135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
"""Tests for sparse cutlass kernels
|
|
|
|
Run `pytest tests/kernels/test_semi_structured.py`.
|
|
"""
|
|
from typing import Optional, Tuple, Type
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from vllm import _custom_ops as ops
|
|
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
|
|
sparse_cutlass_supported)
|
|
from vllm.platforms import current_platform
|
|
|
|
CUDA_DEVICES = [
|
|
f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
|
|
]
|
|
|
|
capability = current_platform.get_device_capability()
|
|
capability = capability[0] * 10 + capability[1]
|
|
|
|
|
|
def to_fp8(tensor: torch.Tensor):
|
|
finfo = torch.finfo(torch.float8_e4m3fn)
|
|
return torch.round(tensor.clamp(
|
|
min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
|
|
|
|
|
|
def to_int8(tensor: torch.Tensor):
|
|
return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8)
|
|
|
|
|
|
def rand_int8(shape: tuple, device: str = "cuda"):
|
|
return to_int8(torch.rand(shape, device=device) * 255 - 128)
|
|
|
|
|
|
def to_bf16(tensor: torch.Tensor) -> torch.Tensor:
|
|
return tensor.to(dtype=torch.bfloat16)
|
|
|
|
|
|
def to_fp16(tensor: torch.Tensor) -> torch.Tensor:
|
|
return tensor.to(dtype=torch.float16)
|
|
|
|
|
|
def prune_to_2_4(tensor):
|
|
# Reshape tensor to [N, 4] where N is number of groups of 4
|
|
original_shape = tensor.shape
|
|
reshaped = tensor.reshape(-1, 4)
|
|
|
|
# Get indices of top 2 absolute values in each group of 4
|
|
_, indices = torch.topk(torch.abs(reshaped), k=2, dim=1)
|
|
|
|
# Create binary mask
|
|
mask = torch.zeros_like(reshaped)
|
|
mask.scatter_(dim=1,
|
|
index=indices,
|
|
src=torch.ones_like(indices, dtype=mask.dtype))
|
|
|
|
# Apply mask and reshape back
|
|
pruned = reshaped * mask
|
|
|
|
# Turn all -0.0 to 0.0
|
|
pruned[pruned == -0.0] = 0.0
|
|
|
|
return pruned.reshape(original_shape)
|
|
|
|
|
|
def make_rand_sparse_tensors(
|
|
dtype: torch.dtype, m: int, n: int, k: int
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
a = torch.randn((m, k), device='cuda') * 5
|
|
b = torch.randn((n, k), device='cuda').t() * 5
|
|
|
|
b = prune_to_2_4(b.t()).t()
|
|
|
|
if dtype == torch.int8:
|
|
a, b = to_int8(a), to_int8(b)
|
|
elif dtype == torch.float8_e4m3fn:
|
|
a, b = to_fp8(a), to_fp8(b)
|
|
elif dtype == torch.float16:
|
|
a, b = to_fp16(a), to_fp16(b)
|
|
elif dtype == torch.bfloat16:
|
|
a, b = to_bf16(a), to_bf16(b)
|
|
else:
|
|
raise ValueError("unsupported dtype")
|
|
|
|
b_compressed, e = ops.cutlass_sparse_compress(b.t())
|
|
|
|
# Compressed B, Metadata, Original A, B
|
|
return b_compressed, e, a, b
|
|
|
|
|
|
def baseline_scaled_mm(a: torch.Tensor,
|
|
b: torch.Tensor,
|
|
scale_a: torch.Tensor,
|
|
scale_b: torch.Tensor,
|
|
out_dtype: Type[torch.dtype],
|
|
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
output = (scale_a * (scale_b * (torch.mm(
|
|
a.to(dtype=torch.float32), b.to(dtype=torch.float32))))).to(out_dtype)
|
|
if bias is not None:
|
|
output = output + bias
|
|
|
|
return output
|
|
|
|
|
|
@pytest.mark.skipif(not sparse_cutlass_supported(),
|
|
reason="Sparse FP8 is not yet supported on this GPU type.")
|
|
# Test working with a subset of A and B for sparse matmul
|
|
def test_cutlass_sparse_subset():
|
|
|
|
big_m = 1024
|
|
m, n, k = 512, 512, 512
|
|
|
|
# Create tensors
|
|
b_comp, e, whole_a, b = make_rand_sparse_tensors(torch.float8_e4m3fn,
|
|
big_m, n, k)
|
|
a = whole_a[0:m, 0:k]
|
|
scale_a = torch.randn((1, 1), device="cuda", dtype=torch.float32) / 10
|
|
scale_b = torch.randn((1, 1), device="cuda", dtype=torch.float32) / 10
|
|
|
|
out = ops.cutlass_scaled_sparse_mm(a,
|
|
b_comp,
|
|
e,
|
|
scale_a,
|
|
scale_b,
|
|
out_dtype=torch.bfloat16)
|
|
baseline = baseline_scaled_mm(a,
|
|
b,
|
|
scale_a,
|
|
scale_b,
|
|
out_dtype=torch.bfloat16)
|
|
|
|
torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)
|