Merge branch 'main' into jingyux/2-3-diffusion-export

Author: jingyu-ml

.vscode/settings.json

@@ -40,7 +40,7 @@
         "--no-cov",
     ],
     "evenBetterToml.schema.enabled": false, // disable toml/json schema since we have custom fields
-    "python.analysis.extraPaths": [
+    "cursorpyright.analysis.extraPaths": [
         "./tests/" // add tests to python path just like pytest does in pyproject.toml
     ],
     "git.alwaysSignOff": true,

CHANGELOG.rst

@@ -1,7 +1,7 @@
 NVIDIA Model Optimizer Changelog (Linux)
 ========================================
 
-0.42 (TBD)
+0.42 (2026-02-xx)
 ^^^^^^^^^^^^^^^^^
 
 **Bug Fixes**
@@ -12,6 +12,7 @@ NVIDIA Model Optimizer Changelog (Linux)
 
 - Add standalone type inference option (``--use_standalone_type_inference``) in ONNX AutoCast as an alternative to ONNX's ``infer_shapes``. This experimental feature performs type-only inference without shape inference, useful as a workaround when shape inference fails or to avoid unnecessary shape inference overhead.
 - Add support for Kimi K2 Thinking model quantization from the original int4 checkpoint.
+- Add support for ``params`` constraint based automatic neural architecture search in Minitron pruning (``mcore_minitron``) as an alternative to manual pruning (using ``export_config``). See `examples/pruning/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/pruning>`_ for more details on its usage.
 
 0.41 (2026-01-19)
 ^^^^^^^^^^^^^^^^^
@@ -96,7 +97,7 @@ NVIDIA Model Optimizer Changelog (Linux)
 
 **Documentation**
 
-- Add general guidelines for Minitron pruning and distillation. See `examples/pruning/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/pruning#pruning-guidelines>`_ for more details.
+- Add general guidelines for Minitron pruning and distillation. See `pruning guidelines <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/pruning#pruning-guidelines>`_ for more details.
 - Added example for exporting QLoRA checkpoint for vLLM deployment. Refer to `examples/llm_qat/README.md <https://github.com/NVIDIA/Model-Optimizer/blob/79ef31bc7269ba4da0cfab446da5b64509cbfcef/examples/llm_qat/README.md#qlora-deployment>`_ for more details
 
 0.37 (2025-10-08)

examples/pruning/README.md

@@ -18,26 +18,18 @@
 import types
 from abc import ABC
 from collections.abc import Callable, Sequence
-from typing import Any
 
 import torch
 import torch.nn as nn
 from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
 from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
 from megatron.core.models.gpt import GPTModel
-from megatron.core.parallel_state import (
-    get_data_parallel_group,
-    get_pipeline_model_parallel_group,
-    get_tensor_model_parallel_group,
-    is_pipeline_first_stage,
-    is_pipeline_last_stage,
-)
+from megatron.core.parallel_state import is_pipeline_first_stage, is_pipeline_last_stage
 from megatron.core.tensor_parallel.layers import (
     ColumnParallelLinear,
     RowParallelLinear,
     VocabParallelEmbedding,
 )
-from megatron.core.transformer import MegatronModule
 from megatron.core.transformer.attention import SelfAttention
 from megatron.core.transformer.dot_product_attention import DotProductAttention
 from megatron.core.transformer.mlp import MLP
@@ -51,29 +43,14 @@
 from modelopt.torch.nas.modules import DynamicModuleList
 from modelopt.torch.opt.dynamic import DynamicModule
 from modelopt.torch.opt.hparam import HPType
-from modelopt.torch.opt.searcher import ConstraintsDict
 from modelopt.torch.trace import Symbol
 from modelopt.torch.utils import distributed as dist
-from modelopt.torch.utils import (
-    get_module_device,
-    make_divisible,
-    param_num_from_forward,
-    print_rank_0,
-    random,
-)
+from modelopt.torch.utils import make_divisible
 
-from ..algorithms import (
-    MODULE_TYPE_TO_CONSTRAINTS_FUNC,
-    ConstraintEvalFunc,
-    ConstraintInterpolator,
-    ConstraintsFunc,
-    ConstraintsRes,
-)
 from ..hparams.concat import build_concat_hp
 from ..modules import _DynamicLayerNorm
 from ..modules.utils import get_sliced_tensor, get_sliced_tensor_by_slices
 from ..registry import DMRegistry
-from ..search_space import SampleFunc
 from ..traced_hp import TracedHp
 
 SUPPORTED_MODELS = {GPTModel: "megatron.core.models.gpt.GPTModel"}
@@ -634,7 +611,6 @@ def modify(
 
     def _export_reinit_token_dispatcher(self) -> None:
         """Reinitialize the token dispatcher after pruning."""
-        print_rank_0("Reinitializing token dispatcher after pruning")
         if hasattr(moe_utils, "get_default_model_comm_pgs"):
             model_comm_pgs = moe_utils.get_default_model_comm_pgs()
         else:
@@ -1045,27 +1021,30 @@ def modify(
         *,
         hidden_size_divisor: int = 1,
         ffn_hidden_size_divisor: int = 1,
-        mamba_num_heads_divisor: int = 1,
         mamba_head_dim_divisor: int = 1,
         num_moe_experts_divisor: int = 1,
+        num_layers_divisor: int = 1,
     ):
         """Modify the dynamic choices of the module according to provided keyword arguments.
 
         Args:
             hidden_size_divisor: The divisor of the hidden_size.
             ffn_hidden_size_divisor: The divisor of the mlp ffn_hidden_size.
-            mamba_num_heads_divisor: The divisor of the mamba num_heads.
             mamba_head_dim_divisor: The divisor of the mamba head_dim.
             num_moe_experts_divisor: The divisor of the number of MoE experts.
+            num_layers_divisor: The divisor of the number of layers.
         """
-        hp = self.get_hparam("hidden_size")
-        choices = {int(make_divisible(c, hidden_size_divisor)) for c in hp.choices}  # type: ignore[arg-type]
-        hp.choices = list(set(hp.choices) & choices | {hp.original})
+        for hp_name, divisor in [
+            ("hidden_size", hidden_size_divisor),
+            ("num_layers", num_layers_divisor),
+        ]:
+            hp = self.get_hparam(hp_name)
+            choices = {int(make_divisible(c, divisor)) for c in hp.choices}  # type: ignore[arg-type]
+            hp.choices = list(set(hp.choices) & choices | {hp.original})
 
         for layer in self.decoder.layers:
             layer.modify(
                 ffn_hidden_size_divisor=ffn_hidden_size_divisor,
-                mamba_num_heads_divisor=mamba_num_heads_divisor,
                 mamba_head_dim_divisor=mamba_head_dim_divisor,
                 num_moe_experts_divisor=num_moe_experts_divisor,
             )
@@ -1084,86 +1063,3 @@ def export(self) -> torch.nn.Module:
             ).export()
             self.output_layer.export()
         return super().export()
-
-
-class MegatronConstraintsFunc(ConstraintsFunc):
-    """A Functor class to check if sub-net satisfied all provided constraints.
-
-    We intentionally expose some attributes like `limits` s.t. we can modify it manually.
-    """
-
-    _sample_points_dict: dict[tuple[str, ...], dict[str, SampleFunc]] = {
-        ("params",): {"min": min, "centroid": random.centroid, "max": max},
-    }
-
-    def __init__(
-        self,
-        model: MegatronModule,
-        constraints: ConstraintsDict,
-        dummy_input: Any | tuple[Any, ...],
-        deployment: dict | None = None,
-        fast_eval: bool = True,
-    ):
-        """Initialize with additional data parallel group info from megatron."""
-        for key in constraints:
-            if key != "params":
-                raise ValueError("Only params constraints is supported for MegatronModule!")
-
-        self.model = model
-        self.dummy_input = dummy_input
-        self.deployment = deployment
-        self._fast_eval = fast_eval
-
-        # Getting data parallel group for
-        self.dp_group = get_data_parallel_group()
-
-        # initialize latency interpolator
-        keys_for_interpolation = ("params",)
-        if ConstraintsFunc.is_configurable(self.model, "depth"):
-            keys_for_interpolation += ("flops_min_depth",)
-        self._latency_interpolator = ConstraintInterpolator(
-            self.model,
-            points_funcs={k: self.constraint_eval_funcs[k] for k in keys_for_interpolation},
-            value_func=self._get_true_latency,
-        )
-        # set fast/regular mode for latency interpolator
-        self._latency_interpolator.collect_mode = not self.fast_eval
-
-        # set limit at the end with setter to use sanity checks on constraints
-        self._limits = {}
-        self.limits = constraints
-
-    @property
-    def constraint_eval_funcs(self) -> dict[str, ConstraintEvalFunc]:
-        """Get constraint eval fns."""
-        return {
-            "params": self._get_params,
-        }
-
-    def _get_params(self, _: ConstraintsRes | None = None) -> float:
-        """Get number of model parameters from forward pass."""
-        params = param_num_from_forward(self.model, args=self.dummy_input, unit=1.0)
-        reduced_params = torch.Tensor([params]).to(device=get_module_device(self.model))
-        torch.distributed.all_reduce(reduced_params, group=get_pipeline_model_parallel_group())
-        torch.distributed.all_reduce(reduced_params, group=get_tensor_model_parallel_group())
-        return reduced_params.item()
-
-    def _get_flops(self, _: ConstraintsRes | None = None) -> float:
-        """Get inference FLOPs."""
-        raise NotImplementedError
-
-    def _get_flops_min_depth(self, _: ConstraintsRes | None = None) -> float:
-        """Get inference FLOPs with depth set to minimum."""
-        raise NotImplementedError
-
-    def _get_true_latency(self, _: ConstraintsRes | None = None) -> float:
-        """Get true inference latency."""
-        raise NotImplementedError
-
-    def _get_latency(self, precomputed: ConstraintsRes | None = None) -> float:
-        """Get inference latency from interpolator."""
-        raise NotImplementedError
-
-
-# Clear the mapping and reinsert.
-MODULE_TYPE_TO_CONSTRAINTS_FUNC[MegatronModule] = MegatronConstraintsFunc

modelopt/torch/nas/plugins/megatron.py

@@ -135,9 +135,7 @@ def sort_parameters(self, hps_to_sort: set[str] | None = None, verbose: bool = F
             hps_to_sort: A set of hparam names to sort. If not provided or empty, all hparams will be sorted.
             verbose: Whether to print the search space and hparam importances.
         """
-        print_rank_0("Sorting parameters...")
-        if verbose:
-            self.print_summary()
+        print_rank_0("\nSorting parameters...")
 
         # get config and set to max
         config = self.config()

modelopt/torch/nas/search_space.py

@@ -35,7 +35,7 @@
 import torch.nn as nn
 
 from modelopt.torch.utils import distributed as dist
-from modelopt.torch.utils import no_stdout, run_forward_loop
+from modelopt.torch.utils import no_stdout, print_rank_0, run_forward_loop
 
 LimitsTuple = tuple[float, float]
 ConstraintsDict = dict[str, str | float | dict | None]
@@ -212,6 +212,7 @@ def construct_forward_loop(
             return None
 
         def forward_loop_with_silence_check(m: nn.Module) -> None:
+            print_rank_0("Running forward loop...")
             with no_stdout() if silent else nullcontext():
                 if data_loader is not None:
                     run_forward_loop(

modelopt/torch/opt/searcher.py

@@ -19,8 +19,6 @@
 simplifies the overall workflow to accommodate for the simpler nature of pruning algorithms.
 """
 
-# nas is a required - so let's check if it's available
-import modelopt.torch.nas
 from modelopt.torch.utils import import_plugin
 
 from . import fastnas, gradnas, plugins