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[https://nvbugs/5613089][fix] Fix the rank to access all_rank_chunk_size_list when chunked MoE is used #8723

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Merged
merged 1 commit into from
Nov 17, 2025
Merged

[https://nvbugs/5613089][fix] Fix the rank to access all_rank_chunk_size_list when chunked MoE is used #8723

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2 changes: 1 addition & 1 deletion tensorrt_llm/_torch/autotuner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -727,10 +727,10 @@ def choose_one(
new_tuning_failure_occured = False

for p in profiles:
tensors = self._prepare_input_tensors(p, inputs)
is_cache_hit, *_ = self.profiling_cache.search_cache(
custom_op, runners, p.get_opt_shapes(), tuning_config)
if not is_cache_hit:
tensors = self._prepare_input_tensors(p, inputs)
# Initialize runner and tactic as None in case of no valid tactic or runners are found
best_runner_id, best_tactic, min_time, has_tuning_failure_occured = self._profile_runners(
custom_op, runners, tensors, p, tuning_config, **kwargs)
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6 changes: 3 additions & 3 deletions tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -626,7 +626,7 @@ def forward_impl(
all_rank_num_tokens_list = [[
val[idx_chunk] for val in all_rank_chunk_size_list
] for idx_chunk in range(num_chunks)]
chunk_size_list = all_rank_chunk_size_list[self.rank]
chunk_size_list = all_rank_chunk_size_list[self.parallel_rank]
else:
all_rank_num_tokens_list = [None] * num_chunks
chunk_size_list = self.split_chunk(x.shape[0], num_chunks)
Expand Down Expand Up @@ -685,7 +685,7 @@ def _reducescatter_or_allreduce(x_, idx):
outputs = torch.cat(outputs_list)

if self.use_dp and self.parallel_size > 1:
rank = self.mapping.tp_rank
rank = self.parallel_rank
outputs = outputs[:all_rank_num_tokens[rank]]
return outputs

Expand Down Expand Up @@ -714,7 +714,7 @@ def forward_fake(
is_nvfp4_input = isinstance(x, Fp4QuantizedTensor)
data_type = output_dtype if is_nvfp4_input else x.dtype
num_tokens = all_rank_num_tokens[
self.mapping.tp_rank] if all_rank_num_tokens else x.shape[0]
self.parallel_rank] if all_rank_num_tokens else x.shape[0]
hidden_size = x.shape[1] * (2 if is_nvfp4_input else 1)
top_k = self.routing_method.experts_per_token
return x.new_empty((num_tokens, top_k, hidden_size),
Expand Down
4 changes: 2 additions & 2 deletions tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -706,7 +706,7 @@ def forward_impl(
all_rank_num_tokens_list = [[
val[idx_chunk] for val in all_rank_chunk_size_list
] for idx_chunk in range(num_chunks)]
chunk_size_list = all_rank_chunk_size_list[self.rank]
chunk_size_list = all_rank_chunk_size_list[self.parallel_rank]
else:
all_rank_num_tokens_list = [None] * num_chunks
chunk_size_list = self.split_chunk(x.shape[0], num_chunks)
Expand Down Expand Up @@ -778,6 +778,6 @@ def _reducescatter_or_allreduce(x_, idx):
outputs = torch.cat(outputs_list)

if self.use_dp and self.parallel_size > 1:
rank = self.mapping.tp_rank
rank = self.parallel_rank
outputs = outputs[:all_rank_num_tokens[rank]]
return outputs
2 changes: 1 addition & 1 deletion tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -661,7 +661,7 @@ def forward_impl(
)

if use_dp_padding:
rank = self.mapping.tp_rank
rank = self.parallel_rank
final_hidden_states = final_hidden_states[:
all_rank_num_tokens[rank]]
return final_hidden_states
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4 changes: 2 additions & 2 deletions tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py
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Original file line number Diff line number Diff line change
Expand Up @@ -828,7 +828,7 @@ def split_chunk(split_token_num: int, split_num_chunks: int):
] for idx_chunk in range(num_chunks)]
all_rank_max_num_tokens_list = split_chunk(all_rank_max_num_tokens,
num_chunks)
chunk_size_list = all_rank_chunk_size_list[self.rank]
chunk_size_list = all_rank_chunk_size_list[self.parallel_rank]
if use_all_to_all:
all_rank_num_tokens_list = [[
1 if val == 0 else val for val in val_list
Expand Down Expand Up @@ -916,7 +916,7 @@ def split_chunk(split_token_num: int, split_num_chunks: int):
self.event_dict[EventType.MoeChunkingOverlap].record()
self.event_dict[EventType.MoeChunkingOverlap].wait()
outputs = torch.cat(outputs_list)
rank = self.mapping.tp_rank
rank = self.parallel_rank
outputs = outputs[:all_rank_num_tokens[rank]]
self.repeat_idx = 0 if self.repeat_idx == self.repeat_count - 1 else self.repeat_idx + 1
return outputs
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1 change: 1 addition & 0 deletions tensorrt_llm/_torch/modules/fused_moe/interface.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,6 +181,7 @@ def __init__(

# All ranks participate in allreduce regardless of EP/TP combination
self.mapping = model_config.mapping
self.parallel_rank = self.mapping.tp_rank
self.parallel_size = self.mapping.tp_size
self.intermediate_size_per_partition = intermediate_size // self.tp_size

Expand Down