5d02eed16f
### What this PR does / why we need it?
Add a control to enable the exponential distribution operator
overlapping with model executing (default is OFF due to this feature
might not perform well on MOE models, i.e. For Qwen3-30B).
Enable async exponential overlapping will provides performance
improvement.
Also, overlapping the exponential operator with module execution can
cover the performance drop introduced by AICPU-version's exponential
operator.
**UPDATE**: (12/12)
Now our overlap will use the same stream that introduced in this pr:
#4908 .
We move the `do_async_exponential` from `model_runner_v1.py` to
`sampler.py`.
Now we are using `additional_config` to enable async exponential:
Add `"enable_async_exponential": 1` in `addition_config`.
Now we **ONLY** support default exponential/AI-CPU exponential, the old
`"enable_async_exponential": 2` option has been aborted to keep
consistency.
### Does this PR introduce _any_ user-facing change?
**YES**, added a new `additional_config` : `"enable_async_exponential":
1`.
When `enable_async_exponential` is set to 1, we enable the async
exponential and overlap with model runner.
When `enable_async_exponential` is set to 0 (default is 0), we disable
the async exponential, but exponential will still running on a different
stream using stream introduced in #4908.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: YuhanBai <yuhan.bai0830@gmail.com>
Signed-off-by: YuhanBai yuhan.bai0830@gmail.com
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/entrypoints/llm/test_guided_generate.py
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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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#
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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def test_models_topk() -> None:
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example_prompts = [
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"Hello, my name is",
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]
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sampling_params = SamplingParams(max_tokens=5,
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temperature=0.0,
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top_k=50,
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top_p=0.9)
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with VllmRunner("Qwen/Qwen3-0.6B",
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max_model_len=8192,
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gpu_memory_utilization=0.7) as runner:
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runner.generate(example_prompts, sampling_params)
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def test_models_prompt_logprobs() -> None:
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example_prompts = [
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"Hello, my name is",
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]
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with VllmRunner("Qwen/Qwen3-0.6B",
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max_model_len=8192,
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gpu_memory_utilization=0.7) as runner:
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runner.generate_greedy_logprobs(example_prompts,
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max_tokens=5,
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num_logprobs=1)
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def test_exponential_overlap() -> None:
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example_prompts = [
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"Hello, my name is",
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]
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sampling_params = SamplingParams(max_tokens=5,
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temperature=1.0,
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top_k=50,
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top_p=0.9)
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with VllmRunner("Qwen/Qwen3-0.6B",
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max_model_len=8192,
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gpu_memory_utilization=0.7,
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additional_config={
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"enable_async_exponential": 1,
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}) as runner:
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runner.generate(example_prompts, sampling_params)
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