move the model modification into the quantization modifier logic

tests/sparseml/transformers/sparsification/modification/test_modifying_mobilebert.py → src/sparseml/modifiers/quantization/modification/__init__.py

@@ -11,21 +11,5 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-from copy import deepcopy
-
-from torch import nn
-
-from sparseml.transformers.sparsification.modification import modify_model
-from sparseml.transformers.sparsification.modification.modification_objects import (
-    QATLinear,
-)
-
-
-def test_modifying_mobilebert(mobilebert_model):
-
-    mobilebert_ = deepcopy(mobilebert_model)
-    mobilebert = modify_model(mobilebert_model)
-
-    assert isinstance(mobilebert_.embeddings.embedding_transformation, nn.Linear)
-    assert isinstance(mobilebert.embeddings.embedding_transformation, QATLinear)
+# flake8: noqa
+from .modify_model import modify_model

src/sparseml/transformers/sparsification/modification/modification_objects.py → src/sparseml/modifiers/quantization/modification/modification_objects.py

@@ -14,7 +14,7 @@
 
 """
 Set of helper objects that are used to modify
-the HuggingFace transformer models
+the quantized models
 """
 
 import torch

src/sparseml/transformers/sparsification/modification/modify_model.py → src/sparseml/modifiers/quantization/modification/modify_model.py

@@ -13,33 +13,31 @@
 # limitations under the License.
 
 import logging
-import os
 
 import torch
 
-from sparseml.transformers.sparsification.modification.registry import (
-    ModificationRegistry,
-)
+from sparseml.modifiers.quantization.modification.registry import ModificationRegistry
 
 
 _LOGGER = logging.getLogger(__name__)
 
 
-def modify_model(model: torch.nn.Module, disable: int = False) -> torch.nn.Module:
+def modify_model(model: torch.nn.Module) -> torch.nn.Module:
     """
-    Modify the original transformers model so that it is
-    compatible with the SparseML library.
+    Modify the original model so that it is
+    compatible with the quantization format required by the
+    SparseML library.
+
     The model will be modified, if there exist a modification
     function for the model in the registry of modifications.
     Otherwise, the original model will be returned.
 
-    :param model: The original HuggingFace transformers model
-    :return: The potentially modified model
+    :param model: The original model to be modified
+    :return: The potentially modified model to support
+        SparseML quantization
     """
     model_name = model.__class__.__name__
-    NM_DISABLE_TRANSFORMERS_MODIFICATION = os.environ.get(
-        "NM_DISABLE_TRANSFORMERS_MODIFICATION", "False"
-    ).lower() in ["true", "1"]
+
     try:
         modification_func = ModificationRegistry.get_value_from_registry(model_name)
     except KeyError:
@@ -50,21 +48,7 @@ def modify_model(model: torch.nn.Module, disable: int = False) -> torch.nn.Modul
         )
         return model
 
-    if NM_DISABLE_TRANSFORMERS_MODIFICATION:
-        _LOGGER.debug(
-            "Application of the modification function to model "
-            "disabled through the environment variable."
-        )
-        return model
-
-    if disable:
-        _LOGGER.debug(
-            "Application of the modification function for to model "
-            "disabled through the `disable` argument."
-        )
-        return model
-
     _LOGGER.info(
-        f"Modifying the model {model_name} to be compatible with SparseML library"
+        f"Modifying the model {model_name} to be compatible with SparseML quantization"
     )
     return modification_func(model)

src/sparseml/transformers/sparsification/modification/registry.py → src/sparseml/modifiers/quantization/modification/registry.py

@@ -11,27 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-from sparseml.transformers.sparsification.modification.base import (
-    check_transformers_version,
-)
 from sparsezoo.utils.registry import RegistryMixin
 
 
 class ModificationRegistry(RegistryMixin):
     """
     A registry for modification functions that can be applied to models
-    so that they can be used in the context of sparseml.transformers
+    so that they can be compatible with the quantization format required by the
+    SparseML library.
     """
-
-    @classmethod
-    def get_value_from_registry(cls, name: str):
-        """
-        Extends the base class method to check the transformers version after
-        successfully retrieving the value from the registry. The motivation is
-        to ensure that the transformers version falls within the supported range
-        before we proceed with model modification.
-        """
-        retrieved_value = super().get_value_from_registry(name)
-        check_transformers_version()
-        return retrieved_value

src/sparseml/modifiers/quantization/pytorch.py

@@ -20,6 +20,7 @@
 
 from sparseml.core import Event, EventType, State
 from sparseml.modifiers.quantization.base import QuantizationModifier
+from sparseml.modifiers.quantization.modification import modify_model
 from sparseml.modifiers.quantization.utils.helpers import (
     configure_module_bn_wrappers,
     freeze_bn_stats,
@@ -73,6 +74,9 @@ def __init__(self, **kwargs):
 
     def on_initialize_structure(self, state: State, **kwargs):
         module = state.model.model
+        # before the structure is modified to support quantization,
+        # we need to potentially modify the model architecture
+        module = modify_model(module)
         self._enable_module_qat(module)
         state.model.model.apply(torch.quantization.disable_observer)
 

src/sparseml/transformers/sparsification/__init__.py

@@ -19,6 +19,7 @@
 
 # flake8: noqa
 
+from .modification import *
 from .question_answering import *
 from .sparse_config import *
 from .sparse_model import *

src/sparseml/transformers/sparsification/modification/__init__.py

@@ -11,11 +11,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 # flake8: noqa
-from .modify_model import modify_model
-from .modifying_bert import *
-from .modifying_distilbert import *
-from .modifying_llama import *
-from .modifying_mistral import *
-from .modifying_mobilebert import *
-from .modifying_opt import *
+# isort:skip_file
+
+from .base import check_transformers_version
+
+check_transformers_version()
+
+from .modifying_bert import modify
+from .modifying_llama import modify
+from .modifying_mistral import modify
+from .modifying_distilbert import modify
+from .modifying_mobilebert import modify
+from .modifying_opt import modify

src/sparseml/transformers/sparsification/modification/modifying_bert.py

@@ -18,64 +18,49 @@
 """
 
 
-import logging
 import math
 from typing import Optional, Tuple
 
 import torch
 from torch import nn
-from transformers.models.bert.modeling_bert import BertAttention, BertSelfAttention
+from transformers.models.bert.modeling_bert import BertSelfAttention
 
+from sparseml.modifiers.quantization.modification.modification_objects import QATMatMul
+from sparseml.modifiers.quantization.modification.registry import ModificationRegistry
 from sparseml.pytorch.utils.helpers import swap_modules
-from sparseml.transformers.sparsification.modification.modification_objects import (
-    QATMatMul,
-)
-from sparseml.transformers.sparsification.modification.registry import (
-    ModificationRegistry,
-)
-
-
-_LOGGER = logging.getLogger(__name__)
 
 
 @ModificationRegistry.register(name="BertModel", alias=["BertForQuestionAnswering"])
 def modify(model: nn.Module) -> nn.Module:
     """
     Modify the Bert model to be compatible with SparseML
+    quantization
 
-    1. Replaces the MultiHeadSelfAttention modules with
-        MultiHeadSelfAttentionWithQuantizableMatmuls modules
-
-    Note: This function will not alter any of the alternatives
-    to the MultiHeadSelfAttention module such as BertAttention
-
+    Replaces the attention modules with
+    MultiHeadSelfAttentionWithQuantizableMatmuls modules
     :param model: the original Bert model
     :return: the modified Bert model
     """
     for name, submodule in model.named_modules():
-        if type(submodule) is BertSelfAttention:
+        if isinstance(submodule, BertSelfAttention):
             swap_modules(
                 model, name, BertSelfAttentionWithQuantizableMatmuls(submodule)
             )
-        elif type(submodule) is BertAttention:
-            _LOGGER.debug(
-                f"The model contains {submodule.__class__.__name__} "
-                "module, which will not be modified"
-            )
     return model
 
 
 class BertSelfAttentionWithQuantizableMatmuls(BertSelfAttention):
     """
-    Wrapper around the original BertSelfAttention module to replace the
+    Wrapper around the original attention module to replace the
     matmul operations with quantizable matmul operations
 
-    :param bert_self_attention: the original BertSelfAttention module
+    :param bert_self_attention: the original attention module to be
+        wrapped and modified
     """
 
     def __init__(self, bert_self_attention: BertSelfAttention):
         self.__class__ = type(
-            bert_self_attention.__class__.__name__,
+            self.__class__.__name__,
             (self.__class__, bert_self_attention.__class__),
             {},
         )

src/sparseml/transformers/sparsification/modification/modifying_distilbert.py

@@ -28,13 +28,9 @@
     MultiHeadSelfAttention,
 )
 
+from sparseml.modifiers.quantization.modification.modification_objects import QATMatMul
+from sparseml.modifiers.quantization.modification.registry import ModificationRegistry
 from sparseml.pytorch.utils.helpers import swap_modules
-from sparseml.transformers.sparsification.modification.modification_objects import (
-    QATMatMul,
-)
-from sparseml.transformers.sparsification.modification.registry import (
-    ModificationRegistry,
-)
 
 
 _LOGGER = logging.getLogger(__name__)
@@ -44,40 +40,34 @@
 def modify(model: nn.Module) -> nn.Module:
     """
     Modify the DistilBert model to be compatible with SparseML
+    quantization
 
-    1. Replaces the MultiHeadSelfAttention modules with
-        MultiHeadSelfAttentionWithQuantizableMatmuls modules
-
-    Note: This function will not alter any of the alternatives
-    to the MultiHeadSelfAttention module such as DistilBertFlashAttention2
+    Replaces the attention modules with
+    MultiHeadSelfAttentionWithQuantizableMatmuls modules
 
     :param model: the original DistilBert model
     :return: the modified DistilBert model
     """
     for name, submodule in model.named_modules():
-        if type(submodule) is MultiHeadSelfAttention:
+        if isinstance(submodule, (MultiHeadSelfAttention, DistilBertFlashAttention2)):
             swap_modules(
                 model, name, MultiHeadSelfAttentionWithQuantizableMatmuls(submodule)
             )
-        if type(submodule) is DistilBertFlashAttention2:
-            _LOGGER.debug(
-                f"The model contains {submodule.__class__.__name__} "
-                "module, which will not be modified"
-            )
     return model
 
 
 class MultiHeadSelfAttentionWithQuantizableMatmuls(MultiHeadSelfAttention):
     """
-    Wrapper around the original MultiHeadSelfAttention module to replace the
-    matmul operations with quantizable matmul operations
+    Wrapper around the original attention module to introduce
+    MultiHeadSelfAttention  with quantizable matmul operations
 
-    :param mhs_attention: the original MultiHeadSelfAttention module
+    :param mhs_attention: the original attention module to be
+        wrapped and modified
     """
 
     def __init__(self, mhs_attention: MultiHeadSelfAttention):
         self.__class__ = type(
-            mhs_attention.__class__.__name__,
+            self.__class__.__name__,
             (self.__class__, mhs_attention.__class__),
             {},
         )