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import torch
from flashinfer import (
scaled_fp4_grouped_quantize,
silu_and_mul_scaled_nvfp4_experts_quantize,
)
from sgl_kernel import scaled_fp4_quant, silu_and_mul
skip_condition = torch.cuda.get_device_capability() < (10, 0)
DTYPES = [torch.float16, torch.bfloat16]
SHAPES = [(128, 64), (128, 128), (256, 64), (256, 128)]
PAD_SHAPES = [
(90, 64),
(150, 64),
(128, 48),
(128, 80),
(150, 80),
(90, 48),
(90, 128),
(150, 128),
(150, 48),
(90, 80),
]
FLOAT4_E2M1_MAX = 6.0
FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
# E2M1 to float
# 0111 -> 6
# 0110 -> 4
# 0101 -> 3
# 0100 -> 2
# 0011 -> 1.5
# 0010 -> 1
# 0001 -> 0.5
# 0000 -> 0
E2M1_TO_FLOAT32 = [
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,
]
BLOCK_SIZE = 16
def cast_from_fp4(x, m, n):
# The fp4 values are packed in uint8 as [v_1st | v_2nd]
v_2nd = x & 0xF
v_1st = (x >> 4) & 0xF
c = torch.stack((v_2nd, v_1st), dim=-1)
out = torch.tensor([E2M1_TO_FLOAT32[x] for x in c.flatten()])
out = out.reshape(m, n).to(torch.float32)
return out
def cast_to_fp4(x):
sign = torch.sign(x)
x = torch.abs(x)
x[(x >= 0.0) & (x <= 0.25)] = 0.0
x[(x > 0.25) & (x < 0.75)] = 0.5
x[(x >= 0.75) & (x <= 1.25)] = 1.0
x[(x > 1.25) & (x < 1.75)] = 1.5
x[(x >= 1.75) & (x <= 2.5)] = 2.0
x[(x > 2.5) & (x < 3.5)] = 3.0
x[(x >= 3.5) & (x <= 5.0)] = 4.0
x[x > 5.0] = 6.0
return x * sign
def get_reciprocal(x):
if isinstance(x, torch.Tensor):
return torch.where(x == 0, torch.tensor(0.0, dtype=x.dtype), 1.0 / x)
elif isinstance(x, (float, int)):
return 0.0 if x == 0 else 1.0 / x
else:
raise TypeError("Input must be a float, int, or a torch.Tensor.")
def ref_nvfp4_quant(x, global_scale):
assert global_scale.dtype == torch.float32
assert x.ndim == 2
m, n = x.shape
x = torch.reshape(x, (m, n // BLOCK_SIZE, BLOCK_SIZE))
vec_max = torch.max(torch.abs(x), dim=-1, keepdim=True)[0].to(torch.float32)
scale = global_scale * (vec_max * get_reciprocal(FLOAT4_E2M1_MAX))
scale = scale.to(torch.float8_e4m3fn).to(torch.float32)
output_scale = get_reciprocal(scale * get_reciprocal(global_scale))
scaled_x = x.to(torch.float32) * output_scale
clipped_x = torch.clamp(scaled_x, -6.0, 6.0).reshape(m, n)
return cast_to_fp4(clipped_x), scale.squeeze(-1)
def recover_swizzled_scales(scale, m, n):
rounded_m = ((m + 128 - 1) // 128) * 128
scale_n = n // BLOCK_SIZE
rounded_n = ((scale_n + 4 - 1) // 4) * 4
# Recover the swizzled scaling factor to linear layout
tmp = torch.reshape(scale, (1, rounded_m // 128, rounded_n // 4, 32, 4, 4))
tmp = torch.permute(tmp, (0, 1, 4, 3, 2, 5))
result = torch.reshape(tmp, (rounded_m, rounded_n)).to(torch.float32)
return result[:m, :scale_n]
@pytest.mark.skipif(
skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("shape", SHAPES)
@torch.inference_mode()
def test_quantize_to_fp4(
dtype: torch.dtype,
shape: tuple[int, int],
) -> None:
torch.manual_seed(42)
torch.set_default_device("cuda:0")
m, n = shape
x = torch.randn((m, n), dtype=dtype)
tensor_amax = torch.abs(x).max().to(torch.float32)
global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / tensor_amax
out_ref, scale_ref = ref_nvfp4_quant(x, global_scale)
out, out_scale = scaled_fp4_quant(x, global_scale)
scale_ans = recover_swizzled_scales(out_scale, m, n)
out_ans = cast_from_fp4(out, m, n)
torch.testing.assert_close(out_ans, out_ref)
torch.testing.assert_close(scale_ans, scale_ref)
@pytest.mark.skipif(
skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
)
@pytest.mark.parametrize("pad_shape", PAD_SHAPES)
@torch.inference_mode()
def test_quantize_to_fp4_padded(pad_shape: tuple[int, int]) -> None:
torch.manual_seed(42)
dtype = torch.float16
torch.set_default_device("cuda:0")
m, n = pad_shape
x = torch.randn((m, n), dtype=dtype)
tensor_amax = torch.abs(x).max().to(torch.float32)
global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / tensor_amax
out_ref, scale_ref = ref_nvfp4_quant(x, global_scale)
out, out_scale = scaled_fp4_quant(x, global_scale)
scale_ans = recover_swizzled_scales(out_scale, m, n)
out_ans = cast_from_fp4(out, m, n)
torch.testing.assert_close(out_ans, out_ref)
torch.testing.assert_close(scale_ans, scale_ref)
@pytest.mark.skipif(
skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
)
@pytest.mark.parametrize("shape", [(2, 512, 2048), (2, 100, 128), (2, 128, 96)])
def test_quantize_to_fp4_grouped(shape):
torch.manual_seed(42)
torch.set_default_device("cuda:0")
l, m, k = shape
x = torch.randn((l, m, k), dtype=torch.bfloat16)
max_m = m // 2
assert max_m <= m
mask = torch.randint(1, max_m, (l,), dtype=torch.int32)
tensor_amax = x.abs().amax(dim=(1, 2)).to(torch.float32)
x_sf_global = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / tensor_amax
output, output_scales = scaled_fp4_grouped_quantize(
x,
mask,
x_sf_global,
)
# output in logical (m, k, l), but its physical layout is (l, m, k).
# So permute first to (l, m, k).
output = output.permute(2, 0, 1)
# output_scale in logical (32, 4, rm, 4, rk, l), but its physical layout is (l, rm, rk, 32, 4, 4).
# So permute first to (l, rm, rk, 32, 4, 4).
padded_m = ((m + 128 - 1) // 128) * 128
output_scales = output_scales.permute(5, 2, 4, 0, 1, 3).view(l, padded_m, -1)
for i in range(l):
a_fp4, a_scale_interleaved = scaled_fp4_quant(x[i], x_sf_global[i])
torch.testing.assert_close(a_fp4[: mask[i]], output[i][: mask[i]])
# Recover swizzled scales to linear layout and drop padded values, so
# no extra checks on padding are needed.
scale_ref = recover_swizzled_scales(a_scale_interleaved, m, k)
scale_ans = recover_swizzled_scales(output_scales[i], m, k)
torch.testing.assert_close(scale_ref[: mask[i]], scale_ans[: mask[i]])
@pytest.mark.skipif(
skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
)
@pytest.mark.parametrize("shape", [(32, 100, 2048), (32, 512, 2048), (6, 6144, 2048)])
def test_silu_and_mul_quantize_to_fp4_grouped(shape):
torch.manual_seed(42)
torch.set_default_device("cuda:0")
l, m, k = shape
x = torch.randn((l, m, k * 2), dtype=torch.bfloat16)
max_m = m // 2
assert max_m <= m
mask = torch.randint(1, max_m, (l,), dtype=torch.int32)
ref_y = silu_and_mul(x)
tensor_amax = ref_y.abs().amax(dim=(1, 2)).to(torch.float32)
y_sf_global = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / tensor_amax
ref_output, ref_output_scales = scaled_fp4_grouped_quantize(
ref_y,
mask,
y_sf_global,
)
output, output_scales = silu_and_mul_scaled_nvfp4_experts_quantize(
x,
mask,
y_sf_global,
)
# output in logical (m, k, l), but its physical layout is (l, m, k).
# So permute first to (l, m, k).
output = output.permute(2, 0, 1)
ref_output = ref_output.permute(2, 0, 1)
# output_scale in logical (32, 4, rm, 4, rk, l), but its physical layout is (l, rm, rk, 32, 4, 4).
# So permute first to (l, rm, rk, 32, 4, 4).
padded_m = ((m + 128 - 1) // 128) * 128
output_scales = output_scales.permute(5, 2, 4, 0, 1, 3).view(l, padded_m, -1)
ref_output_scales = ref_output_scales.permute(5, 2, 4, 0, 1, 3).view(
l, padded_m, -1
)
for i in range(l):
torch.testing.assert_close(ref_output[i, : mask[i]], output[i, : mask[i]])
# We need to recover the swizzled scales to linear layout before applying mask slice.
scale_ref = recover_swizzled_scales(ref_output_scales[i], m, k)
scale_ans = recover_swizzled_scales(output_scales[i], m, k)
torch.testing.assert_close(scale_ref[: mask[i]], scale_ans[: mask[i]])
if __name__ == "__main__":
pytest.main([__file__])
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