[CI/Build] Reorganize models tests (#17459)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/language/pooling/test_scoring.py

@@ -0,0 +1,190 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Compare the scoring outputs of HF and vLLM models.
+
+Run `pytest tests/models/embedding/language/test_scoring.py`.
+"""
+import math
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+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.",
+]
+
+
+@pytest.fixture(scope="module", params=MODELS)
+def model_name(request):
+    yield request.param
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str):
+
+    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, task="score", 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 math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):
+
+    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, task="score", 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 math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+def test_llm_N_to_N(vllm_runner, hf_runner, model_name, dtype: str):
+
+    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, task="score", 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 math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
+
+
+@pytest.fixture(scope="module", params=EMBEDDING_MODELS)
+def emb_model_name(request):
+    yield request.param
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+def test_llm_1_to_1_embedding(vllm_runner, hf_runner, emb_model_name,
+                              dtype: str):
+
+    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,
+                     task="embed",
+                     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 math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+def test_llm_1_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
+                              dtype: str):
+
+    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,
+                     task="embed",
+                     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 math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+def test_llm_N_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
+                              dtype: str):
+
+    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,
+                     task="embed",
+                     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 math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)