biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Remove deprecated fields from CompilationConfig (#27593)

Browse Source 
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-11-12 16:10:28 +00:00
committed by GitHub
parent 728a9eb70e
commit a742134cc5
13 changed files with 122 additions and 164 deletions
Download Patch File Download Diff File Expand all files Collapse all files

151
tests/compile/test_config.py
View File

@@ -2,8 +2,10 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import copy
from contextlib import nullcontext
from unittest.mock import patch
import pytest
from pydantic import ValidationError
from vllm.compilation.counter import compilation_counter
from vllm.compilation.fix_functionalization import FixFunctionalizationPass
@@ -11,7 +13,7 @@ from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig
from vllm.config.compilation import CompilationMode
from vllm.engine.arg_utils import EngineArgs
from vllm.platforms import current_platform
from vllm.utils.torch_utils import _is_torch_equal_or_newer, is_torch_equal_or_newer
from vllm.utils.torch_utils import _is_torch_equal_or_newer
def test_version():
@@ -23,14 +25,6 @@ def test_version():
assert not _is_torch_equal_or_newer("2.7.1", "2.8.0.dev")
def test_use_cudagraphs_dynamic():
vllm_config = VllmConfig()
# Default V1 configuration now starts without cudagraphs enabled; the
# engine decides when to capture based on runtime settings instead of a
# blanket default.
assert vllm_config.compilation_config.use_cudagraph
def test_copy_pass():
vllm_config = VllmConfig()
inductor_pass = FixFunctionalizationPass(vllm_config)
@@ -65,7 +59,7 @@ def test_VLLM_DISABLE_COMPILE_CACHE(vllm_runner, monkeypatch, val):
monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", val)
compilation_config = {
"use_cudagraph": False, # speed things up a bit
"cudagraph_mode": CUDAGraphMode.NONE, # speed things up a bit
}
with (
compilation_counter.expect(
@@ -83,20 +77,31 @@ def test_VLLM_DISABLE_COMPILE_CACHE(vllm_runner, monkeypatch, val):
# forked needed to workaround https://github.com/vllm-project/vllm/issues/21073
@pytest.mark.forked
@pytest.mark.parametrize("enabled", [True, False])
def test_use_cudagraphs(vllm_runner, monkeypatch, enabled):
@pytest.mark.parametrize(
"cudagraph_mode,num_cudagraph_captured",
[
(CUDAGraphMode.NONE, 0),
(CUDAGraphMode.FULL_DECODE_ONLY, 1),
(CUDAGraphMode.PIECEWISE, 13),
(CUDAGraphMode.FULL_AND_PIECEWISE, 14),
],
)
def test_use_cudagraphs(
vllm_runner, monkeypatch, cudagraph_mode, num_cudagraph_captured
):
# Disable multiprocessing so that the counter is in the same process
monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
compilation_config = {
"cudagraph_capture_sizes": [100],
"use_cudagraph": enabled,
"cudagraph_mode": cudagraph_mode,
}
num_gpu_runner_capture_triggers = 1 if cudagraph_mode != CUDAGraphMode.NONE else 0
with (
compilation_counter.expect(
num_graphs_seen=1,
num_gpu_runner_capture_triggers=1 if enabled else 0,
num_cudagraph_captured=13 if enabled else 0,
num_gpu_runner_capture_triggers=num_gpu_runner_capture_triggers,
num_cudagraph_captured=num_cudagraph_captured,
),
# loading the model causes compilation (if enabled) to happen
vllm_runner(
@@ -168,19 +173,18 @@ def test_splitting_ops_dynamic():
assert not config.compilation_config.splitting_ops_contain_attention()
# When use_inductor_graph_partition=True
if is_torch_equal_or_newer("2.9.0.dev"):
config = VllmConfig(
compilation_config=CompilationConfig(
mode=CompilationMode.VLLM_COMPILE,
use_inductor_graph_partition=True,
splitting_ops=["vllm::unified_attention"],
)
config = VllmConfig(
compilation_config=CompilationConfig(
mode=CompilationMode.VLLM_COMPILE,
use_inductor_graph_partition=True,
splitting_ops=["vllm::unified_attention"],
)
# with inductor partition we use splitting_ops directly for
# partition rules
assert config.compilation_config.splitting_ops == ["vllm::unified_attention"]
)
# with inductor partition we use splitting_ops directly for
# partition rules
assert config.compilation_config.splitting_ops == ["vllm::unified_attention"]
# When attn_fusion pass enabled, splitting_ops now default to attention ops.
# When attn_fusion pass enabled.
config = VllmConfig(
compilation_config=CompilationConfig(
mode=CompilationMode.VLLM_COMPILE,
@@ -189,29 +193,41 @@ def test_splitting_ops_dynamic():
cudagraph_mode=CUDAGraphMode.PIECEWISE,
)
)
# With the new simplified logic, attention fusion works with splitting_ops
assert config.compilation_config.splitting_ops_contain_attention()
# cudagraph mode remains PIECEWISE
assert config.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE
assert config.compilation_config.splitting_ops == []
# cudagraph mode also fall back to FULL
assert config.compilation_config.cudagraph_mode == CUDAGraphMode.FULL
# When both use_inductor_graph_partition and attn_fusion pass enabled.
if is_torch_equal_or_newer("2.9.0.dev"):
# splitting_ops can not contain attention ops when attn_fusion
# pass enabled.
with pytest.raises(ValidationError):
config = VllmConfig(
compilation_config=CompilationConfig(
mode=CompilationMode.VLLM_COMPILE,
use_inductor_graph_partition=True,
pass_config={"enable_attn_fusion": True, "enable_noop": True},
custom_ops=["+quant_fp8"],
cudagraph_mode=CUDAGraphMode.PIECEWISE,
# work around for accessing all attntion ops
splitting_ops=CompilationConfig()._attention_ops,
)
)
# With inductor graph partition, attn_fusion and splitting_ops
# work together. Default splitting_ops include attention ops.
assert config.compilation_config.splitting_ops_contain_attention()
# enable_attn_fusion is directly supported under
# use_inductor_graph_partition=True, and cudagraph_mode
# is unchanged.
assert config.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE
# When both use_inductor_graph_partition and attn_fusion pass enabled.
config = VllmConfig(
compilation_config=CompilationConfig(
mode=CompilationMode.VLLM_COMPILE,
use_inductor_graph_partition=True,
pass_config={"enable_attn_fusion": True, "enable_noop": True},
custom_ops=["+quant_fp8"],
cudagraph_mode=CUDAGraphMode.PIECEWISE,
)
)
# With inductor graph partition, attn_fusion and splitting_ops
# work together. Default splitting_ops include attention ops.
assert config.compilation_config.splitting_ops_contain_attention()
# enable_attn_fusion is directly supported under
# use_inductor_graph_partition=True, and cudagraph_mode
# is unchanged.
assert config.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE
def test_should_split():
@@ -293,25 +309,36 @@ def test_should_split():
"tp_size",
"enable_sequence_parallelism",
"max_num_batched_tokens",
"use_cudagraph",
"cudagraph_mode",
"expected_max_size",
),
[
(None, None, 1, False, 2048, True, 512),
([1, 2, 4], 4, 1, False, 2048, True, 4),
([1, 2, 4], 8, 1, False, 2048, True, RuntimeError),
([1, 256], None, 1, False, 2048, 256),
([], None, 1, False, 2048, False, 0),
(None, 0, 1, False, 2048, False, 0),
(None, None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 256),
([1, 2, 4], 4, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
(
[1, 2, 4],
8,
1,
False,
2048,
CUDAGraphMode.FULL_AND_PIECEWISE,
ValidationError,
),
([1, 256], None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 256),
([], None, 1, False, 2048, CUDAGraphMode.NONE, 0),
(None, 0, 1, False, 2048, CUDAGraphMode.NONE, 0),
# truncated to nearest multiple of 8 or 16
(None, 257, 1, False, 2048, True, 256),
([1, 2, 4, 15], None, 1, False, 2048, True, 15), # max from list
([1, 2, 4, 15], None, 2, True, 2048, True, 4), # filtered out 15 due to SP
([1, 2, 4, 15], None, 1, False, 8, True, 4), # limited by the max_tokens
(None, 257, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 256),
# max from list
([1, 2, 4, 15], None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 15),
# filtered out 15 due to SP
([1, 2, 4, 15], None, 2, True, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
# limited by the max_tokens
([1, 2, 4, 15], None, 1, False, 8, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
# the list should contain at least 1 element when use cudagraph
([], None, 1, False, 2048, True, RuntimeError),
([], None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, ValidationError),
# the max capturing size should be >= 1 when use cudagraph
(None, 0, 1, False, 2048, True, RuntimeError),
(None, 0, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, ValidationError),
],
)
def test_cudagraph_sizes_post_init(
@@ -320,15 +347,17 @@ def test_cudagraph_sizes_post_init(
tp_size,
enable_sequence_parallelism,
max_num_batched_tokens,
use_cudagraph,
cudagraph_mode,
expected_max_size,
):
ctx = nullcontext()
if isinstance(expected_max_size, Exception):
if expected_max_size == ValidationError:
ctx = pytest.raises(expected_max_size)
cudagraph_mode = CUDAGraphMode.PIECEWISE if use_cudagraph else CUDAGraphMode.NONE
with ctx:
with (
ctx,
patch("vllm.config.parallel.cuda_device_count_stateless", return_value=tp_size),
):
compilation_config = CompilationConfig(
cudagraph_capture_sizes=cudagraph_capture_sizes,
max_cudagraph_capture_size=max_cudagraph_capture_size,
@@ -342,11 +371,13 @@ def test_cudagraph_sizes_post_init(
engine_args = EngineArgs(
model="facebook/opt-125m",
tensor_parallel_size=tp_size,
max_num_seqs=min(max_num_batched_tokens, 128),
max_num_batched_tokens=max_num_batched_tokens,
compilation_config=compilation_config,
)
vllm_config = engine_args.create_engine_config()
assert (
vllm_config.compilation_config.max_cudagraph_capture_size == expected_max_size
)
assert (
vllm_config.compilation_config.max_cudagraph_capture_size
== expected_max_size
)