c11a05c4e1
### What this PR does / why we need it?
Upgrade vllm commit to 0113 (11b6af5280d6d6dfb8953af16e67b25f819b3be9)
- Modify import paths due to the refactors
https://github.com/vllm-project/vllm/pull/31916
https://github.com/vllm-project/vllm/pull/32054
- Fix `TypeError: NPUOffloadingSpec.__init__() takes 2 positional
arguments but 3 were given` due to
https://github.com/vllm-project/vllm/pull/24498
- Skip the async-scheduling tests in
`tests/e2e/multicard/4-cards/long_sequence/test_mtp.py`, which are never
verified
https://github.com/vllm-project/vllm/pull/31998
- Skip some pooling tests, which are caused by
https://github.com/vllm-project/vllm/pull/32148
where vllm is also failed
https://buildkite.com/vllm/ci/builds/46705/steps/canvas?jid=019bb329-3834-4685-862b-1613b8e0f5d4
We will reopen those tests when main2main reachs
https://github.com/vllm-project/vllm/pull/32243
- Skip some cases in
`tests/e2e/multicard/4-cards/long_sequence/test_mtp.py`, which are
broken by
https://github.com/vllm-project/vllm/pull/32118
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wjunLu <wjunlu217@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
201 lines
6.1 KiB
Python
201 lines
6.1 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 pytest
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import torch
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import torch.nn.functional as F
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from modelscope import snapshot_download # type: ignore[import-untyped]
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from vllm_ascend.utils import vllm_version_is
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from tests.e2e.conftest import HfRunner, VllmRunner
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CROSS_ENCODER_MODELS = [
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"dengcao/ms-marco-MiniLM-L6-v2", # Bert
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"BAAI/bge-reranker-v2-m3", # Roberta
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]
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EMBEDDING_MODELS = [
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"sentence-transformers/all-MiniLM-L12-v2",
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]
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TEXTS_1 = [
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"What is the capital of France?",
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"What is the capital of Germany?",
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]
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TEXTS_2 = [
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"The capital of France is Paris.",
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"The capital of Germany is Berlin.",
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]
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DTYPE = "half"
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@pytest.fixture(scope="module", params=CROSS_ENCODER_MODELS)
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def model_name(request):
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yield snapshot_download(request.param)
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@pytest.mark.skipif(
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not vllm_version_is('0.13.0'),
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reason="vLLM PR-32148 changed the behavior of cross scoring",
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)
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def test_cross_encoder_score_1_to_1(model_name):
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text_pair = [TEXTS_1[0], TEXTS_2[0]]
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with HfRunner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
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hf_outputs = hf_model.predict([text_pair]).tolist()
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with VllmRunner(model_name,
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runner="pooling",
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dtype=DTYPE,
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cudagraph_capture_sizes=[4],
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max_model_len=None) as vllm_model:
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vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
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assert len(vllm_outputs) == 1
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assert len(hf_outputs) == 1
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assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
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@pytest.mark.skipif(
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not vllm_version_is('0.13.0'),
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reason="vLLM PR-32148 changed the behavior of cross scoring",
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)
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def test_cross_encoder_score_1_to_N(model_name):
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text_pairs = [
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[TEXTS_1[0], TEXTS_2[0]],
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[TEXTS_1[0], TEXTS_2[1]],
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]
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with HfRunner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
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hf_outputs = hf_model.predict(text_pairs).tolist()
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with VllmRunner(model_name,
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runner="pooling",
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dtype=DTYPE,
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cudagraph_capture_sizes=[4],
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max_model_len=None) as vllm_model:
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vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
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assert len(vllm_outputs) == 2
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assert len(hf_outputs) == 2
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assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
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assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
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@pytest.mark.skipif(
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not vllm_version_is('0.13.0'),
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reason="vLLM PR-32148 changed the behavior of cross scoring",
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)
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def test_cross_encoder_score_N_to_N(model_name):
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text_pairs = [
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[TEXTS_1[0], TEXTS_2[0]],
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[TEXTS_1[1], TEXTS_2[1]],
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]
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with HfRunner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
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hf_outputs = hf_model.predict(text_pairs).tolist()
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with VllmRunner(model_name,
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runner="pooling",
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dtype=DTYPE,
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cudagraph_capture_sizes=[4],
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max_model_len=None) as vllm_model:
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vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
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assert len(vllm_outputs) == 2
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assert len(hf_outputs) == 2
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assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
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assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
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@pytest.fixture(scope="module", params=EMBEDDING_MODELS)
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def emb_model_name(request):
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yield snapshot_download(request.param)
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def test_embedding_score_1_to_1(emb_model_name):
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text_pair = [TEXTS_1[0], TEXTS_2[0]]
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with HfRunner(emb_model_name, dtype=DTYPE,
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is_sentence_transformer=True) as hf_model:
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hf_embeddings = hf_model.encode(text_pair)
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hf_outputs = [
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F.cosine_similarity(*map(torch.tensor, hf_embeddings), dim=0)
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]
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with VllmRunner(emb_model_name,
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runner="pooling",
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dtype=DTYPE,
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cudagraph_capture_sizes=[4],
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max_model_len=None) as vllm_model:
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vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
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assert len(vllm_outputs) == 1
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assert len(hf_outputs) == 1
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assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
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def test_embedding_score_1_to_N(emb_model_name):
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text_pairs = [
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[TEXTS_1[0], TEXTS_2[0]],
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[TEXTS_1[0], TEXTS_2[1]],
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]
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with HfRunner(emb_model_name, dtype=DTYPE,
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is_sentence_transformer=True) as hf_model:
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hf_embeddings = [
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hf_model.encode(text_pair) for text_pair in text_pairs
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]
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hf_outputs = [
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F.cosine_similarity(*map(torch.tensor, pair), dim=0)
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for pair in hf_embeddings
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]
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with VllmRunner(emb_model_name,
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runner="pooling",
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dtype=DTYPE,
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cudagraph_capture_sizes=[4],
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max_model_len=None) as vllm_model:
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vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
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assert len(vllm_outputs) == 2
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assert len(hf_outputs) == 2
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assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
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assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
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def test_embedding_score_N_to_N(emb_model_name):
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text_pairs = [
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[TEXTS_1[0], TEXTS_2[0]],
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[TEXTS_1[1], TEXTS_2[1]],
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]
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with HfRunner(emb_model_name, dtype=DTYPE,
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is_sentence_transformer=True) as hf_model:
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hf_embeddings = [
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hf_model.encode(text_pair) for text_pair in text_pairs
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]
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hf_outputs = [
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F.cosine_similarity(*map(torch.tensor, pair), dim=0)
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for pair in hf_embeddings
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]
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with VllmRunner(emb_model_name,
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runner="pooling",
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dtype=DTYPE,
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cudagraph_capture_sizes=[4],
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max_model_len=None) as vllm_model:
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vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
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assert len(vllm_outputs) == 2
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assert len(hf_outputs) == 2
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assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
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assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
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