamba_expand * config.hidden_size
-        self.ssm_state_size = config.mamba_d_state
-        self.conv_kernel_size = config.mamba_d_conv
-        self.n_mamba_heads = config.n_mamba_heads
-        self.conv_states = []
-        self.ssm_states = []
-        self.transformer_layers = []
-        self._modules = {}
-        self._parameters = {}
-        self._buffers = {}
-        for i in range(config.num_hidden_layers):
-            self.conv_states += [
-                torch.zeros(batch_size, self.intermediate_size, self.conv_kernel_size, device=device, dtype=dtype)
-            ]
-            cache_shape = (
-                batch_size,
-                self.n_mamba_heads,
-                self.intermediate_size // self.n_mamba_heads,
-                self.ssm_state_size,
-            )
-            self.ssm_states += [torch.zeros(cache_shape, device=device, dtype=dtype)]
-            if self.layers_block_type[i] == "hybrid":
-                self.transformer_layers.append(i)
-
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.update
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Update the cache
-        if self.key_cache[layer_idx].shape[-1] == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.reorder_cache
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.get_seq_length
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    # Copied from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache.get_mask_sizes
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-
 def eager_attention_forward(
     module: nn.Module,
     query: torch.Tensor,
@@ -243,7 +141,7 @@ def forward(
         hidden_states: torch.Tensor,
         layer_idx: int,
         attention_mask: torch.Tensor | None,
-        past_key_values: ZambaHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
         input_shape = hidden_states.shape[:-1]
@@ -369,7 +267,7 @@ def __init__(self, config: ZambaConfig, layer_idx):
             )
 
     def cuda_kernels_forward(
-        self, hidden_states: torch.Tensor, cache_params: ZambaHybridDynamicCache = None, attention_mask=None
+        self, hidden_states: torch.Tensor, cache_params: Cache | None = None, attention_mask=None
     ):
         batch_size, seq_len, _ = hidden_states.shape
         use_precomputed_states = cache_params is not None and cache_params.has_previous_state and seq_len == 1
@@ -387,7 +285,7 @@ def cuda_kernels_forward(
         if use_precomputed_states:
             hidden_states = causal_conv1d_update(
                 hidden_states.squeeze(-1),
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 conv_weights,
                 self.conv1d.bias,
                 self.activation,
@@ -398,7 +296,7 @@ def cuda_kernels_forward(
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
             if cache_params is not None:
                 conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
             hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation)
             if attention_mask is not None and not torch.all(attention_mask == 1):
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
@@ -424,7 +322,7 @@ def cuda_kernels_forward(
         if use_precomputed_states:
             for n in range(self.n_mamba_heads):
                 scan_outputs_ = selective_state_update(
-                    cache_params.ssm_states[self.layer_idx][:, n],
+                    cache_params.layers[self.layer_idx].ssm_states[:, n],
                     hidden_states[n, ..., 0],
                     discrete_time_step[n, ..., 0],
                     A[n],
@@ -459,13 +357,13 @@ def cuda_kernels_forward(
                 scan_outputs = torch.cat((scan_outputs, scan_outputs_), dim=1).contiguous()
                 ssm_state = torch.cat((ssm_state, ssm_state_.unsqueeze(1)), dim=1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
         return contextualized_states
 
-    def slow_forward(self, input_states, cache_params: ZambaHybridDynamicCache = None, attention_mask=None):
+    def slow_forward(self, input_states, cache_params: Cache | None = None, attention_mask=None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # 1. Gated linear projection
@@ -476,26 +374,18 @@ def slow_forward(self, input_states, cache_params: ZambaHybridDynamicCache = Non
         gate = gate.squeeze(2)
         gate = gate.reshape(batch_size, self.n_mamba_heads, -1, seq_len).transpose(0, 1)
 
-        use_cache = isinstance(cache_params, ZambaHybridDynamicCache)
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
+            # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
+            )
+
         # 2. Convolution sequence transformation
-        if use_cache and cache_params.ssm_states[self.layer_idx].shape[0] == batch_size:
-            if self.training:
-                # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-                ssm_state = cache_params.ssm_states[self.layer_idx].clone()
-            else:
-                ssm_state = cache_params.ssm_states[self.layer_idx]
-
-            ssm_state = ssm_state.to(hidden_states.device)
-
-            if (
-                cache_params.has_previous_state
-                and seq_len == 1
-                and cache_params.conv_states[self.layer_idx].shape[0] == batch_size
-            ):
-                conv_state = cache_params.conv_states[self.layer_idx]
-                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
-                conv_state[:, :, -1] = hidden_states[:, :, 0]
-                cache_params.conv_states[self.layer_idx] = conv_state
+        if cache_params is not None:
+            if cache_params.has_previous_state(self.layer_idx) and seq_len == 1:
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
                     hidden_states += self.conv1d.bias
@@ -504,16 +394,11 @@ def slow_forward(self, input_states, cache_params: ZambaHybridDynamicCache = Non
                 if attention_mask is not None:
                     hidden_states = hidden_states * attention_mask[:, -hidden_states.shape[-1] :].unsqueeze(1)
                 conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
                 if attention_mask is not None:
                     hidden_states = hidden_states * attention_mask[:, -hidden_states.shape[-1] :].unsqueeze(1)
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.n_mamba_heads, self.mamba_head_dim, self.ssm_state_size),
-                device=hidden_states.device,
-                dtype=dtype,
-            )
             if attention_mask is not None:
                 hidden_states = hidden_states * attention_mask.unsqueeze(1)
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
@@ -549,8 +434,8 @@ def slow_forward(self, input_states, cache_params: ZambaHybridDynamicCache = Non
         scan_output = scan_output + (hidden_states * self.D[:, None, :, None])
         scan_output = scan_output * self.act(gate)
 
-        if use_cache:
-            cache_params.ssm_states[self.layer_idx] = ssm_state
+        if cache_params is not None:
+            cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(
@@ -558,7 +443,7 @@ def slow_forward(self, input_states, cache_params: ZambaHybridDynamicCache = Non
         )
         return contextualized_states
 
-    def forward(self, hidden_states, cache_params: ZambaHybridDynamicCache = None, attention_mask=None, **kwargs):
+    def forward(self, hidden_states, cache_params: Cache | None = None, attention_mask=None, **kwargs):
         is_fast_path_available = all(
             (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
         )
@@ -606,7 +491,7 @@ def forward(
         original_hidden_states: torch.Tensor,
         layer_idx: int,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: ZambaHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
@@ -620,7 +505,7 @@ def forward(
             layer_idx (`int`): layer_idx in the forward pass. Used to distinguish Zamba's tied transformer layers.
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`ZambaHybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -656,7 +541,7 @@ def forward(
         layer_idx: int | None = None,
         attention_mask: torch.Tensor | None = None,
         causal_mask: torch.Tensor | None = None,
-        past_key_values: ZambaHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_ids: torch.LongTensor | None = None,
         transformer_hidden_states: torch.Tensor | None = None,
@@ -667,7 +552,7 @@ def forward(
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`ZambaHybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -708,7 +593,7 @@ def forward(
         layer_idx: int | None = None,
         attention_mask: torch.Tensor | None = None,
         causal_mask: torch.Tensor | None = None,
-        past_key_values: ZambaHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
@@ -720,7 +605,7 @@ def forward(
             layer_idx (`int`): layer number.
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`ZambaHybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -758,7 +643,6 @@ class ZambaPreTrainedModel(PreTrainedModel):
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn = False
     _supports_sdpa = False
-    # Note: only supports ZambaHybridDynamicCache
     _is_stateful = True
     _can_record_outputs = {
         "hidden_states": ZambaMambaDecoderLayer,
@@ -835,7 +719,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: ZambaHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -854,10 +738,7 @@ def forward(
         # original_hidden_states: word embedding output that will be concatenated with hidden activations to form the input of the shared transformer layer
 
         if use_cache and past_key_values is None:
-            logger.warning_once(
-                "Zamba requires an initialized `ZambaHybridDynamicCache` to return a cache. None was "
-                "provided, so no cache will be returned."
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -915,7 +796,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: ZambaHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
@@ -987,13 +868,6 @@ def prepare_inputs_for_generation(
         is_first_iteration=False,
         **kwargs,
     ):
-        # Overwritten -- has a unique cache type, `ZambaHybridDynamicCache`
-
-        if past_key_values is None:
-            past_key_values = ZambaHybridDynamicCache(
-                self.config, input_ids.shape[0], dtype=self.dtype, device=self.device
-            )
-
         kwargs["logits_to_keep"] = self.config.num_logits_to_keep
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index 775d0d45d009..c882353a8639 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -21,13 +21,14 @@
 import math
 from collections.abc import Callable
 from itertools import cycle
-from typing import Any, Optional
+from typing import Optional
 
 import torch
 from torch import nn
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_func_from_hub
 from ...integrations.hub_kernels import lazy_load_kernel
@@ -88,107 +89,6 @@ def extra_repr(self):
         return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
 
 
-class Zamba2HybridDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(
-        self, config: Zamba2Config, batch_size: int, dtype: torch.dtype = torch.float16, device: str | None = None
-    ):
-        self.dtype = dtype
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False
-        self.intermediate_size = int(config.mamba_expand * config.hidden_size)
-        self.ssm_state_size = config.mamba_d_state
-        self.conv_kernel_size = config.mamba_d_conv
-        self.n_mamba_heads = config.n_mamba_heads
-        self.transformer_layers = []
-        self._modules = {}
-        self._parameters = {}
-        self._buffers = {}
-        self.conv_states = {}
-        self.ssm_states = {}
-        for i in range(config.num_hidden_layers):
-            self.conv_states[i] = torch.zeros(
-                batch_size,
-                self.intermediate_size + 2 * config.mamba_ngroups * config.mamba_d_state,
-                self.conv_kernel_size,
-                device=device,
-                dtype=dtype,
-            )
-            self.ssm_states[i] = torch.zeros(
-                batch_size, self.n_mamba_heads, config.mamba_headdim, self.ssm_state_size, device=device, dtype=dtype
-            )
-            if self.layers_block_type[i] == "hybrid":
-                self.transformer_layers.append(i)
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Update the cache
-        if self.key_cache[layer_idx].shape[-1] == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-    def reset(self):
-        self.conv_states.zero_()
-        self.ssm_states.zero_()
-
-
 class Zamba2RotaryEmbedding(nn.Module):
     inv_freq: torch.Tensor  # fix linting for `register_buffer`
 
@@ -401,7 +301,7 @@ def forward(
         hidden_states: torch.Tensor,
         layer_idx: int,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
@@ -604,7 +504,7 @@ def __init__(self, config: Zamba2Config, layer_idx: int | None = None):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Zamba2HybridDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         # set up dimensions for reshapes later
@@ -614,7 +514,7 @@ def cuda_kernels_forward(
         d_to_remove = 2 * self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.num_heads
 
         # getting projected states from cache if it exists
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             in_projected_states = self.in_proj(hidden_states.squeeze(1))  # (B 2D)
             d_mlp = (in_projected_states.shape[-1] - d_to_remove) // 2
             split_projection_dim = [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads]
@@ -622,7 +522,7 @@ def cuda_kernels_forward(
 
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -643,7 +543,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -707,7 +607,7 @@ def cuda_kernels_forward(
                     conv_state = nn.functional.pad(
                         hidden_states_B_C_t, (self.conv_kernel_size - hidden_states_B_C_t.shape[-1], 0)
                     )
-                    cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                    conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 if causal_conv1d_fn is None or self.activation not in ["silu", "swish"]:
                     hidden_states_B_C = self.act(
                         self.conv1d(hidden_states_B_C.transpose(1, 2)).transpose(1, 2)[:, :seq_len]
@@ -744,7 +644,7 @@ def cuda_kernels_forward(
                     **dt_limit_kwargs,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 scan_output = self.norm(scan_output, gate)
@@ -752,11 +652,11 @@ def cuda_kernels_forward(
         return out
 
     # fmt: off
-    def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | None=None, attention_mask: torch.Tensor | None=None):
+    def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None=None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # Gated MLP's linear projection
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             projected_states = self.in_proj(input_states.squeeze(1))
         else:
             if attention_mask is not None:
@@ -768,17 +668,15 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
 
+        has_previous_state = cache_params.has_previous_state(self.layer_idx)
+
         # Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if cache_params.has_previous_state:
+            if has_previous_state:
                 gate = gate.unsqueeze(1)
-                conv_state = cache_params.conv_states[self.layer_idx]                   # [batch, intermediate_size, conv_kernel_size]
-                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
-                # handle batched generation - states are copied through
-                conv_state[:, :, -1] = hidden_states[:, 0, :] if hidden_states.ndim == 3 else hidden_states
-                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
                     hidden_states += self.conv1d.bias
@@ -789,7 +687,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :]     # [batch, intermediate_size, seq_len]
                 if attention_mask is not None:
                     dtype = hidden_states.dtype
@@ -803,7 +701,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
         hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
         A = -torch.exp(self.A_log.float())                            # [num_heads]
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and has_previous_state:
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
             dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
@@ -832,9 +730,8 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_state = ssm_state * dA + dBx
+            ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -843,7 +740,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_state.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -904,10 +801,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None]
             # permute back B * decay states
             states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None]  * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3)
-            if cache_params is not None and cache_params.has_previous_state:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...]
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
 
@@ -935,7 +829,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
                 y = y[:, :seq_len, :, :]
             y = y.reshape(batch_size, seq_len, -1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
@@ -949,7 +843,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
     def forward(
         self,
         hidden_states,
-        cache_params: Zamba2HybridDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -1017,7 +911,7 @@ def forward(
         original_hidden_states: torch.Tensor,
         layer_idx: int,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         position_embeddings: torch.LongTensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.FloatTensor]:
@@ -1030,7 +924,7 @@ def forward(
                 (see fig. 2 in https://huggingface.co/papers/2405.16712).
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`Zamba2HybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -1069,7 +963,7 @@ def forward(
         layer_idx: int | None = None,
         attention_mask: torch.Tensor | None = None,
         causal_mask: torch.Tensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_ids: torch.LongTensor | None = None,
         transformer_hidden_states: torch.Tensor | None = None,
@@ -1080,7 +974,7 @@ def forward(
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`Zamba2HybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -1123,7 +1017,7 @@ def forward(
         layer_idx: int | None = None,
         attention_mask: torch.Tensor | None = None,
         causal_mask: torch.Tensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_embeddings: torch.LongTensor | None = None,
         position_ids: torch.LongTensor | None = None,
@@ -1137,7 +1031,7 @@ def forward(
             layer_idx (`int`): layer number.
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`Zamba2HybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -1245,7 +1139,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -1264,8 +1158,7 @@ def forward(
         # original_hidden_states: word embedding output that will be concatenated with hidden activations to form the input of the shared transformer layer
 
         if use_cache and past_key_values is None:
-            batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]
-            past_key_values = Zamba2HybridDynamicCache(self.config, batch_size, dtype=self.dtype, device=self.device)
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -1365,7 +1258,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
@@ -1437,13 +1330,6 @@ def prepare_inputs_for_generation(
         is_first_iteration=False,
         **kwargs,
     ):
-        # Overwritten -- has a unique cache type, `Zamba2HybridDynamicCache`
-
-        if past_key_values is None:
-            past_key_values = Zamba2HybridDynamicCache(
-                self.config, input_ids.shape[0], dtype=self.dtype, device=self.device
-            )
-
         kwargs["logits_to_keep"] = self.config.num_logits_to_keep
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
@@ -1490,7 +1376,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index 2d57dc94046c..fd32a7d217a8 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -21,6 +21,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...integrations.hub_kernels import lazy_load_kernel
 from ...masking_utils import create_causal_mask
 from ...modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast
@@ -37,7 +38,6 @@
     ZambaAttentionDecoderLayer,
     ZambaForCausalLM,
     ZambaForSequenceClassification,
-    ZambaHybridDynamicCache,
     ZambaHybridLayer,
     ZambaMambaDecoderLayer,
     ZambaModel,
@@ -77,71 +77,6 @@ class Zamba2RMSNorm(ZambaRMSNorm):
     pass
 
 
-class Zamba2HybridDynamicCache(ZambaHybridDynamicCache):
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    def __init__(
-        self, config: Zamba2Config, batch_size: int, dtype: torch.dtype = torch.float16, device: str | None = None
-    ):
-        self.dtype = dtype
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False
-        self.intermediate_size = int(config.mamba_expand * config.hidden_size)
-        self.ssm_state_size = config.mamba_d_state
-        self.conv_kernel_size = config.mamba_d_conv
-        self.n_mamba_heads = config.n_mamba_heads
-        self.transformer_layers = []
-        self._modules = {}
-        self._parameters = {}
-        self._buffers = {}
-        self.conv_states = {}
-        self.ssm_states = {}
-        for i in range(config.num_hidden_layers):
-            self.conv_states[i] = torch.zeros(
-                batch_size,
-                self.intermediate_size + 2 * config.mamba_ngroups * config.mamba_d_state,
-                self.conv_kernel_size,
-                device=device,
-                dtype=dtype,
-            )
-            self.ssm_states[i] = torch.zeros(
-                batch_size, self.n_mamba_heads, config.mamba_headdim, self.ssm_state_size, device=device, dtype=dtype
-            )
-            if self.layers_block_type[i] == "hybrid":
-                self.transformer_layers.append(i)
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def reset(self):
-        self.conv_states.zero_()
-        self.ssm_states.zero_()
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-
 class Zamba2RotaryEmbedding(LlamaRotaryEmbedding):
     pass
 
@@ -208,7 +143,7 @@ def forward(
         hidden_states: torch.Tensor,
         layer_idx: int,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
@@ -357,7 +292,7 @@ def __init__(self, config: Zamba2Config, layer_idx: int | None = None):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Zamba2HybridDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         # set up dimensions for reshapes later
@@ -367,7 +302,7 @@ def cuda_kernels_forward(
         d_to_remove = 2 * self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.num_heads
 
         # getting projected states from cache if it exists
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             in_projected_states = self.in_proj(hidden_states.squeeze(1))  # (B 2D)
             d_mlp = (in_projected_states.shape[-1] - d_to_remove) // 2
             split_projection_dim = [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads]
@@ -375,7 +310,7 @@ def cuda_kernels_forward(
 
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -396,7 +331,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -460,7 +395,7 @@ def cuda_kernels_forward(
                     conv_state = nn.functional.pad(
                         hidden_states_B_C_t, (self.conv_kernel_size - hidden_states_B_C_t.shape[-1], 0)
                     )
-                    cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                    conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 if causal_conv1d_fn is None or self.activation not in ["silu", "swish"]:
                     hidden_states_B_C = self.act(
                         self.conv1d(hidden_states_B_C.transpose(1, 2)).transpose(1, 2)[:, :seq_len]
@@ -497,7 +432,7 @@ def cuda_kernels_forward(
                     **dt_limit_kwargs,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 scan_output = self.norm(scan_output, gate)
@@ -505,11 +440,11 @@ def cuda_kernels_forward(
         return out
 
     # fmt: off
-    def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | None=None, attention_mask: torch.Tensor | None=None):
+    def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None=None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # Gated MLP's linear projection
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             projected_states = self.in_proj(input_states.squeeze(1))
         else:
             if attention_mask is not None:
@@ -521,17 +456,15 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
 
+        has_previous_state = cache_params.has_previous_state(self.layer_idx)
+
         # Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state.to(hidden_states.device)
-            if cache_params.has_previous_state:
+            if has_previous_state:
                 gate = gate.unsqueeze(1)
-                conv_state = cache_params.conv_states[self.layer_idx]                   # [batch, intermediate_size, conv_kernel_size]
-                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
-                # handle batched generation - states are copied through
-                conv_state[:, :, -1] = hidden_states[:, 0, :] if hidden_states.ndim == 3 else hidden_states
-                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
                 hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
                 if self.use_conv_bias:
                     hidden_states += self.conv1d.bias
@@ -542,7 +475,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :]     # [batch, intermediate_size, seq_len]
                 if attention_mask is not None:
                     dtype = hidden_states.dtype
@@ -556,7 +489,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
         hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
         A = -torch.exp(self.A_log.float())                            # [num_heads]
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and has_previous_state:
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
             dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
@@ -585,9 +518,8 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_state = ssm_state * dA + dBx
+            ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -596,7 +528,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_state.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -657,10 +589,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
             B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None]
             # permute back B * decay states
             states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None]  * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3)
-            if cache_params is not None and cache_params.has_previous_state:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...]
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
 
@@ -688,7 +617,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
                 y = y[:, :seq_len, :, :]
             y = y.reshape(batch_size, seq_len, -1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
@@ -702,7 +631,7 @@ def torch_forward(self, input_states, cache_params: Zamba2HybridDynamicCache | N
     def forward(
         self,
         hidden_states,
-        cache_params: Zamba2HybridDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -768,7 +697,7 @@ def forward(
         original_hidden_states: torch.Tensor,
         layer_idx: int,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         position_embeddings: torch.LongTensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.FloatTensor]:
@@ -781,7 +710,7 @@ def forward(
                 (see fig. 2 in https://huggingface.co/papers/2405.16712).
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`Zamba2HybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -828,7 +757,7 @@ def forward(
         layer_idx: int | None = None,
         attention_mask: torch.Tensor | None = None,
         causal_mask: torch.Tensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_embeddings: torch.LongTensor | None = None,
         position_ids: torch.LongTensor | None = None,
@@ -842,7 +771,7 @@ def forward(
             layer_idx (`int`): layer number.
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`Zamba2HybridDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -983,7 +912,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -1002,8 +931,7 @@ def forward(
         # original_hidden_states: word embedding output that will be concatenated with hidden activations to form the input of the shared transformer layer
 
         if use_cache and past_key_values is None:
-            batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]
-            past_key_values = Zamba2HybridDynamicCache(self.config, batch_size, dtype=self.dtype, device=self.device)
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -1069,7 +997,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Zamba2HybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,

From bd8f9e9a75997866c54dc086e2d70e6ee0ca25e8 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 13:03:13 +0100
Subject: [PATCH 15/56] =?UTF-8?q?add=20config=20map=C3=A8ping?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/transformers/models/zamba/configuration_zamba.py   | 1 +
 src/transformers/models/zamba2/configuration_zamba2.py | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/transformers/models/zamba/configuration_zamba.py b/src/transformers/models/zamba/configuration_zamba.py
index f0793f9c165e..2fd99acf2207 100644
--- a/src/transformers/models/zamba/configuration_zamba.py
+++ b/src/transformers/models/zamba/configuration_zamba.py
@@ -53,6 +53,7 @@ class ZambaConfig(PreTrainedConfig):
 
     model_type = "zamba"
     keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"layer_types": "layers_block_type"}
 
     vocab_size: int = 32000
     tie_word_embeddings: bool = True
diff --git a/src/transformers/models/zamba2/configuration_zamba2.py b/src/transformers/models/zamba2/configuration_zamba2.py
index d5f2673dc08d..f9b56fabf6d4 100644
--- a/src/transformers/models/zamba2/configuration_zamba2.py
+++ b/src/transformers/models/zamba2/configuration_zamba2.py
@@ -66,7 +66,7 @@ class Zamba2Config(PreTrainedConfig):
     ```"""
 
     model_type = "zamba2"
-    attribute_map = {"head_dim": "attention_head_dim"}
+    attribute_map = {"head_dim": "attention_head_dim", "layer_types": "layers_block_type"}
     keys_to_ignore_at_inference = ["past_key_values"]
 
     vocab_size: int = 32000

From b2f1bb8edd26dc362093a1d23dd89b7fd3ad5a37 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 13:12:55 +0100
Subject: [PATCH 16/56] adjust tests

---
 tests/models/zamba/test_modeling_zamba.py   | 43 ++++++++++-----------
 tests/models/zamba2/test_modeling_zamba2.py | 43 ++++++++++-----------
 2 files changed, 42 insertions(+), 44 deletions(-)

diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index c5d0c81be98c..2bd7dd2c3bc9 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -38,14 +38,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        ZambaForCausalLM,
-        ZambaForSequenceClassification,
-        ZambaModel,
-    )
-    from transformers.models.zamba.modeling_zamba import (
-        ZambaHybridDynamicCache,
-    )
+    from transformers import DynamicCache, ZambaForCausalLM, ZambaForSequenceClassification, ZambaModel
+    from transformers.cache_utils import MambaLayer
 
 
 class ZambaModelTester:
@@ -212,12 +206,9 @@ def create_and_check_decoder_model_past_large_inputs(
         model.eval()
 
         # first forward pass
-        # Attention: Zamba needs the cache to be initialized to return a cache!
-        past_key_values = ZambaHybridDynamicCache(config, input_ids.shape[0], model.dtype, device=model.device)
         outputs = model(
             input_ids,
             attention_mask=input_mask,
-            past_key_values=past_key_values,
             use_cache=True,
         )
         past_key_values = outputs.past_key_values
@@ -299,7 +290,7 @@ class ZambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     )
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, ZambaHybridDynamicCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -314,14 +305,16 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         for idx in range(len(past_key_values)):
             if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-                self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
             else:
-                self.assertEqual(past_key_values.key_cache[idx].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[idx].shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
+                self.assertEqual(past_key_values.layers[idx].key_cache.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].value_cache.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1: ZambaHybridDynamicCache, cache2: ZambaHybridDynamicCache):
-        if not isinstance(cache1, ZambaHybridDynamicCache) or not isinstance(cache2, ZambaHybridDynamicCache):
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -329,10 +322,16 @@ def _check_caches_are_equal(self, cache1: ZambaHybridDynamicCache, cache2: Zamba
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx])
-            torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx])
+            # Mamba layer
+            if type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Hybrid mamba + attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].key_cache, cache2.layers[idx].key_cache)
+                torch.testing.assert_close(cache1.layers[idx].value_cache, cache2.layers[idx].value_cache)
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
 
     def setUp(self):
         self.model_tester = ZambaModelTester(self)
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index 8a9d168fe0c5..632855f2138d 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -39,14 +39,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        Zamba2ForCausalLM,
-        Zamba2ForSequenceClassification,
-        Zamba2Model,
-    )
-    from transformers.models.zamba2.modeling_zamba2 import (
-        Zamba2HybridDynamicCache,
-    )
+    from transformers import DynamicCache, Zamba2ForCausalLM, Zamba2ForSequenceClassification, Zamba2Model
+    from transformers.cache_utils import MambaLayer
 
 
 class Zamba2ModelTester:
@@ -222,12 +216,9 @@ def create_and_check_decoder_model_past_large_inputs(
         model.eval()
 
         # first forward pass
-        # Attention: Zamba2 needs the cache to be initialized to return a cache!
-        past_key_values = Zamba2HybridDynamicCache(config, input_ids.shape[0], model.dtype, device=model.device)
         outputs = model(
             input_ids,
             attention_mask=input_mask,
-            past_key_values=past_key_values,
             use_cache=True,
         )
         past_key_values = outputs.past_key_values
@@ -309,7 +300,7 @@ class Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
     )
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, Zamba2HybridDynamicCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -328,14 +319,16 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         for idx in range(len(past_key_values)):
             if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-                self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
             else:
-                self.assertEqual(past_key_values.key_cache[idx].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[idx].shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
+                self.assertEqual(past_key_values.layers[idx].key_cache.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].value_cache.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1: Zamba2HybridDynamicCache, cache2: Zamba2HybridDynamicCache):
-        if not isinstance(cache1, Zamba2HybridDynamicCache) or not isinstance(cache2, Zamba2HybridDynamicCache):
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -343,10 +336,16 @@ def _check_caches_are_equal(self, cache1: Zamba2HybridDynamicCache, cache2: Zamb
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx])
-            torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx])
+            # Mamba layer
+            if type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Hybrid mamba + attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].key_cache, cache2.layers[idx].key_cache)
+                torch.testing.assert_close(cache1.layers[idx].value_cache, cache2.layers[idx].value_cache)
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
 
     def setUp(self):
         self.model_tester = Zamba2ModelTester(self)

From 7795808775ad7a68b88a708355597794c213bb2e Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 13:27:51 +0100
Subject: [PATCH 17/56] fix

---
 src/transformers/configuration_utils.py         | 1 +
 src/transformers/models/zamba/modeling_zamba.py | 4 +++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/transformers/configuration_utils.py b/src/transformers/configuration_utils.py
index 5ff548c523f6..4d7034f9b1d3 100755
--- a/src/transformers/configuration_utils.py
+++ b/src/transformers/configuration_utils.py
@@ -68,6 +68,7 @@
     "attention",
     "sparse",
     "dense",
+    "hybrid",  # for layers that have both mamba and attention in zamba and zamba2
 )
 
 
diff --git a/src/transformers/models/zamba/modeling_zamba.py b/src/transformers/models/zamba/modeling_zamba.py
index dd6bf0ed5414..feefa4d7d55c 100644
--- a/src/transformers/models/zamba/modeling_zamba.py
+++ b/src/transformers/models/zamba/modeling_zamba.py
@@ -379,7 +379,9 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
             ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
         else:
             ssm_state = torch.zeros(
-                (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
+                (batch_size, self.n_mamba_heads, self.mamba_head_dim, self.ssm_state_size),
+                device=hidden_states.device,
+                dtype=dtype,
             )
 
         # 2. Convolution sequence transformation

From 18685c605bc5ae83e56b0fcde1faf4b4f57b4bb8 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 14:28:43 +0100
Subject: [PATCH 18/56] fix

---
 tests/models/zamba/test_modeling_zamba.py   | 8 ++++----
 tests/models/zamba2/test_modeling_zamba2.py | 8 ++++----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index 2bd7dd2c3bc9..4ce37dd638a7 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -310,8 +310,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             else:
                 self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
                 self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-                self.assertEqual(past_key_values.layers[idx].key_cache.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].value_cache.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
     def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
         if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
@@ -328,8 +328,8 @@ def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
                 torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
             # Hybrid mamba + attention layer
             else:
-                torch.testing.assert_close(cache1.layers[idx].key_cache, cache2.layers[idx].key_cache)
-                torch.testing.assert_close(cache1.layers[idx].value_cache, cache2.layers[idx].value_cache)
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
                 torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
 
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index 632855f2138d..0760c5e60789 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -324,8 +324,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             else:
                 self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
                 self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-                self.assertEqual(past_key_values.layers[idx].key_cache.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].value_cache.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
     def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
         if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
@@ -342,8 +342,8 @@ def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
                 torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
             # Hybrid mamba + attention layer
             else:
-                torch.testing.assert_close(cache1.layers[idx].key_cache, cache2.layers[idx].key_cache)
-                torch.testing.assert_close(cache1.layers[idx].value_cache, cache2.layers[idx].value_cache)
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
                 torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
 

From fcec6bc0f9f9e3337d5cc1c1756bef9890b0380f Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 14:45:50 +0100
Subject: [PATCH 19/56] fix

---
 src/transformers/models/zamba2/modeling_zamba2.py | 14 ++++++++------
 src/transformers/models/zamba2/modular_zamba2.py  | 14 ++++++++------
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index c882353a8639..2feaf7f7bcba 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -670,10 +670,16 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
 
         has_previous_state = cache_params.has_previous_state(self.layer_idx)
 
+        if cache_params is not None and has_previous_state:
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
+                device=hidden_states.device, dtype=dtype
+            )
+
         # Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state.to(hidden_states.device)
             if has_previous_state:
                 gate = gate.unsqueeze(1)
                 conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
@@ -694,10 +700,6 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
                     # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
                     hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
             hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
         hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
         A = -torch.exp(self.A_log.float())                            # [num_heads]
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index fd32a7d217a8..6a4ab477d731 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -458,10 +458,16 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
 
         has_previous_state = cache_params.has_previous_state(self.layer_idx)
 
+        if cache_params is not None and has_previous_state:
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
+                device=hidden_states.device, dtype=dtype
+            )
+
         # Convolution sequence transformation
         if cache_params is not None:
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state.to(hidden_states.device)
             if has_previous_state:
                 gate = gate.unsqueeze(1)
                 conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
@@ -482,10 +488,6 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
                     # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
                     hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
         else:
-            ssm_state = torch.zeros(
-                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
             hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
         hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
         A = -torch.exp(self.A_log.float())                            # [num_heads]

From b1df43f64fe4d9821c1fc541eb14235edd82b1ba Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 15:19:42 +0100
Subject: [PATCH 20/56] more models

---
 .../models/bamba/configuration_bamba.py       |   1 +
 .../models/bamba/modeling_bamba.py            | 191 +++-------------
 .../models/bamba/modular_bamba.py             | 162 ++++----------
 .../configuration_granitemoehybrid.py         |   9 +-
 .../modeling_granitemoehybrid.py              | 207 +++---------------
 .../modular_granitemoehybrid.py               |  38 +---
 .../models/zamba2/modeling_zamba2.py          |  49 ++---
 .../models/zamba2/modular_zamba2.py           |  49 ++---
 tests/models/bamba/test_modeling_bamba.py     |  42 ++--
 .../test_modeling_granitemoehybrid.py         |  37 ++--
 10 files changed, 175 insertions(+), 610 deletions(-)

diff --git a/src/transformers/models/bamba/configuration_bamba.py b/src/transformers/models/bamba/configuration_bamba.py
index d9b88cb8c95d..5755ff3d1d55 100644
--- a/src/transformers/models/bamba/configuration_bamba.py
+++ b/src/transformers/models/bamba/configuration_bamba.py
@@ -43,6 +43,7 @@ class BambaConfig(PreTrainedConfig):
     """
 
     model_type = "bamba"
+    attribute_map = {"layer_types": "layers_block_type"}
     keys_to_ignore_at_inference = ["past_key_values"]
 
     vocab_size: int = 128000
diff --git a/src/transformers/models/bamba/modeling_bamba.py b/src/transformers/models/bamba/modeling_bamba.py
index 8888fa5ddbdf..d9a8ca68576d 100644
--- a/src/transformers/models/bamba/modeling_bamba.py
+++ b/src/transformers/models/bamba/modeling_bamba.py
@@ -24,15 +24,14 @@
 # limitations under the License.
 
 from collections.abc import Callable
-from typing import Any, Optional, TypedDict
+from typing import Optional, TypedDict
 
 import torch
 from torch import nn
 
-from transformers.activations import ACT2FN
-
 from ... import initialization as init
-from ...cache_utils import Cache
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernelized_func
 from ...integrations.hub_kernels import lazy_load_kernel
@@ -76,112 +75,6 @@ class BambaFlashAttentionKwargs(TypedDict, total=False):
     seq_idx: torch.IntTensor
 
 
-class HybridMambaAttentionDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config: BambaConfig, batch_size, dtype=torch.float16, device=None):
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False  # only used by mamba
-        conv_kernel_size = config.mamba_d_conv
-        ssm_state_size = config.mamba_d_state
-
-        self.conv_states = []
-        self.ssm_states = []
-        self.transformer_layers = []
-        for i in range(config.num_hidden_layers):
-            if self.layers_block_type[i] == "mamba":
-                self.conv_states += [
-                    torch.zeros(
-                        batch_size,
-                        (config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * ssm_state_size),
-                        conv_kernel_size,
-                        device=device,
-                        dtype=dtype,
-                    )
-                ]
-                self.ssm_states += [
-                    torch.zeros(
-                        batch_size,
-                        config.mamba_n_heads,
-                        config.mamba_d_head,
-                        ssm_state_size,
-                        device=device,
-                        dtype=dtype,
-                    )
-                ]
-            else:
-                self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.transformer_layers.append(i)
-
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    def __getitem__(self, layer_idx):
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Update the cache
-        if self.key_cache[layer_idx].shape[-1] == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-
 class BambaRotaryEmbedding(nn.Module):
     inv_freq: torch.Tensor  # fix linting for `register_buffer`
 
@@ -592,7 +485,7 @@ def __init__(self, config: BambaConfig, layer_idx: int):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         seq_idx: torch.IntTensor | None = None,
     ):
@@ -605,12 +498,7 @@ def cuda_kernels_forward(
         groups_time_state_size = self.n_groups * self.ssm_state_size
 
         use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
 
         # getting projected states from cache if it exists
@@ -622,7 +510,7 @@ def cuda_kernels_forward(
             # 2. Convolution sequence transformation
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -644,7 +532,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -704,7 +592,7 @@ def cuda_kernels_forward(
                         hidden_states_B_C_transposed,
                         (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
                     )
-                    cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                    conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
                 if self.activation not in ["silu", "swish"]:
                     hidden_states_B_C = self.act(
@@ -745,7 +633,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -759,7 +647,7 @@ def cuda_kernels_forward(
     def torch_forward(
         self,
         input_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         batch_size, seq_len, _ = input_states.shape
@@ -771,23 +659,13 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split(
                 [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
         )
+        hidden_states_B_C = hidden_states_B_C.transpsose(1,2)
 
-        use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
-        )
+        use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
         # 2. Convolution sequence transformation
         if use_precomputed_states:
-            cache_params.conv_states[self.layer_idx] = cache_params.conv_states[self.layer_idx].roll(shifts=-1, dims=-1)
-            cache_params.conv_states[self.layer_idx][:, :, -1] = hidden_states_B_C[:, 0, :].to(cache_params.conv_states[self.layer_idx].device)
-
-            # We need to guarantee that anything regarding the cache is on the same device
-            conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
+            conv_states = cache_params.update_conv_state(hidden_states_B_C, self.layer_idx)
 
             hidden_states_B_C = torch.sum(
                 conv_states * self.conv1d.weight.squeeze(1), dim=-1
@@ -798,13 +676,12 @@ def torch_forward(
         else:
             # Init cache
             if cache_params is not None:
-                hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                 conv_states = nn.functional.pad(
-                    hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+                    hidden_states_B_C, (self.conv_kernel_size - hidden_states_B_C.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
-            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C)[..., :seq_len].transpose(1, 2))
 
         hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
         hidden_states, B, C = torch.split(
@@ -817,7 +694,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.ssm_states[self.layer_idx].device
+            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -847,9 +724,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
+            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -858,7 +734,7 @@ def torch_forward(
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -919,10 +795,7 @@ def torch_forward(
 
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
-            if use_precomputed_states:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
             decay_chunk = decay_chunk.transpose(1, 3)
@@ -949,7 +822,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
@@ -963,7 +836,7 @@ def torch_forward(
     def forward(
         self,
         hidden_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         seq_idx: torch.IntTensor | None = None,
         **kwargs,
@@ -1042,7 +915,7 @@ def forward(
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs: Unpack[BambaFlashAttentionKwargs],
@@ -1133,7 +1006,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[BambaFlashAttentionKwargs],
@@ -1146,10 +1019,7 @@ def forward(
         hidden_states = inputs_embeds
 
         if use_cache and past_key_values is None:
-            logger.warning_once(
-                "Bamba requires an initialized `HybridMambaAttentionDynamicCache` to return a cache. None was "
-                "provided, so no cache will be returned."
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
@@ -1226,7 +1096,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
@@ -1295,13 +1165,6 @@ def prepare_inputs_for_generation(
         is_first_iteration=False,
         **kwargs,
     ):
-        # Overwritten -- has a unique cache type, `HybridMambaAttentionDynamicCache`
-
-        if past_key_values is None:
-            past_key_values = HybridMambaAttentionDynamicCache(
-                self.config, input_ids.shape[0], self.dtype, device=self.device
-            )
-
         kwargs["logits_to_keep"] = self.config.num_logits_to_keep
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
diff --git a/src/transformers/models/bamba/modular_bamba.py b/src/transformers/models/bamba/modular_bamba.py
index 4961025f1743..a4ba833f5565 100644
--- a/src/transformers/models/bamba/modular_bamba.py
+++ b/src/transformers/models/bamba/modular_bamba.py
@@ -23,9 +23,20 @@
 import torch
 from torch import nn
 
-from transformers.activations import ACT2FN
-from transformers.models.jamba.modeling_jamba import HybridMambaAttentionDynamicCache, JambaAttentionDecoderLayer
-from transformers.models.llama.modeling_llama import (
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...integrations.hub_kernels import lazy_load_kernel
+from ...masking_utils import create_causal_mask
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
+from ...utils.generic import merge_with_config_defaults
+from ...utils.import_utils import resolve_internal_import
+from ...utils.output_capturing import capture_outputs
+from ..jamba.modeling_jamba import JambaAttentionDecoderLayer
+from ..llama.modeling_llama import (
     LlamaAttention,
     LlamaForCausalLM,
     LlamaMLP,
@@ -33,24 +44,13 @@
     LlamaRotaryEmbedding,
     rotate_half,
 )
-from transformers.models.mamba2.modeling_mamba2 import (
+from ..mamba2.modeling_mamba2 import (
     MambaRMSNormGated,
     apply_mask_to_padding_states,
     pad_tensor_by_size,
     reshape_into_chunks,
     segment_sum,
 )
-
-from ... import initialization as init
-from ...integrations.hub_kernels import lazy_load_kernel
-from ...masking_utils import create_causal_mask
-from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from ...modeling_utils import PreTrainedModel
-from ...processing_utils import Unpack
-from ...utils import auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
-from ...utils.generic import merge_with_config_defaults
-from ...utils.import_utils import resolve_internal_import
-from ...utils.output_capturing import capture_outputs
 from .configuration_bamba import BambaConfig
 
 
@@ -81,60 +81,6 @@ class BambaFlashAttentionKwargs(TypedDict, total=False):
     seq_idx: torch.IntTensor
 
 
-# Adapted from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache for the v2 mixer
-class HybridMambaAttentionDynamicCache(HybridMambaAttentionDynamicCache):
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    def __init__(self, config: BambaConfig, batch_size, dtype=torch.float16, device=None):
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False  # only used by mamba
-        conv_kernel_size = config.mamba_d_conv
-        ssm_state_size = config.mamba_d_state
-
-        self.conv_states = []
-        self.ssm_states = []
-        self.transformer_layers = []
-        for i in range(config.num_hidden_layers):
-            if self.layers_block_type[i] == "mamba":
-                self.conv_states += [
-                    torch.zeros(
-                        batch_size,
-                        (config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * ssm_state_size),
-                        conv_kernel_size,
-                        device=device,
-                        dtype=dtype,
-                    )
-                ]
-                self.ssm_states += [
-                    torch.zeros(
-                        batch_size,
-                        config.mamba_n_heads,
-                        config.mamba_d_head,
-                        ssm_state_size,
-                        device=device,
-                        dtype=dtype,
-                    )
-                ]
-            else:
-                self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.transformer_layers.append(i)
-
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-
 class BambaRotaryEmbedding(LlamaRotaryEmbedding):
     pass
 
@@ -296,7 +242,7 @@ def __init__(self, config: BambaConfig, layer_idx: int):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         seq_idx: torch.IntTensor | None = None,
     ):
@@ -309,12 +255,7 @@ def cuda_kernels_forward(
         groups_time_state_size = self.n_groups * self.ssm_state_size
 
         use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
 
         # getting projected states from cache if it exists
@@ -326,7 +267,7 @@ def cuda_kernels_forward(
             # 2. Convolution sequence transformation
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -348,7 +289,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -408,7 +349,7 @@ def cuda_kernels_forward(
                         hidden_states_B_C_transposed,
                         (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
                     )
-                    cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                    conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
                 if self.activation not in ["silu", "swish"]:
                     hidden_states_B_C = self.act(
@@ -449,7 +390,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -463,7 +404,7 @@ def cuda_kernels_forward(
     def torch_forward(
         self,
         input_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         batch_size, seq_len, _ = input_states.shape
@@ -475,23 +416,13 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split(
                 [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
         )
+        hidden_states_B_C = hidden_states_B_C.transpsose(1,2)
 
-        use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
-        )
+        use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
         # 2. Convolution sequence transformation
         if use_precomputed_states:
-            cache_params.conv_states[self.layer_idx] = cache_params.conv_states[self.layer_idx].roll(shifts=-1, dims=-1)
-            cache_params.conv_states[self.layer_idx][:, :, -1] = hidden_states_B_C[:, 0, :].to(cache_params.conv_states[self.layer_idx].device)
-
-            # We need to guarantee that anything regarding the cache is on the same device
-            conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
+            conv_states = cache_params.update_conv_state(hidden_states_B_C, self.layer_idx)
 
             hidden_states_B_C = torch.sum(
                 conv_states * self.conv1d.weight.squeeze(1), dim=-1
@@ -502,13 +433,12 @@ def torch_forward(
         else:
             # Init cache
             if cache_params is not None:
-                hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                 conv_states = nn.functional.pad(
-                    hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+                    hidden_states_B_C, (self.conv_kernel_size - hidden_states_B_C.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
-            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C)[..., :seq_len].transpose(1, 2))
 
         hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
         hidden_states, B, C = torch.split(
@@ -521,7 +451,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.ssm_states[self.layer_idx].device
+            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -551,9 +481,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
+            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -562,7 +491,7 @@ def torch_forward(
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -623,10 +552,7 @@ def torch_forward(
 
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
-            if use_precomputed_states:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
             decay_chunk = decay_chunk.transpose(1, 3)
@@ -653,7 +579,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
@@ -667,7 +593,7 @@ def torch_forward(
     def forward(
         self,
         hidden_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         seq_idx: torch.IntTensor | None = None,
         **kwargs,
@@ -717,7 +643,7 @@ def forward(
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs: Unpack[BambaFlashAttentionKwargs],
@@ -808,7 +734,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[BambaFlashAttentionKwargs],
@@ -821,10 +747,7 @@ def forward(
         hidden_states = inputs_embeds
 
         if use_cache and past_key_values is None:
-            logger.warning_once(
-                "Bamba requires an initialized `HybridMambaAttentionDynamicCache` to return a cache. None was "
-                "provided, so no cache will be returned."
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
@@ -893,7 +816,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: HybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
@@ -962,13 +885,6 @@ def prepare_inputs_for_generation(
         is_first_iteration=False,
         **kwargs,
     ):
-        # Overwritten -- has a unique cache type, `HybridMambaAttentionDynamicCache`
-
-        if past_key_values is None:
-            past_key_values = HybridMambaAttentionDynamicCache(
-                self.config, input_ids.shape[0], self.dtype, device=self.device
-            )
-
         kwargs["logits_to_keep"] = self.config.num_logits_to_keep
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
diff --git a/src/transformers/models/granitemoehybrid/configuration_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/configuration_granitemoehybrid.py
index 48278a265572..1277b107d09e 100644
--- a/src/transformers/models/granitemoehybrid/configuration_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/configuration_granitemoehybrid.py
@@ -45,9 +45,7 @@ class GraniteMoeHybridConfig(PreTrainedConfig):
     ```"""
 
     model_type = "granitemoehybrid"
-    attribute_map = {
-        "layers_block_type": "layer_types",
-    }
+    attribute_map = {"layers_block_type": "layer_types"}
     keys_to_ignore_at_inference = ["past_key_values"]
 
     vocab_size: int = 32000
@@ -116,10 +114,5 @@ def validate_architecture(self):
         if self.mamba_d_head * self.mamba_n_heads != mamba_intermediate:
             raise ValueError("The dimensions for the Mamba head state do not match the model intermediate_size")
 
-    # overwrite the function to use in `HybridMambaAttentionDynamicCache`
-    @property
-    def layers_block_type(self):
-        return self.layer_types
-
 
 __all__ = ["GraniteMoeHybridConfig"]
diff --git a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
index 676ec8f93773..63ba0b0a9dcc 100644
--- a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
@@ -19,16 +19,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections.abc import Callable
-from typing import Any, Optional, TypedDict
+from typing import Optional, TypedDict
 
 import torch
 from torch import nn
 from torch.nn import functional as F
 
-from transformers.activations import ACT2FN
-
 from ... import initialization as init
-from ...cache_utils import Cache
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
 from ...integrations.hub_kernels import lazy_load_kernel
@@ -187,112 +186,6 @@ def forward(
         return attn_output, attn_weights
 
 
-class HybridMambaAttentionDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config: GraniteMoeHybridConfig, batch_size, dtype=torch.float16, device=None):
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False  # only used by mamba
-        conv_kernel_size = config.mamba_d_conv
-        ssm_state_size = config.mamba_d_state
-
-        self.conv_states = []
-        self.ssm_states = []
-        self.transformer_layers = []
-        for i in range(config.num_hidden_layers):
-            if self.layers_block_type[i] == "mamba":
-                self.conv_states += [
-                    torch.zeros(
-                        batch_size,
-                        (config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * ssm_state_size),
-                        conv_kernel_size,
-                        device=device,
-                        dtype=dtype,
-                    )
-                ]
-                self.ssm_states += [
-                    torch.zeros(
-                        batch_size,
-                        config.mamba_n_heads,
-                        config.mamba_d_head,
-                        ssm_state_size,
-                        device=device,
-                        dtype=dtype,
-                    )
-                ]
-            else:
-                self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
-                self.transformer_layers.append(i)
-
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    def __getitem__(self, layer_idx):
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Update the cache
-        if self.key_cache[layer_idx].shape[-1] == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-
 # Helper methods for segment sum computation
 
 
@@ -469,7 +362,7 @@ def __init__(self, config: GraniteMoeHybridConfig, layer_idx: int):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         seq_idx: torch.IntTensor | None = None,
     ):
@@ -482,12 +375,7 @@ def cuda_kernels_forward(
         groups_time_state_size = self.n_groups * self.ssm_state_size
 
         use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
 
         # getting projected states from cache if it exists
@@ -499,7 +387,7 @@ def cuda_kernels_forward(
             # 2. Convolution sequence transformation
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -521,7 +409,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -581,7 +469,7 @@ def cuda_kernels_forward(
                         hidden_states_B_C_transposed,
                         (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
                     )
-                    cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                    conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
                 if self.activation not in ["silu", "swish"]:
                     hidden_states_B_C = self.act(
@@ -622,7 +510,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -636,7 +524,7 @@ def cuda_kernels_forward(
     def torch_forward(
         self,
         input_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         batch_size, seq_len, _ = input_states.shape
@@ -648,23 +536,13 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split(
                 [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
         )
+        hidden_states_B_C = hidden_states_B_C.transpsose(1,2)
 
-        use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
-        )
+        use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
         # 2. Convolution sequence transformation
         if use_precomputed_states:
-            cache_params.conv_states[self.layer_idx] = cache_params.conv_states[self.layer_idx].roll(shifts=-1, dims=-1)
-            cache_params.conv_states[self.layer_idx][:, :, -1] = hidden_states_B_C[:, 0, :].to(cache_params.conv_states[self.layer_idx].device)
-
-            # We need to guarantee that anything regarding the cache is on the same device
-            conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
+            conv_states = cache_params.update_conv_state(hidden_states_B_C, self.layer_idx)
 
             hidden_states_B_C = torch.sum(
                 conv_states * self.conv1d.weight.squeeze(1), dim=-1
@@ -675,13 +553,12 @@ def torch_forward(
         else:
             # Init cache
             if cache_params is not None:
-                hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                 conv_states = nn.functional.pad(
-                    hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+                    hidden_states_B_C, (self.conv_kernel_size - hidden_states_B_C.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx].copy_(conv_states)
+                conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
-            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C)[..., :seq_len].transpose(1, 2))
 
         hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
         hidden_states, B, C = torch.split(
@@ -694,7 +571,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.ssm_states[self.layer_idx].device
+            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -724,9 +601,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
+            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -735,7 +611,7 @@ def torch_forward(
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -796,10 +672,7 @@ def torch_forward(
 
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
-            if use_precomputed_states:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
             decay_chunk = decay_chunk.transpose(1, 3)
@@ -826,7 +699,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
@@ -840,7 +713,7 @@ def torch_forward(
     def forward(
         self,
         hidden_states,
-        cache_params: HybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         seq_idx: torch.IntTensor | None = None,
         **kwargs,
@@ -1293,6 +1166,9 @@ def forward(
 
         inputs_embeds = inputs_embeds * self.embedding_multiplier
 
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
             position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
@@ -1534,36 +1410,5 @@ def forward(
             router_logits=outputs.router_logits,
         )
 
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        position_ids=None,
-        use_cache=True,
-        is_first_iteration=False,
-        **kwargs,
-    ):
-        # Overwritten -- has a unique cache type, `HybridMambaAttentionDynamicCache`
-
-        if past_key_values is None and use_cache:
-            past_key_values = HybridMambaAttentionDynamicCache(
-                self.config, input_ids.shape[0], self.dtype, device=self.device
-            )
-
-        model_inputs = super().prepare_inputs_for_generation(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            position_ids=position_ids,
-            use_cache=use_cache,
-            is_first_iteration=is_first_iteration,
-            **kwargs,
-        )
-
-        return model_inputs
-
 
 __all__ = ["GraniteMoeHybridForCausalLM", "GraniteMoeHybridModel", "GraniteMoeHybridPreTrainedModel"]
diff --git a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
index 4c72531bddb5..ccf8c58bfb51 100644
--- a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
@@ -18,7 +18,7 @@
 from torch import nn
 
 from ... import initialization as init
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
 from ...modeling_outputs import BaseModelOutputWithPast, MoeModelOutputWithPast
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
@@ -27,7 +27,7 @@
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..bamba.configuration_bamba import BambaConfig
-from ..bamba.modeling_bamba import BambaMixer, BambaRMSNormGated, HybridMambaAttentionDynamicCache
+from ..bamba.modeling_bamba import BambaMixer, BambaRMSNormGated
 from ..gemma2.modeling_gemma2 import Gemma2RotaryEmbedding
 from ..granitemoeshared.modeling_granitemoeshared import (
     GraniteFlashAttentionKwargs,
@@ -226,6 +226,9 @@ def forward(
 
         inputs_embeds = inputs_embeds * self.embedding_multiplier
 
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
             position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
@@ -315,36 +318,5 @@ def forward(self, **super_kwargs):
         ```"""
         return super().forward(**super_kwargs)
 
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        position_ids=None,
-        use_cache=True,
-        is_first_iteration=False,
-        **kwargs,
-    ):
-        # Overwritten -- has a unique cache type, `HybridMambaAttentionDynamicCache`
-
-        if past_key_values is None and use_cache:
-            past_key_values = HybridMambaAttentionDynamicCache(
-                self.config, input_ids.shape[0], self.dtype, device=self.device
-            )
-
-        model_inputs = super().prepare_inputs_for_generation(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            position_ids=position_ids,
-            use_cache=use_cache,
-            is_first_iteration=is_first_iteration,
-            **kwargs,
-        )
-
-        return model_inputs
-
 
 __all__ = ["GraniteMoeHybridForCausalLM", "GraniteMoeHybridModel", "GraniteMoeHybridPreTrainedModel"]
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index 2feaf7f7bcba..899bd737d3c5 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -652,7 +652,7 @@ def cuda_kernels_forward(
         return out
 
     # fmt: off
-    def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None=None):
+    def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None = None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # Gated MLP's linear projection
@@ -667,43 +667,35 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         _, _, gate, hidden_states, dt = projected_states.split(
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
+        hidden_states = hidden_states.transpose(1, 2)
 
-        has_previous_state = cache_params.has_previous_state(self.layer_idx)
-
-        if cache_params is not None and has_previous_state:
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-        else:
-            ssm_state = torch.zeros(
-                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
+        use_precomputed_state = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
         # Convolution sequence transformation
-        if cache_params is not None:
-            if has_previous_state:
-                gate = gate.unsqueeze(1)
-                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
-                hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
-                if self.use_conv_bias:
-                    hidden_states += self.conv1d.bias
-                hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
-            else:
-                hidden_states = hidden_states.transpose(1,2)
+        if use_precomputed_state:
+            gate = gate.unsqueeze(1)
+            conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
+            hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+            if self.use_conv_bias:
+                hidden_states += self.conv1d.bias
+            hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
+        else:
+            if cache_params is not None:
                 conv_state = nn.functional.pad(
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
                 conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
-                hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :]     # [batch, intermediate_size, seq_len]
-                if attention_mask is not None:
-                    dtype = hidden_states.dtype
-                    # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
-                    hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
-        else:
-            hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+
+            hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len].transpose(1, 2))
+            if attention_mask is not None:
+                dtype = hidden_states.dtype
+                # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+
         hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
         A = -torch.exp(self.A_log.float())                            # [num_heads]
-        if cache_params is not None and has_previous_state:
+        if use_precomputed_state:
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
             dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
@@ -732,6 +724,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state * dA + dBx
             ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index 6a4ab477d731..c6e7af8c3cba 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -440,7 +440,7 @@ def cuda_kernels_forward(
         return out
 
     # fmt: off
-    def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None=None):
+    def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None = None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # Gated MLP's linear projection
@@ -455,43 +455,35 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         _, _, gate, hidden_states, dt = projected_states.split(
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
+        hidden_states = hidden_states.transpose(1, 2)
 
-        has_previous_state = cache_params.has_previous_state(self.layer_idx)
-
-        if cache_params is not None and has_previous_state:
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-        else:
-            ssm_state = torch.zeros(
-                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
+        use_precomputed_state = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
         # Convolution sequence transformation
-        if cache_params is not None:
-            if has_previous_state:
-                gate = gate.unsqueeze(1)
-                conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
-                hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
-                if self.use_conv_bias:
-                    hidden_states += self.conv1d.bias
-                hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
-            else:
-                hidden_states = hidden_states.transpose(1,2)
+        if use_precomputed_state:
+            gate = gate.unsqueeze(1)
+            conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
+            hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+            if self.use_conv_bias:
+                hidden_states += self.conv1d.bias
+            hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
+        else:
+            if cache_params is not None:
                 conv_state = nn.functional.pad(
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
                 conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
-                hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :]     # [batch, intermediate_size, seq_len]
-                if attention_mask is not None:
-                    dtype = hidden_states.dtype
-                    # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
-                    hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
-        else:
-            hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+
+            hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len].transpose(1, 2))
+            if attention_mask is not None:
+                dtype = hidden_states.dtype
+                # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+
         hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
         A = -torch.exp(self.A_log.float())                            # [num_heads]
-        if cache_params is not None and has_previous_state:
+        if use_precomputed_state:
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
             dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
@@ -520,6 +512,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
             ssm_state = ssm_state * dA + dBx
             ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
diff --git a/tests/models/bamba/test_modeling_bamba.py b/tests/models/bamba/test_modeling_bamba.py
index ee8143f31c14..da236c72790c 100644
--- a/tests/models/bamba/test_modeling_bamba.py
+++ b/tests/models/bamba/test_modeling_bamba.py
@@ -47,11 +47,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        BambaForCausalLM,
-        BambaModel,
-    )
-    from transformers.models.bamba.modeling_bamba import HybridMambaAttentionDynamicCache
+    from transformers import BambaForCausalLM, BambaModel, DynamicCache
+    from transformers.cache_utils import MambaLayer
 
 
 class BambaModelTester:
@@ -228,14 +225,9 @@ def create_and_check_decoder_model_past_large_inputs(
         model.eval()
 
         # first forward pass
-        # Attention: Jamba needs the cache to be initialized to return a cache!
-        past_key_values = HybridMambaAttentionDynamicCache(
-            config, input_ids.shape[0], model.dtype, device=model.device
-        )
         outputs = model(
             input_ids,
             attention_mask=input_mask,
-            past_key_values=past_key_values,
             use_cache=True,
         )
         past_key_values = outputs.past_key_values
@@ -289,7 +281,7 @@ class BambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     model_split_percents = [0.5, 0.7, 0.8]
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, HybridMambaAttentionDynamicCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -307,18 +299,14 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         for idx in range(len(past_key_values)):
             if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-                self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
             else:
-                self.assertEqual(past_key_values.key_cache[idx].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[idx].shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(
-        self, cache1: HybridMambaAttentionDynamicCache, cache2: HybridMambaAttentionDynamicCache
-    ):
-        if not isinstance(cache1, HybridMambaAttentionDynamicCache) or not isinstance(
-            cache2, HybridMambaAttentionDynamicCache
-        ):
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -326,10 +314,14 @@ def _check_caches_are_equal(
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx])
-            torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx])
+            # Mamba layer
+            if type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
     def setUp(self):
         self.model_tester = self.model_tester_class(self)
diff --git a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
index 8cb946d0aa2e..e102ff9c93ea 100644
--- a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
+++ b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
@@ -46,11 +46,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        GraniteMoeHybridForCausalLM,
-        GraniteMoeHybridModel,
-    )
-    from transformers.models.granitemoehybrid.modeling_granitemoehybrid import HybridMambaAttentionDynamicCache
+    from transformers import DynamicCache, GraniteMoeHybridForCausalLM, GraniteMoeHybridModel
+    from transformers.cache_utils import MambaLayer
 
 
 class GraniteMoeHybridModelTester(BambaModelTester):
@@ -109,12 +106,8 @@ class GraniteMoeHybridModelTest(ModelTesterMixin, GenerationTesterMixin, Pipelin
     # This is because we are hitting edge cases with the causal_mask buffer
     model_split_percents = [0.5, 0.7, 0.8]
 
-    def _check_caches_are_equal(
-        self, cache1: HybridMambaAttentionDynamicCache, cache2: HybridMambaAttentionDynamicCache
-    ):
-        if not isinstance(cache1, HybridMambaAttentionDynamicCache) or not isinstance(
-            cache2, HybridMambaAttentionDynamicCache
-        ):
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -122,10 +115,14 @@ def _check_caches_are_equal(
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx])
-            torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx])
+            # Mamba layer
+            if type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
     def setUp(self):
         self.model_tester = self.model_tester_class(self)
@@ -325,7 +322,7 @@ def test_flash_attention_2_padding_matches_padding_free_with_position_ids_seq_id
                 torch.testing.assert_close(loss_padded, loss_padfree)
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, HybridMambaAttentionDynamicCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -343,11 +340,11 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         for idx in range(len(past_key_values)):
             if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-                self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
             else:
-                self.assertEqual(past_key_values.key_cache[idx].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[idx].shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
     def test_config_requires_mamba_or_attention_layers(self):
         """Ensure we can't create a config with disallowed layers."""

From fdb1579124cc9dfebe2c33d46ac2862d9fc58ff2 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 15:46:00 +0100
Subject: [PATCH 21/56] final mambas

---
 .../falcon_h1/configuration_falcon_h1.py      |   1 +
 .../models/falcon_h1/modeling_falcon_h1.py    | 243 ++----------------
 .../models/falcon_h1/modular_falcon_h1.py     | 235 +++--------------
 .../models/nemotron_h/modeling_nemotron_h.py  | 220 +++-------------
 .../models/nemotron_h/modular_nemotron_h.py   |  66 +----
 .../falcon_h1/test_modeling_falcon_h1.py      |  54 ++--
 .../nemotron_h/test_modeling_nemotron_h.py    |  52 ++--
 7 files changed, 168 insertions(+), 703 deletions(-)

diff --git a/src/transformers/models/falcon_h1/configuration_falcon_h1.py b/src/transformers/models/falcon_h1/configuration_falcon_h1.py
index caa0e6288528..19401fbbc632 100644
--- a/src/transformers/models/falcon_h1/configuration_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/configuration_falcon_h1.py
@@ -54,6 +54,7 @@ class FalconH1Config(PreTrainedConfig):
     """
 
     model_type = "falcon_h1"
+    attribute_map = {"layer_types": "layers_block_type"}
     keys_to_ignore_at_inference = ["past_key_values"]
 
     vocab_size: int = 128000
diff --git a/src/transformers/models/falcon_h1/modeling_falcon_h1.py b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
index 237c04c8d28d..2911f76a15cb 100644
--- a/src/transformers/models/falcon_h1/modeling_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
@@ -24,16 +24,15 @@
 # limitations under the License.
 
 from collections.abc import Callable
-from typing import Any, Optional
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
 from torch import nn
 
-from transformers.activations import ACT2FN
-
 from ... import initialization as init
-from ...cache_utils import Cache
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
 from ...integrations.hub_kernels import lazy_load_kernel
@@ -54,161 +53,6 @@
 logger = logging.get_logger(__name__)
 
 
-class FalconHybridMambaAttentionDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(
-        self,
-        config: FalconH1Config,
-        batch_size: int,
-        dtype: torch.dtype = torch.float16,
-        devices: list[str] | None = None,
-    ):
-        self.seqlen_offset = 0
-        self.dtype = dtype
-        self.has_previous_state = False
-        self.conv_kernel_size = config.mamba_d_conv
-
-        self.intermediate_size = (
-            config.mamba_d_ssm if config.mamba_d_ssm is not None else int(config.mamba_expand * config.hidden_size)
-        )
-
-        self.conv_states = {
-            i: torch.zeros(
-                batch_size,
-                self.intermediate_size + 2 * config.mamba_n_groups * config.mamba_d_state,
-                self.conv_kernel_size,
-                device=devices[i],
-                dtype=dtype,
-            )
-            for i in range(config.num_hidden_layers)
-        }
-        self.ssm_states = {
-            i: torch.zeros(
-                batch_size,
-                config.mamba_n_heads,
-                config.mamba_d_head,
-                config.mamba_d_state,
-                device=devices[i],
-                dtype=dtype,
-            )
-            for i in range(config.num_hidden_layers)
-        }
-
-        self.transformer_layers = []
-        for i in range(config.num_hidden_layers):
-            self.transformer_layers.append(i)
-
-        self.key_cache: list[torch.Tensor] = []
-        self.value_cache: list[torch.Tensor] = []
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    def __getitem__(self, layer_idx):
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
-
-        Parameters:
-            key_states (`torch.Tensor`):
-                The new key states to cache.
-            value_states (`torch.Tensor`):
-                The new value states to cache.
-            layer_idx (`int`):
-                The index of the layer to cache the states for.
-            cache_kwargs (`dict[str, Any]`, `optional`):
-                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
-
-        Return:
-            A tuple containing the updated key and value states.
-        """
-        # Update the cache
-        if len(self.key_cache) <= layer_idx:
-            # There may be skipped layers, fill them with empty lists
-            for _ in range(len(self.key_cache), layer_idx):
-                self.key_cache.append([])
-                self.value_cache.append([])
-            self.key_cache.append(key_states)
-            self.value_cache.append(value_states)
-        elif len(self.key_cache[layer_idx]) == 0:  # fills previously skipped layers; checking for tensor causes errors
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_states[layer_idx] = new_conv_state.to(self.conv_states[layer_idx].device)
-        else:
-            self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
-            self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states[layer_idx].device)
-
-        return self.conv_states[layer_idx]
-
-    def reset(self):
-        self.has_previous_state = False
-        self.conv_states.zero_()
-        self.ssm_states.zero_()
-
-
 class FalconH1RotaryEmbedding(nn.Module):
     inv_freq: torch.Tensor  # fix linting for `register_buffer`
 
@@ -633,7 +477,7 @@ def __init__(self, config: FalconH1Config, layer_idx: int):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: FalconHybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         # 1. Gated MLP's linear projection
@@ -649,12 +493,7 @@ def cuda_kernels_forward(
         groups_time_state_size = self.n_groups * self.ssm_state_size
 
         use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
 
         # getting projected states from cache if it exists
@@ -668,7 +507,7 @@ def cuda_kernels_forward(
             # 2. Convolution sequence transformation
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -690,7 +529,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -763,7 +602,7 @@ def cuda_kernels_forward(
                         hidden_states_B_C.permute(0, 2, 1),
                         (self.conv_kernel_size - hidden_states_B_C.shape[-2], 0),
                     )
-                    cache_params.update_conv_state(self.layer_idx, conv_states, cache_init=True)
+                    conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
                 time_step = nn.functional.softplus(dt + self.dt_bias)
                 # 1D Convolution
@@ -810,7 +649,7 @@ def cuda_kernels_forward(
                         **dt_limit_kwargs,
                     )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 if self.mamba_rms_norm:
@@ -824,7 +663,7 @@ def cuda_kernels_forward(
     def torch_forward(
         self,
         input_states,
-        cache_params: FalconHybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         batch_size, seq_len, _ = input_states.shape
@@ -839,19 +678,13 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split([
                 self.intermediate_size, self.conv_dim, self.num_heads
             ], dim=-1)
+        hidden_states_B_C = hidden_states_B_C.transpose(1,2)
 
-        use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
-        )
+        use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
         # 2. Convolution sequence transformation
         if use_precomputed_states:
-            conv_states = cache_params.update_conv_state(self.layer_idx, hidden_states_B_C, cache_init=False)
+            conv_states = cache_params.update_conv_state(hidden_states_B_C, self.layer_idx)
             # We need to guarantee that anything regarding the cache is on the same device
             conv_states = conv_states.to(device=self.conv1d.weight.device)
 
@@ -864,13 +697,12 @@ def torch_forward(
         else:
             # Init cache
             if cache_params is not None:
-                hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                 conv_states = nn.functional.pad(
-                    hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+                    hidden_states_B_C, (self.conv_kernel_size - hidden_states_B_C.shape[-1], 0)
                 )
-                conv_states = cache_params.update_conv_state(self.layer_idx, conv_states, cache_init=True)
+                conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
-            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C)[..., :seq_len].transpose(1, 2))
 
         hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
         hidden_states, B, C = torch.split(
@@ -883,7 +715,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.ssm_states[self.layer_idx].device
+            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -913,9 +745,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
+            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -924,7 +755,7 @@ def torch_forward(
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -985,10 +816,7 @@ def torch_forward(
 
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
-            if use_precomputed_states:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
             decay_chunk = decay_chunk.transpose(1, 3)
@@ -1015,7 +843,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                ssm_state = cache_params.update_ssm_states(ssm_state, self.layer_idx)
 
         if self.mamba_rms_norm:
             scan_output = self.norm(y, gate)
@@ -1032,7 +860,7 @@ def torch_forward(
     def forward(
         self,
         hidden_states,
-        cache_params: FalconHybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -1110,7 +938,7 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         mamba_attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: FalconHybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs,
@@ -1120,7 +948,7 @@ def forward(
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`FalconHybridMambaAttentionDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -1271,7 +1099,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: FalconHybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -1284,10 +1112,7 @@ def forward(
         hidden_states = inputs_embeds
 
         if use_cache and past_key_values is None:
-            logger.warning_once(
-                "FalconH1 requires an initialized `FalconHybridMambaAttentionDynamicCache` to return a cache. None was "
-                "provided, so no cache will be returned."
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -1365,7 +1190,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: FalconHybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
@@ -1427,18 +1252,6 @@ def prepare_inputs_for_generation(
         is_first_iteration=False,
         **kwargs,
     ):
-        # Overwritten -- has a unique cache type, `FalconHybridMambaAttentionDynamicCache`
-
-        if past_key_values is None:
-            past_key_values = FalconHybridMambaAttentionDynamicCache(
-                self.config,
-                input_ids.shape[0],
-                self.dtype,
-                devices=[
-                    self.model.layers[i].mamba.conv1d.weight.device for i in range(self.config.num_hidden_layers)
-                ],
-            )
-
         kwargs["logits_to_keep"] = self.config.num_logits_to_keep
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
diff --git a/src/transformers/models/falcon_h1/modular_falcon_h1.py b/src/transformers/models/falcon_h1/modular_falcon_h1.py
index 4bd7301cd3fd..985e14dda2c4 100644
--- a/src/transformers/models/falcon_h1/modular_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modular_falcon_h1.py
@@ -19,15 +19,26 @@
 """PyTorch FalconH1 model."""
 
 from collections.abc import Callable
-from typing import Any
 
 import torch
 import torch.nn.functional as F
 from torch import nn
 
-from transformers.activations import ACT2FN
-from transformers.models.jamba.modeling_jamba import HybridMambaAttentionDynamicCache
-from transformers.models.llama.modeling_llama import (
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...integrations.hub_kernels import lazy_load_kernel
+from ...masking_utils import create_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
+from ...utils.generic import merge_with_config_defaults
+from ...utils.import_utils import resolve_internal_import
+from ...utils.output_capturing import capture_outputs
+from ..llama.modeling_llama import (
     LlamaAttention,
     LlamaForCausalLM,
     LlamaMLP,
@@ -36,152 +47,19 @@
     apply_rotary_pos_emb,
     eager_attention_forward,
 )
-from transformers.models.mamba2.modeling_mamba2 import (
+from ..mamba2.modeling_mamba2 import (
     MambaRMSNormGated,
     apply_mask_to_padding_states,
     pad_tensor_by_size,
     reshape_into_chunks,
     segment_sum,
 )
-
-from ... import initialization as init
-from ...cache_utils import Cache
-from ...integrations.hub_kernels import lazy_load_kernel
-from ...masking_utils import create_causal_mask
-from ...modeling_flash_attention_utils import FlashAttentionKwargs
-from ...modeling_layers import GradientCheckpointingLayer
-from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
-from ...utils.generic import merge_with_config_defaults
-from ...utils.import_utils import resolve_internal_import
-from ...utils.output_capturing import capture_outputs
 from .configuration_falcon_h1 import FalconH1Config
 
 
 logger = logging.get_logger(__name__)
 
 
-class FalconHybridMambaAttentionDynamicCache(HybridMambaAttentionDynamicCache):
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    def __init__(
-        self,
-        config: FalconH1Config,
-        batch_size: int,
-        dtype: torch.dtype = torch.float16,
-        devices: list[str] | None = None,
-    ):
-        self.seqlen_offset = 0
-        self.dtype = dtype
-        self.has_previous_state = False
-        self.conv_kernel_size = config.mamba_d_conv
-
-        self.intermediate_size = (
-            config.mamba_d_ssm if config.mamba_d_ssm is not None else int(config.mamba_expand * config.hidden_size)
-        )
-
-        self.conv_states = {
-            i: torch.zeros(
-                batch_size,
-                self.intermediate_size + 2 * config.mamba_n_groups * config.mamba_d_state,
-                self.conv_kernel_size,
-                device=devices[i],
-                dtype=dtype,
-            )
-            for i in range(config.num_hidden_layers)
-        }
-        self.ssm_states = {
-            i: torch.zeros(
-                batch_size,
-                config.mamba_n_heads,
-                config.mamba_d_head,
-                config.mamba_d_state,
-                device=devices[i],
-                dtype=dtype,
-            )
-            for i in range(config.num_hidden_layers)
-        }
-
-        self.transformer_layers = []
-        for i in range(config.num_hidden_layers):
-            self.transformer_layers.append(i)
-
-        self.key_cache: list[torch.Tensor] = []
-        self.value_cache: list[torch.Tensor] = []
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
-
-        Parameters:
-            key_states (`torch.Tensor`):
-                The new key states to cache.
-            value_states (`torch.Tensor`):
-                The new value states to cache.
-            layer_idx (`int`):
-                The index of the layer to cache the states for.
-            cache_kwargs (`dict[str, Any]`, `optional`):
-                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
-
-        Return:
-            A tuple containing the updated key and value states.
-        """
-        # Update the cache
-        if len(self.key_cache) <= layer_idx:
-            # There may be skipped layers, fill them with empty lists
-            for _ in range(len(self.key_cache), layer_idx):
-                self.key_cache.append([])
-                self.value_cache.append([])
-            self.key_cache.append(key_states)
-            self.value_cache.append(value_states)
-        elif len(self.key_cache[layer_idx]) == 0:  # fills previously skipped layers; checking for tensor causes errors
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_states[layer_idx] = new_conv_state.to(self.conv_states[layer_idx].device)
-        else:
-            self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
-            self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states[layer_idx].device)
-
-        return self.conv_states[layer_idx]
-
-    def reset(self):
-        self.has_previous_state = False
-        self.conv_states.zero_()
-        self.ssm_states.zero_()
-
-
 class FalconH1RotaryEmbedding(LlamaRotaryEmbedding):
     pass
 
@@ -386,7 +264,7 @@ def __init__(self, config: FalconH1Config, layer_idx: int):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: FalconHybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         # 1. Gated MLP's linear projection
@@ -402,12 +280,7 @@ def cuda_kernels_forward(
         groups_time_state_size = self.n_groups * self.ssm_state_size
 
         use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
 
         # getting projected states from cache if it exists
@@ -421,7 +294,7 @@ def cuda_kernels_forward(
             # 2. Convolution sequence transformation
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -443,7 +316,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -516,7 +389,7 @@ def cuda_kernels_forward(
                         hidden_states_B_C.permute(0, 2, 1),
                         (self.conv_kernel_size - hidden_states_B_C.shape[-2], 0),
                     )
-                    cache_params.update_conv_state(self.layer_idx, conv_states, cache_init=True)
+                    conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
                 time_step = nn.functional.softplus(dt + self.dt_bias)
                 # 1D Convolution
@@ -563,7 +436,7 @@ def cuda_kernels_forward(
                         **dt_limit_kwargs,
                     )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 if self.mamba_rms_norm:
@@ -577,7 +450,7 @@ def cuda_kernels_forward(
     def torch_forward(
         self,
         input_states,
-        cache_params: FalconHybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         batch_size, seq_len, _ = input_states.shape
@@ -592,19 +465,13 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split([
                 self.intermediate_size, self.conv_dim, self.num_heads
             ], dim=-1)
+        hidden_states_B_C = hidden_states_B_C.transpose(1,2)
 
-        use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_params.conv_states[self.layer_idx].shape[0]
-            == cache_params.ssm_states[self.layer_idx].shape[0]
-            == batch_size
-        )
+        use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
         # 2. Convolution sequence transformation
         if use_precomputed_states:
-            conv_states = cache_params.update_conv_state(self.layer_idx, hidden_states_B_C, cache_init=False)
+            conv_states = cache_params.update_conv_state(hidden_states_B_C, self.layer_idx)
             # We need to guarantee that anything regarding the cache is on the same device
             conv_states = conv_states.to(device=self.conv1d.weight.device)
 
@@ -617,13 +484,12 @@ def torch_forward(
         else:
             # Init cache
             if cache_params is not None:
-                hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
                 conv_states = nn.functional.pad(
-                    hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+                    hidden_states_B_C, (self.conv_kernel_size - hidden_states_B_C.shape[-1], 0)
                 )
-                conv_states = cache_params.update_conv_state(self.layer_idx, conv_states, cache_init=True)
+                conv_states = cache_params.update_conv_state(conv_states, self.layer_idx)
 
-            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+            hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C)[..., :seq_len].transpose(1, 2))
 
         hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
         hidden_states, B, C = torch.split(
@@ -636,7 +502,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.ssm_states[self.layer_idx].device
+            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -666,9 +532,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
+            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -677,7 +542,7 @@ def torch_forward(
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(device=C.device, dtype=C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -738,10 +603,7 @@ def torch_forward(
 
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
-            if use_precomputed_states:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
             decay_chunk = decay_chunk.transpose(1, 3)
@@ -768,7 +630,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                ssm_state = cache_params.update_ssm_states(ssm_state, self.layer_idx)
 
         if self.mamba_rms_norm:
             scan_output = self.norm(y, gate)
@@ -785,7 +647,7 @@ def torch_forward(
     def forward(
         self,
         hidden_states,
-        cache_params: FalconHybridMambaAttentionDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -839,7 +701,7 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         mamba_attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: FalconHybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = False,
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs,
@@ -849,7 +711,7 @@ def forward(
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
             attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                 `(batch, sequence_length)` where padding elements are indicated by 0.
-            past_key_values (`FalconHybridMambaAttentionDynamicCache`, *optional*): cached past key and value projection states
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
             use_cache (`bool`, *optional*):
                 If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                 (see `past_key_values`).
@@ -1000,7 +862,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: FalconHybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -1013,10 +875,7 @@ def forward(
         hidden_states = inputs_embeds
 
         if use_cache and past_key_values is None:
-            logger.warning_once(
-                "FalconH1 requires an initialized `FalconHybridMambaAttentionDynamicCache` to return a cache. None was "
-                "provided, so no cache will be returned."
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -1080,7 +939,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: FalconHybridMambaAttentionDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
@@ -1142,18 +1001,6 @@ def prepare_inputs_for_generation(
         is_first_iteration=False,
         **kwargs,
     ):
-        # Overwritten -- has a unique cache type, `FalconHybridMambaAttentionDynamicCache`
-
-        if past_key_values is None:
-            past_key_values = FalconHybridMambaAttentionDynamicCache(
-                self.config,
-                input_ids.shape[0],
-                self.dtype,
-                devices=[
-                    self.model.layers[i].mamba.conv1d.weight.device for i in range(self.config.num_hidden_layers)
-                ],
-            )
-
         kwargs["logits_to_keep"] = self.config.num_logits_to_keep
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index 4455c9ba49b7..87c456cf4ecf 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -22,7 +22,6 @@
 
 import math
 from collections.abc import Callable
-from typing import Any
 
 import torch
 import torch.nn.functional as F
@@ -30,6 +29,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import (
     lazy_load_kernel,
@@ -54,119 +54,6 @@
 logger = logging.get_logger(__name__)
 
 
-class NemotronHHybridDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
-    (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(
-        self, config: NemotronHConfig, batch_size: int, dtype: torch.dtype = torch.float16, device: str | None = None
-    ):
-        self.dtype = dtype
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False
-        self.intermediate_size = int(config.mamba_num_heads * config.mamba_head_dim)
-        self.ssm_state_size = config.ssm_state_size
-        self.conv_kernel_size = config.conv_kernel
-        self.n_mamba_heads = config.mamba_num_heads
-        self.transformer_layers = []
-        self._modules = {}
-        self._parameters = {}
-        self._buffers = {}
-        self.conv_states = {}
-        self.ssm_states = {}
-        for i in range(config.num_hidden_layers):
-            if self.layers_block_type[i] == "mamba":
-                # Only allocate mamba cache for mamba layers
-                self.conv_states[i] = torch.zeros(
-                    batch_size,
-                    self.intermediate_size + 2 * config.n_groups * self.ssm_state_size,
-                    self.conv_kernel_size,
-                    device=device,
-                    dtype=dtype,
-                )
-                self.ssm_states[i] = torch.zeros(
-                    batch_size,
-                    self.n_mamba_heads,
-                    config.mamba_head_dim,
-                    self.ssm_state_size,
-                    device=device,
-                    dtype=dtype,
-                )
-            else:
-                # For attention and moe layers, use empty tensors
-                self.conv_states[i] = torch.tensor([[]] * batch_size, device=device)
-                self.ssm_states[i] = torch.tensor([[]] * batch_size, device=device)
-
-            if self.layers_block_type[i] == "attention":
-                self.transformer_layers.append(i)
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.key_cache)
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Update the cache
-        if self.key_cache[layer_idx].shape[-1] == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        if self.get_seq_length() > 0:
-            for layer_idx in range(len(self.key_cache)):
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-                device = self.conv_states[layer_idx].device
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.ssm_states[layer_idx].device
-                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """Return the length and offset of the cache, used to generate the mask"""
-        kv_offset = 0
-        kv_length = self.get_seq_length(layer_idx) + query_length
-        return kv_length, kv_offset
-
-    def reset(self):
-        self.conv_states.zero_()
-        self.ssm_states.zero_()
-
-
 # Helper methods for segment sum computation
 
 
@@ -329,7 +216,7 @@ def __init__(self, config: NemotronHConfig, layer_idx: int | None = None):
     def cuda_kernels_forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: NemotronHHybridDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         # set up dimensions for reshapes later
@@ -339,7 +226,7 @@ def cuda_kernels_forward(
         d_to_remove = 2 * self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.num_heads
 
         # getting projected states from cache if it exists
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             in_projected_states = self.in_proj(hidden_states.squeeze(1))  # (B 2D)
             d_mlp = (in_projected_states.shape[-1] - d_to_remove) // 2
             split_projection_dim = [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads]
@@ -347,7 +234,7 @@ def cuda_kernels_forward(
 
             hidden_states_B_C = causal_conv1d_update(
                 hidden_states_B_C,
-                cache_params.conv_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].conv_states,
                 self.conv1d.weight.squeeze(1),
                 self.conv1d.bias,
                 self.activation,
@@ -368,7 +255,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.ssm_states[self.layer_idx],
+                cache_params.layers[self.layer_idx].ssm_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -432,7 +319,7 @@ def cuda_kernels_forward(
                     conv_state = nn.functional.pad(
                         hidden_states_B_C_t, (self.conv_kernel_size - hidden_states_B_C_t.shape[-1], 0)
                     )
-                    cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                    conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
                 if causal_conv1d_fn is None or self.activation not in ["silu", "swish"]:
                     hidden_states_B_C = self.act(
                         self.conv1d(hidden_states_B_C.transpose(1, 2)).transpose(1, 2)[:, :seq_len]
@@ -469,7 +356,7 @@ def cuda_kernels_forward(
                     **dt_limit_kwargs,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 scan_output = self.norm(scan_output, gate)
@@ -477,11 +364,11 @@ def cuda_kernels_forward(
         return out
 
     # fmt: off
-    def torch_forward(self, input_states, cache_params: NemotronHHybridDynamicCache | None=None, attention_mask: torch.Tensor | None=None):
+    def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None = None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # Gated MLP's linear projection
-        if cache_params is not None and cache_params.has_previous_state:
+        if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             projected_states = self.in_proj(input_states.squeeze(1))
         else:
             if attention_mask is not None:
@@ -492,43 +379,35 @@ def torch_forward(self, input_states, cache_params: NemotronHHybridDynamicCache
         _, _, gate, hidden_states, dt = projected_states.split(
                 [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
         )
+        hidden_states = hidden_states.transpose(1, 2)
+
+        use_precomputed_state = cache_params is not None and cache_params.has_previous_state(self.layer_idx)
 
         # Convolution sequence transformation
-        if cache_params is not None:
-            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
-            ssm_state = ssm_state.to(hidden_states.device)
-            if cache_params.has_previous_state:
-                gate = gate.unsqueeze(1)
-                conv_state = cache_params.conv_states[self.layer_idx]                   # [batch, intermediate_size, conv_kernel_size]
-                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
-                # handle batched generation - states are copied through
-                conv_state[:, :, -1] = hidden_states[:, 0, :] if hidden_states.ndim == 3 else hidden_states
-                cache_params.conv_states[self.layer_idx].copy_(conv_state)
-                hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
-                if self.use_conv_bias:
-                    hidden_states += self.conv1d.bias
-                hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
-            else:
-                hidden_states = hidden_states.transpose(1,2)
+        if use_precomputed_state:
+            gate = gate.unsqueeze(1)
+            conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
+            hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+            if self.use_conv_bias:
+                hidden_states += self.conv1d.bias
+            hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
+        else:
+            if cache_params is not None:
                 conv_state = nn.functional.pad(
                     hidden_states,
                     (self.conv_kernel_size - hidden_states.shape[-1], 0)
                 )
-                cache_params.conv_states[self.layer_idx].copy_(conv_state)
-                hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :]     # [batch, intermediate_size, seq_len]
-                if attention_mask is not None:
-                    dtype = hidden_states.dtype
-                    # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
-                    hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
-        else:
-            ssm_state = torch.zeros(
-                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
-                device=hidden_states.device, dtype=dtype
-            )
-            hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
+
+            hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len].transpose(1, 2))
+            if attention_mask is not None:
+                dtype = hidden_states.dtype
+                # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+
         hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
         A = -torch.exp(self.A_log.float())                            # [num_heads]
-        if cache_params is not None and cache_params.has_previous_state:
+        if use_precomputed_state:
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
             dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
@@ -557,9 +436,9 @@ def torch_forward(self, input_states, cache_params: NemotronHHybridDynamicCache
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            cache_params.ssm_states[self.layer_idx].copy_(
-                cache_params.ssm_states[self.layer_idx] * dA + dBx
-            )
+            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+            ssm_state = ssm_state * dA + dBx
+            ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -568,7 +447,7 @@ def torch_forward(self, input_states, cache_params: NemotronHHybridDynamicCache
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = cache_params.ssm_states[self.layer_idx].to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_state.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -629,10 +508,7 @@ def torch_forward(self, input_states, cache_params: NemotronHHybridDynamicCache
             B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None]
             # permute back B * decay states
             states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None]  * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3)
-            if cache_params is not None and cache_params.has_previous_state:
-                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...]
-            else:
-                previous_states = torch.zeros_like(states[:, :1])
+            previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
 
@@ -660,7 +536,7 @@ def torch_forward(self, input_states, cache_params: NemotronHHybridDynamicCache
                 y = y[:, :seq_len, :, :]
             y = y.reshape(batch_size, seq_len, -1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
@@ -674,7 +550,7 @@ def torch_forward(self, input_states, cache_params: NemotronHHybridDynamicCache
     def forward(
         self,
         hidden_states,
-        cache_params: NemotronHHybridDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -968,7 +844,7 @@ def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         input_shape = hidden_states.shape[:-1]
@@ -1036,7 +912,7 @@ def __init__(self, config, layer_idx):
     def forward(
         self,
         hidden_states,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
         use_cache: bool | None = False,
@@ -1162,7 +1038,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         inputs_embeds: torch.LongTensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -1174,12 +1050,7 @@ def forward(
             inputs_embeds = self.embeddings(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = NemotronHHybridDynamicCache(
-                config=self.config,
-                batch_size=inputs_embeds.shape[0],
-                dtype=inputs_embeds.dtype,
-                device=inputs_embeds.device,
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         hidden_states = inputs_embeds
 
@@ -1260,7 +1131,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
@@ -1327,13 +1198,6 @@ def prepare_inputs_for_generation(
         is_first_iteration=False,
         **kwargs,
     ):
-        # Overwritten -- has a unique cache type, `NemotronHHybridDynamicCache`
-
-        if past_key_values is None:
-            past_key_values = NemotronHHybridDynamicCache(
-                self.config, input_ids.shape[0], dtype=self.dtype, device=self.device
-            )
-
         kwargs["logits_to_keep"] = self.config.num_logits_to_keep
         model_inputs = super().prepare_inputs_for_generation(
             input_ids,
diff --git a/src/transformers/models/nemotron_h/modular_nemotron_h.py b/src/transformers/models/nemotron_h/modular_nemotron_h.py
index 8412be1dc5f9..5a849833cc89 100644
--- a/src/transformers/models/nemotron_h/modular_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modular_nemotron_h.py
@@ -22,6 +22,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
 from ...integrations import use_experts_implementation
 from ...masking_utils import create_causal_mask
 from ...modeling_layers import GradientCheckpointingLayer
@@ -32,7 +33,7 @@
 from ...models.llama.modeling_llama import LlamaRMSNorm
 from ...models.nemotron.modeling_nemotron import NemotronMLP
 from ...models.zamba.modeling_zamba import ZambaForCausalLM
-from ...models.zamba2.modeling_zamba2 import Zamba2HybridDynamicCache, Zamba2MambaMixer, Zamba2RMSNormGated
+from ...models.zamba2.modeling_zamba2 import Zamba2MambaMixer, Zamba2RMSNormGated
 from ...processing_utils import Unpack
 from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
 from ...utils.generic import merge_with_config_defaults
@@ -45,52 +46,6 @@
 is_fast_path_available = False
 
 
-class NemotronHHybridDynamicCache(Zamba2HybridDynamicCache):
-    def __init__(
-        self, config: NemotronHConfig, batch_size: int, dtype: torch.dtype = torch.float16, device: str | None = None
-    ):
-        self.dtype = dtype
-        self.layers_block_type = config.layers_block_type
-        self.has_previous_state = False
-        self.intermediate_size = int(config.mamba_num_heads * config.mamba_head_dim)
-        self.ssm_state_size = config.ssm_state_size
-        self.conv_kernel_size = config.conv_kernel
-        self.n_mamba_heads = config.mamba_num_heads
-        self.transformer_layers = []
-        self._modules = {}
-        self._parameters = {}
-        self._buffers = {}
-        self.conv_states = {}
-        self.ssm_states = {}
-        for i in range(config.num_hidden_layers):
-            if self.layers_block_type[i] == "mamba":
-                # Only allocate mamba cache for mamba layers
-                self.conv_states[i] = torch.zeros(
-                    batch_size,
-                    self.intermediate_size + 2 * config.n_groups * self.ssm_state_size,
-                    self.conv_kernel_size,
-                    device=device,
-                    dtype=dtype,
-                )
-                self.ssm_states[i] = torch.zeros(
-                    batch_size,
-                    self.n_mamba_heads,
-                    config.mamba_head_dim,
-                    self.ssm_state_size,
-                    device=device,
-                    dtype=dtype,
-                )
-            else:
-                # For attention and moe layers, use empty tensors
-                self.conv_states[i] = torch.tensor([[]] * batch_size, device=device)
-                self.ssm_states[i] = torch.tensor([[]] * batch_size, device=device)
-
-            if self.layers_block_type[i] == "attention":
-                self.transformer_layers.append(i)
-        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
-
-
 class NemotronHMamba2Mixer(Zamba2MambaMixer):
     def __init__(self, config: NemotronHConfig, layer_idx: int | None = None):
         super().__init__(config, layer_idx)
@@ -133,7 +88,7 @@ def __init__(self, config: NemotronHConfig, layer_idx: int | None = None):
     def forward(
         self,
         hidden_states,
-        cache_params: NemotronHHybridDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs,
     ):
@@ -277,7 +232,7 @@ def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         return super().forward(hidden_states, attention_mask, past_key_values, **kwargs)
@@ -318,7 +273,7 @@ def __init__(self, config, layer_idx):
     def forward(
         self,
         hidden_states,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
         use_cache: bool | None = False,
@@ -444,7 +399,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         inputs_embeds: torch.LongTensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         use_cache: bool | None = None,
         attention_mask: torch.Tensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -456,12 +411,7 @@ def forward(
             inputs_embeds = self.embeddings(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = NemotronHHybridDynamicCache(
-                config=self.config,
-                batch_size=inputs_embeds.shape[0],
-                dtype=inputs_embeds.dtype,
-                device=inputs_embeds.device,
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         hidden_states = inputs_embeds
 
@@ -532,7 +482,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: NemotronHHybridDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
diff --git a/tests/models/falcon_h1/test_modeling_falcon_h1.py b/tests/models/falcon_h1/test_modeling_falcon_h1.py
index 88c656dce1e0..e236a818835b 100644
--- a/tests/models/falcon_h1/test_modeling_falcon_h1.py
+++ b/tests/models/falcon_h1/test_modeling_falcon_h1.py
@@ -36,10 +36,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import AutoTokenizer, FalconH1ForCausalLM, FalconH1Model
-    from transformers.models.falcon_h1.modeling_falcon_h1 import (
-        FalconHybridMambaAttentionDynamicCache,
-    )
+    from transformers import AutoTokenizer, DynamicCache, FalconH1ForCausalLM, FalconH1Model
+    from transformers.cache_utils import MambaLayer
 
 
 class FalconH1ModelTester:
@@ -206,17 +204,9 @@ def create_and_check_decoder_model_past_large_inputs(
         model.eval()
 
         # first forward pass
-        # Attention: Jamba needs the cache to be initialized to return a cache!
-        past_key_values = FalconHybridMambaAttentionDynamicCache(
-            config,
-            input_ids.shape[0],
-            model.dtype,
-            devices=[model.device for _ in range(model.config.num_hidden_layers)],
-        )
         outputs = model(
             input_ids,
             attention_mask=input_mask,
-            past_key_values=past_key_values,
             use_cache=True,
         )
         past_key_values = outputs.past_key_values
@@ -265,26 +255,34 @@ class FalconH1ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
     )
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, FalconHybridMambaAttentionDynamicCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
         head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        expected_shape = (batch_size, num_heads, seq_length, head_dim)
+        attention_shape = (batch_size, num_heads, seq_length, head_dim)
 
-        self.assertListEqual(
-            [key_tensor.shape for key_tensor in past_key_values.key_cache],
-            [expected_shape] * len(past_key_values.key_cache),
+        conv_kernel_size = config.mamba_d_conv
+        intermediate_size = (
+            config.mamba_d_ssm if config.mamba_d_ssm is not None else int(config.mamba_expand * config.hidden_size)
         )
-        self.assertListEqual(
-            [value_cache.shape for value_cache in past_key_values.value_cache],
-            [expected_shape] * len(past_key_values.value_cache),
+        conv_shape = (
+            batch_size,
+            intermediate_size + 2 * config.mamba_n_groups * config.mamba_d_state,
+            conv_kernel_size,
         )
+        ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
+
+        for idx in range(len(past_key_values)):
+            if config.layers_block_type[idx] == "mamba":
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
+            else:
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
     def _check_caches_are_equal(self, cache1, cache2):
-        if not isinstance(cache1, FalconHybridMambaAttentionDynamicCache) or not isinstance(
-            cache2, FalconHybridMambaAttentionDynamicCache
-        ):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -292,10 +290,12 @@ def _check_caches_are_equal(self, cache1, cache2):
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx])
-            torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx])
+            if isinstance(cache1.layers[idx], MambaLayer):
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
     def setUp(self):
         self.model_tester = FalconH1ModelTester(self)
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index 467971673065..43a3ea8af9b1 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -40,13 +40,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        NemotronHForCausalLM,
-        NemotronHModel,
-    )
-    from transformers.models.nemotron_h.modeling_nemotron_h import (
-        NemotronHHybridDynamicCache,
-    )
+    from transformers import DynamicCache, NemotronHForCausalLM, NemotronHModel
+    from transformers.cache_utils import MambaLayer
 
 
 class NemotronHModelTester:
@@ -237,12 +232,9 @@ def create_and_check_decoder_model_past_large_inputs(
         model.eval()
 
         # first forward pass
-        # Attention: NemotronH needs the cache to be initialized to return a cache!
-        past_key_values = NemotronHHybridDynamicCache(config, input_ids.shape[0], model.dtype, device=model.device)
         outputs = model(
             input_ids,
             attention_mask=input_mask,
-            past_key_values=past_key_values,
             use_cache=True,
         )
         past_key_values = outputs.past_key_values
@@ -319,17 +311,11 @@ def create_and_check_mamba2_slow_vs_fast_forward(self, config, input_ids, *args)
         self.parent.assertTrue(torch.allclose(outputs_fast, outputs_slow, atol=1e-3, rtol=1e-3))
 
         # Test with cache
-        batch_size = input_ids.shape[0]
-        cache_params = NemotronHHybridDynamicCache(
-            config=config, batch_size=batch_size, dtype=token_emb.dtype, device=torch_device
-        )
-
+        cache_params = DynamicCache(config=config)
         outputs_fast_cached = mamba_mixer.cuda_kernels_forward(token_emb, cache_params=cache_params)
 
         # Reset cache for fair comparison
-        cache_params_slow = NemotronHHybridDynamicCache(
-            config=config, batch_size=batch_size, dtype=token_emb.dtype, device=torch_device
-        )
+        cache_params_slow = DynamicCache(config=config)
         outputs_slow_cached = mamba_mixer.torch_forward(token_emb, cache_params=cache_params_slow)
 
         self.parent.assertTrue(torch.allclose(outputs_fast_cached, outputs_slow_cached, atol=1e-3, rtol=1e-3))
@@ -368,7 +354,7 @@ class NemotronHModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
     )
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, NemotronHHybridDynamicCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -388,14 +374,14 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         for idx in range(len(past_key_values)):
             if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape)
-                self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape)
-            elif config.layers_block_type[idx] == "attention":
-                self.assertEqual(past_key_values.key_cache[idx].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[idx].shape, attention_shape)
-
-    def _check_caches_are_equal(self, cache1: NemotronHHybridDynamicCache, cache2: NemotronHHybridDynamicCache):
-        if not isinstance(cache1, NemotronHHybridDynamicCache) or not isinstance(cache2, NemotronHHybridDynamicCache):
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
+            else:
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -403,10 +389,14 @@ def _check_caches_are_equal(self, cache1: NemotronHHybridDynamicCache, cache2: N
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx])
-            torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx])
+            # Mamba layer
+            if type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
     def setUp(self):
         self.model_tester = NemotronHModelTester(self)

From b156adeb278eb94059e202a4fcdc74f6db1b6107 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 15:51:11 +0100
Subject: [PATCH 22/56] config

---
 src/transformers/models/nemotron_h/configuration_nemotron_h.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/transformers/models/nemotron_h/configuration_nemotron_h.py b/src/transformers/models/nemotron_h/configuration_nemotron_h.py
index f2ab3ed25ebd..cf6d51bc2c45 100644
--- a/src/transformers/models/nemotron_h/configuration_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/configuration_nemotron_h.py
@@ -80,6 +80,7 @@ class NemotronHConfig(PreTrainedConfig):
     ```"""
 
     model_type = "nemotron_h"
+    attribute_map = {"layer_types": "layers_block_type"}
     keys_to_ignore_at_inference = ["past_key_values"]
 
     vocab_size: int = 131072

From 330e397d5aa4461141a95177e76c12644edcb78a Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 16:03:03 +0100
Subject: [PATCH 23/56] finalize almost everything

---
 src/transformers/models/lfm2/modeling_lfm2.py | 187 ++---------------
 src/transformers/models/lfm2/modular_lfm2.py  | 185 ++---------------
 .../models/lfm2_moe/modeling_lfm2_moe.py      | 189 ++----------------
 .../models/lfm2_moe/modular_lfm2_moe.py       |  15 +-
 tests/models/lfm2/test_modeling_lfm2.py       |  32 +--
 .../models/lfm2_moe/test_modeling_lfm2_moe.py |  32 +--
 6 files changed, 87 insertions(+), 553 deletions(-)

diff --git a/src/transformers/models/lfm2/modeling_lfm2.py b/src/transformers/models/lfm2/modeling_lfm2.py
index ad3d154c2d06..ef753e3b2893 100644
--- a/src/transformers/models/lfm2/modeling_lfm2.py
+++ b/src/transformers/models/lfm2/modeling_lfm2.py
@@ -18,13 +18,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections.abc import Callable
-from typing import Any, Optional
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
 from torch import nn
 
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
 from ...masking_utils import create_causal_mask
@@ -152,160 +152,6 @@ def forward(self, x):
         return self.w2(F.silu(self.w1(x)) * self.w3(x))
 
 
-class Lfm2HybridConvCache:
-    """
-    Attention and conv cache for Lfm2.
-
-    It stores the Key and Value states as a list of tensors, one for each layer.
-    Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
-    Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
-    """
-
-    # Override @property existing in Cache
-    max_batch_size = None
-    is_compileable = False
-    key_cache = None
-    value_cache = None
-
-    def __init__(
-        self,
-        config: Lfm2Config,
-        max_batch_size: int,
-        dtype: torch.dtype = torch.float32,
-        device: torch.device | str | None = None,
-    ):
-        self.key_cache = []
-        self.value_cache = []
-        self.max_batch_size = max_batch_size
-        self.layer_types = config.layer_types
-        self.first_attention_layer = self.layer_types.index("full_attention")
-        self.last_conv_layer = len(self.layer_types) - self.layer_types[::-1].index("conv") - 1
-        self.conv_L_cache = config.conv_L_cache
-        self._dtype = dtype
-        self.has_previous_state = False
-
-        self.conv_cache: list[torch.Tensor] = []
-        device = torch.device(device) if device is not None else None
-
-        for _ in range(config.num_hidden_layers):
-            conv_state = torch.zeros(
-                self.max_batch_size,
-                config.hidden_size,
-                self.conv_L_cache,
-                dtype=self._dtype,
-                device=device,
-            )
-            self.conv_cache.append(conv_state)
-            self.key_cache.append(torch.tensor([]))
-            self.value_cache.append(torch.tensor([]))
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
-
-        Parameters:
-            key_states (`torch.Tensor`):
-                The new key states to cache.
-            value_states (`torch.Tensor`):
-                The new value states to cache.
-            layer_idx (`int`):
-                The index of the layer to cache the states for.
-            cache_kwargs (`Dict[str, Any]`, `optional`):
-                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
-
-        Return:
-            A tuple containing the updated key and value states.
-        """
-        # Update the cache
-        if self.key_cache[layer_idx].numel() == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_cache[layer_idx] = new_conv_state.to(self.conv_cache[layer_idx].device)
-        else:
-            self.conv_cache[layer_idx] = self.conv_cache[layer_idx].roll(shifts=-1, dims=-1)
-            self.conv_cache[layer_idx][:, :, -1] = new_conv_state[:, :, -1].to(self.conv_cache[layer_idx].device)
-
-        # If last layer is updated, set the flag
-        if layer_idx == self.last_conv_layer:
-            self.has_previous_state = True
-
-        return self.conv_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx].numel():
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-            if self.conv_cache[layer_idx].numel():
-                device = self.conv_cache[layer_idx].device
-                self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
-        for each layer.
-        """
-        full_mask_kv_offset = 0
-        past_seen_tokens = self.get_seq_length()
-        kv_length = query_length + past_seen_tokens
-        return kv_length, full_mask_kv_offset
-
-    def crop(self, max_length: int):
-        """Crop the cache to the given length"""
-        if max_length < 0:
-            max_length = self.get_seq_length() - abs(max_length)
-
-        if self.get_seq_length() <= max_length:
-            return
-
-        for idx in range(len(self.key_cache)):
-            if self.key_cache[idx].numel():
-                self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
-                self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
-
-    def __len__(self) -> int:
-        return len(self.key_cache)
-
-    def reset(self):
-        self.has_previous_state = False
-        for layer_idx in range(len(self.conv_cache)):
-            # In-place ops prevent breaking the static address
-            self.conv_cache[layer_idx].zero_()
-
-
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -400,7 +246,7 @@ def forward(
         hidden_states: torch.Tensor,
         position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: torch.Tensor | None,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         input_shape = hidden_states.shape[:-1]
@@ -477,7 +323,7 @@ def __init__(
     def cuda_kernels_forward(
         self,
         x: torch.Tensor,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         x = apply_mask_to_padding_states(x, attention_mask)
@@ -487,10 +333,10 @@ def cuda_kernels_forward(
         Bx = B * x
 
         conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
-        if past_key_values is not None and past_key_values.has_previous_state:
+        if past_key_values is not None and past_key_values.has_previous_state(self.layer_idx):
             conv_out = causal_conv1d_update(
                 Bx.squeeze(-1),
-                past_key_values.conv_cache[self.layer_idx],
+                past_key_values.layers[self.layer_idx].conv_states,
                 conv_weights,
                 self.conv.bias,
                 None,
@@ -499,7 +345,7 @@ def cuda_kernels_forward(
         else:
             if past_key_values is not None:
                 conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                past_key_values.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                conv_state = past_key_values.update_conv_state(conv_state, self.layer_idx)
 
             conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
 
@@ -510,7 +356,7 @@ def cuda_kernels_forward(
     def slow_forward(
         self,
         x: torch.Tensor,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         seqlen = x.shape[1]
@@ -521,8 +367,8 @@ def slow_forward(
 
         Bx = B * x
 
-        if past_key_values is not None and past_key_values.has_previous_state:
-            conv_state = past_key_values.update_conv_state(self.layer_idx, Bx, cache_init=False)
+        if past_key_values is not None and past_key_values.has_previous_state(self.layer_idx):
+            conv_state = past_key_values.update_conv_state(Bx, self.layer_idx)
             conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
             if self.bias:
                 conv_out += self.conv.bias
@@ -531,7 +377,7 @@ def slow_forward(
         else:
             if past_key_values is not None:
                 conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                conv_state = past_key_values.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                conv_state = past_key_values.update_conv_state(conv_state, self.layer_idx)
 
             conv_out = self.conv(Bx)[..., :seqlen]
 
@@ -543,7 +389,7 @@ def slow_forward(
     def forward(
         self,
         hidden_states: torch.Tensor,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         if is_fast_path_available and "cuda" in hidden_states.device.type and not is_torchdynamo_compiling():
@@ -570,7 +416,7 @@ def forward(
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs,
     ) -> torch.Tensor:
         residual = hidden_states
@@ -639,7 +485,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -651,10 +497,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            batch_size = inputs_embeds.shape[0]
-            past_key_values = Lfm2HybridConvCache(
-                config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
diff --git a/src/transformers/models/lfm2/modular_lfm2.py b/src/transformers/models/lfm2/modular_lfm2.py
index 6f3a754d69ae..c08b61956081 100644
--- a/src/transformers/models/lfm2/modular_lfm2.py
+++ b/src/transformers/models/lfm2/modular_lfm2.py
@@ -12,12 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections.abc import Callable
-from typing import Any
 
 import torch
 import torch.nn.functional as F
 from torch import nn
 
+from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_outputs import BaseModelOutputWithPast
@@ -80,160 +80,6 @@ def forward(self, x):
         return self.w2(F.silu(self.w1(x)) * self.w3(x))
 
 
-class Lfm2HybridConvCache:
-    """
-    Attention and conv cache for Lfm2.
-
-    It stores the Key and Value states as a list of tensors, one for each layer.
-    Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
-    Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
-    """
-
-    # Override @property existing in Cache
-    max_batch_size = None
-    is_compileable = False
-    key_cache = None
-    value_cache = None
-
-    def __init__(
-        self,
-        config: Lfm2Config,
-        max_batch_size: int,
-        dtype: torch.dtype = torch.float32,
-        device: torch.device | str | None = None,
-    ):
-        self.key_cache = []
-        self.value_cache = []
-        self.max_batch_size = max_batch_size
-        self.layer_types = config.layer_types
-        self.first_attention_layer = self.layer_types.index("full_attention")
-        self.last_conv_layer = len(self.layer_types) - self.layer_types[::-1].index("conv") - 1
-        self.conv_L_cache = config.conv_L_cache
-        self._dtype = dtype
-        self.has_previous_state = False
-
-        self.conv_cache: list[torch.Tensor] = []
-        device = torch.device(device) if device is not None else None
-
-        for _ in range(config.num_hidden_layers):
-            conv_state = torch.zeros(
-                self.max_batch_size,
-                config.hidden_size,
-                self.conv_L_cache,
-                dtype=self._dtype,
-                device=device,
-            )
-            self.conv_cache.append(conv_state)
-            self.key_cache.append(torch.tensor([]))
-            self.value_cache.append(torch.tensor([]))
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
-
-        Parameters:
-            key_states (`torch.Tensor`):
-                The new key states to cache.
-            value_states (`torch.Tensor`):
-                The new value states to cache.
-            layer_idx (`int`):
-                The index of the layer to cache the states for.
-            cache_kwargs (`Dict[str, Any]`, `optional`):
-                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
-
-        Return:
-            A tuple containing the updated key and value states.
-        """
-        # Update the cache
-        if self.key_cache[layer_idx].numel() == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_cache[layer_idx] = new_conv_state.to(self.conv_cache[layer_idx].device)
-        else:
-            self.conv_cache[layer_idx] = self.conv_cache[layer_idx].roll(shifts=-1, dims=-1)
-            self.conv_cache[layer_idx][:, :, -1] = new_conv_state[:, :, -1].to(self.conv_cache[layer_idx].device)
-
-        # If last layer is updated, set the flag
-        if layer_idx == self.last_conv_layer:
-            self.has_previous_state = True
-
-        return self.conv_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx].numel():
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-            if self.conv_cache[layer_idx].numel():
-                device = self.conv_cache[layer_idx].device
-                self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
-        for each layer.
-        """
-        full_mask_kv_offset = 0
-        past_seen_tokens = self.get_seq_length()
-        kv_length = query_length + past_seen_tokens
-        return kv_length, full_mask_kv_offset
-
-    def crop(self, max_length: int):
-        """Crop the cache to the given length"""
-        if max_length < 0:
-            max_length = self.get_seq_length() - abs(max_length)
-
-        if self.get_seq_length() <= max_length:
-            return
-
-        for idx in range(len(self.key_cache)):
-            if self.key_cache[idx].numel():
-                self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
-                self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
-
-    def __len__(self) -> int:
-        return len(self.key_cache)
-
-    def reset(self):
-        self.has_previous_state = False
-        for layer_idx in range(len(self.conv_cache)):
-            # In-place ops prevent breaking the static address
-            self.conv_cache[layer_idx].zero_()
-
-
 class Lfm2Attention(LlamaAttention):
     def __init__(self, config: Lfm2Config, layer_idx: int):
         super().__init__(config, layer_idx)
@@ -251,7 +97,7 @@ def forward(
         hidden_states: torch.Tensor,
         position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: torch.Tensor | None,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         input_shape = hidden_states.shape[:-1]
@@ -312,7 +158,7 @@ def __init__(
     def cuda_kernels_forward(
         self,
         x: torch.Tensor,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         x = apply_mask_to_padding_states(x, attention_mask)
@@ -322,10 +168,10 @@ def cuda_kernels_forward(
         Bx = B * x
 
         conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
-        if past_key_values is not None and past_key_values.has_previous_state:
+        if past_key_values is not None and past_key_values.has_previous_state(self.layer_idx):
             conv_out = causal_conv1d_update(
                 Bx.squeeze(-1),
-                past_key_values.conv_cache[self.layer_idx],
+                past_key_values.layers[self.layer_idx].conv_states,
                 conv_weights,
                 self.conv.bias,
                 None,
@@ -334,7 +180,7 @@ def cuda_kernels_forward(
         else:
             if past_key_values is not None:
                 conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                past_key_values.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                conv_state = past_key_values.update_conv_state(conv_state, self.layer_idx)
 
             conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
 
@@ -345,7 +191,7 @@ def cuda_kernels_forward(
     def slow_forward(
         self,
         x: torch.Tensor,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         seqlen = x.shape[1]
@@ -356,8 +202,8 @@ def slow_forward(
 
         Bx = B * x
 
-        if past_key_values is not None and past_key_values.has_previous_state:
-            conv_state = past_key_values.update_conv_state(self.layer_idx, Bx, cache_init=False)
+        if past_key_values is not None and past_key_values.has_previous_state(self.layer_idx):
+            conv_state = past_key_values.update_conv_state(Bx, self.layer_idx)
             conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
             if self.bias:
                 conv_out += self.conv.bias
@@ -366,7 +212,7 @@ def slow_forward(
         else:
             if past_key_values is not None:
                 conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                conv_state = past_key_values.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                conv_state = past_key_values.update_conv_state(conv_state, self.layer_idx)
 
             conv_out = self.conv(Bx)[..., :seqlen]
 
@@ -378,7 +224,7 @@ def slow_forward(
     def forward(
         self,
         hidden_states: torch.Tensor,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         if is_fast_path_available and "cuda" in hidden_states.device.type and not is_torchdynamo_compiling():
@@ -405,7 +251,7 @@ def forward(
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs,
     ) -> torch.Tensor:
         residual = hidden_states
@@ -445,7 +291,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Lfm2HybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -457,10 +303,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            batch_size = inputs_embeds.shape[0]
-            past_key_values = Lfm2HybridConvCache(
-                config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
diff --git a/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py b/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py
index 2a5d4564a1e1..0369ae31b8ae 100644
--- a/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py
+++ b/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py
@@ -19,14 +19,14 @@
 # limitations under the License.
 
 from collections.abc import Callable
-from typing import Any, Optional
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
 from torch import nn
 
 from ... import initialization as init
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import (
     use_experts_implementation,
@@ -228,160 +228,6 @@ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tens
         return final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
 
 
-class Lfm2MoeHybridConvCache:
-    """
-    Attention and conv cache for Lfm2Moe.
-
-    It stores the Key and Value states as a list of tensors, one for each layer.
-    Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
-    Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
-    """
-
-    # Override @property existing in Cache
-    max_batch_size = None
-    is_compileable = False
-    key_cache = None
-    value_cache = None
-
-    def __init__(
-        self,
-        config: Lfm2MoeConfig,
-        max_batch_size: int,
-        dtype: torch.dtype = torch.float32,
-        device: torch.device | str | None = None,
-    ):
-        self.key_cache = []
-        self.value_cache = []
-        self.max_batch_size = max_batch_size
-        self.layer_types = config.layer_types
-        self.first_attention_layer = self.layer_types.index("full_attention")
-        self.last_conv_layer = len(self.layer_types) - self.layer_types[::-1].index("conv") - 1
-        self.conv_L_cache = config.conv_L_cache
-        self._dtype = dtype
-        self.has_previous_state = False
-
-        self.conv_cache: list[torch.Tensor] = []
-        device = torch.device(device) if device is not None else None
-
-        for _ in range(config.num_hidden_layers):
-            conv_state = torch.zeros(
-                self.max_batch_size,
-                config.hidden_size,
-                self.conv_L_cache,
-                dtype=self._dtype,
-                device=device,
-            )
-            self.conv_cache.append(conv_state)
-            self.key_cache.append(torch.tensor([]))
-            self.value_cache.append(torch.tensor([]))
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
-
-        Parameters:
-            key_states (`torch.Tensor`):
-                The new key states to cache.
-            value_states (`torch.Tensor`):
-                The new value states to cache.
-            layer_idx (`int`):
-                The index of the layer to cache the states for.
-            cache_kwargs (`Dict[str, Any]`, `optional`):
-                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
-
-        Return:
-            A tuple containing the updated key and value states.
-        """
-        # Update the cache
-        if self.key_cache[layer_idx].numel() == 0:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update_conv_state(
-        self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
-    ) -> torch.Tensor:
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
-        if cache_init:
-            self.conv_cache[layer_idx] = new_conv_state.to(self.conv_cache[layer_idx].device)
-        else:
-            self.conv_cache[layer_idx] = self.conv_cache[layer_idx].roll(shifts=-1, dims=-1)
-            self.conv_cache[layer_idx][:, :, -1] = new_conv_state[:, :, -1].to(self.conv_cache[layer_idx].device)
-
-        # If last layer is updated, set the flag
-        if layer_idx == self.last_conv_layer:
-            self.has_previous_state = True
-
-        return self.conv_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx].numel():
-                device = self.key_cache[layer_idx].device
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-                device = self.value_cache[layer_idx].device
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-            if self.conv_cache[layer_idx].numel():
-                device = self.conv_cache[layer_idx].device
-                self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
-        for each layer.
-        """
-        full_mask_kv_offset = 0
-        past_seen_tokens = self.get_seq_length()
-        kv_length = query_length + past_seen_tokens
-        return kv_length, full_mask_kv_offset
-
-    def crop(self, max_length: int):
-        """Crop the cache to the given length"""
-        if max_length < 0:
-            max_length = self.get_seq_length() - abs(max_length)
-
-        if self.get_seq_length() <= max_length:
-            return
-
-        for idx in range(len(self.key_cache)):
-            if self.key_cache[idx].numel():
-                self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
-                self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
-
-    def __len__(self) -> int:
-        return len(self.key_cache)
-
-    def reset(self):
-        self.has_previous_state = False
-        for layer_idx in range(len(self.conv_cache)):
-            # In-place ops prevent breaking the static address
-            self.conv_cache[layer_idx].zero_()
-
-
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -476,7 +322,7 @@ def forward(
         hidden_states: torch.Tensor,
         position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: torch.Tensor | None,
-        past_key_values: Lfm2MoeHybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         input_shape = hidden_states.shape[:-1]
@@ -553,7 +399,7 @@ def __init__(
     def cuda_kernels_forward(
         self,
         x: torch.Tensor,
-        past_key_values: Lfm2MoeHybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         x = apply_mask_to_padding_states(x, attention_mask)
@@ -563,10 +409,10 @@ def cuda_kernels_forward(
         Bx = B * x
 
         conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
-        if past_key_values is not None and past_key_values.has_previous_state:
+        if past_key_values is not None and past_key_values.has_previous_state(self.layer_idx):
             conv_out = causal_conv1d_update(
                 Bx.squeeze(-1),
-                past_key_values.conv_cache[self.layer_idx],
+                past_key_values.layers[self.layer_idx].conv_states,
                 conv_weights,
                 self.conv.bias,
                 None,
@@ -575,7 +421,7 @@ def cuda_kernels_forward(
         else:
             if past_key_values is not None:
                 conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                past_key_values.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                conv_state = past_key_values.update_conv_state(conv_state, self.layer_idx)
 
             conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
 
@@ -586,7 +432,7 @@ def cuda_kernels_forward(
     def slow_forward(
         self,
         x: torch.Tensor,
-        past_key_values: Lfm2MoeHybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         seqlen = x.shape[1]
@@ -597,8 +443,8 @@ def slow_forward(
 
         Bx = B * x
 
-        if past_key_values is not None and past_key_values.has_previous_state:
-            conv_state = past_key_values.update_conv_state(self.layer_idx, Bx, cache_init=False)
+        if past_key_values is not None and past_key_values.has_previous_state(self.layer_idx):
+            conv_state = past_key_values.update_conv_state(Bx, self.layer_idx)
             conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
             if self.bias:
                 conv_out += self.conv.bias
@@ -607,7 +453,7 @@ def slow_forward(
         else:
             if past_key_values is not None:
                 conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                conv_state = past_key_values.update_conv_state(self.layer_idx, conv_state, cache_init=True)
+                conv_state = past_key_values.update_conv_state(conv_state, self.layer_idx)
 
             conv_out = self.conv(Bx)[..., :seqlen]
 
@@ -619,7 +465,7 @@ def slow_forward(
     def forward(
         self,
         hidden_states: torch.Tensor,
-        past_key_values: Lfm2MoeHybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         if is_fast_path_available and "cuda" in hidden_states.device.type and not is_torchdynamo_compiling():
@@ -650,7 +496,7 @@ def forward(
         position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Lfm2MoeHybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         **kwargs,
     ) -> torch.Tensor:
         residual = hidden_states
@@ -685,7 +531,7 @@ class Lfm2MoePreTrainedModel(PreTrainedModel):
     _supports_flash_attn = True
     _supports_sdpa = True
     _supports_flex_attn = True
-    _can_compile_fullgraph = False  # uses a non-compilable custom cache class Lfm2MoeHybridConvCache
+    _can_compile_fullgraph = False  # uses a non-compilable cache class
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": Lfm2MoeDecoderLayer,
@@ -729,7 +575,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Lfm2MoeHybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -741,10 +587,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            batch_size = inputs_embeds.shape[0]
-            past_key_values = Lfm2MoeHybridConvCache(
-                config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
diff --git a/src/transformers/models/lfm2_moe/modular_lfm2_moe.py b/src/transformers/models/lfm2_moe/modular_lfm2_moe.py
index 7d97d8b70dd5..a1b2799d2bae 100644
--- a/src/transformers/models/lfm2_moe/modular_lfm2_moe.py
+++ b/src/transformers/models/lfm2_moe/modular_lfm2_moe.py
@@ -17,6 +17,7 @@
 from torch import nn
 
 from ... import initialization as init
+from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
 from ...modeling_outputs import MoeModelOutputWithPast
 from ...modeling_utils import PreTrainedModel
@@ -26,7 +27,6 @@
 from ..lfm2.modeling_lfm2 import (
     Lfm2Attention,
     Lfm2DecoderLayer,
-    Lfm2HybridConvCache,
     Lfm2MLP,
     Lfm2RotaryEmbedding,
     Lfm2ShortConv,
@@ -110,10 +110,6 @@ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tens
         return final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
 
 
-class Lfm2MoeHybridConvCache(Lfm2HybridConvCache):
-    pass
-
-
 class Lfm2MoeAttention(Lfm2Attention):
     pass
 
@@ -133,7 +129,7 @@ def __init__(self, config: Lfm2MoeConfig, layer_idx: int):
 
 
 class Lfm2MoePreTrainedModel(LlamaPreTrainedModel):
-    _can_compile_fullgraph = False  # uses a non-compilable custom cache class Lfm2MoeHybridConvCache
+    _can_compile_fullgraph = False  # uses a non-compilable cache class
 
     @torch.no_grad()
     def _init_weights(self, module):
@@ -159,7 +155,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Lfm2MoeHybridConvCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
@@ -171,10 +167,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            batch_size = inputs_embeds.shape[0]
-            past_key_values = Lfm2MoeHybridConvCache(
-                config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
-            )
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
diff --git a/tests/models/lfm2/test_modeling_lfm2.py b/tests/models/lfm2/test_modeling_lfm2.py
index 13afd1c2726b..f44ae5632641 100644
--- a/tests/models/lfm2/test_modeling_lfm2.py
+++ b/tests/models/lfm2/test_modeling_lfm2.py
@@ -29,8 +29,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import Lfm2ForCausalLM, Lfm2Model
-    from transformers.models.lfm2.modeling_lfm2 import Lfm2HybridConvCache
+    from transformers import DynamicCache, Lfm2ForCausalLM, Lfm2Model
+    from transformers.cache_utils import MambaLayer
 
 
 class Lfm2ModelTester(CausalLMModelTester):
@@ -53,7 +53,7 @@ class Lfm2ModelTest(CausalLMModelTest, unittest.TestCase):
     _torch_compile_train_cls = Lfm2ForCausalLM if is_torch_available() else None
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, Lfm2HybridConvCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -61,15 +61,16 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         attention_shape = (batch_size, num_heads, seq_length, head_dim)
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
 
-        for i in range(config.num_hidden_layers):
-            if config.layer_types[i] == "full_attention":
-                self.assertEqual(past_key_values.key_cache[i].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[i].shape, attention_shape)
+        for idx in range(config.num_hidden_layers):
+            if config.layer_types[idx] == "full_attention":
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
             else:
-                self.assertEqual(past_key_values.conv_cache[i].shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, (1,))
 
-    def _check_caches_are_equal(self, cache1: Lfm2HybridConvCache, cache2: Lfm2HybridConvCache):
-        if not isinstance(cache1, Lfm2HybridConvCache) or not isinstance(cache2, Lfm2HybridConvCache):
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -77,9 +78,14 @@ def _check_caches_are_equal(self, cache1: Lfm2HybridConvCache, cache2: Lfm2Hybri
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_cache[idx], cache2.conv_cache[idx])
+            # Mamba layer
+            if type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""
diff --git a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
index fa8aecc99707..a5ed1b9fb9b9 100644
--- a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
+++ b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
@@ -32,8 +32,8 @@
 if is_torch_available():
     import torch
 
-    from transformers import Lfm2MoeConfig, Lfm2MoeForCausalLM, Lfm2MoeModel
-    from transformers.models.lfm2_moe.modeling_lfm2_moe import Lfm2MoeHybridConvCache
+    from transformers import DynamicCache, Lfm2MoeConfig, Lfm2MoeForCausalLM, Lfm2MoeModel
+    from transformers.cache_utils import MambaLayer
 
 
 class Lfm2MoeModelTester(CausalLMModelTester):
@@ -71,7 +71,7 @@ class Lfm2MoeModelTest(CausalLMModelTest, unittest.TestCase):
     _torch_compile_train_cls = Lfm2MoeForCausalLM if is_torch_available() else None
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, Lfm2MoeHybridConvCache)
+        self.assertIsInstance(past_key_values, DynamicCache)
 
         # (batch, kv heads, seq_length, head_dim)
         num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
@@ -79,15 +79,16 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         attention_shape = (batch_size, num_heads, seq_length, head_dim)
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
 
-        for i in range(config.num_hidden_layers):
-            if config.layer_types[i] == "full_attention":
-                self.assertEqual(past_key_values.key_cache[i].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[i].shape, attention_shape)
+        for idx in range(config.num_hidden_layers):
+            if config.layer_types[idx] == "full_attention":
+                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
+                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
             else:
-                self.assertEqual(past_key_values.conv_cache[i].shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
+                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, (1,))
 
-    def _check_caches_are_equal(self, cache1: Lfm2MoeHybridConvCache, cache2: Lfm2MoeHybridConvCache):
-        if not isinstance(cache1, Lfm2MoeHybridConvCache) or not isinstance(cache2, Lfm2MoeHybridConvCache):
+    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
+        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
             raise ValueError("The wrong cache is being used!")
 
         if not len(cache1) == len(cache2):
@@ -95,9 +96,14 @@ def _check_caches_are_equal(self, cache1: Lfm2MoeHybridConvCache, cache2: Lfm2Mo
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_cache[idx], cache2.conv_cache[idx])
+            # Mamba layer
+            if type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""

From b60c6f59d102a16490d355ce6ac7519b34521b7f Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 16:08:56 +0100
Subject: [PATCH 24/56] simplify tests

---
 tests/generation/test_utils.py                | 19 +++++++++++++++--
 tests/models/bamba/test_modeling_bamba.py     | 19 -----------------
 .../falcon_h1/test_modeling_falcon_h1.py      | 17 ---------------
 .../test_modeling_granitemoehybrid.py         | 19 -----------------
 tests/models/jamba/test_modeling_jamba.py     | 17 ---------------
 tests/models/lfm2/test_modeling_lfm2.py       | 19 -----------------
 .../models/lfm2_moe/test_modeling_lfm2_moe.py | 19 -----------------
 tests/models/lfm2_vl/test_modeling_lfm2_vl.py | 14 -------------
 .../nemotron_h/test_modeling_nemotron_h.py    | 19 -----------------
 tests/models/zamba/test_modeling_zamba.py     | 21 -------------------
 tests/models/zamba2/test_modeling_zamba2.py   | 21 -------------------
 11 files changed, 17 insertions(+), 187 deletions(-)

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 7d764b55d008..149ef8911176 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -81,6 +81,8 @@
         Cache,
         DynamicCache,
         EncoderDecoderCache,
+        MambaAndAttentionLayer,
+        MambaLayer,
         QuantoQuantizedLayer,
         StaticCache,
     )
@@ -2616,8 +2618,21 @@ def _check_caches_are_equal(self, cache1: Cache, cache2: Cache):
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-            torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
+            self.assertEqual(type(cache1.layers[idx], cache2.layers[idx]))
+            # Mamba layer
+            if isinstance(cache1.layers[idx], MambaLayer):
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Mamba + Attention layer
+            elif isinstance(cache1.layers[idx], MambaAndAttentionLayer):
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Attention layer
+            else:
+                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
+                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
 
 
 @require_torch
diff --git a/tests/models/bamba/test_modeling_bamba.py b/tests/models/bamba/test_modeling_bamba.py
index da236c72790c..6b9eb77dd64a 100644
--- a/tests/models/bamba/test_modeling_bamba.py
+++ b/tests/models/bamba/test_modeling_bamba.py
@@ -48,7 +48,6 @@
     import torch
 
     from transformers import BambaForCausalLM, BambaModel, DynamicCache
-    from transformers.cache_utils import MambaLayer
 
 
 class BambaModelTester:
@@ -305,24 +304,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
                 self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            # Mamba layer
-            if type(cache1.layers[idx]) is MambaLayer:
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            # Attention layer
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-
     def setUp(self):
         self.model_tester = self.model_tester_class(self)
         self.config_tester = ConfigTester(self, config_class=self.model_tester.config_class, hidden_size=64)
diff --git a/tests/models/falcon_h1/test_modeling_falcon_h1.py b/tests/models/falcon_h1/test_modeling_falcon_h1.py
index e236a818835b..0cb54ff547f9 100644
--- a/tests/models/falcon_h1/test_modeling_falcon_h1.py
+++ b/tests/models/falcon_h1/test_modeling_falcon_h1.py
@@ -37,7 +37,6 @@
     import torch
 
     from transformers import AutoTokenizer, DynamicCache, FalconH1ForCausalLM, FalconH1Model
-    from transformers.cache_utils import MambaLayer
 
 
 class FalconH1ModelTester:
@@ -281,22 +280,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
                 self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1, cache2):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            if isinstance(cache1.layers[idx], MambaLayer):
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-
     def setUp(self):
         self.model_tester = FalconH1ModelTester(self)
         self.config_tester = ConfigTester(self, config_class=FalconH1Config, hidden_size=64)
diff --git a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
index e102ff9c93ea..c05e496808c9 100644
--- a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
+++ b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
@@ -47,7 +47,6 @@
     import torch
 
     from transformers import DynamicCache, GraniteMoeHybridForCausalLM, GraniteMoeHybridModel
-    from transformers.cache_utils import MambaLayer
 
 
 class GraniteMoeHybridModelTester(BambaModelTester):
@@ -106,24 +105,6 @@ class GraniteMoeHybridModelTest(ModelTesterMixin, GenerationTesterMixin, Pipelin
     # This is because we are hitting edge cases with the causal_mask buffer
     model_split_percents = [0.5, 0.7, 0.8]
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            # Mamba layer
-            if type(cache1.layers[idx]) is MambaLayer:
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            # Attention layer
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-
     def setUp(self):
         self.model_tester = self.model_tester_class(self)
         self.config_tester = ConfigTester(self, config_class=self.model_tester.config_class, hidden_size=64)
diff --git a/tests/models/jamba/test_modeling_jamba.py b/tests/models/jamba/test_modeling_jamba.py
index 499941fcbe81..2b3853d4b051 100644
--- a/tests/models/jamba/test_modeling_jamba.py
+++ b/tests/models/jamba/test_modeling_jamba.py
@@ -44,7 +44,6 @@
     import torch
 
     from transformers import DynamicCache, JambaForCausalLM, JambaForSequenceClassification, JambaModel
-    from transformers.cache_utils import MambaLayer
 
 
 class JambaConfigTester(ConfigTester):
@@ -343,22 +342,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
                 self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            if isinstance(cache1.layers[idx], MambaLayer):
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-
     def setUp(self):
         self.model_tester = JambaModelTester(self)
         self.config_tester = JambaConfigTester(self, config_class=JambaConfig, hidden_size=32)
diff --git a/tests/models/lfm2/test_modeling_lfm2.py b/tests/models/lfm2/test_modeling_lfm2.py
index f44ae5632641..12cc3fdb38f9 100644
--- a/tests/models/lfm2/test_modeling_lfm2.py
+++ b/tests/models/lfm2/test_modeling_lfm2.py
@@ -30,7 +30,6 @@
     import torch
 
     from transformers import DynamicCache, Lfm2ForCausalLM, Lfm2Model
-    from transformers.cache_utils import MambaLayer
 
 
 class Lfm2ModelTester(CausalLMModelTester):
@@ -69,24 +68,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
                 self.assertEqual(past_key_values.layers[idx].ssm_states.shape, (1,))
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            # Mamba layer
-            if type(cache1.layers[idx]) is MambaLayer:
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            # Attention layer
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
diff --git a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
index a5ed1b9fb9b9..8b7c2a55004f 100644
--- a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
+++ b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
@@ -33,7 +33,6 @@
     import torch
 
     from transformers import DynamicCache, Lfm2MoeConfig, Lfm2MoeForCausalLM, Lfm2MoeModel
-    from transformers.cache_utils import MambaLayer
 
 
 class Lfm2MoeModelTester(CausalLMModelTester):
@@ -87,24 +86,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
                 self.assertEqual(past_key_values.layers[idx].ssm_states.shape, (1,))
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            # Mamba layer
-            if type(cache1.layers[idx]) is MambaLayer:
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            # Attention layer
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
diff --git a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
index aa153563e4f8..4ccdf539f453 100644
--- a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
+++ b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
@@ -188,20 +188,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             else:
                 self.assertEqual(past_key_values.conv_cache[i].shape, conv_shape)
 
-    def _check_caches_are_equal(self, cache1: Lfm2HybridConvCache, cache2: Lfm2HybridConvCache):
-        """Text model uses lfm2, which has non-standard cache"""
-        if not isinstance(cache1, Lfm2HybridConvCache) or not isinstance(cache2, Lfm2HybridConvCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
-            torch.testing.assert_close(cache1.conv_cache[idx], cache2.conv_cache[idx])
-
     def test_config(self):
         self.config_tester.run_common_tests()
 
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index 43a3ea8af9b1..9ea2dffc5f74 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -41,7 +41,6 @@
     import torch
 
     from transformers import DynamicCache, NemotronHForCausalLM, NemotronHModel
-    from transformers.cache_utils import MambaLayer
 
 
 class NemotronHModelTester:
@@ -380,24 +379,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
                 self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            # Mamba layer
-            if type(cache1.layers[idx]) is MambaLayer:
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            # Attention layer
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-
     def setUp(self):
         self.model_tester = NemotronHModelTester(self)
         self.config_tester = ConfigTester(
diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index 4ce37dd638a7..e59205417458 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -39,7 +39,6 @@
     import torch
 
     from transformers import DynamicCache, ZambaForCausalLM, ZambaForSequenceClassification, ZambaModel
-    from transformers.cache_utils import MambaLayer
 
 
 class ZambaModelTester:
@@ -313,26 +312,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
                 self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            # Mamba layer
-            if type(cache1.layers[idx]) is MambaLayer:
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            # Hybrid mamba + attention layer
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-
     def setUp(self):
         self.model_tester = ZambaModelTester(self)
         self.config_tester = ConfigTester(self, config_class=ZambaConfig, hidden_size=32)
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index 0760c5e60789..545f94e97c02 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -40,7 +40,6 @@
     import torch
 
     from transformers import DynamicCache, Zamba2ForCausalLM, Zamba2ForSequenceClassification, Zamba2Model
-    from transformers.cache_utils import MambaLayer
 
 
 class Zamba2ModelTester:
@@ -327,26 +326,6 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
                 self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
 
-    def _check_caches_are_equal(self, cache1: DynamicCache, cache2: DynamicCache):
-        if not isinstance(cache1, DynamicCache) or not isinstance(cache2, DynamicCache):
-            raise ValueError("The wrong cache is being used!")
-
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            # Mamba layer
-            if type(cache1.layers[idx]) is MambaLayer:
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-            # Hybrid mamba + attention layer
-            else:
-                torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
-                torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
-
     def setUp(self):
         self.model_tester = Zamba2ModelTester(self)
         self.config_tester = ConfigTester(self, config_class=Zamba2Config, hidden_size=32)

From 0e8ca28529438f77e559925032152b4f7d991ba8 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 16:29:59 +0100
Subject: [PATCH 25/56] simplify tests further

---
 tests/generation/test_utils.py                | 33 +++++++++++++++----
 tests/models/bamba/test_modeling_bamba.py     | 23 +++----------
 .../falcon_h1/test_modeling_falcon_h1.py      | 20 ++---------
 .../test_modeling_falcon_mamba.py             | 12 -------
 .../test_modeling_granitemoehybrid.py         | 22 ++-----------
 tests/models/jamba/test_modeling_jamba.py     | 21 ++----------
 tests/models/lfm2/test_modeling_lfm2.py       | 20 +++--------
 .../models/lfm2_moe/test_modeling_lfm2_moe.py | 20 +++--------
 tests/models/lfm2_vl/test_modeling_lfm2_vl.py | 18 ++--------
 tests/models/mamba/test_modeling_mamba.py     | 12 -------
 tests/models/mamba2/test_modeling_mamba2.py   |  9 ++---
 .../nemotron_h/test_modeling_nemotron_h.py    | 21 ++----------
 tests/models/zamba/test_modeling_zamba.py     | 24 ++------------
 tests/models/zamba2/test_modeling_zamba2.py   | 24 ++------------
 14 files changed, 61 insertions(+), 218 deletions(-)

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 149ef8911176..b086117b0549 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2537,6 +2537,12 @@ def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, c
             [encoder_expected_shape] * len(hidden_states),
         )
 
+    def _get_mamba_cache_shapes(batch_size: int, config):
+        # Default mamba cache shape - can vary based on models so this function is convenient to easily check caches
+        conv_shape = (batch_size, config.intermediate_size, config.conv_kernel)
+        ssm_shape = (batch_size, config.intermediate_size, config.state_size)
+        return conv_shape, ssm_shape
+
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
         # Raise a useful error, asking to explicitly override the method
         if not isinstance(past_key_values, Cache):
@@ -2560,12 +2566,15 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         head_dim = getattr(config, "head_dim", hidden_size // config.num_attention_heads)
 
         # For cross attention cache, the seq_length depends on the model, so we remove that dim
-        expected_shape = (
+        attention_shape = (
             (batch_size, num_heads, seq_length, head_dim)
             if seq_length is not None
             else (batch_size, num_heads, head_dim)
         )
 
+        # For mamba layers
+        conv_shape, ssm_shape = self._get_mamba_cache_shapes(batch_size, config)
+
         # Check the size is coherent
         num_hidden_layers = config.num_hidden_layers
         if getattr(config, "num_kv_shared_layers", None) is not None:
@@ -2574,11 +2583,23 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         # Check each layer has the correct shape
         for layer in past_key_values.layers:
-            # Remove the seq_length dim for cross-attention cache (it changes based on the model)
-            keys = layer.keys if seq_length is not None else layer.keys[:, :, 0, :]
-            values = layer.values if seq_length is not None else layer.values[:, :, 0, :]
-            self.assertEqual(keys.shape, expected_shape)
-            self.assertEqual(values.shape, expected_shape)
+            if isinstance(layer, MambaLayer):
+                self.assertEqual(layer.conv_states.shape, conv_shape)
+                self.assertEqual(layer.ssm_states.shape, ssm_shape)
+            elif isinstance(layer, MambaAndAttentionLayer):
+                # Remove the seq_length dim for cross-attention cache (it changes based on the model)
+                keys = layer.keys if seq_length is not None else layer.keys[:, :, 0, :]
+                values = layer.values if seq_length is not None else layer.values[:, :, 0, :]
+                self.assertEqual(keys.shape, attention_shape)
+                self.assertEqual(values.shape, attention_shape)
+                self.assertEqual(layer.conv_states.shape, conv_shape)
+                self.assertEqual(layer.ssm_states.shape, ssm_shape)
+            else:
+                # Remove the seq_length dim for cross-attention cache (it changes based on the model)
+                keys = layer.keys if seq_length is not None else layer.keys[:, :, 0, :]
+                values = layer.values if seq_length is not None else layer.values[:, :, 0, :]
+                self.assertEqual(keys.shape, attention_shape)
+                self.assertEqual(values.shape, attention_shape)
 
     def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
         # check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
diff --git a/tests/models/bamba/test_modeling_bamba.py b/tests/models/bamba/test_modeling_bamba.py
index 6b9eb77dd64a..9ca8127b5616 100644
--- a/tests/models/bamba/test_modeling_bamba.py
+++ b/tests/models/bamba/test_modeling_bamba.py
@@ -47,7 +47,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import BambaForCausalLM, BambaModel, DynamicCache
+    from transformers import BambaForCausalLM, BambaModel
 
 
 class BambaModelTester:
@@ -279,30 +279,15 @@ class BambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     # This is because we are hitting edge cases with the causal_mask buffer
     model_split_percents = [0.5, 0.7, 0.8]
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
-
+    def _get_mamba_cache_shapes(batch_size: int, config):
+        # For mamba layers
         conv_shape = (
             batch_size,
             config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * config.mamba_d_state,
             config.mamba_d_conv,
         )
         ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+        return conv_shape, ssm_shape
 
     def setUp(self):
         self.model_tester = self.model_tester_class(self)
diff --git a/tests/models/falcon_h1/test_modeling_falcon_h1.py b/tests/models/falcon_h1/test_modeling_falcon_h1.py
index 0cb54ff547f9..a562ff404c59 100644
--- a/tests/models/falcon_h1/test_modeling_falcon_h1.py
+++ b/tests/models/falcon_h1/test_modeling_falcon_h1.py
@@ -36,7 +36,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import AutoTokenizer, DynamicCache, FalconH1ForCausalLM, FalconH1Model
+    from transformers import AutoTokenizer, FalconH1ForCausalLM, FalconH1Model
 
 
 class FalconH1ModelTester:
@@ -253,14 +253,7 @@ class FalconH1ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
         {"feature-extraction": FalconH1Model, "text-generation": FalconH1ForCausalLM} if is_torch_available() else {}
     )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
-
+    def _get_mamba_cache_shapes(batch_size: int, config):
         conv_kernel_size = config.mamba_d_conv
         intermediate_size = (
             config.mamba_d_ssm if config.mamba_d_ssm is not None else int(config.mamba_expand * config.hidden_size)
@@ -271,14 +264,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             conv_kernel_size,
         )
         ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
-
-        for idx in range(len(past_key_values)):
-            if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+        return conv_shape, ssm_shape
 
     def setUp(self):
         self.model_tester = FalconH1ModelTester(self)
diff --git a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
index 8aea217bfbc1..3c674b1302eb 100644
--- a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
+++ b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
@@ -274,18 +274,6 @@ def setUp(self):
             self, config_class=FalconMambaConfig, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
         )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        conv_shape = (batch_size, config.intermediate_size, config.conv_kernel)
-        ssm_shape = (batch_size, config.intermediate_size, config.state_size)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-            self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-
     def assertInterval(self, member, container, msg=None):
         r"""
         Simple utility function to check if a member is inside an interval.
diff --git a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
index c05e496808c9..41faac093dd4 100644
--- a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
+++ b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
@@ -46,7 +46,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import DynamicCache, GraniteMoeHybridForCausalLM, GraniteMoeHybridModel
+    from transformers import GraniteMoeHybridForCausalLM, GraniteMoeHybridModel
 
 
 class GraniteMoeHybridModelTester(BambaModelTester):
@@ -302,30 +302,14 @@ def test_flash_attention_2_padding_matches_padding_free_with_position_ids_seq_id
                 loss_padfree = res_padfree.loss
                 torch.testing.assert_close(loss_padded, loss_padfree)
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
-
+    def _get_mamba_cache_shapes(batch_size: int, config):
         conv_shape = (
             batch_size,
             config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * config.mamba_d_state,
             config.mamba_d_conv,
         )
         ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+        return conv_shape, ssm_shape
 
     def test_config_requires_mamba_or_attention_layers(self):
         """Ensure we can't create a config with disallowed layers."""
diff --git a/tests/models/jamba/test_modeling_jamba.py b/tests/models/jamba/test_modeling_jamba.py
index 2b3853d4b051..3f0b2548cea9 100644
--- a/tests/models/jamba/test_modeling_jamba.py
+++ b/tests/models/jamba/test_modeling_jamba.py
@@ -43,7 +43,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import DynamicCache, JambaForCausalLM, JambaForSequenceClassification, JambaModel
+    from transformers import JambaForCausalLM, JambaForSequenceClassification, JambaModel
 
 
 class JambaConfigTester(ConfigTester):
@@ -322,25 +322,10 @@ class JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         else {}
     )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
+    def _get_mamba_cache_shapes(batch_size: int, config):
         conv_shape = (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_conv)
         ssm_shape = (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_state)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+        return conv_shape, ssm_shape
 
     def setUp(self):
         self.model_tester = JambaModelTester(self)
diff --git a/tests/models/lfm2/test_modeling_lfm2.py b/tests/models/lfm2/test_modeling_lfm2.py
index 12cc3fdb38f9..ad3425ab4512 100644
--- a/tests/models/lfm2/test_modeling_lfm2.py
+++ b/tests/models/lfm2/test_modeling_lfm2.py
@@ -29,7 +29,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import DynamicCache, Lfm2ForCausalLM, Lfm2Model
+    from transformers import Lfm2ForCausalLM, Lfm2Model
 
 
 class Lfm2ModelTester(CausalLMModelTester):
@@ -51,22 +51,10 @@ class Lfm2ModelTest(CausalLMModelTest, unittest.TestCase):
     # used in `test_torch_compile_for_training`
     _torch_compile_train_cls = Lfm2ForCausalLM if is_torch_available() else None
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
+    def _get_mamba_cache_shapes(batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-
-        for idx in range(config.num_hidden_layers):
-            if config.layer_types[idx] == "full_attention":
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, (1,))
+        ssm_shape = (1,)
+        return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""
diff --git a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
index 8b7c2a55004f..ed8a2b8366ee 100644
--- a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
+++ b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
@@ -32,7 +32,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import DynamicCache, Lfm2MoeConfig, Lfm2MoeForCausalLM, Lfm2MoeModel
+    from transformers import Lfm2MoeConfig, Lfm2MoeForCausalLM, Lfm2MoeModel
 
 
 class Lfm2MoeModelTester(CausalLMModelTester):
@@ -69,22 +69,10 @@ class Lfm2MoeModelTest(CausalLMModelTest, unittest.TestCase):
     # used in `test_torch_compile_for_training`
     _torch_compile_train_cls = Lfm2MoeForCausalLM if is_torch_available() else None
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
+    def _get_mamba_cache_shapes(batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-
-        for idx in range(config.num_hidden_layers):
-            if config.layer_types[idx] == "full_attention":
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, (1,))
+        ssm_shape = (1,)
+        return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""
diff --git a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
index 4ccdf539f453..9415b3a55ef7 100644
--- a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
+++ b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
@@ -44,7 +44,6 @@
     import torch
 
     from transformers import Lfm2VlConfig, Lfm2VlForConditionalGeneration, Lfm2VlModel
-    from transformers.models.lfm2.modeling_lfm2 import Lfm2HybridConvCache
 
 
 class Lfm2VlModelTester(CausalLMModelTester):
@@ -172,21 +171,10 @@ def setUp(self):
             self, config_class=Lfm2VlConfig, has_text_modality=False, common_properties=common_properties
         )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, Lfm2HybridConvCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
+    def _get_mamba_cache_shapes(batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-
-        for i in range(config.num_hidden_layers):
-            if config.layer_types[i] == "full_attention":
-                self.assertEqual(past_key_values.key_cache[i].shape, attention_shape)
-                self.assertEqual(past_key_values.value_cache[i].shape, attention_shape)
-            else:
-                self.assertEqual(past_key_values.conv_cache[i].shape, conv_shape)
+        ssm_shape = (1,)
+        return conv_shape, ssm_shape
 
     def test_config(self):
         self.config_tester.run_common_tests()
diff --git a/tests/models/mamba/test_modeling_mamba.py b/tests/models/mamba/test_modeling_mamba.py
index f68a17491590..5d56d1e724b2 100644
--- a/tests/models/mamba/test_modeling_mamba.py
+++ b/tests/models/mamba/test_modeling_mamba.py
@@ -242,18 +242,6 @@ def setUp(self):
     def test_enable_input_require_grads(self):
         self.skipTest("Mamba currently requires CUDA/Metal/XPU to run enable_input_require_grads.")
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        conv_shape = (batch_size, config.intermediate_size, config.conv_kernel)
-        ssm_shape = (batch_size, config.intermediate_size, config.state_size)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-            self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-
     def assertInterval(self, member, container, msg=None):
         r"""
         Simple utility function to check if a member is inside an interval.
diff --git a/tests/models/mamba2/test_modeling_mamba2.py b/tests/models/mamba2/test_modeling_mamba2.py
index 6aa16ddeecd2..e9704a2bd2dc 100644
--- a/tests/models/mamba2/test_modeling_mamba2.py
+++ b/tests/models/mamba2/test_modeling_mamba2.py
@@ -244,9 +244,7 @@ def setUp(self):
             self, config_class=Mamba2Config, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
         )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
+    def _get_mamba_cache_shapes(batch_size: int, config):
         intermediate_size = config.expand * config.hidden_size
         conv_shape = (
             batch_size,
@@ -254,10 +252,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             config.conv_kernel,
         )
         ssm_shape = (batch_size, config.num_heads, config.head_dim, config.state_size)
-
-        for idx in range(len(past_key_values)):
-            self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-            self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
+        return conv_shape, ssm_shape
 
     def test_mamba2_caching(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index 9ea2dffc5f74..f90bf0276fbf 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -352,15 +352,7 @@ class NemotronHModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
         else {}
     )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
-
-        # Mamba cache shapes
+    def _get_mamba_cache_shapes(batch_size: int, config):
         intermediate_size = config.mamba_num_heads * config.mamba_head_dim
         conv_shape = (
             batch_size,
@@ -368,16 +360,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             config.conv_kernel,
         )
         ssm_shape = (batch_size, config.mamba_num_heads, config.mamba_head_dim, config.ssm_state_size)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+        return conv_shape, ssm_shape
 
     def setUp(self):
         self.model_tester = NemotronHModelTester(self)
diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index e59205417458..34b5eb9d611f 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -38,7 +38,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import DynamicCache, ZambaForCausalLM, ZambaForSequenceClassification, ZambaModel
+    from transformers import ZambaForCausalLM, ZambaForSequenceClassification, ZambaModel
 
 
 class ZambaModelTester:
@@ -288,29 +288,11 @@ class ZambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         else {}
     )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "attention_head_dim")
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
-
+    def _get_mamba_cache_shapes(batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
         conv_shape = (batch_size, intermediate_size, config.mamba_d_conv)
         ssm_shape = (batch_size, config.n_mamba_heads, intermediate_size // config.n_mamba_heads, config.mamba_d_state)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+        return conv_shape, ssm_shape
 
     def setUp(self):
         self.model_tester = ZambaModelTester(self)
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index 545f94e97c02..03530ad7db83 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -39,7 +39,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import DynamicCache, Zamba2ForCausalLM, Zamba2ForSequenceClassification, Zamba2Model
+    from transformers import Zamba2ForCausalLM, Zamba2ForSequenceClassification, Zamba2Model
 
 
 class Zamba2ModelTester:
@@ -298,14 +298,7 @@ class Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
         else {}
     )
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        self.assertIsInstance(past_key_values, DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        attention_shape = (batch_size, num_heads, seq_length, head_dim)
-
+    def _get_mamba_cache_shapes(batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
         conv_shape = (
             batch_size,
@@ -313,18 +306,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             config.mamba_d_conv,
         )
         ssm_shape = (batch_size, config.n_mamba_heads, config.mamba_headdim, config.mamba_d_state)
-
-        self.assertTrue(config.num_hidden_layers, len(past_key_values))
-
-        for idx in range(len(past_key_values)):
-            if config.layers_block_type[idx] == "mamba":
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-            else:
-                self.assertEqual(past_key_values.layers[idx].conv_states.shape, conv_shape)
-                self.assertEqual(past_key_values.layers[idx].ssm_states.shape, ssm_shape)
-                self.assertEqual(past_key_values.layers[idx].keys.shape, attention_shape)
-                self.assertEqual(past_key_values.layers[idx].values.shape, attention_shape)
+        return conv_shape, ssm_shape
 
     def setUp(self):
         self.model_tester = Zamba2ModelTester(self)

From c2ddcf9b1e73d1668eb86cf0e1e5a8641f2cd03e Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 16:37:07 +0100
Subject: [PATCH 26/56] fix tests

---
 tests/generation/test_utils.py | 24 ++++++++++++++----------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index b086117b0549..9fab4d6a63dd 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2583,10 +2583,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         # Check each layer has the correct shape
         for layer in past_key_values.layers:
-            if isinstance(layer, MambaLayer):
-                self.assertEqual(layer.conv_states.shape, conv_shape)
-                self.assertEqual(layer.ssm_states.shape, ssm_shape)
-            elif isinstance(layer, MambaAndAttentionLayer):
+            # Mamba + Attention layer cache
+            if type(layer) is MambaAndAttentionLayer:
                 # Remove the seq_length dim for cross-attention cache (it changes based on the model)
                 keys = layer.keys if seq_length is not None else layer.keys[:, :, 0, :]
                 values = layer.values if seq_length is not None else layer.values[:, :, 0, :]
@@ -2594,6 +2592,11 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(values.shape, attention_shape)
                 self.assertEqual(layer.conv_states.shape, conv_shape)
                 self.assertEqual(layer.ssm_states.shape, ssm_shape)
+            # Mamba only layer cache
+            elif type(layer) is MambaLayer:
+                self.assertEqual(layer.conv_states.shape, conv_shape)
+                self.assertEqual(layer.ssm_states.shape, ssm_shape)
+            # Attention only layer type
             else:
                 # Remove the seq_length dim for cross-attention cache (it changes based on the model)
                 keys = layer.keys if seq_length is not None else layer.keys[:, :, 0, :]
@@ -2639,17 +2642,18 @@ def _check_caches_are_equal(self, cache1: Cache, cache2: Cache):
 
         num_layers = len(cache1)
         for idx in range(num_layers):
-            self.assertEqual(type(cache1.layers[idx], cache2.layers[idx]))
-            # Mamba layer
-            if isinstance(cache1.layers[idx], MambaLayer):
-                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            self.assertEqual(type(cache1.layers[idx]), type(cache2.layers[idx]))
+
             # Mamba + Attention layer
-            elif isinstance(cache1.layers[idx], MambaAndAttentionLayer):
+            if type(cache1.layers[idx]) is MambaAndAttentionLayer:
                 torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
                 torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
                 torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+            # Mamba layer
+            elif type(cache1.layers[idx]) is MambaLayer:
+                torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
+                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
             # Attention layer
             else:
                 torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)

From b23708fa3580d85e6b374accc5b5a06319183d8d Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 16:45:40 +0100
Subject: [PATCH 27/56] oupsi

---
 tests/generation/test_utils.py                             | 7 ++++---
 tests/models/bamba/test_modeling_bamba.py                  | 2 +-
 tests/models/falcon_h1/test_modeling_falcon_h1.py          | 2 +-
 .../granitemoehybrid/test_modeling_granitemoehybrid.py     | 2 +-
 tests/models/jamba/test_modeling_jamba.py                  | 2 +-
 tests/models/lfm2/test_modeling_lfm2.py                    | 2 +-
 tests/models/lfm2_moe/test_modeling_lfm2_moe.py            | 2 +-
 tests/models/lfm2_vl/test_modeling_lfm2_vl.py              | 2 +-
 tests/models/mamba2/test_modeling_mamba2.py                | 2 +-
 tests/models/nemotron_h/test_modeling_nemotron_h.py        | 2 +-
 tests/models/zamba/test_modeling_zamba.py                  | 2 +-
 tests/models/zamba2/test_modeling_zamba2.py                | 2 +-
 12 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 9fab4d6a63dd..906380997763 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2537,7 +2537,7 @@ def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, c
             [encoder_expected_shape] * len(hidden_states),
         )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         # Default mamba cache shape - can vary based on models so this function is convenient to easily check caches
         conv_shape = (batch_size, config.intermediate_size, config.conv_kernel)
         ssm_shape = (batch_size, config.intermediate_size, config.state_size)
@@ -2561,9 +2561,10 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         config = config.get_text_config(decoder=True)
 
         # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
+        # Only pure mamba models do not have num_attention_heads defined in config, so it can never be 1 in practice for attention models
+        num_heads = getattr(config, "num_key_value_heads", getattr(config, "num_attention_heads", 1))
         hidden_size = getattr(config, "d_model", config.hidden_size)
-        head_dim = getattr(config, "head_dim", hidden_size // config.num_attention_heads)
+        head_dim = getattr(config, "head_dim", hidden_size // num_heads)
 
         # For cross attention cache, the seq_length depends on the model, so we remove that dim
         attention_shape = (
diff --git a/tests/models/bamba/test_modeling_bamba.py b/tests/models/bamba/test_modeling_bamba.py
index 9ca8127b5616..bf180d0fedf0 100644
--- a/tests/models/bamba/test_modeling_bamba.py
+++ b/tests/models/bamba/test_modeling_bamba.py
@@ -279,7 +279,7 @@ class BambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     # This is because we are hitting edge cases with the causal_mask buffer
     model_split_percents = [0.5, 0.7, 0.8]
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         # For mamba layers
         conv_shape = (
             batch_size,
diff --git a/tests/models/falcon_h1/test_modeling_falcon_h1.py b/tests/models/falcon_h1/test_modeling_falcon_h1.py
index a562ff404c59..2537a76b0847 100644
--- a/tests/models/falcon_h1/test_modeling_falcon_h1.py
+++ b/tests/models/falcon_h1/test_modeling_falcon_h1.py
@@ -253,7 +253,7 @@ class FalconH1ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
         {"feature-extraction": FalconH1Model, "text-generation": FalconH1ForCausalLM} if is_torch_available() else {}
     )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_kernel_size = config.mamba_d_conv
         intermediate_size = (
             config.mamba_d_ssm if config.mamba_d_ssm is not None else int(config.mamba_expand * config.hidden_size)
diff --git a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
index 41faac093dd4..83fdc436b9a9 100644
--- a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
+++ b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
@@ -302,7 +302,7 @@ def test_flash_attention_2_padding_matches_padding_free_with_position_ids_seq_id
                 loss_padfree = res_padfree.loss
                 torch.testing.assert_close(loss_padded, loss_padfree)
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (
             batch_size,
             config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * config.mamba_d_state,
diff --git a/tests/models/jamba/test_modeling_jamba.py b/tests/models/jamba/test_modeling_jamba.py
index 3f0b2548cea9..77500ad06251 100644
--- a/tests/models/jamba/test_modeling_jamba.py
+++ b/tests/models/jamba/test_modeling_jamba.py
@@ -322,7 +322,7 @@ class JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         else {}
     )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_conv)
         ssm_shape = (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_state)
         return conv_shape, ssm_shape
diff --git a/tests/models/lfm2/test_modeling_lfm2.py b/tests/models/lfm2/test_modeling_lfm2.py
index ad3425ab4512..88d8583a8aec 100644
--- a/tests/models/lfm2/test_modeling_lfm2.py
+++ b/tests/models/lfm2/test_modeling_lfm2.py
@@ -51,7 +51,7 @@ class Lfm2ModelTest(CausalLMModelTest, unittest.TestCase):
     # used in `test_torch_compile_for_training`
     _torch_compile_train_cls = Lfm2ForCausalLM if is_torch_available() else None
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
         ssm_shape = (1,)
         return conv_shape, ssm_shape
diff --git a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
index ed8a2b8366ee..7068d62f01fc 100644
--- a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
+++ b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
@@ -69,7 +69,7 @@ class Lfm2MoeModelTest(CausalLMModelTest, unittest.TestCase):
     # used in `test_torch_compile_for_training`
     _torch_compile_train_cls = Lfm2MoeForCausalLM if is_torch_available() else None
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
         ssm_shape = (1,)
         return conv_shape, ssm_shape
diff --git a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
index 9415b3a55ef7..3c3fd4d844dc 100644
--- a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
+++ b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
@@ -171,7 +171,7 @@ def setUp(self):
             self, config_class=Lfm2VlConfig, has_text_modality=False, common_properties=common_properties
         )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
         ssm_shape = (1,)
         return conv_shape, ssm_shape
diff --git a/tests/models/mamba2/test_modeling_mamba2.py b/tests/models/mamba2/test_modeling_mamba2.py
index e9704a2bd2dc..6c0b3b865dd9 100644
--- a/tests/models/mamba2/test_modeling_mamba2.py
+++ b/tests/models/mamba2/test_modeling_mamba2.py
@@ -244,7 +244,7 @@ def setUp(self):
             self, config_class=Mamba2Config, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
         )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         intermediate_size = config.expand * config.hidden_size
         conv_shape = (
             batch_size,
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index f90bf0276fbf..7d06790a2016 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -352,7 +352,7 @@ class NemotronHModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
         else {}
     )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         intermediate_size = config.mamba_num_heads * config.mamba_head_dim
         conv_shape = (
             batch_size,
diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index 34b5eb9d611f..899c67252583 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -288,7 +288,7 @@ class ZambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         else {}
     )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
         conv_shape = (batch_size, intermediate_size, config.mamba_d_conv)
         ssm_shape = (batch_size, config.n_mamba_heads, intermediate_size // config.n_mamba_heads, config.mamba_d_state)
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index 03530ad7db83..d8d2d2cb41b6 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -298,7 +298,7 @@ class Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
         else {}
     )
 
-    def _get_mamba_cache_shapes(batch_size: int, config):
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
         conv_shape = (
             batch_size,

From 18feef288e0c8c4c1fb1ed90e62e18dea92af7e9 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 17:00:19 +0100
Subject: [PATCH 28/56] fix

---
 tests/generation/test_utils.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 906380997763..fbb133896618 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2539,8 +2539,13 @@ def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, c
 
     def _get_mamba_cache_shapes(self, batch_size: int, config):
         # Default mamba cache shape - can vary based on models so this function is convenient to easily check caches
-        conv_shape = (batch_size, config.intermediate_size, config.conv_kernel)
-        ssm_shape = (batch_size, config.intermediate_size, config.state_size)
+        # Note that non-mamba models will NOT have the config fields - it does not matter as they will only use attention cache layers
+        # so the None default values will not be used
+        intermediate_size = getattr(config, "intermediate_size", None)
+        conv_kernel = getattr(config, "conv_kernel", None)
+        state_size = getattr(config, "state_size", None)
+        conv_shape = (batch_size, intermediate_size, conv_kernel)
+        ssm_shape = (batch_size, intermediate_size, state_size)
         return conv_shape, ssm_shape
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):

From ce92f3dc0690e92abcf6f21dcdf8f7096bcb7fb7 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 17:05:21 +0100
Subject: [PATCH 29/56] fix broken no_split_modules

---
 src/transformers/models/zamba/modeling_zamba.py   | 2 +-
 src/transformers/models/zamba2/modeling_zamba2.py | 2 +-
 src/transformers/models/zamba2/modular_zamba2.py  | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/transformers/models/zamba/modeling_zamba.py b/src/transformers/models/zamba/modeling_zamba.py
index feefa4d7d55c..88174e03b861 100644
--- a/src/transformers/models/zamba/modeling_zamba.py
+++ b/src/transformers/models/zamba/modeling_zamba.py
@@ -641,7 +641,7 @@ class ZambaPreTrainedModel(PreTrainedModel):
     config: ZambaConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["ZambaAttentionDecoderLayer", "ZambaMambaDecoderLayer"]
+    _no_split_modules = ["ZambaHybridLayer", "ZambaMambaDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn = False
     _supports_sdpa = False
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index 899bd737d3c5..42dba140204d 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -1065,7 +1065,7 @@ class Zamba2PreTrainedModel(PreTrainedModel):
     config: Zamba2Config
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["Zamba2AttentionDecoderLayer", "Zamba2MambaDecoderLayer"]
+    _no_split_modules = ["Zamba2HybridLayer", "Zamba2MambaDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn = True
     _supports_flex_attn = True
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index c6e7af8c3cba..d2d44be47721 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -805,7 +805,7 @@ class Zamba2PreTrainedModel(PreTrainedModel):
     config: Zamba2Config
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["Zamba2AttentionDecoderLayer", "Zamba2MambaDecoderLayer"]
+    _no_split_modules = ["Zamba2HybridLayer", "Zamba2MambaDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn = True
     _supports_flex_attn = True

From ab4472bf9ae8316019ac09cb77f322f52c626431 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 17:16:12 +0100
Subject: [PATCH 30/56] fix

---
 tests/generation/test_utils.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index fbb133896618..b23b004c75ea 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2567,15 +2567,16 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         # (batch, kv heads, seq_length, head_dim)
         # Only pure mamba models do not have num_attention_heads defined in config, so it can never be 1 in practice for attention models
-        num_heads = getattr(config, "num_key_value_heads", getattr(config, "num_attention_heads", 1))
+        num_attention_heads = getattr(config, "num_attention_heads", 1)
+        num_kv_heads = getattr(config, "num_key_value_heads", num_attention_heads)
         hidden_size = getattr(config, "d_model", config.hidden_size)
-        head_dim = getattr(config, "head_dim", hidden_size // num_heads)
+        head_dim = getattr(config, "head_dim", hidden_size // num_attention_heads)
 
         # For cross attention cache, the seq_length depends on the model, so we remove that dim
         attention_shape = (
-            (batch_size, num_heads, seq_length, head_dim)
+            (batch_size, num_kv_heads, seq_length, head_dim)
             if seq_length is not None
-            else (batch_size, num_heads, head_dim)
+            else (batch_size, num_kv_heads, head_dim)
         )
 
         # For mamba layers

From 08e62658a4f9c0865f5697320aa033778ad423c1 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 17:28:37 +0100
Subject: [PATCH 31/56] fixes

---
 src/transformers/models/bamba/modeling_bamba.py                 | 2 +-
 src/transformers/models/bamba/modular_bamba.py                  | 2 +-
 src/transformers/models/falcon_h1/configuration_falcon_h1.py    | 2 +-
 .../models/granitemoehybrid/modeling_granitemoehybrid.py        | 2 +-
 tests/models/lfm2/test_modeling_lfm2.py                         | 2 +-
 tests/models/lfm2_moe/test_modeling_lfm2_moe.py                 | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/transformers/models/bamba/modeling_bamba.py b/src/transformers/models/bamba/modeling_bamba.py
index d9a8ca68576d..4491f64df39b 100644
--- a/src/transformers/models/bamba/modeling_bamba.py
+++ b/src/transformers/models/bamba/modeling_bamba.py
@@ -659,7 +659,7 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split(
                 [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
         )
-        hidden_states_B_C = hidden_states_B_C.transpsose(1,2)
+        hidden_states_B_C = hidden_states_B_C.transpose(1,2)
 
         use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
diff --git a/src/transformers/models/bamba/modular_bamba.py b/src/transformers/models/bamba/modular_bamba.py
index a4ba833f5565..6af5cb7bd5b5 100644
--- a/src/transformers/models/bamba/modular_bamba.py
+++ b/src/transformers/models/bamba/modular_bamba.py
@@ -416,7 +416,7 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split(
                 [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
         )
-        hidden_states_B_C = hidden_states_B_C.transpsose(1,2)
+        hidden_states_B_C = hidden_states_B_C.transpose(1,2)
 
         use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
diff --git a/src/transformers/models/falcon_h1/configuration_falcon_h1.py b/src/transformers/models/falcon_h1/configuration_falcon_h1.py
index 19401fbbc632..64cd746e0c93 100644
--- a/src/transformers/models/falcon_h1/configuration_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/configuration_falcon_h1.py
@@ -133,7 +133,7 @@ def validate_architecture(self):
 
     @property
     def layers_block_type(self):
-        return ["attention" for i in range(self.num_hidden_layers)]
+        return ["hybrid" for i in range(self.num_hidden_layers)]
 
 
 __all__ = ["FalconH1Config"]
diff --git a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
index 63ba0b0a9dcc..ad1b7776dbd9 100644
--- a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
@@ -536,7 +536,7 @@ def torch_forward(
         gate, hidden_states_B_C, dt = projected_states.split(
                 [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
         )
-        hidden_states_B_C = hidden_states_B_C.transpsose(1,2)
+        hidden_states_B_C = hidden_states_B_C.transpose(1,2)
 
         use_precomputed_states = cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
 
diff --git a/tests/models/lfm2/test_modeling_lfm2.py b/tests/models/lfm2/test_modeling_lfm2.py
index 88d8583a8aec..a44c303c6c97 100644
--- a/tests/models/lfm2/test_modeling_lfm2.py
+++ b/tests/models/lfm2/test_modeling_lfm2.py
@@ -53,7 +53,7 @@ class Lfm2ModelTest(CausalLMModelTest, unittest.TestCase):
 
     def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-        ssm_shape = (1,)
+        ssm_shape = (0,)
         return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
diff --git a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
index 7068d62f01fc..8264cf4f6118 100644
--- a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
+++ b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
@@ -71,7 +71,7 @@ class Lfm2MoeModelTest(CausalLMModelTest, unittest.TestCase):
 
     def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-        ssm_shape = (1,)
+        ssm_shape = (0,)
         return conv_shape, ssm_shape
 
     def test_attention_outputs(self):

From 66d071646045693af6d6920a051b84a0bbf2503a Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 17:39:26 +0100
Subject: [PATCH 32/56] fix

---
 src/transformers/cache_utils.py                               | 3 ---
 .../models/falcon_mamba/configuration_falcon_mamba.py         | 4 ++++
 src/transformers/models/falcon_mamba/modular_falcon_mamba.py  | 4 ++++
 src/transformers/models/mamba/configuration_mamba.py          | 4 ++++
 src/transformers/models/mamba2/configuration_mamba2.py        | 4 ++++
 5 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 42c1d8acf849..7ef92d2e7ef6 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -1166,15 +1166,12 @@ def __init__(
             sliding_window = getattr(decoder_config, "sliding_window", None) or getattr(
                 decoder_config, "attention_chunk_size", None
             )
-            conv_kernel = getattr(decoder_config, "conv_kernel", None)
             layer_types = getattr(decoder_config, "layer_types", None)
             if layer_types is None:
                 layer_types = []
                 for _ in range(decoder_config.num_hidden_layers):
                     if sliding_window is not None:
                         layer_types.append("sliding_attention")
-                    elif conv_kernel is not None:
-                        layer_types.append("mamba")
                     else:
                         layer_types.append("full_attention")
             # Some models have shared layers thus no cache is needed for them (e.g. Gemma3n)
diff --git a/src/transformers/models/falcon_mamba/configuration_falcon_mamba.py b/src/transformers/models/falcon_mamba/configuration_falcon_mamba.py
index 0da599e463ad..ba8f96d3ec44 100644
--- a/src/transformers/models/falcon_mamba/configuration_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/configuration_falcon_mamba.py
@@ -105,5 +105,9 @@ def __post_init__(self, **kwargs):
         )
         super().__post_init__(**kwargs)
 
+    @property
+    def layer_types(self):
+        return ["mamba"] * self.num_hidden_layers
+
 
 __all__ = ["FalconMambaConfig"]
diff --git a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
index b53b85465cc2..fb90a60a4a8f 100644
--- a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
@@ -101,6 +101,10 @@ class FalconMambaConfig(MambaConfig):
     use_associative_scan: bool = True
     mixer_rms_eps: float = 1e-6
 
+    @property
+    def layer_types(self):
+        return ["mamba"] * self.num_hidden_layers
+
 
 def rms_forward(hidden_states, variance_epsilon=1e-6):
     """
diff --git a/src/transformers/models/mamba/configuration_mamba.py b/src/transformers/models/mamba/configuration_mamba.py
index 61bb91416b0f..8b3e87d32ae1 100644
--- a/src/transformers/models/mamba/configuration_mamba.py
+++ b/src/transformers/models/mamba/configuration_mamba.py
@@ -98,5 +98,9 @@ def __post_init__(self, **kwargs):
         )
         super().__post_init__(**kwargs)
 
+    @property
+    def layer_types(self):
+        return ["mamba"] * self.num_hidden_layers
+
 
 __all__ = ["MambaConfig"]
diff --git a/src/transformers/models/mamba2/configuration_mamba2.py b/src/transformers/models/mamba2/configuration_mamba2.py
index d692c1b90c9b..7e513c6a9402 100644
--- a/src/transformers/models/mamba2/configuration_mamba2.py
+++ b/src/transformers/models/mamba2/configuration_mamba2.py
@@ -103,5 +103,9 @@ def validate_architecture(self):
                 f"({self.num_heads * self.head_dim})."
             )
 
+    @property
+    def layer_types(self):
+        return ["mamba"] * self.num_hidden_layers
+
 
 __all__ = ["Mamba2Config"]

From c86f9bb896eab8e8ef29bc783df6637f1791f0fe Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 18:27:46 +0100
Subject: [PATCH 33/56] fix

---
 src/transformers/models/falcon_h1/modeling_falcon_h1.py   | 2 +-
 src/transformers/models/falcon_h1/modular_falcon_h1.py    | 2 +-
 src/transformers/models/nemotron_h/modeling_nemotron_h.py | 4 ++--
 src/transformers/models/zamba/configuration_zamba.py      | 2 +-
 src/transformers/models/zamba2/configuration_zamba2.py    | 2 +-
 src/transformers/models/zamba2/modeling_zamba2.py         | 4 ++--
 src/transformers/models/zamba2/modular_zamba2.py          | 4 ++--
 7 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/transformers/models/falcon_h1/modeling_falcon_h1.py b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
index 2911f76a15cb..a15d59717c5d 100644
--- a/src/transformers/models/falcon_h1/modeling_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
@@ -843,7 +843,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                ssm_state = cache_params.update_ssm_states(ssm_state, self.layer_idx)
+                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         if self.mamba_rms_norm:
             scan_output = self.norm(y, gate)
diff --git a/src/transformers/models/falcon_h1/modular_falcon_h1.py b/src/transformers/models/falcon_h1/modular_falcon_h1.py
index 985e14dda2c4..bef05f97649e 100644
--- a/src/transformers/models/falcon_h1/modular_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modular_falcon_h1.py
@@ -630,7 +630,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                ssm_state = cache_params.update_ssm_states(ssm_state, self.layer_idx)
+                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
 
         if self.mamba_rms_norm:
             scan_output = self.norm(y, gate)
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index 87c456cf4ecf..aa8e2aee597d 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -369,7 +369,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         dtype = input_states.dtype
         # Gated MLP's linear projection
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
-            projected_states = self.in_proj(input_states.squeeze(1))
+            projected_states = self.in_proj(input_states)
         else:
             if attention_mask is not None:
                 # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
@@ -387,7 +387,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         if use_precomputed_state:
             gate = gate.unsqueeze(1)
             conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
-            hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+            hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
             if self.use_conv_bias:
                 hidden_states += self.conv1d.bias
             hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
diff --git a/src/transformers/models/zamba/configuration_zamba.py b/src/transformers/models/zamba/configuration_zamba.py
index 2fd99acf2207..588d2efc2cc3 100644
--- a/src/transformers/models/zamba/configuration_zamba.py
+++ b/src/transformers/models/zamba/configuration_zamba.py
@@ -53,7 +53,7 @@ class ZambaConfig(PreTrainedConfig):
 
     model_type = "zamba"
     keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {"layer_types": "layers_block_type"}
+    attribute_map = {"layer_types": "layers_block_type", "head_dim": "attention_head_dim"}
 
     vocab_size: int = 32000
     tie_word_embeddings: bool = True
diff --git a/src/transformers/models/zamba2/configuration_zamba2.py b/src/transformers/models/zamba2/configuration_zamba2.py
index f9b56fabf6d4..d77491bd984a 100644
--- a/src/transformers/models/zamba2/configuration_zamba2.py
+++ b/src/transformers/models/zamba2/configuration_zamba2.py
@@ -66,7 +66,7 @@ class Zamba2Config(PreTrainedConfig):
     ```"""
 
     model_type = "zamba2"
-    attribute_map = {"head_dim": "attention_head_dim", "layer_types": "layers_block_type"}
+    attribute_map = {"layer_types": "layers_block_type", "head_dim": "attention_head_dim"}
     keys_to_ignore_at_inference = ["past_key_values"]
 
     vocab_size: int = 32000
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index 42dba140204d..576dfc71d016 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -657,7 +657,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         dtype = input_states.dtype
         # Gated MLP's linear projection
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
-            projected_states = self.in_proj(input_states.squeeze(1))
+            projected_states = self.in_proj(input_states)
         else:
             if attention_mask is not None:
                 # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
@@ -675,7 +675,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         if use_precomputed_state:
             gate = gate.unsqueeze(1)
             conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
-            hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+            hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
             if self.use_conv_bias:
                 hidden_states += self.conv1d.bias
             hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index d2d44be47721..e61455c4fc5b 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -445,7 +445,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         dtype = input_states.dtype
         # Gated MLP's linear projection
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
-            projected_states = self.in_proj(input_states.squeeze(1))
+            projected_states = self.in_proj(input_states)
         else:
             if attention_mask is not None:
                 # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
@@ -463,7 +463,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
         if use_precomputed_state:
             gate = gate.unsqueeze(1)
             conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
-            hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+            hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
             if self.use_conv_bias:
                 hidden_states += self.conv1d.bias
             hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding

From ba1b7d688f1a0a2338c82318c5af057c5d78d719 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 18:54:09 +0100
Subject: [PATCH 34/56] fixes

---
 src/transformers/cache_utils.py               | 22 ++++++++++++++-----
 .../models/nemotron_h/modeling_nemotron_h.py  |  1 -
 .../models/zamba2/modeling_zamba2.py          |  1 -
 .../models/zamba2/modular_zamba2.py           |  1 -
 4 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 7ef92d2e7ef6..d6c5c5ad0f3a 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -715,7 +715,13 @@ def reorder_cache(self, beam_idx: torch.LongTensor):
         """Reorders the cache for beam search, given the selected beam indices."""
         if self.has_previous_state:
             self.conv_states = self.conv_states.index_select(0, beam_idx.to(self.device))
-            self.ssm_states = self.ssm_states.index_select(0, beam_idx.to(self.device))
+            # ssm_states can stay empty sometimes, see e.g. lfm2 which only uses the conv_states
+            if self.ssm_states.numel() > 0:
+                self.ssm_states = self.ssm_states.index_select(0, beam_idx.to(self.device))
+
+    def crop(self, max_length: int):
+        # We don't crop the mamba cache, so simply do nothing
+        pass
 
 
 class MambaLayer(MambaCacheLayerMixin):
@@ -777,10 +783,16 @@ def __init__(self):
         DynamicLayer.__init__(self)
         MambaLayer.__init__(self)
 
-    def lazy_initialization(self, states_1: torch.Tensor, states_2: torch.Tensor | None = None) -> None:
-        MambaLayer.lazy_initialization(self, states_1)
-        self.keys = torch.tensor([], dtype=self.dtype, device=self.device)
-        self.values = torch.tensor([], dtype=self.dtype, device=self.device)
+    def lazy_initialization(self, states1: torch.Tensor, states2: torch.Tensor | None = None) -> None:
+        MambaLayer.lazy_initialization(self, states1)
+        DynamicLayer.lazy_initialization(self, states1, states2)
+
+    def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
+        # We need this as lazy initialization may be called first from `update` from the attention part, so the inferred
+        # conv_kernel_size may not be correct - make sure we grab the correct one during the first call to `update_conv_state`
+        if not self.has_previous_state:
+            self.conv_kernel_size = conv_states.shape[-1]
+        super().update_conv_state(conv_states, **kwargs)
 
     def reset(self) -> None:
         MambaLayer.reset(self)
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index aa8e2aee597d..6aa8fa6a7332 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -385,7 +385,6 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
 
         # Convolution sequence transformation
         if use_precomputed_state:
-            gate = gate.unsqueeze(1)
             conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
             hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
             if self.use_conv_bias:
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index 576dfc71d016..c1c8ebca01f4 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -673,7 +673,6 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
 
         # Convolution sequence transformation
         if use_precomputed_state:
-            gate = gate.unsqueeze(1)
             conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
             hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
             if self.use_conv_bias:
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index e61455c4fc5b..fd4f25a5196d 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -461,7 +461,6 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
 
         # Convolution sequence transformation
         if use_precomputed_state:
-            gate = gate.unsqueeze(1)
             conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx)
             hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
             if self.use_conv_bias:

From 17856217ad23d3f9f2b839c8e940beb4a2edf1b9 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 18:57:36 +0100
Subject: [PATCH 35/56] add layer type

---
 src/transformers/configuration_utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/transformers/configuration_utils.py b/src/transformers/configuration_utils.py
index 4d7034f9b1d3..56c519a6e92b 100755
--- a/src/transformers/configuration_utils.py
+++ b/src/transformers/configuration_utils.py
@@ -69,6 +69,7 @@
     "sparse",
     "dense",
     "hybrid",  # for layers that have both mamba and attention in zamba and zamba2
+    "moe",  # for nemotron_h, which uses either attention, mamba or moe
 )
 
 

From f684133e0c52fec126edd8c8379c900bde43bfc1 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 19:12:46 +0100
Subject: [PATCH 36/56] oupsi

---
 src/transformers/cache_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index d6c5c5ad0f3a..4e8ca7bf1401 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -792,7 +792,7 @@ def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor
         # conv_kernel_size may not be correct - make sure we grab the correct one during the first call to `update_conv_state`
         if not self.has_previous_state:
             self.conv_kernel_size = conv_states.shape[-1]
-        super().update_conv_state(conv_states, **kwargs)
+        return super().update_conv_state(conv_states, **kwargs)
 
     def reset(self) -> None:
         MambaLayer.reset(self)

From 8ca92a96f511eb4bc5b9d30e2c58565a1506e346 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 19:25:20 +0100
Subject: [PATCH 37/56] fix

---
 tests/models/lfm2_vl/test_modeling_lfm2_vl.py       | 2 +-
 tests/models/nemotron_h/test_modeling_nemotron_h.py | 5 -----
 tests/models/zamba2/test_modeling_zamba2.py         | 5 -----
 3 files changed, 1 insertion(+), 11 deletions(-)

diff --git a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
index 3c3fd4d844dc..f4ef47a97402 100644
--- a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
+++ b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
@@ -173,7 +173,7 @@ def setUp(self):
 
     def _get_mamba_cache_shapes(self, batch_size: int, config):
         conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-        ssm_shape = (1,)
+        ssm_shape = (0,)
         return conv_shape, ssm_shape
 
     def test_config(self):
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index 7d06790a2016..0475d3f061c6 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -540,11 +540,6 @@ def test_flash_attn_2_fp32_ln(self):
                 # with attention mask
                 _ = model(dummy_input, attention_mask=dummy_attention_mask)
 
-    @unittest.skip(reason="NemotronH has its own special cache type")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
     @require_torch_accelerator
     def test_flex_attention_with_grads(self):
         """
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index d8d2d2cb41b6..18cf3746690d 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -468,11 +468,6 @@ def test_flash_attn_2_fp32_ln(self):
                 # with attention mask
                 _ = model(dummy_input, attention_mask=dummy_attention_mask)
 
-    @unittest.skip(reason="Zamba2 has its own special cache type")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
-
     @require_torch_accelerator
     def test_flex_attention_with_grads(self):
         """

From 0d991d74107c72bb607f85e35df7f87e757c25f9 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 19:26:30 +0100
Subject: [PATCH 38/56] style

---
 tests/models/nemotron_h/test_modeling_nemotron_h.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index 0475d3f061c6..ffda5868abb5 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -18,7 +18,6 @@
 
 import pytest
 from huggingface_hub.errors import StrictDataclassClassValidationError
-from parameterized import parameterized
 
 from transformers import AutoTokenizer, NemotronHConfig, NemotronHForCausalLM, is_torch_available
 from transformers.testing_utils import (

From 670d09a3578ae1fd2d6bc4d8a8d77308fb0607d8 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 19:41:43 +0100
Subject: [PATCH 39/56] fix

---
 .../nemotron_h/test_modeling_nemotron_h.py    | 37 +++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index ffda5868abb5..b27f781a3db4 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -361,6 +361,43 @@ def _get_mamba_cache_shapes(self, batch_size: int, config):
         ssm_shape = (batch_size, config.mamba_num_heads, config.mamba_head_dim, config.ssm_state_size)
         return conv_shape, ssm_shape
 
+    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
+        # Raise a useful error, asking to explicitly override the method
+        if not isinstance(past_key_values, DynamicCache):
+            raise ValueError("The cache does not use the correct Cache")
+
+        # Use the correct config
+        config = config.get_text_config(decoder=True)
+
+        # (batch, kv heads, seq_length, head_dim)
+        # Only pure mamba models do not have num_attention_heads defined in config, so it can never be 1 in practice for attention models
+        num_attention_heads = getattr(config, "num_attention_heads", 1)
+        num_kv_heads = getattr(config, "num_key_value_heads", num_attention_heads)
+        hidden_size = getattr(config, "d_model", config.hidden_size)
+        head_dim = getattr(config, "head_dim", hidden_size // num_attention_heads)
+
+        # For cross attention cache, the seq_length depends on the model, so we remove that dim
+        attention_shape = (batch_size, num_kv_heads, seq_length, head_dim)
+        # For mamba layers
+        conv_shape, ssm_shape = self._get_mamba_cache_shapes(batch_size, config)
+
+        # Check each layer has the correct shape
+        for layer, layer_type in zip(past_key_values.layers, config.layer_types):
+            # Moe layers have a default attention cache instantiated, but it stays empty as the layer does not use it
+            if layer_type == "moe":
+                self.assertEqual(layer.keys, None)
+                self.assertEqual(layer.values, None)
+            # Attention layer cache
+            elif layer_type == "attention":
+                self.assertEqual(layer.keys.shape, attention_shape)
+                self.assertEqual(layer.values.shape, attention_shape)
+            # Mamba layer cache
+            elif layer_type == "mamba":
+                self.assertEqual(layer.conv_states.shape, conv_shape)
+                self.assertEqual(layer.ssm_states.shape, ssm_shape)
+            else:
+                raise ValueError("Unknown layer type.")
+
     def setUp(self):
         self.model_tester = NemotronHModelTester(self)
         self.config_tester = ConfigTester(

From 63e0b93987bd62e7a7188fbc5b23b744f3451f48 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 20:29:04 +0100
Subject: [PATCH 40/56] fixes

---
 src/transformers/cache_utils.py                             | 6 +++++-
 src/transformers/models/bamba/modeling_bamba.py             | 2 +-
 src/transformers/models/bamba/modular_bamba.py              | 2 +-
 src/transformers/models/falcon_h1/modeling_falcon_h1.py     | 2 +-
 src/transformers/models/falcon_h1/modular_falcon_h1.py      | 2 +-
 .../models/granitemoehybrid/modeling_granitemoehybrid.py    | 2 +-
 .../models/granitemoehybrid/modular_granitemoehybrid.py     | 2 +-
 src/transformers/models/nemotron_h/modeling_nemotron_h.py   | 2 +-
 src/transformers/models/nemotron_h/modular_nemotron_h.py    | 2 +-
 src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py | 2 +-
 src/transformers/models/qwen3_5/modeling_qwen3_5.py         | 2 +-
 src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py | 2 +-
 src/transformers/models/qwen3_next/modeling_qwen3_next.py   | 2 +-
 src/transformers/models/qwen3_next/modular_qwen3_next.py    | 2 +-
 tests/models/nemotron_h/test_modeling_nemotron_h.py         | 6 +++---
 15 files changed, 21 insertions(+), 17 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 4e8ca7bf1401..54cd06b42079 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -1195,7 +1195,11 @@ def __init__(
                 # states they should return - only the mask changes to make them different at the end!
                 if layer_type in ("sliding_attention", "chunked_attention"):
                     layers.append(DynamicSlidingWindowLayer(sliding_window=sliding_window))
-                elif layer_type in ("mamba", "conv"):
+                # Note: we want moe layers to be MambaLayer, so that we can correctly grab sequence length etc from attention layers.
+                # Since moe layers will stay empty (they don't need any cache), we don't want them to collide for mask creation etc
+                # TODO: maybe use a dummy layer in those cases, or a dictionary {idx: Layer} for self.layers, so that we can skip
+                # the indices we don't need
+                elif layer_type in ("mamba", "conv", "moe"):
                     layers.append(MambaLayer())
                 elif layer_type == "hybrid":
                     layers.append(MambaAndAttentionLayer())
diff --git a/src/transformers/models/bamba/modeling_bamba.py b/src/transformers/models/bamba/modeling_bamba.py
index 4491f64df39b..caf7d08cc56a 100644
--- a/src/transformers/models/bamba/modeling_bamba.py
+++ b/src/transformers/models/bamba/modeling_bamba.py
@@ -1066,7 +1066,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/bamba/modular_bamba.py b/src/transformers/models/bamba/modular_bamba.py
index 6af5cb7bd5b5..a1a440be3597 100644
--- a/src/transformers/models/bamba/modular_bamba.py
+++ b/src/transformers/models/bamba/modular_bamba.py
@@ -794,7 +794,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/falcon_h1/modeling_falcon_h1.py b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
index a15d59717c5d..b85d02b00b81 100644
--- a/src/transformers/models/falcon_h1/modeling_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
@@ -1161,7 +1161,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/falcon_h1/modular_falcon_h1.py b/src/transformers/models/falcon_h1/modular_falcon_h1.py
index bef05f97649e..6408200a081e 100644
--- a/src/transformers/models/falcon_h1/modular_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modular_falcon_h1.py
@@ -924,7 +924,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
index ad1b7776dbd9..11dc138d17dc 100644
--- a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
@@ -1217,7 +1217,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
index ccf8c58bfb51..d0d2329ec202 100644
--- a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
@@ -277,7 +277,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index 6aa8fa6a7332..fe85ab4b8065 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -1103,7 +1103,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/nemotron_h/modular_nemotron_h.py b/src/transformers/models/nemotron_h/modular_nemotron_h.py
index 5a849833cc89..05a0b25de2b6 100644
--- a/src/transformers/models/nemotron_h/modular_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modular_nemotron_h.py
@@ -465,7 +465,7 @@ def _update_mamba_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         mamba_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             mamba_mask = None
diff --git a/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py b/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
index e3b83bff770a..58ac37ced520 100644
--- a/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
+++ b/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
@@ -1024,7 +1024,7 @@ def _update_linear_attn_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         linear_attn_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             linear_attn_mask = None
diff --git a/src/transformers/models/qwen3_5/modeling_qwen3_5.py b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
index 57589b70b94f..efa661e4373e 100644
--- a/src/transformers/models/qwen3_5/modeling_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
@@ -1384,7 +1384,7 @@ def _update_linear_attn_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         linear_attn_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             linear_attn_mask = None
diff --git a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
index 801156d236c3..243d7bee0fe3 100644
--- a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
+++ b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
@@ -1509,7 +1509,7 @@ def _update_linear_attn_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         linear_attn_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             linear_attn_mask = None
diff --git a/src/transformers/models/qwen3_next/modeling_qwen3_next.py b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
index 7b45f0ea4838..002d879f8135 100644
--- a/src/transformers/models/qwen3_next/modeling_qwen3_next.py
+++ b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
@@ -1068,7 +1068,7 @@ def _update_linear_attn_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         linear_attn_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             linear_attn_mask = None
diff --git a/src/transformers/models/qwen3_next/modular_qwen3_next.py b/src/transformers/models/qwen3_next/modular_qwen3_next.py
index a22b85bf9278..7dbd15e6d91b 100644
--- a/src/transformers/models/qwen3_next/modular_qwen3_next.py
+++ b/src/transformers/models/qwen3_next/modular_qwen3_next.py
@@ -824,7 +824,7 @@ def _update_linear_attn_mask(self, attention_mask, past_key_values):
             2. Attending to all inputs
         """
         linear_attn_mask = attention_mask
-        if (past_key_values is not None and past_key_values.has_previous_state) or (
+        if (past_key_values is not None and past_key_values.has_previous_state()) or (
             attention_mask is not None and torch.all(attention_mask == 1)
         ):
             linear_attn_mask = None
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index b27f781a3db4..1ca4225cb7a9 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -383,10 +383,10 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
 
         # Check each layer has the correct shape
         for layer, layer_type in zip(past_key_values.layers, config.layer_types):
-            # Moe layers have a default attention cache instantiated, but it stays empty as the layer does not use it
+            # Moe layers have a default mamba cache instantiated, but it stays empty as the layer does not use it
             if layer_type == "moe":
-                self.assertEqual(layer.keys, None)
-                self.assertEqual(layer.values, None)
+                self.assertEqual(layer.conv_states, None)
+                self.assertEqual(layer.ssm_states, None)
             # Attention layer cache
             elif layer_type == "attention":
                 self.assertEqual(layer.keys.shape, attention_shape)

From eb018e7e519fc776bdc37226c7d54530ef84b64e Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 20:34:09 +0100
Subject: [PATCH 41/56] final fix

---
 src/transformers/models/bamba/modeling_bamba.py            | 7 +------
 src/transformers/models/bamba/modular_bamba.py             | 7 +------
 src/transformers/models/falcon_h1/modeling_falcon_h1.py    | 7 +------
 src/transformers/models/falcon_h1/modular_falcon_h1.py     | 7 +------
 .../models/granitemoehybrid/modeling_granitemoehybrid.py   | 3 ---
 .../models/granitemoehybrid/modular_granitemoehybrid.py    | 3 ---
 src/transformers/models/jamba/modeling_jamba.py            | 3 ---
 src/transformers/models/jamba/modular_jamba.py             | 3 ---
 src/transformers/models/nemotron_h/modeling_nemotron_h.py  | 3 ---
 src/transformers/models/nemotron_h/modular_nemotron_h.py   | 3 ---
 src/transformers/models/zamba/modeling_zamba.py            | 3 ---
 src/transformers/models/zamba2/modeling_zamba2.py          | 3 ---
 src/transformers/models/zamba2/modular_zamba2.py           | 3 ---
 13 files changed, 4 insertions(+), 51 deletions(-)

diff --git a/src/transformers/models/bamba/modeling_bamba.py b/src/transformers/models/bamba/modeling_bamba.py
index caf7d08cc56a..0b599490c5ff 100644
--- a/src/transformers/models/bamba/modeling_bamba.py
+++ b/src/transformers/models/bamba/modeling_bamba.py
@@ -1049,14 +1049,9 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
-        next_cache = None if not use_cache else past_key_values
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
-            past_key_values=next_cache,
+            past_key_values=past_key_values,
         )
 
     def _update_mamba_mask(self, attention_mask, past_key_values):
diff --git a/src/transformers/models/bamba/modular_bamba.py b/src/transformers/models/bamba/modular_bamba.py
index a1a440be3597..f60d8c405b4d 100644
--- a/src/transformers/models/bamba/modular_bamba.py
+++ b/src/transformers/models/bamba/modular_bamba.py
@@ -777,14 +777,9 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
-        next_cache = None if not use_cache else past_key_values
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
-            past_key_values=next_cache,
+            past_key_values=past_key_values,
         )
 
     def _update_mamba_mask(self, attention_mask, past_key_values):
diff --git a/src/transformers/models/falcon_h1/modeling_falcon_h1.py b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
index b85d02b00b81..7aaba6aacb4d 100644
--- a/src/transformers/models/falcon_h1/modeling_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
@@ -1144,14 +1144,9 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
-        next_cache = None if not use_cache else past_key_values
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
-            past_key_values=next_cache,
+            past_key_values=past_key_values,
         )
 
     def _update_mamba_mask(self, attention_mask, past_key_values):
diff --git a/src/transformers/models/falcon_h1/modular_falcon_h1.py b/src/transformers/models/falcon_h1/modular_falcon_h1.py
index 6408200a081e..61fab3117d96 100644
--- a/src/transformers/models/falcon_h1/modular_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modular_falcon_h1.py
@@ -907,14 +907,9 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
-        next_cache = None if not use_cache else past_key_values
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
-            past_key_values=next_cache,
+            past_key_values=past_key_values,
         )
 
     def _update_mamba_mask(self, attention_mask, past_key_values):
diff --git a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
index 11dc138d17dc..baeaf65bbd45 100644
--- a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
@@ -1202,9 +1202,6 @@ def forward(
             )
         hidden_states = self.norm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return MoeModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values,
diff --git a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
index d0d2329ec202..93291275b5b7 100644
--- a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
@@ -262,9 +262,6 @@ def forward(
             )
         hidden_states = self.norm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return MoeModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values,
diff --git a/src/transformers/models/jamba/modeling_jamba.py b/src/transformers/models/jamba/modeling_jamba.py
index 753e703ff715..5ffd16302e06 100755
--- a/src/transformers/models/jamba/modeling_jamba.py
+++ b/src/transformers/models/jamba/modeling_jamba.py
@@ -739,9 +739,6 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return MoeModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values,
diff --git a/src/transformers/models/jamba/modular_jamba.py b/src/transformers/models/jamba/modular_jamba.py
index 2a5b21aad005..e24be7640f40 100644
--- a/src/transformers/models/jamba/modular_jamba.py
+++ b/src/transformers/models/jamba/modular_jamba.py
@@ -584,9 +584,6 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return MoeModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values,
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index fe85ab4b8065..41aba6122448 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -1088,9 +1088,6 @@ def forward(
 
         hidden_states = self.norm_f(hidden_states)
 
-        if past_key_values is not None and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
diff --git a/src/transformers/models/nemotron_h/modular_nemotron_h.py b/src/transformers/models/nemotron_h/modular_nemotron_h.py
index 05a0b25de2b6..a7433a982f1c 100644
--- a/src/transformers/models/nemotron_h/modular_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modular_nemotron_h.py
@@ -450,9 +450,6 @@ def forward(
 
         hidden_states = self.norm_f(hidden_states)
 
-        if past_key_values is not None and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
diff --git a/src/transformers/models/zamba/modeling_zamba.py b/src/transformers/models/zamba/modeling_zamba.py
index 88174e03b861..405434004ca9 100644
--- a/src/transformers/models/zamba/modeling_zamba.py
+++ b/src/transformers/models/zamba/modeling_zamba.py
@@ -769,9 +769,6 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index c1c8ebca01f4..4de0979a71c0 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -1189,9 +1189,6 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values is not None and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index fd4f25a5196d..2f858fc2eeb8 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -962,9 +962,6 @@ def forward(
 
         hidden_states = self.final_layernorm(hidden_states)
 
-        if past_key_values is not None and not past_key_values.has_previous_state:
-            past_key_values.has_previous_state = True
-
         return BaseModelOutputWithPast(
             last_hidden_state=hidden_states,
             past_key_values=past_key_values if use_cache else None,

From f8a070292843da2bb13ce20a4f17c0cd54dc928b Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 22:07:39 +0100
Subject: [PATCH 42/56] forgot those qwens

---
 src/transformers/cache_utils.py               |   2 +-
 .../olmo_hybrid/modeling_olmo_hybrid.py       |   6 +-
 .../models/olmo_hybrid/modular_olmo_hybrid.py |  62 ++++++++--
 .../models/qwen3_5/modeling_qwen3_5.py        | 109 ++---------------
 .../models/qwen3_5/modular_qwen3_5.py         |  25 ++--
 .../qwen3_5_moe/modeling_qwen3_5_moe.py       | 111 ++---------------
 .../models/qwen3_5_moe/modular_qwen3_5_moe.py |   5 -
 .../models/qwen3_next/modeling_qwen3_next.py  | 114 ++----------------
 .../models/qwen3_next/modular_qwen3_next.py   | 114 ++----------------
 tests/models/qwen3_5/test_modeling_qwen3_5.py |  39 ++----
 .../qwen3_5_moe/test_modeling_qwen3_5_moe.py  |  39 ++----
 .../qwen3_next/test_modeling_qwen3_next.py    |  39 ++----
 12 files changed, 146 insertions(+), 519 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 54cd06b42079..1c4d7ed22513 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -1199,7 +1199,7 @@ def __init__(
                 # Since moe layers will stay empty (they don't need any cache), we don't want them to collide for mask creation etc
                 # TODO: maybe use a dummy layer in those cases, or a dictionary {idx: Layer} for self.layers, so that we can skip
                 # the indices we don't need
-                elif layer_type in ("mamba", "conv", "moe"):
+                elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
                     layers.append(MambaLayer())
                 elif layer_type == "hybrid":
                     layers.append(MambaAndAttentionLayer())
diff --git a/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py b/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
index 58ac37ced520..bf72e06d94eb 100644
--- a/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
+++ b/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
@@ -61,8 +61,7 @@ class OlmoHybridDynamicCache:
     """
     Cache for hybrid model supporting both attention KV cache and linear attention state.
 
-    Inherits from Qwen3NextDynamicCache. The main difference is that this cache
-    stores separate conv states for q, k, v (instead of a single conv_states list).
+    The main difference is that this cache stores separate conv states for q, k, v (instead of a single conv_states).
     """
 
     is_compileable = False
@@ -155,7 +154,6 @@ def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
         kv_length = query_length + past_seen_tokens
         return kv_length, kv_offset
 
-    @property
     def has_previous_state(self):
         """We have a previous state if the last linear (conv) layer was already updated."""
         return self.conv_states_q[self.last_linear_layer] is not None
@@ -725,7 +723,7 @@ def forward(
         batch_size, seq_len, _ = hidden_states.shape
 
         use_cache = cache_params is not None
-        use_precomputed = use_cache and getattr(cache_params, "has_previous_state", False) and seq_len == 1
+        use_precomputed = use_cache and cache_params.has_previous_state() and seq_len == 1
 
         conv_state_q = cache_params.conv_states_q[self.layer_idx] if cache_params else None
         conv_state_k = cache_params.conv_states_k[self.layer_idx] if cache_params else None
diff --git a/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py b/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py
index 74f6a70ce6de..089f29309007 100644
--- a/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py
+++ b/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py
@@ -16,6 +16,7 @@
 
 import math
 from collections.abc import Callable
+from typing import Any
 
 import torch
 import torch.nn as nn
@@ -48,7 +49,6 @@
     eager_attention_forward,
 )
 from ..qwen3_next.modeling_qwen3_next import (
-    Qwen3NextDynamicCache,
     Qwen3NextModel,
     Qwen3NextPreTrainedModel,
     Qwen3NextRMSNormGated,
@@ -189,22 +189,49 @@ def validate_architecture(self):
             raise ValueError("OLMoHybrid expects at least one attention layer.")
 
 
-class OlmoHybridDynamicCache(Qwen3NextDynamicCache):
+class OlmoHybridDynamicCache:
     """
     Cache for hybrid model supporting both attention KV cache and linear attention state.
 
-    Inherits from Qwen3NextDynamicCache. The main difference is that this cache
-    stores separate conv states for q, k, v (instead of a single conv_states list).
+    The main difference is that this cache stores separate conv states for q, k, v (instead of a single conv_states).
     """
 
+    is_compileable = False
+
     def __init__(self, config: OlmoHybridConfig):
-        super().__init__(config)
-        del self.conv_states
+        super().__init__()
+        self.layer_types = config.layer_types
+        self.transformer_layers = [
+            i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention"
+        ]
+        self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention")
+        self.recurrent_states = [None for _ in range(config.num_hidden_layers)]
+        self.key_cache = [None for _ in range(config.num_hidden_layers)]
+        self.value_cache = [None for _ in range(config.num_hidden_layers)]
         # Replace single conv_states with separate q, k, v conv states
         self.conv_states_q = [None for _ in range(config.num_hidden_layers)]
         self.conv_states_k = [None for _ in range(config.num_hidden_layers)]
         self.conv_states_v = [None for _ in range(config.num_hidden_layers)]
 
+    def __len__(self):
+        return len(self.layer_types)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: dict[str, Any] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if self.key_cache[layer_idx] is None:
+            self.key_cache[layer_idx] = key_states
+            self.value_cache[layer_idx] = value_states
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
     def reorder_cache(self, beam_idx: torch.LongTensor):
         """Reorders the cache for beam search, given the selected beam indices."""
         batch_size = beam_idx.shape[0]
@@ -240,8 +267,27 @@ def reorder_cache(self, beam_idx: torch.LongTensor):
                     0, beam_idx.to(device)
                 )
 
-    @property
+    def get_seq_length(self, layer_idx: int | None = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # take any layer that contains cache and not empty tensor
+        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
+        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
+        """
+        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
+        the given layer at `layer_idx`.
+        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
+        """
+        kv_offset = 0
+        past_seen_tokens = self.get_seq_length(layer_idx)
+        kv_length = query_length + past_seen_tokens
+        return kv_length, kv_offset
+
     def has_previous_state(self):
+        """We have a previous state if the last linear (conv) layer was already updated."""
         return self.conv_states_q[self.last_linear_layer] is not None
 
 
@@ -495,7 +541,7 @@ def forward(
         batch_size, seq_len, _ = hidden_states.shape
 
         use_cache = cache_params is not None
-        use_precomputed = use_cache and getattr(cache_params, "has_previous_state", False) and seq_len == 1
+        use_precomputed = use_cache and cache_params.has_previous_state() and seq_len == 1
 
         conv_state_q = cache_params.conv_states_q[self.layer_idx] if cache_params else None
         conv_state_k = cache_params.conv_states_k[self.layer_idx] if cache_params else None
diff --git a/src/transformers/models/qwen3_5/modeling_qwen3_5.py b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
index efa661e4373e..feda0bd67a2b 100644
--- a/src/transformers/models/qwen3_5/modeling_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
@@ -29,7 +29,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernelized_func
 from ...masking_utils import create_causal_mask
@@ -66,95 +66,6 @@
 logger = logging.get_logger(__name__)
 
 
-class Qwen3_5DynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the linear attention
-    cache (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for gated deltanet cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For linear attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `recurrent_states` represents the recurrent state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config: Qwen3_5Config):
-        super().__init__()
-        self.layer_types = config.layer_types
-        self.transformer_layers = [
-            i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention"
-        ]
-        self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention")
-
-        # Initialize everything to None -> will be lazy initialized to allow multi-gpu (device_map) inference
-        self.conv_states = [None for _ in range(config.num_hidden_layers)]
-        self.recurrent_states = [None for _ in range(config.num_hidden_layers)]
-        self.key_cache = [None for _ in range(config.num_hidden_layers)]
-        self.value_cache = [None for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.layer_types)
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self.key_cache[layer_idx] is None:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx] is not None:
-                device = self.key_cache[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx)
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx)
-
-            if self.conv_states[layer_idx] is not None:
-                device = self.conv_states[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx)
-                self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx)
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
-        """
-        kv_offset = 0
-        past_seen_tokens = self.get_seq_length(layer_idx)
-        kv_length = query_length + past_seen_tokens
-        return kv_length, kv_offset
-
-    @property
-    def has_previous_state(self):
-        """We have a previous state if the last linear (conv) layer was already updated."""
-        return self.conv_states[self.last_linear_layer] is not None
-
-
 class Qwen3_5VisionRotaryEmbedding(nn.Module):
     inv_freq: torch.Tensor  # fix linting for `register_buffer`
 
@@ -511,7 +422,7 @@ def __init__(self, config: Qwen3_5Config, layer_idx: int):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Qwen3_5DynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
@@ -519,12 +430,14 @@ def forward(
         # Set up dimensions for reshapes later
         batch_size, seq_len, _ = hidden_states.shape
 
-        use_precomputed_states = cache_params is not None and cache_params.has_previous_state and seq_len == 1
+        use_precomputed_states = (
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
+        )
 
         # getting projected states from cache if it exists
-        if cache_params is not None:
-            conv_state = cache_params.conv_states[self.layer_idx]
-            recurrent_state = cache_params.recurrent_states[self.layer_idx]
+        if use_precomputed_states:
+            conv_state = cache_params.layers[self.layer_idx].conv_states
+            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
 
         mixed_qkv = self.in_proj_qkv(hidden_states)
         mixed_qkv = mixed_qkv.transpose(1, 2)
@@ -548,7 +461,7 @@ def forward(
         else:
             if cache_params is not None:
                 conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
             if self.causal_conv1d_fn is not None:
                 mixed_qkv = self.causal_conv1d_fn(
                     x=mixed_qkv,
@@ -608,7 +521,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
+            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
 
         # reshape input data into 2D tensor
         core_attn_out = core_attn_out.reshape(-1, self.head_v_dim)
@@ -1328,7 +1241,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = Qwen3_5DynamicCache(config=self.config)
+            past_key_values = DynamicCache(config=self.config)
 
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
diff --git a/src/transformers/models/qwen3_5/modular_qwen3_5.py b/src/transformers/models/qwen3_5/modular_qwen3_5.py
index b76991426f13..69e9bdb0faf2 100644
--- a/src/transformers/models/qwen3_5/modular_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/modular_qwen3_5.py
@@ -21,7 +21,7 @@
 from torch import nn
 
 from ... import initialization as init
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
 from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
 from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling
@@ -34,7 +34,6 @@
 from ..qwen3_next.configuration_qwen3_next import Qwen3NextConfig
 from ..qwen3_next.modeling_qwen3_next import (
     Qwen3NextAttention,
-    Qwen3NextDynamicCache,
     Qwen3NextGatedDeltaNet,
     Qwen3NextMLP,
     Qwen3NextModel,
@@ -160,10 +159,6 @@ class Qwen3_5Config(Qwen3VLConfig):
     vision_end_token_id: int = 248054
 
 
-class Qwen3_5DynamicCache(Qwen3NextDynamicCache):
-    pass
-
-
 class Qwen3_5VisionRotaryEmbedding(Qwen3VLVisionRotaryEmbedding):
     pass
 
@@ -212,7 +207,7 @@ def fix_query_key_value_ordering(self):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Qwen3_5DynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
@@ -220,12 +215,14 @@ def forward(
         # Set up dimensions for reshapes later
         batch_size, seq_len, _ = hidden_states.shape
 
-        use_precomputed_states = cache_params is not None and cache_params.has_previous_state and seq_len == 1
+        use_precomputed_states = (
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
+        )
 
         # getting projected states from cache if it exists
-        if cache_params is not None:
-            conv_state = cache_params.conv_states[self.layer_idx]
-            recurrent_state = cache_params.recurrent_states[self.layer_idx]
+        if use_precomputed_states:
+            conv_state = cache_params.layers[self.layer_idx].conv_states
+            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
 
         mixed_qkv = self.in_proj_qkv(hidden_states)
         mixed_qkv = mixed_qkv.transpose(1, 2)
@@ -249,7 +246,7 @@ def forward(
         else:
             if cache_params is not None:
                 conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
             if self.causal_conv1d_fn is not None:
                 mixed_qkv = self.causal_conv1d_fn(
                     x=mixed_qkv,
@@ -309,7 +306,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
+            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
 
         # reshape input data into 2D tensor
         core_attn_out = core_attn_out.reshape(-1, self.head_v_dim)
@@ -501,7 +498,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = Qwen3_5DynamicCache(config=self.config)
+            past_key_values = DynamicCache(config=self.config)
 
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
diff --git a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
index 243d7bee0fe3..3c578e6fe836 100644
--- a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
+++ b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
@@ -29,7 +29,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_experts_implementation, use_kernelized_func
 from ...masking_utils import create_causal_mask
@@ -173,95 +173,6 @@ def apply_interleaved_mrope(self, freqs, mrope_section):
         return freqs_t
 
 
-class Qwen3_5MoeDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the linear attention
-    cache (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for gated deltanet cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For linear attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `recurrent_states` represents the recurrent state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config: Qwen3_5MoeConfig):
-        super().__init__()
-        self.layer_types = config.layer_types
-        self.transformer_layers = [
-            i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention"
-        ]
-        self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention")
-
-        # Initialize everything to None -> will be lazy initialized to allow multi-gpu (device_map) inference
-        self.conv_states = [None for _ in range(config.num_hidden_layers)]
-        self.recurrent_states = [None for _ in range(config.num_hidden_layers)]
-        self.key_cache = [None for _ in range(config.num_hidden_layers)]
-        self.value_cache = [None for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.layer_types)
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self.key_cache[layer_idx] is None:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx] is not None:
-                device = self.key_cache[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx)
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx)
-
-            if self.conv_states[layer_idx] is not None:
-                device = self.conv_states[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx)
-                self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx)
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
-        """
-        kv_offset = 0
-        past_seen_tokens = self.get_seq_length(layer_idx)
-        kv_length = query_length + past_seen_tokens
-        return kv_length, kv_offset
-
-    @property
-    def has_previous_state(self):
-        """We have a previous state if the last linear (conv) layer was already updated."""
-        return self.conv_states[self.last_linear_layer] is not None
-
-
 class Qwen3_5MoeRMSNormGated(nn.Module):
     def __init__(self, hidden_size, eps=1e-6, **kwargs):
         super().__init__()
@@ -512,7 +423,7 @@ def __init__(self, config: Qwen3_5MoeConfig, layer_idx: int):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Qwen3_5MoeDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
@@ -520,12 +431,14 @@ def forward(
         # Set up dimensions for reshapes later
         batch_size, seq_len, _ = hidden_states.shape
 
-        use_precomputed_states = cache_params is not None and cache_params.has_previous_state and seq_len == 1
+        use_precomputed_states = (
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
+        )
 
         # getting projected states from cache if it exists
-        if cache_params is not None:
-            conv_state = cache_params.conv_states[self.layer_idx]
-            recurrent_state = cache_params.recurrent_states[self.layer_idx]
+        if use_precomputed_states:
+            conv_state = cache_params.layers[self.layer_idx].conv_states
+            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
 
         mixed_qkv = self.in_proj_qkv(hidden_states)
         mixed_qkv = mixed_qkv.transpose(1, 2)
@@ -549,7 +462,7 @@ def forward(
         else:
             if cache_params is not None:
                 conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
             if self.causal_conv1d_fn is not None:
                 mixed_qkv = self.causal_conv1d_fn(
                     x=mixed_qkv,
@@ -609,7 +522,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
+            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
 
         # reshape input data into 2D tensor
         core_attn_out = core_attn_out.reshape(-1, self.head_v_dim)
@@ -1453,7 +1366,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = Qwen3_5MoeDynamicCache(config=self.config)
+            past_key_values = DynamicCache(config=self.config)
 
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
@@ -2005,7 +1918,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Qwen3_5MoeDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
diff --git a/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py b/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py
index f8684ddd83db..312b22bc88ed 100644
--- a/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py
+++ b/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py
@@ -35,7 +35,6 @@
 from ..qwen3_next.modeling_qwen3_next import (
     Qwen3NextAttention,
     Qwen3NextDecoderLayer,
-    Qwen3NextDynamicCache,
     Qwen3NextExperts,
     Qwen3NextForCausalLM,
     Qwen3NextPreTrainedModel,
@@ -156,10 +155,6 @@ class Qwen3_5MoeTextRotaryEmbedding(Qwen3_5TextRotaryEmbedding):
     pass
 
 
-class Qwen3_5MoeDynamicCache(Qwen3NextDynamicCache):
-    pass
-
-
 class Qwen3_5MoeGatedDeltaNet(Qwen3_5GatedDeltaNet):
     pass
 
diff --git a/src/transformers/models/qwen3_next/modeling_qwen3_next.py b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
index 002d879f8135..da31a5eb8e7d 100644
--- a/src/transformers/models/qwen3_next/modeling_qwen3_next.py
+++ b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
@@ -19,7 +19,7 @@
 # limitations under the License.
 
 from collections.abc import Callable
-from typing import Any, Optional
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -27,7 +27,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_experts_implementation, use_kernelized_func
 from ...masking_utils import create_causal_mask
@@ -82,95 +82,6 @@ def forward(self, hidden_states, gate=None):
         return hidden_states.to(input_dtype)
 
 
-class Qwen3NextDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the linear attention
-    cache (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for gated deltanet cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For linear attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `recurrent_states` represents the recurrent state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config: Qwen3NextConfig):
-        super().__init__()
-        self.layer_types = config.layer_types
-        self.transformer_layers = [
-            i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention"
-        ]
-        self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention")
-
-        # Initialize everything to None -> will be lazy initialized to allow multi-gpu (device_map) inference
-        self.conv_states = [None for _ in range(config.num_hidden_layers)]
-        self.recurrent_states = [None for _ in range(config.num_hidden_layers)]
-        self.key_cache = [None for _ in range(config.num_hidden_layers)]
-        self.value_cache = [None for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.layer_types)
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self.key_cache[layer_idx] is None:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx] is not None:
-                device = self.key_cache[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx)
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx)
-
-            if self.conv_states[layer_idx] is not None:
-                device = self.conv_states[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx)
-                self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx)
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
-        """
-        kv_offset = 0
-        past_seen_tokens = self.get_seq_length(layer_idx)
-        kv_length = query_length + past_seen_tokens
-        return kv_length, kv_offset
-
-    @property
-    def has_previous_state(self):
-        """We have a previous state if the last linear (conv) layer was already updated."""
-        return self.conv_states[self.last_linear_layer] is not None
-
-
 class Qwen3NextRotaryEmbedding(nn.Module):
     inv_freq: torch.Tensor  # fix linting for `register_buffer`
 
@@ -681,7 +592,7 @@ def fix_query_key_value_ordering(self, mixed_qkvz, mixed_ba):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Qwen3NextDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
@@ -689,12 +600,14 @@ def forward(
         # Set up dimensions for reshapes later
         batch_size, seq_len, _ = hidden_states.shape
 
-        use_precomputed_states = cache_params is not None and cache_params.has_previous_state and seq_len == 1
+        use_precomputed_states = (
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
+        )
 
         # getting projected states from cache if it exists
-        if cache_params is not None:
-            conv_state = cache_params.conv_states[self.layer_idx]
-            recurrent_state = cache_params.recurrent_states[self.layer_idx]
+        if use_precomputed_states:
+            conv_state = cache_params.layers[self.layer_idx].conv_states
+            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
 
         projected_states_qkvz = self.in_proj_qkvz(hidden_states)
         projected_states_ba = self.in_proj_ba(hidden_states)
@@ -717,7 +630,7 @@ def forward(
         else:
             if cache_params is not None:
                 conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
             if self.causal_conv1d_fn is not None:
                 mixed_qkv = self.causal_conv1d_fn(
                     x=mixed_qkv,
@@ -761,7 +674,6 @@ def forward(
                 output_final_state=cache_params is not None,
                 use_qk_l2norm_in_kernel=True,
             )
-
         else:
             core_attn_out, last_recurrent_state = self.recurrent_gated_delta_rule(
                 query,
@@ -776,7 +688,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
+            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
 
         z_shape_og = z.shape
         # reshape input data into 2D tensor
@@ -1021,7 +933,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = Qwen3NextDynamicCache(config=self.config)
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -1182,7 +1094,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Qwen3NextDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
diff --git a/src/transformers/models/qwen3_next/modular_qwen3_next.py b/src/transformers/models/qwen3_next/modular_qwen3_next.py
index 7dbd15e6d91b..80fa61c3229b 100644
--- a/src/transformers/models/qwen3_next/modular_qwen3_next.py
+++ b/src/transformers/models/qwen3_next/modular_qwen3_next.py
@@ -14,7 +14,7 @@
 """PyTorch Qwen3-Next model."""
 
 from collections.abc import Callable
-from typing import Any, Optional
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -22,7 +22,7 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
-from ...cache_utils import Cache
+from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
@@ -92,95 +92,6 @@ def forward(self, hidden_states, gate=None):
         return hidden_states.to(input_dtype)
 
 
-class Qwen3NextDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the linear attention
-    cache (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for gated deltanet cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For linear attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `recurrent_states` represents the recurrent state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config: Qwen3NextConfig):
-        super().__init__()
-        self.layer_types = config.layer_types
-        self.transformer_layers = [
-            i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention"
-        ]
-        self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention")
-
-        # Initialize everything to None -> will be lazy initialized to allow multi-gpu (device_map) inference
-        self.conv_states = [None for _ in range(config.num_hidden_layers)]
-        self.recurrent_states = [None for _ in range(config.num_hidden_layers)]
-        self.key_cache = [None for _ in range(config.num_hidden_layers)]
-        self.value_cache = [None for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.layer_types)
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: dict[str, Any] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self.key_cache[layer_idx] is None:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx] is not None:
-                device = self.key_cache[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx)
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx)
-
-            if self.conv_states[layer_idx] is not None:
-                device = self.conv_states[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx)
-                self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx)
-
-    def get_seq_length(self, layer_idx: int | None = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
-        """
-        kv_offset = 0
-        past_seen_tokens = self.get_seq_length(layer_idx)
-        kv_length = query_length + past_seen_tokens
-        return kv_length, kv_offset
-
-    @property
-    def has_previous_state(self):
-        """We have a previous state if the last linear (conv) layer was already updated."""
-        return self.conv_states[self.last_linear_layer] is not None
-
-
 class Qwen3NextRotaryEmbedding(Gemma2RotaryEmbedding):
     @staticmethod
     def compute_default_rope_parameters(
@@ -520,7 +431,7 @@ def fix_query_key_value_ordering(self, mixed_qkvz, mixed_ba):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        cache_params: Qwen3NextDynamicCache | None = None,
+        cache_params: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
     ):
         hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
@@ -528,12 +439,14 @@ def forward(
         # Set up dimensions for reshapes later
         batch_size, seq_len, _ = hidden_states.shape
 
-        use_precomputed_states = cache_params is not None and cache_params.has_previous_state and seq_len == 1
+        use_precomputed_states = (
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
+        )
 
         # getting projected states from cache if it exists
-        if cache_params is not None:
-            conv_state = cache_params.conv_states[self.layer_idx]
-            recurrent_state = cache_params.recurrent_states[self.layer_idx]
+        if use_precomputed_states:
+            conv_state = cache_params.layers[self.layer_idx].conv_states
+            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
 
         projected_states_qkvz = self.in_proj_qkvz(hidden_states)
         projected_states_ba = self.in_proj_ba(hidden_states)
@@ -556,7 +469,7 @@ def forward(
         else:
             if cache_params is not None:
                 conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
+                conv_state = cache_params.update_conv_state(conv_state, self.layer_idx)
             if self.causal_conv1d_fn is not None:
                 mixed_qkv = self.causal_conv1d_fn(
                     x=mixed_qkv,
@@ -600,7 +513,6 @@ def forward(
                 output_final_state=cache_params is not None,
                 use_qk_l2norm_in_kernel=True,
             )
-
         else:
             core_attn_out, last_recurrent_state = self.recurrent_gated_delta_rule(
                 query,
@@ -615,7 +527,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
+            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
 
         z_shape_og = z.shape
         # reshape input data into 2D tensor
@@ -777,7 +689,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if use_cache and past_key_values is None:
-            past_key_values = Qwen3NextDynamicCache(config=self.config)
+            past_key_values = DynamicCache(config=self.config)
 
         if position_ids is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
@@ -841,7 +753,7 @@ def forward(
         input_ids: torch.LongTensor | None = None,
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
-        past_key_values: Qwen3NextDynamicCache | None = None,
+        past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
diff --git a/tests/models/qwen3_5/test_modeling_qwen3_5.py b/tests/models/qwen3_5/test_modeling_qwen3_5.py
index 191a8cf788e4..04b815c04422 100644
--- a/tests/models/qwen3_5/test_modeling_qwen3_5.py
+++ b/tests/models/qwen3_5/test_modeling_qwen3_5.py
@@ -71,35 +71,16 @@ class Qwen3_5TextModelTest(CausalLMModelTest, unittest.TestCase):
     config_class = Qwen3_5TextConfig
     model_split_percents = [0.5, 0.8, 0.9]
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        "Qwen3.5 has a special Cache as it alternates with gated deltanet layers"
-        self.assertIsInstance(past_key_values, Qwen3_5DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        expected_shape = (batch_size, num_heads, seq_length, head_dim)
-
-        attention_layer_indices = past_key_values.transformer_layers
-        self.assertListEqual(
-            [past_key_values.key_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-        self.assertListEqual(
-            [past_key_values.value_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-
-    def _check_caches_are_equal(self, cache1, cache2):
-        "Qwen3.5 has a special Cache as it alternates with gated deltanet layers"
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            if cache1.key_cache[idx] is not None:
-                torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-                torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        num_k_heads = config.linear_num_key_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+        intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
+
+        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
+        return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 alternates between attention layers and gated deltanet layers."
diff --git a/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py b/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
index 27cef6196313..c249d77d045a 100644
--- a/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
+++ b/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
@@ -74,35 +74,16 @@ class Qwen3_5MoeTextModelTest(CausalLMModelTest, unittest.TestCase):
     model_tester_class = Qwen3_5MoeTextModelTester
     config_class = Qwen3_5MoeTextConfig
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        "Qwen3.5 Moe has a special Cache as it alternates with gated deltanet layers"
-        self.assertIsInstance(past_key_values, Qwen3_5MoeDynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        expected_shape = (batch_size, num_heads, seq_length, head_dim)
-
-        attention_layer_indices = past_key_values.transformer_layers
-        self.assertListEqual(
-            [past_key_values.key_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-        self.assertListEqual(
-            [past_key_values.value_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-
-    def _check_caches_are_equal(self, cache1, cache2):
-        "Qwen3.5 Moe has a special Cache as it alternates with gated deltanet layers"
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            if cache1.key_cache[idx] is not None:
-                torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-                torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        num_k_heads = config.linear_num_key_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+        intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
+
+        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
+        return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 Moe alternates between attention layers and gated deltanet layers."
diff --git a/tests/models/qwen3_next/test_modeling_qwen3_next.py b/tests/models/qwen3_next/test_modeling_qwen3_next.py
index 29e5f51705de..f597b8e28192 100644
--- a/tests/models/qwen3_next/test_modeling_qwen3_next.py
+++ b/tests/models/qwen3_next/test_modeling_qwen3_next.py
@@ -25,10 +25,8 @@
     import torch
 
     from transformers import (
-        Cache,
         Qwen3NextModel,
     )
-    from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextDynamicCache
 
 from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
 from ...test_modeling_common import (
@@ -59,35 +57,16 @@ def __init__(self, parent):
 class Qwen3NextModelTest(CausalLMModelTest, unittest.TestCase):
     model_tester_class = Qwen3NextModelTester
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        "Qwen3-Next has a special Cache as it alternates with gated deltanet layers"
-        self.assertIsInstance(past_key_values, Qwen3NextDynamicCache)
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        num_k_heads = config.linear_num_key_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+        intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        expected_shape = (batch_size, num_heads, seq_length, head_dim)
-
-        attention_layer_indices = past_key_values.transformer_layers
-        self.assertListEqual(
-            [past_key_values.key_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-        self.assertListEqual(
-            [past_key_values.value_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-
-    def _check_caches_are_equal(self, cache1: Cache, cache2: Cache):
-        "Qwen3-Next has a special Cache as it alternates with gated deltanet layers"
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
-
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            if cache1.key_cache[idx] is not None:
-                torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-                torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
+        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
+        return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3-Next alternates between attention layers and gated deltanet layers."

From fc27c37fab09f412575f0defc329652d4e2cadf1 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 24 Mar 2026 22:09:08 +0100
Subject: [PATCH 43/56] tests

---
 tests/models/qwen3_5/test_modeling_qwen3_5.py | 38 +++++--------------
 .../qwen3_5_moe/test_modeling_qwen3_5_moe.py  | 38 +++++--------------
 2 files changed, 18 insertions(+), 58 deletions(-)

diff --git a/tests/models/qwen3_5/test_modeling_qwen3_5.py b/tests/models/qwen3_5/test_modeling_qwen3_5.py
index 04b815c04422..839b4f6c7fc4 100644
--- a/tests/models/qwen3_5/test_modeling_qwen3_5.py
+++ b/tests/models/qwen3_5/test_modeling_qwen3_5.py
@@ -46,7 +46,6 @@
         Qwen3_5TextConfig,
         Qwen3_5TextModel,
     )
-    from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5DynamicCache
 
 
 class Qwen3_5TextModelTester(CausalLMModelTester):
@@ -300,35 +299,16 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        "Qwen3.5 has a special Cache as it alternates with gated deltanet layers"
-        self.assertIsInstance(past_key_values, Qwen3_5DynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        expected_shape = (batch_size, num_heads, seq_length, head_dim)
-
-        attention_layer_indices = past_key_values.transformer_layers
-        self.assertListEqual(
-            [past_key_values.key_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-        self.assertListEqual(
-            [past_key_values.value_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-
-    def _check_caches_are_equal(self, cache1, cache2):
-        "Qwen3.5 has a special Cache as it alternates with gated deltanet layers"
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        num_k_heads = config.linear_num_key_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+        intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            if cache1.key_cache[idx] is not None:
-                torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-                torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
+        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
+        return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 alternates between attention layers and gated deltanet layers."
diff --git a/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py b/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
index c249d77d045a..55b58a1aea34 100644
--- a/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
+++ b/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
@@ -51,7 +51,6 @@
         Qwen3_5MoeTextConfig,
         Qwen3_5MoeTextModel,
     )
-    from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import Qwen3_5MoeDynamicCache
 
 
 class Qwen3_5MoeTextModelTester(CausalLMModelTester):
@@ -382,35 +381,16 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
-        "Qwen3.5 Moe has a special Cache as it alternates with gated deltanet layers"
-        self.assertIsInstance(past_key_values, Qwen3_5MoeDynamicCache)
-
-        # (batch, kv heads, seq_length, head_dim)
-        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
-        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        expected_shape = (batch_size, num_heads, seq_length, head_dim)
-
-        attention_layer_indices = past_key_values.transformer_layers
-        self.assertListEqual(
-            [past_key_values.key_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-        self.assertListEqual(
-            [past_key_values.value_cache[idx].shape for idx in attention_layer_indices],
-            [expected_shape] * len(attention_layer_indices),
-        )
-
-    def _check_caches_are_equal(self, cache1, cache2):
-        "Qwen3.5 Moe has a special Cache as it alternates with gated deltanet layers"
-        if not len(cache1) == len(cache2):
-            raise ValueError("Both caches do not have the same number of layers.")
+    def _get_mamba_cache_shapes(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        num_k_heads = config.linear_num_key_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+        intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        num_layers = len(cache1)
-        for idx in range(num_layers):
-            if cache1.key_cache[idx] is not None:
-                torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
-                torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
+        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
+        return conv_shape, ssm_shape
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 Moe alternates between attention layers and gated deltanet layers."

From 9c616dd759260940dd407fd17814b0b26bcd4656 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Wed, 25 Mar 2026 11:35:33 +0100
Subject: [PATCH 44/56] offloading

---
 tests/models/zamba/test_modeling_zamba.py   | 13 +++++++++++++
 tests/models/zamba2/test_modeling_zamba2.py |  1 +
 tests/test_modeling_common.py               |  2 +-
 3 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index 899c67252583..037f157f79db 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -287,6 +287,7 @@ class ZambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         if is_torch_available()
         else {}
     )
+    model_split_percents = [0.5, 0.8, 0.9]
 
     def _get_mamba_cache_shapes(self, batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
@@ -298,6 +299,18 @@ def setUp(self):
         self.model_tester = ZambaModelTester(self)
         self.config_tester = ConfigTester(self, config_class=ZambaConfig, hidden_size=32)
 
+    @unittest.skip(
+        "Same as zamba2 -> investigate, it's probably due to their tied weights that accelerate does not work"
+    )
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip(
+        "Same as zamba2 -> investigate, it's probably due to their tied weights that accelerate does not work"
+    )
+    def test_disk_offload_safetensors(self):
+        pass
+
     def test_config(self):
         self.config_tester.run_common_tests()
 
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index 18cf3746690d..1695cc9d5556 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -297,6 +297,7 @@ class Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
         if is_torch_available()
         else {}
     )
+    model_split_percents = [0.5, 0.8, 0.9]
 
     def _get_mamba_cache_shapes(self, batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py
index 13b81855aaa6..5ae3a88fb3b8 100755
--- a/tests/test_modeling_common.py
+++ b/tests/test_modeling_common.py
@@ -2945,7 +2945,7 @@ def test_cpu_offload(self):
                 model.cpu().save_pretrained(tmp_dir)
 
                 for max_size in max_gpu_sizes:
-                    max_memory = {0: max_size, "cpu": model_size * 2}
+                    max_memory = {0: max_size, "cpu": model_size * 3}
                     new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
                     # Making sure part of the model will actually end up offloaded
                     self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"})

From 6f85f54912665d89fb49816ff7000ff1dfa89654 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Wed, 25 Mar 2026 14:46:11 +0100
Subject: [PATCH 45/56] much better static shape native design

---
 src/transformers/cache_utils.py               | 81 ++++++++++++-------
 src/transformers/generation/utils.py          | 42 +++++-----
 .../models/mamba2/modeling_mamba2.py          |  1 +
 tests/models/mamba/test_modeling_mamba.py     |  8 +-
 4 files changed, 78 insertions(+), 54 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 1c4d7ed22513..df9057d57723 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -672,19 +672,23 @@ def _dequantize(self, qtensor):
 class MambaCacheLayerMixin(ABC):
     """Base, abstract class for a mamba single layer's cache."""
 
-    is_compileable = False
+    # All shapes are static by essence in a Mamba layer, so it is compileable
+    is_compileable = True
 
     def __init__(self):
         self.conv_states: torch.Tensor | None = None
         self.ssm_states: torch.Tensor | None = None
-        self.is_initialized = False
+        self.is_conv_states_initialized = False
+        self.is_ssm_states_initialized = False
         self.has_previous_state = False
 
     def __repr__(self):
         return f"{self.__class__.__name__}"
 
     @abstractmethod
-    def lazy_initialization(self, conv_states: torch.Tensor) -> None: ...
+    def lazy_initialization(
+        self, conv_states: torch.Tensor | None = None, ssm_states: torch.Tensor | None = None
+    ) -> None: ...
 
     @abstractmethod
     def update_conv_state(self, conv_states: torch.Tensor) -> torch.Tensor: ...
@@ -694,44 +698,55 @@ def update_ssm_state(self, ssm_states: torch.Tensor) -> torch.Tensor: ...
 
     def offload(self):
         """Offload this layer's data to CPU device."""
-        if self.is_initialized:
+        if self.is_conv_states_initialized:
             self.conv_states = self.conv_states.to("cpu", non_blocking=True)
+        if self.is_ssm_states_initialized:
             self.ssm_states = self.ssm_states.to("cpu", non_blocking=True)
 
     def prefetch(self):
         """In case of layer offloading, this allows to move the data back to the layer's device ahead of time."""
-        if self.is_initialized and self.conv_states.device != self.device:
+        if self.is_conv_states_initialized and self.conv_states.device != self.device:
             self.conv_states = self.conv_states.to(self.device, non_blocking=True)
+        if self.is_ssm_states_initialized and self.ssm_states.device != self.device:
             self.ssm_states = self.ssm_states.to(self.device, non_blocking=True)
 
     def reset(self) -> None:
         """Resets the cache values while preserving the objects"""
-        if self.is_initialized:
+        if self.is_conv_states_initialized:
             self.conv_states.zero_()
+        if self.is_ssm_states_initialized:
             self.ssm_states.zero_()
         self.has_previous_state = False
 
     def reorder_cache(self, beam_idx: torch.LongTensor):
         """Reorders the cache for beam search, given the selected beam indices."""
-        if self.has_previous_state:
+        if self.is_conv_states_initialized:
             self.conv_states = self.conv_states.index_select(0, beam_idx.to(self.device))
-            # ssm_states can stay empty sometimes, see e.g. lfm2 which only uses the conv_states
-            if self.ssm_states.numel() > 0:
-                self.ssm_states = self.ssm_states.index_select(0, beam_idx.to(self.device))
+        # ssm_states can stay empty sometimes, see e.g. lfm2 which only uses the conv_states
+        if self.is_ssm_states_initialized:
+            self.ssm_states = self.ssm_states.index_select(0, beam_idx.to(self.device))
 
     def crop(self, max_length: int):
-        # We don't crop the mamba cache, so simply do nothing
+        # We don't crop the mamba cache, so simply do nothing here
         pass
 
 
 class MambaLayer(MambaCacheLayerMixin):
-    def lazy_initialization(self, conv_states: torch.Tensor) -> None:
-        self.dtype, self.device = conv_states.dtype, conv_states.device
-        # Even if prefill is larfer/shorter than the conv_size, the tensor is always either padded or truncated
-        self.conv_kernel_size = conv_states.shape[-1]
-        self.conv_states = torch.tensor([], dtype=self.dtype, device=self.device)
-        self.ssm_states = torch.tensor([], dtype=self.dtype, device=self.device)
-        self.is_initialized = True
+    def lazy_initialization(
+        self, conv_states: torch.Tensor | None = None, ssm_states: torch.Tensor | None = None
+    ) -> None:
+        # Here, we will lazy init both states separately, each in their own update function
+        if conv_states is not None:
+            self.dtype, self.device = conv_states.dtype, conv_states.device
+            # Even if prefill is larfer/shorter than the conv_size, the tensor is always either padded or truncated
+            self.max_batch_size, self.conv_kernel_size = conv_states.shape[0], conv_states.shape[-1]
+            # The shape is always static, so we init as such
+            self.conv_states = torch.zeros_like(conv_states, dtype=self.dtype, device=self.device)
+            self.is_conv_states_initialized = True
+        if ssm_states is not None:
+            # The shape is always static, so we init as such
+            self.ssm_states = torch.zeros_like(ssm_states, dtype=self.dtype, device=self.device)
+            self.is_ssm_states_initialized = True
 
     def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
         """
@@ -744,8 +759,8 @@ def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor
             `torch.Tensor`: The updated conv states.
         """
         # Lazy initialization
-        if not self.is_initialized:
-            self.lazy_initialization(conv_states)
+        if not self.is_conv_states_initialized:
+            self.lazy_initialization(conv_states=conv_states)
 
         # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
         # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
@@ -774,25 +789,28 @@ def update_ssm_state(self, ssm_states: torch.Tensor, **kwargs) -> torch.Tensor:
         Returns:
             `torch.Tensor`: The updated ssm states.
         """
+        if not self.is_ssm_states_initialized:
+            self.lazy_initialization(ssm_states=ssm_states)
         self.ssm_states = ssm_states
         return self.ssm_states
 
 
 class MambaAndAttentionLayer(MambaLayer, DynamicLayer):
+    # The dynamic Attention part makes it non-compileable
+    is_compileable = False
+
     def __init__(self):
         DynamicLayer.__init__(self)
         MambaLayer.__init__(self)
 
-    def lazy_initialization(self, states1: torch.Tensor, states2: torch.Tensor | None = None) -> None:
-        MambaLayer.lazy_initialization(self, states1)
-        DynamicLayer.lazy_initialization(self, states1, states2)
-
-    def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
-        # We need this as lazy initialization may be called first from `update` from the attention part, so the inferred
-        # conv_kernel_size may not be correct - make sure we grab the correct one during the first call to `update_conv_state`
-        if not self.has_previous_state:
-            self.conv_kernel_size = conv_states.shape[-1]
-        return super().update_conv_state(conv_states, **kwargs)
+    def lazy_initialization(self, *args, **kwargs) -> None:
+        # When the Attention cache is used with `update`, `lazy_initialization` is called with 2 positional args
+        if len(args) == 2 and len(kwargs) == 0:
+            DynamicLayer.lazy_initialization(self, *args)
+        # Otherwise, for the Mamba cache, when it's called in `update_conv_state` or `update_ssm_state`, it's
+        # always called with 1 single kwarg (cause it needs to know if it's for the conv or ssm states)
+        if len(args) == 0 and len(kwargs) == 1:
+            MambaLayer.lazy_initialization(self, **kwargs)
 
     def reset(self) -> None:
         MambaLayer.reset(self)
@@ -1312,6 +1330,9 @@ def __init__(
                 layer = StaticSlidingWindowLayer(
                     max_cache_len=max_cache_len, sliding_window=config.attention_chunk_size
                 )
+            # Mamba layers are static by essence - using `"moe"` as well is a trick, see the comment about it on DynamicCache
+            elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
+                layers.append(MambaLayer())
             else:
                 layer = StaticLayer(max_cache_len=max_cache_len)
             layers.append(layer)
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index 8a55c184b0f0..d00aaee37db8 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1768,19 +1768,19 @@ def _prepare_static_cache(
     def _supports_default_dynamic_cache(cls: type["GenerativePreTrainedModel"]) -> bool:
         """
         Return `True` if current model can use a `DynamicCache` instance when initializing the `past_key_values`.
-        This adds exception for some models like `Mamba` models which use their own caches.
         """
         # NOTE: remove xlnet/reformer when the models are deprecated, non-standard model architecture/cache name
-        return not cls._is_stateful and all(
-            special_model_name not in cls.__name__.lower()
-            or "minimaxm2" in cls.__name__.lower()  # name clash between minimax and minimax m2
-            for special_model_name in [
-                "reformer",
-                "minimax",
-                "xlnet",
-                "lfm2",
-                "lfm2_vl",
-            ]
+        unsupported_model_names = (
+            "reformer",
+            "minimax",
+            "xlnet",
+            "olmo_hybrid",
+            "rwkv",
+            "xlstm",
+        )
+        # name clash between minimax and minimax m2, so we add this "or"
+        return "minimaxm2" in cls.__name__.lower() or all(
+            unsupported_name not in cls.__name__.lower() for unsupported_name in unsupported_model_names
         )
 
     def _prepare_cache_for_generation(
@@ -1855,7 +1855,7 @@ def _prepare_cache_for_generation(
                     f"and will be removed in v5.13. Please only use one of {STATIC_CACHE_IMPLEMENTATIONS}, "
                     "and the layer structure will be inferred automatically."
                 )
-            model_kwargs["past_key_values"] = self._prepare_static_cache(
+            model_kwargs[cache_name] = self._prepare_static_cache(
                 cache_implementation=generation_config.cache_implementation,
                 batch_size=max(generation_config.num_beams, generation_config.num_return_sequences) * batch_size,
                 max_cache_len=max_cache_length,
@@ -1871,19 +1871,19 @@ def _prepare_cache_for_generation(
             cache_config = generation_config.cache_config if generation_config.cache_config is not None else {}
             cache_config.setdefault("config", self.config.get_text_config(decoder=True))
             backend = cache_config.pop("backend", "quanto")
-            model_kwargs["past_key_values"] = QuantizedCache(backend=backend, **cache_config)
+            model_kwargs[cache_name] = QuantizedCache(backend=backend, **cache_config)
         # i.e. `cache_implementation` in [None, "dynamic", "offloaded", "dynamic_full"]
         # TODO: prepare linear cache from a single API, instead of creating in modeling code
         else:
-            model_kwargs["past_key_values"] = DynamicCache(**dynamic_cache_kwargs)
+            model_kwargs[cache_name] = DynamicCache(**dynamic_cache_kwargs)
 
         if (
             self.config.is_encoder_decoder
-            and "past_key_values" in model_kwargs
-            and not isinstance(model_kwargs["past_key_values"], EncoderDecoderCache)
+            and cache_name in model_kwargs
+            and not isinstance(model_kwargs[cache_name], EncoderDecoderCache)
         ):
-            model_kwargs["past_key_values"] = EncoderDecoderCache(
-                model_kwargs["past_key_values"],  # self-attention cache
+            model_kwargs[cache_name] = EncoderDecoderCache(
+                model_kwargs[cache_name],  # self-attention cache
                 DynamicCache(**dynamic_cache_kwargs),  # cross-attention cache
             )
 
@@ -1983,13 +1983,13 @@ def _valid_auto_compile_criteria(
         if generation_config.disable_compile:
             return False
 
+        cache = model_kwargs.get("past_key_values", model_kwargs.get("cache_params"))
+
         # Base logic
         valid_hardware = self.device.type in ["cuda", "xpu"] or bool(
             generation_config.compile_config is not None and generation_config.compile_config._compile_all_devices
         )
-        using_compilable_cache = (
-            isinstance(model_kwargs.get("past_key_values"), Cache) and model_kwargs["past_key_values"].is_compileable
-        )
+        using_compilable_cache = cache is not None and cache.is_compileable
         can_compile = valid_hardware and using_compilable_cache
 
         # Exception 1: Some quantization methods do not support compilation
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 43b70dab9185..32bda17d976e 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -487,6 +487,7 @@ def torch_forward(
             # [bsz, num_heads, head_dim]
 
             # Reshape ssm_states to merge the first two dimensions
+            ssm_states = ssm_states.to(device=C.device, dtype=C.dtype)
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
             y = torch.bmm(ssm_states_reshaped, C_reshaped)
diff --git a/tests/models/mamba/test_modeling_mamba.py b/tests/models/mamba/test_modeling_mamba.py
index 5d56d1e724b2..78ec5789085d 100644
--- a/tests/models/mamba/test_modeling_mamba.py
+++ b/tests/models/mamba/test_modeling_mamba.py
@@ -31,7 +31,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import DynamicCache, MambaForCausalLM, MambaModel
+    from transformers import CompileConfig, DynamicCache, MambaForCausalLM, MambaModel
 
 
 class MambaModelTester:
@@ -452,8 +452,10 @@ def test_compile_mamba_cache(self):
         output_sentence = self.tokenizer.decode(output[0].tolist())
         self.assertEqual(output_sentence, expected_output)
 
-        model.forward = torch.compile(model.forward, fullgraph=True, mode="reduce-overhead")
-        output = model.generate(input_ids, max_new_tokens=20)
+        compile_config = CompileConfig(fullgraph=True, mode="reduce-overhead")
+        output = model.generate(
+            input_ids, max_new_tokens=20, cache_implementation="static", compile_config=compile_config
+        )
         output_sentence = self.tokenizer.decode(output[0].tolist())
         self.assertEqual(output_sentence, expected_output)
 

From f4fc80170aa90e97b91b1e19440a2881d0293d0e Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Wed, 25 Mar 2026 14:53:25 +0100
Subject: [PATCH 46/56] oupsi

---
 src/transformers/generation/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index d00aaee37db8..49b864fc48b3 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1774,7 +1774,7 @@ def _supports_default_dynamic_cache(cls: type["GenerativePreTrainedModel"]) -> b
             "reformer",
             "minimax",
             "xlnet",
-            "olmo_hybrid",
+            "olmohybrid",
             "rwkv",
             "xlstm",
         )

From 6aca24ed518acf1d4ae7220108aa36aa0a0ad163 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Wed, 25 Mar 2026 16:59:29 +0100
Subject: [PATCH 47/56] adjustments in generate

---
 src/transformers/generation/utils.py | 15 +++++++++------
 tests/generation/test_utils.py       | 16 ++++++++++++----
 2 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index 49b864fc48b3..c02448b51331 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1842,7 +1842,9 @@ def _prepare_cache_for_generation(
             generation_config.cache_implementation = "dynamic_full"
 
         dynamic_cache_kwargs = {}
-        if generation_config.cache_implementation != "dynamic_full":
+        # mamba models always need to pass the config, otherwise it will use an Attention cache
+        is_mamba = any(x in ("mamba", "conv", "linear_attention") for x in getattr(self.config, "layer_types", []))
+        if generation_config.cache_implementation != "dynamic_full" or is_mamba:
             dynamic_cache_kwargs["config"] = self.config.get_text_config(decoder=True)
 
         if generation_config.cache_implementation == "offloaded":
@@ -1989,7 +1991,9 @@ def _valid_auto_compile_criteria(
         valid_hardware = self.device.type in ["cuda", "xpu"] or bool(
             generation_config.compile_config is not None and generation_config.compile_config._compile_all_devices
         )
-        using_compilable_cache = cache is not None and cache.is_compileable
+        # Note: for full mamba models, even a DynamicCache is compileable since all layers are mamba, but we don't want
+        # to ALWAYS compile when calling `generate`, so we check the type
+        using_compilable_cache = cache is not None and cache.is_compileable and type(cache) is not DynamicCache
         can_compile = valid_hardware and using_compilable_cache
 
         # Exception 1: Some quantization methods do not support compilation
@@ -3460,10 +3464,9 @@ def _assisted_decoding(
         # The cache must be dynamic for assisted generation, and the check must happen AFTER preparing cache
         if not model_kwargs["use_cache"]:
             raise ValueError("assisted generate requires `use_cache=True`")
-        if generation_config.cache_implementation in ["static", "hybrid", "sliding_window"] or (
-            "past_key_values" in model_kwargs
-            and hasattr(model_kwargs["past_key_values"], "layers")
-            and any(getattr(l, "is_compileable", False) for l in model_kwargs["past_key_values"].layers)
+        if (
+            generation_config.cache_implementation in ["static", "hybrid", "sliding_window"]
+            or type(model_kwargs.get("past_key_values")) is StaticCache
         ):
             raise ValueError("assisted generate is not supported with Static cache classes`")
         # Get the candidate generator, given the parameterization
diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index b23b004c75ea..e91b5dc3aeb3 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2598,11 +2598,15 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(keys.shape, attention_shape)
                 self.assertEqual(values.shape, attention_shape)
                 self.assertEqual(layer.conv_states.shape, conv_shape)
-                self.assertEqual(layer.ssm_states.shape, ssm_shape)
+                # May not be used (e.g. lfm2)
+                if layer.is_ssm_states_initialized:
+                    self.assertEqual(layer.ssm_states.shape, ssm_shape)
             # Mamba only layer cache
             elif type(layer) is MambaLayer:
                 self.assertEqual(layer.conv_states.shape, conv_shape)
-                self.assertEqual(layer.ssm_states.shape, ssm_shape)
+                # May not be used (e.g. lfm2)
+                if layer.is_ssm_states_initialized:
+                    self.assertEqual(layer.ssm_states.shape, ssm_shape)
             # Attention only layer type
             else:
                 # Remove the seq_length dim for cross-attention cache (it changes based on the model)
@@ -2656,11 +2660,15 @@ def _check_caches_are_equal(self, cache1: Cache, cache2: Cache):
                 torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
                 torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+                # May not be used (e.g. lfm2)
+                if cache1.layers[idx].is_ssm_states_initialized:
+                    torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
             # Mamba layer
             elif type(cache1.layers[idx]) is MambaLayer:
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
-                torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+                # May not be used (e.g. lfm2)
+                if cache1.layers[idx].is_ssm_states_initialized:
+                    torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
             # Attention layer
             else:
                 torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)

From 13781f17a18ef2bd3d0c245d126e267d2ce48658 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Wed, 25 Mar 2026 17:04:03 +0100
Subject: [PATCH 48/56] allow cudagraphs

---
 src/transformers/cache_utils.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index df9057d57723..21fbaefcba29 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -742,10 +742,16 @@ def lazy_initialization(
             self.max_batch_size, self.conv_kernel_size = conv_states.shape[0], conv_states.shape[-1]
             # The shape is always static, so we init as such
             self.conv_states = torch.zeros_like(conv_states, dtype=self.dtype, device=self.device)
+            # Mark as static address to be able to use cudagraphs
+            if not is_torchdynamo_compiling():
+                torch._dynamo.mark_static_address(self.conv_states)
             self.is_conv_states_initialized = True
         if ssm_states is not None:
             # The shape is always static, so we init as such
             self.ssm_states = torch.zeros_like(ssm_states, dtype=self.dtype, device=self.device)
+            # Mark as static address to be able to use cudagraphs
+            if not is_torchdynamo_compiling():
+                torch._dynamo.mark_static_address(self.ssm_states)
             self.is_ssm_states_initialized = True
 
     def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
@@ -762,20 +768,22 @@ def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor
         if not self.is_conv_states_initialized:
             self.lazy_initialization(conv_states=conv_states)
 
-        # Technically, those update are not logically correct if the prefill is smaller than `conv_kernel_size`,
-        # as it will `roll` anyway in the first decoding step even though it should `roll` ONLY if the cache is already full.
-        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
         if not self.has_previous_state:
-            self.conv_states = conv_states
+            # Note that we copy instead of assigning, to preserve the static address for cudagraphs
+            self.conv_states.copy_(conv_states)
             self.has_previous_state = True
+        # Technically, this update is not logically correct if the prefill is smaller than `conv_kernel_size`,
+        # as it will `roll` anyway in the first decoding step, even though it should `roll` ONLY if the cache is already full.
+        # But since `conv_kernel_size=4` in practice, it's almost impossible to have a smaller prefill so it's mostly fine for now
         else:
+            # Note that we copy instead of assigning, to preserve the static address for cudagraphs
             num_new_tokens = conv_states.shape[-1]
             if num_new_tokens >= self.conv_kernel_size:
-                self.conv_states = conv_states[..., -self.conv_kernel_size :]
+                self.conv_states.copy_(conv_states[..., -self.conv_kernel_size :])
             else:
                 new_conv_states = self.conv_states.roll(shifts=-num_new_tokens, dims=-1)
                 new_conv_states[:, :, -num_new_tokens:] = conv_states
-                self.conv_states = new_conv_states
+                self.conv_states.copy_(new_conv_states)
 
         return self.conv_states
 
@@ -791,7 +799,8 @@ def update_ssm_state(self, ssm_states: torch.Tensor, **kwargs) -> torch.Tensor:
         """
         if not self.is_ssm_states_initialized:
             self.lazy_initialization(ssm_states=ssm_states)
-        self.ssm_states = ssm_states
+        # Note that we copy instead of assigning, to preserve the static address for cudagraphs
+        self.ssm_states.copy_(ssm_states)
         return self.ssm_states
 
 
@@ -1332,7 +1341,7 @@ def __init__(
                 )
             # Mamba layers are static by essence - using `"moe"` as well is a trick, see the comment about it on DynamicCache
             elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
-                layers.append(MambaLayer())
+                layer = MambaLayer()
             else:
                 layer = StaticLayer(max_cache_len=max_cache_len)
             layers.append(layer)

From 3df0d85ae30889ae50f484d284bc0c2b73d35846 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Wed, 25 Mar 2026 17:12:47 +0100
Subject: [PATCH 49/56] small oupsi

---
 src/transformers/generation/utils.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index c02448b51331..1f6125d52957 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1842,8 +1842,11 @@ def _prepare_cache_for_generation(
             generation_config.cache_implementation = "dynamic_full"
 
         dynamic_cache_kwargs = {}
-        # mamba models always need to pass the config, otherwise it will use an Attention cache
-        is_mamba = any(x in ("mamba", "conv", "linear_attention") for x in getattr(self.config, "layer_types", []))
+        # mamba models always need to pass the config, otherwise it will use an Attention cache for the Mamba layers
+        is_mamba = any(
+            x in ("mamba", "conv", "linear_attention")
+            for x in getattr(self.config.get_text_config(decoder=True), "layer_types", [])
+        )
         if generation_config.cache_implementation != "dynamic_full" or is_mamba:
             dynamic_cache_kwargs["config"] = self.config.get_text_config(decoder=True)
 

From eadcfa47d04ebdaf0b4aadec46ebc9062ed8dc39 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 31 Mar 2026 10:58:58 +0200
Subject: [PATCH 50/56] start renaming

---
 src/transformers/cache_utils.py               |   96 +-
 .../models/align/configuration_align.py       |    1 +
 .../models/bamba/modeling_bamba.py            |   12 +-
 .../models/bamba/modular_bamba.py             |   12 +-
 .../models/falcon_h1/modeling_falcon_h1.py    |   12 +-
 .../models/falcon_h1/modular_falcon_h1.py     |   12 +-
 .../falcon_mamba/modeling_falcon_mamba.py     |    8 +-
 .../falcon_mamba/modular_falcon_mamba.py      |    8 +-
 .../modeling_granitemoehybrid.py              |   12 +-
 .../models/jamba/modeling_jamba.py            |    8 +-
 .../models/jamba/modular_jamba.py             |    8 +-
 .../models/mamba/modeling_mamba.py            |    8 +-
 .../models/mamba2/modeling_mamba2.py          |   10 +-
 .../models/nemotron_h/modeling_nemotron_h.py  |   14 +-
 .../models/qwen3_5/modeling_qwen3_5.py        |    4 +-
 .../models/qwen3_5/modular_qwen3_5.py         |    4 +-
 .../qwen3_5_moe/modeling_qwen3_5_moe.py       |    4 +-
 .../models/qwen3_next/modeling_qwen3_next.py  |    4 +-
 .../models/qwen3_next/modular_qwen3_next.py   |    4 +-
 .../models/zamba/modeling_zamba.py            |    8 +-
 .../models/zamba2/modeling_zamba2.py          |   14 +-
 .../models/zamba2/modular_zamba2.py           |   14 +-
 tests/generation/test_utils.py                |   32 +-
 .../test_modeling_falcon_mamba.py             |    4 +-
 tests/models/mamba/test_modeling_mamba.py     |    4 +-
 tests/models/mamba2/test_modeling_mamba2.py   |    4 +-
 .../nemotron_h/test_modeling_nemotron_h.py    |    4 +-
 utils/mlinter/.mlinter_cache.json             | 1073 +++++++++++++++++
 28 files changed, 1242 insertions(+), 156 deletions(-)
 create mode 100644 utils/mlinter/.mlinter_cache.json

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 21fbaefcba29..297059a5b1e2 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -669,7 +669,7 @@ def _dequantize(self, qtensor):
         return tensor
 
 
-class MambaCacheLayerMixin(ABC):
+class LinearAttentionCacheLayerMixin(ABC):
     """Base, abstract class for a mamba single layer's cache."""
 
     # All shapes are static by essence in a Mamba layer, so it is compileable
@@ -677,9 +677,9 @@ class MambaCacheLayerMixin(ABC):
 
     def __init__(self):
         self.conv_states: torch.Tensor | None = None
-        self.ssm_states: torch.Tensor | None = None
+        self.recurrent_states: torch.Tensor | None = None
         self.is_conv_states_initialized = False
-        self.is_ssm_states_initialized = False
+        self.is_recurrent_states_initialized = False
         self.has_previous_state = False
 
     def __repr__(self):
@@ -687,53 +687,53 @@ def __repr__(self):
 
     @abstractmethod
     def lazy_initialization(
-        self, conv_states: torch.Tensor | None = None, ssm_states: torch.Tensor | None = None
+        self, conv_states: torch.Tensor | None = None, recurrent_states: torch.Tensor | None = None
     ) -> None: ...
 
     @abstractmethod
     def update_conv_state(self, conv_states: torch.Tensor) -> torch.Tensor: ...
 
     @abstractmethod
-    def update_ssm_state(self, ssm_states: torch.Tensor) -> torch.Tensor: ...
+    def update_recurrent_state(self, recurrent_states: torch.Tensor) -> torch.Tensor: ...
 
     def offload(self):
         """Offload this layer's data to CPU device."""
         if self.is_conv_states_initialized:
             self.conv_states = self.conv_states.to("cpu", non_blocking=True)
-        if self.is_ssm_states_initialized:
-            self.ssm_states = self.ssm_states.to("cpu", non_blocking=True)
+        if self.is_recurrent_states_initialized:
+            self.recurrent_states = self.recurrent_states.to("cpu", non_blocking=True)
 
     def prefetch(self):
         """In case of layer offloading, this allows to move the data back to the layer's device ahead of time."""
         if self.is_conv_states_initialized and self.conv_states.device != self.device:
             self.conv_states = self.conv_states.to(self.device, non_blocking=True)
-        if self.is_ssm_states_initialized and self.ssm_states.device != self.device:
-            self.ssm_states = self.ssm_states.to(self.device, non_blocking=True)
+        if self.is_recurrent_states_initialized and self.recurrent_states.device != self.device:
+            self.recurrent_states = self.recurrent_states.to(self.device, non_blocking=True)
 
     def reset(self) -> None:
         """Resets the cache values while preserving the objects"""
         if self.is_conv_states_initialized:
             self.conv_states.zero_()
-        if self.is_ssm_states_initialized:
-            self.ssm_states.zero_()
+        if self.is_recurrent_states_initialized:
+            self.recurrent_states.zero_()
         self.has_previous_state = False
 
     def reorder_cache(self, beam_idx: torch.LongTensor):
         """Reorders the cache for beam search, given the selected beam indices."""
         if self.is_conv_states_initialized:
             self.conv_states = self.conv_states.index_select(0, beam_idx.to(self.device))
-        # ssm_states can stay empty sometimes, see e.g. lfm2 which only uses the conv_states
-        if self.is_ssm_states_initialized:
-            self.ssm_states = self.ssm_states.index_select(0, beam_idx.to(self.device))
+        # recurrent_states can stay empty sometimes, see e.g. lfm2 which only uses the conv_states
+        if self.is_recurrent_states_initialized:
+            self.recurrent_states = self.recurrent_states.index_select(0, beam_idx.to(self.device))
 
     def crop(self, max_length: int):
         # We don't crop the mamba cache, so simply do nothing here
         pass
 
 
-class MambaLayer(MambaCacheLayerMixin):
+class LinearAttentionLayer(LinearAttentionCacheLayerMixin):
     def lazy_initialization(
-        self, conv_states: torch.Tensor | None = None, ssm_states: torch.Tensor | None = None
+        self, conv_states: torch.Tensor | None = None, recurrent_states: torch.Tensor | None = None
     ) -> None:
         # Here, we will lazy init both states separately, each in their own update function
         if conv_states is not None:
@@ -746,13 +746,13 @@ def lazy_initialization(
             if not is_torchdynamo_compiling():
                 torch._dynamo.mark_static_address(self.conv_states)
             self.is_conv_states_initialized = True
-        if ssm_states is not None:
+        if recurrent_states is not None:
             # The shape is always static, so we init as such
-            self.ssm_states = torch.zeros_like(ssm_states, dtype=self.dtype, device=self.device)
+            self.recurrent_states = torch.zeros_like(recurrent_states, dtype=self.dtype, device=self.device)
             # Mark as static address to be able to use cudagraphs
             if not is_torchdynamo_compiling():
-                torch._dynamo.mark_static_address(self.ssm_states)
-            self.is_ssm_states_initialized = True
+                torch._dynamo.mark_static_address(self.recurrent_states)
+            self.is_recurrent_states_initialized = True
 
     def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
         """
@@ -787,7 +787,7 @@ def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor
 
         return self.conv_states
 
-    def update_ssm_state(self, ssm_states: torch.Tensor, **kwargs) -> torch.Tensor:
+    def update_recurrent_state(self, recurrent_states: torch.Tensor, **kwargs) -> torch.Tensor:
         """
         Update the mamba cache in-place, and return the necessary ssm states.
 
@@ -797,37 +797,37 @@ def update_ssm_state(self, ssm_states: torch.Tensor, **kwargs) -> torch.Tensor:
         Returns:
             `torch.Tensor`: The updated ssm states.
         """
-        if not self.is_ssm_states_initialized:
-            self.lazy_initialization(ssm_states=ssm_states)
+        if not self.is_recurrent_states_initialized:
+            self.lazy_initialization(recurrent_states=recurrent_states)
         # Note that we copy instead of assigning, to preserve the static address for cudagraphs
-        self.ssm_states.copy_(ssm_states)
-        return self.ssm_states
+        self.recurrent_states.copy_(recurrent_states)
+        return self.recurrent_states
 
 
-class MambaAndAttentionLayer(MambaLayer, DynamicLayer):
+class LinearAttentionAndAttentionLayer(LinearAttentionLayer, DynamicLayer):
     # The dynamic Attention part makes it non-compileable
     is_compileable = False
 
     def __init__(self):
         DynamicLayer.__init__(self)
-        MambaLayer.__init__(self)
+        LinearAttentionLayer.__init__(self)
 
     def lazy_initialization(self, *args, **kwargs) -> None:
         # When the Attention cache is used with `update`, `lazy_initialization` is called with 2 positional args
         if len(args) == 2 and len(kwargs) == 0:
             DynamicLayer.lazy_initialization(self, *args)
-        # Otherwise, for the Mamba cache, when it's called in `update_conv_state` or `update_ssm_state`, it's
+        # Otherwise, for the Mamba cache, when it's called in `update_conv_state` or `update_recurrent_state`, it's
         # always called with 1 single kwarg (cause it needs to know if it's for the conv or ssm states)
         if len(args) == 0 and len(kwargs) == 1:
-            MambaLayer.lazy_initialization(self, **kwargs)
+            LinearAttentionLayer.lazy_initialization(self, **kwargs)
 
     def reset(self) -> None:
-        MambaLayer.reset(self)
+        LinearAttentionLayer.reset(self)
         DynamicLayer.reset(self)
 
     def reorder_cache(self, beam_idx: torch.LongTensor):
         """Reorders the cache for beam search, given the selected beam indices."""
-        MambaLayer.reorder_cache(self, beam_idx)
+        LinearAttentionLayer.reorder_cache(self, beam_idx)
         DynamicLayer.reorder_cache(self, beam_idx)
 
 
@@ -838,9 +838,9 @@ class Cache:
 
     Args:
         layers (`Optional`, *optional*):
-            A list of pre-created `CacheLayerMixin` or `MambaCacheLayerMixin`. If omitted (`None`), then `layer_class_to_replicate`
+            A list of pre-created `CacheLayerMixin` or `LinearAttentionCacheLayerMixin`. If omitted (`None`), then `layer_class_to_replicate`
             will be used.
-        layer_class_to_replicate (`type[CacheLayerMixin | MambaCacheLayerMixin]`, *optional*):
+        layer_class_to_replicate (`type[CacheLayerMixin | LinearAttentionCacheLayerMixin]`, *optional*):
             Only used if `layers` is omitted (`None`), in which case it will be used as the base class for each layer,
             and the layers will be added lazily as soon as `update` is called with a `layer_idx` greater than the current
             list of layers.
@@ -853,8 +853,8 @@ class Cache:
 
     def __init__(
         self,
-        layers: list[CacheLayerMixin | MambaCacheLayerMixin] | None = None,
-        layer_class_to_replicate: type[CacheLayerMixin | MambaCacheLayerMixin] | None = None,
+        layers: list[CacheLayerMixin | LinearAttentionCacheLayerMixin] | None = None,
+        layer_class_to_replicate: type[CacheLayerMixin | LinearAttentionCacheLayerMixin] | None = None,
         offloading: bool = False,
         offload_only_non_sliding: bool = True,
     ):
@@ -956,14 +956,14 @@ def update_conv_state(self, conv_states: torch.Tensor, layer_idx: int, **kwargs)
         """
         # NOTE: if we slightly break `update` arg order, we could combine this with it, and allow offloading support
         # out of the box
-        if not isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+        if not isinstance(self.layers[layer_idx], LinearAttentionCacheLayerMixin):
             raise ValueError("Cannot call `update_conv_state` on a non-Mamba layer!")
         conv_states = self.layers[layer_idx].update_conv_state(conv_states, **kwargs)
         return conv_states
 
-    def update_ssm_state(self, ssm_states: torch.Tensor, layer_idx: int, **kwargs) -> torch.Tensor:
+    def update_recurrent_state(self, recurrent_states: torch.Tensor, layer_idx: int, **kwargs) -> torch.Tensor:
         """
-        Updates the cache with the new `ssm_states` for the layer `layer_idx`.
+        Updates the cache with the new `recurrent_states` for the layer `layer_idx`.
 
         Parameters:
             smm_states (`torch.Tensor`):
@@ -976,10 +976,10 @@ def update_ssm_state(self, ssm_states: torch.Tensor, layer_idx: int, **kwargs) -
         """
         # NOTE: if we slightly break `update` arg order, we could combine this with it, and allow offloading support
         # out of the box
-        if not isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+        if not isinstance(self.layers[layer_idx], LinearAttentionCacheLayerMixin):
             raise ValueError("Cannot call `update_conv_state` on a non-Mamba layer!")
-        ssm_states = self.layers[layer_idx].update_ssm_state(ssm_states, **kwargs)
-        return ssm_states
+        recurrent_states = self.layers[layer_idx].update_recurrent_state(recurrent_states, **kwargs)
+        return recurrent_states
 
     def early_initialization(
         self, batch_size: int, num_heads: int, head_dim: int, dtype: torch.dtype, device: torch.device
@@ -1029,14 +1029,16 @@ def has_previous_state(self, layer_idx: int | None = None) -> bool:
         if layer_idx is None:
             try:
                 layer_idx = next(
-                    idx for idx in range(len(self) - 1, -1, -1) if isinstance(self.layers[idx], MambaCacheLayerMixin)
+                    idx
+                    for idx in range(len(self) - 1, -1, -1)
+                    if isinstance(self.layers[idx], LinearAttentionCacheLayerMixin)
                 )
             except StopIteration:
                 raise ValueError(
                     "`has_previous_state` can only be called on Mamba layers, and the current Cache seem to only contain "
                     "Attention layers."
                 )
-        elif not isinstance(self.layers[layer_idx], MambaCacheLayerMixin):
+        elif not isinstance(self.layers[layer_idx], LinearAttentionCacheLayerMixin):
             raise ValueError(
                 f"You called `has_previous_state` on layer index {layer_idx}, but this layer is an Attention layer, which "
                 "does not support calling it."
@@ -1222,14 +1224,14 @@ def __init__(
                 # states they should return - only the mask changes to make them different at the end!
                 if layer_type in ("sliding_attention", "chunked_attention"):
                     layers.append(DynamicSlidingWindowLayer(sliding_window=sliding_window))
-                # Note: we want moe layers to be MambaLayer, so that we can correctly grab sequence length etc from attention layers.
+                # Note: we want moe layers to be LinearAttentionLayer, so that we can correctly grab sequence length etc from attention layers.
                 # Since moe layers will stay empty (they don't need any cache), we don't want them to collide for mask creation etc
                 # TODO: maybe use a dummy layer in those cases, or a dictionary {idx: Layer} for self.layers, so that we can skip
                 # the indices we don't need
                 elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
-                    layers.append(MambaLayer())
+                    layers.append(LinearAttentionLayer())
                 elif layer_type == "hybrid":
-                    layers.append(MambaAndAttentionLayer())
+                    layers.append(LinearAttentionAndAttentionLayer())
                 else:
                     layers.append(DynamicLayer())
 
@@ -1341,7 +1343,7 @@ def __init__(
                 )
             # Mamba layers are static by essence - using `"moe"` as well is a trick, see the comment about it on DynamicCache
             elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
-                layer = MambaLayer()
+                layer = LinearAttentionLayer()
             else:
                 layer = StaticLayer(max_cache_len=max_cache_len)
             layers.append(layer)
diff --git a/src/transformers/models/align/configuration_align.py b/src/transformers/models/align/configuration_align.py
index cde6445cf62f..babf97d4572a 100644
--- a/src/transformers/models/align/configuration_align.py
+++ b/src/transformers/models/align/configuration_align.py
@@ -59,6 +59,7 @@ class AlignTextConfig(PreTrainedConfig):
     pad_token_id: int | None = 0
     bos_token_id: int | None = None
     eos_token_id: int | list[int] | None = None
+    tie_word_embeddings: True
 
 
 @auto_docstring(checkpoint="kakaobrain/align-base")
diff --git a/src/transformers/models/bamba/modeling_bamba.py b/src/transformers/models/bamba/modeling_bamba.py
index 0b599490c5ff..90129fc998b1 100644
--- a/src/transformers/models/bamba/modeling_bamba.py
+++ b/src/transformers/models/bamba/modeling_bamba.py
@@ -532,7 +532,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -633,7 +633,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -694,7 +694,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
+            cache_device = cache_params.layers[self.layer_idx].recurrent_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -724,8 +724,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
-            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -822,7 +822,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
diff --git a/src/transformers/models/bamba/modular_bamba.py b/src/transformers/models/bamba/modular_bamba.py
index f60d8c405b4d..a79b26ff8fe9 100644
--- a/src/transformers/models/bamba/modular_bamba.py
+++ b/src/transformers/models/bamba/modular_bamba.py
@@ -289,7 +289,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -390,7 +390,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -451,7 +451,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
+            cache_device = cache_params.layers[self.layer_idx].recurrent_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -481,8 +481,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
-            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -579,7 +579,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
diff --git a/src/transformers/models/falcon_h1/modeling_falcon_h1.py b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
index 7aaba6aacb4d..37b5da9df4b3 100644
--- a/src/transformers/models/falcon_h1/modeling_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
@@ -529,7 +529,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -649,7 +649,7 @@ def cuda_kernels_forward(
                         **dt_limit_kwargs,
                     )
                 if ssm_state is not None and cache_params is not None:
-                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 if self.mamba_rms_norm:
@@ -715,7 +715,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
+            cache_device = cache_params.layers[self.layer_idx].recurrent_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -745,8 +745,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
-            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -843,7 +843,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         if self.mamba_rms_norm:
             scan_output = self.norm(y, gate)
diff --git a/src/transformers/models/falcon_h1/modular_falcon_h1.py b/src/transformers/models/falcon_h1/modular_falcon_h1.py
index 61fab3117d96..75cbed28a646 100644
--- a/src/transformers/models/falcon_h1/modular_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modular_falcon_h1.py
@@ -316,7 +316,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -436,7 +436,7 @@ def cuda_kernels_forward(
                         **dt_limit_kwargs,
                     )
                 if ssm_state is not None and cache_params is not None:
-                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 if self.mamba_rms_norm:
@@ -502,7 +502,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
+            cache_device = cache_params.layers[self.layer_idx].recurrent_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -532,8 +532,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
-            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -630,7 +630,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         if self.mamba_rms_norm:
             scan_output = self.norm(y, gate)
diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index b2fc8afdae75..32fd1bf4a358 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -280,7 +280,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params.layers[self.layer_idx].ssm_states,
+                    cache_params.layers[self.layer_idx].recurrent_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -305,7 +305,7 @@ def cuda_kernels_forward(
                     return_last_state=True,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
             # 4. Final linear projection
             contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -327,7 +327,7 @@ def slow_forward(self,
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].recurrent_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
@@ -429,7 +429,7 @@ def combine_fn(left, right):
             scan_output = scan_output * self.act(gate)
 
             if cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))  # [batch, seq_len, hidden_size]
diff --git a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
index 2e1b940cf48d..6962c66c2973 100644
--- a/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modular_falcon_mamba.py
@@ -245,7 +245,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params.layers[self.layer_idx].ssm_states,
+                    cache_params.layers[self.layer_idx].recurrent_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -270,7 +270,7 @@ def cuda_kernels_forward(
                     return_last_state=True,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
             # 4. Final linear projection
             contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -292,7 +292,7 @@ def slow_forward(
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].recurrent_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype
@@ -394,7 +394,7 @@ def combine_fn(left, right):
             scan_output = scan_output * self.act(gate)
 
             if cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))  # [batch, seq_len, hidden_size]
diff --git a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
index baeaf65bbd45..dadffaea0072 100644
--- a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
@@ -409,7 +409,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -510,7 +510,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -571,7 +571,7 @@ def torch_forward(
         A = -torch.exp(self.A_log.float())                            # [num_heads]
         if use_precomputed_states:
             # We need to guarantee that anything regarding the cache is on the same device
-            cache_device = cache_params.layers[self.layer_idx].ssm_states.device
+            cache_device = cache_params.layers[self.layer_idx].recurrent_states.device
 
             # Note: there is no need to pad parameter matrices here, as there is just one new token
             # for batched generation
@@ -601,8 +601,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
-            ssm_states = cache_params.update_ssm_state(ssm_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -699,7 +699,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                ssm_state = cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
diff --git a/src/transformers/models/jamba/modeling_jamba.py b/src/transformers/models/jamba/modeling_jamba.py
index 5ffd16302e06..ae618fb4a2b3 100755
--- a/src/transformers/models/jamba/modeling_jamba.py
+++ b/src/transformers/models/jamba/modeling_jamba.py
@@ -343,7 +343,7 @@ def cuda_kernels_forward(
         time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
         if use_precomputed_states:
             scan_outputs = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states[..., 0],
                 discrete_time_step[..., 0],
                 A,
@@ -368,7 +368,7 @@ def cuda_kernels_forward(
                 return_last_state=True,
             )
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -388,7 +388,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].recurrent_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.intermediate_size, self.ssm_state_size),
@@ -446,7 +446,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
         scan_output = (scan_output * self.act(gate))
 
         if cache_params is not None:
-            cache_params.update_ssm_state(ssm_state, self.layer_idx)
+            cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))
diff --git a/src/transformers/models/jamba/modular_jamba.py b/src/transformers/models/jamba/modular_jamba.py
index e24be7640f40..21e6623d3296 100644
--- a/src/transformers/models/jamba/modular_jamba.py
+++ b/src/transformers/models/jamba/modular_jamba.py
@@ -236,7 +236,7 @@ def cuda_kernels_forward(
         time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
         if use_precomputed_states:
             scan_outputs = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states[..., 0],
                 discrete_time_step[..., 0],
                 A,
@@ -261,7 +261,7 @@ def cuda_kernels_forward(
                 return_last_state=True,
             )
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -281,7 +281,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].recurrent_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.intermediate_size, self.ssm_state_size),
@@ -339,7 +339,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
         scan_output = (scan_output * self.act(gate))
 
         if cache_params is not None:
-            cache_params.update_ssm_state(ssm_state, self.layer_idx)
+            cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index 1591583112dc..2e5695c1d4fa 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -235,7 +235,7 @@ def cuda_kernels_forward(
             time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
             if is_decoding:
                 scan_outputs = selective_state_update(
-                    cache_params.layers[self.layer_idx].ssm_states,
+                    cache_params.layers[self.layer_idx].recurrent_states,
                     hidden_states[..., 0],
                     discrete_time_step[..., 0],
                     A,
@@ -260,7 +260,7 @@ def cuda_kernels_forward(
                     return_last_state=True,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
             # 4. Final linear projection
             contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -278,7 +278,7 @@ def slow_forward(self, input_states, cache_params: Cache | None=None, attention_
             hidden_states = hidden_states * attention_mask.unsqueeze(1)
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].recurrent_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.intermediate_size, self.ssm_state_size),
@@ -356,7 +356,7 @@ def combine_fn(left, right):
             scan_output = (scan_output * self.act(gate))
 
             if cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_output.transpose(1, 2))  # [batch, seq_len, hidden_size]
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 32bda17d976e..d0a47ef9dc63 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -286,7 +286,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -384,7 +384,7 @@ def cuda_kernels_forward(
 
                 # Init cache
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(ssm_state, layer_idx=self.layer_idx)
+                    cache_params.update_recurrent_state(ssm_state, layer_idx=self.layer_idx)
 
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
@@ -476,8 +476,8 @@ def torch_forward(
             dBx = (dB * hidden_states[..., None]).to(device=cache_device)
 
             # State calculation
-            ssm_states = cache_params.layers[self.layer_idx].ssm_states * dA + dBx
-            ssm_states = cache_params.update_ssm_state(ssm_states, layer_idx=self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, layer_idx=self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -574,7 +574,7 @@ def torch_forward(
 
             # Init cache
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, layer_idx=self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, layer_idx=self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index 41aba6122448..af09fdfaf36f 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -255,7 +255,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -356,7 +356,7 @@ def cuda_kernels_forward(
                     **dt_limit_kwargs,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    cache_params.update_recurrent_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 scan_output = self.norm(scan_output, gate)
@@ -435,9 +435,9 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state * dA + dBx
-            ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+            recurrent_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
+            recurrent_states = recurrent_states * dA + dBx
+            recurrent_states = cache_params.update_recurrent_state(recurrent_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -446,7 +446,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = ssm_state.to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = recurrent_states.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -535,7 +535,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
                 y = y[:, :seq_len, :, :]
             y = y.reshape(batch_size, seq_len, -1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
diff --git a/src/transformers/models/qwen3_5/modeling_qwen3_5.py b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
index feda0bd67a2b..eba3eec02fdd 100644
--- a/src/transformers/models/qwen3_5/modeling_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
@@ -437,7 +437,7 @@ def forward(
         # getting projected states from cache if it exists
         if use_precomputed_states:
             conv_state = cache_params.layers[self.layer_idx].conv_states
-            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
+            recurrent_state = cache_params.layers[self.layer_idx].recurrent_states
 
         mixed_qkv = self.in_proj_qkv(hidden_states)
         mixed_qkv = mixed_qkv.transpose(1, 2)
@@ -521,7 +521,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
+            cache_params.update_recurrent_state(last_recurrent_state, self.layer_idx)
 
         # reshape input data into 2D tensor
         core_attn_out = core_attn_out.reshape(-1, self.head_v_dim)
diff --git a/src/transformers/models/qwen3_5/modular_qwen3_5.py b/src/transformers/models/qwen3_5/modular_qwen3_5.py
index 69e9bdb0faf2..8fddbc6115c1 100644
--- a/src/transformers/models/qwen3_5/modular_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/modular_qwen3_5.py
@@ -222,7 +222,7 @@ def forward(
         # getting projected states from cache if it exists
         if use_precomputed_states:
             conv_state = cache_params.layers[self.layer_idx].conv_states
-            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
+            recurrent_state = cache_params.layers[self.layer_idx].recurrent_states
 
         mixed_qkv = self.in_proj_qkv(hidden_states)
         mixed_qkv = mixed_qkv.transpose(1, 2)
@@ -306,7 +306,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
+            cache_params.update_recurrent_state(last_recurrent_state, self.layer_idx)
 
         # reshape input data into 2D tensor
         core_attn_out = core_attn_out.reshape(-1, self.head_v_dim)
diff --git a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
index 3c578e6fe836..be4501d34903 100644
--- a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
+++ b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
@@ -438,7 +438,7 @@ def forward(
         # getting projected states from cache if it exists
         if use_precomputed_states:
             conv_state = cache_params.layers[self.layer_idx].conv_states
-            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
+            recurrent_state = cache_params.layers[self.layer_idx].recurrent_states
 
         mixed_qkv = self.in_proj_qkv(hidden_states)
         mixed_qkv = mixed_qkv.transpose(1, 2)
@@ -522,7 +522,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
+            cache_params.update_recurrent_state(last_recurrent_state, self.layer_idx)
 
         # reshape input data into 2D tensor
         core_attn_out = core_attn_out.reshape(-1, self.head_v_dim)
diff --git a/src/transformers/models/qwen3_next/modeling_qwen3_next.py b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
index da31a5eb8e7d..9e7fa7e01c69 100644
--- a/src/transformers/models/qwen3_next/modeling_qwen3_next.py
+++ b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
@@ -607,7 +607,7 @@ def forward(
         # getting projected states from cache if it exists
         if use_precomputed_states:
             conv_state = cache_params.layers[self.layer_idx].conv_states
-            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
+            recurrent_state = cache_params.layers[self.layer_idx].recurrent_states
 
         projected_states_qkvz = self.in_proj_qkvz(hidden_states)
         projected_states_ba = self.in_proj_ba(hidden_states)
@@ -688,7 +688,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
+            cache_params.update_recurrent_state(last_recurrent_state, self.layer_idx)
 
         z_shape_og = z.shape
         # reshape input data into 2D tensor
diff --git a/src/transformers/models/qwen3_next/modular_qwen3_next.py b/src/transformers/models/qwen3_next/modular_qwen3_next.py
index 80fa61c3229b..417a9a59cf8b 100644
--- a/src/transformers/models/qwen3_next/modular_qwen3_next.py
+++ b/src/transformers/models/qwen3_next/modular_qwen3_next.py
@@ -446,7 +446,7 @@ def forward(
         # getting projected states from cache if it exists
         if use_precomputed_states:
             conv_state = cache_params.layers[self.layer_idx].conv_states
-            recurrent_state = cache_params.layers[self.layer_idx].ssm_states
+            recurrent_state = cache_params.layers[self.layer_idx].recurrent_states
 
         projected_states_qkvz = self.in_proj_qkvz(hidden_states)
         projected_states_ba = self.in_proj_ba(hidden_states)
@@ -527,7 +527,7 @@ def forward(
 
         # Update cache
         if cache_params is not None:
-            cache_params.update_ssm_state(last_recurrent_state, self.layer_idx)
+            cache_params.update_recurrent_state(last_recurrent_state, self.layer_idx)
 
         z_shape_og = z.shape
         # reshape input data into 2D tensor
diff --git a/src/transformers/models/zamba/modeling_zamba.py b/src/transformers/models/zamba/modeling_zamba.py
index 405434004ca9..3e891f9a3baf 100644
--- a/src/transformers/models/zamba/modeling_zamba.py
+++ b/src/transformers/models/zamba/modeling_zamba.py
@@ -322,7 +322,7 @@ def cuda_kernels_forward(
         if use_precomputed_states:
             for n in range(self.n_mamba_heads):
                 scan_outputs_ = selective_state_update(
-                    cache_params.layers[self.layer_idx].ssm_states[:, n],
+                    cache_params.layers[self.layer_idx].recurrent_states[:, n],
                     hidden_states[n, ..., 0],
                     discrete_time_step[n, ..., 0],
                     A[n],
@@ -357,7 +357,7 @@ def cuda_kernels_forward(
                 scan_outputs = torch.cat((scan_outputs, scan_outputs_), dim=1).contiguous()
                 ssm_state = torch.cat((ssm_state, ssm_state_.unsqueeze(1)), dim=1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
@@ -376,7 +376,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
 
         if cache_params is not None and cache_params.has_previous_state(self.layer_idx):
             # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
+            ssm_state = cache_params.layers[self.layer_idx].recurrent_states.clone()
         else:
             ssm_state = torch.zeros(
                 (batch_size, self.n_mamba_heads, self.mamba_head_dim, self.ssm_state_size),
@@ -437,7 +437,7 @@ def slow_forward(self, input_states, cache_params: Cache | None = None, attentio
         scan_output = scan_output * self.act(gate)
 
         if cache_params is not None:
-            cache_params.update_ssm_state(ssm_state, self.layer_idx)
+            cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         # 4. Final linear projection
         contextualized_states = self.out_proj(
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index 4de0979a71c0..ea5054194de6 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -543,7 +543,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -644,7 +644,7 @@ def cuda_kernels_forward(
                     **dt_limit_kwargs,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    cache_params.update_recurrent_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 scan_output = self.norm(scan_output, gate)
@@ -723,9 +723,9 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state * dA + dBx
-            ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+            recurrent_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
+            recurrent_states = recurrent_states * dA + dBx
+            recurrent_states = cache_params.update_recurrent_state(recurrent_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -734,7 +734,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = ssm_state.to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = recurrent_states.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -823,7 +823,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
                 y = y[:, :seq_len, :, :]
             y = y.reshape(batch_size, seq_len, -1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index 2f858fc2eeb8..f39b6de31ff6 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -331,7 +331,7 @@ def cuda_kernels_forward(
             C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
             hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
             hidden_states = selective_state_update(
-                cache_params.layers[self.layer_idx].ssm_states,
+                cache_params.layers[self.layer_idx].recurrent_states,
                 hidden_states_reshaped,
                 dt,
                 A,
@@ -432,7 +432,7 @@ def cuda_kernels_forward(
                     **dt_limit_kwargs,
                 )
                 if ssm_state is not None and cache_params is not None:
-                    cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                    cache_params.update_recurrent_state(ssm_state, self.layer_idx)
                 scan_output = scan_output.view(batch_size, seq_len, -1)
                 # Multiply "gate" branch and apply extra normalization layer
                 scan_output = self.norm(scan_output, gate)
@@ -511,9 +511,9 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            ssm_state = cache_params.layers[self.layer_idx].ssm_states.clone()
-            ssm_state = ssm_state * dA + dBx
-            ssm_state = cache_params.update_ssm_state(ssm_state, self.layer_idx)
+            recurrent_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
+            recurrent_states = recurrent_states * dA + dBx
+            recurrent_states = cache_params.update_recurrent_state(recurrent_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -522,7 +522,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = ssm_state.to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = recurrent_states.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
@@ -611,7 +611,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
                 y = y[:, :seq_len, :, :]
             y = y.reshape(batch_size, seq_len, -1)
             if ssm_state is not None and cache_params is not None:
-                cache_params.update_ssm_state(ssm_state, self.layer_idx)
+                cache_params.update_recurrent_state(ssm_state, self.layer_idx)
 
         scan_output = self.norm(y, gate)
 
diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index e91b5dc3aeb3..c0d24abda5c7 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -81,8 +81,8 @@
         Cache,
         DynamicCache,
         EncoderDecoderCache,
-        MambaAndAttentionLayer,
-        MambaLayer,
+        LinearAttentionAndAttentionLayer,
+        LinearAttentionLayer,
         QuantoQuantizedLayer,
         StaticCache,
     )
@@ -2591,7 +2591,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         # Check each layer has the correct shape
         for layer in past_key_values.layers:
             # Mamba + Attention layer cache
-            if type(layer) is MambaAndAttentionLayer:
+            if type(layer) is LinearAttentionAndAttentionLayer:
                 # Remove the seq_length dim for cross-attention cache (it changes based on the model)
                 keys = layer.keys if seq_length is not None else layer.keys[:, :, 0, :]
                 values = layer.values if seq_length is not None else layer.values[:, :, 0, :]
@@ -2599,14 +2599,14 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(values.shape, attention_shape)
                 self.assertEqual(layer.conv_states.shape, conv_shape)
                 # May not be used (e.g. lfm2)
-                if layer.is_ssm_states_initialized:
-                    self.assertEqual(layer.ssm_states.shape, ssm_shape)
+                if layer.is_recurrent_states_initialized:
+                    self.assertEqual(layer.recurrent_states.shape, ssm_shape)
             # Mamba only layer cache
-            elif type(layer) is MambaLayer:
+            elif type(layer) is LinearAttentionLayer:
                 self.assertEqual(layer.conv_states.shape, conv_shape)
                 # May not be used (e.g. lfm2)
-                if layer.is_ssm_states_initialized:
-                    self.assertEqual(layer.ssm_states.shape, ssm_shape)
+                if layer.is_recurrent_states_initialized:
+                    self.assertEqual(layer.recurrent_states.shape, ssm_shape)
             # Attention only layer type
             else:
                 # Remove the seq_length dim for cross-attention cache (it changes based on the model)
@@ -2656,19 +2656,23 @@ def _check_caches_are_equal(self, cache1: Cache, cache2: Cache):
             self.assertEqual(type(cache1.layers[idx]), type(cache2.layers[idx]))
 
             # Mamba + Attention layer
-            if type(cache1.layers[idx]) is MambaAndAttentionLayer:
+            if type(cache1.layers[idx]) is LinearAttentionAndAttentionLayer:
                 torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
                 torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
                 # May not be used (e.g. lfm2)
-                if cache1.layers[idx].is_ssm_states_initialized:
-                    torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+                if cache1.layers[idx].is_recurrent_states_initialized:
+                    torch.testing.assert_close(
+                        cache1.layers[idx].recurrent_states, cache2.layers[idx].recurrent_states
+                    )
             # Mamba layer
-            elif type(cache1.layers[idx]) is MambaLayer:
+            elif type(cache1.layers[idx]) is LinearAttentionLayer:
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)
                 # May not be used (e.g. lfm2)
-                if cache1.layers[idx].is_ssm_states_initialized:
-                    torch.testing.assert_close(cache1.layers[idx].ssm_states, cache2.layers[idx].ssm_states)
+                if cache1.layers[idx].is_recurrent_states_initialized:
+                    torch.testing.assert_close(
+                        cache1.layers[idx].recurrent_states, cache2.layers[idx].recurrent_states
+                    )
             # Attention layer
             else:
                 torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
diff --git a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
index 3c674b1302eb..b14bc4227efe 100644
--- a/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
+++ b/tests/models/falcon_mamba/test_modeling_falcon_mamba.py
@@ -337,7 +337,9 @@ def recursive_check(tuple_object, dict_object):
                     if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
                         for idx in range(len(tuple_object)):
                             recursive_check(tuple_object.layers[idx].conv_states, dict_object.layers[idx].conv_states)
-                            recursive_check(tuple_object.layers[idx].ssm_states, dict_object.layers[idx].ssm_states)
+                            recursive_check(
+                                tuple_object.layers[idx].recurrent_states, dict_object.layers[idx].recurrent_states
+                            )
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)
diff --git a/tests/models/mamba/test_modeling_mamba.py b/tests/models/mamba/test_modeling_mamba.py
index 78ec5789085d..276c03d65099 100644
--- a/tests/models/mamba/test_modeling_mamba.py
+++ b/tests/models/mamba/test_modeling_mamba.py
@@ -304,7 +304,9 @@ def recursive_check(tuple_object, dict_object):
                     if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
                         for idx in range(len(tuple_object)):
                             recursive_check(tuple_object.layers[idx].conv_states, dict_object.layers[idx].conv_states)
-                            recursive_check(tuple_object.layers[idx].ssm_states, dict_object.layers[idx].ssm_states)
+                            recursive_check(
+                                tuple_object.layers[idx].recurrent_states, dict_object.layers[idx].recurrent_states
+                            )
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)
diff --git a/tests/models/mamba2/test_modeling_mamba2.py b/tests/models/mamba2/test_modeling_mamba2.py
index 6c0b3b865dd9..f64047907acc 100644
--- a/tests/models/mamba2/test_modeling_mamba2.py
+++ b/tests/models/mamba2/test_modeling_mamba2.py
@@ -286,7 +286,9 @@ def recursive_check(tuple_object, dict_object):
                     if isinstance(tuple_object, DynamicCache):  # MODIFIED PART START
                         for idx in range(len(tuple_object)):
                             recursive_check(tuple_object.layers[idx].conv_states, dict_object.layers[idx].conv_states)
-                            recursive_check(tuple_object.layers[idx].ssm_states, dict_object.layers[idx].ssm_states)
+                            recursive_check(
+                                tuple_object.layers[idx].recurrent_states, dict_object.layers[idx].recurrent_states
+                            )
                     elif isinstance(tuple_object, (list, tuple)):  # MODIFIED PART END
                         for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                             recursive_check(tuple_iterable_value, dict_iterable_value)
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index 1ca4225cb7a9..2c45f4c5485c 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -386,7 +386,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             # Moe layers have a default mamba cache instantiated, but it stays empty as the layer does not use it
             if layer_type == "moe":
                 self.assertEqual(layer.conv_states, None)
-                self.assertEqual(layer.ssm_states, None)
+                self.assertEqual(layer.recurrent_states, None)
             # Attention layer cache
             elif layer_type == "attention":
                 self.assertEqual(layer.keys.shape, attention_shape)
@@ -394,7 +394,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             # Mamba layer cache
             elif layer_type == "mamba":
                 self.assertEqual(layer.conv_states.shape, conv_shape)
-                self.assertEqual(layer.ssm_states.shape, ssm_shape)
+                self.assertEqual(layer.recurrent_states.shape, ssm_shape)
             else:
                 raise ValueError("Unknown layer type.")
 
diff --git a/utils/mlinter/.mlinter_cache.json b/utils/mlinter/.mlinter_cache.json
new file mode 100644
index 000000000000..39154b73c1d7
--- /dev/null
+++ b/utils/mlinter/.mlinter_cache.json
@@ -0,0 +1,1073 @@
+{
+  "src/transformers/models/afmoe/configuration_afmoe.py": "cb18784c5d578c0352c0f2958afb473b5aadd3ef4ecdc5216150204ab553493d",
+  "src/transformers/models/afmoe/modeling_afmoe.py": "a5681d5e3ed6e4c25c8cc5d207a40b1757c7f21e342c982380ec1119ac44861c",
+  "src/transformers/models/afmoe/modular_afmoe.py": "ae6dfcbc3fcf34c2fd3b217128d1e33db0d70db9045a19fb6da66e40f1f50a2c",
+  "src/transformers/models/aimv2/configuration_aimv2.py": "89696ddda44298d16a3c5c5e40741588d5d66a0679339479099a5ff42f6a23f1",
+  "src/transformers/models/aimv2/modeling_aimv2.py": "cb9287ca4946a51b16c99eb49bb4eafbad9df2a81fab1aa61d3beb7cab8c0a80",
+  "src/transformers/models/aimv2/modular_aimv2.py": "97d66d6d0756d07e7e0d1d730b4c89da394412d4507d92ede09fd7f5b11623f3",
+  "src/transformers/models/albert/configuration_albert.py": "e3c4d4e4c87111b669cc285cadb206d58731e0c88d166a1113a0f096f4a8909f",
+  "src/transformers/models/albert/modeling_albert.py": "fc64e43d93f5bbbe8734c663ef6740a559799cabf441e436cb2901cac9b27a38",
+  "src/transformers/models/align/configuration_align.py": "c4dc28a3ba2be74752f6ffecbb75c425332f2670a9de6d13584f6779a5ee1058",
+  "src/transformers/models/align/modeling_align.py": "73ddf7860acecd9ba61dc269ab54bb5e949c227016eefc3a66d823dcab8ef94f",
+  "src/transformers/models/altclip/configuration_altclip.py": "9487f951824ef5c5f60eb001604ead445b48968d8738ba674b4823cf5f7e298f",
+  "src/transformers/models/altclip/modeling_altclip.py": "b2d926d1f63e86913b12cdeaf3cbee1b28c95b6c80765a30a63ac910f9dd02b9",
+  "src/transformers/models/apertus/configuration_apertus.py": "ae34c92ef6630fdf3b4875f5b4b6fc08fb67a6b11533515411ae37b7a4dc4ed5",
+  "src/transformers/models/apertus/modeling_apertus.py": "f6e3cb98e5dfd454dafe50c36b52734e554700dce7cd0dced4ded6791c586b26",
+  "src/transformers/models/apertus/modular_apertus.py": "7c0c5f12ce3e6bbe163501d0a476eeb52c88255de081e5a03cebcfb42f7a5e67",
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+  "src/transformers/models/arcee/modeling_arcee.py": "1fdfa8f1d32d2f2193d3146ed6baceafb3122e72f6888c4b471f9cd3be44a087",
+  "src/transformers/models/arcee/modular_arcee.py": "bb3829636df7ae7428960147106cbbc23f73503f57919371c8d79ca75c7fa45d",
+  "src/transformers/models/aria/configuration_aria.py": "8cabf127bb9ead8ae62278279aebac74aaecf8eb78a28be1e19af8e984aec4e6",
+  "src/transformers/models/aria/modeling_aria.py": "9828aae955a915bfc59b9714d6ae9c560f0791131d1a15d95218564fadffb66b",
+  "src/transformers/models/aria/modular_aria.py": "11784c387433a2ffa27e5eefadc8eda4c3bb75191a3077fc4d63a01421d80203",
+  "src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py": "fd56d62a882a8c4baaa6e07b437635c24360a1e6f782125480baa6fbec35f18f",
+  "src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py": "0c744ebdb41884f72e9fd983da1f5b4a62ead979ac0d3f0010edeb968056401a",
+  "src/transformers/models/audioflamingo3/configuration_audioflamingo3.py": "23203bcec4b5df4af65faf6dc95d2ea6e9a3c36f38bdb4ac200a5b311674a6e2",
+  "src/transformers/models/audioflamingo3/modeling_audioflamingo3.py": "14b9c7e3a43c55ea507b41e815b3acb6d02fb0ff917b55a616e5451b70dc42d0",
+  "src/transformers/models/audioflamingo3/modular_audioflamingo3.py": "ba453855d1764eada7316808882e1bcca9a54505d58d97fbe701730d84485eba",
+  "src/transformers/models/auto/configuration_auto.py": "9bc2674a59eac35c771a46cc4059aaeb37e8e6602f17d36f8b10c3d18babc502",
+  "src/transformers/models/auto/modeling_auto.py": "b01d5c96f8d7125b2f70b630032dab0ce24a42f45e4f46b963ba34492bf5ef16",
+  "src/transformers/models/autoformer/configuration_autoformer.py": "243ec867b1578384d0860237d85a86451dce375b6b013f209a06c74411bfc810",
+  "src/transformers/models/autoformer/modeling_autoformer.py": "4d2f8c8cedd2a220a9d48dfcbe8dff1b8605390ced03a860293e3b81a3524d7e",
+  "src/transformers/models/aya_vision/configuration_aya_vision.py": "e8039bc9df5ac44693f533fd50b53b0b30dd3cd3f17f66a9df156b139e1f1b1f",
+  "src/transformers/models/aya_vision/modeling_aya_vision.py": "bfebfb56427cf3428c25fd5c2869cd3d5f95061220f67abca10fd102b9330f5d",
+  "src/transformers/models/aya_vision/modular_aya_vision.py": "68f24cffef72590c7f59074803d806942e2e290bade6737387c1e36faddbc9bb",
+  "src/transformers/models/bamba/configuration_bamba.py": "cf1b9f81f03a825255dc9546022356a6eb3b1e6f4cd664c3278759fe7d3f3f08",
+  "src/transformers/models/bamba/modeling_bamba.py": "420ea53746e558276e350de794ff5b234a79e1ec9170eba5fafa1f3f2a82b9c9",
+  "src/transformers/models/bamba/modular_bamba.py": "23adb0e8edbc56ba4bd0bea5ad53904d5963a7e36f42d6fcbc66d17691cac65f",
+  "src/transformers/models/bark/configuration_bark.py": "237c42af22103f191e551a5a1192a9bf4794e2b7861189a58cf6a2bbb75daa7b",
+  "src/transformers/models/bark/modeling_bark.py": "3bbfc82ff3f700a4b5deb860a423bf644ea26e7c3bc469407597351dda4533e0",
+  "src/transformers/models/bart/configuration_bart.py": "5f56eecabf2ff9bbf8f53c5df4b7368287e4626ea526bc1e7dc1776fc6922e5c",
+  "src/transformers/models/bart/modeling_bart.py": "8e8d6b713dc94cbced6cc05c54b0fc9217dcede141e79891def2b4c65cf205b6",
+  "src/transformers/models/beit/configuration_beit.py": "cc1c33ab0e97b5a6b1c7274d96b185d0484b7d18989720f18294dc656dac67f2",
+  "src/transformers/models/beit/modeling_beit.py": "e5e3be58febade51052456042defdb04f8bac95bc00ccaa7c299c2f847947a71",
+  "src/transformers/models/bert/configuration_bert.py": "924e13540f603e40b2b4ed51d7139eb95ed646a851d7cbb2ee6186da7c9829b9",
+  "src/transformers/models/bert/modeling_bert.py": "bc1c375d781fdbeb424e4592ea82be8ec25c4841a057f5f85e08f20f50d2068a",
+  "src/transformers/models/bert_generation/configuration_bert_generation.py": "ee10f065b884880ba1f6c9d08a966b2ed26812ee7602a7f565e7d87e683c1c6a",
+  "src/transformers/models/bert_generation/modeling_bert_generation.py": "5a0cfaf598a7be00806d9e3aa2a59c31aa7d4c671652e2e15cad600b14fe5080",
+  "src/transformers/models/big_bird/configuration_big_bird.py": "c328fdd36de02bef93bc9c02c34aac7d61e23ce32cd23932c894dc1b8ce5d35a",
+  "src/transformers/models/big_bird/modeling_big_bird.py": "1700b3b52a5ae85e5321367be5edfa1e3a4cc8e0709fc5c19b88ff4eb9be5523",
+  "src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py": "dc782ebd96e20824279e1c60c9dccdabfb4569da461a1054e6f46c1c15e5e8dd",
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+  "src/transformers/models/bit/configuration_bit.py": "db00bd41886ba99305fde95b0b3ebad06ebfb143438441bbdf496697db50a78f",
+  "src/transformers/models/bit/modeling_bit.py": "44724c5dc21875fb438691a8b86039d60a34c903c6b0c92d88fc543bd1a57f8c",
+  "src/transformers/models/bitnet/configuration_bitnet.py": "7d79cd088e7cf06e5f27084c2aa98971ae146dc51623fd22611c3b29e23fb684",
+  "src/transformers/models/bitnet/modeling_bitnet.py": "1f589083a3b6d7836763de0b641b2555eafddb20c89aa7c985d7185aa30a7ee2",
+  "src/transformers/models/bitnet/modular_bitnet.py": "1bde132b6ae0857152632e990006a8872eebbadd76ac975c53194fe051896df1",
+  "src/transformers/models/blenderbot/configuration_blenderbot.py": "170ced3b7b82617975dfc05f34bb64d3c82dbcf1ef6a78b76794d554e7466f4e",
+  "src/transformers/models/blenderbot/modeling_blenderbot.py": "14505b502d4ec2d94bf15934752ba5b896caf478ba775a66f2e6d00ee51189ab",
+  "src/transformers/models/blenderbot_small/configuration_blenderbot_small.py": "03c1529efa869494c1293229409c9903184af564d857465d6c50eb8196433653",
+  "src/transformers/models/blenderbot_small/modeling_blenderbot_small.py": "64f35af1e75ac11f88bb294ec370ccde54782b1594bd96597870b9d7d2555508",
+  "src/transformers/models/blip/configuration_blip.py": "ac921b6675d31bae5a879571d49ac1fc60ebb5da0536fe636b7718d2f72304f3",
+  "src/transformers/models/blip/modeling_blip.py": "015f1b98b382a666db8c31711c6f295acd3c89254292401ff937f8c877f660b7",
+  "src/transformers/models/blip/modeling_blip_text.py": "eb0378f5c76c4a9ba5c1ea5ad3ad5287bf40e492afdce059bd84ddd4c1856087",
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+  "src/transformers/models/bloom/configuration_bloom.py": "47d054ceb7654b1328441e6f59d1afdffbd8f80685f708e5d1d30c5e53974324",
+  "src/transformers/models/bloom/modeling_bloom.py": "798a7dd43aa1e9584219255c36740873ea4d1f204c002ddb8ec7296bcf70128e",
+  "src/transformers/models/blt/configuration_blt.py": "9cfb74b1e0eb9ce9611a19942b6e82c32573470ecb8195c043e312e7b23abbce",
+  "src/transformers/models/blt/modeling_blt.py": "81e4bf6e0737688e8aa552d802c56362af7a171ab3464c546f5e73adc8505d9c",
+  "src/transformers/models/blt/modular_blt.py": "529435735e3413bcfc5c3a8096b56e58d57aa176a6628989c5d64039f8c2d0bb",
+  "src/transformers/models/bridgetower/configuration_bridgetower.py": "dabf6a2d4fdd1d38ae9f7b32c620ec7da52730e9533e668861513d7adfc7f7d7",
+  "src/transformers/models/bridgetower/modeling_bridgetower.py": "7e99550d2f015f6ce304bc73a461211080adfa52433298efb821ac81a75db03a",
+  "src/transformers/models/bros/configuration_bros.py": "af1f360c52851af9e08aa6767c39274d99f224e4b559fda04599461a9d6f50df",
+  "src/transformers/models/bros/modeling_bros.py": "0efca639237d5be48c6d28148d268c1728a5062f4422493fea5660e72c4a0cbf",
+  "src/transformers/models/camembert/configuration_camembert.py": "65bbc35964cbb42e7d06ea05128302237b80c16f8fb38b794e48299831bb0d31",
+  "src/transformers/models/camembert/modeling_camembert.py": "463f946576635f1db3013d5b5c77a98d99aea36c31b4e472f6a8efbbc47b24d3",
+  "src/transformers/models/camembert/modular_camembert.py": "12b8cfeeac5270dd43827aad7f3f3f61d67d781fd458b7d033f4daad44a386f6",
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+  "src/transformers/models/xlm_roberta/modular_xlm_roberta.py": "8c8469c3867eb85c2474eec51406d32187b1f8169c6517d77b79cdd8ac41dde0",
+  "src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py": "fff0d189298232584d020e3b0b12184855bd49c3c2821c7385d29ae564be0664",
+  "src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py": "d9e4579799bfce9a55ea16ca5915033a1e463a6e1f2aa37d6fb852b780e1b7c3",
+  "src/transformers/models/xlm_roberta_xl/modular_xlm_roberta_xl.py": "0db7040a77c0db9c71adb37374a6fe11655698bdd511c744327a5f90584b4f66",
+  "src/transformers/models/xlnet/configuration_xlnet.py": "b23ca312b54597df3c9515bd14e2bb8b0bccf7d71722c2030900eeb05f06ac2c",
+  "src/transformers/models/xlnet/modeling_xlnet.py": "26ef15cc9e19c5f1ad78e7beac1d02620659b4b7811ee07944b082cea346d827",
+  "src/transformers/models/xlstm/configuration_xlstm.py": "2fa1e1de35a743aaf4faa4399be2f6669990ab5370abf6a5670278ddacbd86ad",
+  "src/transformers/models/xlstm/modeling_xlstm.py": "658246eb2c90671f0912bc2bae93385a32f8a74b87c21ec334e34a1d6ccbff22",
+  "src/transformers/models/xmod/configuration_xmod.py": "fc8326fb791bf9165dad3af262e172b8c5e1985f4eb96ec6369a47de27ff3ebd",
+  "src/transformers/models/xmod/modeling_xmod.py": "019b42bd4b6373dddf2b15a16b9c20b3f0915dae92a7633bb5ef9f2ee1bb5ef9",
+  "src/transformers/models/yolos/configuration_yolos.py": "8970bd7e2ef458e6063b5fccf136c237b063465b4f7fd7be41c0f0b4e1fc1aa4",
+  "src/transformers/models/yolos/modeling_yolos.py": "32933319a7ef76a7d2d026abb2def66f2576349759836b23ebbe95682024c56f",
+  "src/transformers/models/yolos/modular_yolos.py": "67b24ac8dd457ab763f88b08a368c43fe32fb1487989dac63cd60c56e98badd1",
+  "src/transformers/models/yoso/configuration_yoso.py": "18fcac752ecd69aac9cda2ceadb444204a1b8b2536bb25030d2b34a34571279a",
+  "src/transformers/models/yoso/modeling_yoso.py": "ee3856557776db9294841314d24f39db87253677984766fcad6c78139a3db423",
+  "src/transformers/models/youtu/configuration_youtu.py": "72981b4a97b2f39069e4d3a74a008ab8acfc600c898c494662ee2f0c7683315d",
+  "src/transformers/models/youtu/modeling_youtu.py": "fbc8374c50df05da935a1b040aaf0a90e93689804c7b4e35e8d6b8c0813f66ae",
+  "src/transformers/models/youtu/modular_youtu.py": "ddbfc95d009cf94a6f3487c0d064c7a3aa2ef6c6015898c51988b5dab9c452c8",
+  "src/transformers/models/zamba/configuration_zamba.py": "23566c8ef76743f6be134cd1d5853d323c87677161ba2b477a57722e0215f376",
+  "src/transformers/models/zamba/modeling_zamba.py": "205a0b6cc541fe9d05e811226cfa8e0649cdb6fa634cfa888dd79f6b4f5263a6",
+  "src/transformers/models/zamba2/configuration_zamba2.py": "c98074770b92db6d888aaf530279b2b4f156357a6d4365cfce7298e88452ab12",
+  "src/transformers/models/zamba2/modeling_zamba2.py": "c4dbed4799ed9a2374a7eb6c55ebda6c7710310ed3b6d8508469a32c7babb999",
+  "src/transformers/models/zamba2/modular_zamba2.py": "546cc01d3910e159e566a41a8d86ed1c955eac41391871cf3e3cb2aef7ef24be",
+  "src/transformers/models/zoedepth/configuration_zoedepth.py": "1a2474f62a1d0e91bb183c1c2845dbbd4af92e5aa5ac2e7d1db96499c5b39907",
+  "src/transformers/models/zoedepth/modeling_zoedepth.py": "97e08f50416ab8ac3b6c52e6b3471aa3573b8619e85765acc6d0001b1bb8f657"
+}

From 908f0da1dbea78b0c02e03e5d25fb3318c09c5cd Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 31 Mar 2026 11:06:14 +0200
Subject: [PATCH 51/56] revert unrelated what are they doing here

---
 .../models/align/configuration_align.py       |    1 -
 .../models/nemotron_h/modeling_nemotron_h.py  |    8 +-
 .../models/zamba2/modeling_zamba2.py          |    8 +-
 .../models/zamba2/modular_zamba2.py           |    8 +-
 utils/mlinter/.mlinter_cache.json             | 1073 -----------------
 5 files changed, 12 insertions(+), 1086 deletions(-)
 delete mode 100644 utils/mlinter/.mlinter_cache.json

diff --git a/src/transformers/models/align/configuration_align.py b/src/transformers/models/align/configuration_align.py
index babf97d4572a..cde6445cf62f 100644
--- a/src/transformers/models/align/configuration_align.py
+++ b/src/transformers/models/align/configuration_align.py
@@ -59,7 +59,6 @@ class AlignTextConfig(PreTrainedConfig):
     pad_token_id: int | None = 0
     bos_token_id: int | None = None
     eos_token_id: int | list[int] | None = None
-    tie_word_embeddings: True
 
 
 @auto_docstring(checkpoint="kakaobrain/align-base")
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index af09fdfaf36f..9e264e5cfdcc 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -435,9 +435,9 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            recurrent_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
-            recurrent_states = recurrent_states * dA + dBx
-            recurrent_states = cache_params.update_recurrent_state(recurrent_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
+            ssm_states = ssm_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -446,7 +446,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = recurrent_states.to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index ea5054194de6..6e4ea7dcf2d8 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -723,9 +723,9 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            recurrent_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
-            recurrent_states = recurrent_states * dA + dBx
-            recurrent_states = cache_params.update_recurrent_state(recurrent_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
+            ssm_states = ssm_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -734,7 +734,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = recurrent_states.to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
diff --git a/src/transformers/models/zamba2/modular_zamba2.py b/src/transformers/models/zamba2/modular_zamba2.py
index f39b6de31ff6..d7716301ad4a 100644
--- a/src/transformers/models/zamba2/modular_zamba2.py
+++ b/src/transformers/models/zamba2/modular_zamba2.py
@@ -511,9 +511,9 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             dBx = dB * hidden_states[..., None]
 
             # State calculation
-            recurrent_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
-            recurrent_states = recurrent_states * dA + dBx
-            recurrent_states = cache_params.update_recurrent_state(recurrent_states, self.layer_idx)
+            ssm_states = cache_params.layers[self.layer_idx].recurrent_states.clone()
+            ssm_states = ssm_states * dA + dBx
+            ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx)
 
             # Subsequent output
             # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
@@ -522,7 +522,7 @@ def torch_forward(self, input_states, cache_params: Cache | None=None, attention
             C = C.reshape(batch_size, -1, C.shape[-1])
             # [bsz, num_heads, head_dim]
 
-            ssm_states = recurrent_states.to(C.dtype)  # Shape: [b, h, d, n]
+            ssm_states = ssm_states.to(C.dtype)  # Shape: [b, h, d, n]
             # Reshape ssm_states to merge the first two dimensions
             ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
             C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
diff --git a/utils/mlinter/.mlinter_cache.json b/utils/mlinter/.mlinter_cache.json
deleted file mode 100644
index 39154b73c1d7..000000000000
--- a/utils/mlinter/.mlinter_cache.json
+++ /dev/null
@@ -1,1073 +0,0 @@
-{
-  "src/transformers/models/afmoe/configuration_afmoe.py": "cb18784c5d578c0352c0f2958afb473b5aadd3ef4ecdc5216150204ab553493d",
-  "src/transformers/models/afmoe/modeling_afmoe.py": "a5681d5e3ed6e4c25c8cc5d207a40b1757c7f21e342c982380ec1119ac44861c",
-  "src/transformers/models/afmoe/modular_afmoe.py": "ae6dfcbc3fcf34c2fd3b217128d1e33db0d70db9045a19fb6da66e40f1f50a2c",
-  "src/transformers/models/aimv2/configuration_aimv2.py": "89696ddda44298d16a3c5c5e40741588d5d66a0679339479099a5ff42f6a23f1",
-  "src/transformers/models/aimv2/modeling_aimv2.py": "cb9287ca4946a51b16c99eb49bb4eafbad9df2a81fab1aa61d3beb7cab8c0a80",
-  "src/transformers/models/aimv2/modular_aimv2.py": "97d66d6d0756d07e7e0d1d730b4c89da394412d4507d92ede09fd7f5b11623f3",
-  "src/transformers/models/albert/configuration_albert.py": "e3c4d4e4c87111b669cc285cadb206d58731e0c88d166a1113a0f096f4a8909f",
-  "src/transformers/models/albert/modeling_albert.py": "fc64e43d93f5bbbe8734c663ef6740a559799cabf441e436cb2901cac9b27a38",
-  "src/transformers/models/align/configuration_align.py": "c4dc28a3ba2be74752f6ffecbb75c425332f2670a9de6d13584f6779a5ee1058",
-  "src/transformers/models/align/modeling_align.py": "73ddf7860acecd9ba61dc269ab54bb5e949c227016eefc3a66d823dcab8ef94f",
-  "src/transformers/models/altclip/configuration_altclip.py": "9487f951824ef5c5f60eb001604ead445b48968d8738ba674b4823cf5f7e298f",
-  "src/transformers/models/altclip/modeling_altclip.py": "b2d926d1f63e86913b12cdeaf3cbee1b28c95b6c80765a30a63ac910f9dd02b9",
-  "src/transformers/models/apertus/configuration_apertus.py": "ae34c92ef6630fdf3b4875f5b4b6fc08fb67a6b11533515411ae37b7a4dc4ed5",
-  "src/transformers/models/apertus/modeling_apertus.py": "f6e3cb98e5dfd454dafe50c36b52734e554700dce7cd0dced4ded6791c586b26",
-  "src/transformers/models/apertus/modular_apertus.py": "7c0c5f12ce3e6bbe163501d0a476eeb52c88255de081e5a03cebcfb42f7a5e67",
-  "src/transformers/models/arcee/configuration_arcee.py": "c1bdb413f20fda66604002b0c1d2fd06cb67c1e6bc3090b0282e34a77a52e387",
-  "src/transformers/models/arcee/modeling_arcee.py": "1fdfa8f1d32d2f2193d3146ed6baceafb3122e72f6888c4b471f9cd3be44a087",
-  "src/transformers/models/arcee/modular_arcee.py": "bb3829636df7ae7428960147106cbbc23f73503f57919371c8d79ca75c7fa45d",
-  "src/transformers/models/aria/configuration_aria.py": "8cabf127bb9ead8ae62278279aebac74aaecf8eb78a28be1e19af8e984aec4e6",
-  "src/transformers/models/aria/modeling_aria.py": "9828aae955a915bfc59b9714d6ae9c560f0791131d1a15d95218564fadffb66b",
-  "src/transformers/models/aria/modular_aria.py": "11784c387433a2ffa27e5eefadc8eda4c3bb75191a3077fc4d63a01421d80203",
-  "src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py": "fd56d62a882a8c4baaa6e07b437635c24360a1e6f782125480baa6fbec35f18f",
-  "src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py": "0c744ebdb41884f72e9fd983da1f5b4a62ead979ac0d3f0010edeb968056401a",
-  "src/transformers/models/audioflamingo3/configuration_audioflamingo3.py": "23203bcec4b5df4af65faf6dc95d2ea6e9a3c36f38bdb4ac200a5b311674a6e2",
-  "src/transformers/models/audioflamingo3/modeling_audioflamingo3.py": "14b9c7e3a43c55ea507b41e815b3acb6d02fb0ff917b55a616e5451b70dc42d0",
-  "src/transformers/models/audioflamingo3/modular_audioflamingo3.py": "ba453855d1764eada7316808882e1bcca9a54505d58d97fbe701730d84485eba",
-  "src/transformers/models/auto/configuration_auto.py": "9bc2674a59eac35c771a46cc4059aaeb37e8e6602f17d36f8b10c3d18babc502",
-  "src/transformers/models/auto/modeling_auto.py": "b01d5c96f8d7125b2f70b630032dab0ce24a42f45e4f46b963ba34492bf5ef16",
-  "src/transformers/models/autoformer/configuration_autoformer.py": "243ec867b1578384d0860237d85a86451dce375b6b013f209a06c74411bfc810",
-  "src/transformers/models/autoformer/modeling_autoformer.py": "4d2f8c8cedd2a220a9d48dfcbe8dff1b8605390ced03a860293e3b81a3524d7e",
-  "src/transformers/models/aya_vision/configuration_aya_vision.py": "e8039bc9df5ac44693f533fd50b53b0b30dd3cd3f17f66a9df156b139e1f1b1f",
-  "src/transformers/models/aya_vision/modeling_aya_vision.py": "bfebfb56427cf3428c25fd5c2869cd3d5f95061220f67abca10fd102b9330f5d",
-  "src/transformers/models/aya_vision/modular_aya_vision.py": "68f24cffef72590c7f59074803d806942e2e290bade6737387c1e36faddbc9bb",
-  "src/transformers/models/bamba/configuration_bamba.py": "cf1b9f81f03a825255dc9546022356a6eb3b1e6f4cd664c3278759fe7d3f3f08",
-  "src/transformers/models/bamba/modeling_bamba.py": "420ea53746e558276e350de794ff5b234a79e1ec9170eba5fafa1f3f2a82b9c9",
-  "src/transformers/models/bamba/modular_bamba.py": "23adb0e8edbc56ba4bd0bea5ad53904d5963a7e36f42d6fcbc66d17691cac65f",
-  "src/transformers/models/bark/configuration_bark.py": "237c42af22103f191e551a5a1192a9bf4794e2b7861189a58cf6a2bbb75daa7b",
-  "src/transformers/models/bark/modeling_bark.py": "3bbfc82ff3f700a4b5deb860a423bf644ea26e7c3bc469407597351dda4533e0",
-  "src/transformers/models/bart/configuration_bart.py": "5f56eecabf2ff9bbf8f53c5df4b7368287e4626ea526bc1e7dc1776fc6922e5c",
-  "src/transformers/models/bart/modeling_bart.py": "8e8d6b713dc94cbced6cc05c54b0fc9217dcede141e79891def2b4c65cf205b6",
-  "src/transformers/models/beit/configuration_beit.py": "cc1c33ab0e97b5a6b1c7274d96b185d0484b7d18989720f18294dc656dac67f2",
-  "src/transformers/models/beit/modeling_beit.py": "e5e3be58febade51052456042defdb04f8bac95bc00ccaa7c299c2f847947a71",
-  "src/transformers/models/bert/configuration_bert.py": "924e13540f603e40b2b4ed51d7139eb95ed646a851d7cbb2ee6186da7c9829b9",
-  "src/transformers/models/bert/modeling_bert.py": "bc1c375d781fdbeb424e4592ea82be8ec25c4841a057f5f85e08f20f50d2068a",
-  "src/transformers/models/bert_generation/configuration_bert_generation.py": "ee10f065b884880ba1f6c9d08a966b2ed26812ee7602a7f565e7d87e683c1c6a",
-  "src/transformers/models/bert_generation/modeling_bert_generation.py": "5a0cfaf598a7be00806d9e3aa2a59c31aa7d4c671652e2e15cad600b14fe5080",
-  "src/transformers/models/big_bird/configuration_big_bird.py": "c328fdd36de02bef93bc9c02c34aac7d61e23ce32cd23932c894dc1b8ce5d35a",
-  "src/transformers/models/big_bird/modeling_big_bird.py": "1700b3b52a5ae85e5321367be5edfa1e3a4cc8e0709fc5c19b88ff4eb9be5523",
-  "src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py": "dc782ebd96e20824279e1c60c9dccdabfb4569da461a1054e6f46c1c15e5e8dd",
-  "src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py": "78b36af511d3a2e4df5ed526bb609638488ea5574d10b75f4d349b907fcf3077",
-  "src/transformers/models/biogpt/configuration_biogpt.py": "a34f6a9c53d25bea7d3df6b8a73612b287e754157e557c7061d45e4b367d36fb",
-  "src/transformers/models/biogpt/modeling_biogpt.py": "47a87ee17f7d5cca6221d5c87f1836af8c15da04ab5a16c0d3c5c67937d6b154",
-  "src/transformers/models/biogpt/modular_biogpt.py": "82a58e3790d5d236add39b6d7233da5f2d98108e1150e640e43f213952936e5b",
-  "src/transformers/models/bit/configuration_bit.py": "db00bd41886ba99305fde95b0b3ebad06ebfb143438441bbdf496697db50a78f",
-  "src/transformers/models/bit/modeling_bit.py": "44724c5dc21875fb438691a8b86039d60a34c903c6b0c92d88fc543bd1a57f8c",
-  "src/transformers/models/bitnet/configuration_bitnet.py": "7d79cd088e7cf06e5f27084c2aa98971ae146dc51623fd22611c3b29e23fb684",
-  "src/transformers/models/bitnet/modeling_bitnet.py": "1f589083a3b6d7836763de0b641b2555eafddb20c89aa7c985d7185aa30a7ee2",
-  "src/transformers/models/bitnet/modular_bitnet.py": "1bde132b6ae0857152632e990006a8872eebbadd76ac975c53194fe051896df1",
-  "src/transformers/models/blenderbot/configuration_blenderbot.py": "170ced3b7b82617975dfc05f34bb64d3c82dbcf1ef6a78b76794d554e7466f4e",
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-  "src/transformers/models/wav2vec2_bert/modular_wav2vec2_bert.py": "651a7c381d07e473f8dab7e0598a306650553230868770bfcfa5221fd2be2aa1",
-  "src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py": "2e138af1532aca4c5a1eb922203933309f1f4c12d7ee5007be36d68d593f37db",
-  "src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py": "1baeb85979dc5237962b8d2689033647c5e56ad76d8a27603edfd615175e1c26",
-  "src/transformers/models/wav2vec2_conformer/modular_wav2vec2_conformer.py": "d8d459a537db62a26ad90dcdfad25160f17ebde81269e21db64d310c89fb66a1",
-  "src/transformers/models/wavlm/configuration_wavlm.py": "6c07717bea00a8c6b622b9cd97123c19d3e0744fa76ab3791676ae9a97d0324d",
-  "src/transformers/models/wavlm/modeling_wavlm.py": "43cd2c54edadfd5f697f68ad64c462d45707ccfabd9d15811213bd5e409340a3",
-  "src/transformers/models/wavlm/modular_wavlm.py": "146c101d45ef4442ce295f6ca1e0e2cbaf031038f918e7c54b379eef23c10b35",
-  "src/transformers/models/whisper/configuration_whisper.py": "91a5cc9d8e2284490628c48f5470d1b89a233b16ab219ad9d300b3ffc978de7c",
-  "src/transformers/models/whisper/modeling_whisper.py": "0d711e4623341a3da969d5fe90841a9ee28a4ac464ca42a7c8ea8f3e1d1d54da",
-  "src/transformers/models/x_clip/configuration_x_clip.py": "d39e1d1d73090f322369c5679d18f65ac0b897e76d66620fcf66390e3dffe346",
-  "src/transformers/models/x_clip/modeling_x_clip.py": "3cea422f2a284b6135e7e0efa09a5c10509bb142861c59bd66ad37ef8f241b4b",
-  "src/transformers/models/xcodec/configuration_xcodec.py": "d2cd3b2c86368b597476825ad2eac62fe8f0409a2c8b1f12cef97784ca733500",
-  "src/transformers/models/xcodec/modeling_xcodec.py": "03be2a880f1429d8722f700cdd5479f2da555ee4d241be774a122779c71b734c",
-  "src/transformers/models/xglm/configuration_xglm.py": "2dc8cea98578cb05cbeaddf4ca1e016860f7365c785d1c1a1af6f3f3eb3fa9d4",
-  "src/transformers/models/xglm/modeling_xglm.py": "7119c77597c7966720bf8c04228c41755a3e225a7b68f74e5d15f02964e9a023",
-  "src/transformers/models/xlm/configuration_xlm.py": "f0b5d2b6b9669d845540b2b9df5a2b7951354796a47d1bc985f14b23d631daa2",
-  "src/transformers/models/xlm/modeling_xlm.py": "baec462f1e4308c31b084ade8618fb2041a6a760a6090a4bd57ceaf4a8ef7dee",
-  "src/transformers/models/xlm_roberta/configuration_xlm_roberta.py": "7569a207cea00ea0eb1e22bf725e071d409f705c512ff1e33cc30a798665a193",
-  "src/transformers/models/xlm_roberta/modeling_xlm_roberta.py": "c8394866e7f785a6c2652c8341ff0d649dbe76ca619bf4078f66e2ec689b142b",
-  "src/transformers/models/xlm_roberta/modular_xlm_roberta.py": "8c8469c3867eb85c2474eec51406d32187b1f8169c6517d77b79cdd8ac41dde0",
-  "src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py": "fff0d189298232584d020e3b0b12184855bd49c3c2821c7385d29ae564be0664",
-  "src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py": "d9e4579799bfce9a55ea16ca5915033a1e463a6e1f2aa37d6fb852b780e1b7c3",
-  "src/transformers/models/xlm_roberta_xl/modular_xlm_roberta_xl.py": "0db7040a77c0db9c71adb37374a6fe11655698bdd511c744327a5f90584b4f66",
-  "src/transformers/models/xlnet/configuration_xlnet.py": "b23ca312b54597df3c9515bd14e2bb8b0bccf7d71722c2030900eeb05f06ac2c",
-  "src/transformers/models/xlnet/modeling_xlnet.py": "26ef15cc9e19c5f1ad78e7beac1d02620659b4b7811ee07944b082cea346d827",
-  "src/transformers/models/xlstm/configuration_xlstm.py": "2fa1e1de35a743aaf4faa4399be2f6669990ab5370abf6a5670278ddacbd86ad",
-  "src/transformers/models/xlstm/modeling_xlstm.py": "658246eb2c90671f0912bc2bae93385a32f8a74b87c21ec334e34a1d6ccbff22",
-  "src/transformers/models/xmod/configuration_xmod.py": "fc8326fb791bf9165dad3af262e172b8c5e1985f4eb96ec6369a47de27ff3ebd",
-  "src/transformers/models/xmod/modeling_xmod.py": "019b42bd4b6373dddf2b15a16b9c20b3f0915dae92a7633bb5ef9f2ee1bb5ef9",
-  "src/transformers/models/yolos/configuration_yolos.py": "8970bd7e2ef458e6063b5fccf136c237b063465b4f7fd7be41c0f0b4e1fc1aa4",
-  "src/transformers/models/yolos/modeling_yolos.py": "32933319a7ef76a7d2d026abb2def66f2576349759836b23ebbe95682024c56f",
-  "src/transformers/models/yolos/modular_yolos.py": "67b24ac8dd457ab763f88b08a368c43fe32fb1487989dac63cd60c56e98badd1",
-  "src/transformers/models/yoso/configuration_yoso.py": "18fcac752ecd69aac9cda2ceadb444204a1b8b2536bb25030d2b34a34571279a",
-  "src/transformers/models/yoso/modeling_yoso.py": "ee3856557776db9294841314d24f39db87253677984766fcad6c78139a3db423",
-  "src/transformers/models/youtu/configuration_youtu.py": "72981b4a97b2f39069e4d3a74a008ab8acfc600c898c494662ee2f0c7683315d",
-  "src/transformers/models/youtu/modeling_youtu.py": "fbc8374c50df05da935a1b040aaf0a90e93689804c7b4e35e8d6b8c0813f66ae",
-  "src/transformers/models/youtu/modular_youtu.py": "ddbfc95d009cf94a6f3487c0d064c7a3aa2ef6c6015898c51988b5dab9c452c8",
-  "src/transformers/models/zamba/configuration_zamba.py": "23566c8ef76743f6be134cd1d5853d323c87677161ba2b477a57722e0215f376",
-  "src/transformers/models/zamba/modeling_zamba.py": "205a0b6cc541fe9d05e811226cfa8e0649cdb6fa634cfa888dd79f6b4f5263a6",
-  "src/transformers/models/zamba2/configuration_zamba2.py": "c98074770b92db6d888aaf530279b2b4f156357a6d4365cfce7298e88452ab12",
-  "src/transformers/models/zamba2/modeling_zamba2.py": "c4dbed4799ed9a2374a7eb6c55ebda6c7710310ed3b6d8508469a32c7babb999",
-  "src/transformers/models/zamba2/modular_zamba2.py": "546cc01d3910e159e566a41a8d86ed1c955eac41391871cf3e3cb2aef7ef24be",
-  "src/transformers/models/zoedepth/configuration_zoedepth.py": "1a2474f62a1d0e91bb183c1c2845dbbd4af92e5aa5ac2e7d1db96499c5b39907",
-  "src/transformers/models/zoedepth/modeling_zoedepth.py": "97e08f50416ab8ac3b6c52e6b3471aa3573b8619e85765acc6d0001b1bb8f657"
-}

From 86de2bc8bd6a5435520ff0d14b2a85d8de73f49e Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 31 Mar 2026 11:16:18 +0200
Subject: [PATCH 52/56] more renaming

---
 src/transformers/cache_utils.py      | 38 ++++++++++++++--------------
 src/transformers/generation/utils.py |  6 ++---
 2 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 297059a5b1e2..b73f2658ed80 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -670,9 +670,9 @@ def _dequantize(self, qtensor):
 
 
 class LinearAttentionCacheLayerMixin(ABC):
-    """Base, abstract class for a mamba single layer's cache."""
+    """Base, abstract class for a linear attention single layer's cache."""
 
-    # All shapes are static by essence in a Mamba layer, so it is compileable
+    # All shapes are static by essence in a LinearAttention layer, so it is compileable
     is_compileable = True
 
     def __init__(self):
@@ -727,7 +727,7 @@ def reorder_cache(self, beam_idx: torch.LongTensor):
             self.recurrent_states = self.recurrent_states.index_select(0, beam_idx.to(self.device))
 
     def crop(self, max_length: int):
-        # We don't crop the mamba cache, so simply do nothing here
+        # We don't crop the linear attention cache, so simply do nothing here
         pass
 
 
@@ -756,7 +756,7 @@ def lazy_initialization(
 
     def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor:
         """
-        Update the mamba cache in-place, and return the necessary conv states.
+        Update the linear attention cache in-place, and return the necessary conv states.
 
         Args:
             conv_states (`torch.Tensor`): The new conv states to cache.
@@ -789,7 +789,7 @@ def update_conv_state(self, conv_states: torch.Tensor, **kwargs) -> torch.Tensor
 
     def update_recurrent_state(self, recurrent_states: torch.Tensor, **kwargs) -> torch.Tensor:
         """
-        Update the mamba cache in-place, and return the necessary ssm states.
+        Update the linear attention cache in-place, and return the necessary ssm states.
 
         Args:
             smm_states (`torch.Tensor`): The new ssm states to cache.
@@ -816,7 +816,7 @@ def lazy_initialization(self, *args, **kwargs) -> None:
         # When the Attention cache is used with `update`, `lazy_initialization` is called with 2 positional args
         if len(args) == 2 and len(kwargs) == 0:
             DynamicLayer.lazy_initialization(self, *args)
-        # Otherwise, for the Mamba cache, when it's called in `update_conv_state` or `update_recurrent_state`, it's
+        # Otherwise, for the LinearAttention cache, when it's called in `update_conv_state` or `update_recurrent_state`, it's
         # always called with 1 single kwarg (cause it needs to know if it's for the conv or ssm states)
         if len(args) == 0 and len(kwargs) == 1:
             LinearAttentionLayer.lazy_initialization(self, **kwargs)
@@ -957,7 +957,7 @@ def update_conv_state(self, conv_states: torch.Tensor, layer_idx: int, **kwargs)
         # NOTE: if we slightly break `update` arg order, we could combine this with it, and allow offloading support
         # out of the box
         if not isinstance(self.layers[layer_idx], LinearAttentionCacheLayerMixin):
-            raise ValueError("Cannot call `update_conv_state` on a non-Mamba layer!")
+            raise ValueError("Cannot call `update_conv_state` on a non-LinearAttention layer!")
         conv_states = self.layers[layer_idx].update_conv_state(conv_states, **kwargs)
         return conv_states
 
@@ -977,7 +977,7 @@ def update_recurrent_state(self, recurrent_states: torch.Tensor, layer_idx: int,
         # NOTE: if we slightly break `update` arg order, we could combine this with it, and allow offloading support
         # out of the box
         if not isinstance(self.layers[layer_idx], LinearAttentionCacheLayerMixin):
-            raise ValueError("Cannot call `update_conv_state` on a non-Mamba layer!")
+            raise ValueError("Cannot call `update_conv_state` on a non-LinearAttention layer!")
         recurrent_states = self.layers[layer_idx].update_recurrent_state(recurrent_states, **kwargs)
         return recurrent_states
 
@@ -1001,12 +1001,12 @@ def get_seq_length(self, layer_idx: int = 0) -> int:
         if layer_idx >= len(self.layers):
             return 0
 
-        # For alternating attention-mamba caches, `get_seq_length` needs to use attention layer idx when called with default layer_idx
+        # For alternating attention/linear attention  caches, `get_seq_length` needs to use attention layer idx when called with default layer_idx
         if not isinstance(self.layers[layer_idx], CacheLayerMixin):
             # If this is called with non-default arg, raise
             if layer_idx != 0:
                 raise ValueError(
-                    f"You called `get_seq_length` on layer index {layer_idx}, but this layer is a Mamba layer, which "
+                    f"You called `get_seq_length` on layer index {layer_idx}, but this layer is a LinearAttention layer, which "
                     "does not track sequence length."
                 )
             try:
@@ -1015,17 +1015,17 @@ def get_seq_length(self, layer_idx: int = 0) -> int:
             except StopIteration:
                 raise ValueError(
                     "`get_seq_length` can only be called on Attention layers, and the current Cache seem to only contain "
-                    "Mamba layers."
+                    "LinearAttention layers."
                 )
 
         return self.layers[layer_idx].get_seq_length()
 
     def has_previous_state(self, layer_idx: int | None = None) -> bool:
-        """Returns whether the Mamba layer at index `layer_idx` has previous state or not."""
+        """Returns whether the LinearAttention layer at index `layer_idx` has previous state or not."""
         if layer_idx is not None and layer_idx >= len(self.layers):
             return False
 
-        # In this case, use last Mamba layer
+        # In this case, use last LinearAttention layer
         if layer_idx is None:
             try:
                 layer_idx = next(
@@ -1035,8 +1035,8 @@ def has_previous_state(self, layer_idx: int | None = None) -> bool:
                 )
             except StopIteration:
                 raise ValueError(
-                    "`has_previous_state` can only be called on Mamba layers, and the current Cache seem to only contain "
-                    "Attention layers."
+                    "`has_previous_state` can only be called on LinearAttention layers, and the current Cache seem to "
+                    "only contain Attention layers."
                 )
         elif not isinstance(self.layers[layer_idx], LinearAttentionCacheLayerMixin):
             raise ValueError(
@@ -1057,12 +1057,12 @@ def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
         if layer_idx >= len(self.layers):
             return query_length, 0
 
-        # For alternating attention-mamba caches, `get_mask_sizes` needs to use attention layer idx when called with default layer_idx
+        # For alternating attention/linear attention caches, `get_mask_sizes` needs to use attention layer idx when called with default layer_idx
         if not isinstance(self.layers[layer_idx], CacheLayerMixin):
             # If this is called with non-default arg, raise
             if layer_idx != 0:
                 raise ValueError(
-                    f"You called `get_mask_sizes` on layer index {layer_idx}, but this layer is a Mamba layer, which "
+                    f"You called `get_mask_sizes` on layer index {layer_idx}, but this layer is a LinearAttention layer, which "
                     "does not track sequence length."
                 )
             try:
@@ -1071,7 +1071,7 @@ def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]:
             except StopIteration:
                 raise ValueError(
                     "`get_mask_sizes` can only be called on Attention layers, and the current Cache seem to only contain "
-                    "Mamba layers."
+                    "LinearAttention layers."
                 )
 
         return self.layers[layer_idx].get_mask_sizes(query_length)
@@ -1341,7 +1341,7 @@ def __init__(
                 layer = StaticSlidingWindowLayer(
                     max_cache_len=max_cache_len, sliding_window=config.attention_chunk_size
                 )
-            # Mamba layers are static by essence - using `"moe"` as well is a trick, see the comment about it on DynamicCache
+            # LinearAttention layers are static by essence - using `"moe"` as well is a trick, see the comment about it on DynamicCache
             elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
                 layer = LinearAttentionLayer()
             else:
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index ed8e3947fd5f..d28ac6744023 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1849,12 +1849,12 @@ def _prepare_cache_for_generation(
             generation_config.cache_implementation = "dynamic_full"
 
         dynamic_cache_kwargs = {}
-        # mamba models always need to pass the config, otherwise it will use an Attention cache for the Mamba layers
-        is_mamba = any(
+        # linear attention models always need to pass the config, otherwise it will use an Attention cache for the LinearAttention layers
+        is_linear_attention = any(
             x in ("mamba", "conv", "linear_attention")
             for x in getattr(self.config.get_text_config(decoder=True), "layer_types", [])
         )
-        if generation_config.cache_implementation != "dynamic_full" or is_mamba:
+        if generation_config.cache_implementation != "dynamic_full" or is_linear_attention:
             dynamic_cache_kwargs["config"] = self.config.get_text_config(decoder=True)
 
         if generation_config.cache_implementation == "offloaded":

From f5dfd79bfb4c140fadd26fecd1a51b86dfd0b9d0 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 31 Mar 2026 11:43:43 +0200
Subject: [PATCH 53/56] revert offloading change

---
 .../models/musicflamingo/configuration_musicflamingo.py         | 2 +-
 src/transformers/models/musicflamingo/modular_musicflamingo.py  | 2 +-
 tests/test_modeling_common.py                                   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/transformers/models/musicflamingo/configuration_musicflamingo.py b/src/transformers/models/musicflamingo/configuration_musicflamingo.py
index e7e7a15dcde1..562a3bf13071 100644
--- a/src/transformers/models/musicflamingo/configuration_musicflamingo.py
+++ b/src/transformers/models/musicflamingo/configuration_musicflamingo.py
@@ -32,7 +32,7 @@
 class MusicFlamingoConfig(PreTrainedConfig):
     r"""
     audio_bos_token_id (`int`, *optional*, defaults to 151670):
-            The beginning-of-audio token index used to mark the start of audio spans.
+        The beginning-of-audio token index used to mark the start of audio spans.
     audio_eos_token_id (`int`, *optional*, defaults to 151671):
         The end-of-audio token index used to mark the end of audio spans.
     audio_frame_step (`float`, *optional*, defaults to 0.01):
diff --git a/src/transformers/models/musicflamingo/modular_musicflamingo.py b/src/transformers/models/musicflamingo/modular_musicflamingo.py
index 5da937bab052..2a9735f78ce0 100644
--- a/src/transformers/models/musicflamingo/modular_musicflamingo.py
+++ b/src/transformers/models/musicflamingo/modular_musicflamingo.py
@@ -43,7 +43,7 @@
 class MusicFlamingoConfig(AudioFlamingo3Config):
     r"""
     audio_bos_token_id (`int`, *optional*, defaults to 151670):
-            The beginning-of-audio token index used to mark the start of audio spans.
+        The beginning-of-audio token index used to mark the start of audio spans.
     audio_eos_token_id (`int`, *optional*, defaults to 151671):
         The end-of-audio token index used to mark the end of audio spans.
     audio_frame_step (`float`, *optional*, defaults to 0.01):
diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py
index a68cdba04cd9..d4e8ca71a60a 100755
--- a/tests/test_modeling_common.py
+++ b/tests/test_modeling_common.py
@@ -2945,7 +2945,7 @@ def test_cpu_offload(self):
                 model.cpu().save_pretrained(tmp_dir)
 
                 for max_size in max_gpu_sizes:
-                    max_memory = {0: max_size, "cpu": model_size * 3}
+                    max_memory = {0: max_size, "cpu": model_size * 2}
                     new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
                     # Making sure part of the model will actually end up offloaded
                     self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"})

From 476aaafe4e79fe00d4f766ef8e55901f4e8153e7 Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 31 Mar 2026 11:49:39 +0200
Subject: [PATCH 54/56] add offloading skips

---
 tests/models/zamba/test_modeling_zamba.py   | 10 ++++++++--
 tests/models/zamba2/test_modeling_zamba2.py |  6 ++++++
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index 037f157f79db..48e8bdb64a40 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -300,17 +300,23 @@ def setUp(self):
         self.config_tester = ConfigTester(self, config_class=ZambaConfig, hidden_size=32)
 
     @unittest.skip(
-        "Same as zamba2 -> investigate, it's probably due to their tied weights that accelerate does not work"
+        "Same as zamba2 -> investigate, it's probably due to their mixed layer classes or tied weights that accelerate does not work"
     )
     def test_disk_offload_bin(self):
         pass
 
     @unittest.skip(
-        "Same as zamba2 -> investigate, it's probably due to their tied weights that accelerate does not work"
+        "Same as zamba2 -> investigate, it's probably due to their mixed layer classes or tied weights that accelerate does not work"
     )
     def test_disk_offload_safetensors(self):
         pass
 
+    @unittest.skip(
+        "Same as zamba2 -> investigate, it's probably due to their mixed layer classes or tied weights that accelerate does not work"
+    )
+    def test_cpu_offload(self):
+        pass
+
     def test_config(self):
         self.config_tester.run_common_tests()
 
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index 1695cc9d5556..a240a4a71334 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -329,6 +329,12 @@ def test_disk_offload_bin(self):
     def test_disk_offload_safetensors(self):
         pass
 
+    @unittest.skip(
+        "Offloading does not work correctly for zamba2 - probably due to their mixed layer classes or tied weights"
+    )
+    def test_cpu_offload(self):
+        pass
+
     @unittest.skip("position_ids cannot be used to pad due to Mamba2 layers")
     def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
         pass

From 7a6928718bb8c812999e62bf229fa9484fd1401d Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 31 Mar 2026 12:14:31 +0200
Subject: [PATCH 55/56] split shapes for tests

---
 tests/generation/test_utils.py                | 28 ++++++++++++-------
 tests/models/bamba/test_modeling_bamba.py     |  9 +++---
 .../falcon_h1/test_modeling_falcon_h1.py      | 11 ++++----
 .../test_modeling_granitemoehybrid.py         |  8 ++++--
 tests/models/jamba/test_modeling_jamba.py     |  9 +++---
 tests/models/lfm2/test_modeling_lfm2.py       |  6 ++--
 .../models/lfm2_moe/test_modeling_lfm2_moe.py |  6 ++--
 tests/models/lfm2_vl/test_modeling_lfm2_vl.py |  6 ++--
 tests/models/mamba2/test_modeling_mamba2.py   |  8 ++++--
 .../nemotron_h/test_modeling_nemotron_h.py    | 13 +++++----
 tests/models/qwen3_5/test_modeling_qwen3_5.py | 26 +++++++++++------
 .../qwen3_5_moe/test_modeling_qwen3_5_moe.py  | 26 +++++++++++------
 .../qwen3_next/test_modeling_qwen3_next.py    | 13 ++++++---
 tests/models/zamba/test_modeling_zamba.py     | 10 ++++---
 tests/models/zamba2/test_modeling_zamba2.py   |  8 ++++--
 15 files changed, 114 insertions(+), 73 deletions(-)

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index c0d24abda5c7..2184827b0891 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2537,16 +2537,23 @@ def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, c
             [encoder_expected_shape] * len(hidden_states),
         )
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
-        # Default mamba cache shape - can vary based on models so this function is convenient to easily check caches
-        # Note that non-mamba models will NOT have the config fields - it does not matter as they will only use attention cache layers
-        # so the None default values will not be used
+    def _get_conv_state_shape(self, batch_size: int, config):
+        # Default conv state shape, for linear attention models - can vary based on models so this function is convenient
+        # to easily check caches
+        # Note that non-mamba models will NOT have the config fields - it does not matter as they will only use attention
+        # cache layers, so the None default values will not be used
         intermediate_size = getattr(config, "intermediate_size", None)
         conv_kernel = getattr(config, "conv_kernel", None)
+        return (batch_size, intermediate_size, conv_kernel)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        # Default recurrent state shape, for linear attention models - can vary based on models so this function is convenient
+        # to easily check caches
+        # Note that non-mamba models will NOT have the config fields - it does not matter as they will only use attention
+        # cache layers, so the None default values will not be used
+        intermediate_size = getattr(config, "intermediate_size", None)
         state_size = getattr(config, "state_size", None)
-        conv_shape = (batch_size, intermediate_size, conv_kernel)
-        ssm_shape = (batch_size, intermediate_size, state_size)
-        return conv_shape, ssm_shape
+        return (batch_size, intermediate_size, state_size)
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
         # Raise a useful error, asking to explicitly override the method
@@ -2580,7 +2587,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         )
 
         # For mamba layers
-        conv_shape, ssm_shape = self._get_mamba_cache_shapes(batch_size, config)
+        conv_shape = self._get_conv_state_shape(batch_size, config)
+        recurrent_shape = self._get_recurrent_state_shape(batch_size, config)
 
         # Check the size is coherent
         num_hidden_layers = config.num_hidden_layers
@@ -2600,13 +2608,13 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
                 self.assertEqual(layer.conv_states.shape, conv_shape)
                 # May not be used (e.g. lfm2)
                 if layer.is_recurrent_states_initialized:
-                    self.assertEqual(layer.recurrent_states.shape, ssm_shape)
+                    self.assertEqual(layer.recurrent_states.shape, recurrent_shape)
             # Mamba only layer cache
             elif type(layer) is LinearAttentionLayer:
                 self.assertEqual(layer.conv_states.shape, conv_shape)
                 # May not be used (e.g. lfm2)
                 if layer.is_recurrent_states_initialized:
-                    self.assertEqual(layer.recurrent_states.shape, ssm_shape)
+                    self.assertEqual(layer.recurrent_states.shape, recurrent_shape)
             # Attention only layer type
             else:
                 # Remove the seq_length dim for cross-attention cache (it changes based on the model)
diff --git a/tests/models/bamba/test_modeling_bamba.py b/tests/models/bamba/test_modeling_bamba.py
index bf180d0fedf0..fd028512f16c 100644
--- a/tests/models/bamba/test_modeling_bamba.py
+++ b/tests/models/bamba/test_modeling_bamba.py
@@ -279,15 +279,16 @@ class BambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     # This is because we are hitting edge cases with the causal_mask buffer
     model_split_percents = [0.5, 0.7, 0.8]
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
-        # For mamba layers
+    def _get_conv_state_shape(self, batch_size: int, config):
         conv_shape = (
             batch_size,
             config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * config.mamba_d_state,
             config.mamba_d_conv,
         )
-        ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
-        return conv_shape, ssm_shape
+        return conv_shape
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
 
     def setUp(self):
         self.model_tester = self.model_tester_class(self)
diff --git a/tests/models/falcon_h1/test_modeling_falcon_h1.py b/tests/models/falcon_h1/test_modeling_falcon_h1.py
index 2537a76b0847..a6429f2da621 100644
--- a/tests/models/falcon_h1/test_modeling_falcon_h1.py
+++ b/tests/models/falcon_h1/test_modeling_falcon_h1.py
@@ -253,18 +253,19 @@ class FalconH1ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
         {"feature-extraction": FalconH1Model, "text-generation": FalconH1ForCausalLM} if is_torch_available() else {}
     )
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
-        conv_kernel_size = config.mamba_d_conv
+    def _get_conv_state_shape(self, batch_size: int, config):
         intermediate_size = (
             config.mamba_d_ssm if config.mamba_d_ssm is not None else int(config.mamba_expand * config.hidden_size)
         )
         conv_shape = (
             batch_size,
             intermediate_size + 2 * config.mamba_n_groups * config.mamba_d_state,
-            conv_kernel_size,
+            config.mamba_d_conv,
         )
-        ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
-        return conv_shape, ssm_shape
+        return conv_shape
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
 
     def setUp(self):
         self.model_tester = FalconH1ModelTester(self)
diff --git a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
index 83fdc436b9a9..919bf79deac3 100644
--- a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
+++ b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
@@ -302,14 +302,16 @@ def test_flash_attention_2_padding_matches_padding_free_with_position_ids_seq_id
                 loss_padfree = res_padfree.loss
                 torch.testing.assert_close(loss_padded, loss_padfree)
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         conv_shape = (
             batch_size,
             config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * config.mamba_d_state,
             config.mamba_d_conv,
         )
-        ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
-        return conv_shape, ssm_shape
+        return conv_shape
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
 
     def test_config_requires_mamba_or_attention_layers(self):
         """Ensure we can't create a config with disallowed layers."""
diff --git a/tests/models/jamba/test_modeling_jamba.py b/tests/models/jamba/test_modeling_jamba.py
index 77500ad06251..28d4b7a18c61 100644
--- a/tests/models/jamba/test_modeling_jamba.py
+++ b/tests/models/jamba/test_modeling_jamba.py
@@ -322,10 +322,11 @@ class JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         else {}
     )
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
-        conv_shape = (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_conv)
-        ssm_shape = (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_state)
-        return conv_shape, ssm_shape
+    def _get_conv_state_shape(self, batch_size: int, config):
+        return (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_conv)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.mamba_expand * config.hidden_size, config.mamba_d_state)
 
     def setUp(self):
         self.model_tester = JambaModelTester(self)
diff --git a/tests/models/lfm2/test_modeling_lfm2.py b/tests/models/lfm2/test_modeling_lfm2.py
index a44c303c6c97..67698c564092 100644
--- a/tests/models/lfm2/test_modeling_lfm2.py
+++ b/tests/models/lfm2/test_modeling_lfm2.py
@@ -51,10 +51,8 @@ class Lfm2ModelTest(CausalLMModelTest, unittest.TestCase):
     # used in `test_torch_compile_for_training`
     _torch_compile_train_cls = Lfm2ForCausalLM if is_torch_available() else None
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
-        conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-        ssm_shape = (0,)
-        return conv_shape, ssm_shape
+    def _get_conv_state_shape(self, batch_size: int, config):
+        return (batch_size, config.hidden_size, config.conv_L_cache)
 
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""
diff --git a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
index 8264cf4f6118..2015a4a83e31 100644
--- a/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
+++ b/tests/models/lfm2_moe/test_modeling_lfm2_moe.py
@@ -69,10 +69,8 @@ class Lfm2MoeModelTest(CausalLMModelTest, unittest.TestCase):
     # used in `test_torch_compile_for_training`
     _torch_compile_train_cls = Lfm2MoeForCausalLM if is_torch_available() else None
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
-        conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-        ssm_shape = (0,)
-        return conv_shape, ssm_shape
+    def _get_conv_state_shape(self, batch_size: int, config):
+        return (batch_size, config.hidden_size, config.conv_L_cache)
 
     def test_attention_outputs(self):
         """Lfm2Moe alternates between attention and short-conv layers."""
diff --git a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
index f4ef47a97402..c14e3933f77b 100644
--- a/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
+++ b/tests/models/lfm2_vl/test_modeling_lfm2_vl.py
@@ -171,10 +171,8 @@ def setUp(self):
             self, config_class=Lfm2VlConfig, has_text_modality=False, common_properties=common_properties
         )
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
-        conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)
-        ssm_shape = (0,)
-        return conv_shape, ssm_shape
+    def _get_conv_state_shape(self, batch_size: int, config):
+        return (batch_size, config.hidden_size, config.conv_L_cache)
 
     def test_config(self):
         self.config_tester.run_common_tests()
diff --git a/tests/models/mamba2/test_modeling_mamba2.py b/tests/models/mamba2/test_modeling_mamba2.py
index f64047907acc..6e7116afdc36 100644
--- a/tests/models/mamba2/test_modeling_mamba2.py
+++ b/tests/models/mamba2/test_modeling_mamba2.py
@@ -244,15 +244,17 @@ def setUp(self):
             self, config_class=Mamba2Config, n_embd=37, common_properties=["hidden_size", "num_hidden_layers"]
         )
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         intermediate_size = config.expand * config.hidden_size
         conv_shape = (
             batch_size,
             intermediate_size + 2 * config.n_groups * config.state_size,
             config.conv_kernel,
         )
-        ssm_shape = (batch_size, config.num_heads, config.head_dim, config.state_size)
-        return conv_shape, ssm_shape
+        return conv_shape
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.num_heads, config.head_dim, config.state_size)
 
     def test_mamba2_caching(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
diff --git a/tests/models/nemotron_h/test_modeling_nemotron_h.py b/tests/models/nemotron_h/test_modeling_nemotron_h.py
index 2c45f4c5485c..19ca9f4f77fd 100644
--- a/tests/models/nemotron_h/test_modeling_nemotron_h.py
+++ b/tests/models/nemotron_h/test_modeling_nemotron_h.py
@@ -351,15 +351,17 @@ class NemotronHModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
         else {}
     )
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         intermediate_size = config.mamba_num_heads * config.mamba_head_dim
         conv_shape = (
             batch_size,
             intermediate_size + 2 * config.n_groups * config.ssm_state_size,
             config.conv_kernel,
         )
-        ssm_shape = (batch_size, config.mamba_num_heads, config.mamba_head_dim, config.ssm_state_size)
-        return conv_shape, ssm_shape
+        return conv_shape
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.mamba_num_heads, config.mamba_head_dim, config.ssm_state_size)
 
     def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
         # Raise a useful error, asking to explicitly override the method
@@ -379,7 +381,8 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         # For cross attention cache, the seq_length depends on the model, so we remove that dim
         attention_shape = (batch_size, num_kv_heads, seq_length, head_dim)
         # For mamba layers
-        conv_shape, ssm_shape = self._get_mamba_cache_shapes(batch_size, config)
+        conv_shape = self._get_conv_state_shape(batch_size, config)
+        recurrent_shape = self._get_recurrent_state_shape(batch_size, config)
 
         # Check each layer has the correct shape
         for layer, layer_type in zip(past_key_values.layers, config.layer_types):
@@ -394,7 +397,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
             # Mamba layer cache
             elif layer_type == "mamba":
                 self.assertEqual(layer.conv_states.shape, conv_shape)
-                self.assertEqual(layer.recurrent_states.shape, ssm_shape)
+                self.assertEqual(layer.recurrent_states.shape, recurrent_shape)
             else:
                 raise ValueError("Unknown layer type.")
 
diff --git a/tests/models/qwen3_5/test_modeling_qwen3_5.py b/tests/models/qwen3_5/test_modeling_qwen3_5.py
index 839b4f6c7fc4..f90fb09546d6 100644
--- a/tests/models/qwen3_5/test_modeling_qwen3_5.py
+++ b/tests/models/qwen3_5/test_modeling_qwen3_5.py
@@ -70,16 +70,21 @@ class Qwen3_5TextModelTest(CausalLMModelTest, unittest.TestCase):
     config_class = Qwen3_5TextConfig
     model_split_percents = [0.5, 0.8, 0.9]
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         num_v_heads = config.linear_num_value_heads
         num_k_heads = config.linear_num_key_heads
         head_k_dim = config.linear_key_head_dim
         head_v_dim = config.linear_value_head_dim
         intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
-        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
-        return conv_shape, ssm_shape
+        return (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+
+        return (batch_size, num_v_heads, head_k_dim, head_v_dim)
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 alternates between attention layers and gated deltanet layers."
@@ -299,16 +304,21 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         num_v_heads = config.linear_num_value_heads
         num_k_heads = config.linear_num_key_heads
         head_k_dim = config.linear_key_head_dim
         head_v_dim = config.linear_value_head_dim
         intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
-        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
-        return conv_shape, ssm_shape
+        return (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+
+        return (batch_size, num_v_heads, head_k_dim, head_v_dim)
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 alternates between attention layers and gated deltanet layers."
diff --git a/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py b/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
index 55b58a1aea34..d949f777f8a4 100644
--- a/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
+++ b/tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
@@ -73,16 +73,21 @@ class Qwen3_5MoeTextModelTest(CausalLMModelTest, unittest.TestCase):
     model_tester_class = Qwen3_5MoeTextModelTester
     config_class = Qwen3_5MoeTextConfig
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         num_v_heads = config.linear_num_value_heads
         num_k_heads = config.linear_num_key_heads
         head_k_dim = config.linear_key_head_dim
         head_v_dim = config.linear_value_head_dim
         intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
-        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
-        return conv_shape, ssm_shape
+        return (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+
+        return (batch_size, num_v_heads, head_k_dim, head_v_dim)
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 Moe alternates between attention layers and gated deltanet layers."
@@ -381,16 +386,21 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         num_v_heads = config.linear_num_value_heads
         num_k_heads = config.linear_num_key_heads
         head_k_dim = config.linear_key_head_dim
         head_v_dim = config.linear_value_head_dim
         intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
-        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
-        return conv_shape, ssm_shape
+        return (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+
+        return (batch_size, num_v_heads, head_k_dim, head_v_dim)
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3.5 Moe alternates between attention layers and gated deltanet layers."
diff --git a/tests/models/qwen3_next/test_modeling_qwen3_next.py b/tests/models/qwen3_next/test_modeling_qwen3_next.py
index f597b8e28192..4cb53fb6c695 100644
--- a/tests/models/qwen3_next/test_modeling_qwen3_next.py
+++ b/tests/models/qwen3_next/test_modeling_qwen3_next.py
@@ -57,16 +57,21 @@ def __init__(self, parent):
 class Qwen3NextModelTest(CausalLMModelTest, unittest.TestCase):
     model_tester_class = Qwen3NextModelTester
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         num_v_heads = config.linear_num_value_heads
         num_k_heads = config.linear_num_key_heads
         head_k_dim = config.linear_key_head_dim
         head_v_dim = config.linear_value_head_dim
         intermediate_size = 2 * num_k_heads * head_k_dim + num_v_heads * head_v_dim
 
-        conv_shape = (batch_size, intermediate_size, config.linear_conv_kernel_dim)
-        ssm_shape = (batch_size, num_v_heads, head_k_dim, head_v_dim)
-        return conv_shape, ssm_shape
+        return (batch_size, intermediate_size, config.linear_conv_kernel_dim)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        num_v_heads = config.linear_num_value_heads
+        head_k_dim = config.linear_key_head_dim
+        head_v_dim = config.linear_value_head_dim
+
+        return (batch_size, num_v_heads, head_k_dim, head_v_dim)
 
     def test_attention_outputs(self):
         "Needs to be overwritten as Qwen3-Next alternates between attention layers and gated deltanet layers."
diff --git a/tests/models/zamba/test_modeling_zamba.py b/tests/models/zamba/test_modeling_zamba.py
index 48e8bdb64a40..9e7ee869b88d 100644
--- a/tests/models/zamba/test_modeling_zamba.py
+++ b/tests/models/zamba/test_modeling_zamba.py
@@ -289,11 +289,13 @@ class ZambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     )
     model_split_percents = [0.5, 0.8, 0.9]
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
-        conv_shape = (batch_size, intermediate_size, config.mamba_d_conv)
-        ssm_shape = (batch_size, config.n_mamba_heads, intermediate_size // config.n_mamba_heads, config.mamba_d_state)
-        return conv_shape, ssm_shape
+        return (batch_size, intermediate_size, config.mamba_d_conv)
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        intermediate_size = config.mamba_expand * config.hidden_size
+        return (batch_size, config.n_mamba_heads, intermediate_size // config.n_mamba_heads, config.mamba_d_state)
 
     def setUp(self):
         self.model_tester = ZambaModelTester(self)
diff --git a/tests/models/zamba2/test_modeling_zamba2.py b/tests/models/zamba2/test_modeling_zamba2.py
index a240a4a71334..66b9093ee4e8 100644
--- a/tests/models/zamba2/test_modeling_zamba2.py
+++ b/tests/models/zamba2/test_modeling_zamba2.py
@@ -299,15 +299,17 @@ class Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
     )
     model_split_percents = [0.5, 0.8, 0.9]
 
-    def _get_mamba_cache_shapes(self, batch_size: int, config):
+    def _get_conv_state_shape(self, batch_size: int, config):
         intermediate_size = config.mamba_expand * config.hidden_size
         conv_shape = (
             batch_size,
             intermediate_size + 2 * config.mamba_ngroups * config.mamba_d_state,
             config.mamba_d_conv,
         )
-        ssm_shape = (batch_size, config.n_mamba_heads, config.mamba_headdim, config.mamba_d_state)
-        return conv_shape, ssm_shape
+        return conv_shape
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.n_mamba_heads, config.mamba_headdim, config.mamba_d_state)
 
     def setUp(self):
         self.model_tester = Zamba2ModelTester(self)

From 3600b89ce12991f04ae48bc6bacf58d23c7374ee Mon Sep 17 00:00:00 2001
From: Cyril Vallez <cyril.vallez@gmail.com>
Date: Tue, 31 Mar 2026 14:24:24 +0200
Subject: [PATCH 56/56] comments and renaming

---
 src/transformers/cache_utils.py      | 4 ++--
 src/transformers/generation/utils.py | 6 +++---
 tests/generation/test_utils.py       | 6 +++---
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index b73f2658ed80..ac324ebb62b4 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -804,7 +804,7 @@ def update_recurrent_state(self, recurrent_states: torch.Tensor, **kwargs) -> to
         return self.recurrent_states
 
 
-class LinearAttentionAndAttentionLayer(LinearAttentionLayer, DynamicLayer):
+class LinearAttentionAndFullAttentionLayer(LinearAttentionLayer, DynamicLayer):
     # The dynamic Attention part makes it non-compileable
     is_compileable = False
 
@@ -1231,7 +1231,7 @@ def __init__(
                 elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
                     layers.append(LinearAttentionLayer())
                 elif layer_type == "hybrid":
-                    layers.append(LinearAttentionAndAttentionLayer())
+                    layers.append(LinearAttentionAndFullAttentionLayer())
                 else:
                     layers.append(DynamicLayer())
 
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index d28ac6744023..d07cf05b1ed7 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1781,7 +1781,7 @@ def _supports_default_dynamic_cache(cls: type["GenerativePreTrainedModel"]) -> b
             "reformer",
             "minimax",
             "xlnet",
-            "olmohybrid",
+            "olmohybrid",  # olmo_hybrid cannot use linear attention cache for now as it uses split k,q,v conv states
             "rwkv",
             "xlstm",
         )
@@ -2001,8 +2001,8 @@ def _valid_auto_compile_criteria(
         valid_hardware = self.device.type in ["cuda", "xpu"] or bool(
             generation_config.compile_config is not None and generation_config.compile_config._compile_all_devices
         )
-        # Note: for full mamba models, even a DynamicCache is compileable since all layers are mamba, but we don't want
-        # to ALWAYS compile when calling `generate`, so we check the type
+        # Note: for some models that only use linear attention (e.g. Mamba), even a DynamicCache is compileable since all
+        # layers are, but we don't want to ALWAYS compile when calling `generate`, so we check the type
         using_compilable_cache = cache is not None and cache.is_compileable and type(cache) is not DynamicCache
         can_compile = valid_hardware and using_compilable_cache
 
diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 2184827b0891..055b852be0b3 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -81,7 +81,7 @@
         Cache,
         DynamicCache,
         EncoderDecoderCache,
-        LinearAttentionAndAttentionLayer,
+        LinearAttentionAndFullAttentionLayer,
         LinearAttentionLayer,
         QuantoQuantizedLayer,
         StaticCache,
@@ -2599,7 +2599,7 @@ def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_l
         # Check each layer has the correct shape
         for layer in past_key_values.layers:
             # Mamba + Attention layer cache
-            if type(layer) is LinearAttentionAndAttentionLayer:
+            if type(layer) is LinearAttentionAndFullAttentionLayer:
                 # Remove the seq_length dim for cross-attention cache (it changes based on the model)
                 keys = layer.keys if seq_length is not None else layer.keys[:, :, 0, :]
                 values = layer.values if seq_length is not None else layer.values[:, :, 0, :]
@@ -2664,7 +2664,7 @@ def _check_caches_are_equal(self, cache1: Cache, cache2: Cache):
             self.assertEqual(type(cache1.layers[idx]), type(cache2.layers[idx]))
 
             # Mamba + Attention layer
-            if type(cache1.layers[idx]) is LinearAttentionAndAttentionLayer:
+            if type(cache1.layers[idx]) is LinearAttentionAndFullAttentionLayer:
                 torch.testing.assert_close(cache1.layers[idx].keys, cache2.layers[idx].keys)
                 torch.testing.assert_close(cache1.layers[idx].values, cache2.layers[idx].values)
                 torch.testing.assert_close(cache1.layers[idx].conv_states, cache2.layers[idx].conv_states)