[Bugfix] DeepSeek V4: skip expert tensor when no mapping matches

[dparikh79]

Purpose

Closes #42769.

When load_weights in DeepseekV4ForCausalLM processes a tensor whose name contains .experts. but whose suffix doesn't match any entry in expert_mapping (e.g. a quantization-specific layout such as .tq_packed, outside the standard gate_proj / up_proj / down_proj set), the inner for mapping in expert_mapping: loop hits the if weight_name not in name: continue guard on every iteration, so name_mapped is never bound. The next statement, loaded_params.add(name_mapped), then raises:

UnboundLocalError: cannot access local variable 'name_mapped' where it is not associated with a value

Fix: initialize name_mapped = None before the loop and skip the loaded_params.add when the loop never matched. This matches the implicit contract that unrecognized expert tensors are skipped (consistent with how the sibling deepseek_v4_mtp.py handles loaded_params.add only inside the if success: branch).

Duplicate-work check

gh issue view 42769 --repo vllm-project/vllm --comments    # 0 comments on the issue
gh pr list --repo vllm-project/vllm --state open --search "42769 in:body"   # no results
gh pr list --repo vllm-project/vllm --state open --search "deepseek_v4 name_mapped UnboundLocalError"   # no results

No existing PR addresses this bug.

Test Plan

This is a control-flow bug that doesn't require model weights or GPU to reproduce. a standalone Python repro mirroring the buggy block triggers the UnboundLocalError in three lines. Pasting the structure for reviewer convenience:

def buggy_expert_load(name, expert_mapping, params_dict, loaded_params):
    for mapping in expert_mapping:
        param_name, weight_name, expert_id, shard_id = mapping
        if weight_name not in name:
            continue
        name_mapped = name.replace(weight_name, param_name)
        # ... weight_loader call elided ...
    loaded_params.add(name_mapped)  # <-- UnboundLocalError when no iteration matched

Run with name = 'model.layers.0.ffn.experts.0.gate_proj.tq_packed' and a stock expert_mapping whose weight_names only cover gate_proj.weight / up_proj.weight / down_proj.weight.

Test Result

Before fix:

UnboundLocalError: cannot access local variable 'name_mapped' where it is not associated with a value

After fix (with name_mapped = None init + if name_mapped is None: continue guard):

loaded_params = set()      # unrecognized tensor skipped cleanly

Happy path (mapping matches) still behaves identically:

loaded_params = {'model.layers.0.ffn.experts.0.w13_weight'}

ruff check and ruff format --check pass on the modified file.

AI Assistance Disclosure

Per AGENTS.md, disclosing that this PR was drafted with AI assistance (Claude Code). I reviewed every changed line, traced the control flow in deepseek_v4.py:1532-1558 end-to-end, validated the fix against the sibling deepseek_v4_mtp.py:408-432 pattern, and ran the Python-level repro both pre- and post-fix to confirm the UnboundLocalError reproduces under the old code and is gone under the new code. The fix is the minimal change matching the issue author's exact proposal.

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[gemini-code-assist]

Code Review

This pull request modifies the weight loading logic in the DeepSeek V4 model to prevent UnboundLocalError exceptions when expert mappings do not match. The reviewer suggests improving the robustness of this logic by using a success flag instead of checking if name_mapped is None. This approach ensures that parameters are only marked as loaded if the weight loader actually succeeds, which also maintains consistency with other model implementations in the codebase.

[gemini-code-assist]

Instead of initializing name_mapped, it is better to initialize success = False. This allows for a more robust check after the loop to ensure that the parameter is only marked as loaded if the weight loader actually succeeded. This is also consistent with the logic in other parts of the codebase (e.g., the MTP implementation mentioned in the PR description).

Suggested change

	name_mapped = None
	success = False

[gemini-code-assist]

Check if not success instead of if name_mapped is None. This ensures that loaded_params.add(name_mapped) is only called if a mapping was successfully loaded into the model. This correctly handles cases where a mapping string matches but the expert is not on the current rank (where weight_loader returns False), and it also avoids the UnboundLocalError since name_mapped will only be accessed if success is True.

Suggested change

	if name_mapped is None:
	# ``.experts.`` is in the name but no entry in
	# ``expert_mapping`` matched (e.g. a quantization
	# suffix outside gate_proj/up_proj/down_proj). Skip
	# rather than raising UnboundLocalError below.
	continue
	if not success:
	# ``.experts.`` is in the name but no entry in
	# ``expert_mapping`` matched or the weight loader
	# returned False. Skip rather than adding to
	# loaded_params or raising UnboundLocalError below.
	continue

[dparikh79]

Thanks for the review. The success flag suggestion is cleaner and also catches the silent failure cases (is_pp_missing_parameter returns False on the wrong rank, weight_loader returns False) that the original name_mapped is None check would miss. Pushed an amended commit that switches to success = False + if not success: continue, mirroring the pattern in deepseek_v4_mtp.py. Also added the Signed-off-by line so DCO passes.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @dparikh79.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[dparikh79]

Closing for #44030 (same fix at the post-#43004 path, now in both AMD + NVIDIA forks).