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