39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
|
import os
|
||
|
|
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from tests.kernels.utils import opcheck
|
||
|
|
from vllm import _custom_ops as ops # noqa: F401
|
||
|
|
|
||
|
|
|
||
|
|
def test_awq_dequantize_opcheck():
|
||
|
|
os.environ["VLLM_USE_TRITON_AWQ"] = "0"
|
||
|
|
qweight = torch.randint(-2000000000,
|
||
|
|
2000000000, (8192, 256),
|
||
|
|
device='cuda',
|
||
|
|
dtype=torch.int32)
|
||
|
|
scales = torch.rand((64, 2048), device='cuda', dtype=torch.float16)
|
||
|
|
zeros = torch.empty((64, 256), device='cuda', dtype=torch.int32)
|
||
|
|
split_k_iters = 0
|
||
|
|
thx = 0
|
||
|
|
thy = 0
|
||
|
|
opcheck(torch.ops._C.awq_dequantize,
|
||
|
|
(qweight, scales, zeros, split_k_iters, thx, thy))
|
||
|
|
|
||
|
|
|
||
|
|
def test_awq_gemm_opcheck():
|
||
|
|
os.environ["VLLM_USE_TRITON_AWQ"] = "0"
|
||
|
|
input = torch.rand((2, 8192), device='cuda', dtype=torch.float16)
|
||
|
|
qweight = torch.randint(-2000000000,
|
||
|
|
2000000000, (8192, 256),
|
||
|
|
device='cuda',
|
||
|
|
dtype=torch.int32)
|
||
|
|
scales = torch.randint(-2000000000,
|
||
|
|
2000000000, (64, 256),
|
||
|
|
device='cuda',
|
||
|
|
dtype=torch.int32)
|
||
|
|
qzeros = torch.empty((64, 2048), device='cuda', dtype=torch.float16)
|
||
|
|
split_k_iters = 8
|
||
|
|
opcheck(torch.ops._C.awq_gemm,
|
||
|
|
(input, qweight, qzeros, scales, split_k_iters))
|