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https://github.com/ROCm/composable_kernel.git
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Move grouped conv fwd client examples (#1299)
* Move grouped conv fwd client examples * Update existing examples * Format
This commit is contained in:
Rostyslav Geyyer
@@ -4,4 +4,22 @@ if(GPU_TARGETS MATCHES "gfx9")
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add_executable(client_grouped_conv1d_fwd grouped_conv1d_fwd.cpp)
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target_link_libraries(client_grouped_conv1d_fwd PRIVATE composable_kernel::device_conv_operations)
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endif()
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if((DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
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add_executable(client_grouped_conv3d_fwd_fp8 grouped_conv3d_fwd_fp8.cpp)
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target_link_libraries(client_grouped_conv3d_fwd_fp8 PRIVATE composable_kernel::device_conv_operations)
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endif()
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if((DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
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add_executable(client_grouped_conv3d_fwd_bf8 grouped_conv3d_fwd_bf8.cpp)
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target_link_libraries(client_grouped_conv3d_fwd_bf8 PRIVATE composable_kernel::device_conv_operations)
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endif()
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if((DTYPES MATCHES "fp8" AND DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
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add_executable(client_grouped_conv3d_fwd_fp8_bf8 grouped_conv3d_fwd_fp8_bf8.cpp)
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target_link_libraries(client_grouped_conv3d_fwd_fp8_bf8 PRIVATE composable_kernel::device_conv_operations)
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add_executable(client_grouped_conv3d_fwd_bf8_fp8 grouped_conv3d_fwd_bf8_fp8.cpp)
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target_link_libraries(client_grouped_conv3d_fwd_bf8_fp8 PRIVATE composable_kernel::device_conv_operations)
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endif()
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endif()
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304
client_example/07_grouped_convnd_fwd/common.hpp
Normal file
304
client_example/07_grouped_convnd_fwd/common.hpp
Normal file
@@ -0,0 +1,304 @@
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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 <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 <string>
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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.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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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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template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetFlops(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths,
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const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
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{
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// 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
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ck::index_t G = weights_lengths[0];
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ck::index_t N = output_lengths[1];
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ck::index_t K = weights_lengths[1];
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ck::index_t C = weights_lengths[2];
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return static_cast<std::size_t>(2) * G * N * K * C *
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std::accumulate(std::next(std::begin(output_lengths), NumNonSpatialDim),
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std::end(output_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>()) *
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std::accumulate(std::next(std::begin(weights_lengths), NumNonSpatialDim),
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std::end(weights_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>());
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}
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template <typename InDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetInputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& input_lengths)
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{
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// sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
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return sizeof(InDataType) * std::accumulate(std::begin(input_lengths),
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std::end(input_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>());
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}
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template <typename WeiDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetWeightByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
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{
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// sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
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return sizeof(WeiDataType) * std::accumulate(std::begin(weights_lengths),
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std::end(weights_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<>());
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}
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template <typename OutDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
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std::size_t
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GetOutputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths)
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{
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// sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
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return sizeof(OutDataType) * std::accumulate(std::begin(output_lengths),
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std::end(output_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<std::size_t>());
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}
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template <ck::index_t NumDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout,
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ck::index_t NumNonSpatialDim = 3,
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typename AComputeType = InDataType,
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typename BComputeType = AComputeType>
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bool run_grouped_conv_fwd(std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_lengths,
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_lengths,
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_lengths)
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{
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std::size_t in_mem_size = GetInputByte<InDataType, NumDimSpatial>(in_lengths);
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std::size_t wei_mem_size = GetWeightByte<WeiDataType, NumDimSpatial>(wei_lengths);
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std::size_t out_mem_size = GetOutputByte<OutDataType, NumDimSpatial>(out_lengths);
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SimpleDeviceMem in(in_mem_size);
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SimpleDeviceMem wei(wei_mem_size);
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SimpleDeviceMem out(out_mem_size);
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_strides;
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_strides;
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std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_strides;
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in_strides.fill(0);
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wei_strides.fill(0);
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out_strides.fill(0);
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in_strides.back() = 1;
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wei_strides.back() = 1;
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out_strides.back() = 1;
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std::partial_sum(rbegin(in_lengths),
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std::prev(rend(in_lengths)),
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std::next(rbegin(in_strides)),
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std::multiplies<>{});
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std::partial_sum(rbegin(wei_lengths),
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std::prev(rend(wei_lengths)),
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std::next(rbegin(wei_strides)),
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std::multiplies<>{});
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std::partial_sum(rbegin(out_lengths),
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std::prev(rend(out_lengths)),
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std::next(rbegin(out_strides)),
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std::multiplies<>{});
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// transpose NDHWGC/KZYXGC/NDHWGK to GNDHWC/GKZYXC/GNDHWK to GNCDHW/GKCZYX/GNKDHW
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std::rotate(std::next(rbegin(in_lengths)), std::next(rbegin(in_lengths), 2), rend(in_lengths));
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std::rotate(rbegin(in_lengths),
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std::next(rbegin(in_lengths)),
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std::next(rbegin(in_lengths), NumDimSpatial + 1));
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std::rotate(std::next(rbegin(in_strides)), std::next(rbegin(in_strides), 2), rend(in_strides));
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std::rotate(rbegin(in_strides),
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std::next(rbegin(in_strides)),
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std::next(rbegin(in_strides), NumDimSpatial + 1));
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std::rotate(rbegin(wei_lengths),
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std::next(rbegin(wei_lengths)),
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std::next(rbegin(wei_lengths), NumDimSpatial + 1));
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std::rotate(rbegin(wei_strides),
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std::next(rbegin(wei_strides)),
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std::next(rbegin(wei_strides), NumDimSpatial + 1));
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std::rotate(
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std::next(rbegin(out_lengths)), std::next(rbegin(out_lengths), 2), rend(out_lengths));
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std::rotate(rbegin(out_lengths),
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std::next(rbegin(out_lengths)),
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std::next(rbegin(out_lengths), NumDimSpatial + 1));
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std::rotate(
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std::next(rbegin(out_strides)), std::next(rbegin(out_strides), 2), rend(out_strides));
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std::rotate(rbegin(out_strides),
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std::next(rbegin(out_strides)),
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std::next(rbegin(out_strides), NumDimSpatial + 1));
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std::array<ck::index_t, NumDimSpatial> conv_filter_strides;
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std::array<ck::index_t, NumDimSpatial> conv_filter_dilations;
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std::array<ck::index_t, NumDimSpatial> input_left_pads;
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std::array<ck::index_t, NumDimSpatial> input_right_pads;
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conv_filter_strides.fill(1);
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conv_filter_dilations.fill(1);
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input_left_pads.fill(1);
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input_right_pads.fill(1);
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std::size_t flop = GetFlops<NumDimSpatial>(out_lengths, wei_lengths);
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std::size_t num_bytes = in_mem_size + wei_mem_size + out_mem_size;
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<>,
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OutLayout,
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InDataType,
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WeiDataType,
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ck::Tuple<>,
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OutDataType,
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PassThrough,
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PassThrough,
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PassThrough,
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AComputeType,
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BComputeType>;
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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(
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in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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std::array<const void*, 0>{},
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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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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
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out_lengths,
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out_strides,
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conv_filter_strides,
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conv_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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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 false;
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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(
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in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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std::array<const void*, 0>{},
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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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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
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std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
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out_lengths,
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out_strides,
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conv_filter_strides,
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conv_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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return true;
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}
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@@ -1,17 +1,10 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
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|
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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 "common.hpp"
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|
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.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 InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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@@ -31,199 +24,16 @@ static constexpr ck::index_t X = 3;
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static constexpr ck::index_t Wi = 28;
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static constexpr ck::index_t Wo = 28;
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|
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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_{}
|
||||
{
|
||||
(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, Wi, C};
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std::array<ck::index_t, NumDimSpatial + 3> in_strides{0, 0, 0, 1};
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std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, X, C};
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std::array<ck::index_t, NumDimSpatial + 3> wei_strides{0, 0, 0, 1};
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std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, Wo, K};
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std::array<ck::index_t, NumDimSpatial + 3> out_strides{0, 0, 0, 1};
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std::partial_sum(rbegin(in_lengths),
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std::prev(rend(in_lengths)),
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||||
std::next(rbegin(in_strides)),
|
||||
std::multiplies<>{});
|
||||
std::partial_sum(rbegin(wei_lengths),
|
||||
std::prev(rend(wei_lengths)),
|
||||
std::next(rbegin(wei_strides)),
|
||||
std::multiplies<>{});
|
||||
std::partial_sum(rbegin(out_lengths),
|
||||
std::prev(rend(out_lengths)),
|
||||
std::next(rbegin(out_strides)),
|
||||
std::multiplies<>{});
|
||||
|
||||
// transpose GNWC/GKXC/GNWK to GNCW/GKCX/GNCW
|
||||
std::rotate(rbegin(in_lengths),
|
||||
std::next(rbegin(in_lengths)),
|
||||
std::next(rbegin(in_lengths), NumDimSpatial + 1));
|
||||
std::rotate(rbegin(in_strides),
|
||||
std::next(rbegin(in_strides)),
|
||||
std::next(rbegin(in_strides), NumDimSpatial + 1));
|
||||
std::rotate(rbegin(wei_lengths),
|
||||
std::next(rbegin(wei_lengths)),
|
||||
std::next(rbegin(wei_lengths), NumDimSpatial + 1));
|
||||
std::rotate(rbegin(wei_strides),
|
||||
std::next(rbegin(wei_strides)),
|
||||
std::next(rbegin(wei_strides), NumDimSpatial + 1));
|
||||
std::rotate(rbegin(out_lengths),
|
||||
std::next(rbegin(out_lengths)),
|
||||
std::next(rbegin(out_lengths), NumDimSpatial + 1));
|
||||
std::rotate(rbegin(out_strides),
|
||||
std::next(rbegin(out_strides)),
|
||||
std::next(rbegin(out_strides), NumDimSpatial + 1));
|
||||
|
||||
std::array<ck::index_t, NumDimSpatial> filter_strides{1};
|
||||
std::array<ck::index_t, NumDimSpatial> filter_dilations{1};
|
||||
std::array<ck::index_t, NumDimSpatial> input_left_pads{1};
|
||||
std::array<ck::index_t, NumDimSpatial> input_right_pads{1};
|
||||
|
||||
SimpleDeviceMem in(sizeof(InDataType) * G * N * Wi * C);
|
||||
SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * X * C);
|
||||
SimpleDeviceMem out(sizeof(OutDataType) * G * N * Wo * K);
|
||||
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
ck::Tuple<>,
|
||||
OutLayout,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
ck::Tuple<>,
|
||||
OutDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>;
|
||||
|
||||
// get device op instances
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
std::string best_op_name;
|
||||
int best_op_id = -1;
|
||||
float best_avg_time = std::numeric_limits<float>::max();
|
||||
float best_gb_per_sec = 0;
|
||||
float best_tflops = 0;
|
||||
|
||||
// profile device operation instances
|
||||
std::cout << "Run all instances and do timing" << std::endl;
|
||||
|
||||
for(int i = 0; i < op_ptrs.size(); ++i)
|
||||
{
|
||||
auto& op_ptr = op_ptrs[i];
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
|
||||
wei.GetDeviceBuffer(),
|
||||
{},
|
||||
out.GetDeviceBuffer(),
|
||||
in_lengths,
|
||||
in_strides,
|
||||
wei_lengths,
|
||||
wei_strides,
|
||||
{},
|
||||
{},
|
||||
out_lengths,
|
||||
out_strides,
|
||||
filter_strides,
|
||||
filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
PassThrough{},
|
||||
PassThrough{},
|
||||
PassThrough{});
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
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 * Wo * X;
|
||||
std::size_t num_bytes = sizeof(InDataType) * G * N * Wi * C +
|
||||
sizeof(WeiDataType) * G * K * X * C +
|
||||
sizeof(OutDataType) * G * N * Wo * K;
|
||||
|
||||
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, "
|
||||
<< 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(in.GetDeviceBuffer(),
|
||||
wei.GetDeviceBuffer(),
|
||||
{},
|
||||
out.GetDeviceBuffer(),
|
||||
in_lengths,
|
||||
in_strides,
|
||||
wei_lengths,
|
||||
wei_strides,
|
||||
{},
|
||||
{},
|
||||
out_lengths,
|
||||
out_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;
|
||||
}
|
||||
return run_grouped_conv_fwd<NumDimSpatial,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
OutDataType,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout,
|
||||
3>({N, Wi, G, C}, {G, K, X, C}, {N, Wo, G, K})
|
||||
? EXIT_SUCCESS
|
||||
: EXIT_FAILURE;
|
||||
}
|
||||
|
||||
@@ -1,17 +1,10 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <cstdlib>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <iterator>
|
||||
#include <numeric>
|
||||
#include <vector>
|
||||
#include "common.hpp"
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
using InDataType = ck::half_t;
|
||||
using WeiDataType = ck::half_t;
|
||||
@@ -34,167 +27,16 @@ static constexpr ck::index_t Wi = 28; // input W
|
||||
static constexpr ck::index_t Ho = 28; // output H
|
||||
static constexpr ck::index_t Wo = 28; // output W
|
||||
|
||||
struct SimpleDeviceMem
|
||||
{
|
||||
SimpleDeviceMem() = delete;
|
||||
|
||||
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
|
||||
{
|
||||
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
|
||||
}
|
||||
|
||||
void* GetDeviceBuffer() { return p_mem_; }
|
||||
|
||||
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
|
||||
|
||||
void* p_mem_;
|
||||
};
|
||||
|
||||
int main()
|
||||
{
|
||||
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
|
||||
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
|
||||
// Hence, we need to adjust the order of stride
|
||||
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
|
||||
std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
|
||||
std::array<ck::index_t, 5> wei_lengths{G, K, C, Y, X};
|
||||
std::array<ck::index_t, 5> wei_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
|
||||
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
|
||||
std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
|
||||
|
||||
std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
|
||||
std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
|
||||
std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1};
|
||||
std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
|
||||
|
||||
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
|
||||
SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
|
||||
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
|
||||
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
ck::Tuple<>,
|
||||
OutLayout,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
ck::Tuple<>,
|
||||
OutDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>;
|
||||
|
||||
// get device op instances
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
std::string best_op_name;
|
||||
int best_op_id = -1;
|
||||
float best_avg_time = std::numeric_limits<float>::max();
|
||||
float best_gb_per_sec = 0;
|
||||
float best_tflops = 0;
|
||||
|
||||
// profile device operation instances
|
||||
std::cout << "Run all instances and do timing" << std::endl;
|
||||
|
||||
for(int i = 0; i < op_ptrs.size(); ++i)
|
||||
{
|
||||
auto& op_ptr = op_ptrs[i];
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
|
||||
wei.GetDeviceBuffer(),
|
||||
{},
|
||||
out.GetDeviceBuffer(),
|
||||
in_lengths,
|
||||
in_strides,
|
||||
wei_lengths,
|
||||
wei_strides,
|
||||
{},
|
||||
{},
|
||||
out_lengths,
|
||||
out_strides,
|
||||
filter_strides,
|
||||
filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
PassThrough{},
|
||||
PassThrough{},
|
||||
PassThrough{});
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
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 * Ho * Wo * Y * X;
|
||||
std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * G * C +
|
||||
sizeof(WeiDataType) * G * K * Y * X * C +
|
||||
sizeof(OutDataType) * N * Ho * Wo * G * K;
|
||||
|
||||
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, "
|
||||
<< 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(in.GetDeviceBuffer(),
|
||||
wei.GetDeviceBuffer(),
|
||||
{},
|
||||
out.GetDeviceBuffer(),
|
||||
in_lengths,
|
||||
in_strides,
|
||||
wei_lengths,
|
||||
wei_strides,
|
||||
{},
|
||||
{},
|
||||
out_lengths,
|
||||
out_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;
|
||||
}
|
||||
return run_grouped_conv_fwd<NumDimSpatial,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
OutDataType,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout,
|
||||
3>({N, Hi, Wi, G, C}, {G, K, Y, X, C}, {N, Ho, Wo, G, K})
|
||||
? EXIT_SUCCESS
|
||||
: EXIT_FAILURE;
|
||||
}
|
||||
|
||||
@@ -7,22 +7,6 @@ endif()
|
||||
if((DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
|
||||
add_executable(client_conv3d_fwd_fp16_comp_fp8 conv3d_fwd_fp16_comp_fp8.cpp)
|
||||
target_link_libraries(client_conv3d_fwd_fp16_comp_fp8 PRIVATE composable_kernel::device_conv_operations)
|
||||
|
||||
add_executable(client_conv3d_fwd_fp8 conv3d_fwd_fp8.cpp)
|
||||
target_link_libraries(client_conv3d_fwd_fp8 PRIVATE composable_kernel::device_conv_operations)
|
||||
endif()
|
||||
|
||||
if((DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
|
||||
add_executable(client_conv3d_fwd_bf8 conv3d_fwd_bf8.cpp)
|
||||
target_link_libraries(client_conv3d_fwd_bf8 PRIVATE composable_kernel::device_conv_operations)
|
||||
endif()
|
||||
|
||||
if((DTYPES MATCHES "fp8" AND DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
|
||||
add_executable(client_conv3d_fwd_fp8_bf8 conv3d_fwd_fp8_bf8.cpp)
|
||||
target_link_libraries(client_conv3d_fwd_fp8_bf8 PRIVATE composable_kernel::device_conv_operations)
|
||||
|
||||
add_executable(client_conv3d_fwd_bf8_fp8 conv3d_fwd_bf8_fp8.cpp)
|
||||
target_link_libraries(client_conv3d_fwd_bf8_fp8 PRIVATE composable_kernel::device_conv_operations)
|
||||
endif()
|
||||
|
||||
if((DTYPES MATCHES "fp32") OR NOT DEFINED DTYPES)
|
||||
|
||||
Reference in New Issue
Block a user