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[Bugfix]Support Qwen3-MOE on aclgraph mode in sizes capture and add new ut (#2511)

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[Bugfix]Support Qwen3-MOE on aclgraph mode in sizes capture and add new
ut

What this PR does / why we need it?
This PR solves the problem of sizes capture and stream error caused by
using ACLgraph on the Qwen3-30B MOE model.
Add new ut.

Does this PR introduce any user-facing change?
no

How was this patch tested?
ut

- vLLM version: v0.10.1.1
- vLLM main:
6fad29b11b

Signed-off-by: lilinsiman <lilinsiman@gmail.com>
This commit is contained in:
lilinsiman
2025-08-26 12:39:21 +08:00
committed by GitHub
parent b3fdd78a6b
commit cfe77e83ae
3 changed files with 80 additions and 7 deletions
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35
tests/e2e/multicard/test_qwen3_moe.py
View File

@@ -21,6 +21,8 @@
Run `pytest tests/e2e/multicard/test_qwen3_moe.py`.
"""
import os
from modelscope import snapshot_download # type: ignore
from tests.e2e.conftest import VllmRunner
@@ -72,3 +74,36 @@ def test_models_distributed_Qwen3_MOE_W8A8():
enforce_eager=False,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH_AIV():
os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
example_prompts = [
"Hello, my name is",
]
dtype = "auto"
max_tokens = 5
with VllmRunner(
"Qwen/Qwen3-30B-A3B",
dtype=dtype,
tensor_parallel_size=2,
enforce_eager=False,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH():
if 'HCCL_OP_EXPANSION_MODE' in os.environ:
del os.environ['HCCL_OP_EXPANSION_MODE']
example_prompts = [
"Hello, my name is",
]
dtype = "auto"
max_tokens = 5
with VllmRunner(
"Qwen/Qwen3-30B-A3B",
dtype=dtype,
tensor_parallel_size=2,
enforce_eager=False,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)

6
tests/ut/test_utils.py
View File

@@ -255,6 +255,9 @@ class TestUtils(TestBase):
parallel_config=test_parallel_config,
)
utils.update_aclgraph_sizes(test_vllm_config)
os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
utils.update_aclgraph_sizes(test_vllm_config)
del os.environ['HCCL_OP_EXPANSION_MODE']
self.assertEqual(
147,
len(test_vllm_config.compilation_config.cudagraph_capture_sizes))
@@ -267,6 +270,9 @@ class TestUtils(TestBase):
parallel_config=test_parallel_config,
)
utils.update_aclgraph_sizes(test_vllm_config)
os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
utils.update_aclgraph_sizes(test_vllm_config)
del os.environ['HCCL_OP_EXPANSION_MODE']
self.assertEqual(
3,
len(test_vllm_config.compilation_config.cudagraph_capture_sizes))

46
vllm_ascend/utils.py
View File

@@ -20,6 +20,7 @@
import atexit
import functools
import math
import os
from contextlib import contextmanager
from enum import Enum
from threading import Lock
@@ -303,16 +304,47 @@ def update_aclgraph_sizes(vllm_config: VllmConfig) -> None:
parallel_config = vllm_config.parallel_config
# TODO: Find out whether we need to take into account the pp_size
parallel_factor = 1 + sum(size > 1 for size in [
parallel_config.data_parallel_size_local,
num_comm_groups = sum(size > 1 for size in [
parallel_config.data_parallel_size,
parallel_config.tensor_parallel_size,
])
# Calculate maximum supported batch sizes considering model architecture
max_num_batch_sizes = math.floor(MAX_CAPTURE_SIZE /
(num_hidden_layers + 1) / parallel_factor)
logger.info("Calculated maximum supported batch sizes for ACL graph: %s",
max_num_batch_sizes)
if os.getenv("HCCL_OP_EXPANSION_MODE") == 'AIV':
# TODO: Find out whether we need to take into account the pp_size
parallel_factor = 1 + num_comm_groups + int(
parallel_config.enable_expert_parallel)
# Calculate maximum supported batch sizes considering model architecture on the A2 Hardware Device
# Assume the following case:
# MAX_CAPTURE_SIZE = 1920, num_hidden_layers = 48, data_parallel_size is 1, tensor_parallel_size is 4,
# According to the formula, max_num_batch_sizes = math.floor(1920 / (48 + 1) / 2) = 19
max_num_batch_sizes = math.floor(
MAX_CAPTURE_SIZE / (num_hidden_layers + 1) / parallel_factor)
logger.info(
"Calculated maximum supported batch sizes for ACL graph: %s",
max_num_batch_sizes)
else:
# The above describes an empirical formula applicable to the A2 hardware.
# Under this configuration, HCCL employs the FFTS+ method for execution unfolding,
# which adds only 1 concurrent stream without consuming collective communication execution unfolding streams.
# On A3 hardware, HCCL defaults to the AICPU method.
# This approach may additionally allocate up to rank_size (max 16) - 1 streams per collective communication domain on the device (worst case).
# Using the default collective communication unfolding method on A3 will lead to a significant reduction in the maximum supported sizes.
# Therefore, the calculation formula has been modified as follows:
# Assume the following case:
# MAX_CAPTURE_SIZE = 1920, num_hidden_layers = 48, data_parallel_size is 1, tensor_parallel_size is 4,
# According to the formula, max_num_batch_sizes = math.floor((1920 - 1 * 40) / (48 + 1) / (1 + 1 * 2)) = 12
max_num_batch_sizes = math.floor(
(MAX_CAPTURE_SIZE - num_comm_groups * 40) /
(num_hidden_layers + 1) / (1 + num_comm_groups * 2))
logger.info(
"Calculated maximum supported batch sizes for ACL graph: %s",
max_num_batch_sizes)
logger.warning(
"Currently, communication is performed using FFTS+ method, which reduces "
"the number of available streams and, as a result, limits the range of runtime "
"shapes that can be handled. To both improve communication performance and "
"increase the number of supported shapes, set HCCL_OP_EXPANSION_MODE=AIV."
)
# If original sizes exceed maximum, sample a representative subset
if max_num_batch_sizes < len(original_sizes):