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https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 17:26:00 +00:00
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This commit is contained in:
carlushuang
@@ -1,11 +0,0 @@
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add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp32 grouped_conv_fwd_scaleadd_scaleadd_relu_fp32.cpp)
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target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp32 PRIVATE composable_kernel::device_conv_operations)
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add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp16 grouped_conv_fwd_scaleadd_scaleadd_relu_fp16.cpp)
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target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_fp16 PRIVATE composable_kernel::device_conv_operations)
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add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_bf16 grouped_conv_fwd_scaleadd_scaleadd_relu_bf16.cpp)
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target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_bf16 PRIVATE composable_kernel::device_conv_operations)
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add_executable(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_int8 grouped_conv_fwd_scaleadd_scaleadd_relu_int8.cpp)
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target_link_libraries(client_grouped_convnd_fwd_scaleadd_scaleadd_relu_int8 PRIVATE composable_kernel::device_conv_operations)
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@@ -1,216 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <iterator>
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#include <numeric>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_scaleadd_scaleadd_relu.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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using InLayout = ck::tensor_layout::convolution::NDHWGC;
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using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
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using OutLayout = ck::tensor_layout::convolution::NDHWGK;
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using BiasLayout = ck::tensor_layout::convolution::G_K;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ScaleAddScaleAddRelu = ck::tensor_operation::element_wise::ScaleAddScaleAddRelu;
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static constexpr ck::index_t NumDimSpatial = 3;
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static constexpr ck::index_t G = 32;
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static constexpr ck::index_t N = 64; // batch size
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static constexpr ck::index_t K = 64; // output channel
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static constexpr ck::index_t C = 32; // input channel (per group)
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static constexpr ck::index_t Z = 3; // filter D
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static constexpr ck::index_t Y = 3; // filter H
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static constexpr ck::index_t X = 3; // filter W
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static constexpr ck::index_t Di = 14; // input D
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static constexpr ck::index_t Hi = 14; // input H
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static constexpr ck::index_t Wi = 14; // input W
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static constexpr ck::index_t Do = 14; // output D
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static constexpr ck::index_t Ho = 14; // output H
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static constexpr ck::index_t Wo = 14; // output W
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int execute_conv_fwd_scaleadd_scaleadd_relu()
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{
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// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space.
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// However, CK's API only accepts lengths and strides with order of GNCDHW/GKCZYX/GNKDHW.
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// Hence, we need to adjust the order of strides.
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std::array<ck::index_t, 6> in_lengths{G, N, C, Di, Hi, Wi};
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std::array<ck::index_t, 6> in_strides{
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C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
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std::array<ck::index_t, 6> wei_lengths{G, K, C, Z, Y, X};
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std::array<ck::index_t, 6> wei_strides{
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K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
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std::array<ck::index_t, 6> out_lengths{G, N, K, Do, Ho, Wo};
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std::array<ck::index_t, 6> out_strides{
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K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
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// Logical broadcast bias (we have to pass bias lengths in the same format as output - GNKDHW)
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std::array<ck::index_t, 6> bias_lengths{G, 1, K, 1, 1, 1};
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std::array<ck::index_t, 6> bias_strides{K, 0, 1, 0, 0, 0};
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std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1, 1};
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std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1, 1};
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std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1, 1};
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std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1, 1};
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SimpleDeviceMem in(sizeof(InDataType) * N * Di * Hi * Wi * G * C);
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SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Z * Y * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * N * Do * Ho * Wo * G * K);
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SimpleDeviceMem d0(sizeof(std::tuple_element_t<0, DDataTypes>) * N * Do * Ho * Wo * G * K);
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SimpleDeviceMem d1(sizeof(std::tuple_element_t<1, DDataTypes>) * G * K);
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<
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NumDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<OutLayout, BiasLayout>,
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OutLayout,
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InDataType,
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WeiDataType,
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ck::Tuple<std::tuple_element_t<0, DDataTypes>, std::tuple_element_t<1, DDataTypes>>,
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OutDataType,
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PassThrough,
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PassThrough,
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ScaleAddScaleAddRelu>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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int best_op_id = -1;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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float best_tflops = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr =
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op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{d0.GetDeviceBuffer(), d1.GetDeviceBuffer()},
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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wei_lengths,
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wei_strides,
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{out_lengths, bias_lengths},
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{out_strides, bias_strides},
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out_lengths,
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out_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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input_right_pads,
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PassThrough{},
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PassThrough{},
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ScaleAddScaleAddRelu{2.f, 2.f});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t flop =
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std::size_t(2) * G * N * K * C * Ho * Wo * Y * X + 2 * N * Ho * Wo * G * K;
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std::size_t num_bytes =
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sizeof(InDataType) * N * Hi * Wi * G * C + sizeof(WeiDataType) * G * K * Y * X * C +
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(sizeof(OutDataType) + sizeof(std::tuple_element_t<0, DDataTypes>) +
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sizeof(std::tuple_element_t<1, DDataTypes>)) *
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N * Ho * Wo * G * K;
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_op_id = i;
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best_op_name = op_name;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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best_tflops = tflops;
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}
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}
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else
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{
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std::cerr << op_name << " does not support this problem" << std::endl;
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}
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}
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if(best_op_id < 0)
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{
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std::cerr << "no suitable instance" << std::endl;
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return EXIT_FAILURE;
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}
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std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
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<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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// run the best intance
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr =
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op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{d0.GetDeviceBuffer(), d1.GetDeviceBuffer()},
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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wei_lengths,
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wei_strides,
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{out_lengths, bias_lengths},
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{out_strides, bias_strides},
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out_lengths,
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out_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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input_right_pads,
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PassThrough{},
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PassThrough{},
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ScaleAddScaleAddRelu{2.f, 2.f});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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@@ -1,18 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <tuple>
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/tuple.hpp"
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using InDataType = ck::bhalf_t;
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using WeiDataType = ck::bhalf_t;
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using OutDataType = ck::bhalf_t;
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// Use std tuple instead of ck tuple to avoid clang
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// implicit instantiation of undefined template error.
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using DDataTypes = std::tuple<ck::bhalf_t, ck::bhalf_t>;
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#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
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int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }
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@@ -1,18 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <tuple>
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/tuple.hpp"
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using InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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using OutDataType = ck::half_t;
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// Use std tuple instead of ck tuple to avoid clang
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// implicit instantiation of undefined template error.
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using DDataTypes = std::tuple<ck::half_t, ck::half_t>;
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#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
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int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }
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@@ -1,18 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <tuple>
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/tuple.hpp"
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using InDataType = float;
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using WeiDataType = float;
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using OutDataType = float;
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// Use std tuple instead of ck tuple to avoid clang
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// implicit instantiation of undefined template error.
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using DDataTypes = std::tuple<float, float>;
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#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
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int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }
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@@ -1,18 +0,0 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <tuple>
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/tuple.hpp"
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using InDataType = int8_t;
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using WeiDataType = int8_t;
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using OutDataType = int8_t;
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// Use std tuple instead of ck tuple to avoid clang
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// implicit instantiation of undefined template error.
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using DDataTypes = std::tuple<float, float>;
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#include "grouped_conv_fwd_scaleadd_scaleadd_relu.inc"
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int main() { return execute_conv_fwd_scaleadd_scaleadd_relu(); }
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