109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
import multiprocessing as mp
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import unittest
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from typing import Optional
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import torch
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from transformers import AutoConfig, AutoTokenizer
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from sglang.test.ci.ci_register import register_npu_ci
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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
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register_npu_ci(
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est_time=400,
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suite="nightly-1-npu-a3",
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nightly=True,
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disabled="embeddings are not all close",
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)
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MODELS = [
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("/root/.cache/modelscope/hub/models/iic/gte_Qwen2-1.5B-instruct", 1, 1e-5),
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("/root/.cache/modelscope/hub/models/Qwen/Qwen3-Embedding-8B", 1, 1e-5),
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]
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TORCH_DTYPES = [torch.bfloat16]
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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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matryoshka_dim: Optional[int] = None,
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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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matryoshka_dim=matryoshka_dim,
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) as hf_runner:
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hf_outputs = hf_runner.forward(truncated_prompts)
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attention_backend = "ascend"
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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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json_model_override_args=(
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{"matryoshka_dimensions": [matryoshka_dim]} if matryoshka_dim else None
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),
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) as srt_runner:
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srt_outputs = srt_runner.forward(
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truncated_prompts, dimensions=matryoshka_dim
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)
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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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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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