[None][fix] Eagle: Attention DP #7939
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📝 WalkthroughWalkthroughAdjusts tensor-parallel handling when attention data parallelism is enabled: forces TP size to 1 for attention-related components, routes QKV projection mapping from layer-local config, updates Embedding construction paths based on enable_attention_dp, and extends GatedMLP to accept an optional overridden_tp_size. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant C as Config
participant M as Model Init
participant E as Embedding
participant Q as QKV Projection
participant G as GatedMLP
Note over C,M: Start initialization with enable_attention_dp flag
C->>M: provide enable_attention_dp, model_config
alt enable_attention_dp == true
M->>M: set tp_size = 1 (override)
M->>E: construct(vocab_size, hidden_size, dtype)
M->>Q: use self.qkv_proj.mapping (layer-local)
M->>G: construct(..., overridden_tp_size=1)
else enable_attention_dp == false
M->>E: construct(vocab, hidden, dtype, mapping, tp_mode, gather_output)
M->>Q: use model_config.mapping
M->>G: construct(..., overridden_tp_size=None)
end
Note over M: Components initialized according to attention DP setting
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Actionable comments posted: 0
🧹 Nitpick comments (1)
tensorrt_llm/_torch/models/modeling_speculative.py (1)
189-203: Inconsistent Embedding construction approaches.The dual path for Embedding construction based on
enable_attention_dpis correctly implemented, but there are some considerations:
Missing tensor_parallel_mode: When
enable_attention_dpis false (Lines 196-203), the constructor includestensor_parallel_mode=TensorParallelMode.COLUMN, but when true (Lines 190-194), this parameter is omitted.Different parameter sets: The attention DP path uses a minimal constructor while the standard path uses the full parameter set including
mapping,tensor_parallel_mode, andgather_output.This approach is functionally correct for the attention DP use case but may cause inconsistent behavior between the two modes.
Consider documenting why the attention DP path uses minimal Embedding construction, or ensure consistent parameter usage:
if model_config.mapping.enable_attention_dp: self.embed_tokens = Embedding( config.vocab_size, config.hidden_size, dtype=config.torch_dtype, + # Minimal construction for attention DP - no tensor parallel needed )
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tensorrt_llm/_torch/models/modeling_speculative.py (3)
tensorrt_llm/_torch/models/checkpoints/base_weight_mapper.py (2)
mapping(152-153)config(156-159)tensorrt_llm/_torch/modules/linear.py (2)
Linear(1784-2003)TensorParallelMode(45-57)tensorrt_llm/_torch/modules/embedding.py (1)
Embedding(184-266)
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🔇 Additional comments (3)
tensorrt_llm/_torch/models/modeling_speculative.py (3)
51-52: Force TP size to 1 when attention DP is enabled.The logic correctly forces tensor-parallel size to 1 when attention data parallelism is enabled, which aligns with the attention DP strategy.
94-95: GatedMLP supports overridden_tp_size — no action required.GatedMLP.init defines overridden_tp_size: Optional[int] = None and uses it (assert + mapping adjustments) in tensorrt_llm/_torch/modules/gated_mlp.py, so passing overridden_tp_size is supported.
60-60: Incorrect — referencing self.qkv_proj.mapping is valid here.Attention.init (tensorrt_llm/_torch/modules/attention.py) constructs self.qkv_proj during super().init, so Eagle3Attention's override can safely reuse self.qkv_proj.mapping; no change required.
Likely an incorrect or invalid review comment.
IzzyPutterman
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I think I'll try to implement TP LM Head |
Signed-off-by: Izzy Putterman <iputterman@nvidia.com>
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