[Misc] Clean up mark to fork process in BNB tests (#20692)
Signed-off-by: Isotr0py <2037008807@qq.com>
This commit is contained in:
Isotr0py
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tests/models/quantization/test_bitsandbytes.py
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tests/models/quantization/test_bitsandbytes.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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'''Tests whether bitsandbytes computation is enabled correctly.
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Run `pytest tests/quantization/test_bitsandbytes.py`.
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'''
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import gc
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import pytest
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import torch
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from transformers import BitsAndBytesConfig
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from tests.quantization.utils import is_quant_method_supported
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from ...utils import compare_two_settings, multi_gpu_test
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from ..utils import check_embeddings_close
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models_4bit_to_test = [
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("facebook/opt-125m", "quantize opt model inflight"),
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("mistralai/Mistral-7B-Instruct-v0.3",
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"quantize inflight model with both HF and Mistral format weights")
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]
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models_4bit_to_embedding_test = [
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("intfloat/e5-mistral-7b-instruct", "quantize embedding model inflight"),
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]
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models_pre_qaunt_4bit_to_test = [
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('PrunaAI/Einstein-v6.1-Llama3-8B-bnb-4bit-smashed',
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'read pre-quantized 4-bit FP4 model'),
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('poedator/opt-125m-bnb-4bit', 'read pre-quantized 4-bit NF4 opt model'),
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]
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models_pre_quant_8bit_to_test = [
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('meta-llama/Llama-Guard-3-8B-INT8',
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'read pre-quantized llama 8-bit model'),
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("yec019/fbopt-350m-8bit", "read pre-quantized 8-bit opt model"),
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]
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@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
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reason='bitsandbytes is not supported on this GPU type.')
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@pytest.mark.parametrize("model_name, description", models_4bit_to_test)
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def test_load_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
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model_name, description) -> None:
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hf_model_kwargs = dict(quantization_config=BitsAndBytesConfig(
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load_in_4bit=True))
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validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1],
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model_name, False, hf_model_kwargs)
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@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
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reason='bitsandbytes is not supported on this GPU type.')
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@pytest.mark.parametrize("model_name, description",
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models_pre_qaunt_4bit_to_test)
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def test_load_pre_quant_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
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model_name, description) -> None:
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validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1],
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model_name, True)
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@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
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reason='bitsandbytes is not supported on this GPU type.')
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@pytest.mark.parametrize("model_name, description",
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models_pre_quant_8bit_to_test)
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def test_load_8bit_bnb_model(hf_runner, vllm_runner, example_prompts,
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model_name, description) -> None:
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validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1],
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model_name, True)
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@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
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reason='bitsandbytes is not supported on this GPU type.')
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@pytest.mark.parametrize("model_name, description", models_4bit_to_test)
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@multi_gpu_test(num_gpus=2)
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def test_load_tp_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
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model_name, description) -> None:
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hf_model_kwargs = dict(quantization_config=BitsAndBytesConfig(
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load_in_4bit=True))
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validate_generated_texts(hf_runner,
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vllm_runner,
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example_prompts[:1],
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model_name,
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False,
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hf_model_kwargs,
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vllm_tp_size=2)
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@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
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reason='bitsandbytes is not supported on this GPU type.')
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@pytest.mark.parametrize("model_name, description", models_4bit_to_test)
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@multi_gpu_test(num_gpus=2)
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def test_load_pp_4bit_bnb_model(model_name, description) -> None:
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common_args = [
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"--disable-log-stats",
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"--disable-log-requests",
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"--dtype",
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"bfloat16",
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"--enable-prefix-caching",
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"--quantization",
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"bitsandbytes",
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"--gpu-memory-utilization",
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"0.7",
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]
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pp_args = [
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*common_args,
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"--pipeline-parallel-size",
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"2",
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]
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compare_two_settings(model_name, common_args, pp_args)
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@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
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reason='bitsandbytes is not supported on this GPU type.')
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@pytest.mark.parametrize("model_name, description",
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models_4bit_to_embedding_test)
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@pytest.mark.parametrize("dtype", ["half"])
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def test_4bit_bnb_embedding_model(
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model_name,
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description,
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hf_runner,
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vllm_runner,
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example_prompts,
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dtype: str,
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) -> None:
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# The example_prompts has ending "\n", for example:
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# "Write a short story about a robot that dreams for the first time.\n"
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# sentence_transformers will strip the input texts, see:
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# https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
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# This makes the input_ids different between hf_model and vllm_model.
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# So we need to strip the input texts to avoid test failing.
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example_prompts = [str(s).strip() for s in example_prompts]
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# Inflight 4bit quantization
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with vllm_runner(model_name,
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task="embed",
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dtype=dtype,
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gpu_memory_utilization=0.5,
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quantization="bitsandbytes") as vllm_model:
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vllm_outputs = vllm_model.embed(example_prompts)
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hf_model_kwargs = dict(quantization_config=BitsAndBytesConfig(
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load_in_4bit=True))
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with hf_runner(
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model_name,
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dtype=dtype,
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model_kwargs=hf_model_kwargs,
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is_sentence_transformer=True,
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) as hf_model:
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hf_outputs = hf_model.encode(example_prompts)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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tol=5e-2,
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)
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def log_generated_texts(prompts, outputs, runner_name):
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logged_texts = []
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for i, (_, generated_text) in enumerate(outputs):
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log_entry = {
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"prompt": prompts[i],
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"runner_name": runner_name,
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"generated_text": generated_text,
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}
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logged_texts.append(log_entry)
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return logged_texts
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def validate_generated_texts(hf_runner,
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vllm_runner,
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prompts,
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model_name,
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pre_quant=False,
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hf_model_kwargs=None,
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vllm_tp_size=1):
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# NOTE: run vLLM first, as it requires a clean process
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# when using distributed inference
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with vllm_runner(model_name,
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quantization=None if pre_quant else 'bitsandbytes',
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tensor_parallel_size=vllm_tp_size,
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enforce_eager=False) as llm:
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vllm_outputs = llm.generate_greedy(prompts, 8)
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vllm_logs = log_generated_texts(prompts, vllm_outputs, "VllmRunner")
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# Clean up the GPU memory for the next test
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gc.collect()
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torch.cuda.empty_cache()
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if hf_model_kwargs is None:
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hf_model_kwargs = {}
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# Run with HF runner
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with hf_runner(model_name, model_kwargs=hf_model_kwargs) as llm:
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hf_outputs = llm.generate_greedy(prompts, 8)
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hf_logs = log_generated_texts(prompts, hf_outputs, "HfRunner")
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# Clean up the GPU memory for the next test
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gc.collect()
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torch.cuda.empty_cache()
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# Compare the generated strings
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for hf_log, vllm_log in zip(hf_logs, vllm_logs):
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hf_str = hf_log["generated_text"]
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vllm_str = vllm_log["generated_text"]
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prompt = hf_log["prompt"]
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assert hf_str == vllm_str, (f"Model: {model_name}"
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f"Mismatch between HF and vLLM outputs:\n"
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f"Prompt: {prompt}\n"
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f"HF Output: '{hf_str}'\n"
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f"vLLM Output: '{vllm_str}'")
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