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example/35_splitK_gemm/splitK_gemm_xdl_int4.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_splitk_c_shuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/literals.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = ck::int4_t;
using BDataType = ck::int4_t;
using AccDataType = int32_t;
using CDataType = int32_t;
using KernelADataType = int8_t;
using KernelBDataType = int8_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdlSplitKCShuffle
// clang-format off
<KernelADataType, //ADataType
KernelBDataType, //BDataType
CDataType, //EDataType
AccDataType, //AccDataType
ALayout, //ALayout
BLayout, //BLayout
CLayout, //ELayout
AElementOp, //AElementwiseOperation
BElementOp, //BElementwiseOperation
CElementOp, //CElementwiseOperation
GemmDefault, //GEMMSpecialization
256, // BlockSize
256, // MPerBlock
128, // NPerBlock
4, // KPerBlock
16, // K1
32, // MPerXdl
32, // NPerXdl
4, // MXdlPerWave
2, // NXdlPerWave
S<1, 4, 64, 1>, // ABlockTransfer ThreadCluster Lengths_K0_M_K1
S<0, 2, 1, 3>, // ABlockTransfer ThreadCluster ArrangeOrder
S<0, 2, 1, 3>, // ABlockTransfer SrcAccessOrder
3, // ABlockTransfer SrcVectorDim
16, // ABlockTransfer SrcScalarPerVector
16, // ABlockTransfer DstScalarPerVector_K1
true, // ABlockLdsExtraM
S<1, 4, 64, 1>, // BBlockTransfer ThreadCluster Lengths_K0_N_K1
S<0, 1, 3, 2>, // BBlockTransfer ThreadCluster ArrangeOrder
S<0, 1, 3, 2>, // BBlockTransfer SrcAccessOrder
3, // BBlockTransfer SrcVectorDim
16, // BBlockTransfer SrcScalarPerVector
16, // BBlockTransfer DstScalarPerVector_K1
true, // BBlockLdsExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CBlockTransferClusterLengths _MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
4>; // CBlockTransferScalarPerVector_NWaveNPerXdl
// clang-format on
#define BUILD_INT4_EXAMPLE
#include "run_splitK_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_splitK_gemm_example(argc, argv); }
example/35_splitK_gemm/splitK_gemm_xdl_int8.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_splitk_c_shuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/literals.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = int8_t;
using BDataType = int8_t;
using AccDataType = int32_t;
using CDataType = int32_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdlSplitKCShuffle
// clang-format off
//######| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| KPer| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//######| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Spacialization| 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_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, GemmDefault, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 16, 16, true, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 3, 16, 16, true, 1, 1, S<1, 32, 1, 8>, 4>;
// clang-format on
#include "run_splitK_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_splitK_gemm_example(argc, argv); }
example/36_sparse_embedding/CMakeLists.txt 0 → 100644
View file @ 4cccaba1
add_example_executable(example_sparse_embedding3_forward_layernorm sparse_embedding3_forward_layernorm.cpp)
example/36_sparse_embedding/sparse_embedding3_forward_layernorm.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#include <ctime>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_sparse_embedding3_forward_layernorm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_sparse_embedding3_forward_layernorm.hpp"
// using EmbType = float;
// using IndexType = int64_t;
// using GammaDataType = float;
// using BetaDataType = float;
// using AccDataType = float;
// using OutType = float;
using EmbType = ck::half_t;
using IndexType = int64_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using AccDataType = float;
using OutType = ck::half_t;
// clang-format off
// BlockSize, DimClusterSize, RowClusterSize, DimPerBlock, RowPerBlock, DimThreadSize, RowVectorSize
using DeviceInstance_fp32_e256 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 256, 1, 1>;
using DeviceInstance_fp32_e512 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 512, 1, 1>;
using DeviceInstance_fp32_e768 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 768, 1, 1>;
using DeviceInstance_fp32_e1024 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 1024, 1, 1>;
using DeviceInstance_fp32_e1536 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 1536, 1, 1>;
using DeviceInstance_fp32_e2048 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 2048, 1, 4>;
using DeviceInstance_fp32_e4096 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 4096, 1, 4>;
using DeviceInstance_fp32_e8192 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 8192, 1, 4>;
using DeviceInstance_fp32_e16384 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 16384, 1, 4>;
using DeviceInstance_fp16_e256 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 256, 1, 1>;
using DeviceInstance_fp16_e512 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 512, 1, 2>;
using DeviceInstance_fp16_e768 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 768, 1, 1>;
using DeviceInstance_fp16_e1024 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 1024, 1, 2>;
using DeviceInstance_fp16_e1536 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 1536, 1, 2>;
using DeviceInstance_fp16_e2048 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 2048, 1, 2>;
using DeviceInstance_fp16_e4096 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 4096, 1, 8>;
using DeviceInstance_fp16_e8192 = ck::tensor_operation::device::DeviceSparseEmbedding3ForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, 256, 1, 256, 1, 8192, 1, 8>;
template<typename emb_type, ck::index_t dim> struct emb_kernel{};
template<> struct emb_kernel<float, 256> { using kernel_type = DeviceInstance_fp32_e256; };
template<> struct emb_kernel<float, 512> { using kernel_type = DeviceInstance_fp32_e512; };
template<> struct emb_kernel<float, 768> { using kernel_type = DeviceInstance_fp32_e768; };
template<> struct emb_kernel<float, 1024> { using kernel_type = DeviceInstance_fp32_e1024;};
template<> struct emb_kernel<float, 1536> { using kernel_type = DeviceInstance_fp32_e1536;};
template<> struct emb_kernel<float, 2048> { using kernel_type = DeviceInstance_fp32_e2048;};
template<> struct emb_kernel<float, 4096> { using kernel_type = DeviceInstance_fp32_e4096;};
template<> struct emb_kernel<float, 8192> { using kernel_type = DeviceInstance_fp32_e8192;};
template<> struct emb_kernel<float, 16384>{ using kernel_type = DeviceInstance_fp32_e16384;};
template<> struct emb_kernel<ck::half_t, 256> { using kernel_type = DeviceInstance_fp16_e256; };
template<> struct emb_kernel<ck::half_t, 512> { using kernel_type = DeviceInstance_fp16_e512; };
template<> struct emb_kernel<ck::half_t, 768> { using kernel_type = DeviceInstance_fp16_e768; };
template<> struct emb_kernel<ck::half_t, 1024> { using kernel_type = DeviceInstance_fp16_e1024; };
template<> struct emb_kernel<ck::half_t, 1536> { using kernel_type = DeviceInstance_fp16_e1536; };
template<> struct emb_kernel<ck::half_t, 2048> { using kernel_type = DeviceInstance_fp16_e2048; };
template<> struct emb_kernel<ck::half_t, 4096> { using kernel_type = DeviceInstance_fp16_e4096; };
template<> struct emb_kernel<ck::half_t, 8192> { using kernel_type = DeviceInstance_fp16_e8192; };
// clang-format on
int main()
{
bool time_kernel = true;
constexpr auto num_rows = 65536;
constexpr auto dims = ck::Sequence<256, 512, 768, 1024, 1536, 2048, 4096, 8192>{};
// constexpr auto dims = ck::Sequence<256, 512>{};
constexpr auto index_length = 2048;
constexpr AccDataType epsilon = 1e-4;
auto f_host_tensor_desc_1d = [](std::size_t len_) { return HostTensorDescriptor({len_}); };
auto f_host_tensor_desc_2d = [](std::size_t rows_, std::size_t cols_) {
return HostTensorDescriptor({rows_, cols_});
};
using ReferenceInstance =
ck::tensor_operation::host::ReferenceSparseEmbedding3ForwardLayernorm<EmbType,
IndexType,
GammaDataType,
BetaDataType,
AccDataType,
OutType>;
ck::static_for<0, dims.Size(), 1>{}([&](auto I) {
std::srand(std::time(nullptr));
constexpr auto current_dim = dims.At(I);
Tensor<EmbType> emb_a(f_host_tensor_desc_2d(num_rows, current_dim));
Tensor<EmbType> emb_b(f_host_tensor_desc_2d(num_rows, current_dim));
Tensor<EmbType> emb_c(f_host_tensor_desc_2d(num_rows, current_dim));
Tensor<IndexType> index_a(f_host_tensor_desc_1d(index_length));
Tensor<IndexType> index_b(f_host_tensor_desc_1d(index_length));
Tensor<IndexType> index_c(f_host_tensor_desc_1d(index_length));
Tensor<GammaDataType> gamma(f_host_tensor_desc_1d(current_dim));
Tensor<BetaDataType> beta(f_host_tensor_desc_1d(current_dim));
Tensor<OutType> out(f_host_tensor_desc_2d(index_length, current_dim));
emb_a.GenerateTensorValue(GeneratorTensor_3<EmbType>{0.0, 1.0});
emb_b.GenerateTensorValue(GeneratorTensor_3<EmbType>{0.0, 1.0});
emb_c.GenerateTensorValue(GeneratorTensor_3<EmbType>{0.0, 1.0});
index_a.GenerateTensorValue(GeneratorTensor_2<IndexType>{0, num_rows});
index_b.GenerateTensorValue(GeneratorTensor_2<IndexType>{0, num_rows});
index_c.GenerateTensorValue(GeneratorTensor_2<IndexType>{0, num_rows});
gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});
DeviceMem emb_a_dev(sizeof(EmbType) * emb_a.mDesc.GetElementSpaceSize());
DeviceMem emb_b_dev(sizeof(EmbType) * emb_b.mDesc.GetElementSpaceSize());
DeviceMem emb_c_dev(sizeof(EmbType) * emb_c.mDesc.GetElementSpaceSize());
DeviceMem index_a_dev(sizeof(IndexType) * index_a.mDesc.GetElementSpaceSize());
DeviceMem index_b_dev(sizeof(IndexType) * index_b.mDesc.GetElementSpaceSize());
DeviceMem index_c_dev(sizeof(IndexType) * index_c.mDesc.GetElementSpaceSize());
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
DeviceMem out_dev(sizeof(OutType) * out.mDesc.GetElementSpaceSize());
emb_a_dev.ToDevice(emb_a.mData.data());
emb_b_dev.ToDevice(emb_b.mData.data());
emb_c_dev.ToDevice(emb_c.mData.data());
index_a_dev.ToDevice(index_a.mData.data());
index_b_dev.ToDevice(index_b.mData.data());
index_c_dev.ToDevice(index_c.mData.data());
gamma_dev.ToDevice(gamma.mData.data());
beta_dev.ToDevice(beta.mData.data());
auto device_instance = typename emb_kernel<EmbType, current_dim>::kernel_type{};
auto argument_ptr = device_instance.MakeArgumentPointer(out_dev.GetDeviceBuffer(),
emb_a_dev.GetDeviceBuffer(),
emb_b_dev.GetDeviceBuffer(),
emb_c_dev.GetDeviceBuffer(),
index_a_dev.GetDeviceBuffer(),
index_b_dev.GetDeviceBuffer(),
index_c_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
num_rows,
current_dim,
index_length,
epsilon);
std::cout << "Dim:" << current_dim << ", kernel:" << device_instance.GetTypeString()
<< std::endl
<< std::flush;
bool is_supported = device_instance.IsSupportedArgument(argument_ptr.get());
if(!is_supported)
{
std::cout << "Runtime parameters are not supported" << std::endl;
return;
}
auto invoker_ptr = device_instance.MakeInvokerPointer();
float time_ms = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
bool pass = true;
{
Tensor<OutType> out_from_dev(f_host_tensor_desc_2d(index_length, current_dim));
ReferenceInstance ref;
auto ref_argument = ref.MakeArgument(out,
emb_a,
emb_b,
emb_c,
index_a,
index_b,
index_c,
gamma,
beta,
num_rows,
current_dim,
index_length,
epsilon);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument);
out_dev.FromDevice(out_from_dev.mData.data());
pass &= ck::utils::check_err(out_from_dev, out, "Error: Incorrect results", 1e-3, 1e-3);
}
double total_read = current_dim * index_length * 3 * sizeof(EmbType) +
current_dim * sizeof(GammaDataType) +
current_dim * sizeof(BetaDataType);
double total_write = current_dim * index_length * sizeof(OutType);
double gbps = (total_read + total_write) / time_ms / 1e6;
std::cout << ", total bytes:" << (total_read + total_write) << ", time:" << time_ms
<< ", gbps:" << gbps << ", valid:" << (pass ? "y" : "n") << std::endl
<< std::flush;
});
return 0;
}
example/37_batched_gemm_add_add_relu_gemm_add/CMakeLists.txt 0 → 100644
View file @ 4cccaba1
add_example_executable(example_batched_gemm_add_add_relu_gemm_add_xdl_fp16 batched_gemm_add_add_relu_gemm_add_xdl_fp16.cpp)
example/37_batched_gemm_add_add_relu_gemm_add/batched_gemm_add_add_relu_gemm_add_xdl_fp16.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
/*
Computes C_m_o = Relu(A0[m, k] * B0[n, k] + D00[m, n] + D01[mn]) * B1[n, o] + D1[m, o]
*/
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using A0DataType = F16;
using B0DataType = F16;
using Acc0DataType = F32;
using D00DataType = F16;
using D01DataType = F16;
using B1DataType = F16;
using Acc1DataType = F32;
using C1ShuffleDataType = F32;
using D1DataType = F16;
using E1DataType = F16;
using A0Layout = Row;
using B0Layout = Col;
using D00Layout = Row;
using D01Layout = Row;
using B1Layout = Row;
using D1Layout = Row;
using E1Layout = Row;
// E = Relu(C + D0 + D1)
struct AddAddRelu
{
__host__ __device__ void
operator()(ck::half_t& e, const ck::half_t& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const ck::half_t x = c + d0 + d1;
ck::tensor_operation::element_wise::Relu{}.template operator()<ck::half_t>(e, x);
}
__host__ __device__ void
operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const float x = c + (d0 + d1);
ck::tensor_operation::element_wise::Relu{}.template operator()<float>(e, x);
}
};
// E = Gelu(C + D0 + D1)
struct AddAddGelu
{
__host__ __device__ void
operator()(ck::half_t& e, const ck::half_t& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const ck::half_t x = c + d0 + d1;
ck::tensor_operation::element_wise::Gelu{}.template operator()<ck::half_t, ck::half_t>(e,
x);
}
__host__ __device__ void
operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const float x = c + (d0 + d1);
ck::tensor_operation::element_wise::Gelu{}.template operator()<float, float>(e, x);
}
};
// E = FastGelu(C + D0 + D1)
struct AddAddFastGelu
{
__host__ __device__ void
operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const float x = c + (d0 + d1);
ck::tensor_operation::element_wise::FastGelu{}.template operator()<float, float>(e, x);
}
};
using A0ElementOp = PassThrough;
using B0ElementOp = PassThrough;
using CDE0ElementOp = AddAddRelu;
using A1ElementOp = PassThrough;
using B1ElementOp = PassThrough;
using CDE1ElementOp = ck::tensor_operation::element_wise::Add;
static constexpr bool PadGemm0M = false;
static constexpr bool PadGemm0N = false;
static constexpr bool PadGemm0K = false;
static constexpr bool PadGemm1N = false;
static constexpr bool PadGemm1K = false;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle<
A0Layout,
B0Layout,
ck::Tuple<D00Layout, D01Layout>,
B1Layout,
ck::Tuple<D1Layout>,
E1Layout,
A0DataType,
B0DataType,
Acc0DataType,
ck::Tuple<D00DataType, D01DataType>,
B1DataType,
Acc1DataType,
C1ShuffleDataType,
ck::Tuple<D1DataType>,
E1DataType,
A0ElementOp,
B0ElementOp,
CDE0ElementOp,
B1ElementOp,
CDE1ElementOp,
PadGemm0M,
PadGemm0N,
PadGemm0K,
PadGemm1N,
PadGemm1K,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8>; // CShuffleBlockTransferScalarPerVector_NPerBlock
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 64;
ck::index_t O = 128;
ck::index_t BatchCount = 4;
ck::index_t StrideA0 = -1;
ck::index_t StrideB0 = -1;
ck::index_t StrideD00 = -1;
ck::index_t StrideD01 = -1;
ck::index_t StrideB1 = -1;
ck::index_t StrideD1 = -1;
ck::index_t StrideE1 = -1;
ck::index_t BatchStrideA0 = -1;
ck::index_t BatchStrideB0 = -1;
ck::index_t BatchStrideD00 = -1;
ck::index_t BatchStrideD01 = -1;
ck::index_t BatchStrideB1 = -1;
ck::index_t BatchStrideD1 = -1;
ck::index_t BatchStrideE1 = -1;
if(argc == 1)
{
// use default case
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 9)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
BatchCount = std::stoi(argv[8]);
}
else if(argc == 23)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
BatchCount = std::stoi(argv[8]);
StrideA0 = std::stoi(argv[9]);
StrideB0 = std::stoi(argv[10]);
StrideD00 = std::stoi(argv[11]);
StrideD01 = std::stoi(argv[12]);
StrideB1 = std::stoi(argv[13]);
StrideD1 = std::stoi(argv[14]);
StrideE1 = std::stoi(argv[15]);
BatchStrideA0 = std::stoi(argv[16]);
BatchStrideB0 = std::stoi(argv[17]);
BatchStrideD00 = std::stoi(argv[18]);
BatchStrideD01 = std::stoi(argv[19]);
BatchStrideB1 = std::stoi(argv[20]);
BatchStrideD1 = std::stoi(argv[21]);
BatchStrideE1 = std::stoi(argv[22]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 8: M, N, K, O, Batch\n");
printf(
"arg9 to 15: StrideA0, StrideB0, StrideD00, StrideD01, StrideB1, StrideD1, StrideE1\n");
printf("arg16 to 22: BatchStrideA0, BatchStrideB0, BatchStrideD00, BatchStrideD01, "
"BatchStrideB1, BatchStrideD1, BatchStrideE1 \n");
exit(0);
}
const int DefaultStrideA0 = ck::is_same_v<A0Layout, Row> ? K : M;
const int DefaultStrideB0 = ck::is_same_v<B0Layout, Row> ? N : K;
const int DefaultStrideD00 = ck::is_same_v<D00Layout, Row> ? N : M;
const int DefaultStrideD01 = ck::is_same_v<D01Layout, Row> ? N : M;
const int DefaultStrideB1 = ck::is_same_v<B1Layout, Row> ? O : N;
const int DefaultStrideD1 = ck::is_same_v<D1Layout, Row> ? O : M;
const int DefaultStrideE1 = ck::is_same_v<E1Layout, Row> ? O : M;
StrideA0 = (StrideA0 < 0) ? DefaultStrideA0 : StrideA0;
StrideB0 = (StrideB0 < 0) ? DefaultStrideB0 : StrideB0;
StrideD00 = (StrideD00 < 0) ? DefaultStrideD00 : StrideD00;
StrideD01 = (StrideD01 < 0) ? DefaultStrideD01 : StrideD01;
StrideB1 = (StrideB1 < 0) ? DefaultStrideB1 : StrideB1;
StrideD1 = (StrideD1 < 0) ? DefaultStrideD1 : StrideD1;
StrideE1 = (StrideE1 < 0) ? DefaultStrideE1 : StrideE1;
const int DefaultBatchStrideA0 = (ck::is_same_v<A0Layout, Col> ? K : M) * StrideA0;
const int DefaultBatchStrideB0 = (ck::is_same_v<B0Layout, Col> ? N : K) * StrideB0;
const int DefaultBatchStrideD00 = (ck::is_same_v<D00Layout, Col> ? N : M) * StrideD00;
const int DefaultBatchStrideD01 = (ck::is_same_v<D01Layout, Col> ? N : M) * StrideD01;
const int DefaultBatchStrideB1 = (ck::is_same_v<B1Layout, Col> ? O : N) * StrideB1;
const int DefaultBatchStrideD1 = (ck::is_same_v<D1Layout, Col> ? O : M) * StrideD1;
const int DefaultBatchStrideE1 = (ck::is_same_v<E1Layout, Col> ? O : M) * StrideE1;
BatchStrideA0 = BatchStrideA0 < 0 ? DefaultBatchStrideA0 : BatchStrideA0;
BatchStrideB0 = BatchStrideB0 < 0 ? DefaultBatchStrideB0 : BatchStrideB0;
BatchStrideD00 = BatchStrideD00 < 0 ? DefaultBatchStrideD00 : BatchStrideD00;
BatchStrideD01 = BatchStrideD01 < 0 ? DefaultBatchStrideD01 : BatchStrideD01;
BatchStrideB1 = BatchStrideB1 < 0 ? DefaultBatchStrideB1 : BatchStrideB1;
BatchStrideD1 = BatchStrideD1 < 0 ? DefaultBatchStrideD1 : BatchStrideD1;
BatchStrideE1 = BatchStrideE1 < 0 ? DefaultBatchStrideE1 : BatchStrideE1;
auto f_host_tensor_descriptor = [](std::size_t batch_count,
std::size_t row,
std::size_t col,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), Row>::value)
{
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
}
else
{
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
}
};
// E_m_o = A_m_k * B0_k_n * B1_n_o
Tensor<A0DataType> a0_g_m_k(
f_host_tensor_descriptor(BatchCount, M, K, StrideA0, BatchStrideA0, A0Layout{}));
Tensor<B0DataType> b0_g_k_n(
f_host_tensor_descriptor(BatchCount, K, N, StrideB0, BatchStrideB0, B0Layout{}));
Tensor<D00DataType> d00_g_m_n(
f_host_tensor_descriptor(BatchCount, M, N, StrideD00, BatchStrideD00, D00Layout{}));
Tensor<D01DataType> d01_g_m_n(
f_host_tensor_descriptor(BatchCount, M, N, StrideD01, BatchStrideD01, D01Layout{}));
Tensor<B1DataType> b1_g_n_o(
f_host_tensor_descriptor(BatchCount, N, O, StrideB1, BatchStrideB1, B1Layout{}));
Tensor<D1DataType> d1_g_m_o(
f_host_tensor_descriptor(BatchCount, M, O, StrideD1, BatchStrideD1, D1Layout{}));
Tensor<E1DataType> e1_g_m_o_host_result(
f_host_tensor_descriptor(BatchCount, M, O, StrideE1, BatchStrideE1, E1Layout{}));
Tensor<E1DataType> e1_g_m_o_device_result(
f_host_tensor_descriptor(BatchCount, M, O, StrideE1, BatchStrideE1, E1Layout{}));
std::cout << "a0_g_m_k: " << a0_g_m_k.mDesc << std::endl;
std::cout << "b0_g_k_n: " << b0_g_k_n.mDesc << std::endl;
std::cout << "d00_g_m_n: " << d00_g_m_n.mDesc
<< " size: " << d00_g_m_n.mDesc.GetElementSpaceSize() << std::endl;
std::cout << "d01_g_m_n: " << d01_g_m_n.mDesc
<< " size: " << d01_g_m_n.mDesc.GetElementSpaceSize() << std::endl;
std::cout << "b1_g_n_o: " << b1_g_n_o.mDesc << std::endl;
std::cout << "e1_g_m_o: " << e1_g_m_o_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a0_g_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 3});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 3});
d00_g_m_n.GenerateTensorValue(GeneratorTensor_2<D00DataType>{-2, 3});
d01_g_m_n.GenerateTensorValue(GeneratorTensor_2<D01DataType>{-2, 3});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 3});
d1_g_m_o.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 3});
break;
case 2:
a0_g_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{0.0, 1.0});
d00_g_m_n.GenerateTensorValue(GeneratorTensor_3<D00DataType>{0.0, 1.0});
d01_g_m_n.GenerateTensorValue(GeneratorTensor_3<D01DataType>{0.0, 1.0});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
d1_g_m_o.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
break;
default:
a0_g_m_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{1});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
d00_g_m_n.GenerateTensorValue(GeneratorTensor_1<D00DataType>{1});
d01_g_m_n.GenerateTensorValue(GeneratorTensor_1<D01DataType>{1});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
d1_g_m_o.GenerateTensorValue(GeneratorTensor_1<D1DataType>{1});
}
DeviceMem a0_g_m_k_device_buf(sizeof(A0DataType) * a0_g_m_k.mDesc.GetElementSize());
DeviceMem b0_g_k_n_device_buf(sizeof(B0DataType) * b0_g_k_n.mDesc.GetElementSize());
DeviceMem d00_g_m_n_device_buf(sizeof(D00DataType) * d00_g_m_n.mDesc.GetElementSpaceSize());
DeviceMem d01_g_m_n_device_buf(sizeof(D01DataType) * d01_g_m_n.mDesc.GetElementSpaceSize());
DeviceMem b1_g_n_o_device_buf(sizeof(B1DataType) * b1_g_n_o.mDesc.GetElementSize());
DeviceMem e1_g_m_o_device_buf(sizeof(E1DataType) *
e1_g_m_o_device_result.mDesc.GetElementSize());
DeviceMem d1_g_m_o_device_buf(sizeof(D1DataType) * d1_g_m_o.mDesc.GetElementSpaceSize());
a0_g_m_k_device_buf.ToDevice(a0_g_m_k.mData.data());
b0_g_k_n_device_buf.ToDevice(b0_g_k_n.mData.data());
d00_g_m_n_device_buf.ToDevice(d00_g_m_n.mData.data());
d01_g_m_n_device_buf.ToDevice(d01_g_m_n.mData.data());
b1_g_n_o_device_buf.ToDevice(b1_g_n_o.mData.data());
d1_g_m_o_device_buf.ToDevice(d1_g_m_o.mData.data());
auto a0_element_op = A0ElementOp{};
auto b0_element_op = B0ElementOp{};
auto cde0_element_op = CDE0ElementOp{};
auto b1_element_op = B1ElementOp{};
auto cde1_element_op = CDE1ElementOp{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument =
gemm.MakeArgument(static_cast<A0DataType*>(a0_g_m_k_device_buf.GetDeviceBuffer()),
static_cast<B0DataType*>(b0_g_k_n_device_buf.GetDeviceBuffer()),
std::array<const void*, 2>{d00_g_m_n_device_buf.GetDeviceBuffer(),
d01_g_m_n_device_buf.GetDeviceBuffer()},
static_cast<B1DataType*>(b1_g_n_o_device_buf.GetDeviceBuffer()),
std::array<const void*, 1>{d1_g_m_o_device_buf.GetDeviceBuffer()},
static_cast<E1DataType*>(e1_g_m_o_device_buf.GetDeviceBuffer()),
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
std::array<ck::index_t, 2>{StrideD00, StrideD01},
StrideB1,
std::array<ck::index_t, 1>{StrideD1},
StrideE1,
BatchStrideA0,
BatchStrideB0,
std::array<ck::index_t, 2>{BatchStrideD00, BatchStrideD01},
BatchStrideB1,
std::array<ck::index_t, 1>{BatchStrideD1},
BatchStrideE1,
a0_element_op,
b0_element_op,
cde0_element_op,
b1_element_op,
cde1_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
return 0;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
std::size_t num_btype =
(sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(D00DataType) * N +
sizeof(D01DataType) * N + sizeof(B1DataType) * N * O + sizeof(E1DataType) * M * O +
sizeof(D1DataType) * O) *
BatchCount;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
e1_g_m_o_device_buf.FromDevice(e1_g_m_o_device_result.mData.data());
if(do_verification)
{
using ReferenceGemm0Instance =
ck::tensor_operation::host::ReferenceBatchedGemm<A0DataType,
B0DataType,
Acc0DataType,
Acc0DataType,
A0ElementOp,
B0ElementOp,
PassThrough>;
using ReferenceGemm1Instance =
ck::tensor_operation::host::ReferenceBatchedGemm<Acc0DataType,
B1DataType,
Acc1DataType,
Acc1DataType,
PassThrough,
B1ElementOp,
PassThrough>;
// Output of Gemm0 is input A of Gemm1
Tensor<Acc0DataType> c0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
Tensor<Acc0DataType> e0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
Tensor<Acc1DataType> c1_g_m_o(f_host_tensor_descriptor(BatchCount, M, O, O, M * O, Row{}));
auto ref_gemm0 = ReferenceGemm0Instance{};
auto ref_gemm0_invoker = ref_gemm0.MakeInvoker();
auto ref_gemm0_argument = ref_gemm0.MakeArgument(
a0_g_m_k, b0_g_k_n, c0_g_m_n, a0_element_op, b0_element_op, PassThrough{});
ref_gemm0_invoker.Run(ref_gemm0_argument);
// bias+bias+relu
e0_g_m_n.ForEach([&](auto&, auto idx) {
cde0_element_op(e0_g_m_n(idx), c0_g_m_n(idx), d00_g_m_n(idx), d01_g_m_n(idx));
});
auto ref_gemm1 = ReferenceGemm1Instance{};
auto ref_gemm1_invoker = ref_gemm1.MakeInvoker();
auto ref_gemm1_argument = ref_gemm1.MakeArgument(
e0_g_m_n, b1_g_n_o, c1_g_m_o, PassThrough{}, b1_element_op, PassThrough{});
ref_gemm1_invoker.Run(ref_gemm1_argument);
// bias
e1_g_m_o_host_result.ForEach([&](auto&, auto idx) {
cde1_element_op(e1_g_m_o_host_result(idx), c1_g_m_o(idx), d1_g_m_o(idx));
});
return ck::utils::check_err(e1_g_m_o_device_result, e1_g_m_o_host_result) ? 0 : 1;
}
return 0;
}
example/38_grouped_conv_bwd_data_multiple_d/CMakeLists.txt 0 → 100644
View file @ 4cccaba1
add_custom_target(example_grouped_conv_bwd_data)
add_example_executable(example_grouped_conv_bwd_data_fp16 grouped_conv_bwd_data_fp16.cpp)
add_example_executable(example_grouped_conv_bwd_data_bias_relu_fp16 grouped_conv_bwd_data_bias_relu_fp16.cpp)
add_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_fp16)
add_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_bias_relu_fp16)
example/38_grouped_conv_bwd_data_multiple_d/common.hpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <initializer_list>
#include <iostream>
#include <numeric>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static inline constexpr ck::index_t NDimSpatial = 2;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Default;
using FP16 = ck::half_t;
using FP32 = float;
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = true;
};
#define DefaultConvParams \
ck::utils::conv::ConvParam \
{ \
NDimSpatial, 32, 4, 192, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {1, 1}, { 1, 1 } \
}
inline void print_help_msg()
{
std::cerr << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
}
inline bool parse_cmd_args(int argc,
char* argv[],
ExecutionConfig& config,
ck::utils::conv::ConvParam& conv_params)
{
constexpr int num_execution_config_args =
3; // arguments for do_verification, init_method, time_kernel
constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
constexpr int threshold_to_catch_all_args =
threshold_to_catch_partial_args + num_conv_param_leading_args;
if(argc == 1)
{
// use default
config = ExecutionConfig{};
}
// catch only ExecutionConfig arguments
else if(argc == threshold_to_catch_partial_args)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
// catch both ExecutionConfig & ConvParam arguments
else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
conv_params = ck::utils::conv::parse_conv_param(
num_dim_spatial, threshold_to_catch_partial_args, argv);
}
else
{
print_help_msg();
return false;
}
return true;
}
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv_bwd_data_bias_relu_fp16.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
using OutDataType = FP16;
using WeiDataType = FP16;
using AccDataType = FP32;
using CShuffleDataType = FP16;
using BiasDataType = FP16; // bias
using InDataType = FP16;
using OutLayout = ck::tensor_layout::convolution::GNHWK;
using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using BiasLayout = ck::Tuple<ck::tensor_layout::convolution::G_C>;
using InLayout = ck::tensor_layout::convolution::GNHWC;
using OutElementOp = PassThrough;
using WeiElementOp = PassThrough;
using InElementOp = ck::tensor_operation::element_wise::AddRelu;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, BiasLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, ck::Tuple<BiasDataType>, InDataType, OutElementOp, WeiElementOp, InElementOp, ConvBwdDataDefault, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
#include "run_grouped_conv_bwd_data_bias_relu_example.inc"
int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_bias_relu_example(argc, argv); }
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv_bwd_data_fp16.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
using OutDataType = FP16;
using WeiDataType = FP16;
using AccDataType = FP32;
using CShuffleDataType = FP16;
using DsDataType = ck::Tuple<>;
using InDataType = FP16;
using OutLayout = ck::tensor_layout::convolution::GNHWK;
using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using DsLayout = ck::Tuple<>;
using InLayout = ck::tensor_layout::convolution::GNHWC;
using OutElementOp = PassThrough;
using WeiElementOp = PassThrough;
using InElementOp = PassThrough;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, DsLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, DsDataType, InDataType, OutElementOp, WeiElementOp, InElementOp, ConvBwdDataDefault, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
#include "run_grouped_conv_bwd_data_example.inc"
int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_example(argc, argv); }
example/38_grouped_conv_bwd_data_multiple_d/run_grouped_conv_bwd_data_bias_relu_example.inc 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
bool run_conv_bwd_data_bias_relu(const ExecutionConfig& config,
const ck::utils::conv::ConvParam& conv_params,
const HostTensorDescriptor& out_g_n_k_wos_desc,
const HostTensorDescriptor& wei_g_k_c_xs_desc,
const HostTensorDescriptor& bias_g_n_c_wis_desc,
const HostTensorDescriptor& in_g_n_c_wis_desc,
const OutElementOp& out_element_op,
const WeiElementOp& wei_element_op,
const InElementOp& in_element_op)
{
Tensor<OutDataType> out(out_g_n_k_wos_desc);
Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
Tensor<BiasDataType> bias(bias_g_n_c_wis_desc);
Tensor<InDataType> in_host(in_g_n_c_wis_desc);
Tensor<InDataType> in_device(in_g_n_c_wis_desc);
std::cout << "out: " << out.mDesc << std::endl;
std::cout << "wei: " << wei.mDesc << std::endl;
std::cout << "bias: " << bias.mDesc << std::endl;
std::cout << "in: " << in_host.mDesc << std::endl;
switch(config.init_method)
{
case 0: break;
case 1:
out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
bias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5});
break;
default:
out.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
bias.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{0.0, 1.0});
}
DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
DeviceMem bias_device_buf(sizeof(BiasDataType) * bias.mDesc.GetElementSpaceSize());
DeviceMem in_device_buf(sizeof(InDataType) * in_device.mDesc.GetElementSpaceSize());
out_device_buf.ToDevice(out.mData.data());
wei_device_buf.ToDevice(wei.mData.data());
bias_device_buf.ToDevice(bias.mData.data());
// reset input to zero
in_device_buf.SetZero();
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_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> d0_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> d0_g_n_c_wis_strides{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_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{};
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_strides);
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
copy(bias_g_n_c_wis_desc.GetLengths(), d0_g_n_c_wis_lengths);
copy(bias_g_n_c_wis_desc.GetStrides(), d0_g_n_c_wis_strides);
copy(in_g_n_c_wis_desc.GetLengths(), e_g_n_c_wis_lengths);
copy(in_g_n_c_wis_desc.GetStrides(), e_g_n_c_wis_strides);
copy(conv_params.conv_filter_strides_, conv_filter_strides);
copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
copy(conv_params.input_left_pads_, input_left_pads);
copy(conv_params.input_right_pads_, input_right_pads);
static_assert(std::is_default_constructible_v<DeviceConvInstance>);
// do conv
auto conv = DeviceConvInstance{};
auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(
out_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{bias_device_buf.GetDeviceBuffer()},
in_device_buf.GetDeviceBuffer(),
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{d0_g_n_c_wis_lengths},
std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{d0_g_n_c_wis_strides},
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
out_element_op,
wei_element_op,
in_element_op);
if(!conv.IsSupportedArgument(argument))
{
std::cerr << "wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem"
<< std::endl;
return false;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = conv_params.GetFlops();
std::size_t num_btype = conv_params.GetByte<InDataType, WeiDataType, OutDataType>();
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
if(config.do_verification)
{
// c doesn't physically exist, any layout is fine
Tensor<float> c_host(in_g_n_c_wis_desc);
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdData<NDimSpatial,
float,
WeiDataType,
OutDataType,
PassThrough,
WeiElementOp,
OutElementOp>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(c_host,
wei,
out,
conv_params.conv_filter_strides_,
conv_params.conv_filter_dilations_,
conv_params.input_left_pads_,
conv_params.input_right_pads_,
PassThrough{},
wei_element_op,
out_element_op);
ref_invoker.Run(ref_argument);
// TODO: implement elementwise operation for host
in_host.ForEach(
[&](auto&, auto idx) { in_element_op(in_host(idx), c_host(idx), bias(idx)); });
in_device_buf.FromDevice(in_device.mData.data());
return ck::utils::check_err(in_device, in_host);
}
return true;
}
int run_grouped_conv_bwd_data_bias_relu_example(int argc, char* argv[])
{
namespace ctc = ck::tensor_layout::convolution;
ExecutionConfig config;
ck::utils::conv::ConvParam conv_params = DefaultConvParams;
if(!parse_cmd_args(argc, argv, config, conv_params))
{
return EXIT_FAILURE;
}
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
if(conv_params.num_dim_spatial_ != NDimSpatial)
{
std::cerr << "unsupported # of spatials dimensions" << std::endl;
return EXIT_FAILURE;
}
// output image: GNHWK
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_params);
// weight: GKYXC
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_params);
// input image bias: G_C
const auto bias_g_n_c_wis_desc = HostTensorDescriptor({conv_params.G_,
conv_params.N_,
conv_params.C_,
conv_params.input_spatial_lengths_[0],
conv_params.input_spatial_lengths_[1]},
{
conv_params.C_, // g
0, // n
1, // c
0, // hi
0 // wi
});
// input image: GNHWC
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_params);
return !run_conv_bwd_data_bias_relu(config,
conv_params,
out_g_n_k_wos_desc,
wei_g_k_c_xs_desc,
bias_g_n_c_wis_desc,
in_g_n_c_wis_desc,
wei_element_op,
out_element_op,
in_element_op);
}
example/38_grouped_conv_bwd_data_multiple_d/run_grouped_conv_bwd_data_example.inc 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
bool run_conv_bwd_data(const ExecutionConfig& config,
const ck::utils::conv::ConvParam& conv_params,
const HostTensorDescriptor& out_g_n_k_wos_desc,
const HostTensorDescriptor& wei_g_k_c_xs_desc,
const HostTensorDescriptor& in_g_n_c_wis_desc,
const OutElementOp& out_element_op,
const WeiElementOp& wei_element_op,
const InElementOp& in_element_op)
{
Tensor<OutDataType> out(out_g_n_k_wos_desc);
Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
Tensor<InDataType> in_host(in_g_n_c_wis_desc);
Tensor<InDataType> in_device(in_g_n_c_wis_desc);
std::cout << "out: " << out.mDesc << std::endl;
std::cout << "wei: " << wei.mDesc << std::endl;
std::cout << "in: " << in_host.mDesc << std::endl;
switch(config.init_method)
{
case 0: break;
case 1:
out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
break;
default:
out.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
}
DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
DeviceMem in_device_buf(sizeof(InDataType) * in_device.mDesc.GetElementSpaceSize());
out_device_buf.ToDevice(out.mData.data());
wei_device_buf.ToDevice(wei.mData.data());
// reset input to zero
in_device_buf.SetZero();
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_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_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_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{};
auto copy = [](auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_strides);
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
copy(in_g_n_c_wis_desc.GetLengths(), e_g_n_c_wis_lengths);
copy(in_g_n_c_wis_desc.GetStrides(), e_g_n_c_wis_strides);
copy(conv_params.conv_filter_strides_, conv_filter_strides);
copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
copy(conv_params.input_left_pads_, input_left_pads);
copy(conv_params.input_right_pads_, input_right_pads);
static_assert(std::is_default_constructible_v<DeviceConvInstance>);
// do conv
auto conv = DeviceConvInstance{};
auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(out_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
std::array<const void*, 0>{},
in_device_buf.GetDeviceBuffer(),
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{},
std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{},
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
out_element_op,
wei_element_op,
in_element_op);
if(!conv.IsSupportedArgument(argument))
{
std::cerr << "wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem"
<< std::endl;
return false;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = conv_params.GetFlops();
std::size_t num_btype = conv_params.GetByte<InDataType, WeiDataType, OutDataType>();
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
if(config.do_verification)
{
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdData<NDimSpatial,
InDataType,
WeiDataType,
OutDataType,
PassThrough,
WeiElementOp,
OutElementOp>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(in_host,
wei,
out,
conv_params.conv_filter_strides_,
conv_params.conv_filter_dilations_,
conv_params.input_left_pads_,
conv_params.input_right_pads_,
PassThrough{},
wei_element_op,
out_element_op);
ref_invoker.Run(ref_argument);
in_device_buf.FromDevice(in_device.mData.data());
return ck::utils::check_err(in_device.mData, in_host.mData);
}
return true;
}
int run_grouped_conv_bwd_data_example(int argc, char* argv[])
{
namespace ctc = ck::tensor_layout::convolution;
ExecutionConfig config;
ck::utils::conv::ConvParam conv_params = DefaultConvParams;
if(!parse_cmd_args(argc, argv, config, conv_params))
{
return EXIT_FAILURE;
}
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
if(conv_params.num_dim_spatial_ != NDimSpatial)
{
std::cerr << "unsupported # of spatials dimensions" << std::endl;
return EXIT_FAILURE;
}
// output image: GNHWK
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_params);
// weight: GKYXC
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_params);
// input image: GNHWC
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_params);
return !run_conv_bwd_data(config,
conv_params,
out_g_n_k_wos_desc,
wei_g_k_c_xs_desc,
in_g_n_c_wis_desc,
wei_element_op,
out_element_op,
in_element_op);
}
example/39_permute/CMakeLists.txt 0 → 100644
View file @ 4cccaba1
add_custom_target(example_permute)
add_example_executable(example_permute_1xHxW_fp16 permute_1xHxW_fp16.cpp)
add_example_executable(example_permute_NxHxW_fp16 permute_NxHxW_fp16.cpp)
add_example_executable(example_permute_HxWx4_fp16 permute_HxWx4_fp16.cpp)
add_dependencies(example_permute example_permute_1xHxW_fp16)
add_dependencies(example_permute example_permute_NxHxW_fp16)
add_dependencies(example_permute example_permute_HxWx4_fp16)
example/39_permute/common.hpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <algorithm>
#include <cassert>
#include <cstddef>
#include <cstdlib>
#include <cstring>
#include <iostream>
#include <iterator>
#include <numeric>
#include <type_traits>
#include <utility>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_permute_impl.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/utility/type.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
using F16 = ck::half_t;
using F32 = float;
using F64 = double;
struct Problem final
{
static constexpr std::size_t NumDim = 3;
using Shape = std::array<std::size_t, NumDim>;
using Axes = Shape;
Problem() = delete;
explicit Problem(const Shape& default_shape, const Axes& default_axes)
: shape(default_shape), axes(default_axes)
{
}
Shape shape;
Axes axes;
};
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
namespace detail {
template <typename Array, std::size_t Difference>
struct enlarge_array_size;
template <typename T, std::size_t Size, std::size_t Difference>
struct enlarge_array_size<std::array<T, Size>, Difference>
{
using type = std::array<T, Size + Difference>;
};
template <typename Array, std::size_t Difference>
using enlarge_array_size_t = typename enlarge_array_size<Array, Difference>::type;
template <typename Array>
struct get_array_size;
template <typename T, std::size_t Size>
struct get_array_size<std::array<T, Size>> : std::integral_constant<std::size_t, Size>
{
};
template <typename Array>
inline constexpr std::size_t get_array_size_v = get_array_size<Array>::value;
template <typename T, typename = void>
struct is_iterator : std::false_type
{
};
template <typename T>
struct is_iterator<T,
std::void_t<decltype(*std::declval<T>()),
decltype(++std::declval<std::add_lvalue_reference_t<T>>()),
decltype(std::declval<std::add_lvalue_reference_t<T>>()++)>>
: std::true_type
{
};
template <typename T>
inline constexpr bool is_iterator_v = is_iterator<T>::value;
struct Placeholder final
{
template <typename T>
constexpr inline operator T() const noexcept;
};
template <typename Iterator, typename = void>
struct is_output_iterator : std::false_type
{
};
template <typename Iterator>
struct is_output_iterator<
Iterator,
std::void_t<decltype(*std::declval<Iterator>() = std::declval<Placeholder>())>>
: std::bool_constant<is_iterator_v<Iterator>>
{
};
template <typename T>
inline constexpr bool is_output_iterator_v = is_output_iterator<T>::value;
template <typename Iterator, typename = void>
struct is_bidirectional_iterator : std::false_type
{
};
template <typename Iterator>
struct is_bidirectional_iterator<
Iterator,
std::void_t<decltype(--std::declval<std::add_lvalue_reference_t<Iterator>>()),
decltype(std::declval<std::add_lvalue_reference_t<Iterator>>()--)>>
: std::bool_constant<is_iterator_v<Iterator>>
{
};
template <typename Iterator>
inline constexpr bool is_bidirectional_iterator_v = is_bidirectional_iterator<Iterator>::value;
template <typename Iterator, typename = void>
struct is_random_access_iterator : std::false_type
{
};
template <typename Iterator>
struct is_random_access_iterator<Iterator,
std::void_t<decltype(std::declval<Iterator>() + 1),
decltype(std::declval<Iterator>() - 1),
decltype(std::declval<Iterator>()[1])>>
: std::bool_constant<is_iterator_v<Iterator>>
{
};
template <typename Iterator>
inline constexpr bool is_random_access_iterator_v = is_random_access_iterator<Iterator>::value;
template <typename T, typename = void>
struct is_range : std::false_type
{
};
template <typename T>
struct is_range<T,
std::void_t<decltype(begin(std::declval<T>())),
decltype(end(std::declval<T>())),
decltype(begin(std::declval<T>()) != end(std::declval<T>()))>>
: std::bool_constant<is_iterator_v<ck::remove_cvref_t<decltype(begin(std::declval<T>()))>>>
{
};
template <typename T>
inline constexpr bool is_range_v = is_range<T>::value;
template <typename Range, typename = void>
struct is_sized_range : std::false_type
{
};
template <typename Range>
struct is_sized_range<Range, std::void_t<decltype(size(std::declval<Range>()))>>
: std::bool_constant<is_range_v<Range>>
{
};
template <typename Range>
inline constexpr bool is_sized_range_v = is_sized_range<Range>::value;
template <typename Range, typename = void>
struct is_bidirectional_range : std::false_type
{
};
template <typename Range>
struct is_bidirectional_range<Range, std::void_t<>>
: std::bool_constant<
is_range_v<Range> &&
is_bidirectional_iterator_v<ck::remove_cvref_t<decltype(begin(std::declval<Range>()))>>>
{
};
template <typename Range>
inline constexpr bool is_bidirectional_range_v = is_bidirectional_range<Range>::value;
template <typename Range, typename = void>
struct is_random_access_range : std::false_type
{
};
template <typename Range>
struct is_random_access_range<Range, std::void_t<>>
: std::bool_constant<
is_range_v<Range> &&
is_random_access_iterator_v<ck::remove_cvref_t<decltype(begin(std::declval<Range>()))>>>
{
};
template <typename Range>
inline constexpr bool is_random_access_range_v = is_random_access_range<Range>::value;
template <typename Range>
class to_array_proxy
{
static_assert(is_range_v<Range>);
public:
explicit to_array_proxy(const Range& source) noexcept : source_(source) {}
template <typename T, std::size_t Size>
operator std::array<T, Size>() const
{
std::array<T, Size> destination;
std::copy_n(std::begin(source_),
std::min<std::size_t>(Size, std::size(source_)),
std::begin(destination));
return destination;
}
private:
const Range& source_;
};
} // namespace detail
template <typename Range>
inline auto to_array(Range& range) noexcept
-> std::enable_if_t<detail::is_range_v<Range>,
detail::to_array_proxy<ck::remove_cvref_t<Range>>>
{
return detail::to_array_proxy<ck::remove_cvref_t<Range>>{range};
}
template <typename Axes>
inline auto is_valid_axes(const Axes& axes)
-> std::enable_if_t<detail::is_random_access_range_v<Axes>, bool>
{
using std::empty;
if(empty(axes))
{
return false;
}
using std::begin, std::end;
std::vector<std::size_t> sorted_axes(begin(axes), end(axes));
std::sort(begin(sorted_axes), end(sorted_axes));
const auto last = std::unique(begin(sorted_axes), end(sorted_axes));
return (last == end(sorted_axes)) && (*begin(sorted_axes) == 0) &&
(*std::prev(last) == size(axes) - 1);
}
template <typename Shape>
inline auto is_valid_shape(const Shape& shape) -> std::enable_if_t<detail::is_range_v<Shape>, bool>
{
static_assert(std::is_unsigned_v<ck::remove_cvref_t<decltype(*std::begin(shape))>>);
using std::begin, std::end;
using std::empty;
return !empty(shape) && std::all_of(begin(shape), end(shape), [](auto dim) { return 0 < dim; });
}
template <typename Shape, typename Indices>
inline auto is_valid_indices(const Shape& shape, const Indices& indices)
-> std::enable_if_t<detail::is_sized_range_v<Shape> && detail::is_sized_range_v<Indices>, bool>
{
static_assert(std::is_unsigned_v<ck::remove_cvref_t<decltype(*std::begin(indices))>>);
if(!is_valid_shape(shape))
{
return false;
}
using std::empty;
if(empty(indices))
{
return false;
}
using std::size;
if(size(shape) != size(indices))
{
return false;
}
using std::begin, std::end;
auto dim = begin(shape);
auto idx = begin(indices);
for(; dim != end(shape) && idx != end(indices); ++dim, ++idx)
{
if(*dim <= *idx)
{
return false;
}
}
return true;
}
template <std::size_t Size>
std::array<std::size_t, Size> transpose(const std::array<std::size_t, Size>& shape,
const std::array<std::size_t, Size>& axes)
{
assert(is_valid_shape(shape) && is_valid_axes(axes));
std::array<std::size_t, Size> transposed;
auto iter = std::begin(transposed);
for(const auto axis : axes)
{
*iter++ = shape[axis];
}
return transposed;
}
auto extend_shape(const Problem::Shape& shape, std::size_t new_dim)
{
detail::enlarge_array_size_t<Problem::Shape, 1> extended_shape;
using std::begin, std::end;
ck::ranges::copy(shape, begin(extended_shape));
extended_shape.back() = new_dim;
return extended_shape;
}
auto extend_axes(const Problem::Axes& axes)
{
detail::enlarge_array_size_t<Problem::Axes, 1> extended_axes;
using std::begin, std::end;
ck::ranges::copy(axes, begin(extended_axes));
extended_axes.back() = detail::get_array_size_v<Problem::Axes>;
return extended_axes;
}
template <typename Shape, typename Indices>
auto advance_indices(const Shape& shape, Indices& indices) -> std::enable_if_t<
detail::is_bidirectional_range_v<Shape> && detail::is_sized_range_v<Shape> &&
detail::is_bidirectional_range_v<Indices> && detail::is_sized_range_v<Indices>,
bool>
{
using std::size;
if(!(is_valid_shape(shape) && is_valid_indices(shape, indices) && size(shape) == size(indices)))
{
return false;
}
bool carry = true;
using std::rbegin, std::rend;
auto dim = rbegin(shape);
auto idx = rbegin(indices);
for(; carry && dim != rend(shape) && idx != rend(indices); ++dim, ++idx)
{
*idx = (*idx + carry);
carry = ((*idx == *dim) ? (*idx = 0, true) : false);
}
return !carry;
}
template <typename Src, typename Axes, typename Functor, typename Dest>
auto host_permute(const Tensor<Src>& src, const Axes& axes, Functor functor, Tensor<Dest>& dest)
-> std::enable_if_t<detail::is_random_access_range_v<Axes> && detail::is_sized_range_v<Axes> &&
std::is_invocable_v<Functor,
std::add_lvalue_reference_t<Dest>,
std::add_lvalue_reference_t<Src>>,
bool>
{
const auto& shape = src.mDesc.GetLengths();
const auto& transposed_shape = dest.mDesc.GetLengths();
if(!(is_valid_shape(shape) && is_valid_shape(transposed_shape)))
{
return false;
}
using std::size;
if(!is_valid_axes(axes))
{
return false;
}
static_assert(detail::is_sized_range_v<ck::remove_cvref_t<decltype(shape)>> &&
detail::is_sized_range_v<ck::remove_cvref_t<decltype(transposed_shape)>>);
if(size(shape) != size(transposed_shape))
{
return false;
}
static_assert(detail::is_random_access_range_v<ck::remove_cvref_t<decltype(shape)>> &&
detail::is_random_access_range_v<ck::remove_cvref_t<decltype(transposed_shape)>>);
{
for(std::size_t idx = 0; idx < size(shape); ++idx)
{
if(transposed_shape[idx] != shape[axes[idx]])
{
return false;
}
}
}
std::vector<std::size_t> indices(size(shape), 0);
if(!is_valid_indices(shape, indices))
{
return false;
}
switch(size(shape))
{
case 3: {
do
{
Dest output = 0;
functor(output, src(indices[0], indices[1], indices[2]));
dest(indices[axes[0]], indices[axes[1]], indices[axes[2]]) = output;
} while(advance_indices(shape, indices));
}
break;
case 4: {
do
{
Dest output = 0;
functor(output, src(indices[0], indices[1], indices[2], indices[3]));
dest(indices[axes[0]], indices[axes[1]], indices[axes[2]], indices[axes[3]]) = output;
} while(advance_indices(shape, indices));
}
break;
default: return false;
}
return true;
}
example/39_permute/permute_1xHxW_fp16.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
using InDataType = F16;
using OutDataType = F16;
// clang-format off
using DevicePermuteInstance = ck::tensor_operation::device::DevicePermuteImpl
// ######| NumDim| InData| OutData| Elementwise| Block| NPer| HPer| WPer| InBlock| InBlockTransfer| InBlockTransfer| Src| Dst| Src| Dst|
// ######| | Type| Type| Operation| Size| Block| Block| Block| LdsExtraW| ThreadClusterLengths| ThreadClusterArrangeOrder| VectorDim| VectorDim| ScalarPerVector| ScalarPerVector|
// ######| | | | | | | | | | | | | | | |
// ######| | | | | | | | | | | | | | | |
< 3, InDataType, OutDataType, PassThrough, 256, 1, 32, 32, 3, S<1, 32, 8>, S<0, 1, 2>, 2, 1, 2, 1>;
// clang-format on
#include "run_permute_element_example.inc"
int main() { return !run_permute_element_example({1, 32000, 80}, {0, 2, 1}); }
example/39_permute/permute_HxWx4_fp16.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
using DataType = F16;
using BundleType = F64;
static_assert(sizeof(BundleType) % sizeof(DataType) == 0);
// clang-format off
using DevicePermuteInstance = ck::tensor_operation::device::DevicePermuteImpl
// ######| NumDim| InData| OutData| Elementwise| Block| NPer| HPer| WPer| InBlock| InBlockTransfer| InBlockTransfer| Src| Dst| Src| Dst|
// ######| | Type| Type| Operation| Size| Block| Block| Block| LdsExtraW| ThreadClusterLengths| ThreadClusterArrangeOrder| VectorDim| VectorDim| ScalarPerVector| ScalarPerVector|
// ######| | | | | | | | | | | | | | | |
// ######| | | | | | | | | | | | | | | |
< 3, BundleType, BundleType, PassThrough, 256, 1, 32, 32, 5, S<1, 32, 8>, S<0, 1, 2>, 2, 1, 4, 1>;
// clang-format on
#include "run_permute_bundle_example.inc"
int main() { return !run_permute_bundle_example({1, 80, 32000}, {0, 2, 1}); }
example/39_permute/permute_NxHxW_fp16.cpp 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
using InDataType = F16;
using OutDataType = F16;
// clang-format off
using DevicePermuteInstance = ck::tensor_operation::device::DevicePermuteImpl
// ######| NumDim| InData| OutData| Elementwise| Block| NPer| HPer| WPer| InBlock| InBlockTransfer| InBlockTransfer| Src| Dst| Src| Dst|
// ######| | Type| Type| Operation| Size| Block| Block| Block| LdsExtraW| ThreadClusterLengths| ThreadClusterArrangeOrder| VectorDim| VectorDim| ScalarPerVector| ScalarPerVector|
// ######| | | | | | | | | | | | | | | |
// ######| | | | | | | | | | | | | | | |
< 3, InDataType, OutDataType, PassThrough, 128, 4, 16, 8, 6, S<2, 16, 4>, S<0, 1, 2>, 2, 1, 2, 1>;
// clang-format on
#include "run_permute_element_example.inc"
int main() { return !run_permute_element_example({121, 768, 80}, {0, 2, 1}); }
example/39_permute/run_permute_bundle_example.inc 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
bool run_permute_bundle(const Problem& problem)
{
const auto& input_bundle_shape = problem.shape;
const auto& input_bundle_axes = problem.axes;
const auto output_bundle_shape = transpose(input_bundle_shape, input_bundle_axes);
Tensor<BundleType> input_bundle_tensor(input_bundle_shape);
Tensor<BundleType> output_bundle_tensor(output_bundle_shape);
// initialize tensor by assigning DataType values
ck::utils::FillUniformDistribution<DataType>{-1.f, 1.f}(input_bundle_tensor.AsSpan<DataType>());
DeviceMem input_device_buf(input_bundle_tensor.GetElementSpaceSizeInBytes());
DeviceMem output_device_buf(output_bundle_tensor.GetElementSpaceSizeInBytes());
using std::data;
input_device_buf.ToDevice(data(input_bundle_tensor));
static_assert(std::is_default_constructible_v<DevicePermuteInstance>);
auto permute = DevicePermuteInstance{};
auto argument = permute.MakeArgument(to_array(input_bundle_shape),
to_array(input_bundle_tensor.GetStrides()),
to_array(output_bundle_shape),
to_array(output_bundle_tensor.GetStrides()),
input_device_buf.GetDeviceBuffer(),
output_device_buf.GetDeviceBuffer(),
PassThrough{});
if(!permute.IsSupportedArgument(argument))
{
std::cerr << "The runtime parameters seems not supported by the device instance, exiting!"
<< std::endl;
return false;
};
auto invoker = permute.MakeInvoker();
float ave_time = invoker.Run(argument, StreamConfig{nullptr, true});
std::cout << "Perf: " << ave_time << " ms" << std::endl;
output_device_buf.FromDevice(data(output_bundle_tensor));
constexpr std::size_t NumElemsInBundle = sizeof(BundleType) / sizeof(DataType);
// extend tensor shape from [N, H, W] to [N, H, W, NumElemsInBundle]
// axes from [0, 2, 1] to [0, 2, 1, 3]
const auto input_shape = extend_shape(input_bundle_shape, NumElemsInBundle);
const auto input_axes = extend_axes(input_bundle_axes);
using std::begin;
Tensor<DataType> input_tensor(input_shape);
ck::ranges::copy(input_bundle_tensor.AsSpan<const DataType>(), begin(input_tensor));
Tensor<DataType> output_tensor(transpose(input_shape, input_axes));
if(!host_permute(input_tensor, input_axes, PassThrough{}, output_tensor))
{
return false;
}
return ck::utils::check_err(output_bundle_tensor.AsSpan<const DataType>(),
output_tensor.AsSpan<const DataType>(),
"Error: incorrect results in output tensor",
1e-6,
1e-6);
}
bool run_permute_bundle_example(const Problem::Shape& shape, const Problem::Axes& axes)
{
return run_permute_bundle(Problem{shape, axes});
}
example/39_permute/run_permute_element_example.inc 0 → 100644
View file @ 4cccaba1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
bool run_permute_element(const Problem& problem)
{
const auto& input_shape = problem.shape;
const auto& input_axes = problem.axes;
const auto output_shape = transpose(input_shape, input_axes);
Tensor<InDataType> input_tensor(input_shape);
Tensor<OutDataType> output_tensor(output_shape);
ck::utils::FillUniformDistribution<InDataType>{-1.f, 1.f}(input_tensor);
DeviceMem input_device_buf(input_tensor.GetElementSpaceSizeInBytes());
DeviceMem output_device_buf(output_tensor.GetElementSpaceSizeInBytes());
using std::data;
input_device_buf.ToDevice(data(input_tensor));
static_assert(std::is_default_constructible_v<DevicePermuteInstance>);
auto permute = DevicePermuteInstance{};
auto argument = permute.MakeArgument(to_array(input_shape),
to_array(input_tensor.GetStrides()),
to_array(output_shape),
to_array(output_tensor.GetStrides()),
input_device_buf.GetDeviceBuffer(),
output_device_buf.GetDeviceBuffer(),
PassThrough{});
if(!permute.IsSupportedArgument(argument))
{
std::cerr << "The runtime parameters seems not supported by the device instance, exiting!"
<< std::endl;
return false;
};
auto invoker = permute.MakeInvoker();
float ave_time = invoker.Run(argument, StreamConfig{nullptr, true});
std::cout << "Perf: " << ave_time << " ms" << std::endl;
output_device_buf.FromDevice(data(output_tensor));
Tensor<OutDataType> output_tensor_host(output_shape);
if(!host_permute(input_tensor, input_axes, PassThrough{}, output_tensor_host))
{
return false;
}
return ck::utils::check_err(output_tensor.AsSpan<const OutDataType>(),
output_tensor_host.AsSpan<const OutDataType>(),
"Error: incorrect results in output tensor",
1e-6,
1e-6);
}
bool run_permute_element_example(const Problem::Shape& shape, const Problem::Axes& axes)
{
return run_permute_element(Problem{shape, axes});
}
example/41_grouped_conv_conv_fwd/CMakeLists.txt 0 → 100644
View file @ 4cccaba1
add_example_executable(example_grouped_conv_conv_fwd_xdl_fp32 grouped_conv_conv_fwd_xdl_fp32.cpp)
add_example_executable(example_grouped_conv_conv_fwd_xdl_fp16 grouped_conv_conv_fwd_xdl_fp16.cpp)
add_example_executable(example_grouped_conv_conv_fwd_xdl_bf16 grouped_conv_conv_fwd_xdl_bf16.cpp)
add_example_executable(example_grouped_conv_conv_fwd_xdl_int8 grouped_conv_conv_fwd_xdl_int8.cpp)
if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_grouped_conv_conv_fwd_xdl_int4 grouped_conv_conv_fwd_xdl_int4.cpp)
endif(USE_BITINT_EXTENSION_INT4)
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