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layernorm_blockwise.cpp 4.82 KB
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Implement layernorm kernel and deviceOp  
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.

#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>

#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"

#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_common_util.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"

using XDataType     = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType  = ck::half_t;
using YDataType     = ck::half_t;
using AccDataType   = float;

constexpr int Rank         = 2;
constexpr int NumReduceDim = 1;

using DeviceInstance = ck::tensor_operation::device::DeviceLayernorm<XDataType,
                                                                     GammaDataType,
                                                                     BetaDataType,
                                                                     AccDataType,
                                                                     YDataType,
                                                                     Rank,
                                                                     NumReduceDim,
                                                                     256, // BlockSize
                                                                     8,   // ClusterM
                                                                     32,  // ClusterK
                                                                     1,   // SliceM
                                                                     8,   // SliceK
                                                                     1,   // SrcVecDim (0=M, 1=K)
                                                                     8,   // SrcScalarPerVector
                                                                     1,   // AffineVecDim (0=M, 1=K)
                                                                     1,   // AffineScalarPerVector
                                                                     8>;  // OutScalarPerVector

int main()
{
    bool time_kernel = false;

    ck::index_t M      = 1024;
    ck::index_t N      = 1024;
    ck::index_t Stride = 1024;

    auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
        return HostTensorDescriptor(std::vector<std::size_t>({len}),
                                    std::vector<std::size_t>({stride}));
    };

    auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) {
        return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                    std::vector<std::size_t>({stride, 1}));
    };

    Tensor<XDataType> x(f_host_tensor_descriptor2d(M, N, Stride));
    Tensor<GammaDataType> gamma(f_host_tensor_descriptor1d(N, 1));
    Tensor<BetaDataType> beta(f_host_tensor_descriptor1d(N, 1));
    Tensor<YDataType> y(f_host_tensor_descriptor2d(M, N, Stride));

    x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0.0, 1.0});
    gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
    beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});

    DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpace());
    DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpace());
    DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpace());
    DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpace());

    x_dev.ToDevice(x.mData.data());

    auto device_instance = DeviceInstance{};
    auto argument_ptr    = device_instance.MakeArgumentPointer({M, N},
                                                            {Stride, 1},
                                                            {0, 1},
                                                            {1},
                                                            1e-4,
                                                            x_dev.GetDeviceBuffer(),
                                                            gamma_dev.GetDeviceBuffer(),
                                                            beta_dev.GetDeviceBuffer(),
                                                            y_dev.GetDeviceBuffer());

    if(!device_instance.IsSupportedArgument(argument_ptr.get()))
    {
        std::cout << "The runtime parameters are not supported" << std::endl;
        return 1;
    };

    auto invoker_ptr = device_instance.MakeInvokerPointer();
    invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});

    bool pass = true;
    return (pass ? 0 : 1);
}