9239bf718e
Signed-off-by: ElizaWszola <eliza@neuralmagic.com> Signed-off-by: ElizaWszola <ewszola@redhat.com> Co-authored-by: Lucas Wilkinson <wilkinson.lucas@gmail.com>
451 lines
18 KiB
C++
451 lines
18 KiB
C++
#pragma once
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#include "cutlass_extensions/epilogue/broadcast_load_epilogue_c3x.hpp"
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#include "cutlass_extensions/epilogue/broadcast_load_epilogue_array_c3x.hpp"
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/*
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This file defines custom epilogues for fusing channel scales, token scales,
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bias, and activation zero-points onto a GEMM operation using the
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CUTLASS 3.x API, for NVIDIA GPUs with sm90a (Hopper) or later.
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Epilogues must contain a public type named EVTCompute of type Sm90EVT,
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as well as a static prepare_args function that constructs an
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EVTCompute::Arguments struct.
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*/
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namespace vllm::c3x {
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using namespace cute;
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template <typename T>
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struct identity {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs) const { return lhs; }
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};
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template <typename ElementAcc, typename ElementD, typename TileShape>
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struct TrivialEpilogue {
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private:
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using Accum = cutlass::epilogue::fusion::Sm90AccFetch;
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using Compute = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::epilogue::thread::Identity, ElementD, ElementAcc,
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cutlass::FloatRoundStyle::round_to_nearest>;
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public:
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using EVTCompute = cutlass::epilogue::fusion::Sm90EVT<Compute, Accum>;
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using ArgumentType = typename EVTCompute::Arguments;
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template <typename... Args>
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static ArgumentType prepare_args(Args... args) {
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return {};
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}
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};
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/*
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* This class provides the common load descriptors for the
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* ScaledEpilogue[...] classes
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*/
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template <typename ElementAcc, typename ElementD, typename TileShape>
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struct ScaledEpilogueBase {
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protected:
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using Accum = cutlass::epilogue::fusion::Sm90AccFetch;
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template <typename T>
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using ColOrScalarLoad = cutlass::epilogue::fusion::Sm90ColOrScalarBroadcast<
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0 /*Stages*/, TileShape, T, Stride<Int<1>, Int<0>, Int<0>>>;
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template <typename T>
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using RowOrScalarLoad = cutlass::epilogue::fusion::Sm90RowOrScalarBroadcast<
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0 /*Stages*/, TileShape, T, Stride<Int<0>, Int<1>, Int<0>>>;
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// Don't want to support nullptr by default
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template <typename T, bool EnableNullPtr = false>
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using ColLoad = cutlass::epilogue::fusion::Sm90ColBroadcast<
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0 /*Stages*/, TileShape, T, T, Stride<Int<1>, Int<0>, Int<0>>,
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128 / sizeof_bits_v<T>, EnableNullPtr>;
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// Don't want to support nullptr by default
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template <typename T, bool EnableNullPtr = false>
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using RowLoad = cutlass::epilogue::fusion::Sm90RowBroadcast<
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0 /*Stages*/, TileShape, T, T, Stride<Int<0>, Int<1>, Int<0>>,
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128 / sizeof_bits_v<T>, EnableNullPtr>;
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template <typename T>
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using ColOrScalarLoadArray =
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cutlass::epilogue::fusion::Sm90ColOrScalarBroadcastArray<
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0 /*Stages*/, TileShape, T, Stride<Int<1>, Int<0>, Int<0>>>;
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template <typename T>
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using RowOrScalarLoadArray =
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cutlass::epilogue::fusion::Sm90RowOrScalarBroadcastArray<
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0 /*Stages*/, TileShape, T, Stride<Int<0>, Int<1>, Int<0>>>;
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// This utility function constructs the arguments for the load descriptors
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// from a tensor. It can handle both row and column, as well as row/column or
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// scalar cases.
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template <typename Descriptor, typename T>
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static auto args_from_tensor(torch::Tensor const& tensor) {
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using Arguments = typename Descriptor::Arguments;
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auto* data_ptr = static_cast<T*>(tensor.data_ptr());
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if constexpr (std::is_same_v<Descriptor, ColOrScalarLoad<T>> ||
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std::is_same_v<Descriptor, RowOrScalarLoad<T>>) {
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return Arguments{data_ptr, tensor.numel() != 1};
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} else {
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static_assert(!std::is_same_v<Descriptor, ColLoad<T, true>> &&
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!std::is_same_v<Descriptor, RowLoad<T, true>>);
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return Arguments{data_ptr};
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}
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}
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// This overload handles the case where there might not be a tensor, in which
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// case a nullptr is passed and a constant (0) is used.
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template <typename Descriptor, typename T>
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static auto args_from_tensor(std::optional<torch::Tensor> const& tensor) {
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using Arguments = typename Descriptor::Arguments;
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auto* data_ptr = tensor ? static_cast<T*>(tensor->data_ptr()) : nullptr;
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static_assert(std::is_same_v<Descriptor, ColLoad<T, true>> ||
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std::is_same_v<Descriptor, RowLoad<T, true>>);
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return Arguments{data_ptr};
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}
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template <typename Descriptor, typename T>
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static auto args_from_tensor(const T* const* data_ptr, bool do_broadcast) {
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using Arguments = typename Descriptor::Arguments;
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static_assert(std::is_same_v<Descriptor, ColOrScalarLoadArray<T>> ||
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std::is_same_v<Descriptor, RowOrScalarLoadArray<T>>);
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return Arguments{data_ptr, do_broadcast};
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}
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};
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/*
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This epilogue function defines a quantized GEMM operation similar to
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torch.scaled_mm_.
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A and B may be both either int8 or fp8_e4m3. A can be
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quantized per-tensor or per-row. B can be quantized per-tensor or per-column.
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Any combination of per-tensor and per-row or column is supported.
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A and B must have symmetric quantization (zero point == 0).
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So the GEMM operation is D = (a_scales * A) (b_scales * B), where the
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scales are applied elementwise with numpy-style broadcasting.
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ScaleA and ScaleB define the epilogue functions that apply the scales for
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the A and B operands respectively. These scales may be either per-tensor or
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per row or column.
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*/
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template <typename ElementAcc, typename ElementD, typename TileShape>
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struct ScaledEpilogue
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: private ScaledEpilogueBase<ElementAcc, ElementD, TileShape> {
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private:
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using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, TileShape>;
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using Accum = typename SUPER::Accum;
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using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
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using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
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using Compute0 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, float, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTCompute0 =
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cutlass::epilogue::fusion::Sm90EVT<Compute0, ScaleB, Accum>;
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using Compute1 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, ElementD, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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public:
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using EVTCompute =
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cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0>;
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using ArgumentType = typename EVTCompute::Arguments;
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static ArgumentType prepare_args(torch::Tensor const& a_scales,
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torch::Tensor const& b_scales) {
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auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
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auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
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typename EVTCompute0::Arguments evt0_args{b_args, {}, {}};
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return ArgumentType{a_args, evt0_args, {}};
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}
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};
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/*
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* This epilogue performs the same operation as ScaledEpilogue, but adds a bias.
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* This bias can also be used in the per-tensor azp case, where the activation
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* zero point (azp) is used to compute an azp correction term,
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* which is folded into the bias.
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*
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* The bias tensor must be per-output channel.
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* ScaleA and ScaleB can be per-tensor or per-token/per-channel.
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*/
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template <typename ElementAcc, typename ElementD, typename TileShape>
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struct ScaledEpilogueBias
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: private ScaledEpilogueBase<ElementAcc, ElementD, TileShape> {
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private:
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using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, TileShape>;
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using Accum = typename SUPER::Accum;
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using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
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using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
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using Bias = typename SUPER::template RowLoad<ElementD>;
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using Compute0 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, float, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTCompute0 =
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cutlass::epilogue::fusion::Sm90EVT<Compute0, ScaleB, Accum>;
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using Compute1 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiply_add, ElementD, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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public:
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using EVTCompute =
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cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0, Bias>;
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using ArgumentType = typename EVTCompute::Arguments;
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static ArgumentType prepare_args(torch::Tensor const& a_scales,
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torch::Tensor const& b_scales,
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torch::Tensor const& bias) {
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auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
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auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
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auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);
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typename EVTCompute0::Arguments evt0_args{b_args, {}, {}};
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return ArgumentType{a_args, evt0_args, bias_args, {}};
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}
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};
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/*
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* This epilogue performs the same operation as ScaledEpilogueBias, but the
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* bias is a column vector instead of a row vector. Useful e.g. if we are
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* computing a GEMM via C^T += B^T A^T. This happens in the 2:4 sparse kernels.
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*/
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template <typename ElementAcc, typename ElementD, typename TileShape>
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struct ScaledEpilogueColumnBias
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: private ScaledEpilogueBase<ElementAcc, ElementD, TileShape> {
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private:
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using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, TileShape>;
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using Accum = typename SUPER::Accum;
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using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
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using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
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using Bias = typename SUPER::template ColLoad<ElementD>;
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using Compute0 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, float, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTCompute0 =
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cutlass::epilogue::fusion::Sm90EVT<Compute0, ScaleB, Accum>;
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using Compute1 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiply_add, ElementD, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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public:
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using EVTCompute =
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cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0, Bias>;
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using ArgumentType = typename EVTCompute::Arguments;
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static ArgumentType prepare_args(torch::Tensor const& a_scales,
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torch::Tensor const& b_scales,
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torch::Tensor const& bias) {
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auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
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auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
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auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);
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typename EVTCompute0::Arguments evt0_args{b_args, {}, {}};
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return ArgumentType{a_args, evt0_args, bias_args, {}};
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}
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};
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/*
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* This epilogue directly supports per-tensor azp in int32 form.
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* As opposed to the per-token epilogue below, this epilogue only has an azp_adj
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* term, which should already be multiplied with the scalar azp.
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* The azp_adj term is a 1D tensor of shape (1,n), computed as azp * J @ B.
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*
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* This epilogue also supports bias, which remains per-channel.
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*/
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template <typename ElementAcc, typename ElementD, typename TileShape>
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struct ScaledEpilogueBiasAzp
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: private ScaledEpilogueBase<ElementAcc, ElementD, TileShape> {
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private:
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using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, TileShape>;
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using Accum = typename SUPER::Accum;
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using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
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using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
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using Bias = typename SUPER::template RowLoad<ElementD, true>;
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// This is the full AZP term, azp * J @ B, shape (1,n)
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using AzpWithAdj = typename SUPER::template RowLoad<int32_t>;
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// Compute float(accum - azp_adj), both operands are int32_t
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using ComputeAzp = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::minus, float, int32_t,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTComputeAzp =
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cutlass::epilogue::fusion::Sm90EVT<ComputeAzp, Accum, AzpWithAdj>;
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using ComputeScaleB = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, float, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTComputeScaleB =
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cutlass::epilogue::fusion::Sm90EVT<ComputeScaleB, ScaleB, EVTComputeAzp>;
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using ComputeScaleBiasA = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiply_add, ElementD, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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public:
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using EVTCompute =
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cutlass::epilogue::fusion::Sm90EVT<ComputeScaleBiasA, ScaleA,
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EVTComputeScaleB, Bias>;
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using ArgumentType = typename EVTCompute::Arguments;
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static ArgumentType prepare_args(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& bias) {
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auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
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auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
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auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);
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auto azp_adj_args =
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SUPER::template args_from_tensor<AzpWithAdj, int32_t>(azp_adj);
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typename EVTComputeAzp::Arguments evt_azp_args{{}, azp_adj_args, {}};
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typename EVTComputeScaleB::Arguments evt_scale_b_args{
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b_args, evt_azp_args, {}};
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return ArgumentType{a_args, evt_scale_b_args, bias_args, {}};
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}
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};
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/*
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* This epilogue supports per-token azp by computing and applying
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* the correction term using a rank-1 update. If the term were materialized,
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* it would require O(m*n) space, and this way it only requires O(m+n) space.
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* The azp term is a 1D tensor of shape (m,1), and represents the unscaled zero
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* point for each row of A.
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* The azp_adj term is a 1D tensor of shape (1,n), computed as J @ B.
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*
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* This epilogue also supports bias, which remains per-channel.
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*/
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template <typename ElementAcc, typename ElementD, typename TileShape>
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struct ScaledEpilogueBiasAzpToken
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: private ScaledEpilogueBase<ElementAcc, ElementD, TileShape> {
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private:
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using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, TileShape>;
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using Accum = typename SUPER::Accum;
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using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
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using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
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using Bias = typename SUPER::template RowLoad<ElementD, true>;
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// Per-token azp term, shape (m,1)
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using Azp = typename SUPER::template ColLoad<int32_t>;
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// This is the AZP adjustment term, J @ B, shape (1,n)
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using AzpAdj = typename SUPER::template RowLoad<int32_t>;
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// Compute azp * azp_adj
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using ComputeAzp = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, int32_t, int32_t,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTComputeAzp =
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cutlass::epilogue::fusion::Sm90EVT<ComputeAzp, Azp, AzpAdj>;
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// Compute float(accum - azp*azp_adj), all operands are int32_t
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using ComputeAcc = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::minus, float, int32_t,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTComputeAcc =
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cutlass::epilogue::fusion::Sm90EVT<ComputeAcc, Accum, EVTComputeAzp>;
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using ComputeScaleB = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, float, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTComputeScaleB =
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cutlass::epilogue::fusion::Sm90EVT<ComputeScaleB, ScaleB, EVTComputeAcc>;
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using ComputeScaleBiasA = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiply_add, ElementD, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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public:
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using EVTCompute =
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cutlass::epilogue::fusion::Sm90EVT<ComputeScaleBiasA, ScaleA,
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EVTComputeScaleB, Bias>;
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using ArgumentType = typename EVTCompute::Arguments;
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static ArgumentType prepare_args(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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torch::Tensor const& azp,
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std::optional<torch::Tensor> const& bias) {
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auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
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auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
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auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);
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auto azp_args = SUPER::template args_from_tensor<Azp, int32_t>(azp);
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auto azp_adj_args =
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SUPER::template args_from_tensor<AzpAdj, int32_t>(azp_adj);
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typename EVTComputeAzp::Arguments evt_azp_args{azp_args, azp_adj_args, {}};
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typename EVTComputeAcc::Arguments evt_acc_args{{}, evt_azp_args, {}};
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typename EVTComputeScaleB::Arguments evt_scale_b_args{
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b_args, evt_acc_args, {}};
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return ArgumentType{a_args, evt_scale_b_args, bias_args, {}};
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}
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};
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/*
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This epilogue works like ScaledEpilogue, but ScaleA and ScaleB are pointers
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to arrays containing different scales used in group gemm. The number of
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pointers in ScaleA and the number of pointers in ScaleB are equal to the
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group size.
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*/
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template <typename ElementAcc, typename ElementD, typename EpilogueDescriptor>
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struct ScaledEpilogueArray
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: private ScaledEpilogueBase<ElementAcc, ElementD, EpilogueDescriptor> {
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private:
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using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, EpilogueDescriptor>;
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using Accum = typename SUPER::Accum;
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using ScaleA = typename SUPER::template ColOrScalarLoadArray<float>;
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using ScaleB = typename SUPER::template RowOrScalarLoadArray<float>;
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using Compute0 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, float, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTCompute0 =
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cutlass::epilogue::fusion::Sm90EVT<Compute0, ScaleB, Accum>;
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using Compute1 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, ElementD, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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public:
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using EVTCompute =
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cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0>;
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using ArgumentType = typename EVTCompute::Arguments;
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using ScaleAArray = typename SUPER::template ColOrScalarLoadArray<float>;
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using ScaleBArray = typename SUPER::template RowOrScalarLoadArray<float>;
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static ArgumentType prepare_args(float const* const* a_scales_ptr,
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float const* const* b_scales_ptr,
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bool a_col_broadcast, bool b_row_broadcast) {
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auto a_args = SUPER::template args_from_tensor<ScaleAArray, float>(
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a_scales_ptr, a_col_broadcast);
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auto b_args = SUPER::template args_from_tensor<ScaleBArray, float>(
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b_scales_ptr, b_row_broadcast);
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typename EVTCompute0::Arguments evt0_args{b_args, {}, {}};
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return ArgumentType{a_args, evt0_args, {}};
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}
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};
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}; // namespace vllm::c3x
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