[torch.compile] remove compilation_context and simplify code (#10838)

Signed-off-by: youkaichao <youkaichao@gmail.com>

tests/compile/piecewise/test_simple.py

@@ -7,7 +7,6 @@ import torch
 from torch import nn
 from torch.library import Library
 
-from vllm.compilation.compile_context import set_compile_context
 from vllm.compilation.counter import compilation_counter
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (CompilationConfig, CompilationLevel, VllmConfig,
@@ -81,6 +80,7 @@ def test_simple_piecewise_compile():
         use_cudagraph=True,
         splitting_ops=["silly.attention"],
         cudagraph_copy_inputs=True,
+        cudagraph_capture_sizes=[1, 2],
     ))
     with set_current_vllm_config(vllm_config):
         model = SillyModel(vllm_config=vllm_config, prefix='')
@@ -96,11 +96,10 @@ def test_simple_piecewise_compile():
             6,  # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
     ):
 
-        with set_compile_context([1, 2]):
-            model(inputs)
+        model(inputs)
 
-            model(torch.randn(2).cuda())
-            model(torch.randn(1).cuda())
+        model(torch.randn(2).cuda())
+        model(torch.randn(1).cuda())
 
         input = torch.zeros(2).cuda()
         global global_counter

tests/compile/piecewise/test_toy_llama.py

@@ -13,7 +13,6 @@ import torch
 from torch import nn
 from torch.library import Library
 
-from vllm.compilation.compile_context import set_compile_context
 from vllm.compilation.counter import compilation_counter
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (CompilationConfig, CompilationLevel, VllmConfig,
@@ -256,6 +255,7 @@ def run_model(llama_config,
         compilation_config = CompilationConfig(
             level=CompilationLevel.PIECEWISE,
             use_cudagraph=True,
+            cudagraph_capture_sizes=[1, 2],
         )
         if split_attn:
             compilation_config.splitting_ops = ["silly.attention"]
@@ -273,10 +273,9 @@ def run_model(llama_config,
     input_ids = torch.randint(0, llama_config.vocab_size, (B, )).cuda()
     positions = torch.arange(B).cuda()
 
-    with set_compile_context([1, 2]):
-        model(input_ids, positions)
-        model(input_ids[:2], positions[:2])
-        model(input_ids[:1], positions[:1])
+    model(input_ids, positions)
+    model(input_ids[:2], positions[:2])
+    model(input_ids[:1], positions[:1])
 
     input_ids[:2].zero_()
     output = model(input_ids[:2], positions[:2])
@@ -379,10 +378,13 @@ def benchmark():
                 level=CompilationLevel.PIECEWISE,
                 use_cudagraph=True,
                 splitting_ops=["silly.attention"],
+                cudagraph_capture_sizes=cudagraph_sizes,
             )
         else:
             compilation_config = CompilationConfig(
-                level=CompilationLevel.PIECEWISE, )
+                level=CompilationLevel.PIECEWISE,
+                cudagraph_capture_sizes=cudagraph_sizes,
+            )
 
         vllm_config = VllmConfig(compilation_config=compilation_config)
         with set_current_vllm_config(vllm_config):
@@ -396,17 +398,16 @@ def benchmark():
 
         graphs = {}
 
-        with set_compile_context(cudagraph_sizes):
-            model(input_ids, positions)
-            for b in cudagraph_sizes[::-1]:
-                if not piecewise:
-                    graph = torch.cuda.CUDAGraph()
-                    with torch.cuda.graph(graph, pool=pool):
-                        output = model(input_ids[:b], positions[:b])
-                    graphs[b] = (graph, output)
-                else:
+        model(input_ids, positions)
+        for b in cudagraph_sizes[::-1]:
+            if not piecewise:
+                graph = torch.cuda.CUDAGraph()
+                with torch.cuda.graph(graph, pool=pool):
                     output = model(input_ids[:b], positions[:b])
-                    graphs[b] = (model, output)
+                graphs[b] = (graph, output)
+            else:
+                output = model(input_ids[:b], positions[:b])
+                graphs[b] = (model, output)
         for b in cudagraph_sizes:
             if piecewise:
                 # noqa is for `Function definition does not bind loop variable`

tests/models/decoder_only/language/test_jamba.py

@@ -1,8 +1,8 @@
 import pytest
 
 from tests.utils import multi_gpu_test
+from vllm.config import VllmConfig
 from vllm.sampling_params import SamplingParams
-from vllm.worker.model_runner import _get_graph_batch_size
 
 from ...utils import check_outputs_equal
 
@@ -189,7 +189,8 @@ def test_mamba_cache_cg_padding(
     # This test is for verifying that mamba cache is padded to CG captured
     # batch size. If it's not, a torch RuntimeError will be raised because
     # tensor dimensions aren't compatible
-    while len(example_prompts) == _get_graph_batch_size(len(example_prompts)):
+    while len(example_prompts) == VllmConfig.get_graph_batch_size(
+            len(example_prompts)):
         example_prompts.append(example_prompts[0])
 
     try:

tests/models/decoder_only/language/test_mamba.py

@@ -5,8 +5,8 @@ Run `pytest tests/models/test_mamba.py`.
 import pytest
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+from vllm.config import VllmConfig
 from vllm.sampling_params import SamplingParams
-from vllm.worker.model_runner import _get_graph_batch_size
 
 from ...utils import check_outputs_equal
 
@@ -200,7 +200,8 @@ def test_mamba_cache_cg_padding(
     # This test is for verifying that mamba cache is padded to CG captured
     # batch size. If it's not, a torch RuntimeError will be raised because
     # tensor dimensions aren't compatible
-    while len(example_prompts) == _get_graph_batch_size(len(example_prompts)):
+    while len(example_prompts) == VllmConfig.get_graph_batch_size(
+            len(example_prompts)):
         example_prompts.append(example_prompts[0])
 
     try:

tests/worker/test_encoder_decoder_model_runner.py

@@ -4,12 +4,12 @@ from typing import List
 import pytest
 import torch
 
+from vllm.config import VllmConfig
 from vllm.engine.arg_utils import EngineArgs
 from vllm.platforms import current_platform
 from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.utils import make_tensor_with_pad
 from vllm.worker.enc_dec_model_runner import EncoderDecoderModelRunner
-from vllm.worker.model_runner import _get_graph_batch_size
 
 BATCH_SIZES = [1, 4, 16, 64, 256]
 
@@ -548,7 +548,7 @@ def test_prepare_decode_cuda_graph(batch_size, multiple_seqs_per_seq_group):
     # With CUDA Graph capture and replay enabled, the decoder and encoder
     # input sequences will be padded. Create the expected padded tensors
     # accordingly.
-    graph_batch_size = _get_graph_batch_size(expanded_batch_size)
+    graph_batch_size = VllmConfig.get_graph_batch_size(expanded_batch_size)
     cuda_graph_pad_size = graph_batch_size - expanded_batch_size
     padded_seq_lens = seq_lens + list(itertools.repeat(1, cuda_graph_pad_size))
     padded_encoder_seq_lens = encoder_seq_lens + list(

tests/worker/test_model_runner.py

@@ -3,13 +3,14 @@ from typing import List
 import pytest
 import torch
 
+from vllm.config import VllmConfig
 from vllm.distributed.parallel_state import (ensure_model_parallel_initialized,
                                              init_distributed_environment)
 from vllm.engine.arg_utils import EngineArgs
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
 from vllm.utils import get_open_port
-from vllm.worker.model_runner import ModelRunner, _get_graph_batch_size
+from vllm.worker.model_runner import ModelRunner
 
 
 def _create_model_runner(model: str, *args, **kwargs) -> ModelRunner:
@@ -176,7 +177,7 @@ def test_prepare_decode_cuda_graph(batch_size):
         model_input.attn_metadata, model_input.attn_metadata.slot_mapping)
     assert len(slot_mapping) == len(input_tokens)
 
-    expected_bs = _get_graph_batch_size(len(seq_group_metadata_list))
+    expected_bs = VllmConfig.get_graph_batch_size(len(seq_group_metadata_list))
     # Verify input metadata is correct for prompts.
     device = model_runner.device
     assert attn_metadata.num_prefills == 0

vllm/compilation/backends.py

@@ -242,10 +242,6 @@ class VllmBackend:
         assert not self._called, "VllmBackend can only be called once"
 
         self.graph = graph
-        # config is updated now, because only here can
-        # we get the sizes to capture for cudagraph
-        # from compilation context
-        self.compilation_configs.init_during_runtime()
         self.configure_post_pass()
 
         self.split_gm, self.piecewise_graphs = split_graph(

vllm/compilation/compile_context.py

@@ -1,23 +0,0 @@
-from contextlib import contextmanager
-from typing import Any
-
-_compile_context: Any = None
-
-
-def get_compile_context() -> Any:
-    """Get the current compile context."""
-    return _compile_context
-
-
-@contextmanager
-def set_compile_context(context: Any):
-    """A context manager that stores the current compile context,
-    usually it is a list of sizes to specialize.
-    """
-    global _compile_context
-    prev_context = _compile_context
-    _compile_context = context
-    try:
-        yield
-    finally:
-        _compile_context = prev_context

vllm/config.py

@@ -2357,15 +2357,10 @@ class CompilationConfig(BaseModel):
         from vllm.compilation.backends import VllmBackend
         return VllmBackend(self)
 
-    def init_during_runtime(self):
+    def init_with_cudagraph_sizes(self, sizes_to_specialize: List[int]):
         """To complete the initialization of config,
-        we need to know the compile context, which is only available
-        during the first run of the model.
-        """
-        from vllm.compilation.compile_context import get_compile_context
-        context = get_compile_context()
-        context = copy.deepcopy(context) if context is not None else []
-        sizes_to_specialize: List[int] = context
+        we need to know the cudagraph sizes."""
+
         if self.cudagraph_capture_sizes is None:
             self.capture_sizes = sizes_to_specialize
         else:
@@ -2386,6 +2381,21 @@ class CompilationConfig(BaseModel):
                 self.inductor_compile_sizes = []
             self.compile_sizes = self.inductor_compile_sizes
 
+        # sort to make sure cudagraph capture sizes are in descending order
+        self.capture_sizes.sort(reverse=True)
+
+
+_BATCH_SIZE_ALIGNMENT = 8
+# all the token sizes that **can** be captured by cudagraph.
+# they can be arbitrarily large.
+# currently it includes: 1, 2, 4, 8, 16, 24, 32, 40, ..., 8192.
+# the actual sizes to capture will be determined by the model,
+# depending on the model's max_num_seqs.
+# NOTE: get_graph_batch_size needs to be updated if this list is changed.
+_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [
+    _BATCH_SIZE_ALIGNMENT * i for i in range(1, 1025)
+]
+
 
 @dataclass
 class VllmConfig:
@@ -2413,6 +2423,41 @@ class VllmConfig:
     kv_transfer_config: KVTransferConfig = field(default=None,
                                                  init=True)  # type: ignore
 
+    @staticmethod
+    def get_graph_batch_size(batch_size: int) -> int:
+        """Returns the padded batch size given actual batch size.
+
+        Batch sizes are 1, 2, 4, _BATCH_SIZE_ALIGNMENT,
+        2*_BATCH_SIZE_ALIGNMENT, 3*_BATCH_SIZE_ALIGNMENT...
+        """
+        if batch_size <= 2:
+            return batch_size
+        elif batch_size <= 4:
+            return 4
+        else:
+            return ((batch_size + _BATCH_SIZE_ALIGNMENT - 1) //
+                    _BATCH_SIZE_ALIGNMENT * _BATCH_SIZE_ALIGNMENT)
+
+    @staticmethod
+    def get_max_graph_batch_size(max_num_seqs: int) -> int:
+        """
+        max_num_seqs: Maximum number of sequences in a batch.
+        _BATCH_SIZES_TO_CAPTURE: all the sizes that we want to capture.
+
+        pad the max_num_seqs if necessary by calling get_graph_batch_size,
+        which will deal with some edge cases like 1, 2, 4.
+
+        if the padded size is in _BATCH_SIZES_TO_CAPTURE, return the padded
+        size. if not, it means the padded size is larger than the largest size
+        in _BATCH_SIZES_TO_CAPTURE, return the largest size in
+        _BATCH_SIZES_TO_CAPTURE.
+        """
+        padded_size = VllmConfig.get_graph_batch_size(max_num_seqs)
+        if padded_size in _BATCH_SIZES_TO_CAPTURE:
+            return padded_size
+        assert padded_size > _BATCH_SIZES_TO_CAPTURE[-1]
+        return _BATCH_SIZES_TO_CAPTURE[-1]
+
     @staticmethod
     def _get_quantization_config(
             model_config: ModelConfig,
@@ -2496,6 +2541,28 @@ class VllmConfig:
             self.compilation_config.pass_config.enable_reshape = False
             self.compilation_config.level = CompilationLevel.PIECEWISE
 
+        if not envs.VLLM_USE_V1:
+            max_batchsize_to_capture = 0
+            if self.scheduler_config is not None and \
+                self.model_config is not None and \
+                    not self.model_config.enforce_eager:
+                max_batchsize_to_capture = \
+                    self.get_max_graph_batch_size(
+                    self.scheduler_config.max_num_seqs)
+            batch_size_capture_list = [
+                size for size in _BATCH_SIZES_TO_CAPTURE
+                if size <= max_batchsize_to_capture
+            ]
+        else:
+            batch_size_capture_list = []
+            if self.model_config is not None and \
+                not self.model_config.enforce_eager:
+                batch_size_capture_list = [1, 2, 4
+                                           ] + [i for i in range(8, 513, 8)]
+
+        self.compilation_config.init_with_cudagraph_sizes(
+            batch_size_capture_list)
+
         if self.cache_config is not None and \
             self.cache_config.cpu_offload_gb > 0 and \
             self.compilation_config.level != CompilationLevel.NO_COMPILATION:

vllm/model_executor/models/jamba.py

@@ -7,7 +7,7 @@ from transformers import JambaConfig
 
 from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.attention.layer import Attention
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import _BATCH_SIZES_TO_CAPTURE, CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -25,8 +25,6 @@ from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
                                                     MambaCacheParams)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
-from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
-                                      _get_graph_batch_size)
 
 from .interfaces import HasInnerState, SupportsLoRA
 from .utils import maybe_prefix
@@ -404,7 +402,7 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
                 inputs_embeds: Optional[torch.Tensor] = None,
                 **kwargs):
         if self.mamba_cache is None:
-            max_batch_size = (_get_graph_batch_size(
+            max_batch_size = (VllmConfig.get_graph_batch_size(
                 self.scheduler_config.max_num_seqs) if self.scheduler_config
                               else max(_BATCH_SIZES_TO_CAPTURE) + 2)
 

vllm/model_executor/models/mamba.py

@@ -6,7 +6,7 @@ from torch import nn
 from transformers import MambaConfig
 
 from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import _BATCH_SIZES_TO_CAPTURE, CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -23,8 +23,6 @@ from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
                                                     MambaCacheParams)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
-from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
-                                      _get_graph_batch_size)
 
 from .utils import maybe_prefix
 
@@ -187,7 +185,7 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
                 inputs_embeds: Optional[torch.Tensor] = None,
                 **kwargs):
         if self.mamba_cache is None:
-            max_batch_size = (_get_graph_batch_size(
+            max_batch_size = (VllmConfig.get_graph_batch_size(
                 self.scheduler_config.max_num_seqs) if self.scheduler_config
                               else max(_BATCH_SIZES_TO_CAPTURE) + 2)
             self.mamba_cache = MambaCacheManager(

vllm/v1/worker/gpu_model_runner.py

@@ -8,7 +8,6 @@ import torch
 import torch.distributed
 import torch.nn as nn
 
-from vllm.compilation.compile_context import set_compile_context
 from vllm.config import CompilationLevel, VllmConfig
 from vllm.distributed.parallel_state import graph_capture
 from vllm.forward_context import set_forward_context
@@ -100,7 +99,11 @@ class GPUModelRunner:
                                == CompilationLevel.PIECEWISE
                                and not self.model_config.enforce_eager)
         # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
-        self.cudagraph_batch_sizes = [1, 2, 4] + [i for i in range(8, 513, 8)]
+        # The convention is different.
+        # self.cudagraph_batch_sizes sorts in ascending order.
+        # The batch sizes in the config are in descending order.
+        self.cudagraph_batch_sizes = list(
+            reversed(self.vllm_config.compilation_config.capture_sizes))
         self.positions = torch.zeros(self.max_num_tokens,
                                      dtype=torch.int64,
                                      device=self.device)
@@ -548,10 +551,9 @@ class GPUModelRunner:
             torch.tensor([], dtype=torch.float32, device=self.device)
             for _ in range(self.num_attn_layers)
         ]
-        with set_compile_context(self.cudagraph_batch_sizes):
-            # Trigger compilation for general shape.
-            hidden_states = self._dummy_run(self.model, self.max_num_tokens,
-                                            dummy_kv_caches)
+        # Trigger compilation for general shape.
+        hidden_states = self._dummy_run(self.model, self.max_num_tokens,
+                                        dummy_kv_caches)
         logits = self.model.compute_logits(hidden_states, None)
         logits = logits[:self.max_num_tokens]
         # TODO(woosuk): Consider the memory usage of the sampler.

vllm/worker/enc_dec_model_runner.py

@@ -25,8 +25,7 @@ from vllm.sequence import (IntermediateTensors, PoolerOutput,
 from vllm.utils import STR_NOT_IMPL_ENC_DEC_BACKEND, make_tensor_with_pad
 from vllm.worker.model_runner import (GPUModelRunnerBase,
                                       ModelInputForGPUBuilder,
-                                      ModelInputForGPUWithSamplingMetadata,
-                                      _get_graph_batch_size)
+                                      ModelInputForGPUWithSamplingMetadata)
 from vllm.worker.model_runner_base import (
     _add_attn_metadata_broadcastable_dict,
     _add_sampling_metadata_broadcastable_dict)
@@ -465,7 +464,8 @@ class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]):
                 # We will be using CUDA graph replay for this decode.
                 max_len_of_block_table = self.get_max_block_per_batch()
                 batch_size = len(encoder_seq_lens)
-                graph_batch_size = _get_graph_batch_size(batch_size)
+                graph_batch_size = self.vllm_config.get_graph_batch_size(
+                    batch_size)
                 assert graph_batch_size >= batch_size
                 cuda_graph_pad_size = graph_batch_size - batch_size
                 # extend the cross_block_tables and encoder_seq_lens to match

vllm/worker/model_runner.py

@@ -18,7 +18,6 @@ import vllm.envs as envs
 from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.attention.backends.abstract import AttentionState
 from vllm.attention.backends.utils import CommonAttentionState
-from vllm.compilation.compile_context import set_compile_context
 from vllm.config import CompilationLevel, VllmConfig
 from vllm.core.scheduler import SchedulerOutputs
 from vllm.distributed import get_kv_transfer_group, get_pp_group
@@ -63,16 +62,7 @@ if TYPE_CHECKING:
 logger = init_logger(__name__)
 
 LORA_WARMUP_RANK = 8
-_BATCH_SIZE_ALIGNMENT = 8
-# all the token sizes that **can** be captured by cudagraph.
-# they can be arbitrarily large.
-# currently it includes: 1, 2, 4, 8, 16, 24, 32, 40, ..., 8192.
-# the actual sizes to capture will be determined by the model,
-# depending on the model's max_num_seqs.
-# NOTE: _get_graph_batch_size needs to be updated if this list is changed.
-_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [
-    _BATCH_SIZE_ALIGNMENT * i for i in range(1, 1025)
-]
+
 _NUM_WARMUP_ITERS = 2
 
 TModelInputForGPU = TypeVar('TModelInputForGPU', bound="ModelInputForGPU")
@@ -763,7 +753,6 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
                             max_decode_seq_len: int,
                             max_encoder_seq_len: int = 0) -> bool:
         return (decode_only and not self.runner.model_config.enforce_eager
-                and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1]
                 and max_decode_seq_len <= self.runner.max_seq_len_to_capture
                 and max_encoder_seq_len <= self.runner.max_seq_len_to_capture
                 and batch_size <= self.runner.max_batchsize_to_capture)
@@ -811,7 +800,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
                                         max_encoder_seq_len):
             return -1
 
-        graph_batch_size = _get_graph_batch_size(batch_size)
+        graph_batch_size = VllmConfig.get_graph_batch_size(batch_size)
         assert graph_batch_size >= batch_size
         return graph_batch_size - batch_size
 
@@ -1023,7 +1012,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         self.sliding_window = model_config.get_sliding_window()
         self.block_size = cache_config.block_size
         self.max_seq_len_to_capture = self.model_config.max_seq_len_to_capture
-        self.max_batchsize_to_capture = _get_max_graph_batch_size(
+        self.max_batchsize_to_capture = VllmConfig.get_max_graph_batch_size(
             self.scheduler_config.max_num_seqs)
 
         self.graph_runners: List[Dict[int, CUDAGraphRunner]] = [
@@ -1333,14 +1322,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                 dtype=self.model_config.dtype,
                 device=self.device)
 
-        graph_batch_size = self.max_batchsize_to_capture
-        batch_size_capture_list = [
-            bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
-        ]
-        if self.model_config.enforce_eager:
-            batch_size_capture_list = []
-        with set_compile_context(batch_size_capture_list):
-            self.execute_model(model_input, kv_caches, intermediate_tensors)
+        self.execute_model(model_input, kv_caches, intermediate_tensors)
         torch.cuda.synchronize()
         return
 
@@ -1459,18 +1441,14 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                 dtype=self.model_config.dtype,
                 device=self.device)
 
-        graph_batch_size = self.max_batchsize_to_capture
-        batch_size_capture_list = [
-            bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
-        ]
-
         with self.attn_state.graph_capture(
                 max_batch_size), graph_capture() as graph_capture_context:
             # NOTE: Capturing the largest batch size first may help reduce the
             # memory usage of CUDA graph.
             for virtual_engine in range(
                     self.parallel_config.pipeline_parallel_size):
-                for batch_size in reversed(batch_size_capture_list):
+                for batch_size in \
+                    self.vllm_config.compilation_config.capture_sizes:
                     attn_metadata = (
                         self.attn_state.graph_capture_get_metadata_for_batch(
                             batch_size,
@@ -1993,37 +1971,3 @@ class CUDAGraphRunner(nn.Module):
             return self.output_buffers["hidden_states"]
 
         return self.output_buffers
-
-
-def _get_graph_batch_size(batch_size: int) -> int:
-    """Returns the padded batch size given actual batch size.
-
-    Batch sizes are 1, 2, 4, _BATCH_SIZE_ALIGNMENT,
-    2*_BATCH_SIZE_ALIGNMENT, 3*_BATCH_SIZE_ALIGNMENT...
-    """
-    if batch_size <= 2:
-        return batch_size
-    elif batch_size <= 4:
-        return 4
-    else:
-        return ((batch_size + _BATCH_SIZE_ALIGNMENT - 1) //
-                _BATCH_SIZE_ALIGNMENT * _BATCH_SIZE_ALIGNMENT)
-
-
-def _get_max_graph_batch_size(max_num_seqs: int) -> int:
-    """
-    max_num_seqs: Maximum number of sequences in a batch.
-    _BATCH_SIZES_TO_CAPTURE: all the sizes that we want to capture.
-
-    pad the max_num_seqs if necessary by calling _get_graph_batch_size,
-    which will deal with some edge cases like 1, 2, 4.
-
-    if the padded size is in _BATCH_SIZES_TO_CAPTURE, return the padded size.
-    if not, it means the padded size is larger than the largest size in
-    _BATCH_SIZES_TO_CAPTURE, return the largest size in _BATCH_SIZES_TO_CAPTURE.
-    """
-    padded_size = _get_graph_batch_size(max_num_seqs)
-    if padded_size in _BATCH_SIZES_TO_CAPTURE:
-        return padded_size
-    assert padded_size > _BATCH_SIZES_TO_CAPTURE[-1]
-    return _BATCH_SIZES_TO_CAPTURE[-1]