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Unverified Commit 4ec5c52a authored by Bartłomiej Kocot's avatar Bartłomiej Kocot Committed by GitHub
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Add Grouped Conv Fwd Large Tensor kernel (#1432) 

* Support 64 bit indexing

* Add new grouped conv fwd kernel for large tensors

* Add instances large tensor

* Fixes for transform conv to gemm

* Fixes

* fixes

* Remove not needed instances

* examples fixes

* Remove not need ds arrays

* Fix tests

* Add 2GB check in gridwise dl

* Fixes
parent 7f57b2e0
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Showing with 788 additions and 161 deletions
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -69,10 +69,10 @@ struct ReferenceConvFwd : public device::BaseOperator
const Tensor<InDataType>& input,
const Tensor<WeiDataType>& weight,
Tensor<OutDataType>& output,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
std::vector<ck::long_index_t> conv_filter_strides,
std::vector<ck::long_index_t> conv_filter_dilations,
std::vector<ck::long_index_t> input_left_pads,
std::vector<ck::long_index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op,
......@@ -103,10 +103,10 @@ struct ReferenceConvFwd : public device::BaseOperator
const std::array<Tensor<WeiDataType>, NumBElementwiseTensor>& elementwise_b_tensors_;
const std::array<Tensor<OutDataType>, NumDElementwiseTensor>& elementwise_d_tensors_;
std::vector<index_t> conv_strides_;
std::vector<index_t> conv_dilations_;
std::vector<index_t> in_left_pads_;
std::vector<index_t> in_right_pads_;
std::vector<ck::long_index_t> conv_strides_;
std::vector<ck::long_index_t> conv_dilations_;
std::vector<ck::long_index_t> in_left_pads_;
std::vector<ck::long_index_t> in_right_pads_;
InElementwiseOperation in_element_op_;
WeiElementwiseOperation wei_element_op_;
......@@ -416,10 +416,10 @@ struct ReferenceConvFwd : public device::BaseOperator
const Tensor<InDataType>& input,
const Tensor<WeiDataType>& weight,
Tensor<OutDataType>& output,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
std::vector<ck::long_index_t> conv_filter_strides,
std::vector<ck::long_index_t> conv_filter_dilations,
std::vector<ck::long_index_t> input_left_pads,
std::vector<ck::long_index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op,
......
library/include/ck/library/reference_tensor_operation/cpu/reference_image_to_column.hpp
View file @ 4ec5c52a
......@@ -40,11 +40,11 @@ struct ReferenceImageToColumn : public device::BaseOperator
public:
Argument(const Tensor<InDataType>& input,
Tensor<OutDataType>& output,
std::vector<ck::index_t> filter_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads)
std::vector<ck::long_index_t> filter_spatial_lengths,
std::vector<ck::long_index_t> conv_filter_strides,
std::vector<ck::long_index_t> conv_filter_dilations,
std::vector<ck::long_index_t> input_left_pads,
std::vector<ck::long_index_t> input_right_pads)
: input_{input},
output_{output},
conv_strides_{conv_filter_strides},
......@@ -59,13 +59,13 @@ struct ReferenceImageToColumn : public device::BaseOperator
const Tensor<InDataType>& input_;
Tensor<OutDataType>& output_;
std::vector<index_t> conv_strides_;
std::vector<index_t> conv_dilations_;
std::vector<index_t> in_left_pads_;
std::vector<index_t> in_right_pads_;
std::vector<long_index_t> conv_strides_;
std::vector<long_index_t> conv_dilations_;
std::vector<long_index_t> in_left_pads_;
std::vector<long_index_t> in_right_pads_;
std::vector<index_t> filter_spatial_lengths_;
std::vector<index_t> output_spatial_lengths_;
std::vector<long_index_t> filter_spatial_lengths_;
std::vector<long_index_t> output_spatial_lengths_;
private:
void initOutputSpatialLengths()
......@@ -76,7 +76,8 @@ struct ReferenceImageToColumn : public device::BaseOperator
{
// XEff = (X - 1) * conv_dilation_w + 1;
// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
const ck::index_t x_eff = (filter_spatial_lengths_[i] - 1) * conv_dilations_[i] + 1;
const ck::long_index_t x_eff =
(filter_spatial_lengths_[i] - 1) * conv_dilations_[i] + 1;
output_spatial_lengths_.push_back(
(input_.GetLengths()[i + input_offset_to_spatial] + in_left_pads_[i] +
......@@ -99,24 +100,24 @@ struct ReferenceImageToColumn : public device::BaseOperator
throw std::runtime_error("wrong! inconsistent dimension");
}
const index_t G = arg.input_.GetLengths()[0];
const index_t N = arg.input_.GetLengths()[1];
const index_t C = arg.input_.GetLengths()[2];
const long_index_t G = arg.input_.GetLengths()[0];
const long_index_t N = arg.input_.GetLengths()[1];
const long_index_t C = arg.input_.GetLengths()[2];
if constexpr(NDimSpatial == 1)
{
const index_t Wo = arg.output_spatial_lengths_[0];
auto func = [&](auto g, auto n, auto wo) {
index_t row = n * Wo + wo;
index_t column = 0;
const long_index_t Wo = arg.output_spatial_lengths_[0];
auto func = [&](auto g, auto n, auto wo) {
long_index_t row = n * Wo + wo;
long_index_t column = 0;
for(index_t x = 0; x < arg.filter_spatial_lengths_[0]; ++x)
for(long_index_t x = 0; x < arg.filter_spatial_lengths_[0]; ++x)
{
auto wi = static_cast<ck::long_index_t>(wo * arg.conv_strides_[0]) +
static_cast<ck::long_index_t>(x * arg.conv_dilations_[0]) -
static_cast<ck::long_index_t>(arg.in_left_pads_[0]);
for(index_t c = 0; c < C; ++c)
for(long_index_t c = 0; c < C; ++c)
{
if(wi >= 0 &&
ck::type_convert<std::size_t>(wi) < arg.input_.GetLengths()[3])
......@@ -135,26 +136,26 @@ struct ReferenceImageToColumn : public device::BaseOperator
}
else if constexpr(NDimSpatial == 2)
{
const index_t Ho = arg.output_spatial_lengths_[0];
const index_t Wo = arg.output_spatial_lengths_[1];
const long_index_t Ho = arg.output_spatial_lengths_[0];
const long_index_t Wo = arg.output_spatial_lengths_[1];
auto func = [&](auto g, auto n, auto ho, auto wo) {
index_t row = n * Ho * Wo + ho * Wo + wo;
index_t column = 0;
long_index_t row = n * Ho * Wo + ho * Wo + wo;
long_index_t column = 0;
for(index_t y = 0; y < arg.filter_spatial_lengths_[0]; ++y)
for(long_index_t y = 0; y < arg.filter_spatial_lengths_[0]; ++y)
{
auto hi = static_cast<ck::long_index_t>(ho * arg.conv_strides_[0]) +
static_cast<ck::long_index_t>(y * arg.conv_dilations_[0]) -
static_cast<ck::long_index_t>(arg.in_left_pads_[0]);
for(index_t x = 0; x < arg.filter_spatial_lengths_[1]; ++x)
for(long_index_t x = 0; x < arg.filter_spatial_lengths_[1]; ++x)
{
auto wi = static_cast<ck::long_index_t>(wo * arg.conv_strides_[1]) +
static_cast<ck::long_index_t>(x * arg.conv_dilations_[1]) -
static_cast<ck::long_index_t>(arg.in_left_pads_[1]);
for(index_t c = 0; c < C; ++c)
for(long_index_t c = 0; c < C; ++c)
{
if(hi >= 0 &&
......@@ -178,31 +179,31 @@ struct ReferenceImageToColumn : public device::BaseOperator
}
else if constexpr(NDimSpatial == 3)
{
const index_t Do = arg.output_spatial_lengths_[0];
const index_t Ho = arg.output_spatial_lengths_[1];
const index_t Wo = arg.output_spatial_lengths_[2];
const long_index_t Do = arg.output_spatial_lengths_[0];
const long_index_t Ho = arg.output_spatial_lengths_[1];
const long_index_t Wo = arg.output_spatial_lengths_[2];
auto func = [&](auto g, auto n, auto d_o, auto ho, auto wo) {
index_t row = n * Do * Ho * Wo + d_o * Ho * Wo + ho * Wo + wo;
index_t column = 0;
long_index_t row = n * Do * Ho * Wo + d_o * Ho * Wo + ho * Wo + wo;
long_index_t column = 0;
for(index_t z = 0; z < arg.filter_spatial_lengths_[0]; ++z)
for(long_index_t z = 0; z < arg.filter_spatial_lengths_[0]; ++z)
{
auto di = static_cast<ck::long_index_t>(d_o * arg.conv_strides_[0]) +
static_cast<ck::long_index_t>(z * arg.conv_dilations_[0]) -
static_cast<ck::long_index_t>(arg.in_left_pads_[0]);
for(index_t y = 0; y < arg.filter_spatial_lengths_[1]; ++y)
for(long_index_t y = 0; y < arg.filter_spatial_lengths_[1]; ++y)
{
auto hi = static_cast<ck::long_index_t>(ho * arg.conv_strides_[1]) +
static_cast<ck::long_index_t>(y * arg.conv_dilations_[1]) -
static_cast<ck::long_index_t>(arg.in_left_pads_[1]);
for(index_t x = 0; x < arg.filter_spatial_lengths_[2]; ++x)
for(long_index_t x = 0; x < arg.filter_spatial_lengths_[2]; ++x)
{
auto wi =
static_cast<ck::long_index_t>(wo * arg.conv_strides_[2]) +
static_cast<ck::long_index_t>(x * arg.conv_dilations_[2]) -
static_cast<ck::long_index_t>(arg.in_left_pads_[2]);
for(index_t c = 0; c < C; ++c)
for(long_index_t c = 0; c < C; ++c)
{
if(di >= 0 &&
ck::type_convert<std::size_t>(di) <
......@@ -259,15 +260,15 @@ struct ReferenceImageToColumn : public device::BaseOperator
bool IsSupportedArgument(const Argument& arg)
{
const ck::index_t G = arg.input_.GetLengths()[0];
const ck::index_t N = arg.input_.GetLengths()[1];
const ck::index_t C = arg.input_.GetLengths()[2];
const ck::long_index_t G = arg.input_.GetLengths()[0];
const ck::long_index_t N = arg.input_.GetLengths()[1];
const ck::long_index_t C = arg.input_.GetLengths()[2];
const index_t NDoHoWo =
N * ck::accumulate_n<index_t>(
const long_index_t NDoHoWo =
N * ck::accumulate_n<long_index_t>(
arg.output_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
const index_t CZYX =
C * ck::accumulate_n<index_t>(
const long_index_t CZYX =
C * ck::accumulate_n<long_index_t>(
arg.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
if(!(arg.output_.GetLengths()[0] == static_cast<std::size_t>(G) &&
......@@ -291,11 +292,11 @@ struct ReferenceImageToColumn : public device::BaseOperator
static auto MakeArgument(const Tensor<InDataType>& input,
Tensor<OutDataType>& output,
std::vector<ck::index_t> filter_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads)
std::vector<ck::long_index_t> filter_spatial_lengths,
std::vector<ck::long_index_t> conv_filter_strides,
std::vector<ck::long_index_t> conv_filter_dilations,
std::vector<ck::long_index_t> input_left_pads,
std::vector<ck::long_index_t> input_right_pads)
{
return Argument{input,
output,
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_large_tensor_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using Empty_Tuple = ck::Tuple<>;
using namespace ck::tensor_layout::convolution;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvFwdDefault =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_large_tensor_bf16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsLayout, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, DsLayout, BF16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_large_tensor_f16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, DsLayout, F16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, DsLayout, F16, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_large_tensor_f32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsLayout, F32, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 8, 1, 8>, 1>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, DsLayout, F32, PassThrough, PassThrough, PassThrough, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp
View file @ 4ec5c52a
......@@ -17,6 +17,7 @@
#endif
#ifdef CK_USE_XDL
#include "grouped_convolution_forward_xdl.inc"
#include "grouped_convolution_forward_xdl_large_tensor.inc"
#include "grouped_convolution_forward_xdl_merged_groups.inc"
#include "grouped_convolution_forward_comp_xdl.inc"
#include "grouped_convolution_forward_mem_inter_xdl.inc"
......@@ -200,6 +201,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<BComputeType, float>)
{
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_comp_instances(op_ptrs);
......@@ -215,6 +218,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<BComputeType, half_t>)
{
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_comp_instances(op_ptrs);
......@@ -232,6 +237,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<BComputeType, ck::bhalf_t>)
{
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_comp_instances(op_ptrs);
......@@ -291,6 +298,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<BComputeType, float>)
{
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_comp_instances(op_ptrs);
......@@ -347,6 +356,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<BComputeType, half_t>)
{
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_instances(op_ptrs);
......@@ -364,6 +375,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
is_same_v<BComputeType, ck::bhalf_t>)
{
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instances(op_ptrs);
add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
op_ptrs);
add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_comp_instances(op_ptrs);
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_xdl_large_tensor.inc 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// grouped conv2d forward, NHWGC/GKYXC/NHWGK
#ifdef CK_ENABLE_BF16
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP16
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_BF16
// grouped conv3d forward, NDHWGC/GKZYXC/NDHWGK
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP16
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/utility/convolution_parameter.hpp
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -31,23 +31,35 @@ struct ConvParam
const std::vector<ck::index_t>& left_pads,
const std::vector<ck::index_t>& right_pads);
ck::index_t num_dim_spatial_;
ck::index_t G_;
ck::index_t N_;
ck::index_t K_;
ck::index_t C_;
std::vector<ck::index_t> filter_spatial_lengths_;
std::vector<ck::index_t> input_spatial_lengths_;
std::vector<ck::index_t> output_spatial_lengths_;
std::vector<ck::index_t> conv_filter_strides_;
std::vector<ck::index_t> conv_filter_dilations_;
std::vector<ck::index_t> input_left_pads_;
std::vector<ck::index_t> input_right_pads_;
std::vector<ck::index_t> GetOutputSpatialLengths() const;
ConvParam(ck::long_index_t n_dim,
ck::long_index_t group_count,
ck::long_index_t n_batch,
ck::long_index_t n_out_channels,
ck::long_index_t n_in_channels,
const std::vector<ck::long_index_t>& filters_len,
const std::vector<ck::long_index_t>& input_len,
const std::vector<ck::long_index_t>& strides,
const std::vector<ck::long_index_t>& dilations,
const std::vector<ck::long_index_t>& left_pads,
const std::vector<ck::long_index_t>& right_pads);
ck::long_index_t num_dim_spatial_;
ck::long_index_t G_;
ck::long_index_t N_;
ck::long_index_t K_;
ck::long_index_t C_;
std::vector<ck::long_index_t> filter_spatial_lengths_;
std::vector<ck::long_index_t> input_spatial_lengths_;
std::vector<ck::long_index_t> output_spatial_lengths_;
std::vector<ck::long_index_t> conv_filter_strides_;
std::vector<ck::long_index_t> conv_filter_dilations_;
std::vector<ck::long_index_t> input_left_pads_;
std::vector<ck::long_index_t> input_right_pads_;
std::vector<ck::long_index_t> GetOutputSpatialLengths() const;
std::size_t GetFlops() const;
......
library/include/ck/library/utility/host_tensor.hpp
View file @ 4ec5c52a
......@@ -96,9 +96,16 @@ struct HostTensorDescriptor
this->CalculateStrides();
}
HostTensorDescriptor(const std::initializer_list<ck::long_index_t>& lens)
: mLens(lens.begin(), lens.end())
{
this->CalculateStrides();
}
template <typename Lengths,
typename = std::enable_if_t<
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t>>>
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> ||
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t>>>
HostTensorDescriptor(const Lengths& lens) : mLens(lens.begin(), lens.end())
{
this->CalculateStrides();
......@@ -114,11 +121,19 @@ struct HostTensorDescriptor
{
}
HostTensorDescriptor(const std::initializer_list<ck::long_index_t>& lens,
const std::initializer_list<ck::long_index_t>& strides)
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
{
}
template <typename Lengths,
typename Strides,
typename = std::enable_if_t<
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> &&
std::is_convertible_v<ck::ranges::range_value_t<Strides>, std::size_t>>>
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> &&
std::is_convertible_v<ck::ranges::range_value_t<Strides>, std::size_t>) ||
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t> &&
std::is_convertible_v<ck::ranges::range_value_t<Strides>, ck::long_index_t>)>>
HostTensorDescriptor(const Lengths& lens, const Strides& strides)
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
{
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/CMakeLists.txt
View file @ 4ec5c52a
......@@ -9,6 +9,11 @@ add_instance_library(device_grouped_conv2d_fwd_instance
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f16_instance.cpp
xdl/device_grouped_conv2d_fwd_xdl_nhwgc_gkyxc_nhwgk_f32_instance.cpp
# large tensor
# NHWGC, GKYXC, NHWGK
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f16_instance.cpp
xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instance.cpp
# merged groups
# NHWGC, GKYXC, NHWGK
xdl/merged_groups/device_grouped_conv2d_fwd_xdl_merged_groups_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_bf16_instance.cpp 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_bf16_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f16_instance.cpp 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f16_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/xdl/large_tensor/device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instance.cpp 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_fwd_xdl_large_tensor_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_instances<2,
NHWGC,
GKYXC,
Empty_Tuple,
NHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/CMakeLists.txt
View file @ 4ec5c52a
......@@ -9,6 +9,10 @@ set(GROUPED_CONV3D_FWD
xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_int8_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/merged_groups/device_grouped_conv3d_fwd_xdl_merged_groups_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_bf16_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f16_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/large_tensor/device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp 0 → 100644
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_large_tensor_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_large_tensor_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_large_tensor_f32_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/utility/convolution_parameter.cpp
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/host_utility/io.hpp"
......@@ -20,6 +20,63 @@ ConvParam::ConvParam(ck::index_t n_dim,
const std::vector<ck::index_t>& dilations,
const std::vector<ck::index_t>& left_pads,
const std::vector<ck::index_t>& right_pads)
: num_dim_spatial_(static_cast<ck::long_index_t>(n_dim)),
G_(static_cast<ck::long_index_t>(group_count)),
N_(static_cast<ck::long_index_t>(n_batch)),
K_(static_cast<ck::long_index_t>(n_out_channels)),
C_(static_cast<ck::long_index_t>(n_in_channels)),
filter_spatial_lengths_(num_dim_spatial_),
input_spatial_lengths_(num_dim_spatial_),
output_spatial_lengths_(num_dim_spatial_),
conv_filter_strides_(num_dim_spatial_),
conv_filter_dilations_(num_dim_spatial_),
input_left_pads_(num_dim_spatial_),
input_right_pads_(num_dim_spatial_)
{
if(static_cast<ck::index_t>(filter_spatial_lengths_.size()) != num_dim_spatial_ ||
static_cast<ck::index_t>(input_spatial_lengths_.size()) != num_dim_spatial_ ||
static_cast<ck::index_t>(conv_filter_strides_.size()) != num_dim_spatial_ ||
static_cast<ck::index_t>(conv_filter_dilations_.size()) != num_dim_spatial_ ||
static_cast<ck::index_t>(input_left_pads_.size()) != num_dim_spatial_ ||
static_cast<ck::index_t>(input_right_pads_.size()) != num_dim_spatial_)
{
throw(
std::runtime_error("ConvParam::ConvParam: "
"parameter size is different from number of declared dimensions!"));
}
for(ck::index_t i = 0; i < num_dim_spatial_; ++i)
{
filter_spatial_lengths_[i] = static_cast<ck::long_index_t>(filters_len[i]);
input_spatial_lengths_[i] = static_cast<ck::long_index_t>(input_len[i]);
conv_filter_strides_[i] = static_cast<ck::long_index_t>(strides[i]);
conv_filter_dilations_[i] = static_cast<ck::long_index_t>(dilations[i]);
input_left_pads_[i] = static_cast<ck::long_index_t>(left_pads[i]);
input_right_pads_[i] = static_cast<ck::long_index_t>(right_pads[i]);
// XEff = (X - 1) * conv_dilation_w + 1;
// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
const ck::long_index_t x_eff =
(filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
output_spatial_lengths_[i] =
(input_spatial_lengths_[i] + input_left_pads_[i] + input_right_pads_[i] - x_eff) /
conv_filter_strides_[i] +
1;
}
}
ConvParam::ConvParam(ck::long_index_t n_dim,
ck::long_index_t group_count,
ck::long_index_t n_batch,
ck::long_index_t n_out_channels,
ck::long_index_t n_in_channels,
const std::vector<ck::long_index_t>& filters_len,
const std::vector<ck::long_index_t>& input_len,
const std::vector<ck::long_index_t>& strides,
const std::vector<ck::long_index_t>& dilations,
const std::vector<ck::long_index_t>& left_pads,
const std::vector<ck::long_index_t>& right_pads)
: num_dim_spatial_(n_dim),
G_(group_count),
N_(n_batch),
......@@ -49,7 +106,8 @@ ConvParam::ConvParam(ck::index_t n_dim,
{
// XEff = (X - 1) * conv_dilation_w + 1;
// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
const ck::index_t x_eff = (filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
const ck::long_index_t x_eff =
(filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
output_spatial_lengths_[i] =
(input_spatial_lengths_[i] + input_left_pads_[i] + input_right_pads_[i] - x_eff) /
......@@ -63,7 +121,7 @@ ConvParam::ConvParam()
{
}
std::vector<ck::index_t> ConvParam::GetOutputSpatialLengths() const
std::vector<ck::long_index_t> ConvParam::GetOutputSpatialLengths() const
{
return output_spatial_lengths_;
}
......@@ -97,46 +155,46 @@ std::string get_conv_param_parser_helper_msg()
ck::utils::conv::ConvParam parse_conv_param(int num_dim_spatial, int arg_idx, char* const argv[])
{
const ck::index_t G = std::stoi(argv[arg_idx++]);
const ck::index_t N = std::stoi(argv[arg_idx++]);
const ck::index_t K = std::stoi(argv[arg_idx++]);
const ck::index_t C = std::stoi(argv[arg_idx++]);
std::vector<ck::index_t> filter_spatial_lengths(num_dim_spatial);
std::vector<ck::index_t> input_spatial_lengths(num_dim_spatial);
std::vector<ck::index_t> conv_filter_strides(num_dim_spatial);
std::vector<ck::index_t> conv_filter_dilations(num_dim_spatial);
std::vector<ck::index_t> input_left_pads(num_dim_spatial);
std::vector<ck::index_t> input_right_pads(num_dim_spatial);
const ck::long_index_t G = std::stol(argv[arg_idx++]);
const ck::long_index_t N = std::stol(argv[arg_idx++]);
const ck::long_index_t K = std::stol(argv[arg_idx++]);
const ck::long_index_t C = std::stol(argv[arg_idx++]);
std::vector<ck::long_index_t> filter_spatial_lengths(num_dim_spatial);
std::vector<ck::long_index_t> input_spatial_lengths(num_dim_spatial);
std::vector<ck::long_index_t> conv_filter_strides(num_dim_spatial);
std::vector<ck::long_index_t> conv_filter_dilations(num_dim_spatial);
std::vector<ck::long_index_t> input_left_pads(num_dim_spatial);
std::vector<ck::long_index_t> input_right_pads(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
filter_spatial_lengths[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
input_spatial_lengths[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
conv_filter_strides[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
conv_filter_dilations[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
input_left_pads[i] = std::stoi(argv[arg_idx++]);
input_left_pads[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
input_right_pads[i] = std::stoi(argv[arg_idx++]);
input_right_pads[i] = std::stol(argv[arg_idx++]);
}
return ck::utils::conv::ConvParam{num_dim_spatial,
......
profiler/include/profiler/profile_conv_bwd_data_impl.hpp
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -82,6 +82,29 @@ bool profile_conv_bwd_data_impl(int do_verification,
Tensor<WeiDataType> weight(wei_g_k_c_xs_desc);
Tensor<OutDataType> output(out_g_n_k_wos_desc);
std::vector<ck::index_t> input_spatial_lengths_i32(NDimSpatial);
std::vector<ck::index_t> filter_spatial_lengths_i32(NDimSpatial);
std::vector<ck::index_t> output_spatial_lengths_i32(NDimSpatial);
std::vector<ck::index_t> conv_filter_strides_i32(NDimSpatial);
std::vector<ck::index_t> conv_filter_dilations_i32(NDimSpatial);
std::vector<ck::index_t> input_left_pads_i32(NDimSpatial);
std::vector<ck::index_t> input_right_pads_i32(NDimSpatial);
for(ck::index_t d = 0; d < NDimSpatial; d++)
{
input_spatial_lengths_i32[d] =
static_cast<ck::index_t>(conv_param.input_spatial_lengths_[d]);
filter_spatial_lengths_i32[d] =
static_cast<ck::index_t>(conv_param.filter_spatial_lengths_[d]);
output_spatial_lengths_i32[d] =
static_cast<ck::index_t>(conv_param.GetOutputSpatialLengths()[d]);
conv_filter_strides_i32[d] = static_cast<ck::index_t>(conv_param.conv_filter_strides_[d]);
conv_filter_dilations_i32[d] =
static_cast<ck::index_t>(conv_param.conv_filter_dilations_[d]);
input_left_pads_i32[d] = static_cast<ck::index_t>(conv_param.input_left_pads_[d]);
input_right_pads_i32[d] = static_cast<ck::index_t>(conv_param.input_right_pads_[d]);
}
std::cout << "input: " << input_host_result.mDesc << std::endl;
std::cout << "weight: " << weight.mDesc << std::endl;
std::cout << "output: " << output.mDesc << std::endl;
......@@ -161,16 +184,16 @@ bool profile_conv_bwd_data_impl(int do_verification,
op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
conv_param.N_,
conv_param.K_,
conv_param.C_,
conv_param.input_spatial_lengths_,
conv_param.filter_spatial_lengths_,
conv_param.output_spatial_lengths_,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_,
static_cast<ck::index_t>(conv_param.N_),
static_cast<ck::index_t>(conv_param.K_),
static_cast<ck::index_t>(conv_param.C_),
input_spatial_lengths_i32,
filter_spatial_lengths_i32,
output_spatial_lengths_i32,
conv_filter_strides_i32,
conv_filter_dilations_i32,
input_left_pads_i32,
input_right_pads_i32,
in_element_op,
wei_element_op,
out_element_op);
......
profiler/include/profiler/profile_conv_fwd_impl.hpp
View file @ 4ec5c52a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -60,6 +60,29 @@ bool profile_conv_fwd_impl(int do_verification,
Tensor<OutDataType> host_output(out_g_n_k_wos_desc);
Tensor<OutDataType> device_output(out_g_n_k_wos_desc);
std::vector<ck::index_t> input_spatial_lengths_i32(NDimSpatial);
std::vector<ck::index_t> filter_spatial_lengths_i32(NDimSpatial);
std::vector<ck::index_t> output_spatial_lengths_i32(NDimSpatial);
std::vector<ck::index_t> conv_filter_strides_i32(NDimSpatial);
std::vector<ck::index_t> conv_filter_dilations_i32(NDimSpatial);
std::vector<ck::index_t> input_left_pads_i32(NDimSpatial);
std::vector<ck::index_t> input_right_pads_i32(NDimSpatial);
for(ck::index_t d = 0; d < NDimSpatial; d++)
{
input_spatial_lengths_i32[d] =
static_cast<ck::index_t>(conv_param.input_spatial_lengths_[d]);
filter_spatial_lengths_i32[d] =
static_cast<ck::index_t>(conv_param.filter_spatial_lengths_[d]);
output_spatial_lengths_i32[d] =
static_cast<ck::index_t>(conv_param.GetOutputSpatialLengths()[d]);
conv_filter_strides_i32[d] = static_cast<ck::index_t>(conv_param.conv_filter_strides_[d]);
conv_filter_dilations_i32[d] =
static_cast<ck::index_t>(conv_param.conv_filter_dilations_[d]);
input_left_pads_i32[d] = static_cast<ck::index_t>(conv_param.input_left_pads_[d]);
input_right_pads_i32[d] = static_cast<ck::index_t>(conv_param.input_right_pads_[d]);
}
std::cout << "input: " << input.mDesc << std::endl;
std::cout << "weight: " << weight.mDesc << std::endl;
std::cout << "output: " << host_output.mDesc << std::endl;
......@@ -143,16 +166,16 @@ bool profile_conv_fwd_impl(int do_verification,
op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
conv_param.N_,
conv_param.K_,
conv_param.C_,
conv_param.input_spatial_lengths_,
conv_param.filter_spatial_lengths_,
conv_param.GetOutputSpatialLengths(),
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_,
static_cast<ck::index_t>(conv_param.N_),
static_cast<ck::index_t>(conv_param.K_),
static_cast<ck::index_t>(conv_param.C_),
input_spatial_lengths_i32,
filter_spatial_lengths_i32,
output_spatial_lengths_i32,
conv_filter_strides_i32,
conv_filter_dilations_i32,
input_left_pads_i32,
input_right_pads_i32,
in_element_op,
wei_element_op,
out_element_op);
......
profiler/include/profiler/profile_grouped_conv_fwd_impl.hpp
View file @ 4ec5c52a
......@@ -33,7 +33,8 @@ template <ck::index_t NDimSpatial,
typename WeiDataType,
typename OutDataType,
typename AComputeType = InDataType,
typename BComputeType = AComputeType>
typename BComputeType = AComputeType,
typename IndexType = ck::index_t>
bool profile_grouped_conv_fwd_impl(int do_verification,
int init_method,
bool do_log,
......@@ -57,16 +58,16 @@ bool profile_grouped_conv_fwd_impl(int do_verification,
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
std::array<ck::index_t, NDimSpatial> input_right_pads{};
std::array<IndexType, NDimSpatial + 3> a_g_n_c_wis_lengths{};
std::array<IndexType, NDimSpatial + 3> a_g_n_c_wis_strides{};
std::array<IndexType, NDimSpatial + 3> b_g_k_c_xs_lengths{};
std::array<IndexType, NDimSpatial + 3> b_g_k_c_xs_strides{};
std::array<IndexType, NDimSpatial + 3> e_g_n_k_wos_lengths{};
std::array<IndexType, NDimSpatial + 3> e_g_n_k_wos_strides{};
std::array<IndexType, NDimSpatial> conv_filter_strides{};
std::array<IndexType, NDimSpatial> conv_filter_dilations{};
std::array<IndexType, NDimSpatial> input_left_pads{};
std::array<IndexType, NDimSpatial> input_right_pads{};
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
......
profiler/src/profile_grouped_conv_fwd.cpp
View file @ 4ec5c52a
......@@ -29,6 +29,12 @@ enum struct ConvDataType
BF8_F8_F8, // 7
};
enum struct IndexType
{
INDEX_T, // 0
LONG_INDEX_T, // 1
};
#define OP_NAME "grouped_conv_fwd"
#define OP_DESC "Grouped Convolution Forward"
......@@ -45,12 +51,13 @@ static void print_helper_msg()
<< " 5: Input bf8, Weight bf8, Output fp8\n"
<< " 6: Input fp8, Weight bf8, Output fp8\n"
<< " 7: Input bf8, Weight fp8, Output fp8)\n"
<< "arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]\n"
<< "arg3: indexing data type (0: 32-bit, 1: 64-bit)\n"
<< "arg4: tensor layout (0: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]\n"
<< " 1: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K])\n"
<< "arg4: verification (0: no, 1: yes)\n"
<< "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0: no, 1: yes)\n"
<< "arg5: verification (0: no, 1: yes)\n"
<< "arg6: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg7: print tensor value (0: no; 1: yes)\n"
<< "arg8: time kernel (0: no, 1: yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
// clang-format on
}
......@@ -60,7 +67,7 @@ static void print_helper_msg()
int profile_grouped_conv_fwd(int argc, char* argv[])
{
// 8 for control, 1 for num_dim_spatial
if(argc < 9)
if(argc < 10)
{
print_helper_msg();
return 1;
......@@ -68,20 +75,21 @@ int profile_grouped_conv_fwd(int argc, char* argv[])
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ConvLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const int num_dim_spatial = std::stoi(argv[8]);
const auto index_type = static_cast<IndexType>(std::stoi(argv[4]));
const bool do_verification = std::stoi(argv[5]);
const int init_method = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]);
const int num_dim_spatial = std::stoi(argv[9]);
// 8 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
if(argc != 8 + 1 + 4 + 6 * num_dim_spatial)
// 9 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
if(argc != 9 + 1 + 4 + 6 * num_dim_spatial)
{
print_helper_msg();
return 1;
}
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 9, argv);
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 10, argv);
using F32 = float;
using F16 = ck::half_t;
......@@ -138,18 +146,43 @@ int profile_grouped_conv_fwd(int argc, char* argv[])
using AComputeType = decltype(a_compute_type);
using BComputeType = decltype(b_compute_type);
bool pass = ck::profiler::profile_grouped_conv_fwd_impl<NDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
AComputeType,
BComputeType>(
do_verification, init_method, do_log, time_kernel, params);
if(index_type == IndexType::INDEX_T)
{
bool pass = ck::profiler::profile_grouped_conv_fwd_impl<NDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
AComputeType,
BComputeType,
ck::index_t>(
do_verification, init_method, do_log, time_kernel, params);
return pass ? 0 : 1;
}
else if(index_type == IndexType::LONG_INDEX_T)
{
bool pass = ck::profiler::profile_grouped_conv_fwd_impl<NDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
AComputeType,
BComputeType,
ck::long_index_t>(
do_verification, init_method, do_log, time_kernel, params);
return pass ? 0 : 1;
return pass ? 0 : 1;
}
else
{
std::cout << "this indexing data type is not implemented" << std::endl;
return 1;
}
};
// GNHWC_GKYXC_GNHWK
......
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