[ROCm] [CI] Keep original layer count for Eagle3DeepseekV2 init test

[stefankoncarevic]

Purpose

test_can_initialize_large_subset[Eagle3DeepseekV2ForCausalLM] fails in the AMD
ROCm CI (basic models init tests) with:

File "vllm/v1/worker/gpu_model_runner.py", line 6056, in _dummy_run
    hidden_states, _ = outputs
ValueError: too many values to unpack (expected 2)

The Eagle3 draft (AQ-MedAI/Kimi-K25-eagle3) requests auxiliary hidden states
from target layers (2, 30, 58) (log: Using auxiliary layers from speculative config: (2, 30, 58)). The initialization test's default dummy_hf_overrides
collapses the target model (moonshotai/Kimi-K2.5) to a single hidden layer, so
layers 2/30/58 don't exist. In DeepseekV2Model.forward no layer index then
matches, aux_hidden_states stays empty, and the model returns a bare
hidden_states tensor instead of the (hidden_states, aux_hidden_states) tuple:

# vllm/model_executor/models/deepseek_v2.py
if len(aux_hidden_states) > 0:
    return hidden_states, aux_hidden_states
return hidden_states

Because Eagle3 sets use_aux_hidden_state_outputs=True, _dummy_run
unconditionally unpacks two values (hidden_states, _ = outputs). Unpacking a
bare tensor iterates its first dim (num tokens > 2), raising
too many values to unpack (expected 2).

This is the same class of issue as Eagle3Qwen3vlForCausalLM: with reduced
layers the configured aux layers are absent.

Changes

• Set use_original_num_layers=True for Eagle3DeepseekV2ForCausalLM in
  tests/models/registry.py, so the target keeps its full layer count during
  initialization. Layers 2/30/58 then exist, aux hidden states are collected,
  the model returns the (hidden_states, aux_hidden_states) tuple, and the
  unpack succeeds. This matches the existing pattern used by the other Eagle3 /
  speculative entries.
• Also set max_model_len=8192. With the full layer count the target
  (Kimi-K2.5) is large (~169 GiB of dummy weights), and the default
  max_model_len (262144) sizes a KV cache too big to fit alongside the weights,
  causing a torch.OutOfMemoryError during KV cache allocation. Reducing
  max_model_len shrinks the KV cache so the test fits in CI VRAM (same approach
  already used by other heavy entries in this registry).

Test Plan

Environment: AMD MI300 (gfx942), ROCm, VLLM_ROCM_USE_AITER unset (default
False → ROCM_ATTN backend), matching the AMD CI configuration.

pytest -x -s "tests/models/test_initialization.py::test_can_initialize_large_subset[Eagle3DeepseekV2ForCausalLM]"

Test Result

• test_can_initialize_large_subset[Eagle3DeepseekV2ForCausalLM]: 1 passed
  (~288 s), previously failed with
  ValueError: too many values to unpack (expected 2).

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[claude]

Claude Code Review

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[stefankoncarevic]

Putting this on hold pending #47077.

Eagle3DeepseekV2ForCausalLM and Eagle3DeepseekV3ForCausalLM are the same checkpoint (moonshotai/Kimi-K2.5 with the AQ-MedAI/Kimi-K25-eagle3 draft) registered under two arch names, and they share the same root cause: the reduced-layer dummy init returns a bare hidden-states tensor, which the runner unpacks as hidden_states, _ = outputs.

#47077 fixes that for Eagle3DeepseekV3 in shared runtime code (robust unpack + the MLA decode-only assertion), which should also cover Eagle3DeepseekV2 here. To avoid two divergent fixes for the same model, I'll wait for #47077 to merge and then re-check whether Eagle3DeepseekV2 passes on main without this test-only change. If it does, I'll close this PR as no longer needed.

[stefankoncarevic]

Resolved by #48527. Verified locally on MI300 that test_can_initialize_large_subset[Eagle3DeepseekV2ForCausalLM] passes on current main with the default config. Closing.