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[ROCm][INTERNAL][WIP] Cap profiling token count to fix SP all-gather OOM for MLA+DSA at TP>1 #3

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[ROCm][INTERNAL][WIP] Cap profiling token count to fix SP all-gather OOM for MLA+DSA at TP>1 #3

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14 changes: 11 additions & 3 deletions vllm/v1/worker/gpu_model_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6217,10 +6217,18 @@ def profile_run(self) -> None:
for i, output in enumerate(dummy_encoder_outputs):
self.encoder_cache[f"tmp_{i}"] = output

# Add `is_profile` here to pre-allocate communication buffers
# [ROCm] When fused_qkv_a_proj uses sequence-parallel mode
# (disable_tp=True), it all-gathers hidden_states across TP ranks,
# producing query tensors with tp_size * local_tokens (e.g., 65536
# for TP=8 with 8192 local tokens). Surrounding tensors (positions,
# k, residual) remain at local token count, causing shape mismatches
# and GPU OOM in MoE layers during the profiling pass.
# Affected models: GLM-5 (GlmMoeDsaForCausalLM), DeepSeek-V3/R1
# at any TP > 1 with MLA + DSA attention.
_profile_tokens = min(self.max_num_tokens, 64)
hidden_states, last_hidden_states = self._dummy_run(
self.max_num_tokens, is_profile=True
)
_profile_tokens, is_profile=True)

if get_pp_group().is_last_rank:
if self.is_pooling_model:
output = self._dummy_pooler_run(hidden_states)
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