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MBridge pruning minor fix for saving pruned NemotronH #887

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Merged
kevalmorabia97 merged 1 commit into from
Feb 13, 2026
Merged

MBridge pruning minor fix for saving pruned NemotronH #887

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2 changes: 2 additions & 0 deletions examples/megatron_bridge/distill.py
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Original file line number Diff line number Diff line change
Expand Up @@ -198,6 +198,8 @@ def _build_model_provider(hf_path):
manual_gc=True,
manual_gc_interval=100,
),
# TODO: Replace validation args in train with validation config in nemo:26.04
# validation=ValidationConfig(eval_interval=args.eval_interval, eval_iters=args.eval_iters),
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@jenchen13 jenchen13 Feb 13, 2026

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will you implement this in the current PR or a later PR?

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@kevalmorabia97 kevalmorabia97 Feb 13, 2026

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Later. It wont be merged into 26.02 container so will raise error if we do it now but wanted to keep it for reference incase we try to mount latest m-bridge and run the script

optimizer=optimizer_config,
scheduler=scheduler_config,
ddp=DistributedDataParallelConfig(
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4 changes: 3 additions & 1 deletion examples/megatron_bridge/prune_minitron.py
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Expand Up @@ -380,7 +380,9 @@ def score_func_mmlu(m):
AutoModelForCausalLM.from_config(
hf_cfg, trust_remote_code=args.trust_remote_code
).save_pretrained(args.output_hf_path, trust_remote_code=args.trust_remote_code)
pruned_bridge = AutoBridge.from_hf_pretrained(args.output_hf_path)
pruned_bridge = AutoBridge.from_hf_pretrained(
args.output_hf_path, trust_remote_code=args.trust_remote_code
)
pruned_bridge.save_hf_weights(model, args.output_hf_path)
print_rank_0(f"Saved pruned model to {args.output_hf_path} in HF checkpoint format")

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8 changes: 3 additions & 5 deletions modelopt/torch/opt/searcher.py
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Expand Up @@ -27,15 +27,14 @@
from collections.abc import Callable
from contextlib import nullcontext
from typing import Any, final
from warnings import warn

import numpy as np
import pulp
import torch
import torch.nn as nn

from modelopt.torch.utils import distributed as dist
from modelopt.torch.utils import no_stdout, print_rank_0, run_forward_loop
from modelopt.torch.utils import no_stdout, print_rank_0, run_forward_loop, warn_rank_0

LimitsTuple = tuple[float, float]
ConstraintsDict = dict[str, str | float | dict | None]
Expand Down Expand Up @@ -244,12 +243,11 @@ def load_search_checkpoint(self) -> bool:
if checkpoint is None:
return False
if not os.path.exists(checkpoint):
if dist.is_master():
warn(f"Checkpoint {checkpoint} does not exist! Initializing from scratch.")
warn_rank_0(f"Checkpoint {checkpoint} does not exist! Initializing from scratch.")
return False

# iterate through state dict and load keys
print(f"Loading searcher state from {checkpoint}...")
print_rank_0(f"Loading searcher state from {checkpoint}...")
# Security NOTE: weights_only=False is used here on ModelOpt-generated ckpt, not on untrusted user input
state_dict = torch.load(checkpoint, weights_only=False)
assert state_dict.keys() == self.state_dict().keys(), "Keys in checkpoint don't match!"
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2 changes: 2 additions & 0 deletions modelopt/torch/utils/plugins/mbridge.py
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Expand Up @@ -191,6 +191,8 @@ def get_hf_mbridge_calibration_loop(
eval_iters=num_iters,
skip_train=True,
),
# TODO: Replace validation args in train with validation config in nemo:26.04
# validation=ValidationConfig(eval_iters=num_iters, eval_interval=1, skip_train=True),
dataset=_get_dataset_cfg(
dataset_name,
num_samples,
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