112 lines
3.7 KiB
Python
112 lines
3.7 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 random
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import unittest
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import torch
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from transformers import AutoConfig, AutoTokenizer
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from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
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from sglang.test.test_utils import CustomTestCase, get_similarities, is_in_ci
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MODELS = [
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("Alibaba-NLP/gte-Qwen2-1.5B-instruct", 1, 1e-5),
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("intfloat/e5-mistral-7b-instruct", 1, 1e-5),
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("marco/mcdse-2b-v1", 1, 1e-5),
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("Qwen/Qwen3-Embedding-8B", 1, 1e-5),
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# Temporarily disable before this model is fixed
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# ("jason9693/Qwen2.5-1.5B-apeach", 1, 1e-5),
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]
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TORCH_DTYPES = [torch.float16]
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class TestEmbeddingModels(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 _truncate_prompts(self, prompts, model_path):
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config = AutoConfig.from_pretrained(model_path)
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max_length = getattr(config, "max_position_embeddings", 2048)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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truncated_prompts = []
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for prompt in prompts:
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tokens = tokenizer(prompt, return_tensors="pt", truncation=False)
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if len(tokens.input_ids[0]) > max_length:
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truncated_text = tokenizer.decode(
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tokens.input_ids[0][: max_length - 1], skip_special_tokens=True
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)
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truncated_prompts.append(truncated_text)
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else:
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truncated_prompts.append(prompt)
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return truncated_prompts
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def assert_close_prefill_logits(
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self,
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prompts,
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model_path,
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tp_size,
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torch_dtype,
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prefill_tolerance,
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) -> None:
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truncated_prompts = self._truncate_prompts(prompts, model_path)
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with HFRunner(
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model_path,
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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_outputs = hf_runner.forward(truncated_prompts)
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with SRTRunner(
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model_path,
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tp_size=tp_size,
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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_outputs = srt_runner.forward(truncated_prompts)
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for i in range(len(prompts)):
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hf_logits = torch.Tensor(hf_outputs.embed_logits[i])
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srt_logits = torch.Tensor(srt_outputs.embed_logits[i])
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similarity = torch.tensor(get_similarities(hf_logits, srt_logits))
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print("similarity diff", abs(similarity - 1))
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if len(prompts[i]) <= 1000:
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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_prefill_logits(self):
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models_to_test = MODELS
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if is_in_ci():
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models_to_test = [random.choice(MODELS)]
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for model, tp_size, prefill_tolerance in models_to_test:
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for torch_dtype in TORCH_DTYPES:
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self.assert_close_prefill_logits(
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DEFAULT_PROMPTS, model, tp_size, torch_dtype, prefill_tolerance
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)
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if __name__ == "__main__":
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unittest.main()
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