Merge branch 'sa/quant_mod_refactor' of github.com:neuralmagic/sparseml into sa/quant_mod_refactor

Author: horheynm

.github/workflows/test-check.yaml

@@ -165,7 +165,7 @@ jobs:
       - name: "Clean sparsezoo directory"
         run: rm -r sparsezoo/
       - name: "⚙️ Install dependencies"
-        run: pip3 install .[dev,torchvision,onnxruntime]
+        run: pip3 install .[dev,torchvision,onnxruntime,transformers]
       - name: "🔬 Running pytorch tests"
         run: make test TARGETS=pytorch
   compat-pytorch-1_9-pytorch-tests:
@@ -194,7 +194,7 @@ jobs:
       - name: "Clean sparsezoo directory"
         run: rm -r sparsezoo/
       - name: "⚙️ Install dependencies"
-        run: pip3 install .[dev,torchvision,onnxruntime] torch==1.9.1
+        run: pip3 install .[dev,torchvision,onnxruntime,transformers]
       - name: "🔬 Running pytorch tests"
         run: make test TARGETS=pytorch
   compat-pytorch-1_9-onnx-tests:

integrations/huggingface-transformers/tutorials/text-generation/trl_mixin/README.md

@@ -30,17 +30,17 @@ class SFTTrainer(SessionManagerMixIn, TRLSFTTrainer):
 
 The new `SFTTrainer` class can now apply SparseML recipes and modifiers during 
 supervised finetuning, will full support for all of the original TRL features. The full
-class is defined in [sft_trainer.py](sft_trainer.py) and requires very minimal 
+class is defined in the script `sft_trainer.py` and requires very minimal 
 additional code: just a dataset load override to support passing in tokenized datasets 
 to the Trainer. 
 
 ### Examples
 
-[ex_trl_sft_data.py](ex_trl_sft_data.py): finetunes a 50% sparse Llama-7b model,
+* Script `ex_trl_sft_data.py`: finetunes a 50% sparse Llama-7b model,
 using TRL's dataset preprocessing. Sparsity is maintained throughout training by 
 applying a `ConstantPruningModifier` recipe to the `SFTTrainer` 
 
-[ex_trl_distillation.py](ex_trl_distillation.py): finetunes a 50% sparse Llama-7b 
+* Script `ex_trl_distillation.py`: finetunes a 50% sparse Llama-7b 
 model using knowledge distillation from a dense Llama-7b model. Sparsity is maintained 
 throughout training with a `ConstantPruningModifier` and layer-wise knowledge 
 distillation is handled by the `OutputDistillationModifier`

src/sparseml/core/recipe/recipe.py

@@ -580,15 +580,23 @@ def _get_yaml_dict(self) -> Dict[str, Any]:
         # populate stages
         stages = original_recipe_dict["stages"]
         for stage_name, stage_list in stages.items():
-            # stage is always a list of size 1
-            stage = stage_list[0]
-            stage_dict = get_yaml_serializable_stage_dict(modifiers=stage["modifiers"])
+            for idx, stage in enumerate(stage_list):
+                if len(stage_list) > 1:
+                    # resolve name clashes caused by combining recipes with
+                    # duplicate stage names
+                    final_stage_name = f"{stage_name}_{idx}"
+                else:
+                    final_stage_name = stage_name
+                stage_dict = get_yaml_serializable_stage_dict(
+                    modifiers=stage["modifiers"]
+                )
+
+                # infer run_type from stage
+                if run_type := stage.get("run_type"):
+                    stage_dict["run_type"] = run_type
 
-            # infer run_type from stage
-            if run_type := stage.get("run_type"):
-                stage_dict["run_type"] = run_type
+                yaml_recipe_dict[final_stage_name] = stage_dict
 
-            yaml_recipe_dict[stage_name] = stage_dict
         return yaml_recipe_dict
 
 

tests/sparseml/transformers/sparsification/modification/test_modifying_mobilebert.py renamed to 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 renamed to 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 renamed to src/sparseml/modifiers/quantization/modification/modify_model.py

@@ -15,31 +15,34 @@
 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", disable: bool = False  # noqa: F821
+) -> "torch.nn.Module":  # noqa: F821
     """
-    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
+    :param disable: If True, the modification will be disabled
+    :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"
+    NM_DISABLE_QUANTIZATION_MODIFICATION = os.environ.get(
+        "NM_DISABLE_QUANTIZATION_MODIFICATION", "False"
     ).lower() in ["true", "1"]
+
     try:
         modification_func = ModificationRegistry.get_value_from_registry(model_name)
     except KeyError:
@@ -50,7 +53,7 @@ def modify_model(model: torch.nn.Module, disable: int = False) -> torch.nn.Modul
         )
         return model
 
-    if NM_DISABLE_TRANSFORMERS_MODIFICATION:
+    if NM_DISABLE_QUANTIZATION_MODIFICATION:
         _LOGGER.debug(
             "Application of the modification function to model "
             "disabled through the environment variable."
@@ -65,6 +68,6 @@ def modify_model(model: torch.nn.Module, disable: int = False) -> torch.nn.Modul
         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 renamed to 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,
@@ -73,11 +74,16 @@ 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)
 
     def on_initialize(self, state: State, **kwargs) -> bool:
         raise_if_torch_quantization_not_available()
+        module = state.model.model
+        module = modify_model(module)
         if self.end and self.end != -1:
             raise ValueError(
                 "end_epoch is disabled for QuantizationModifier and can only be set to"

src/sparseml/modifiers/smoothquant/base.py

@@ -16,8 +16,6 @@
 from dataclasses import dataclass
 from typing import Dict, Generic, List, Optional, Tuple, TypeVar
 
-from pydantic import Field
-
 from sparseml.core import Modifier
 from sparseml.core.model import ModifiableModel
 from sparseml.core.model.base import LT
@@ -98,7 +96,7 @@ class SmoothQuantModifier(Modifier):
      use the whole dataset
     """
 
-    smoothing_strength: float = Field(validation_alias="alpha", default=0.5)
+    smoothing_strength: float = 0.5
     mappings: List[Tuple]
     ignore: Optional[List[str]] = None
     num_calibration_steps: Optional[int] = None

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 *