MVP for Alternating Flow

src/sparseml/core/recipe/stage.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from enum import Enum
 from typing import Any, Dict, List, Optional
 
 from pydantic import Field, root_validator
@@ -23,14 +24,20 @@
 from sparseml.core.recipe.modifier import RecipeModifier
 
 
-__all__ = ["RecipeStage"]
+__all__ = ["RecipeStage", "StageRunType"]
+
+
+class StageRunType(Enum):
+    TRAIN = "train"
+    ONESHOT = "oneshot"
 
 
 class RecipeStage(RecipeBase):
     """
     Represents a stage in a recipe.
 
     :param group: Name of the current stage
+    :param run_type: Whether this is a oneshot or training stage
     :param args: Optional RecipeArgs to use for this stage
     :param enabled: True to enable the stage, False otherwise
     :param modifiers: list of RecipeModifiers that are a part of this stage
@@ -40,12 +47,28 @@ class RecipeStage(RecipeBase):
     """
 
     group: Optional[str] = None
+    run_type: Optional[StageRunType] = None
     args: Optional[RecipeArgs] = None
     enabled: bool = True
     modifiers: List[RecipeModifier] = Field(default_factory=list)
     exclude_default: bool = False
     args_evaluated: Optional[RecipeArgs] = None
 
+    def infer_run_type(self) -> Optional[StageRunType]:
+        """
+        Infers the stage type from the type attribute or stage name, falls back to None
+
+        :return: string representing stage type, either train or oneshot, or None if
+        stage cannot be inferred
+        """
+        if self.run_type == StageRunType.TRAIN or self.run_type == StageRunType.ONESHOT:
+            return self.run_type
+        if StageRunType.TRAIN.value in self.group:
+            return StageRunType.TRAIN
+        if StageRunType.ONESHOT.value in self.group:
+            return StageRunType.ONESHOT
+        return None
+
     def calculate_start(self) -> int:
         """
         :return: the start epoch for the stage, atleast one modifier

src/sparseml/core/session.py

@@ -269,6 +269,13 @@ def reset(self):
         """
         self._lifecycle.reset()
 
+    def reset_stage(self):
+        """
+        Reset the session for starting a new stage, recipe and model stays intact
+        """
+        self.lifecycle.initialized_ = False
+        self.lifecycle.finalized = False
+
     def get_serialized_recipe(self) -> str:
         """
         :return: serialized string of the current compiled recipe

src/sparseml/pytorch/model_load/helpers.py

@@ -60,6 +60,10 @@ def apply_recipe_structure_to_model(model: Module, recipe_path: str, model_path:
         model=model, recipe=recipe_path, framework=Framework.pytorch
     )
 
+    # no need to reload if no recipe was applied
+    if recipe_path is None:
+        return
+
     session = session_manager.active_session()
     num_stages = len(session.lifecycle.recipe_container.compiled_recipe.stages)
     msg = (
@@ -233,3 +237,16 @@ def parse_dtype(dtype_arg: str) -> torch.dtype:
         dtype = torch.float32
 
     return dtype
+
+
+def get_session_model() -> Module:
+    """
+    :return: pytorch module stored by the active SparseSession, or None if no session
+    is active
+    """
+    session = session_manager.active_session()
+    if not session:
+        return None
+
+    active_model = session.state.model.model
+    return active_model

src/sparseml/transformers/finetune/runner.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 
 import logging
-from typing import List
+import os
+from typing import List, Optional
 
 import torch
 from torch.nn import Module
@@ -22,6 +23,7 @@
 
 import sparseml.core.session as session_manager
 from sparseml.core.framework import Framework
+from sparseml.core.recipe import Recipe, StageRunType
 from sparseml.pytorch.model_load.helpers import fallback_to_cpu, save_model_and_recipe
 from sparseml.transformers.finetune import Trainer, TrainingArguments
 from sparseml.transformers.finetune.data import TextGenerationDataset
@@ -65,6 +67,7 @@ def __init__(
         self.model = model
         self.trainer = None
         self.tokenizer = None
+        self._output_dir = self._training_args.output_dir
 
     def populate_datasets(self, tokenizer: "AutoTokenizer"):
         """
@@ -95,10 +98,10 @@ def populate_datasets(self, tokenizer: "AutoTokenizer"):
 
         self.datasets = make_dataset_splits(
             tokenized_datasets,
-            self._training_args.do_train,
-            self._training_args.do_eval,
-            self._training_args.do_predict,
-            self._training_args.do_oneshot,
+            do_train=self._training_args.do_train or self._training_args.run_stages,
+            do_eval=self._training_args.do_eval,
+            do_predict=self._training_args.do_predict,
+            do_oneshot=self._training_args.do_oneshot or self._training_args.run_stages,
         )
         self.tokenizer = tokenizer
 
@@ -144,9 +147,11 @@ def format_calibration_data(self) -> List[torch.Tensor]:
             : min(self._data_args.num_calibration_samples, len(parsed_calib_data))
         ]
 
-    def one_shot(self):
+    def one_shot(self, stage: Optional[str] = None):
         """
         Run oneshot calibration on the active model
+
+        :param stage: which stage of the recipe to run, or None to run whole recipe
         """
         _LOGGER.info("*** One Shot ***")
 
@@ -155,6 +160,7 @@ def one_shot(self):
         session_manager.apply(
             framework=Framework.pytorch,
             recipe=self._training_args.recipe,
+            recipe_stage=stage,
             model=self.model,
             calib_data=calib_data,
             start=-1,
@@ -164,25 +170,29 @@ def one_shot(self):
 
         save_model_and_recipe(
             model=self.model,
-            save_path=self._training_args.output_dir,
+            save_path=self._output_dir,
             tokenizer=self.tokenizer,
         )
 
-    def train(self, checkpoint: str):
+    def train(self, checkpoint: str, stage: Optional[str] = None):
         """
         Run trainer's training loop on train_dataset, saving the resulting model to
         output_dir
 
         :param checkpoint: Optional checkpoint to resume from
+        :param stage: which stage of the recipe to run, or None to run whole recipe
         """
-        train_result = self.trainer.train(resume_from_checkpoint=checkpoint)
+        _LOGGER.info("*** Train ***")
+        train_result = self.trainer.train(
+            resume_from_checkpoint=checkpoint, stage=stage
+        )
         metrics = train_result.metrics
         metrics["train_samples"] = len(self.get_dataset_split("train"))
         self.trainer.log_metrics("train", metrics)
         self.trainer.save_metrics("train", metrics)
 
         # this includes saving the state, optimizer and scheduler
-        self.trainer.save_model()
+        self.trainer.save_model(output_dir=self._output_dir)
 
     def evaluate(self):
         """
@@ -206,3 +216,42 @@ def predict(self):
         metrics["predict_samples"] = len(self.dataset["test"])
         self.trainer.log_metrics("predict", metrics)
         self.trainer.save_metrics("predict", metrics)
+
+    def run_sequential_stages(self):
+        """
+        Run the recipe stage by stage, allowing for alternating between one-shot and
+        finetuning flows. Optionally save the model output at the end of each stage
+        """
+
+        recipe_obj = Recipe.create_instance(self._training_args.recipe)
+
+        for stage in recipe_obj.stages:
+            # validate stage
+            stage_name = stage.group
+            run_type = stage.infer_run_type()
+            if not run_type:
+                raise ValueError(
+                    f"a valid stage type ({[e.value for e in StageRunType]}) "
+                    "must be provided in run_stages mode. Either add a run_type "
+                    "attribute to each stage in the recipe or include it as part of "
+                    "the stage name."
+                )
+
+            # setup checkpoint dir, TODO: this should be optional
+            self._output_dir = os.path.join(
+                self._training_args.output_dir, "stage_" + stage_name
+            )
+            if not os.path.exists(self._output_dir):
+                os.makedirs(self._output_dir)
+
+            # run stage
+            if run_type is StageRunType.ONESHOT:
+                self.one_shot(stage=stage_name)
+            elif run_type is StageRunType.TRAIN:
+                self.train(checkpoint=None, stage=stage_name)
+
+            # setup for next stage
+            session = session_manager.active_session()
+            session.reset_stage()
+
+        self.trainer.log_model_sparsification()

src/sparseml/transformers/finetune/session_mixin.py

@@ -53,7 +53,6 @@ class SessionManagerMixIn:
     Mix-In class to extend the Hugging Face Trainer class to support SparseML recipes
     for one-shot and finetuning flows.
 
-    :param model: PyTorch model to run training on
     :param model_state_path: path to Pytorch model checkpoint or saved model
     :param recipe: path to recipe file to apply during training
     :param recipe_args: additional kwargs to use for evaluating recipe
@@ -64,9 +63,8 @@ class SessionManagerMixIn:
 
     def __init__(
         self,
-        model: Module,
         model_state_path: str,
-        recipe: Optional[str],
+        recipe: Optional[str] = None,
         recipe_args: Optional[Union[Dict[str, Any], str]] = None,
         metadata_args: Optional[List[str]] = None,
         data_args: Optional["DataTrainingArguments"] = None,  # noqa: F821
@@ -96,7 +94,7 @@ def __init__(
         session_manager.create_session()
 
         # call Trainer initialization
-        super().__init__(model=model, **kwargs)
+        super().__init__(**kwargs)
 
         # setup callbacks and loss
         self.optim_callbacks = TrainingLoopCallbacks(self)
@@ -105,7 +103,7 @@ def __init__(
         self.callback_handler.add_callback(self.callback_disable_fp16)
         self.criterion = torch.nn.CrossEntropyLoss()
 
-        model_signature = inspect.signature(model.forward)
+        model_signature = inspect.signature(self.model.forward)
         self._model_signature_columns = list(model_signature.parameters.keys())
 
         if self.teacher is not None and teacher not in ("disable", "self"):
@@ -114,14 +112,20 @@ def __init__(
         else:
             self._teacher_signature_columns = None
 
-    def initialize_session(self, epoch: float, checkpoint: Optional[str]):
+    def initialize_session(
+        self,
+        epoch: float,
+        checkpoint: Optional[str] = None,
+        stage: Optional[str] = None,
+    ):
         """
         Initialize the SparseSession from the specified epoch, evaluates the recipe
         and initialized the modifiers for the training session
 
         :param epoch: Epoch to initialize session from, usually 0 unless loading
         from a checkpoint
         :param checkpoint: Optional checkpoint to initialize from to continue training
+        :param stage: Optional stage of recipe to run, or None to run all stages
         """
         session = session_manager.active_session()
         if session.lifecycle.initialized_ or session.lifecycle.finalized:
@@ -135,6 +139,7 @@ def initialize_session(self, epoch: float, checkpoint: Optional[str]):
             model=self.model,
             teacher_model=self.teacher,  # TODO: what about for self/disable?
             recipe=self.recipe,
+            recipe_stage=stage,
             recipe_args=self.recipe_args,
             framework=Framework.pytorch,
             train_data=train_data,
@@ -305,19 +310,20 @@ def prediction_step(
         )
         return model_outputs
 
-    def train(self, *args, **kwargs):
+    def train(self, *args, stage: Optional[str] = None, **kwargs):
         """
         Run a sparsification training cycle. Runs initialization for the sparse session
         before calling super().train() and finalization of the session after.
 
         Logs sparsification details for the trained model.
 
         :param args: positional args to pass to super().train()
+        :param stage: Optional stage of recipe to run, or None to run all stages
         :param kwargs: keyword args to pass to super().train()
         :return: the output from super.train()
         """
         checkpoint, epoch = self._calculate_checkpoint_info(kwargs)
-        self.initialize_session(epoch=epoch, checkpoint=checkpoint)
+        self.initialize_session(epoch=epoch, checkpoint=checkpoint, stage=stage)
         self.callback_disable_fp16.check_disable(epoch, force=True)
         self.accelerator.wait_for_everyone()
         output = super().train(*args, **kwargs)

src/sparseml/transformers/finetune/text_generation.py

@@ -34,6 +34,7 @@
 
 from sparseml.pytorch.model_load.helpers import (
     apply_recipe_structure_to_model,
+    get_session_model,
     parse_dtype,
 )
 from sparseml.transformers.finetune import Trainer, TrainingArguments
@@ -192,6 +193,7 @@ def main(
     else:
         if not os.path.exists(recipe_path):
             _LOGGER.warning(f"No recipes were applied for {model_path}.")
+            apply_recipe_structure_to_model(model, None, model_path)
         else:
             _LOGGER.warning(f"Applying recipe {recipe_path} to {model_path}")
             apply_recipe_structure_to_model(model, recipe_path, model_path)
@@ -233,21 +235,28 @@ def main(
 
     # Initialize our Trainer
     trainer = Trainer(
-        model=model,
+        model_init=get_session_model,
         teacher=teacher,
         model_state_path=model_path,
         recipe=training_args.recipe,
         metadata_args=metadata_args,
         recipe_args=training_args.recipe_args,
         args=training_args,
         data_args=data_args,
-        train_dataset=train_dataset if training_args.do_train else None,
-        eval_dataset=eval_dataset if training_args.do_eval else None,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
         tokenizer=tokenizer,
         data_collator=data_collator,
     )
     stage_runner.set_trainer(trainer)
 
+    # alternating Training/One-shot
+    if training_args.run_stages:
+        stage_runner.run_sequential_stages()
+
+        # exit immediately
+        return
+
     # Training
     if training_args.do_train:
         checkpoint = None