8ea5e44a43
[CI/Test] improve robustness of test by replacing del with context manager (vllm_runner) (#5357)
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
"""Compares the outputs of gptq vs gptq_marlin
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Note: GPTQ and Marlin do not have bitwise correctness.
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As a result, in this test, we just confirm that the top selected tokens of the
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Marlin/GPTQ models are in the top 5 selections of each other.
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Note: Marlin internally uses locks to synchronize the threads. This can
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result in very slight nondeterminism for Marlin. As a result, we re-run the test
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up to 3 times to see if we pass.
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Run `pytest tests/models/test_gptq_marlin.py`.
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"""
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import os
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import pytest
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import torch
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from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
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from vllm.model_executor.layers.rotary_embedding import _ROPE_DICT
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from .utils import check_logprobs_close
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os.environ["TOKENIZERS_PARALLELISM"] = "true"
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MAX_MODEL_LEN = 1024
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gptq_marlin_not_supported = True
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if torch.cuda.is_available():
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capability = torch.cuda.get_device_capability()
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capability = capability[0] * 10 + capability[1]
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gptq_marlin_not_supported = (
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capability < QUANTIZATION_METHODS["gptq_marlin"].get_min_capability())
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MODELS = [
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# act_order==False, group_size=channelwise
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("robertgshaw2/zephyr-7b-beta-channelwise-gptq", "main"),
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# act_order==False, group_size=128
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("TheBloke/Llama-2-7B-GPTQ", "main"),
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# act_order==True, group_size=128
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("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "main"),
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# act_order==True, group_size=64
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("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-4bit-64g-actorder_True"),
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# act_order==True, group_size=32
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("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-4bit-32g-actorder_True"),
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# 8-bit, act_order==True, group_size=channelwise
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("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-8bit--1g-actorder_True"),
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# 8-bit, act_order==True, group_size=128
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("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-8bit-128g-actorder_True"),
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# 8-bit, act_order==True, group_size=32
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("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-8bit-32g-actorder_True"),
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]
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@pytest.mark.flaky(reruns=3)
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@pytest.mark.skipif(gptq_marlin_not_supported,
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reason="gptq_marlin is not supported on this GPU type.")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half", "bfloat16"])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_models(
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vllm_runner,
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example_prompts,
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model,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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model_name, revision = model
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# Run marlin.
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with vllm_runner(model_name=model_name,
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revision=revision,
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dtype=dtype,
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quantization="marlin",
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max_model_len=MAX_MODEL_LEN,
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tensor_parallel_size=1) as gptq_marlin_model:
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gptq_marlin_outputs = gptq_marlin_model.generate_greedy_logprobs(
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example_prompts[:-1], max_tokens, num_logprobs)
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_ROPE_DICT.clear() # clear rope cache to avoid rope dtype error
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# Run gptq.
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# The naive gptq kernel doesn't support bf16 yet.
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# Here we always compare fp16/bf16 gpt marlin kernel
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# to fp16 gptq kernel.
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with vllm_runner(model_name=model_name,
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revision=revision,
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dtype="half",
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quantization="gptq",
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max_model_len=MAX_MODEL_LEN,
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tensor_parallel_size=1) as gptq_model:
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gptq_outputs = gptq_model.generate_greedy_logprobs(
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example_prompts[:-1], max_tokens, num_logprobs)
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check_logprobs_close(
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outputs_0_lst=gptq_outputs,
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outputs_1_lst=gptq_marlin_outputs,
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name_0="gptq",
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name_1="gptq_marlin",
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)
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