[Mixtral & Mistral] Add support for sdpa

src/transformers/models/mistral/modeling_mistral.py

@@ -31,7 +31,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
-from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
@@ -612,9 +612,98 @@ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query
         )
 
 
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mistral
+class MistralSdpaAttention(MistralAttention):
+    """
+    Mistral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MistralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MistralAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "MistralModel is using MistralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
 MISTRAL_ATTENTION_CLASSES = {
     "eager": MistralAttention,
     "flash_attention_2": MistralFlashAttention2,
+    "sdpa": MistralSdpaAttention,
 }
 
 
@@ -717,6 +806,7 @@ class MistralPreTrainedModel(PreTrainedModel):
     _no_split_modules = ["MistralDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn_2 = True
+    _supports_sdpa = True
     _supports_cache_class = True
 
     def _init_weights(self, module):
@@ -822,7 +912,7 @@ def __init__(self, config: MistralConfig):
         self.layers = nn.ModuleList(
             [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self._attn_implementation = config._attn_implementation
         self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
         self.gradient_checkpointing = False
@@ -893,7 +983,7 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        if attention_mask is not None and self._use_flash_attention_2 and use_cache:
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
             is_padding_right = attention_mask[:, -1].sum().item() != batch_size
             if is_padding_right:
                 raise ValueError(
@@ -902,9 +992,18 @@ def forward(
                     " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                 )
 
-        if self._use_flash_attention_2:
+        if self._attn_implementation == "flash_attention_2":
             # 2d mask is passed through the layers
             attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._attn_implementation == "sdpa" and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
         else:
             # 4d mask is passed through the layers
             attention_mask = _prepare_4d_causal_attention_mask(

src/transformers/models/mixtral/modeling_mixtral.py

@@ -33,6 +33,7 @@
 from ...cache_utils import Cache, DynamicCache
 from ...modeling_attn_mask_utils import (
     _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
 )
 from ...modeling_outputs import (
     MoeCausalLMOutputWithPast,
@@ -660,6 +661,101 @@ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query
         )
 
 
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mixtral
+class MixtralSdpaAttention(MixtralAttention):
+    """
+    Mixtral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MixtralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MixtralAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "MixtralModel is using MixtralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+MIXTRAL_ATTENTION_CLASSES = {
+    "eager": MixtralAttention,
+    "flash_attention_2": MixtralFlashAttention2,
+    "sdpa": MixtralSdpaAttention,
+}
+
+
 class MixtralBLockSparseTop2MLP(nn.Module):
     def __init__(self, config: MixtralConfig):
         super().__init__()
@@ -678,12 +774,6 @@ def forward(self, hidden_states):
         return current_hidden_states
 
 
-MISTRAL_ATTENTION_CLASSES = {
-    "eager": MixtralAttention,
-    "flash_attention_2": MixtralFlashAttention2,
-}
-
-
 class MixtralSparseMoeBlock(nn.Module):
     """
     This implementation is
@@ -759,7 +849,7 @@ def __init__(self, config: MixtralConfig, layer_idx: int):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+        self.self_attn = MIXTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
 
         self.block_sparse_moe = MixtralSparseMoeBlock(config)
         self.input_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -861,6 +951,7 @@ class MixtralPreTrainedModel(PreTrainedModel):
     _no_split_modules = ["MixtralDecoderLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn_2 = True
+    _supports_sdpa = True
     _supports_cache_class = True
 
     def _init_weights(self, module):
@@ -964,7 +1055,7 @@ def __init__(self, config: MixtralConfig):
         self.layers = nn.ModuleList(
             [MixtralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
         )
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self._attn_implementation = config._attn_implementation
         self.norm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
         self.gradient_checkpointing = False
@@ -1040,7 +1131,7 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        if attention_mask is not None and self._use_flash_attention_2 and use_cache:
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
             is_padding_right = attention_mask[:, -1].sum().item() != batch_size
             if is_padding_right:
                 raise ValueError(
@@ -1049,9 +1140,18 @@ def forward(
                     " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                 )
 
-        if self._use_flash_attention_2:
+        if self._attn_implementation == "flash_attention_2":
             # 2d mask is passed through the layers
             attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._attn_implementation == "sdpa" and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
         else:
             # 4d mask is passed through the layers
             attention_mask = _prepare_4d_causal_attention_mask(

tests/models/mistral/test_modeling_mistral.py

@@ -28,6 +28,7 @@
     require_flash_attn,
     require_torch,
     require_torch_gpu,
+    require_torch_sdpa,
     slow,
     torch_device,
 )
@@ -528,6 +529,44 @@ def test_model_7b_long_prompt(self):
         backend_empty_cache(torch_device)
         gc.collect()
 
+    @slow
+    @require_torch_sdpa
+    def test_model_7b_long_prompt_sdpa(self):
+        EXPECTED_OUTPUT_TOKEN_IDS = [306, 338]
+        # An input with 4097 tokens that is above the size of the sliding window
+        input_ids = [1] + [306, 338] * 2048
+        model = MistralForCausalLM.from_pretrained(
+            "mistralai/Mistral-7B-v0.1",
+            device_map="auto",
+            attn_implementation="sdpa",
+        )
+        input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
+        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
+        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
+
+        # Assisted generation
+        assistant_model = model
+        assistant_model.generation_config.num_assistant_tokens = 2
+        assistant_model.generation_config.num_assistant_tokens_schedule = "constant"
+        generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
+        self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist())
+
+        del assistant_model
+
+        backend_empty_cache(torch_device)
+        gc.collect()
+
+        EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"""
+        prompt = "My favourite condiment is "
+        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
+
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
     @slow
     def test_speculative_generation(self):
         EXPECTED_TEXT_COMPLETION = (

tests/models/mixtral/test_modeling_mixtral.py

@@ -38,11 +38,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import (
-        MixtralForCausalLM,
-        MixtralForSequenceClassification,
-        MixtralModel,
-    )
+    from transformers import MixtralForCausalLM, MixtralForSequenceClassification, MixtralModel
 
 
 class MixtralModelTester: