Support Cache Class for Even Newer Versions of Transformers Library (#1343)

Summary:
Pull Request resolved: #1343

Supports multiple and newer versions of the transformers library. Adds the `packaging` dependency as well to more robustly check package versions.

Reviewed By: vivekmig

Differential Revision: D62468332

fbshipit-source-id: 6cbe984adc867771242dec1bc98ae1ab1962bd93

Author: craymichael

captum/_utils/transformers_typing.py

@@ -2,10 +2,13 @@
 
 # pyre-strict
 
-from typing import Optional, Protocol, Tuple, Type
+from typing import Any, Dict, Optional, Protocol, Tuple, Type
 
 import torch
 
+from packaging.version import Version
+from torch import nn
+
 
 class CacheLike(Protocol):
     """Protocol for cache-like objects."""
@@ -21,12 +24,96 @@ def from_legacy_cache(
     ) -> "DynamicCacheLike": ...
 
 
+transformers_installed: bool
+Cache: Optional[Type[CacheLike]]
+DynamicCache: Optional[Type[DynamicCacheLike]]
+
 try:
-    # pyre-ignore[21]: Could not find a module corresponding to import
-    #  `transformers.cache_utils`
-    from transformers.cache_utils import Cache as _Cache, DynamicCache as _DynamicCache
+    # pyre-ignore[21]: Could not find a module corresponding to import `transformers`.
+    import transformers  # noqa: F401
+
+    transformers_installed = True
 except ImportError:
-    _Cache = _DynamicCache = None
+    transformers_installed = False
+
+if transformers_installed:
+    try:
+        # pyre-ignore[21]: Could not find a module corresponding to import
+        # `transformers.cache_utils`.
+        from transformers.cache_utils import (  # noqa: F401
+            Cache as _Cache,
+            DynamicCache as _DynamicCache,
+        )
+
+        Cache = _Cache
+        # pyre-ignore[9]: Incompatible variable type: DynamicCache is declared to have
+        # type `Optional[Type[DynamicCacheLike]]` but is used as type
+        # `Type[_DynamicCache]`
+        DynamicCache = _DynamicCache
+    except ImportError:
+        Cache = DynamicCache = None
+else:
+    Cache = DynamicCache = None
+
+# GenerationMixin._update_model_kwargs_for_generation
+# "cache_position" at v4.39.0 (only needed for models that support cache class)
+# "use_cache" at v4.41.0 (optional, default is True)
+# "cache_position" is mandatory at v4.43.0 ("use_cache" is still optional, default True)
+_transformers_version: Optional[Version]
+if transformers_installed:
+    _transformers_version = Version(transformers.__version__)
+else:
+    _transformers_version = None
+
+_mandated_cache_version = Version("4.43.0")
+_use_cache_version = Version("4.41.0")
+_cache_position_version = Version("4.39.0")
+
+
+def update_model_kwargs(
+    model_kwargs: Dict[str, Any],
+    model: nn.Module,
+    input_ids: torch.Tensor,
+    caching: bool,
+) -> None:
+    if not supports_caching(model):
+        return
+    assert _transformers_version is not None
+    if caching:
+        # Enable caching
+        if _transformers_version >= _cache_position_version:
+            cache_position = torch.arange(
+                input_ids.shape[1], dtype=torch.int64, device=input_ids.device
+            )
+            model_kwargs["cache_position"] = cache_position
+        # pyre-ignore[58]: Unsupported operand `>=` is not supported for operand types
+        # `Optional[Version]` and `Version`.
+        if _transformers_version >= _use_cache_version:
+            model_kwargs["use_cache"] = True
+    else:
+        # Disable caching
+        if _transformers_version >= _use_cache_version:
+            model_kwargs["use_cache"] = False
+
 
-Cache: Optional[Type[CacheLike]] = _Cache
-DynamicCache: Optional[Type[DynamicCacheLike]] = _DynamicCache
+def supports_caching(model: nn.Module) -> bool:
+    if not transformers_installed:
+        # Not a transformers model
+        return False
+    # Cache may be optional or unsupported depending on model/version
+    try:
+        # pyre-ignore[21]: Could not find a module corresponding to import
+        # `transformers.generation.utils`.
+        from transformers.generation.utils import GenerationMixin
+    except ImportError:
+        return False
+    if not isinstance(model, GenerationMixin):
+        # Model isn't a GenerationMixin, we don't support additional caching logic
+        # for it
+        return False
+    assert _transformers_version is not None
+    if _transformers_version >= _mandated_cache_version:
+        # Cache is mandatory
+        return True
+    # Fallback on _supports_cache_class attribute
+    return getattr(model, "_supports_cache_class", False)

captum/attr/_core/llm_attr.py

@@ -7,7 +7,6 @@
 import numpy as np
 
 import torch
-from captum._utils.transformers_typing import Cache, DynamicCache
 from captum._utils.typing import TokenizerLike
 from captum.attr._core.feature_ablation import FeatureAblation
 from captum.attr._core.kernel_shap import KernelShap
@@ -259,6 +258,15 @@ def _forward_func(
         use_cached_outputs: bool = False,
         _inspect_forward: Optional[Callable[[str, str, List[float]], None]] = None,
     ) -> Tensor:
+        # Lazily import transformers_typing to avoid importing transformers package if
+        # it isn't needed
+        from captum._utils.transformers_typing import (
+            Cache,
+            DynamicCache,
+            supports_caching,
+            update_model_kwargs,
+        )
+
         perturbed_input = self._format_model_input(inp.to_model_input(perturbed_tensor))
         init_model_inp = perturbed_input
 
@@ -267,16 +275,25 @@ def _forward_func(
             [1, model_inp.shape[1]], dtype=torch.long, device=model_inp.device
         )
         model_kwargs = {"attention_mask": attention_mask}
+        # If applicable, update model kwargs for transformers models
+        update_model_kwargs(
+            model_kwargs=model_kwargs,
+            model=self.model,
+            input_ids=model_inp,
+            caching=use_cached_outputs,
+        )
 
         log_prob_list = []
         outputs = None
         for target_token in target_tokens:
             if use_cached_outputs:
                 if outputs is not None:
+                    # If applicable, convert past_key_values to DynamicCache for
+                    # transformers models
                     if (
                         Cache is not None
                         and DynamicCache is not None
-                        and getattr(self.model, "_supports_cache_class", False)
+                        and supports_caching(self.model)
                         and not isinstance(outputs.past_key_values, Cache)
                     ):
                         outputs.past_key_values = DynamicCache.from_legacy_cache(

setup.py

@@ -82,7 +82,9 @@ def report(*args):
 
 # get version string from module
 with open(os.path.join(os.path.dirname(__file__), "captum/__init__.py"), "r") as f:
-    version = re.search(r"__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M).group(1)
+    version_match = re.search(r"__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M)
+    assert version_match is not None, "Unable to find version string."
+    version = version_match.group(1)
     report("-- Building version " + version)
 
 # read in README.md as the long description
@@ -147,7 +149,13 @@ def get_package_files(root, subdirs):
         long_description=long_description,
         long_description_content_type="text/markdown",
         python_requires=">=3.8",
-        install_requires=["matplotlib", "numpy<2.0", "torch>=1.10", "tqdm"],
+        install_requires=[
+            "matplotlib",
+            "numpy<2.0",
+            "packaging",
+            "torch>=1.10",
+            "tqdm",
+        ],
         packages=find_packages(exclude=("tests", "tests.*")),
         extras_require={
             "dev": DEV_REQUIRES,