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[Bugfix][Kernel] Fix per-token/per-channel quantization for Hopper scaled mm #12696

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Merged
merged 2 commits into from
Feb 3, 2025
Merged

[Bugfix][Kernel] Fix per-token/per-channel quantization for Hopper scaled mm #12696

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fd8f32f
Fix per-token/per-channel quantization for Hopper scaled mm
tlrmchlsmth Feb 3, 2025
be7888d
Merge branch 'main' into fix_cutlass_group_checks
tlrmchlsmth Feb 3, 2025
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59 changes: 24 additions & 35 deletions csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -16,55 +16,44 @@ void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
TORCH_CHECK(b_scales.dtype() == torch::kFloat32);

using GroupShape = std::array<int64_t, 2>;

int M = a.size(0), N = b.size(1), K = a.size(1);

GroupShape a_scale_group_shape = [&, &s = a_scales]() -> GroupShape {
if (s.numel() == 1) return {M, K}; // tensor-wise
if (s.dim() == 2)
return {ceil_div(a.size(0), s.size(0)), ceil_div(a.size(1), s.size(1))};
TORCH_CHECK(false, "Unsupported scale shape for scale_a");
}();

GroupShape b_scale_group_shape = [&, &s = b_scales]() -> GroupShape {
if (s.numel() == 1) return {K, N}; // tensor-wise
if (s.dim() == 2)
return {ceil_div(b.size(0), s.size(0)), ceil_div(b.size(1), s.size(1))};
TORCH_CHECK(false, "Unsupported scale shape for scale_b");
}();

if ((a_scale_group_shape == GroupShape{M, K} ||
a_scale_group_shape == GroupShape{1, K}) &&
(b_scale_group_shape == GroupShape{K, N} ||
b_scale_group_shape == GroupShape{K, 1})) {
// "standard per-tensor/per-token/per-channel" scaling
if ((a_scales.numel() == 1 || a_scales.numel() == a.size(0)) &&
(b_scales.numel() == 1 || b_scales.numel() == b.size(1))) {
Comment on lines -37 to +22
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This is the main change

// Standard per-tensor/per-token/per-channel scaling
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (a.dtype() == torch::kFloat8_e4m3fn) {
vllm::cutlass_scaled_mm_sm90_fp8(c, a, b, a_scales, b_scales, bias);
} else {
TORCH_CHECK(a.dtype() == torch::kInt8);
vllm::cutlass_scaled_mm_sm90_int8(c, a, b, a_scales, b_scales, bias);
}
} else if (a_scale_group_shape == GroupShape{1, 128} &&
b_scale_group_shape == GroupShape{128, 128}) {
} else {
using GroupShape = std::array<int64_t, 2>;
auto make_group_shape = [](torch::Tensor const& x,
torch::Tensor const& s) -> GroupShape {
TORCH_CHECK(s.dim() == 2, "cutlass_scaled_mm group scales must be 2D");
return {ceil_div(x.size(0), s.size(0)), ceil_div(x.size(1), s.size(1))};
};

GroupShape a_scale_group_shape = make_group_shape(a, a_scales);
GroupShape b_scale_group_shape = make_group_shape(b, b_scales);

// 1x128 per-token group scales for activations
// 128x128 blockwise scales for weights
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn &&
b.dtype() == torch::kFloat8_e4m3fn,
"Currently only FP8 is supported for A group shape 1x128 and "
"B group shape 128x128");
TORCH_CHECK(!bias, "Bias not yet supported blockwise scaled_mm");

vllm::cutlass_scaled_mm_blockwise_sm90_fp8(c, a, b, a_scales, b_scales);
} else {
TORCH_CHECK(false,
"Unsupported scale group shapes for CUTLASS 3.x GEMM.\n "
"a_scale_group_shape must be [1, 128], got: [",
TORCH_CHECK((a_scale_group_shape == GroupShape{1, 128} &&
b_scale_group_shape == GroupShape{128, 128} &&
a.dtype() == torch::kFloat8_e4m3fn &&
b.dtype() == torch::kFloat8_e4m3fn),
"cutlass_scaled_mm only supports datatype float8_e4m3fn.\n"
"a_scale_group_shape must be [1, 128]. Got: [",
a_scale_group_shape[0], ", ", a_scale_group_shape[1],
"]\n"
"b_scale_group_shape must be [128, 128], got: [",
"b_scale_group_shape must be [128, 128]. Got: [",
b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
TORCH_CHECK(!bias, "Bias not yet supported blockwise scaled_mm");

vllm::cutlass_scaled_mm_blockwise_sm90_fp8(c, a, b, a_scales, b_scales);
}
}

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