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>
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import pytest
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import torch
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from vllm.platforms import current_platform
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MAX_MODEL_LEN = 1024
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MODEL_NAME = os.environ.get(
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"MODEL_NAME", "robertgshaw2/zephyr-7b-beta-channelwise-gptq"
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)
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REVISION = os.environ.get("REVISION", "main")
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QUANTIZATION = os.environ.get("QUANTIZATION", "gptq_marlin")
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MIN_CAPABILITY = os.environ.get("MIN_CAPABILITY", "80")
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@pytest.mark.skipif(
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MODEL_NAME == "casperhansen/deepseek-coder-v2-instruct-awq", reason="OOM in the CI"
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)
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@pytest.mark.skipif(
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not current_platform.has_device_capability(int(MIN_CAPABILITY)),
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reason="Current system does not have minimum capability.",
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)
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def test_weight_loading(vllm_runner):
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"""
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Test parameter weight loading with tp>1.
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"""
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# MoE models need fp16.
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NEEDS_FP16 = (
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QUANTIZATION == "gptq"
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or MODEL_NAME == "nm-testing/test-w4a16-mixtral-actorder-group"
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)
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with vllm_runner(
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model_name=MODEL_NAME,
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revision=REVISION,
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dtype=torch.half if NEEDS_FP16 else "auto",
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quantization=None if QUANTIZATION == "None" else QUANTIZATION,
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max_model_len=MAX_MODEL_LEN,
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tensor_parallel_size=2,
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) as model:
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output = model.generate_greedy("Hello world!", max_tokens=20)
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print(output)
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assert output
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