4e6dbe0956
### What this PR does / why we need it? pr: https://github.com/vllm-project/vllm/pull/37136 break eplb because it filters out redundant experts. pr: https://github.com/vllm-project/vllm/pull/37322 fix it due to use parallel_config.enable_eplb to determine whether to skip the weight loading filter. But in vllm-ascend, parallel_config.enable_eplb is always false. When we use eplb, we temporarily set it to true. ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as API, interface or other behavior changes. Documentation-only updates are not considered user-facing changes. --> ### How was this patch tested?  | dataset | version | metric | mode | vllm-api-stream-chat | |----- | ----- | ----- | ----- | -----| | aime2024 | 604a78 | accuracy | gen | 86.67 | Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
123 lines
4.2 KiB
Python
123 lines
4.2 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 json
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import os
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from unittest.mock import patch
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import openai
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import pytest
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from vllm.utils.network_utils import get_open_port
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from tests.e2e.conftest import RemoteOpenAIServer, VllmRunner
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@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
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def test_qwen3_moe_distributed_mp_tp2_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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"Qwen/Qwen3-30B-A3B",
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tensor_parallel_size=2,
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enable_expert_parallel=True,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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distributed_executor_backend="mp",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_qwen3_moe_w8a8_distributed_tp2():
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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/Qwen3-30B-A3B-W8A8",
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max_model_len=8192,
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tensor_parallel_size=2,
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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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def test_qwen3_moe_distributed_aiv_tp2():
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os.environ["HCCL_OP_EXPANSION_MODE"] = "AIV"
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example_prompts = [
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"Hello, my name is",
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]
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dtype = "auto"
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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dtype=dtype,
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tensor_parallel_size=2,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@pytest.mark.asyncio
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async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
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model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
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port = get_open_port()
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compilation_config = json.dumps({"cudagraph_capture_sizes": [8]})
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server_args = [
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"--max_model_len",
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"8192",
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"--tensor_parallel_size",
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"2",
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"--enable_expert_parallel",
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"--quantization",
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"ascend",
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"--port",
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str(port),
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"--compilation-config",
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compilation_config,
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]
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env_dict = {"HCCL_BUFFSIZE": "1024"}
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with RemoteOpenAIServer(model, server_args, server_port=port, auto_port=False, env_dict=env_dict) as server:
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client = server.get_async_client()
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batch = await client.completions.create(
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model=model, prompt="What is deeplearning?", max_tokens=400, temperature=0, top_p=1.0, n=1
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)
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gt_choices: list[openai.types.CompletionChoice] = batch.choices
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# dynamic eplb test
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# Since pytest runs as a daemon, it conflicts with the dynamic eplb manager
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# during initialization in offline mode, so the online mode is used instead.
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env_dict.update({"DYNAMIC_EPLB": "true"})
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additional_config = {
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"eplb_config": {
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"dynamic_eplb": True,
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"expert_heat_collection_interval": 100,
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"algorithm_execution_interval": 20,
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"num_redundant_experts": 2,
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}
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}
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server_args.extend(["--additional-config", json.dumps(additional_config)])
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with RemoteOpenAIServer(model, server_args, server_port=port, auto_port=False, env_dict=env_dict) as server:
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client = server.get_async_client()
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batch = await client.completions.create(
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model=model, prompt="What is deeplearning?", max_tokens=400, temperature=0, top_p=1.0, n=1
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)
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eplb_choices: list[openai.types.CompletionChoice] = batch.choices
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assert gt_choices[0].text == eplb_choices[0].text, f"{gt_choices[0].text=} \n {eplb_choices[0].text=}"
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