mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
Grouped conv bwd data with fp16 input and bf8fp8 comp (#962)
* Add f8 bf8 gemm example * Add element-wise ops * Add intrinsics * Update reference calculation * Add an additional type option for xdlops gemm * Fix build process * Add bf8 to buffer addressing * Update blockwise op, split typeA and typeB * Update for compatibility * Uppdate naming to f8->fp8 * Update naming * Format * Update naming (#937) * Add a client example * Add computetypes to device and gridwise ops * Add instances, update instance factory * Format * Fix a flag * Add ckProfiler mode * Fix typos * Add an example * Add bf8 generator * add bf8 mfma; fixed type_convert for bf8 * move verfication ahead of timing * Update reference calculation * Fix reference * Narrow down float init range * Fix bf8 bf8 mfma * Add bf8 @ fp8 mfma * Update example * Update instances * Update profiler api * Update for compatibility * Format * Remove extra example * Clean up * workaround convert * added instance of f16_bf8f8, and client example * fixed mfma selector * format --------- Co-authored-by: Rostyslav Geyyer <rosty.geyyer@amd.com> Co-authored-by: Rostyslav Geyyer <46627076+geyyer@users.noreply.github.com> Co-authored-by: Jing Zhang <jizha@amd.com>
This commit is contained in:
zjing14
@@ -1,2 +0,0 @@
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add_executable(client_grouped_conv2d_bwd_data grouped_conv2d_bwd_data.cpp)
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target_link_libraries(client_grouped_conv2d_bwd_data PRIVATE composable_kernel::device_operations)
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8
client_example/10_grouped_convnd_bwd_data/CMakeLists.txt
Normal file
8
client_example/10_grouped_convnd_bwd_data/CMakeLists.txt
Normal file
@@ -0,0 +1,8 @@
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add_executable(client_grouped_conv2d_bwd_data grouped_conv2d_bwd_data.cpp)
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target_link_libraries(client_grouped_conv2d_bwd_data PRIVATE composable_kernel::device_operations)
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add_executable(client_grouped_conv3d_bwd_data grouped_conv3d_bwd_data.cpp)
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target_link_libraries(client_grouped_conv3d_bwd_data PRIVATE composable_kernel::device_operations)
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add_executable(client_grouped_conv3d_bwd_data_input_fp16_comp_bf8f8 grouped_conv3d_bwd_data_input_fp16_comp_bf8f8.cpp)
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target_link_libraries(client_grouped_conv3d_bwd_data_input_fp16_comp_bf8f8 PRIVATE composable_kernel::device_operations)
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@@ -0,0 +1,205 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, 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_backward_data.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.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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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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
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static constexpr ck::index_t NumDimSpatial = 3;
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static constexpr ck::index_t G = 2;
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static constexpr ck::index_t N = 16;
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static constexpr ck::index_t K = 16;
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static constexpr ck::index_t C = 16;
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static constexpr ck::index_t Z = 3;
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static constexpr ck::index_t Y = 3;
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static constexpr ck::index_t X = 3;
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static constexpr ck::index_t Di = 14;
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static constexpr ck::index_t Hi = 14;
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static constexpr ck::index_t Wi = 14;
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static constexpr ck::index_t Do = 14;
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static constexpr ck::index_t Ho = 14;
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static constexpr ck::index_t Wo = 14;
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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 main()
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{
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std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, C, Di, Hi, Wi};
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std::array<ck::index_t, NumDimSpatial + 3> 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, NumDimSpatial + 3> wei_lengths{G, K, C, Z, Y, X};
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std::array<ck::index_t, NumDimSpatial + 3> 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, NumDimSpatial + 3> out_lengths{G, N, K, Do, Ho, Wo};
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std::array<ck::index_t, NumDimSpatial + 3> 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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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) * G * N * Di * Hi * Wi * C);
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SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Z * Y * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * G * N * Do * Ho * Wo * K);
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD<NumDimSpatial,
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OutLayout,
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WeiLayout,
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ck::Tuple<>,
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InLayout,
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OutDataType,
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WeiDataType,
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ck::Tuple<>,
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InDataType,
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PassThrough,
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PassThrough,
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PassThrough>;
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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 = op_ptr->MakeArgumentPointer(out.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{},
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in.GetDeviceBuffer(),
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out_lengths,
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out_strides,
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wei_lengths,
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wei_strides,
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{},
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{},
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in_lengths,
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in_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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PassThrough{});
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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 = std::size_t(2) * G * N * K * C * Do * Ho * Wo * Y * X;
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std::size_t num_bytes = sizeof(InDataType) * G * N * Di * Hi * Wi * C +
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sizeof(WeiDataType) * G * K * Z * Y * X * C +
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sizeof(OutDataType) * G * N * Do * Ho * Wo * 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 = op_ptr->MakeArgumentPointer(out.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{},
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in.GetDeviceBuffer(),
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out_lengths,
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out_strides,
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wei_lengths,
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wei_strides,
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{},
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{},
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in_lengths,
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in_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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PassThrough{});
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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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}
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@@ -0,0 +1,207 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, 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_backward_data.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.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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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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
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static constexpr ck::index_t NumDimSpatial = 3;
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static constexpr ck::index_t G = 2;
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static constexpr ck::index_t N = 16;
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static constexpr ck::index_t K = 16;
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static constexpr ck::index_t C = 16;
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static constexpr ck::index_t Z = 3;
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static constexpr ck::index_t Y = 3;
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static constexpr ck::index_t X = 3;
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static constexpr ck::index_t Di = 14;
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static constexpr ck::index_t Hi = 14;
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static constexpr ck::index_t Wi = 14;
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static constexpr ck::index_t Do = 14;
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static constexpr ck::index_t Ho = 14;
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static constexpr ck::index_t Wo = 14;
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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 main()
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{
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std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, C, Di, Hi, Wi};
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std::array<ck::index_t, NumDimSpatial + 3> 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, NumDimSpatial + 3> wei_lengths{G, K, C, Z, Y, X};
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std::array<ck::index_t, NumDimSpatial + 3> 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, NumDimSpatial + 3> out_lengths{G, N, K, Do, Ho, Wo};
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std::array<ck::index_t, NumDimSpatial + 3> 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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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) * G * N * Di * Hi * Wi * C);
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SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Z * Y * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * G * N * Do * Ho * Wo * K);
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD<NumDimSpatial,
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OutLayout,
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WeiLayout,
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ck::Tuple<>,
|
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InLayout,
|
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OutDataType,
|
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WeiDataType,
|
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ck::Tuple<>,
|
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InDataType,
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PassThrough,
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PassThrough,
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PassThrough,
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ck::bf8_t,
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ck::f8_t>;
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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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|
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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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|
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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 = op_ptr->MakeArgumentPointer(out.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
|
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{},
|
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in.GetDeviceBuffer(),
|
||||
out_lengths,
|
||||
out_strides,
|
||||
wei_lengths,
|
||||
wei_strides,
|
||||
{},
|
||||
{},
|
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in_lengths,
|
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in_strides,
|
||||
filter_strides,
|
||||
filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
PassThrough{},
|
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PassThrough{},
|
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PassThrough{});
|
||||
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()))
|
||||
{
|
||||
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
|
||||
|
||||
std::size_t flop = std::size_t(2) * G * N * K * C * Do * Ho * Wo * Y * X;
|
||||
std::size_t num_bytes = sizeof(InDataType) * G * N * Di * Hi * Wi * C +
|
||||
sizeof(WeiDataType) * G * K * Z * Y * X * C +
|
||||
sizeof(OutDataType) * G * N * Do * Ho * Wo * K;
|
||||
|
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
|
||||
float gb_per_sec = num_bytes / 1.E6 / avg_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
|
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_op_id = i;
|
||||
best_op_name = op_name;
|
||||
best_avg_time = avg_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
best_tflops = tflops;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cerr << op_name << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
if(best_op_id < 0)
|
||||
{
|
||||
std::cerr << "no suitable instance" << std::endl;
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
|
||||
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
|
||||
|
||||
// run the best intance
|
||||
{
|
||||
auto& op_ptr = op_ptrs[best_op_id];
|
||||
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
|
||||
<< std::endl;
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(out.GetDeviceBuffer(),
|
||||
wei.GetDeviceBuffer(),
|
||||
{},
|
||||
in.GetDeviceBuffer(),
|
||||
out_lengths,
|
||||
out_strides,
|
||||
wei_lengths,
|
||||
wei_strides,
|
||||
{},
|
||||
{},
|
||||
in_lengths,
|
||||
in_strides,
|
||||
filter_strides,
|
||||
filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
PassThrough{},
|
||||
PassThrough{},
|
||||
PassThrough{});
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
|
||||
}
|
||||
|
||||
std::cout << "Done" << std::endl;
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user