Sync from v0.13

tests/models/language/pooling/test_scoring.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+import torch.nn.functional as F
+
+CROSS_ENCODER_MODELS = [
+    "cross-encoder/ms-marco-MiniLM-L-6-v2",  # Bert
+    "BAAI/bge-reranker-v2-m3",  # Roberta
+]
+
+EMBEDDING_MODELS = [
+    "sentence-transformers/all-MiniLM-L12-v2",
+]
+
+TEXTS_1 = [
+    "What is the capital of France?",
+    "What is the capital of Germany?",
+]
+
+TEXTS_2 = [
+    "The capital of France is Paris.",
+    "The capital of Germany is Berlin.",
+]
+
+DTYPE = "half"
+
+
+@pytest.fixture(scope="module", params=CROSS_ENCODER_MODELS)
+def model_name(request):
+    yield request.param
+
+
+def test_cross_encoder_1_to_1(vllm_runner, hf_runner, model_name):
+    text_pair = [TEXTS_1[0], TEXTS_2[0]]
+
+    with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
+        hf_outputs = hf_model.predict([text_pair]).tolist()
+
+    with vllm_runner(
+        model_name, runner="pooling", dtype=DTYPE, max_model_len=None
+    ) as vllm_model:
+        vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
+
+    assert len(vllm_outputs) == 1
+    assert len(hf_outputs) == 1
+
+    assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
+
+
+def test_cross_encoder_1_to_N(vllm_runner, hf_runner, model_name):
+    text_pairs = [
+        [TEXTS_1[0], TEXTS_2[0]],
+        [TEXTS_1[0], TEXTS_2[1]],
+    ]
+
+    with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
+        hf_outputs = hf_model.predict(text_pairs).tolist()
+
+    with vllm_runner(
+        model_name, runner="pooling", dtype=DTYPE, max_model_len=None
+    ) as vllm_model:
+        vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
+
+    assert len(vllm_outputs) == 2
+    assert len(hf_outputs) == 2
+
+    assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
+    assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
+
+
+def test_cross_encoder_N_to_N(vllm_runner, hf_runner, model_name):
+    text_pairs = [
+        [TEXTS_1[0], TEXTS_2[0]],
+        [TEXTS_1[1], TEXTS_2[1]],
+    ]
+
+    with hf_runner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
+        hf_outputs = hf_model.predict(text_pairs).tolist()
+
+    with vllm_runner(
+        model_name, runner="pooling", dtype=DTYPE, max_model_len=None
+    ) as vllm_model:
+        vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
+
+    assert len(vllm_outputs) == 2
+    assert len(hf_outputs) == 2
+
+    assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
+    assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
+
+
+@pytest.fixture(scope="module", params=EMBEDDING_MODELS)
+def emb_model_name(request):
+    yield request.param
+
+
+def test_embedding_1_to_1(vllm_runner, hf_runner, emb_model_name):
+    text_pair = [TEXTS_1[0], TEXTS_2[0]]
+
+    with hf_runner(
+        emb_model_name, dtype=DTYPE, is_sentence_transformer=True
+    ) as hf_model:
+        hf_embeddings = hf_model.encode(text_pair)
+        hf_outputs = [F.cosine_similarity(*map(torch.tensor, hf_embeddings), dim=0)]
+
+    with vllm_runner(
+        emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
+    ) as vllm_model:
+        vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
+
+    assert len(vllm_outputs) == 1
+    assert len(hf_outputs) == 1
+
+    assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
+
+
+def test_embedding_1_to_N(vllm_runner, hf_runner, emb_model_name):
+    text_pairs = [
+        [TEXTS_1[0], TEXTS_2[0]],
+        [TEXTS_1[0], TEXTS_2[1]],
+    ]
+
+    with hf_runner(
+        emb_model_name, dtype=DTYPE, is_sentence_transformer=True
+    ) as hf_model:
+        hf_embeddings = [hf_model.encode(text_pair) for text_pair in text_pairs]
+        hf_outputs = [
+            F.cosine_similarity(*map(torch.tensor, pair), dim=0)
+            for pair in hf_embeddings
+        ]
+
+    with vllm_runner(
+        emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
+    ) as vllm_model:
+        vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
+
+    assert len(vllm_outputs) == 2
+    assert len(hf_outputs) == 2
+
+    assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
+    assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
+
+
+def test_embedding_N_to_N(vllm_runner, hf_runner, emb_model_name):
+    text_pairs = [
+        [TEXTS_1[0], TEXTS_2[0]],
+        [TEXTS_1[1], TEXTS_2[1]],
+    ]
+
+    with hf_runner(
+        emb_model_name, dtype=DTYPE, is_sentence_transformer=True
+    ) as hf_model:
+        hf_embeddings = [hf_model.encode(text_pair) for text_pair in text_pairs]
+        hf_outputs = [
+            F.cosine_similarity(*map(torch.tensor, pair), dim=0)
+            for pair in hf_embeddings
+        ]
+
+    with vllm_runner(
+        emb_model_name, runner="pooling", dtype=DTYPE, max_model_len=None
+    ) as vllm_model:
+        vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
+
+    assert len(vllm_outputs) == 2
+    assert len(hf_outputs) == 2
+
+    assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
+    assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)