[Model][Misc] Add e5-mistral-7b-instruct and Embedding API (#3734)

tests/entrypoints/test_openai_server.py

@@ -23,6 +23,7 @@ from vllm.transformers_utils.tokenizer import get_tokenizer
 MAX_SERVER_START_WAIT_S = 600  # wait for server to start for 60 seconds
 # any model with a chat template should work here
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+EMBEDDING_MODEL_NAME = "intfloat/e5-mistral-7b-instruct"
 # technically this needs Mistral-7B-v0.1 as base, but we're not testing
 # generation quality here
 LORA_NAME = "typeof/zephyr-7b-beta-lora"
@@ -121,7 +122,7 @@ def zephyr_lora_files():
     return snapshot_download(repo_id=LORA_NAME)
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture(scope="module")
 def server(zephyr_lora_files):
     ray.init()
     server_runner = ServerRunner.remote([
@@ -150,6 +151,25 @@ def server(zephyr_lora_files):
     ray.shutdown()
 
 
+@pytest.fixture(scope="module")
+def embedding_server(zephyr_lora_files):
+    ray.shutdown()
+    ray.init()
+    server_runner = ServerRunner.remote([
+        "--model",
+        EMBEDDING_MODEL_NAME,
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+    ])
+    ray.get(server_runner.ready.remote())
+    yield server_runner
+    ray.shutdown()
+
+
 @pytest.fixture(scope="module")
 def client():
     client = openai.AsyncOpenAI(
@@ -890,5 +910,79 @@ async def test_long_seed(server, client: openai.AsyncOpenAI):
                 or "less_than_equal" in exc_info.value.message)
 
 
+@pytest.mark.parametrize(
+    "model_name",
+    [EMBEDDING_MODEL_NAME],
+)
+async def test_single_embedding(embedding_server, client: openai.AsyncOpenAI,
+                                model_name: str):
+    input = [
+        "The chef prepared a delicious meal.",
+    ]
+
+    # test single embedding
+    embeddings = await client.embeddings.create(
+        model=model_name,
+        input=input,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert embeddings.data is not None and len(embeddings.data) == 1
+    assert len(embeddings.data[0].embedding) == 4096
+    assert embeddings.usage.completion_tokens == 0
+    assert embeddings.usage.prompt_tokens == 9
+    assert embeddings.usage.total_tokens == 9
+
+    # test using token IDs
+    input = [1, 1, 1, 1, 1]
+    embeddings = await client.embeddings.create(
+        model=model_name,
+        input=input,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert embeddings.data is not None and len(embeddings.data) == 1
+    assert len(embeddings.data[0].embedding) == 4096
+    assert embeddings.usage.completion_tokens == 0
+    assert embeddings.usage.prompt_tokens == 5
+    assert embeddings.usage.total_tokens == 5
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [EMBEDDING_MODEL_NAME],
+)
+async def test_batch_embedding(embedding_server, client: openai.AsyncOpenAI,
+                               model_name: str):
+    # test List[str]
+    inputs = [
+        "The cat sat on the mat.", "A feline was resting on a rug.",
+        "Stars twinkle brightly in the night sky."
+    ]
+    embeddings = await client.embeddings.create(
+        model=model_name,
+        input=inputs,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert embeddings.data is not None and len(embeddings.data) == 3
+    assert len(embeddings.data[0].embedding) == 4096
+
+    # test List[List[int]]
+    inputs = [[4, 5, 7, 9, 20], [15, 29, 499], [24, 24, 24, 24, 24],
+              [25, 32, 64, 77]]
+    embeddings = await client.embeddings.create(
+        model=model_name,
+        input=inputs,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert embeddings.data is not None and len(embeddings.data) == 4
+    assert len(embeddings.data[0].embedding) == 4096
+    assert embeddings.usage.completion_tokens == 0
+    assert embeddings.usage.prompt_tokens == 17
+    assert embeddings.usage.total_tokens == 17
+
+
 if __name__ == "__main__":
     pytest.main([__file__])