[Frontend] Using matryoshka_dimensions control the allowed output dimensions.

docs/source/models/pooling_models.md

@@ -159,14 +159,14 @@ For example, setting `dimensions` parameter while using the `BAAI/bge-m3` model
 
 ### Manually enable Matryoshka Embeddings
 
-There is currently no official interface for specifying support for Matryoshka Embeddings. In vLLM, we simply check the existence of the fields `is_matryoshka` or `matryoshka_dimensions` inside `config.json`.
+There is currently no official interface for specifying support for Matryoshka Embeddings. In vLLM, if `is_matryoshka` is `True` in `config.json,` it is allowed to change the output to arbitrary dimensions. Using `matryoshka_dimensions` can control the allowed output dimensions.
 
-For models that support Matryoshka Embeddings but not recognized by vLLM, please manually override the config using `hf_overrides={"is_matryoshka": True}` (offline) or `--hf_overrides '{"is_matryoshka": true}'` (online).
+For models that support Matryoshka Embeddings but not recognized by vLLM, please manually override the config using `hf_overrides={"is_matryoshka": True}`, `hf_overrides={"matryoshka_dimensions": [<allowed output dimensions>]}` (offline) or `--hf_overrides '{"is_matryoshka": true}'`,  `--hf_overrides '{"matryoshka_dimensions": [<allowed output dimensions>]}'`(online).
 
 Here is an example to serve a model with Matryoshka Embeddings enabled.
 
 ```text
-vllm serve Snowflake/snowflake-arctic-embed-m-v1.5 --hf_overrides '{"is_matryoshka":true}'
+vllm serve Snowflake/snowflake-arctic-embed-m-v1.5 --hf_overrides '{"matryoshka_dimensions":[256]}'
 ```
 
 ### Offline Inference
@@ -204,14 +204,14 @@ curl http://127.0.0.1:8000/v1/embeddings \
     "input": "Follow the white rabbit.",
     "model": "jinaai/jina-embeddings-v3",
     "encoding_format": "float",
-    "dimensions": 1
+    "dimensions": 32
   }'
 ```
 
 Expected output:
 
 ```json
-{"id":"embd-0aab28c384d348c3b8f0eb783109dc5f","object":"list","created":1744195454,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-1.0]}],"usage":{"prompt_tokens":10,"total_tokens":10,"completion_tokens":0,"prompt_tokens_details":null}}
+{"id":"embd-5c21fc9a5c9d4384a1b021daccaf9f64","object":"list","created":1745476417,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-0.3828125,-0.1357421875,0.03759765625,0.125,0.21875,0.09521484375,-0.003662109375,0.1591796875,-0.130859375,-0.0869140625,-0.1982421875,0.1689453125,-0.220703125,0.1728515625,-0.2275390625,-0.0712890625,-0.162109375,-0.283203125,-0.055419921875,-0.0693359375,0.031982421875,-0.04052734375,-0.2734375,0.1826171875,-0.091796875,0.220703125,0.37890625,-0.0888671875,-0.12890625,-0.021484375,-0.0091552734375,0.23046875]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0,"prompt_tokens_details":null}}
 ```
 
 A openai client example can be found here: <gh-file:examples/online_serving/openai_embedding_matryoshka_fy.py>

examples/online_serving/openai_embedding_matryoshka_fy.py

@@ -25,11 +25,11 @@ def main():
     responses = client.embeddings.create(
         input=["Follow the white rabbit."],
         model=model,
-        dimensions=1,
+        dimensions=32,
     )
 
     for data in responses.data:
-        print(data.embedding)  # List of float of len 1
+        print(data.embedding)  # List of float of len 32
 
 
 if __name__ == "__main__":

tests/entrypoints/openai/test_embedding.py

@@ -11,11 +11,12 @@
 from vllm.entrypoints.openai.protocol import EmbeddingResponse
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
-from ...models.embedding.utils import check_embeddings_close
+from ...models.embedding.utils import correctness_test
 from ...utils import RemoteOpenAIServer
 
 MODEL_NAME = "intfloat/multilingual-e5-small"
 DUMMY_CHAT_TEMPLATE = """{% for message in messages %}{{message['role'] + ': ' + message['content'] + '\\n'}}{% endfor %}"""  # noqa: E501
+DTYPE = "bfloat16"
 
 
 @pytest.fixture(scope="module")
@@ -25,7 +26,7 @@ def server():
         "embed",
         # use half precision for speed and memory savings in CI environment
         "--dtype",
-        "bfloat16",
+        DTYPE,
         "--enforce-eager",
         "--max-model-len",
         "512",
@@ -43,9 +44,17 @@ async def client(server):
         yield async_client
 
 
+@pytest.fixture(scope="module")
+def hf_model(hf_runner):
+    with hf_runner(MODEL_NAME, dtype=DTYPE,
+                   is_sentence_transformer=True) as hf_model:
+        yield hf_model
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_single_embedding(client: openai.AsyncOpenAI, model_name: str):
+async def test_single_embedding(hf_model, client: openai.AsyncOpenAI,
+                                model_name: str):
     input_texts = [
         "The chef prepared a delicious meal.",
     ]
@@ -66,6 +75,9 @@ async def test_single_embedding(client: openai.AsyncOpenAI, model_name: str):
     assert embeddings.usage.prompt_tokens == 11
     assert embeddings.usage.total_tokens == 11
 
+    vllm_outputs = [d.embedding for d in embeddings.data]
+    correctness_test(hf_model, input_texts, vllm_outputs)
+
     # test using token IDs
     input_tokens = [1, 1, 1, 1, 1]
     embedding_response = await client.embeddings.create(
@@ -86,7 +98,8 @@ async def test_single_embedding(client: openai.AsyncOpenAI, model_name: str):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_batch_embedding(client: openai.AsyncOpenAI, model_name: str):
+async def test_batch_embedding(hf_model, client: openai.AsyncOpenAI,
+                               model_name: str):
     # test list[str]
     input_texts = [
         "The cat sat on the mat.", "A feline was resting on a rug.",
@@ -107,6 +120,9 @@ async def test_batch_embedding(client: openai.AsyncOpenAI, model_name: str):
     assert embeddings.usage.prompt_tokens == 33
     assert embeddings.usage.total_tokens == 33
 
+    vllm_outputs = [d.embedding for d in embeddings.data]
+    correctness_test(hf_model, input_texts, vllm_outputs)
+
     # test list[list[int]]
     input_tokens = [[4, 5, 7, 9, 20], [15, 29, 499], [24, 24, 24, 24, 24],
                     [25, 32, 64, 77]]
@@ -181,7 +197,7 @@ async def test_conversation_embedding(server: RemoteOpenAIServer,
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_batch_base64_embedding(client: openai.AsyncOpenAI,
+async def test_batch_base64_embedding(hf_model, client: openai.AsyncOpenAI,
                                       model_name: str):
     input_texts = [
         "Hello my name is",
@@ -192,6 +208,7 @@ async def test_batch_base64_embedding(client: openai.AsyncOpenAI,
                                                      model=model_name,
                                                      encoding_format="float")
     float_data = [d.embedding for d in responses_float.data]
+    correctness_test(hf_model, input_texts, float_data)
 
     responses_base64 = await client.embeddings.create(input=input_texts,
                                                       model=model_name,
@@ -202,24 +219,13 @@ async def test_batch_base64_embedding(client: openai.AsyncOpenAI,
             np.frombuffer(base64.b64decode(data.embedding),
                           dtype="float32").tolist())
 
-    check_embeddings_close(
-        embeddings_0_lst=float_data,
-        embeddings_1_lst=base64_data,
-        name_0="float",
-        name_1="base64",
-    )
+    correctness_test(hf_model, input_texts, base64_data)
 
     # Default response is float32 decoded from base64 by OpenAI Client
     responses_default = await client.embeddings.create(input=input_texts,
                                                        model=model_name)
     default_data = [d.embedding for d in responses_default.data]
-
-    check_embeddings_close(
-        embeddings_0_lst=float_data,
-        embeddings_1_lst=default_data,
-        name_0="float",
-        name_1="default",
-    )
+    correctness_test(hf_model, input_texts, default_data)
 
 
 @pytest.mark.asyncio

tests/entrypoints/openai/test_embedding_dimensions.py

@@ -3,73 +3,121 @@
 Run `pytest tests/entrypoints/openai/test_embedding_dimensions.py`.
 """
 
+from typing import Optional
+
 import openai
 import pytest
 
 from vllm.entrypoints.openai.protocol import EmbeddingResponse
 
-from ...models.embedding.utils import EmbedModelInfo
+from ...conftest import HfRunner
+from ...models.embedding.utils import EmbedModelInfo, correctness_test
 from ...utils import RemoteOpenAIServer
 
 MODELS = [
-    EmbedModelInfo(name="BAAI/bge-m3", is_matryoshka=False),
-    EmbedModelInfo(name="jinaai/jina-embeddings-v3", is_matryoshka=True),
+    EmbedModelInfo("intfloat/multilingual-e5-small", is_matryoshka=False),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v1.5",
+                   is_matryoshka=True,
+                   matryoshka_dimensions=[256]),
 ]
 
 input_texts = [
     "The chef prepared a delicious meal.",
-] * 3
+]
 
 
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model", MODELS)
-async def test_validating_dimensions(model: EmbedModelInfo):
+@pytest.fixture(scope="module", params=MODELS)
+def model_info(request):
+    return request.param
+
+
+@pytest.fixture(scope="module", params=["bfloat16"])
+def dtype(request):
+    return request.param
+
+
+@pytest.fixture(scope="module")
+def server(model_info, dtype: str):
     args = [
         "--task",
         "embed",
         # use half precision for speed and memory savings in CI environment
         "--dtype",
-        "bfloat16",
+        dtype,
         "--enforce-eager",
         "--max-model-len",
-        "512",
-        "--trust_remote_code"
+        "512"
     ]
-    with RemoteOpenAIServer(model.name, args) as remote_server:
-        client = remote_server.get_async_client()
-
-        async def make_request(dimensions):
-            embedding_response = await client.embeddings.create(
-                model=model.name,
-                input=input_texts,
-                dimensions=dimensions,
-                encoding_format="float",
-            )
-            embeddings = EmbeddingResponse.model_validate(
-                embedding_response.model_dump(mode="json"))
-
-            assert embeddings.id is not None
-            assert len(embeddings.data) == 3
-            assert len(embeddings.data[0].embedding) > 0
-            assert embeddings.usage.completion_tokens == 0
-            assert embeddings.usage.prompt_tokens > 0
-            assert embeddings.usage.total_tokens > 0
-
-            if dimensions is not None:
-                assert len(embeddings.data[0].embedding) == dimensions
-
-        if model.is_matryoshka:
-            for dimensions in [None, 16]:
-                await make_request(dimensions)
 
+    if model_info.name == "Snowflake/snowflake-arctic-embed-m-v1.5":
+        # Manually enable Matryoshka Embeddings
+        args.extend([
+            "--trust_remote_code", "--hf_overrides",
+            '{"matryoshka_dimensions":[256]}'
+        ])
+
+    with RemoteOpenAIServer(model_info.name, args) as remote_server:
+        yield remote_server
+
+
+@pytest.fixture(scope="module")
+def hf_model(hf_runner, model_info, dtype: str):
+    with hf_runner(model_info.name, dtype=dtype,
+                   is_sentence_transformer=True) as hf_model:
+        yield hf_model
+
+
+@pytest.mark.asyncio
+async def test_matryoshka(model_info: EmbedModelInfo,
+                          server: RemoteOpenAIServer, hf_model: HfRunner):
+    client = server.get_async_client()
+
+    async def make_request_and_correctness_test(dimensions):
+        prompts = input_texts * 3
+
+        embedding_response = await client.embeddings.create(
+            model=model_info.name,
+            input=prompts,
+            dimensions=dimensions,
+            encoding_format="float",
+        )
+        embeddings = EmbeddingResponse.model_validate(
+            embedding_response.model_dump(mode="json"))
+
+        assert embeddings.id is not None
+        assert len(embeddings.data) == 3
+        assert len(embeddings.data[0].embedding) > 0
+        assert embeddings.usage.completion_tokens == 0
+        assert embeddings.usage.prompt_tokens > 0
+        assert embeddings.usage.total_tokens > 0
+
+        if dimensions is not None:
+            assert len(embeddings.data[0].embedding) == dimensions
+
+        vllm_outputs = [d.embedding for d in embeddings.data]
+        correctness_test(hf_model, prompts, vllm_outputs, dimensions)
+
+    if model_info.is_matryoshka:
+        valid_dimensions: list[Optional[int]] = [None]
+        if model_info.matryoshka_dimensions is not None:
+            valid_dimensions += model_info.matryoshka_dimensions[:2]
+
+        for dimensions in valid_dimensions:
+            await make_request_and_correctness_test(dimensions)
+
+        invalid_dimensions: list[Optional[int]] = [-1]
+        if model_info.matryoshka_dimensions is not None:
+            assert 5 not in model_info.matryoshka_dimensions
+            invalid_dimensions.append(5)
+
+        for dimensions in invalid_dimensions:
             with pytest.raises(openai.BadRequestError):
-                for dimensions in [-1]:
-                    await make_request(dimensions)
+                await make_request_and_correctness_test(dimensions)
 
-        else:
-            for dimensions in [None]:
-                await make_request(dimensions)
+    else:
+        for dimensions in [None]:
+            await make_request_and_correctness_test(dimensions)
 
+        for dimensions in [-1, 16]:
             with pytest.raises(openai.BadRequestError):
-                for dimensions in [-1, 16]:
-                    await make_request(dimensions)
+                await make_request_and_correctness_test(dimensions)

tests/models/embedding/language/test_jina.py

@@ -153,14 +153,24 @@ def test_matryoshka(
 
     with vllm_runner(model, task="embed", dtype=dtype,
                      max_model_len=None) as vllm_model:
-        vllm_outputs = vllm_model.encode(
-            example_prompts,
-            pooling_params=PoolingParams(dimensions=dimensions))
-
-    check_embeddings_close(
-        embeddings_0_lst=hf_outputs,
-        embeddings_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-        tol=1e-2,
-    )
+        matryoshka_dimensions = (
+            vllm_model.model.llm_engine.model_config.matryoshka_dimensions)
+        assert matryoshka_dimensions is not None
+
+        if dimensions not in matryoshka_dimensions:
+            with pytest.raises(ValueError):
+                vllm_model.encode(
+                    example_prompts,
+                    pooling_params=PoolingParams(dimensions=dimensions))
+        else:
+            vllm_outputs = vllm_model.encode(
+                example_prompts,
+                pooling_params=PoolingParams(dimensions=dimensions))
+
+            check_embeddings_close(
+                embeddings_0_lst=hf_outputs,
+                embeddings_1_lst=vllm_outputs,
+                name_0="hf",
+                name_1="vllm",
+                tol=1e-2,
+            )

tests/models/embedding/utils.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from collections.abc import Sequence
-from typing import NamedTuple
+from typing import NamedTuple, Optional
 
 import torch
 import torch.nn.functional as F
@@ -43,5 +43,24 @@ def matryoshka_fy(tensor, dimensions):
 class EmbedModelInfo(NamedTuple):
     name: str
     is_matryoshka: bool
+    matryoshka_dimensions: Optional[list[int]] = None
     architecture: str = ""
     enable_test: bool = True
+
+
+def correctness_test(hf_model,
+                     inputs,
+                     vllm_outputs: Sequence[list[float]],
+                     dimensions: Optional[int] = None):
+
+    hf_outputs = hf_model.encode(inputs)
+    if dimensions:
+        hf_outputs = matryoshka_fy(hf_outputs, dimensions)
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+        tol=1e-2,
+    )