[cudagraphs] Refactor cudagraph capture loop

tests/v1/cudagraph/test_cudagraph_dispatch.py

@@ -173,6 +173,68 @@ def test_dispatcher(self, cudagraph_mode_str, compilation_mode, lora_config):
         else:
             assert rt_mode == CUDAGraphMode.NONE
 
+    @pytest.mark.parametrize(
+        "cudagraph_mode_str,compilation_mode,expected_modes",
+        [
+            # FULL mode: only FULL keys, no PIECEWISE
+            ("FULL", CompilationMode.NONE, [CUDAGraphMode.FULL]),
+            # PIECEWISE mode: only PIECEWISE keys
+            ("PIECEWISE", CompilationMode.VLLM_COMPILE, [CUDAGraphMode.PIECEWISE]),
+            # FULL_DECODE_ONLY: only FULL keys for uniform decode
+            ("FULL_DECODE_ONLY", CompilationMode.NONE, [CUDAGraphMode.FULL]),
+            # NONE mode: no keys
+            ("NONE", CompilationMode.NONE, []),
+        ],
+    )
+    def test_get_capture_descs(
+        self, cudagraph_mode_str, compilation_mode, expected_modes
+    ):
+        """Test get_capture_descs returns correctly grouped and ordered descs."""
+        comp_config = CompilationConfig(
+            cudagraph_mode=cudagraph_mode_str,
+            mode=compilation_mode,
+            cudagraph_capture_sizes=[1, 4, 8, 16],
+        )
+
+        config = _create_vllm_config(comp_config, max_num_seqs=16)
+        dispatcher = CudagraphDispatcher(config)
+        dispatcher.initialize_cudagraph_keys(
+            cudagraph_mode=comp_config.cudagraph_mode, uniform_decode_query_len=1
+        )
+
+        capture_descs = dispatcher.get_capture_descs()
+
+        # Verify we get the expected modes
+        actual_modes = [mode for mode, _ in capture_descs]
+        assert actual_modes == expected_modes
+
+        # Verify each group is sorted largest-first
+        for mode, descs in capture_descs:
+            assert len(descs) > 0, "Each group should have at least one descriptor"
+            num_tokens_list = [d.num_tokens for d in descs]
+            assert num_tokens_list == sorted(num_tokens_list, reverse=True), (
+                f"Descriptors for {mode} should be sorted largest-first"
+            )
+
+            # All descriptors in a group should have same uniform value
+            uniform_values = [d.uniform for d in descs]
+            assert len(set(uniform_values)) == 1, (
+                "All descriptors in a group should have the same uniform value"
+            )
+
+    def test_get_capture_descs_empty_when_not_initialized(self):
+        """Test that get_capture_descs returns empty list when keys not initialized."""
+        comp_config = CompilationConfig(
+            cudagraph_mode="FULL",
+            mode=CompilationMode.NONE,
+            cudagraph_capture_sizes=[1, 8],
+        )
+        config = _create_vllm_config(comp_config, max_num_seqs=8)
+        dispatcher = CudagraphDispatcher(config)
+        # Don't initialize keys
+
+        assert dispatcher.get_capture_descs() == []
+
 
 @pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda")
 class TestCUDAGraphWrapper:

vllm/v1/cudagraph_dispatcher.py

@@ -231,3 +231,26 @@ def dispatch(
 
         # finally, just return no cudagraphs and a trivial batch descriptor
         return CUDAGraphMode.NONE, BatchDescriptor(num_tokens)
+
+    def get_capture_descs(self) -> list[tuple[CUDAGraphMode, list[BatchDescriptor]]]:
+        """
+        Returns capture descriptors for cudagraph capturing.
+
+        Returns:
+            List of (runtime_mode, batch_descriptors) tuples, ordered PIECEWISE
+            first then FULL. Batch descriptors are sorted largest-first for
+            memory efficiency.
+        """
+        if not self.keys_initialized or self.cudagraph_mode == CUDAGraphMode.NONE:
+            return []
+
+        result = []
+        # Return in order: PIECEWISE first, then FULL
+        for mode in [CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL]:
+            descs = list(self.cudagraph_keys[mode])
+            if descs:
+                # Sort by num_tokens descending (largest first)
+                descs.sort(key=lambda d: d.num_tokens, reverse=True)
+                result.append((mode, descs))
+
+        return result

vllm/v1/worker/gpu_model_runner.py

@@ -10,7 +10,6 @@
 from contextlib import contextmanager
 from copy import copy, deepcopy
 from functools import reduce
-from itertools import product
 from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
 
 import numpy as np
@@ -4809,50 +4808,14 @@ def freeze_gc():
         set_cudagraph_capturing_enabled(True)
         with freeze_gc(), graph_capture(device=self.device):
             start_free_gpu_memory = torch.cuda.mem_get_info()[0]
-            cudagraph_mode = self.compilation_config.cudagraph_mode
-            assert cudagraph_mode is not None
 
-            if self.lora_config:
-                if self.compilation_config.cudagraph_specialize_lora:
-                    lora_cases = [True, False]
-                else:
-                    lora_cases = [True]
-            else:
-                lora_cases = [False]
-
-            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
-                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
-                # make sure we capture the largest batch size first
-                compilation_cases = list(
-                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
-                )
-                self._capture_cudagraphs(
-                    compilation_cases,
-                    cudagraph_runtime_mode=cudagraph_runtime_mode,
-                    uniform_decode=False,
-                )
-
-            # Capture full cudagraph for uniform decode batches if we
-            # don't already have full mixed prefill-decode cudagraphs.
-            if (
-                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
-                and cudagraph_mode.separate_routine()
-            ):
-                max_num_tokens = (
-                    self.scheduler_config.max_num_seqs * self.uniform_decode_query_len
-                )
-                decode_cudagraph_batch_sizes = [
-                    x
-                    for x in self.cudagraph_batch_sizes
-                    if max_num_tokens >= x >= self.uniform_decode_query_len
-                ]
-                compilation_cases_decode = list(
-                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
-                )
+            for (
+                runtime_mode,
+                batch_descs,
+            ) in self.cudagraph_dispatcher.get_capture_descs():
                 self._capture_cudagraphs(
-                    compilation_cases=compilation_cases_decode,
-                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
-                    uniform_decode=True,
+                    batch_descriptors=batch_descs,
+                    cudagraph_runtime_mode=runtime_mode,
                 )
 
             torch.cuda.synchronize()
@@ -4883,19 +4846,32 @@ def freeze_gc():
 
     def _capture_cudagraphs(
         self,
-        compilation_cases: list[tuple[int, bool]],
+        batch_descriptors: list[BatchDescriptor],
         cudagraph_runtime_mode: CUDAGraphMode,
-        uniform_decode: bool,
     ):
         assert (
             cudagraph_runtime_mode != CUDAGraphMode.NONE
             and cudagraph_runtime_mode.valid_runtime_modes()
         ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
 
+        if not batch_descriptors:
+            return
+
+        uniform_decode = batch_descriptors[0].uniform
+        force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
+
+        dummy_run = functools.partial(
+            self._dummy_run,
+            uniform_decode=uniform_decode,
+            skip_eplb=True,
+            remove_lora=False,
+            force_attention=force_attention,
+        )
+
         # Only rank 0 should print progress bar during capture
         if is_global_first_rank():
-            compilation_cases = tqdm(
-                compilation_cases,
+            batch_descriptors = tqdm(
+                batch_descriptors,
                 disable=not self.load_config.use_tqdm_on_load,
                 desc="Capturing CUDA graphs ({}, {})".format(
                     "decode" if uniform_decode else "mixed prefill-decode",
@@ -4904,7 +4880,10 @@ def _capture_cudagraphs(
             )
 
         # We skip EPLB here since we don't want to record dummy metrics
-        for num_tokens, activate_lora in compilation_cases:
+        for batch_desc in batch_descriptors:
+            num_tokens = batch_desc.num_tokens
+            activate_lora = batch_desc.has_lora
+
             # We currently only capture ubatched graphs when its a FULL
             # cudagraph, a uniform decode batch, and the number of tokens
             # is above the threshold. Otherwise we just capture a non-ubatched
@@ -4922,28 +4901,22 @@ def _capture_cudagraphs(
 
             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
+                # 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(
+                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,
                     activate_lora=activate_lora,
                 )
-            self._dummy_run(
+
+            # Capture run
+            dummy_run(
                 num_tokens,
                 cudagraph_runtime_mode=cudagraph_runtime_mode,
-                uniform_decode=uniform_decode,
                 allow_microbatching=allow_microbatching,
-                skip_eplb=True,
-                remove_lora=False,
                 activate_lora=activate_lora,
                 is_graph_capturing=True,
             )