[BugFix] [DP/EP] Fix slow execution when BS <= DP (#25407)

Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>
Co-authored-by: Chris Bamford <chrisbam4d@gmail.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>

Author: 3 people

vllm/v1/worker/gpu_model_runner.py

@@ -55,7 +55,7 @@
 from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
-                        GiB_bytes, check_use_alibi, get_dtype_size,
+                        GiB_bytes, cdiv, check_use_alibi, get_dtype_size,
                         is_pin_memory_available,
                         length_from_prompt_token_ids_or_embeds, round_up,
                         supports_dynamo)
@@ -2913,12 +2913,13 @@ def _dummy_run(
             # Note: Overriding max_query_len to be the prefill tokens
             max_query_len = num_prefill_tokens
         elif uniform_decode:
-            num_reqs = num_tokens // max_query_len
+            assert not create_mixed_batch
+            num_reqs = cdiv(num_tokens, max_query_len)
             assert num_reqs <= max_num_reqs, \
                 "Do not capture num_reqs > max_num_reqs for uniform batch"
             num_scheduled_tokens_list = [max_query_len] * num_reqs
             if num_tokens % max_query_len != 0:
-                num_scheduled_tokens_list[-1] += num_tokens % max_query_len
+                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
         else:
             num_reqs = min(num_tokens, max_num_reqs)
             min_tokens_per_req = num_tokens // num_reqs

vllm/v1/worker/gpu_worker.py

@@ -487,7 +487,7 @@ def profile(self, is_start: bool = True):
                     sort_by="self_cuda_time_total"))
 
     def execute_dummy_batch(self) -> None:
-        self.model_runner._dummy_run(1)
+        self.model_runner._dummy_run(1, uniform_decode=True)
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
         return self.model_runner.add_lora(lora_request)