[TRTLLM-10064][feat] MoE all-to-all paradigm - #10985
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📝 WalkthroughWalkthroughThe changes introduce Expert Parallelism all-to-all paradigm support to AutoDeploy by adding mapping_config parameters across MoE operators, implementing dual-path execution (all-to-all vs. all-reduce), extending sharding configuration with new transform types and attention DP mode, and updating configuration flags for graph preparation and shape propagation. Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant fused_moe_kernel as Fused MoE Kernel
participant MoeAlltoAll
participant RankN as Rank N
participant RankM as Rank M
participant AllGather
alt All-to-All Path (mapping_config enabled)
Client->>fused_moe_kernel: trtllm_moe_fused(tokens, experts, weights, mapping_config)
fused_moe_kernel->>MoeAlltoAll: create MoeAlltoAll object
MoeAlltoAll->>RankN: dispatch tokens to responsible ranks
MoeAlltoAll->>RankM: dispatch tokens to responsible ranks
RankN->>fused_moe_kernel: compute MoE on local experts
RankM->>fused_moe_kernel: compute MoE on local experts
fused_moe_kernel->>AllGather: gather results from all ranks
AllGather->>Client: reshape to original shape
else All-Reduce Path (default)
Client->>fused_moe_kernel: trtllm_moe_fused(tokens, experts, weights)
fused_moe_kernel->>RankN: compute MoE on local experts
fused_moe_kernel->>AllGather: all-reduce across ranks
AllGather->>Client: return output
end
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes 🚥 Pre-merge checks | ✅ 3 | ❌ 2❌ Failed checks (2 warnings)
✅ Passed checks (3 passed)
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Actionable comments posted: 1
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⚠️ Outside diff range comments (1)
tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/torch_moe.py (1)
268-360: apply_routing_on_input is ignored in quantized MoE ops.
When callers setapply_routing_on_input=True(BMM-style routing), both FP8 and NVFP4 paths still use output-side routing, producing incorrect results. Please pass the flag through to_template_moe.🐛 Proposed fix
- return _template_moe(x, selected_experts, routing_weights, mlps) + return _template_moe( + x, selected_experts, routing_weights, mlps, apply_routing_on_input + )- return _template_moe(x, selected_experts, routing_weights, mlps) + return _template_moe( + x, selected_experts, routing_weights, mlps, apply_routing_on_input + )Also applies to: 404-510
🤖 Fix all issues with AI agents
In `@tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py`:
- Around line 1632-1716: The injection of default_params (is_gated_mlp, act_fn,
apply_routing_on_input, mapping_config) overwrites caller intent and can
conflict with existing node.kwargs; instead, read and preserve any of these
values from node.kwargs (keys like "is_gated_mlp", "act_fn",
"apply_routing_on_input", "mapping_config"), remove those keys from node.kwargs
to avoid duplicate-argument errors, and only fill missing positions in args
starting at params_start_idx with the preserved or default values
(mapping_config kept/inserted last). Modify the logic around
params_start_idx/default_params to prefer node.kwargs values, clear consumed
kwargs, and then extend/assign into args as done currently so
selected_experts_local and final_scales_local assignment remains compatible.
🧹 Nitpick comments (4)
tensorrt_llm/_torch/auto_deploy/transform/library/fused_moe.py (1)
1368-1377: Preserve traceback when re-raising stack errors.
Usingraise eresets the traceback; a bareraisekeeps the original context.♻️ Suggested fix
- except Exception as e: + except Exception: print(f"Error stacking parameters: {e}") print(f"param_list: {param_list}") print(f"dim: {dim}") - raise e + raisetensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/torch_moe.py (1)
181-182: Avoid mutable list defaults for fake ops’ mapping_config.
Shared default lists are a common Python footgun; useNoneto avoid shared state.♻️ Suggested fix
- mapping_config: List[int] = [], + mapping_config: Optional[List[int]] = None,Also applies to: 380-381, 532-533
tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/trtllm_moe.py (1)
256-256: Avoid mutable list defaults for mapping_config in fake op.
UseNoneto prevent shared state across calls.♻️ Suggested fix
- mapping_config: List[int] = [], + mapping_config: Optional[List[int]] = None,tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py (1)
174-186: Remove duplicated enable_attention_dp field.
The second definition overwrites the first; keep a single field to avoid confusion.♻️ Suggested fix
- enable_attention_dp: bool = Field( - default=False, - description="When True, skip TP sharding as attention data parallelism is enabled.", - ) - - dist_mapping: dict[str, int] = Field(default_factory=dict) - - mapping: Mapping = Field(default_factory=Mapping) - - enable_attention_dp: bool = Field( - default=False, - description="When True, skip TP sharding as attention data parallelism is enabled.", - ) + enable_attention_dp: bool = Field( + default=False, + description="When True, skip TP sharding as attention data parallelism is enabled.", + ) + + dist_mapping: dict[str, int] = Field(default_factory=dict) + + mapping: Mapping = Field(default_factory=Mapping)
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Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>
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Left one comment regarding the use of torch.distributed. Please take care of it in a follow-up PR and go ahead and merge this PR as is since the CI has already passed
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Fixes #10064
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