150 lines
5.1 KiB
Python
150 lines
5.1 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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# python -m unittest test_encoder_embedding_models.TestEncoderEmbeddingModels.test_prefill_logits
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import multiprocessing as mp
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import random
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import time
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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 = [("BAAI/bge-small-en", 1, 1e-5), ("BAAI/bge-m3", 1, 1e-5)]
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ATTENTION_BACKEND = ["torch_native", "triton"]
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BATCH_SIZE = [1, 2]
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TORCH_DTYPES = [torch.float32]
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sgl_to_st_ratio = []
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class TestEncoderEmbeddingModels(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", 512) - 20
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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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attention_backend,
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batch_size,
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) -> None:
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truncated_prompts = self._truncate_prompts(prompts, model_path)
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truncated_prompts = truncated_prompts * batch_size
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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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# warm up
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hf_outputs = hf_runner.forward(truncated_prompts)
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st_start_time = time.perf_counter()
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hf_outputs = hf_runner.forward(truncated_prompts)
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st_end_time = time.perf_counter()
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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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attention_backend=attention_backend,
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chunked_prefill_size=-1,
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disable_radix_cache=True,
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) as srt_runner:
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# warm up
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srt_outputs = srt_runner.forward(truncated_prompts)
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sgl_start_time = time.perf_counter()
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srt_outputs = srt_runner.forward(truncated_prompts)
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sgl_end_time = time.perf_counter()
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transformer_time = st_end_time - st_start_time
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sgl_time = sgl_end_time - sgl_start_time
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sgl_to_st_ratio.append(sgl_time / transformer_time)
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for i in range(len(truncated_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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# If something is wrong, uncomment this to observe similarity.
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# print("similarity diff", abs(similarity - 1))
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if len(truncated_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 attention_backend in ATTENTION_BACKEND:
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for batch_size in BATCH_SIZE:
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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,
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model,
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tp_size,
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torch_dtype,
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prefill_tolerance,
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attention_backend,
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batch_size,
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)
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for i in range(len(BATCH_SIZE)):
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print(
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"bacth size: ",
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BATCH_SIZE[i] * 5,
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"sgl_time/st_time",
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round(sgl_to_st_ratio[i], 3),
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)
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if __name__ == "__main__":
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unittest.main()
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