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>
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Sequence
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import openai
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import pytest
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from tests.conftest import HfRunner
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from tests.models.utils import EmbedModelInfo, check_embeddings_close, matryoshka_fy
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def run_embedding_correctness_test(
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hf_model: "HfRunner",
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inputs: list[str],
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vllm_outputs: Sequence[list[float]],
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dimensions: int | None = None,
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):
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hf_outputs = hf_model.encode(inputs)
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if dimensions:
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hf_outputs = matryoshka_fy(hf_outputs, dimensions)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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tol=1e-2,
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)
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def correctness_test_embed_models(
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hf_runner,
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vllm_runner,
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model_info: EmbedModelInfo,
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example_prompts,
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vllm_extra_kwargs=None,
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hf_model_callback=None,
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):
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pytest.skip("Debug only, ci prefers to use mteb test.")
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# The example_prompts has ending "\n", for example:
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# "Write a short story about a robot that dreams for the first time.\n"
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# sentence_transformers will strip the input texts, see:
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# https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
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# This makes the input_ids different between hf_model and vllm_model.
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# So we need to strip the input texts to avoid test failing.
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example_prompts = [str(s).strip() for s in example_prompts]
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vllm_extra_kwargs = vllm_extra_kwargs or {}
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vllm_extra_kwargs["dtype"] = model_info.dtype
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if model_info.hf_overrides is not None:
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vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
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with vllm_runner(
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model_info.name, runner="pooling", max_model_len=None, **vllm_extra_kwargs
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) as vllm_model:
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vllm_outputs = vllm_model.embed(example_prompts)
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with hf_runner(
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model_info.name,
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dtype=model_info.hf_dtype,
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is_sentence_transformer=True,
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) as hf_model:
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if hf_model_callback is not None:
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hf_model_callback(hf_model)
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run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
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async def run_client_embeddings(
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client: openai.AsyncOpenAI,
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model_name: str,
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queries: list[str],
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instruction: str = "",
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) -> list[list[float]]:
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outputs = await client.embeddings.create(
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model=model_name,
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input=[instruction + q for q in queries],
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)
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return [data.embedding for data in outputs.data]
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