Remove unnecessary Tracing use in Minitron Search Space generation

Signed-off-by: Keval Morabia <[email protected]>

Author: kevalmorabia97

modelopt/torch/nas/plugins/megatron.py

@@ -13,12 +13,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Plugin to add NAS/Pruning support for megatron-core GPT model."""
+"""Plugin to add NAS/Pruning support for megatron-core Language models like GPT and Mamba."""
 
 import types
 from collections.abc import Callable, Sequence
 from typing import Any
-from warnings import warn
 
 import torch
 import torch.nn as nn
@@ -98,7 +97,7 @@
 except ImportError:
     HAS_MAMBA = False
 
-__all__ = ["drop_mcore_gpt_layers", "drop_mcore_language_model_layers"]
+__all__ = ["drop_mcore_language_model_layers"]
 
 
 class _DynamicParallelLinear(DynamicModule):
@@ -1457,15 +1456,6 @@ def drop_mcore_language_model_layers(model: nn.Module, *, layers_to_drop: list[i
     model.config.num_layers = new_num_layers
 
 
-def drop_mcore_gpt_layers(model: nn.Module, *, layers_to_drop: list[int]) -> None:
-    """[DEPRECATED] Remove given layers (1-indexed) of the model (works with TP and/or PP)."""
-    warn(
-        "`drop_mcore_gpt_layers` is deprecated in favor of `drop_mcore_language_model_layers`.",
-        DeprecationWarning,
-    )
-    drop_mcore_language_model_layers(model, layers_to_drop=layers_to_drop)
-
-
 class MegatronConstraintsFunc(ConstraintsFunc):
     """A Functor class to check if sub-net satisfied all provided constraints.
 

modelopt/torch/prune/plugins/mcore_minitron.py

@@ -27,23 +27,35 @@
 import copy
 
 import torch
+import torch.nn as nn
 from pydantic import create_model
 
 # isort: off
-# import nas plugin to check if it is enabled else raises an Exception
+# import nas plugin to check if it is enabled else raises an Exception and disables the plugin
 from modelopt.torch.nas.plugins.megatron import *  # noqa: F403
-from modelopt.torch.nas.plugins.megatron import HAS_MAMBA, _DynamicMCoreLanguageModel
+from modelopt.torch.nas.plugins.megatron import (
+    HAS_MAMBA,
+    _DynamicMCoreLanguageModel,
+    SUPPORTED_MODELS,
+)
 # isort: on
 
 from modelopt.torch.nas.conversion import NASModeRegistry
 from modelopt.torch.nas.registry import DMRegistry
-from modelopt.torch.nas.utils import sort_parameters
+from modelopt.torch.nas.utils import get_subnet_config, sort_parameters
 from modelopt.torch.opt.config import ModeloptBaseConfig, get_kwargs_for_create_model_with_rules
+from modelopt.torch.opt.conversion import ApplyModeError
+from modelopt.torch.opt.dynamic import DynamicSpace
+from modelopt.torch.opt.mode import (
+    ConvertEntrypoint,
+    ConvertReturnType,
+    ModeDescriptor,
+    RestoreEntrypoint,
+)
 from modelopt.torch.opt.searcher import BaseSearcher, SearchConfig, SearchStateDict
 from modelopt.torch.opt.utils import named_hparams
 from modelopt.torch.utils import print_rank_0
 
-from ..fastnas import FastNASModeDescriptor
 from ..pruning import PruneModeRegistry
 
 SUPPORTED_HPARAMS = {
@@ -58,6 +70,8 @@
     "num_layers",
 }
 
+__all__ = ["MCoreMinitronConfig", "MCoreMinitronModeDescriptor", "MCoreMinitronSearcher"]
+
 
 class MCoreMinitronSearcher(BaseSearcher):
     """Searcher for Minitron pruning algorithm."""
@@ -218,9 +232,48 @@ def run_search(self) -> None:
 )
 
 
+def _convert_model_to_dynamic_space(
+    model: nn.Module, config: ModeloptBaseConfig | None = None
+) -> DynamicSpace:
+    """Create a dynamic space for the model (in-place)."""
+    dynamic_space = DynamicSpace(model)
+    dynamic_space._should_be_converted = lambda mod: isinstance(mod, tuple(SUPPORTED_MODELS.keys()))
+    dynamic_space.convert_to_dynamic(config.model_dump() if config else None, DMRegistry)
+    if not dynamic_space.is_configurable():
+        raise ApplyModeError(
+            "The model does not contain any configurable hyperparameters! Please check the"
+            " documentation for modules and config and how to get a configurable model."
+        )
+
+    return dynamic_space
+
+
+def convert_mcore_minitron(model: nn.Module, config: ModeloptBaseConfig) -> ConvertReturnType:
+    """Convert the model to the dynamic search space (in-place) and return the converted model and metadata.
+
+    This is a simplified version of convert_fastnas_searchspace that removes the automated recursive tracing
+    and instead directly converts the top-level model to a DynamicModule. Submodules should not need to be explicitly
+    converted as that happens from the top-level model.
+    """
+    _convert_model_to_dynamic_space(model, config)
+
+    # store current config in metadata
+    metadata = {"subnet_config": get_subnet_config(model)}
+
+    # return converted model as well as metadata
+    return model, metadata
+
+
+def restore_mcore_minitron(
+    model: nn.Module, config: ModeloptBaseConfig, metadata: dict
+) -> nn.Module:
+    """Restore the model to the original state."""
+    return convert_mcore_minitron(model, config)[0]
+
+
 @NASModeRegistry.register_mode
 @PruneModeRegistry.register_mode
-class MCoreMinitronModeDescriptor(FastNASModeDescriptor):
+class MCoreMinitronModeDescriptor(ModeDescriptor):
     """Class to describe the ``"mcore_minitron"`` mode.
 
     The properties of this mode can be inspected via the source code.
@@ -236,7 +289,27 @@ def config_class(self) -> type[ModeloptBaseConfig]:
         """Specifies the config class for the mode."""
         return MCoreMinitronConfig
 
+    @property
+    def next_modes(self) -> set[str] | None:
+        """Modes that must immediately follow this mode."""
+        return {"export", "kd_loss", "quantize", "sparse_magnitude", "sparse_gpt"}
+
+    @property
+    def export_mode(self) -> str | None:
+        """The mode that corresponds to the export mode of this mode."""
+        return "export"
+
     @property
     def search_algorithm(self) -> type[BaseSearcher]:
-        """Specifies the search algorithm to use for this mode (if any)."""
+        """Specifies the search algorithm to use for this mode."""
         return MCoreMinitronSearcher
+
+    @property
+    def convert(self) -> ConvertEntrypoint:
+        """The mode's entrypoint for converting a model to a search space."""
+        return convert_mcore_minitron
+
+    @property
+    def restore(self) -> RestoreEntrypoint:
+        """The mode's entrypoint for restoring a model with the modelopt_state."""
+        return restore_mcore_minitron

modelopt/torch/trace/plugins/__init__.py

@@ -15,20 +15,7 @@
 
 """Handles tracing plugins for third-party modules."""
 
-import warnings as _warnings
+from modelopt.torch.utils import import_plugin
 
-try:
-    from .megatron import *
-
-except ImportError:
-    pass
-except Exception as e:
-    _warnings.warn(f"Failed to import megatron plugin due to: {e!r}")
-
-try:
+with import_plugin("transformers"):
     from .transformers import *
-
-except ImportError:
-    pass
-except Exception as e:
-    _warnings.warn(f"Failed to import transformers plugin due to: {e!r}")

modelopt/torch/trace/plugins/megatron.py

@@ -48,8 +48,9 @@
     _DynamicVocabParallelEmbedding,
     expand_head_indices,
 )
-from modelopt.torch.nas.search_space import generate_search_space
+from modelopt.torch.nas.registry import DMRegistry
 from modelopt.torch.opt.utils import named_dynamic_modules, search_space_size
+from modelopt.torch.prune.plugins.mcore_minitron import _convert_model_to_dynamic_space
 from modelopt.torch.utils import flatten_tree
 from modelopt.torch.utils.random import centroid
 
@@ -178,7 +179,7 @@ def _test_gpt_parameter_sorting(activation_func, rank, size):
             m.weight.data = torch.randn_like(m.weight)
 
     model.eval()
-    search_space = generate_search_space(model)
+    dynamic_space = _convert_model_to_dynamic_space(model)
 
     # Compute activations for sorting
     for _ in range(5):
@@ -188,18 +189,18 @@ def _test_gpt_parameter_sorting(activation_func, rank, size):
     prompt_tokens = torch.randint(0, vocab_size, (batch_size, max_sequence_length)).cuda()
     y1 = run_mcore_inference(model, prompt_tokens)
 
-    search_space.sort_parameters()
+    mtn.utils.sort_parameters(model)
 
     # check if all ffn_hidden_size, num_heads_per_group, num_query_groups, hidden_size have been sorted
     sortable_per_pp = [
-        n for n, hp in search_space.named_hparams(configurable=True) if hp.importance is not None
+        n for n, hp in dynamic_space.named_hparams(configurable=True) if hp.importance is not None
     ]
     # 3 hps per layer + 1 for hidden_size (num_layers is not sorted!)
     assert len(sortable_per_pp) == 3 * num_layers // size + 1
 
     # Export since sorting force reassigns SelfAttention weights which we dont want to re-sort!
     # TODO: ideally we shouldn't need this
-    search_space.export()
+    dynamic_space.export(DMRegistry)
 
     # sanity check if the model functionality is preserved after sorting
     y2 = run_mcore_inference(model, prompt_tokens)

tests/gpu/torch/nas/plugins/test_megatron_gpt_dynamic_modules.py

@@ -43,9 +43,9 @@
     _DynamicRowParallelLinear,
     _DynamicVocabParallelEmbedding,
 )
-from modelopt.torch.nas.search_space import generate_search_space
 from modelopt.torch.nas.traced_hp import TracedHp
 from modelopt.torch.opt.utils import named_dynamic_modules, search_space_size
+from modelopt.torch.prune.plugins.mcore_minitron import _convert_model_to_dynamic_space
 from modelopt.torch.utils import flatten_tree
 from modelopt.torch.utils.random import centroid
 
@@ -163,7 +163,7 @@ def _test_mamba_parameter_sorting(rank, size):
             m.weight.data = torch.randn_like(m.weight)
 
     model.eval()
-    search_space = generate_search_space(model)
+    dynamic_space = _convert_model_to_dynamic_space(model)
 
     # Compute activations for sorting
     for _ in range(5):
@@ -173,11 +173,11 @@ def _test_mamba_parameter_sorting(rank, size):
     prompt_tokens = torch.randint(0, vocab_size, (batch_size, max_sequence_length)).cuda()
     y1 = run_mcore_inference(model, prompt_tokens)
 
-    search_space.sort_parameters()
+    dynamic_space.sort_parameters()
 
     # check if all mamba_num_heads, mamba_head_dim, hidden_size have been sorted
     sortable_per_pp = [
-        n for n, hp in search_space.named_hparams(configurable=True) if hp.importance is not None
+        n for n, hp in dynamic_space.named_hparams(configurable=True) if hp.importance is not None
     ]
     # 2 mamba hps per layer + 1 for hidden_size (num_layers is not sorted!)
     assert len(sortable_per_pp) == 2 * num_layers // size + 1

tests/gpu/torch/nas/plugins/test_megatron_mamba_dynamic_modules.py

@@ -139,20 +139,6 @@ def test_sym_map_registry():
     except ImportError:
         pass
 
-    try:
-        from megatron.core.models.gpt import GPTModel
-
-        mods_in_registry.add(GPTModel)
-    except ImportError:
-        pass
-
-    try:
-        from megatron.core.models.mamba import MambaModel
-
-        mods_in_registry.add(MambaModel)
-    except ImportError:
-        pass
-
     not_a_leaf = {nn.Sequential}
     dependent_registry = set()