support clip embedding model (#4506)

test/srt/models/test_clip_models.py

@@ -0,0 +1,80 @@
+# 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 transformers import AutoProcessor
+
+from sglang.srt.utils import load_image
+from sglang.test.runners import DEFAULT_PROMPTS, 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 = [
+    ("openai/clip-vit-large-patch14-336", 1e-5),
+]
+TORCH_DTYPES = [torch.float16]
+
+
+class TestClipModels(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        mp.set_start_method("spawn", force=True)
+
+    def assert_close_embeddings(self, model, prefill_tolerance, torch_dtype):
+
+        with HFRunner(
+            model,
+            torch_dtype=torch_dtype,
+            model_type="embedding",
+        ) as hf_runner:
+            hf_text_embeds = hf_runner.forward(prompts=TEXTS)
+            hf_image_embeds = hf_runner.forward(image_data=IMAGES)
+
+        with SRTRunner(
+            model,
+            tp_size=1,
+            torch_dtype=torch_dtype,
+            model_type="embedding",
+        ) as srt_runner:
+            text_embeds = srt_runner.forward(prompts=TEXTS)
+            image_embeds = srt_runner.forward(prompts="padding", image_data=IMAGES)
+
+        text_similarity = get_similarities(
+            text_embeds.embed_logits[0], hf_text_embeds.embed_logits[0]
+        )
+        image_similarity = get_similarities(
+            image_embeds.embed_logits[0], hf_image_embeds.embed_logits[0]
+        )
+        print("text similarity diff", abs(text_similarity - 1))
+        print("image similarity diff", abs(image_similarity - 1))
+        assert torch.all(
+            abs(text_similarity - 1) < prefill_tolerance
+        ), "embeddings are not all close"
+        assert torch.all(
+            abs(image_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

@@ -22,6 +22,7 @@ suites = {
         TestFile("models/test_qwen_models.py", 82),
         TestFile("models/test_reward_models.py", 83),
         TestFile("models/test_gme_qwen_models.py", 45),
+        TestFile("models/test_clip_models.py", 100),
         TestFile("test_abort.py", 51),
         TestFile("test_block_int8.py", 22),
         TestFile("test_chunked_prefill.py", 336),