[kernels] refactor function kernel calling

src/transformers/integrations/hub_kernels.py

@@ -14,12 +14,16 @@
 import re
 from collections.abc import Callable
 from functools import partial
+from types import ModuleType
 from typing import Optional, Union
 
 from ..modeling_flash_attention_utils import lazy_import_flash_attention
+from ..utils import logging
 from .flash_attention import flash_attention_forward
 
 
+logger = logging.get_logger(__name__)
+
 try:
     from kernels import (
         Device,
@@ -158,6 +162,13 @@ def register_kernel_mapping(*args, **kwargs):
         raise RuntimeError("register_kernel_mapping requires `kernels` to be installed. Run `pip install kernels`.")
 
 
+_HUB_KERNEL_MAPPING: dict[str, str] = {
+    "causal-conv1d": "kernels-community/causal-conv1d",
+}
+
+_KERNEL_MODULE_MAPPING: dict[str, Optional[ModuleType]] = {}
+
+
 def is_kernel(attn_implementation: Optional[str]) -> bool:
     """Check whether `attn_implementation` matches a kernel pattern from the hub."""
     return (
@@ -220,9 +231,53 @@ def load_and_register_attn_kernel(attn_implementation: str, attention_wrapper: O
     ALL_MASK_ATTENTION_FUNCTIONS.register(attn_implementation, ALL_MASK_ATTENTION_FUNCTIONS["flash_attention_2"])
 
 
+def lazy_load_kernel(kernel_name: str, mapping: dict[str, Optional[ModuleType]] = _KERNEL_MODULE_MAPPING):
+    if kernel_name in mapping and isinstance(mapping[kernel_name], ModuleType):
+        return mapping[kernel_name]
+    if kernel_name not in _HUB_KERNEL_MAPPING:
+        logger.warning(f"Kernel {kernel_name} not found in _HUB_KERNEL_MAPPING")
+        mapping[kernel_name] = None
+        return None
+    if _kernels_available:
+        from kernels import get_kernel
+
+        try:
+            kernel = get_kernel(_HUB_KERNEL_MAPPING[kernel_name])
+            mapping[kernel_name] = kernel
+        except FileNotFoundError:
+            mapping[kernel_name] = None
+
+    else:
+        # Try to import is_{kernel_name}_available from ..utils
+        import importlib
+
+        new_kernel_name = kernel_name.replace("-", "_")
+        func_name = f"is_{new_kernel_name}_available"
+
+        try:
+            utils_mod = importlib.import_module("..utils.import_utils", __package__)
+            is_kernel_available = getattr(utils_mod, func_name, None)
+        except Exception:
+            is_kernel_available = None
+
+        if callable(is_kernel_available) and is_kernel_available():
+            # Try to import the module "{kernel_name}" from parent package level
+            try:
+                module = importlib.import_module(f"{kernel_name}")
+                mapping[kernel_name] = module
+                return module
+            except Exception:
+                mapping[kernel_name] = None
+        else:
+            mapping[kernel_name] = None
+
+    return mapping[kernel_name]
+
+
 __all__ = [
     "LayerRepository",
     "use_kernel_forward_from_hub",
     "register_kernel_mapping",
     "replace_kernel_forward_from_hub",
+    "lazy_load_kernel",
 ]

src/transformers/models/falcon_mamba/modeling_falcon_mamba.py

@@ -30,12 +30,11 @@
 from ...activations import ACT2FN
 from ...configuration_utils import PreTrainedConfig
 from ...generation import GenerationMixin
+from ...integrations.hub_kernels import lazy_load_kernel
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_utils import PreTrainedModel
 from ...utils import ModelOutput, auto_docstring, logging
 from ...utils.import_utils import (
-    is_causal_conv1d_available,
-    is_kernels_available,
     is_mamba_ssm_available,
     is_mambapy_available,
 )
@@ -162,33 +161,6 @@ def reset(self):
             self.ssm_states[layer_idx].zero_()
 
 
-def _lazy_load_causal_conv1d():
-    global _causal_conv1d_cache
-    if _causal_conv1d_cache is not None:
-        return _causal_conv1d_cache
-
-    if is_kernels_available():
-        from kernels import get_kernel
-
-        try:
-            _causal_conv1d_kernel = get_kernel("kernels-community/causal-conv1d")
-        except FileNotFoundError:
-            # no kernel binary match, fallback to slow path
-            _causal_conv1d_cache = (None, None)
-        else:
-            _causal_conv1d_cache = (_causal_conv1d_kernel.causal_conv1d_update, _causal_conv1d_kernel.causal_conv1d_fn)
-    elif is_causal_conv1d_available():
-        from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
-
-        _causal_conv1d_cache = (causal_conv1d_update, causal_conv1d_fn)
-    else:
-        _causal_conv1d_cache = (None, None)
-    return _causal_conv1d_cache
-
-
-_causal_conv1d_cache = None
-
-
 def rms_forward(hidden_states, variance_epsilon=1e-6):
     """
     Calculates simple RMSNorm with no learnable weights. `MambaRMSNorm` will
@@ -268,7 +240,12 @@ def __init__(self, config: FalconMambaConfig, layer_idx: int):
         self.rms_eps = config.mixer_rms_eps
 
     def warn_slow_implementation(self):
-        causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+        causal_conv1d = lazy_load_kernel("causal-conv1d")
+        causal_conv1d_update, causal_conv1d_fn = (
+            (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+            if causal_conv1d is not None
+            else (None, None)
+        )
         is_fast_path_available = all(
             (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
         )
@@ -323,7 +300,12 @@ def cuda_kernels_forward(
             )
 
         else:
-            causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+            causal_conv1d = lazy_load_kernel("causal-conv1d")
+            causal_conv1d_update, causal_conv1d_fn = (
+                (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+                if causal_conv1d is not None
+                else (None, None)
+            )
             hidden_states, gate = projected_states.chunk(2, dim=1)
 
             if attention_mask is not None:
@@ -518,7 +500,12 @@ def forward(
         cache_position: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.LongTensor] = None,
     ):
-        causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+        causal_conv1d = lazy_load_kernel("causal-conv1d")
+        causal_conv1d_update, causal_conv1d_fn = (
+            (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+            if causal_conv1d is not None
+            else (None, None)
+        )
         is_fast_path_available = all(
             (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
         )

src/transformers/models/falcon_mamba/modular_falcon_mamba.py

@@ -19,6 +19,7 @@
 import torch
 from torch import nn
 
+from ...integrations.hub_kernels import lazy_load_kernel
 from ...utils import auto_docstring, logging
 from ...utils.import_utils import (
     is_mamba_ssm_available,
@@ -35,7 +36,6 @@
     MambaOutput,
     MambaPreTrainedModel,
     MambaRMSNorm,
-    _lazy_load_causal_conv1d,
 )
 
 
@@ -54,8 +54,6 @@
 else:
     selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None
 
-_causal_conv1d_cache = None
-
 
 class FalconMambaConfig(MambaConfig):
     """
@@ -258,7 +256,12 @@ def rms_forward(hidden_states, variance_epsilon=1e-6):
 
 class FalconMambaMixer(MambaMixer):
     def warn_slow_implementation(self):
-        causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+        causal_conv1d = lazy_load_kernel("causal-conv1d")
+        causal_conv1d_update, causal_conv1d_fn = (
+            (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+            if causal_conv1d is not None
+            else (None, None)
+        )
         is_fast_path_available = all(
             (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
         )
@@ -324,7 +327,12 @@ def cuda_kernels_forward(
             )
 
         else:
-            causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+            causal_conv1d = lazy_load_kernel("causal-conv1d")
+            causal_conv1d_update, causal_conv1d_fn = (
+                (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+                if causal_conv1d is not None
+                else (None, None)
+            )
             hidden_states, gate = projected_states.chunk(2, dim=1)
 
             if attention_mask is not None:
@@ -518,7 +526,12 @@ def forward(
         cache_position: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.LongTensor] = None,
     ):
-        causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+        causal_conv1d = lazy_load_kernel("causal-conv1d")
+        causal_conv1d_update, causal_conv1d_fn = (
+            (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+            if causal_conv1d is not None
+            else (None, None)
+        )
         is_fast_path_available = all(
             (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
         )

src/transformers/models/mamba/modeling_mamba.py

@@ -25,6 +25,7 @@
 from ...activations import ACT2FN
 from ...configuration_utils import PreTrainedConfig
 from ...generation import GenerationMixin
+from ...integrations.hub_kernels import lazy_load_kernel
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
@@ -33,8 +34,6 @@
     logging,
 )
 from ...utils.import_utils import (
-    is_causal_conv1d_available,
-    is_kernels_available,
     is_mamba_ssm_available,
     is_mambapy_available,
 )
@@ -54,32 +53,6 @@
 else:
     selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None
 
-_causal_conv1d_cache = None
-
-
-def _lazy_load_causal_conv1d():
-    global _causal_conv1d_cache
-    if _causal_conv1d_cache is not None:
-        return _causal_conv1d_cache
-
-    if is_kernels_available():
-        from kernels import get_kernel
-
-        try:
-            _causal_conv1d_kernel = get_kernel("kernels-community/causal-conv1d")
-        except FileNotFoundError:
-            # no kernel binary match, fallback to slow path
-            _causal_conv1d_cache = (None, None)
-        else:
-            _causal_conv1d_cache = (_causal_conv1d_kernel.causal_conv1d_update, _causal_conv1d_kernel.causal_conv1d_fn)
-    elif is_causal_conv1d_available():
-        from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
-
-        _causal_conv1d_cache = (causal_conv1d_update, causal_conv1d_fn)
-    else:
-        _causal_conv1d_cache = (None, None)
-    return _causal_conv1d_cache
-
 
 class MambaCache:
     """
@@ -236,7 +209,12 @@ def __init__(self, config: MambaConfig, layer_idx: int):
         self.warn_slow_implementation()
 
     def warn_slow_implementation(self):
-        causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+        causal_conv1d = lazy_load_kernel("causal-conv1d")
+        causal_conv1d_update, causal_conv1d_fn = (
+            (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+            if causal_conv1d is not None
+            else (None, None)
+        )
         is_fast_path_available = all(
             (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
         )
@@ -287,7 +265,12 @@ def cuda_kernels_forward(
             )
 
         else:
-            causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+            causal_conv1d = lazy_load_kernel("causal-conv1d")
+            causal_conv1d_update, causal_conv1d_fn = (
+                (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+                if causal_conv1d is not None
+                else (None, None)
+            )
             hidden_states, gate = projected_states.chunk(2, dim=1)
 
             if attention_mask is not None:
@@ -451,7 +434,12 @@ def forward(
         cache_position: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.LongTensor] = None,
     ):
-        causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
+        causal_conv1d = lazy_load_kernel("causal-conv1d")
+        causal_conv1d_update, causal_conv1d_fn = (
+            (causal_conv1d.causal_conv1d_update, causal_conv1d.causal_conv1d_fn)
+            if causal_conv1d is not None
+            else (None, None)
+        )
         is_fast_path_available = all(
             (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
         )