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Support hunyuan moe v2 #6819
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Signed-off-by: sorenwu <[email protected]>
Signed-off-by: sorenwu <[email protected]>
Signed-off-by: sorenwu <[email protected]>
Signed-off-by: sorenwu <[email protected]>
Signed-off-by: sorenwu <[email protected]>
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Caution Review failedThe pull request is closed. 📝 WalkthroughWalkthroughAdds a CLI flag to optionally template prompts as chat, introduces QK normalization controls in attention, extends RoPE with alpha and dynamic scaling, tightens MLA gating conditions, exposes a new HunYuan MoE causal LM model, updates model exports, and adds tokenizer chat-template access with improved error handling. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant CLI as quickstart_advanced.py
participant LLM
participant Tok as Tokenizer
User->>CLI: Run with/without --apply_chat_template
CLI->>CLI: Build prompts list
alt apply_chat_template=True
CLI->>Tok: apply_chat_template(prompts)
Tok-->>CLI: templated_prompts
CLI->>LLM: generate(templated_prompts)
else
CLI->>LLM: generate(prompts)
end
LLM-->>User: outputs
sequenceDiagram
participant Attn as Attention.forward
participant Rope as RoPE
participant QKN as QK Norm
Attn->>Attn: split qkv
alt qk_norm_type == pre_rope
Attn->>QKN: apply_qk_norm(q,k) pre-RoPE
end
alt rope not fused and position_ids provided
Attn->>Rope: apply_rope(q,k,v)
alt qk_norm_type == post_rope
Attn->>QKN: apply_qk_norm(q,k) post-RoPE
end
end
Attn-->>Attn: proceed with attention scores
sequenceDiagram
participant App
participant HY as HunYuanMoEV1ForCausalLM
participant Core as HunYuanModel
participant Layer as DecoderLayer(MoE/MLP, Attn)
App->>HY: forward(input_ids, position_ids, attn_metadata)
HY->>Core: forward(...)
loop for each layer
Core->>Layer: attention + MLP/MoE
Layer-->>Core: hidden_states
end
Core-->>HY: final hidden_states
HY-->>App: logits / context_logits
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
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✨ Finishing Touches
🧪 Generate unit tests
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Summary by CodeRabbit
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Description
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