Reduce the Cuda Graph memory footprint when running with DBO

vllm/v1/worker/gpu_model_runner.py

@@ -3477,8 +3477,10 @@ def _capture_cudagraphs(self, compilation_cases: list[int],
         # We skip EPLB here since we don't want to record dummy metrics
         for num_tokens in compilation_cases:
             # We currently only capture ubatched graphs when its a FULL
-            # cudagraph and for uniform decode batches.
-            capture_ubatched_graph = self.parallel_config.enable_dbo \
+            # cudagraph, a uniform decode batch, and the number of tokens
+            # is above the threshold. Otherwise we just capture a non-ubatched
+            # version of the graph
+            allow_microbatching = self.parallel_config.enable_dbo \
                 and cudagraph_runtime_mode == CUDAGraphMode.FULL \
                 and uniform_decode \
                 and check_ubatch_thresholds(
@@ -3487,37 +3489,27 @@ def _capture_cudagraphs(self, compilation_cases: list[int],
                     uniform_decode=uniform_decode,
                 )
 
-            # Currently we capture both microbatched and non-microbatched
-            # graphs when capture_ubatched_graph is True, this is because
-            # occasionally we will be forced out of microbatching due to other
-            # DP ranks not microbatching (usually caused by an empty second
-            # microbatch; once we resolve this, we can remove the
-            # non-microbatched graph capture).
-            allow_microbatching_options = [True, False] if \
-                capture_ubatched_graph else [False]
-            for allow_microbatching in allow_microbatching_options:
-                for _ in range(
-                        self.compilation_config.cudagraph_num_of_warmups):
-                    # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
-                    # But be careful, warm up with `NONE`is orthogonal to
-                    # if we want to warm up attention or not. This is
-                    # different from the case where `FULL` implies capture
-                    # attention while `PIECEWISE` implies no attention.
-                    force_attention = (
-                        cudagraph_runtime_mode == CUDAGraphMode.FULL)
-                    self._dummy_run(num_tokens,
-                                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
-                                    force_attention=force_attention,
-                                    uniform_decode=uniform_decode,
-                                    allow_microbatching=allow_microbatching,
-                                    skip_eplb=True,
-                                    remove_lora=False)
+            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
+                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
+                # But be careful, warm up with `NONE`is orthogonal to
+                # if we want to warm up attention or not. This is
+                # different from the case where `FULL` implies capture
+                # attention while `PIECEWISE` implies no attention.
+                force_attention = (
+                    cudagraph_runtime_mode == CUDAGraphMode.FULL)
                 self._dummy_run(num_tokens,
-                                cudagraph_runtime_mode=cudagraph_runtime_mode,
+                                cudagraph_runtime_mode=CUDAGraphMode.NONE,
+                                force_attention=force_attention,
                                 uniform_decode=uniform_decode,
                                 allow_microbatching=allow_microbatching,
                                 skip_eplb=True,
                                 remove_lora=False)
+            self._dummy_run(num_tokens,
+                            cudagraph_runtime_mode=cudagraph_runtime_mode,
+                            uniform_decode=uniform_decode,
+                            allow_microbatching=allow_microbatching,
+                            skip_eplb=True,
+                            remove_lora=False)
         self.maybe_remove_all_loras(self.lora_config)
 
     def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:

vllm/v1/worker/gpu_ubatch_wrapper.py

@@ -330,6 +330,18 @@ def __call__(self, *args, **kwargs):
 
         # If there's no ubatching, just run the runnable object
         if ubatch_slices is None:
+
+            # This is to account for the case where ubatching was aborted.
+            # When we capture full graphs we only capture one graph per shape,
+            # meaning that if we have a ubatched  cudagraph for the current
+            # num_tokens, we don't have a non-ubatched one. Without this
+            # check, the cudagraph wrapper will try to capture a cudagraph
+            # for this shape during a normal run.
+            if cudagraph_runtime_mode is CUDAGraphMode.FULL:
+                assert batch_descriptor is not None
+                if batch_descriptor.num_tokens in self.cudagraphs:
+                    cudagraph_runtime_mode = CUDAGraphMode.NONE
+
             if cudagraph_runtime_mode in (CUDAGraphMode.NONE,
                                           CUDAGraphMode.PIECEWISE):
                 return self.runnable(*args, **kwargs)