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0a2073b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | import torch
from kernels.benchmark import Benchmark
_original_allclose = torch.allclose
def _bf16_max_ulp(input: torch.Tensor, other: torch.Tensor) -> int:
got_bits = input.detach().cpu().view(torch.int16).to(torch.int32) & 0xFFFF
exp_bits = other.detach().cpu().view(torch.int16).to(torch.int32) & 0xFFFF
got_ordered = torch.where((got_bits & 0x8000) != 0, 0x8000 - (got_bits & 0x7FFF), got_bits)
exp_ordered = torch.where((exp_bits & 0x8000) != 0, 0x8000 - (exp_bits & 0x7FFF), exp_bits)
return int((got_ordered - exp_ordered).abs().max().item())
def _flashrt_allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False):
if input.dtype == torch.bfloat16 and other.dtype == torch.bfloat16:
return _bf16_max_ulp(input, other) <= 5
return _original_allclose(input, other, rtol=rtol, atol=atol, equal_nan=equal_nan)
torch.allclose = _flashrt_allclose
DECODE_SHAPES = [
("k4096_n1024", 4096, 1024),
("k4096_n4096", 4096, 4096),
("k4096_n12288", 4096, 12288),
("k12288_n1024", 12288, 1024),
("k12288_n4096", 12288, 4096),
("k12288_n12288", 12288, 12288),
]
def _swizzled_bytes(rows: int, cols: int) -> int:
n_blocks = cols // 16
return ((rows + 127) // 128) * ((n_blocks + 3) // 4) * 512
def _swizzle_constant_scale(rows: int, cols: int, value: int) -> torch.Tensor:
return torch.full((_swizzled_bytes(rows, cols),), value, dtype=torch.uint8)
def _reference_swizzle(scales: torch.Tensor) -> torch.Tensor:
rows, n_blocks = scales.shape
n_col_super = (n_blocks + 3) // 4
src = scales.cpu()
out = torch.zeros(
((rows + 127) // 128) * n_col_super * 512,
dtype=torch.uint8,
)
for row in range(rows):
rb = row // 128
ri = row % 128
for block in range(n_blocks):
cb = block // 4
ci = block % 4
super_idx = rb * n_col_super + cb
inner_off = (ri % 32) * 16 + (ri // 32) * 4 + ci
out[super_idx * 512 + inner_off] = src[row, block]
return out
def _ue4m3_to_float(byte: int) -> float:
sign = -1.0 if (byte & 0x80) else 1.0
exp = (byte >> 3) & 0x0F
mant = byte & 0x07
if exp == 0:
return sign * (mant / 8.0) * (2.0 ** -6)
if exp == 15 and mant == 7:
return 0.0
return sign * (1.0 + mant / 8.0) * (2.0 ** (exp - 7))
def _ue4m3_lut() -> torch.Tensor:
return torch.tensor([_ue4m3_to_float(i) for i in range(256)], dtype=torch.float32)
def _fp4_codebook() -> torch.Tensor:
return torch.tensor(
[
0.0,
0.5,
1.0,
1.5,
2.0,
3.0,
4.0,
6.0,
-0.0,
-0.5,
-1.0,
-1.5,
-2.0,
-3.0,
-4.0,
-6.0,
],
dtype=torch.float32,
)
def _unpack_fp4(packed: torch.Tensor) -> torch.Tensor:
codebook = _fp4_codebook().to(packed.device)
lo = packed & 0x0F
hi = packed >> 4
out = torch.empty(
(packed.shape[0], packed.shape[1] * 2),
device=packed.device,
dtype=torch.float32,
)
out[:, 0::2] = codebook[lo.long()]
out[:, 1::2] = codebook[hi.long()]
return out
def _reference_smallm(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa_linear: torch.Tensor,
sfb_linear: torch.Tensor,
K: int,
alpha: float,
chunk_rows: int = 256,
) -> torch.Tensor:
device = b_packed.device
N = b_packed.shape[0]
lut = _ue4m3_lut().to(device)
a = _unpack_fp4(a_packed.reshape(1, -1)).reshape(K)
a_scale = lut[sfa_linear.reshape(-1).to(device).long()].repeat_interleave(16)
a = a * a_scale
sfb_linear = sfb_linear.to(device)
out = torch.empty((N,), device=device, dtype=torch.bfloat16)
for start in range(0, N, chunk_rows):
end = min(start + chunk_rows, N)
b = _unpack_fp4(b_packed[start:end])
b_scale = lut[sfb_linear[start:end].long()].repeat_interleave(16, dim=1)
expected = (b * b_scale * a.reshape(1, K)).sum(dim=1) * alpha
out[start:end] = expected.to(torch.bfloat16)
return out
class Nvfp4W4A4DecodeMatvecBenchmark(Benchmark):
seed = 23
def _setup_shape(self, K: int, N: int) -> None:
torch.manual_seed(600 + K + N)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(600 + K + N)
self.K = K
self.N = N
self.alpha = 0.5
self.a_packed = torch.randint(
0, 256, (K // 2,), device=self.device, dtype=torch.uint8
)
self.b_packed = torch.randint(
0, 256, (N, K // 2), device=self.device, dtype=torch.uint8
)
self.sfa_linear = torch.randint(0, 0x78, (1, K // 16), dtype=torch.uint8)
self.sfb_linear = torch.randint(0, 0x78, (N, K // 16), dtype=torch.uint8)
self.sfa = _reference_swizzle(self.sfa_linear).to(self.device)
self.sfb = _reference_swizzle(self.sfb_linear).to(self.device)
self.out = torch.empty((N,), device=self.device, dtype=torch.bfloat16)
def _benchmark(self) -> None:
self.kernel.nvfp4_w4a4_decode_matvec_bf16out(
self.a_packed,
self.b_packed,
self.sfa,
self.sfb,
alpha=self.alpha,
out=self.out,
)
def _reference(self) -> torch.Tensor:
return _reference_smallm(
self.a_packed,
self.b_packed,
self.sfa_linear,
self.sfb_linear,
self.K,
self.alpha,
)
def _register_shapes() -> None:
for label, K, N in DECODE_SHAPES:
def setup(self, K=K, N=N) -> None:
self._setup_shape(K, N)
def benchmark(self) -> None:
self._benchmark()
def verify(self) -> torch.Tensor:
return self._reference()
setattr(Nvfp4W4A4DecodeMatvecBenchmark, f"setup_{label}", setup)
setattr(Nvfp4W4A4DecodeMatvecBenchmark, f"benchmark_{label}", benchmark)
setattr(Nvfp4W4A4DecodeMatvecBenchmark, f"verify_{label}", verify)
_register_shapes()
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