forked from Karylab-cklius/vllm
109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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from transformers import AutoModelForSequenceClassification
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from vllm.platforms import current_platform
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@pytest.mark.parametrize(
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"model",
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[
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pytest.param(
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"jason9693/Qwen2.5-1.5B-apeach",
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marks=[
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pytest.mark.core_model,
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pytest.mark.cpu_model,
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pytest.mark.slow_test,
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],
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),
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],
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)
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@pytest.mark.parametrize("dtype", ["half"] if current_platform.is_rocm() else ["float"])
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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) -> None:
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with vllm_runner(model, max_model_len=512, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.classify(example_prompts)
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with hf_runner(
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model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
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) as hf_model:
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hf_outputs = hf_model.classify(example_prompts)
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# check logits difference
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for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
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hf_output = torch.tensor(hf_output)
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vllm_output = torch.tensor(vllm_output)
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# the tolerance value of 1e-2 is selected based on the
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# half datatype tests in
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# tests/models/language/pooling/test_embedding.py
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assert torch.allclose(
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hf_output,
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vllm_output,
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rtol=2e-3 if dtype == "float" else 1e-2,
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)
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@pytest.mark.core_model
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def test_bert_model_runner_v2(hf_runner, vllm_runner, monkeypatch) -> None:
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model = "cross-encoder/ms-marco-TinyBERT-L-2-v2"
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score_inputs = (
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"What is the capital of France?",
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[
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"Paris.",
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"Paris is the capital and largest city of France.",
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"William Shakespeare wrote Hamlet in the early seventeenth century.",
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],
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)
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prompt_batches = [
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["short input"],
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[
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"short input",
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"a longer input that exercises mixed sequence lengths",
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],
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]
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with hf_runner(
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model, dtype="half", auto_cls=AutoModelForSequenceClassification
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) as hf_model:
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# HfRunner uses problem_type to preserve the model's
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# sbert_ce_default_activation_function=Identity raw logits.
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hf_model.config.problem_type = "regression"
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hf_outputs = [hf_model.classify(prompts) for prompts in prompt_batches]
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text_1, text_2 = score_inputs
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text_pairs = [[text_1, document] for document in text_2]
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with hf_runner(model, dtype="half", is_cross_encoder=True) as hf_model:
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hf_scores = hf_model.predict(text_pairs).tolist()
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monkeypatch.setenv("VLLM_USE_V2_MODEL_RUNNER", "1")
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with vllm_runner(
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model,
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runner="pooling",
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dtype="half",
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max_model_len=64,
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) as vllm_model:
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assert vllm_model.llm.llm_engine.vllm_config.use_v2_model_runner
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vllm_outputs = [vllm_model.classify(prompts) for prompts in prompt_batches]
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vllm_scores = vllm_model.score(*score_inputs)
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for hf_batch, vllm_batch in zip(hf_outputs, vllm_outputs):
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hf_tensor = torch.tensor(hf_batch)
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vllm_tensor = torch.tensor(vllm_batch)
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assert vllm_tensor.shape == hf_tensor.shape
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assert torch.allclose(vllm_tensor, hf_tensor, rtol=1e-2, atol=1e-4)
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assert torch.allclose(
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torch.tensor(vllm_scores),
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torch.tensor(hf_scores),
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rtol=1e-2,
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atol=1e-4,
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)
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