[Bug Fix] [MiniMax-M3] Implement EAGLE3 support on the AMD MiniMax M3

[functionstackx]

Overview Problem

#fix #45538

hi @hongxiayang @youkaichao

+viz @andyluo7 @chunfangamd

Speculative decoding with EAGLE3 (e.g. an Inferact/MiniMax-M3-EAGLE3 draft head) works for MiniMax-M3 on CUDA but fails at engine init on ROCm:

RuntimeError: Model does not support EAGLE3 interface but aux_hidden_state_outputs was requested

(raised in vllm/v1/worker/gpu_model_runner.py::_setup_eagle3_aux_hidden_state_outputs)

Fix

Validated https://github.com/SemiAnalysisAI/InferenceX/actions/runs/27477412884/job/81220126744?pr=1745

validated GSM8k evals too. I have ran comprehensive sweep & validated GSM8k on MI355X is same as non-EAGLE3 MI355X and same as B200 vLLM.

More Details about Issue

MiniMax-M3 is platform-split — vllm/models/minimax_m3/__init__.py imports from nvidia/ or amd/ based on current_platform.is_rocm(). The NVIDIA model implements the SupportsEagle3 interface and emits auxiliary hidden states; the AMD model does not. So supports_eagle3(model) (an isinstance check) returns False on ROCm and EAGLE3 aborts. This PR brings amd/model.py to parity with nvidia/model.py.

The EAGLE3 plumbing itself is provided by EagleModelMixin and the SupportsEagle3 base in interfaces.py — no per-model method bodies are needed; the model classes just have to opt in and emit the aux states. Each change below mirrors the NVIDIA implementation.

Changes (vllm/models/minimax_m3/amd/model.py)

1. Import EagleModelMixin and SupportsEagle3

The two symbols the rest of the change depends on. Mirrors the NVIDIA import block: nvidia/model.py#L54-L59 (EagleModelMixin at L55, SupportsEagle3 at L57). The AMD file previously imported only MultiModalEmbeddings/SupportsMultiModal.

2. Inner model inherits EagleModelMixin: class MiniMaxM3Model(nn.Module, EagleModelMixin)

EagleModelMixin supplies aux_hidden_state_layers, _set_aux_hidden_state_layers, and _maybe_add_hidden_state (interfaces.py#L1320-L1338) — the state and helper the forward pass uses to collect aux hidden states. Mirrors nvidia/model.py#L768.

3. MiniMaxM3Model.forward emits aux hidden states

Collect the embedding output (layer 0) and each decoder layer's output via _maybe_add_hidden_state, and return (hidden_states, aux_hidden_states) when aux layers are configured (else just hidden_states). The return-type hint is widened to torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]] to match. This is the actual data EAGLE3's draft consumes. Mirrors nvidia/model.py#L806-L825 (return type at L806; aux collection at L814-L825). _maybe_add_hidden_state only appends when layer_idx is in the configured set (interfaces.py#L1326-L1338), so this is a no-op when EAGLE3 is off.

4. MiniMaxM3SparseForCausalLM(nn.Module, SupportsEagle3)

Opt the causal-LM wrapper into the interface so supports_eagle3() passes. SupportsEagle3.set_aux_hidden_state_layers resolves parent_ref to self here and asserts self.model is an EagleModelMixin (interfaces.py#L1384-L1403) — satisfied by change #2, since this class already sets self.model = MiniMaxM3Model(...). Mirrors nvidia/model.py#L935.

5. MiniMaxM3SparseForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsEagle3)

The top-level (VL) entry point is what gpu_model_runner checks. set_aux_hidden_state_layers resolves parent_ref via self.language_model and then parent_ref.model (interfaces.py#L1384-L1403) — both already present on this class (self.language_model = init_vllm_registered_model(...), whose .model is the EagleModelMixin inner model). So inheriting the interface is sufficient; no model property or method overrides are required. Mirrors nvidia/model.py#L983-L984.

Notes

• The default aux layers come from SupportsEagle3.get_eagle3_default_aux_hidden_state_layers (interfaces.py#L1405-L1430) — (2, num_layers // 2, num_layers - 3) — identical resolution path to NVIDIA; no MiniMax-M3-specific override needed.
• Pure parity change: amd/model.py and nvidia/model.py were already line-for-line equivalent except for these EAGLE3 hooks; this closes the gap.

Testing

amd/model.py is byte-identical to nvidia/model.py for the surrounding code, so the port is mechanical. End-to-end validation on MI355X (gfx950) with --speculative-config '{"method":"eagle3","model":"Inferact/MiniMax-M3-EAGLE3","num_speculative_tokens":3}' is in progress; will update with results.

Generated With Help Of Claude!

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[ywang96]

Looks reasonable to me cc @zixi-qi if you see anything wrong

[mergify]

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