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[CI/Build] Add is_quant_method_supported to control quantization test configurations (#5253)

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This commit is contained in:
Michael Goin
2024-06-12 12:58:02 -04:00
committed by GitHub
parent 8f89d72090
commit 3dd6853bc8
8 changed files with 30 additions and 68 deletions
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13
tests/models/test_aqlm.py
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@ -4,17 +4,8 @@ Run `pytest tests/models/test_aqlm.py`.
"""
import pytest
import torch
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
aqlm_not_supported = True
if torch.cuda.is_available():
capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
aqlm_not_supported = (capability <
QUANTIZATION_METHODS["aqlm"].get_min_capability())
from tests.quantization.utils import is_quant_method_supported
# In this test we hardcode prompts and generations for the model so we don't
# need to require the AQLM package as a dependency
@ -67,7 +58,7 @@ ground_truth_generations = [
]
@pytest.mark.skipif(aqlm_not_supported,
@pytest.mark.skipif(not is_quant_method_supported("aqlm"),
reason="AQLM is not supported on this GPU type.")
@pytest.mark.parametrize("model", ["ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf"])
@pytest.mark.parametrize("dtype", ["half"])

12
tests/models/test_fp8.py
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@ -8,8 +8,8 @@ import pytest
import torch
from transformers import AutoTokenizer
from tests.quantization.utils import is_quant_method_supported
from vllm import LLM, SamplingParams
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
os.environ["TOKENIZERS_PARALLELISM"] = "true"
@ -67,16 +67,8 @@ EXPECTED_STRS_MAP = {
},
}
fp8_not_supported = True
if torch.cuda.is_available():
capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
fp8_not_supported = (capability <
QUANTIZATION_METHODS["fp8"].get_min_capability())
@pytest.mark.skipif(fp8_not_supported,
@pytest.mark.skipif(not is_quant_method_supported("fp8"),
reason="fp8 is not supported on this GPU type.")
@pytest.mark.parametrize("model_name", MODELS)
@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8"])

13
tests/models/test_gptq_marlin.py
View File

@ -11,9 +11,8 @@ Run `pytest tests/models/test_gptq_marlin.py`.
import os
import pytest
import torch
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from tests.quantization.utils import is_quant_method_supported
from vllm.model_executor.layers.rotary_embedding import _ROPE_DICT
from .utils import check_logprobs_close
@ -22,14 +21,6 @@ os.environ["TOKENIZERS_PARALLELISM"] = "true"
MAX_MODEL_LEN = 1024
gptq_marlin_not_supported = True
if torch.cuda.is_available():
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"),
@ -53,7 +44,7 @@ MODELS = [
@pytest.mark.flaky(reruns=3)
@pytest.mark.skipif(gptq_marlin_not_supported,
@pytest.mark.skipif(not is_quant_method_supported("gptq_marlin"),
reason="gptq_marlin is not supported on this GPU type.")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half", "bfloat16"])

13
tests/models/test_gptq_marlin_24.py
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@ -9,18 +9,9 @@ Run `pytest tests/models/test_marlin_24.py`.
from dataclasses import dataclass
import pytest
import torch
from tests.models.utils import check_logprobs_close
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
marlin_not_supported = True
if torch.cuda.is_available():
capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
marlin_not_supported = (
capability < QUANTIZATION_METHODS["marlin"].get_min_capability())
from tests.quantization.utils import is_quant_method_supported
@dataclass
@ -47,7 +38,7 @@ model_pairs = [
@pytest.mark.flaky(reruns=2)
@pytest.mark.skipif(marlin_not_supported,
@pytest.mark.skipif(not is_quant_method_supported("gptq_marlin_24"),
reason="Marlin24 is not supported on this GPU type.")
@pytest.mark.parametrize("model_pair", model_pairs)
@pytest.mark.parametrize("dtype", ["half"])

13
tests/models/test_marlin.py
View File

@ -13,20 +13,11 @@ Run `pytest tests/models/test_marlin.py`.
from dataclasses import dataclass
import pytest
import torch
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from tests.quantization.utils import is_quant_method_supported
from .utils import check_logprobs_close
marlin_not_supported = True
if torch.cuda.is_available():
capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
marlin_not_supported = (
capability < QUANTIZATION_METHODS["marlin"].get_min_capability())
@dataclass
class ModelPair:
@ -45,7 +36,7 @@ model_pairs = [
@pytest.mark.flaky(reruns=2)
@pytest.mark.skipif(marlin_not_supported,
@pytest.mark.skipif(not is_quant_method_supported("marlin"),
reason="Marlin is not supported on this GPU type.")
@pytest.mark.parametrize("model_pair", model_pairs)
@pytest.mark.parametrize("dtype", ["half"])