Add Support for Qwen2-VL Multi-modal Embedding Models (#3694)

test/srt/models/test_gme_qwen_models.py

@@ -0,0 +1,85 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+
+import multiprocessing as mp
+import unittest
+
+import torch
+
+from sglang.test.runners import HFRunner, SRTRunner
+from sglang.test.test_utils import get_similarities
+
+TEXTS = "two Subway Series sandwiches with meats, cheese, lettuce, tomatoes, and onions on a black background, accompanied by the Subway Series logo, highlighting a new sandwich series."
+IMAGES = "https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild/resolve/main/images/023.jpg"
+
+
+MODELS = [
+    ("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct", 1e-3),
+]
+TORCH_DTYPES = [torch.float16]
+
+
+class TestQmeQwenModels(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        mp.set_start_method("spawn", force=True)
+
+    def assert_close_embeddings(self, model, prefill_tolerance, torch_dtype):
+
+        prompts_no_image = f"<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\n{TEXTS}<|im_end|>\n<|im_start|>assistant\n<|endoftext|>"
+        prompts_with_image = f"<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>assistant\n<|endoftext|>"
+        with HFRunner(
+            model,
+            torch_dtype=torch_dtype,
+            model_type="embedding",
+        ) as hf_runner:
+            hf_text_embeddings = hf_runner.forward(prompts=[prompts_no_image])
+            hf_image_embeddings = hf_runner.forward(
+                prompts=[prompts_with_image], image_data=[IMAGES]
+            )
+        with SRTRunner(
+            model,
+            tp_size=1,
+            torch_dtype=torch_dtype,
+            model_type="embedding",
+        ) as srt_runner:
+            srt_text_embeddings = srt_runner.forward(prompts=prompts_no_image)
+            srt_image_embeddings = srt_runner.forward(
+                prompts=prompts_with_image, image_data=IMAGES
+            )
+
+        similarity = get_similarities(
+            hf_text_embeddings.embed_logits[0], srt_text_embeddings.embed_logits[0]
+        )
+        print("texts similarity diff", abs(similarity - 1))
+        assert torch.all(
+            abs(similarity - 1) < prefill_tolerance
+        ), "embeddings are not all close"
+        similarity = get_similarities(
+            hf_image_embeddings.embed_logits[0], srt_image_embeddings.embed_logits[0]
+        )
+        print("images similarity diff", abs(similarity - 1))
+        assert torch.all(
+            abs(similarity - 1) < prefill_tolerance
+        ), "embeddings are not all close"
+
+    def test_accuracy(self):
+        for model, prefill_tolerance in MODELS:
+            for torch_dtype in TORCH_DTYPES:
+                self.assert_close_embeddings(model, prefill_tolerance, torch_dtype)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/run_suite.py

@@ -13,6 +13,7 @@ suites = {
         "models/test_qwen_models.py",
         "models/test_reward_models.py",
         "test_gptqmodel_dynamic.py",
+        "models/test_gme_qwen_models.py",
         "test_abort.py",
         "test_chunked_prefill.py",
         "test_custom_allreduce.py",