94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
|
"""Compares the outputs of gptq vs gptq_marlin
|
||
|
|
Note: GPTQ and Marlin do not have bitwise correctness.
|
||
|
|
As a result, in this test, we just confirm that the top selected tokens of the
|
||
|
|
Marlin/GPTQ models are in the top 3 selections of each other.
|
||
|
|
Note: Marlin internally uses locks to synchronize the threads. This can
|
||
|
|
result in very slight nondeterminism for Marlin. As a result, we re-run the test
|
||
|
|
up to 3 times to see if we pass.
|
||
|
|
Note: This test currently fails running with --forked with the following:
|
||
|
|
RuntimeError: Cannot re-initialize CUDA in forked subprocess.
|
||
|
|
To use CUDA with multiprocessing, you must use the 'spawn' start method
|
||
|
|
Run `pytest tests/models/test_gptq_marlin.py`.
|
||
|
|
"""
|
||
|
|
import os
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from tests.models.utils import check_logprobs_close
|
||
|
|
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
|
||
|
|
|
||
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "true"
|
||
|
|
|
||
|
|
MAX_MODEL_LEN = 1024
|
||
|
|
|
||
|
|
capability = torch.cuda.get_device_capability()
|
||
|
|
capability = capability[0] * 10 + capability[1]
|
||
|
|
gptq_marlin_not_supported = (
|
||
|
|
capability < QUANTIZATION_METHODS["gptq_marlin"].get_min_capability())
|
||
|
|
|
||
|
|
MODELS = [
|
||
|
|
# act_order==False, group_size=channelwise
|
||
|
|
("robertgshaw2/zephyr-7b-beta-channelwise-gptq", "main"),
|
||
|
|
# act_order==False, group_size=128
|
||
|
|
("TheBloke/Llama-2-7B-GPTQ", "main"),
|
||
|
|
|
||
|
|
# act_order==True, group_size=128
|
||
|
|
("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "main"),
|
||
|
|
# act_order==True, group_size=64
|
||
|
|
("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-4bit-64g-actorder_True"),
|
||
|
|
# act_order==True, group_size=32
|
||
|
|
("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-4bit-32g-actorder_True"),
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.flaky(reruns=2)
|
||
|
|
@pytest.mark.skipif(gptq_marlin_not_supported,
|
||
|
|
reason="gptq_marlin is not supported on this GPU type.")
|
||
|
|
@pytest.mark.parametrize("model", MODELS)
|
||
|
|
@pytest.mark.parametrize("dtype", ["half"])
|
||
|
|
@pytest.mark.parametrize("max_tokens", [32])
|
||
|
|
@pytest.mark.parametrize("num_logprobs", [5])
|
||
|
|
def test_models(
|
||
|
|
vllm_runner,
|
||
|
|
example_prompts,
|
||
|
|
model,
|
||
|
|
dtype: str,
|
||
|
|
max_tokens: int,
|
||
|
|
num_logprobs: int,
|
||
|
|
) -> None:
|
||
|
|
model_name, revision = model
|
||
|
|
|
||
|
|
# Run marlin.
|
||
|
|
gptq_marlin_model = vllm_runner(model_name=model_name,
|
||
|
|
revision=revision,
|
||
|
|
dtype=dtype,
|
||
|
|
quantization="marlin",
|
||
|
|
max_model_len=MAX_MODEL_LEN,
|
||
|
|
tensor_parallel_size=1,
|
||
|
|
disable_custom_all_reduce=True)
|
||
|
|
|
||
|
|
gptq_marlin_outputs = gptq_marlin_model.generate_greedy_logprobs(
|
||
|
|
example_prompts, max_tokens, num_logprobs)
|
||
|
|
del gptq_marlin_model
|
||
|
|
|
||
|
|
# Run gptq.
|
||
|
|
gptq_model = vllm_runner(model_name=model_name,
|
||
|
|
revision=revision,
|
||
|
|
dtype=dtype,
|
||
|
|
quantization="gptq",
|
||
|
|
max_model_len=MAX_MODEL_LEN,
|
||
|
|
tensor_parallel_size=1,
|
||
|
|
disable_custom_all_reduce=True)
|
||
|
|
gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
|
||
|
|
max_tokens,
|
||
|
|
num_logprobs)
|
||
|
|
del gptq_model
|
||
|
|
|
||
|
|
check_logprobs_close(
|
||
|
|
outputs_0_lst=gptq_outputs,
|
||
|
|
outputs_1_lst=gptq_marlin_outputs,
|
||
|
|
name_0="gptq",
|
||
|
|
name_1="gptq_marlin",
|
||
|
|
)
|