[Export Refactor] Feature Branch

src/sparseml/export/export.py

@@ -27,7 +27,7 @@
 from sparseml.export.validators import validate_correctness as validate_correctness_
 from sparseml.export.validators import validate_structure as validate_structure_
 from sparseml.pytorch.opset import TORCH_DEFAULT_ONNX_OPSET
-from sparseml.pytorch.utils.helpers import default_device, use_single_gpu
+from sparseml.pytorch.utils.helpers import default_device
 from src.sparseml.integration_helper_functions import (
     IntegrationHelperFunctions,
     resolve_integration,
@@ -46,8 +46,9 @@ def export(
     single_graph_file: bool = True,
     num_export_samples: int = 0,
     batch_size: int = 1,
+    recipe: Optional[Union[Path, str]] = None,
     deployment_directory_name: str = "deployment",
-    device: str = "auto",
+    device: str = "cpu",
     graph_optimizations: Union[str, List[str], None] = "all",
     validate_correctness: bool = False,
     validate_structure: bool = True,
@@ -82,6 +83,9 @@ def export(
         the model to. Defaults to 'deepsparse'.
     :param opset: The ONNX opset to use for exporting the model.
         Defaults to the latest supported opset.
+    :param recipe: The path to the recipe to use for exporting the model.
+        Defaults to None. If a recipe is found in the source_path, it will
+        be automatically used for export.
     :param single_graph_file: Whether to save the model as a single
         file. Defaults to True.
     :param num_export_samples: The number of samples to create for
@@ -109,14 +113,24 @@ def export(
     :param task: Optional task to use for exporting the model.
         Defaults to None.
     """
+    # TODO: Remove with the followin once sparsezoo: #404 lands
+    """
+    from sparsezoo.utils.registry import standardize_lookup_name
+    task = standardize_lookup_name(task)
+    """
+    if task is not None:
+        task = task.replace("_", "-").replace(" ", "-")
+
+    # TODO: Remove once sparsezoo: #404 lands
+    if integration is not None:
+        integration = integration.replace("_", "-").replace(" ", "-")
 
     # create the target path if it doesn't exist
     if not Path(target_path).exists():
         Path(target_path).mkdir(parents=True, exist_ok=True)
 
     # choose the appropriate device
     device = default_device() if device == "auto" else device
-    device = use_single_gpu(device) if "cuda" in device else device
 
     # assert the valid deployment target
     if deployment_target not in AVAILABLE_DEPLOYMENT_TARGETS:
@@ -140,8 +154,14 @@ def export(
     # that were created along with the model and are needed
     # for the export
     model, loaded_model_kwargs = helper_functions.create_model(
-        source_path, device=device, task=task, batch_size=batch_size, **kwargs
+        source_path,
+        device=device,
+        task=task,
+        batch_size=batch_size,
+        recipe=recipe,
+        **kwargs,
     )
+    model.eval()
 
     if loaded_model_kwargs:
         _LOGGER.info(

src/sparseml/integration_helper_functions.py

@@ -51,10 +51,6 @@ def resolve_integration(
         will attempt to infer it from the source_path.
     :return: The name of the integration to use for exporting the model.
     """
-
-    if integration is not None:
-        integration = integration.replace("_", "-")
-
     from sparseml.pytorch.image_classification.utils.helpers import (
         is_image_classification_model,
     )

src/sparseml/pytorch/model_load/helpers.py

@@ -25,7 +25,7 @@
 from sparseml.pytorch.sparsification.quantization.helpers import (
     initialize_channel_wise_scale_zp,
 )
-from sparseml.transformers.utils import SparseAutoModel
+from sparseml.pytorch.utils import ModuleSparsificationInfo
 
 
 __all__ = [
@@ -42,6 +42,45 @@
 RECIPE_FILE_NAME = "recipe.yaml"
 
 
+def log_model_load(
+    model: Module, model_name_or_path: str, model_type: str, delayed_load: bool
+):
+    """
+    Log the state of a loaded model including sparsity and
+    prunable params information.
+
+    :param model: the loaded model
+    :param model_name_or_path: the original name of or path to the model that loaded
+    :param model_type: specify the type of model loaded for logging;
+        ex one of [model, student, teacher]
+    :param delayed_load: True if this model load was delayed until after
+        recipe instantiation due to QAT or other architectural state changes
+    """
+    if delayed_load:
+        _LOGGER.info(
+            f"Delayed load of model {model_name_or_path} detected. "
+            f"Will print out model information once SparseML recipes have loaded"
+        )
+        return
+
+    sparsification_info = ModuleSparsificationInfo(model)
+
+    _LOGGER.info(
+        f"Loaded {model_type} from {model_name_or_path} "
+        f"with {sparsification_info.params_total} total params. "
+        f"Of those there are {sparsification_info.params_prunable_total} prunable "
+        f"params which have {sparsification_info.params_prunable_sparse_percent} "
+        "avg sparsity."
+    )
+    model_type = (
+        "sparse" if sparsification_info.params_prunable_sparse_percent > 5 else "dense"
+    )
+    _LOGGER.info(
+        f"{model_type} model detected, "
+        f"all sparsification info: {sparsification_info}"
+    )
+
+
 def apply_recipe_structure_to_model(model: Module, recipe_path: str, model_path: str):
     """
     Takes a loaded Pytorch model and applies any structural changes such as quantization
@@ -152,7 +191,7 @@ def reload_model_state(
     _LOGGER.info(
         f"Reloaded {total_loaded} model params for SparseML Recipe from {load_path}"
     )
-    SparseAutoModel.log_model_load(
+    log_model_load(
         model,
         load_path,
         model_type="student",

src/sparseml/pytorch/utils/helpers.py

@@ -137,15 +137,6 @@ def default_device() -> str:
     return "cuda:{}".format(",".join(device_ids))
 
 
-def use_single_gpu(device: str) -> str:
-    """
-    return: the first gpu in the device string if multiple are available
-    """
-    if "cuda" not in device:
-        raise ValueError("use_single_gpu should only be called on cuda devices")
-    return device.split(",")[0]
-
-
 def device_of(inputs: Any):
     if isinstance(inputs, Tensor):
         return inputs.device

src/sparseml/transformers/finetune/text_generation.py

@@ -178,11 +178,20 @@ def main(
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     # this calls from_pretrained under the hood so should be FSDP safe
-    model, teacher = SparseAutoModel.text_generation_from_pretrained_distil(
+    model = SparseAutoModel.text_classification_from_pretrained(
         model_name_or_path=model_path,
-        teacher_name_or_path=training_args.distill_teacher,
-        model_kwargs=model_kwargs,
-        teacher_kwargs=teacher_kwargs,
+        model_type="student" if training_args.distill_teacher else "model",
+        **model_kwargs,
+    )
+    teacher = (
+        SparseAutoModel.text_classification_from_pretrained(
+            model_name_or_path=training_args.distill_teacher,
+            model_type="teacher",
+            **teacher_kwargs,
+        )
+        if training_args.distill_teacher
+        and training_args.distill_teacher not in ["self", "disable"]
+        else training_args.distill_teacher
     )
 
     # initialize structure of input model from recipe if needed

src/sparseml/transformers/integration_helper_functions.py

@@ -27,6 +27,7 @@
     NLG_TOKENIZER_FILES,
     OPTIONAL_DEPLOYMENT_FILES,
     TaskNames,
+    resolve_sequence_length,
 )
 from sparseml.transformers.utils.load_task_dataset import load_task_dataset
 from sparseml.transformers.utils.optimizations import apply_kv_cache_injection
@@ -38,10 +39,9 @@
 from src.sparseml.transformers.utils.initializers import (
     _parse_data_args,
     initialize_config,
-    initialize_model,
+    initialize_sparse_model,
     initialize_tokenizer,
     initialize_trainer,
-    resolve_sequence_length,
 )
 
 
@@ -52,6 +52,7 @@ def create_model(
     source_path: Union[Path, str],
     device: Optional[str] = None,
     task: Optional[str] = None,
+    recipe: Optional[str] = None,
     **kwargs,
 ) -> Tuple[torch.nn.Module, Dict[str, Any]]:
     """
@@ -61,6 +62,8 @@ def create_model(
     :param source_path: The path to the model
     :param device: The device to use for the model and dataloader instantiation
     :param task: The task to use for the model and dataloader instantiation
+    :param recipe: The recipe to use for the model and dataloader instantiation.
+        If None, attempt to use the default recipe
 
     :return: A tuple of the
         - torch model
@@ -74,7 +77,6 @@ def create_model(
 
     if task is None:
         raise ValueError("To create a transformer model, a task must be specified")
-    task = task.replace("_", "-")
 
     if not trust_remote_code:
         _LOGGER.warning(
@@ -85,11 +87,13 @@ def create_model(
     config = initialize_config(source_path, trust_remote_code, **config_args)
     sequence_length = sequence_length or resolve_sequence_length(config)
     tokenizer = initialize_tokenizer(source_path, sequence_length, task)
-    model = initialize_model(
+    model = initialize_sparse_model(
         model_path=source_path,
         task=task,
         config=config,
         trust_remote_code=trust_remote_code,
+        recipe=recipe,
+        sequence_length=sequence_length,
         device=device,
     )
 
@@ -108,9 +112,8 @@ def create_model(
     else:
         validation_dataset = None
 
-    model.train()
     trainer = initialize_trainer(model, source_path, validation_dataset)
-    model.eval()
+    # TODO: Parse out dataloader from the trainer
 
     return model, dict(
         trainer=trainer,

src/sparseml/transformers/sparsification/obcq/obcq.py

@@ -36,7 +36,7 @@
     llama_forward,
     opt_forward,
 )
-from sparseml.transformers.utils.model import SparseCausalLM
+from sparseml.transformers.utils.sparse_model import SparseAutoModel
 
 
 __all__ = ["one_shot"]
@@ -93,18 +93,18 @@ def one_shot(
     model_loader_fn = None
     forward_fn = None
     if "opt" in model_type:
-        model_loader_fn = SparseCausalLM.opt_model_from_pretrained
+        model_loader_fn = SparseAutoModel.text_classification_from_pretrained
         forward_fn = opt_forward
     elif "llama" in model_type or "mistral" in model_type:
-        model_loader_fn = SparseCausalLM.auto_model_from_pretrained
+        model_loader_fn = SparseAutoModel.text_classification_from_pretrained
         forward_fn = llama_forward
     else:
         _LOGGER.warning(
             f"A supported model type({SUPPORTED_MODELS}) could not be "
             f"parsed from model_path={model_path}. Defaulting to "
-            "AutoModelForCausalLM loading. "
+            "SparseAutoModel loading. "
         )
-        model_loader_fn = SparseCausalLM.auto_model_from_pretrained
+        model_loader_fn = SparseAutoModel.text_classification_from_pretrained
         forward_fn = llama_forward
     torch_dtype = parse_dtype(precision)
     model = model_loader_fn(

src/sparseml/transformers/sparsification/trainer.py

@@ -37,6 +37,7 @@
 from transformers.trainer_pt_utils import reissue_pt_warnings
 from transformers.trainer_utils import ShardedDDPOption, get_last_checkpoint
 
+from sparseml.pytorch.model_load.helpers import log_model_load
 from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
 from sparseml.pytorch.sparsification.quantization.helpers import (
     initialize_channel_wise_scale_zp,
@@ -47,7 +48,6 @@
     TensorBoardLogger,
     WANDBLogger,
 )
-from sparseml.transformers.utils import SparseAutoModel
 from sparseml.transformers.utils.helpers import RECIPE_NAME
 
 
@@ -638,7 +638,7 @@ def _reload_model_state(self, load_path: str, orig_state_dict: Dict[str, Any]):
         _LOGGER.info(
             f"Reloaded {total_loaded} model params for SparseML Recipe from {load_path}"
         )
-        SparseAutoModel.log_model_load(
+        log_model_load(
             self.model,
             self.model_state_path,
             model_type="student" if self.teacher else "model",

src/sparseml/transformers/utils/__init__.py

@@ -19,4 +19,4 @@
 # flake8: noqa
 from .helpers import *
 from .metrics import *
-from .model import *
+from .sparse_model import *