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>
73 lines
2.1 KiB
Python
73 lines
2.1 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 weakref
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import pytest
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import torch
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import torch.nn.functional as F
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from vllm import LLM, PoolingParams
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from vllm.distributed import cleanup_dist_env_and_memory
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from vllm.platforms import current_platform
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MODEL_NAME = "intfloat/multilingual-e5-small"
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prompts = ["The chef prepared a delicious meal."]
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@pytest.fixture(scope="module")
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def llm():
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# ROCm: Use FLEX_ATTENTION backend as it's the only attention backend
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# that supports encoder-only models on ROCm.
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attention_config = None
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if current_platform.is_rocm():
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attention_config = {"backend": "FLEX_ATTENTION"}
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# pytest caches the fixture so we use weakref.proxy to
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# enable garbage collection
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llm = LLM(
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model=MODEL_NAME,
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max_num_batched_tokens=32768,
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tensor_parallel_size=1,
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gpu_memory_utilization=0.75,
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enforce_eager=True,
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seed=0,
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attention_config=attention_config,
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)
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yield weakref.proxy(llm)
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del llm
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cleanup_dist_env_and_memory()
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@pytest.mark.skip_global_cleanup
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def test_token_embed(llm: LLM):
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outputs = llm.encode(prompts, pooling_task="token_embed", use_tqdm=False)
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multi_vector = outputs[0].outputs.data
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assert multi_vector.shape == (11, 384)
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def test_pooling_params(llm: LLM):
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def get_outputs(normalize):
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outputs = llm.embed(
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prompts,
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pooling_params=PoolingParams(use_activation=normalize),
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use_tqdm=False,
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)
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return torch.tensor([x.outputs.embedding for x in outputs])
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default = get_outputs(normalize=None)
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w_normal = get_outputs(normalize=True)
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wo_normal = get_outputs(normalize=False)
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assert torch.allclose(default, w_normal, atol=1e-2), "Default should use normal."
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assert not torch.allclose(w_normal, wo_normal, atol=1e-2), (
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"wo_normal should not use normal."
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)
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assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
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"w_normal should be close to normal(wo_normal)."
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)
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