a51d6366b9
### What this PR does / why we need it?
Support FlashComm1 for Qwen3-Next. Fix some padding problems in Sequence
Parallel (SP)
and resolve precision problems in shared_out when both FlashComm1 is
enabled.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: zhaojiangjiang <zhaojiangjiang1@h-partners.com>
Co-authored-by: zhaojiangjiang <zhaojiangjiang1@h-partners.com>
110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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import os
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from unittest.mock import patch
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from tests.e2e.conftest import VllmRunner
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def test_qwen3_next_distributed_mp_tp4():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp") as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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def test_qwen3_next_distributed_mp_full_decode_only_tp4():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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compilation_config={
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"cudagraph_capture_sizes": [1, 8, 24, 48, 60]
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}) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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# TODO: will conduct accuracy verification after the subsequent version becomes stable
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@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
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def test_qwen3_next_w8a8dynamic_distributed_tp4_ep():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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"vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8",
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max_model_len=4096,
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tensor_parallel_size=4,
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gpu_memory_utilization=0.4,
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max_num_seqs=1,
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enable_expert_parallel=True,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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quantization="ascend",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
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@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
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def test_qwen3_next_distributed_mp_flash_comm_tp4():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.7,
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distributed_executor_backend="mp",
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enable_expert_parallel=True,
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enforce_eager=True) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
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def test_qwen3_next_distributed_mp_graph_mode_tp4():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.7,
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distributed_executor_backend="mp",
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enable_expert_parallel=True,
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enforce_eager=False) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model |