[nemotron_h] respect _no_reinit flag on dt_bias and out_proj.weight

[vai-minzhou]

Summary

NemotronHPreTrainedModel._init_weights unconditionally overwrites two trained parameters every time it is invoked:

• NemotronHMamba2Mixer.dt_bias — reset to a fresh inv_softplus(random dt) draw
• {…}.out_proj.weight — reset to a kaiming-uniform scaled by 1/sqrt(num_hidden_layers)

It sets module.dt_bias._no_reinit = True after the copy, but that flag is only checked for the nn.Linear.bias branch of the same function — never for dt_bias itself, and out_proj.weight doesn't set the flag at all.

On transformers>=5.0, _init_weights runs a second time after from_pretrained has finished loading the checkpoint (the post-load pass that initialises tensors still on meta). For NemotronHForCausalLM that silently overwrites the on-disk values for dt_bias and out_proj.weight with fresh random ones, while all other tensors keep their trained values.

The resulting model outputs repetitive filler streams like and and and , and and , for any input — sanity is preserved only when loading through vLLM (which bypasses _init_weights) or via an older transformers release.

Reproduction

import json, pathlib, torch
from safetensors.torch import load_file
from transformers import AutoConfig, AutoModelForCausalLM

path = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"   # any Nemotron-H ckpt
cfg = AutoConfig.from_pretrained(path, trust_remote_code=True)
cfg._attn_implementation = "eager"
m = AutoModelForCausalLM.from_pretrained(path, config=cfg, torch_dtype=torch.bfloat16)

idx = json.load(open(pathlib.Path(path) / "model.safetensors.index.json"))["weight_map"]
k = "backbone.layers.0.mixer.dt_bias"
on_disk = load_file(f"{path}/{idx[k]}")[k]
in_mem  = m.backbone.layers[0].mixer.dt_bias
print((on_disk.float() - in_mem.float().cpu()).abs().max().item())
# → ~26.8 before this patch, 0 after

Prompting "Hello, how are you? I am" on an unpatched load returns ' and' ' in' ' the' ' first' ',' as top-5 next tokens — a symptom of Mamba2 with randomised dt_bias and mis-scaled out_proj. After the patch, trained values are preserved and the model generates normally.

The fix

Both changes live in NemotronHPreTrainedModel._init_weights:

1. dt_bias branch: early-return if dt_bias._no_reinit is already set (the flag is set at the end of the current branch, so the first pass initialises normally, the second pass becomes a no-op).
2. out_proj.weight branch: skip when p._no_reinit is set, and set p._no_reinit = True after the initial kaiming scale so a second invocation is a no-op.

Fresh-init training is unaffected — only the second (post-load) invocation is made idempotent. Same edit is mirrored into modular_nemotron_h.py and modeling_nemotron_h.py.

Test plan

• Unpatched load: |on_disk - in_mem|.max() for layer-0 dt_bias ≈ 26.8, next-token logits return stop-word garbage.
• Patched load: diff is 0, next-token logits look sane, eval on our NemotronH-based classifier no longer collapses to 1000/1000 parse failures.
• CI: run tests/models/nemotron_h/ — no behaviour change for fresh-init, only the idempotence of the re-init pass changes.

Please let me know if you'd like the fix to take a different shape (e.g. short-circuit _init_weights entirely when the module's parameters are all materialised, or move the guard to a shared utility in modeling_utils). Happy to adjust.

[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: nemotron_h

[Rocketknight1]

Hey, I'm not sure about this PR! We already have the _is_hf_initialized attribute, so I'm worried about the no_reinit flag that does the same thing, even though it seems like it's already in the codebase. Can you dig a little deeper and figure out why we don't just use _is_hf_initialized here?