350f9e107f
Since #4335 was merged, I've noticed that the definition of ServerRunner in the tests is the same as in the test for OpenAI API. I have moved the class to the test utilities to avoid code duplication. (Although it only has been repeated twice so far, I will add another similar test suite in #4200 which would duplicate the code a third time) Also, I have moved the test utilities file (test_utils.py) to under the test directory (tests/utils.py), since none of its code is actually used in the main package. Note that I have added __init__.py to each test subpackage and updated the ray.init() call in the test utilities file in order to relative import tests/utils.py.
98 lines
3.5 KiB
Python
98 lines
3.5 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 .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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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"])
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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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gptq_marlin_model = 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)
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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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del gptq_marlin_model
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# Run gptq.
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gptq_model = vllm_runner(model_name=model_name,
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revision=revision,
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dtype=dtype,
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quantization="gptq",
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max_model_len=MAX_MODEL_LEN,
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tensor_parallel_size=1)
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gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts[:-1],
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max_tokens,
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num_logprobs)
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del gptq_model
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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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