228 lines
7.2 KiB
Python
228 lines
7.2 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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#
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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/multicard/test_torchair_graph_mode.py`.
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"""
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import os
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from typing import Dict
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import pytest
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from tests.e2e.conftest import VllmRunner
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os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
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def _deepseek_torchair_test_fixture(
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additional_config: Dict,
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*,
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tensor_parallel_size=2,
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use_v1_schduler=False,
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):
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example_prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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kwargs = {}
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if not use_v1_schduler:
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kwargs = {
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"ascend_scheduler_config": {
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"enabled": True,
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},
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"refresh": True,
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}
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additional_config.update(**kwargs)
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with VllmRunner(
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"vllm-ascend/DeepSeek-V3-Pruning",
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dtype="half",
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend="mp",
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additional_config=additional_config,
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) as vllm_model:
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# use greedy sampler to make sure the generated results are fix
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vllm_output = vllm_model.generate_greedy(example_prompts, 5)
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# NOTE: vllm-ascend/DeepSeek-V3-Pruning is a random weight of
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# DeepSeek-V3 with 2 hidden layers, thus the golden results seems
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# inaccurate. This will only change if accuracy improves with the
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# official weights of DeepSeek-V3.
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golden_results = [
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'Hello, my name is下载早点向前很有่อง',
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'The president of the United States isSender)## physiological Albany',
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'The capital of France is Rocky转角 hospitalizedinterval sparked',
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'The future of AI is её asegο BIOS一扫',
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]
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assert len(golden_results) == len(vllm_output)
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for i in range(len(vllm_output)):
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assert golden_results[i] == vllm_output[i][1]
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print(f"Generated text: {vllm_output[i][1]!r}")
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def test_e2e_deepseekv3_with_torchair():
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additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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}
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_deepseek_torchair_test_fixture(additional_config)
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def test_e2e_deepseekv3_with_torchair_ms_mla():
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additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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"enable_multistream_mla": True,
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},
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}
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_deepseek_torchair_test_fixture(additional_config)
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def test_e2e_deepseekv3_with_torchair_v1scheduler():
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additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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}
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_deepseek_torchair_test_fixture(additional_config, use_v1_schduler=True)
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def _pangu_torchair_test_fixture(
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additional_config: Dict,
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*,
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tensor_parallel_size=2,
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):
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example_prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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# torchair is only work without chunked-prefill now
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kwargs = {
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"ascend_scheduler_config": {
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"enabled": True,
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},
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"refresh": True,
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}
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additional_config.update(**kwargs)
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with VllmRunner(
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"vllm-ascend/pangu-pro-moe-pruing",
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dtype="half",
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tensor_parallel_size=tensor_parallel_size,
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distributed_executor_backend="mp",
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additional_config=additional_config,
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enable_expert_parallel=True,
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) as vllm_model:
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# use greedy sampler to make sure the generated results are fix
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vllm_output = vllm_model.generate_greedy(example_prompts, 5)
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# NOTE: vllm-ascend/pangu-pro-moe-pruing is only part of PanguProMoE
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# with 2 hidden layers, thus the golden results seems inaccurate.
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# This will only change if accuracy changes with the official weights
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# of PanguProMoE.
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golden_results = [
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'Hello, my name is Remempondeprecatedmiot忱',
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'The president of the United States is Remem下的一个 rever ceremoni Segnali',
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'The capital of France is Rememvoud administrativ Remem投',
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'The future of AI isotope Segnali Zoeken精细化 supus',
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]
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assert len(golden_results) == len(vllm_output)
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for i in range(len(vllm_output)):
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assert golden_results[i] == vllm_output[i][1]
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print(f"Generated text: {vllm_output[i][1]!r}")
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@pytest.mark.skip("skipping test_e2e_pangu_with_torchair")
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def test_e2e_pangu_with_torchair():
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additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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}
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_pangu_torchair_test_fixture(additional_config)
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def _qwen_torchair_test_fixture(
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model,
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tp,
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enable_expert_parallel,
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):
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# The current access control does not support 16 cards,
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# so the MC2 operator in Qwen's graph mode cannot run.
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# Once 16-card support is available,
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# this e2e can be switched to graph mode.
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example_prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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additional_config = {
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"torchair_graph_config": {
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"enabled": False,
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},
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"ascend_scheduler_config": {
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"enabled": True,
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},
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"refresh": True,
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}
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with VllmRunner(
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model,
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dtype="half",
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tensor_parallel_size=tp,
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distributed_executor_backend="mp",
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enforce_eager=True,
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additional_config=additional_config,
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enable_expert_parallel=enable_expert_parallel,
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) as vllm_model:
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# use greedy sampler to make sure the generated results are fix
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vllm_output = vllm_model.generate_greedy(example_prompts, 5)
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# NOTE: vllm-ascend/pangu-pro-moe-pruing is only part of PanguProMoE
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# with 2 hidden layers, thus the golden results seems inaccurate.
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# This will only change if accuracy changes with the official weights
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# of PanguProMoE.
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golden_results = [
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'Hello, my name is Remempondeprecatedmiot忱',
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'The president of the United States is Remem下的一个 rever ceremoni Segnali',
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'The capital of France is Rememvoud administrativ Remem投',
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'The future of AI isotope Segnali Zoeken精细化 supus',
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]
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assert len(golden_results) == len(vllm_output)
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for i in range(len(vllm_output)):
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print(f"Generated text: {vllm_output[i][1]!r}")
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def test_e2e_qwen2_with_torchair():
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_qwen_torchair_test_fixture("Qwen/Qwen2.5-0.5B-Instruct", 2, False)
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def test_e2e_qwen3_moe_with_torchair():
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_qwen_torchair_test_fixture("Qwen/Qwen3-30B-A3B", 2, True)
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