7caff01a7b
Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com> Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
78 lines
3.3 KiB
Plaintext
78 lines
3.3 KiB
Plaintext
#include <cudaTypedefs.h>
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#include "c3x/scaled_mm_kernels.hpp"
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#include "cuda_utils.h"
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/*
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This file defines quantized GEMM operations using the CUTLASS 3.x API, for
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NVIDIA GPUs with sm90a (Hopper).
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*/
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#if defined ENABLE_SCALED_MM_SM90 && ENABLE_SCALED_MM_SM90
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void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
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torch::Tensor const& b,
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torch::Tensor const& a_scales,
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torch::Tensor const& b_scales,
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std::optional<torch::Tensor> const& bias) {
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TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
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TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
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int M = a.size(0), N = b.size(1), K = a.size(1);
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if ((a_scales.numel() == 1 || a_scales.numel() == a.size(0)) &&
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(b_scales.numel() == 1 || b_scales.numel() == b.size(1))) {
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// Standard per-tensor/per-token/per-channel scaling
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TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
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if (a.dtype() == torch::kFloat8_e4m3fn) {
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vllm::cutlass_scaled_mm_sm90_fp8(c, a, b, a_scales, b_scales, bias);
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} else {
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TORCH_CHECK(a.dtype() == torch::kInt8);
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vllm::cutlass_scaled_mm_sm90_int8(c, a, b, a_scales, b_scales, bias);
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}
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} else {
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using GroupShape = std::array<int64_t, 2>;
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auto make_group_shape = [](torch::Tensor const& x,
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torch::Tensor const& s) -> GroupShape {
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TORCH_CHECK(s.dim() == 2, "cutlass_scaled_mm group scales must be 2D");
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return {cuda_utils::ceil_div(x.size(0), s.size(0)),
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cuda_utils::ceil_div(x.size(1), s.size(1))};
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};
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GroupShape a_scale_group_shape = make_group_shape(a, a_scales);
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GroupShape b_scale_group_shape = make_group_shape(b, b_scales);
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// 1x128 per-token group scales for activations
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// 128x128 blockwise scales for weights
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TORCH_CHECK((a_scale_group_shape == GroupShape{1, 128} &&
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b_scale_group_shape == GroupShape{128, 128} &&
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a.dtype() == torch::kFloat8_e4m3fn &&
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b.dtype() == torch::kFloat8_e4m3fn),
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"cutlass_scaled_mm only supports datatype float8_e4m3fn.\n"
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"a_scale_group_shape must be [1, 128]. Got: [",
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a_scale_group_shape[0], ", ", a_scale_group_shape[1],
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"]\n"
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"b_scale_group_shape must be [128, 128]. Got: [",
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b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
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TORCH_CHECK(!bias, "Bias not yet supported blockwise scaled_mm");
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vllm::cutlass_scaled_mm_blockwise_sm90_fp8(c, a, b, a_scales, b_scales);
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}
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}
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void cutlass_scaled_mm_azp_sm90(torch::Tensor& out, torch::Tensor const& a,
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torch::Tensor const& b,
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torch::Tensor const& a_scales,
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torch::Tensor const& b_scales,
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torch::Tensor const& azp_adj,
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std::optional<torch::Tensor> const& azp,
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std::optional<torch::Tensor> const& bias) {
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TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
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TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
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vllm::cutlass_scaled_mm_azp_sm90_int8(out, a, b, a_scales, b_scales, azp_adj,
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azp, bias);
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}
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#endif
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