Add support for Matryoshka embeddings (#126) (#11142)

Co-authored-by: Satyam Kumar <satyamk@linkedin.com>

test/srt/models/test_embedding_models.py

@@ -15,6 +15,7 @@
 import multiprocessing as mp
 import random
 import unittest
+from typing import Optional
 
 import torch
 from transformers import AutoConfig, AutoTokenizer
@@ -69,6 +70,7 @@ class TestEmbeddingModels(CustomTestCase):
         tp_size,
         torch_dtype,
         prefill_tolerance,
+        matryoshka_dim: Optional[int] = None,
     ) -> None:
         truncated_prompts = self._truncate_prompts(prompts, model_path)
 
@@ -76,6 +78,7 @@ class TestEmbeddingModels(CustomTestCase):
             model_path,
             torch_dtype=torch_dtype,
             model_type="embedding",
+            matryoshka_dim=matryoshka_dim,
         ) as hf_runner:
             hf_outputs = hf_runner.forward(truncated_prompts)
 
@@ -86,8 +89,13 @@ class TestEmbeddingModels(CustomTestCase):
             torch_dtype=torch_dtype,
             model_type="embedding",
             attention_backend=attention_backend,
+            json_model_override_args=(
+                {"matryoshka_dimensions": [matryoshka_dim]} if matryoshka_dim else None
+            ),
         ) as srt_runner:
-            srt_outputs = srt_runner.forward(truncated_prompts)
+            srt_outputs = srt_runner.forward(
+                truncated_prompts, dimensions=matryoshka_dim
+            )
 
         for i in range(len(prompts)):
             hf_logits = torch.Tensor(hf_outputs.embed_logits[i])
@@ -113,6 +121,25 @@ class TestEmbeddingModels(CustomTestCase):
                     DEFAULT_PROMPTS, model, tp_size, torch_dtype, prefill_tolerance
                 )
 
+    def test_matryoshka_embedding(self):
+        models_to_test = [
+            model
+            for model in MODELS
+            if "Alibaba-NLP/gte-Qwen2-1.5B-instruct" == model[0]
+        ]
+        assert len(models_to_test) == 1
+
+        for model, tp_size, prefill_tolerance in models_to_test:
+            for torch_dtype in TORCH_DTYPES:
+                self.assert_close_prefill_logits(
+                    DEFAULT_PROMPTS,
+                    model,
+                    tp_size,
+                    torch_dtype,
+                    prefill_tolerance,
+                    matryoshka_dim=128,
+                )
+
 
 if __name__ == "__main__":
     unittest.main()

test/srt/openai_server/basic/test_openai_embedding.py

@@ -1,5 +1,8 @@
+import json
+import os
 import unittest
 
+import numpy as np
 import openai
 
 from sglang.srt.utils import kill_process_tree
@@ -92,6 +95,105 @@ class TestOpenAIEmbedding(CustomTestCase):
         # check the status code
         self.assertEqual(cm.exception.status_code, 400)
 
+    def test_embedding_with_dimensions_parameter(self):
+        """Test that non-Matryoshka models reject dimensions parameter."""
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        # Test that specifying dimensions fails for non-Matryoshka models
+        with self.assertRaises(openai.BadRequestError) as cm:
+            client.embeddings.create(
+                model=self.model, input="Hello world", dimensions=512
+            )
+
+        self.assertEqual(cm.exception.status_code, 400)
+
+
+class TestMatryoshkaEmbeddingModel(CustomTestCase):
+    """Test class for Model that supports Matryoshka embedding functionality, using OpenAI API."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.api_key = "sk-123456"
+        cls.matryoshka_dims = [128, 256, 512, 768, 1024]
+
+        # Configure embedding-specific args with Matryoshka support via json_model_override_args
+        matryoshka_config = {
+            "is_matryoshka": True,
+            "matryoshka_dimensions": cls.matryoshka_dims,
+        }
+        other_args = [
+            "--is-embedding",
+            "--enable-metrics",
+            "--json-model-override-args",
+            json.dumps(matryoshka_config),
+        ]
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            api_key=cls.api_key,
+            other_args=other_args,
+        )
+        cls.base_url += "/v1"
+
+    @classmethod
+    def tearDownClass(cls):
+        if hasattr(cls, "process"):
+            kill_process_tree(cls.process.pid)
+
+    def test_matryoshka_embedding_valid_dimensions(self):
+        """Test Matryoshka embedding with valid dimensions."""
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        # Test with various valid dimensions
+        for dimensions in self.matryoshka_dims:
+            with self.subTest(dimensions=dimensions):
+                response = client.embeddings.create(
+                    model=self.model, input="Hello world", dimensions=dimensions
+                )
+                self.assertEqual(len(response.data), 1)
+                self.assertEqual(len(response.data[0].embedding), dimensions)
+
+    def test_matryoshka_embedding_batch_same_dimensions(self):
+        """Test Matryoshka embedding with batch input and same dimensions."""
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        response = client.embeddings.create(
+            model=self.model,
+            input=["Hello world", "Test text", "Another example"],
+            dimensions=256,
+        )
+
+        self.assertEqual(len(response.data), 3)
+        for embedding_data in response.data:
+            self.assertEqual(len(embedding_data.embedding), 256)
+
+    def test_matryoshka_embedding_no_dimensions(self):
+        """Test embedding without specifying dimensions (should use full size)."""
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        response = client.embeddings.create(model=self.model, input="Hello world")
+
+        self.assertEqual(len(response.data), 1)
+
+        # Should return full embedding size when no dimensions specified
+        self.assertEqual(len(response.data[0].embedding), 1536)
+
+    def test_matryoshka_embedding_invalid_dimensions(self):
+        """Test Matryoshka embedding with invalid dimensions."""
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        for dimensions in [100, 0, -1, 10000]:
+            with self.assertRaises(openai.BadRequestError) as cm:
+                client.embeddings.create(
+                    model=self.model,
+                    input="Hello world",
+                    dimensions=dimensions,
+                )
+            self.assertEqual(cm.exception.status_code, 400)
+
 
 if __name__ == "__main__":
     unittest.main()