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Reorganize project folders (#6)
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140
example/44_elementwise_permute/elementwise_binary_4D_fp16.cpp
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140
example/44_elementwise_permute/elementwise_binary_4D_fp16.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/element/combined_element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_elementwise.hpp"
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#include "ck/library/utility/algorithm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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using F16 = ck::half_t;
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using F32 = float;
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using ADataType = F16;
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using BDataType = F16;
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using UnaryScale = ck::tensor_operation::element_wise::Scale;
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using UnarySquare = ck::tensor_operation::element_wise::UnarySquare;
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using UnaryScaleSquare =
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ck::tensor_operation::element_wise::UnaryCombinedOp<UnarySquare, UnaryScale>;
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using BinaryAdd = ck::tensor_operation::element_wise::Add;
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// B = alpha * A0 * A0 + beta * A1 * A1
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using BinaryAddUnaryScaleSquare = ck::tensor_operation::element_wise::
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BinaryWithUnaryCombinedOp<BinaryAdd, UnaryScaleSquare, UnaryScaleSquare>;
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using DeviceElementwisePermuteInstance = ck::tensor_operation::device::DeviceElementwiseImpl<
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ck::Tuple<ADataType, ADataType>, // InDataTypeTuple
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ck::Tuple<BDataType>, // OutDataTypeTuple
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BinaryAddUnaryScaleSquare, // ElementwiseOp
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4, // NumDim
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256, // BlockSize
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128, // M0PerBlock
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128, // M1PerBlock
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8, // M0PerThread
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8, // M1PerThread
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ck::Sequence<1, 0>, // ThreadClusterArrangeOrder
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ck::Sequence<8, 8>, // InScalarPerVectorSeq
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ck::Sequence<8>>; // OutScalarPerVectorSeq
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int main()
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{
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bool do_verification = true;
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bool time_kernel = true;
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std::vector<std::size_t> nchw = {16, 128, 32, 64};
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std::array<ck::index_t, 4> ab_lengths;
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std::array<ck::index_t, 4> ab_strides = {static_cast<int>(nchw[1] * nchw[2] * nchw[3]),
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static_cast<int>(nchw[2] * nchw[3]),
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static_cast<int>(nchw[3]),
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1};
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ck::ranges::copy(nchw, ab_lengths.begin());
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std::array<Tensor<ADataType>, 2> as = {Tensor<ADataType>(ab_lengths, ab_strides),
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Tensor<ADataType>(ab_lengths, ab_strides)};
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Tensor<ADataType>& a0 = as[0];
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Tensor<ADataType>& a1 = as[1];
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Tensor<BDataType> b(ab_lengths, ab_strides);
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float alpha = 3.f;
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float beta = 2.f;
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a0.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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a1.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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DeviceMem a0_device_buf(sizeof(ADataType) * a0.mDesc.GetElementSpaceSize());
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DeviceMem a1_device_buf(sizeof(ADataType) * a1.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
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a0_device_buf.ToDevice(a0.mData.data());
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a1_device_buf.ToDevice(a1.mData.data());
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std::array<const void*, 2> inputs = {a0_device_buf.GetDeviceBuffer(),
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a1_device_buf.GetDeviceBuffer()};
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std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
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auto broadcastPermute = DeviceElementwisePermuteInstance{};
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auto unary_scale_op_a0 = UnaryScaleSquare{UnarySquare{}, UnaryScale{alpha}};
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auto unary_scale_op_a1 = UnaryScaleSquare{UnarySquare{}, UnaryScale{beta}};
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auto argument = broadcastPermute.MakeArgumentPointer(
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ab_lengths,
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{ab_strides, ab_strides},
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{ab_strides},
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inputs,
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output,
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BinaryAddUnaryScaleSquare{BinaryAdd{}, unary_scale_op_a0, unary_scale_op_a1});
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if(!broadcastPermute.IsSupportedArgument(argument.get()))
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{
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throw std::runtime_error(
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"The runtime parameters seems not supported by the device instance, exiting!");
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};
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std::cout << "A0 (nchw): " << a0.mDesc << std::endl;
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std::cout << "A1 (nchw): " << a1.mDesc << std::endl;
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std::cout << "B (nchw): " << b.mDesc << std::endl;
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auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
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float ave_time =
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broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(5) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
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std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
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sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
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<< std::endl;
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bool pass = true;
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if(do_verification)
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{
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Tensor<BDataType> host_b(ab_lengths, ab_strides);
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using ReferenceElementwiseInstance = ck::tensor_operation::host::
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ReferenceElementwise<2, ADataType, BDataType, BinaryAddUnaryScaleSquare>;
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auto ref_elementwise = ReferenceElementwiseInstance{};
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auto ref_invoker = ref_elementwise.MakeInvoker();
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auto ref_argument = ref_elementwise.MakeArgument(
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as,
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host_b,
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BinaryAddUnaryScaleSquare{BinaryAdd{}, unary_scale_op_a0, unary_scale_op_a1});
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ref_invoker.Run(ref_argument);
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b_device_buf.FromDevice(b.mData.data());
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pass &=
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ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
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}
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return pass ? 0 : 1;
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}
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