Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# NOTE To avoid overloading the CI pipeline, this test script will
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# not be triggered on CI and is primarily intended for local testing
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# and verification.
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import vllm
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from vllm.lora.request import LoRARequest
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from ..utils import multi_gpu_test
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MODEL_PATH = "deepseek-ai/DeepSeek-V2-Lite-Chat"
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PROMPT_TEMPLATE = "<｜begin▁of▁sentence｜>You are a helpful assistant.\n\nUser: {context}\n\nAssistant:" # noqa: E501
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def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int):
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prompts = [
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PROMPT_TEMPLATE.format(context="Who are you?"),
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]
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sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
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outputs = llm.generate(
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prompts,
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sampling_params,
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lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
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)
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# Print the outputs.
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generated_texts: list[str] = []
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text.strip()
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generated_texts.append(generated_text)
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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# return generated_texts
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expected_lora_output = [
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"I am \u5f20\u5b50\u8c6a, an AI assistant developed by \u9648\u58eb\u680b.", # noqa: E501
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]
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for i in range(len(expected_lora_output)):
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assert generated_texts[i].startswith(expected_lora_output[i])
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def test_deepseekv2_lora(deepseekv2_lora_files):
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# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
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# Otherwise, the lora-test will fail due to CUDA OOM.
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llm = vllm.LLM(
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MODEL_PATH,
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max_model_len=1024,
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enable_lora=True,
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max_loras=4,
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enforce_eager=True,
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trust_remote_code=True,
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enable_chunked_prefill=True,
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)
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generate_and_test(llm, deepseekv2_lora_files, 1)
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def test_deepseekv2(deepseekv2_lora_files):
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# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
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# Otherwise, the lora-test will fail due to CUDA OOM.
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llm = vllm.LLM(
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MODEL_PATH,
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max_model_len=1024,
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enable_lora=True,
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max_loras=4,
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enforce_eager=True,
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trust_remote_code=True,
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)
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generate_and_test(llm, deepseekv2_lora_files, 1)
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@multi_gpu_test(num_gpus=2)
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def test_deepseekv2_tp2(deepseekv2_lora_files):
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# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
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# Otherwise, the lora-test will fail due to CUDA OOM.
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llm = vllm.LLM(
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MODEL_PATH,
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max_model_len=1024,
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enable_lora=True,
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max_loras=4,
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enforce_eager=True,
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trust_remote_code=True,
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tensor_parallel_size=2,
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)
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generate_and_test(llm, deepseekv2_lora_files, 2)
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@multi_gpu_test(num_gpus=4)
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def test_deepseekv2_tp4(deepseekv2_lora_files):
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# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
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# Otherwise, the lora-test will fail due to CUDA OOM.
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llm = vllm.LLM(
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MODEL_PATH,
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max_model_len=1024,
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enable_lora=True,
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max_loras=4,
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enforce_eager=True,
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trust_remote_code=True,
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tensor_parallel_size=4,
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)
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generate_and_test(llm, deepseekv2_lora_files, 2)
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