151 lines
5.1 KiB
Python
151 lines
5.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
import pytest
|
|
import torch
|
|
|
|
from vllm import _custom_ops as ops
|
|
from vllm.platforms import current_platform
|
|
from vllm.scalar_type import scalar_types
|
|
|
|
if not current_platform.has_device_capability(100):
|
|
pytest.skip(reason="Nvfp4 Requires compute capability of 10 or above.",
|
|
allow_module_level=True)
|
|
|
|
DTYPES = [torch.float16, torch.bfloat16]
|
|
# m, n, k
|
|
SHAPES = [(128, 128, 64), (128, 128, 128), (256, 128, 64), (128, 256, 128)]
|
|
PAD_SHAPES = [(150, 128, 64), (128, 128, 96)]
|
|
SHAPES.extend(PAD_SHAPES)
|
|
|
|
SEEDS = [42]
|
|
CUDA_DEVICES = ['cuda:0']
|
|
|
|
FLOAT4_E2M1_MAX = scalar_types.float4_e2m1fn.max()
|
|
FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
|
|
|
|
kE2M1ToFloatArray = [
|
|
0.,
|
|
0.5,
|
|
1.,
|
|
1.5,
|
|
2.,
|
|
3.,
|
|
4.,
|
|
6.,
|
|
]
|
|
|
|
|
|
def e2m1_to_fp32(int4_value):
|
|
signBit = (int4_value & 0x8)
|
|
int4_absValue = int4_value & 0x7
|
|
float_result = kE2M1ToFloatArray[int4_absValue]
|
|
if (signBit):
|
|
float_result = -float_result
|
|
return float_result
|
|
|
|
|
|
def break_fp4_bytes(a, dtype):
|
|
assert (a.dtype == torch.uint8)
|
|
m, n = a.shape
|
|
a = a.flatten()
|
|
# Get upper 4 bits
|
|
highHalfByte = (a & 0xF0) >> 4
|
|
# Get lower 4 bits
|
|
lowHalfByte = a & 0x0F
|
|
fH = torch.tensor([e2m1_to_fp32(x) for x in highHalfByte]).to(a.device)
|
|
fL = torch.tensor([e2m1_to_fp32(x) for x in lowHalfByte]).to(a.device)
|
|
# [0xAB, 0xCD] -> [0xB, 0xA, 0xD, 0xC]
|
|
out = torch.stack((fL, fH), dim=-1).reshape(m, n * 2)
|
|
return out
|
|
|
|
|
|
def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
|
|
sf_m, sf_k = a_sf_swizzled.shape
|
|
m_tiles = (m + 128 - 1) // 128
|
|
f = block_size * 4
|
|
k_tiles = (k + f - 1) // f
|
|
tmp = torch.reshape(a_sf_swizzled, (1, m_tiles, k_tiles, 32, 4, 4))
|
|
tmp = torch.permute(tmp, (0, 1, 4, 3, 2, 5))
|
|
out = tmp.reshape(m_tiles * 128, k_tiles * f // block_size)
|
|
return out[0:m, 0:k]
|
|
|
|
|
|
def dequantize_to_dtype(tensor_fp4,
|
|
tensor_sf,
|
|
global_scale,
|
|
dtype,
|
|
device,
|
|
block_size=16):
|
|
"""Dequantize the fp4 tensor back to high precision."""
|
|
# Two fp4 values are packed into one uint8.
|
|
assert tensor_fp4.dtype == torch.uint8
|
|
m, packed_k = tensor_fp4.shape
|
|
k = packed_k * 2
|
|
tensor_f32 = break_fp4_bytes(tensor_fp4, dtype)
|
|
tensor_f32 = tensor_f32.reshape(m, k // block_size, block_size)
|
|
tensor_sf = tensor_sf.view(torch.float8_e4m3fn)
|
|
tensor_sf = convert_swizzled_to_linear(tensor_sf, m, k, block_size)
|
|
tensor_sf_dtype = tensor_sf.to(torch.float32) / global_scale
|
|
|
|
# scale the tensor
|
|
out = (tensor_f32 * tensor_sf_dtype.unsqueeze(-1)).reshape(m, k)
|
|
return out
|
|
|
|
|
|
def get_ref_results(a_fp4, b_fp4, a_sf, b_sf, a_global_scale, b_global_scale,
|
|
m, n, dtype, block_size, device):
|
|
_, m_k = a_fp4.shape
|
|
_, n_k = b_fp4.shape
|
|
assert (m_k == n_k)
|
|
a_in_dtype = dequantize_to_dtype(a_fp4,
|
|
a_sf,
|
|
a_global_scale,
|
|
dtype=dtype,
|
|
device=device,
|
|
block_size=block_size)
|
|
b_in_dtype = dequantize_to_dtype(b_fp4,
|
|
b_sf,
|
|
b_global_scale,
|
|
dtype=dtype,
|
|
device=device,
|
|
block_size=block_size)
|
|
return torch.matmul(a_in_dtype, b_in_dtype.t())
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("shape", SHAPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
@pytest.mark.parametrize("device", CUDA_DEVICES)
|
|
@torch.inference_mode()
|
|
def test_nvfp4_gemm(
|
|
dtype: torch.dtype,
|
|
shape: tuple[int, int, int],
|
|
seed: int,
|
|
device: str,
|
|
) -> None:
|
|
current_platform.seed_everything(seed)
|
|
m, n, packed_k = shape
|
|
k = packed_k * 2
|
|
block_size = 16
|
|
a_dtype = torch.randn((m, k), dtype=dtype, device=device)
|
|
b_dtype = torch.randn((n, k), dtype=dtype, device=device)
|
|
|
|
a_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) /
|
|
torch.amax(a_dtype.flatten(), dim=-1)).to(torch.float32)
|
|
b_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) /
|
|
torch.amax(b_dtype.flatten(), dim=-1)).to(torch.float32)
|
|
alpha = 1. / (a_global_scale * b_global_scale)
|
|
a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(a_dtype, a_global_scale)
|
|
b_fp4, b_scale_interleaved = ops.scaled_fp4_quant(b_dtype, b_global_scale)
|
|
|
|
expected_out = get_ref_results(a_fp4, b_fp4, a_scale_interleaved,
|
|
b_scale_interleaved, a_global_scale,
|
|
b_global_scale, m, n, dtype, block_size,
|
|
device)
|
|
out = ops.cutlass_scaled_fp4_mm(a_fp4, b_fp4, a_scale_interleaved,
|
|
b_scale_interleaved, alpha, dtype)
|
|
|
|
torch.testing.assert_close(out,
|
|
expected_out.to(dtype=dtype),
|
|
atol=1e-1,
|
|
rtol=1e-1)
|