youngkingdom/vllm
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

[Bugfix][FP8] Fix dynamic FP8 Marlin quantization (#7219)

Browse Source
This commit is contained in:
Michael Goin
2024-08-07 14:23:12 -04:00
committed by GitHub
parent fde47d3bc2
commit 5223199e03
3 changed files with 33 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
tests/quantization/test_fp8.py
View File

@ -9,6 +9,7 @@ from tests.quantization.utils import is_quant_method_supported
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.fp8 import (Fp8KVCacheMethod,
Fp8LinearMethod)
from vllm.platforms import current_platform
MODELS = [
"neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV",
@ -20,7 +21,12 @@ MODELS = [
@pytest.mark.skipif(not is_quant_method_supported("fp8"),
reason="FP8 is not supported on this GPU type.")
@pytest.mark.parametrize("model_id", MODELS)
def test_model_load_and_run(vllm_runner, model_id: str):
@pytest.mark.parametrize("force_marlin", [False, True])
def test_model_load_and_run(vllm_runner, model_id: str, force_marlin: bool,
monkeypatch) -> None:
if force_marlin:
monkeypatch.setenv("VLLM_TEST_FORCE_FP8_MARLIN", "1")
with vllm_runner(model_id) as llm:
# note: this does not test accuracy, just that we can run through
# see lm-eval tests for accuracy
@ -61,7 +67,12 @@ def test_kv_cache_model_load_and_run(vllm_runner, model_id: str):
@pytest.mark.skipif(not is_quant_method_supported("fp8"),
reason="FP8 is not supported on this GPU type.")
@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8"])
def test_load_fp16_model(vllm_runner, kv_cache_dtype: str) -> None:
@pytest.mark.parametrize("force_marlin", [False, True])
def test_load_fp16_model(vllm_runner, kv_cache_dtype: str, force_marlin: bool,
monkeypatch) -> None:
if force_marlin:
monkeypatch.setenv("VLLM_TEST_FORCE_FP8_MARLIN", "1")
with vllm_runner("facebook/opt-125m",
quantization="fp8",
kv_cache_dtype=kv_cache_dtype) as llm:
@ -75,9 +86,9 @@ def test_load_fp16_model(vllm_runner, kv_cache_dtype: str) -> None:
assert attn._k_scale == 1.0
assert attn._v_scale == 1.0
capability = torch.cuda.get_device_capability()
capability = current_platform.get_device_capability()
capability = capability[0] * 10 + capability[1]
if capability >= 89:
if capability >= 89 and not force_marlin:
# For GPUs with hardware support, we keep weights in fp8
assert fc1.weight.dtype == torch.float8_e4m3fn
else: