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Commit f1b53d78 authored by aska-0096's avatar aska-0096
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Merge branch 'develop' of... 

Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/composable_kernel into navi3x_mD_batchedGEMM_GroupConvFwd
parents 0c9cdbce 7494c1c6
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example/21_gemm_layernorm/CMakeLists.txt
View file @ f1b53d78
add_example_executable(example_gemm_bias_relu_add_layernorm_xdl_fp16 gemm_bias_relu_add_layernorm_xdl_fp16.cpp)
add_example_executable(example_gemm_layernorm_xdl_fp16 gemm_layernorm_xdl_fp16.cpp)
add_example_executable(example_gemm_xdl_layernorm_single_kernel_fp16 gemm_xdl_layernorm_single_kernel_fp16.cpp)
add_example_executable(example_gemm_bias_relu_add_layernorm_xdl_welford_fp16 gemm_bias_relu_add_layernorm_xdl_welford_fp16.cpp)
add_example_executable(example_gemm_bias_relu_add_layernorm_xdl_naive_fp16 gemm_bias_relu_add_layernorm_xdl_naive_fp16.cpp)
add_example_executable(example_gemm_layernorm_xdl_naive_fp16 gemm_layernorm_xdl_naive_fp16.cpp)
add_example_executable(example_gemm_xdl_layernorm_naive_single_kernel_fp16 gemm_xdl_layernorm_naive_single_kernel_fp16.cpp)
example/21_gemm_layernorm/gemm_bias_relu_add_layernorm_xdl_fp16.cpp → example/21_gemm_layernorm/gemm_bias_relu_add_layernorm_xdl_naive_fp16.cpp
View file @ f1b53d78
......@@ -10,7 +10,7 @@
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/device_memory.hpp"
......@@ -95,7 +95,7 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataTyp
using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
// A:x, B:E[x], C:E[x^2], D:Gamma, E:Beta , F:y
using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwise<
using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwiseImpl<
ck::Tuple<EDataType,
R0DataType,
R1DataType,
......
example/21_gemm_layernorm/gemm_bias_relu_add_layernorm_xdl_welford_fp16.cpp 0 → 100644
View file @ f1b53d78
// 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/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_layernorm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.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/reference_tensor_operation/cpu/reference_layernorm.hpp"
#include "ck/library/utility/check_err.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 AddReluAdd = ck::tensor_operation::element_wise::AddReluAdd;
// DataType
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using D0DataType = F16;
using D1DataType = F16;
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
using EMeanVarDataType = F16;
using GammaDataType = F16;
using BetaDataType = F16;
using HDataType = F16;
// Layout
using ALayout = Row;
using BLayout = Col;
using D0Layout = Row;
using D1Layout = Row;
using DsLayout = ck::Tuple<D0Layout, D1Layout>;
using HLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AddReluAdd;
using HElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// clang-format off
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleDLayernorm_Xdl_CShuffle
//######| ALayout| BLayout| DsLayout| HLayout| AData| BData| AccData| CShuffle| DsData| EMeanVarData| GammaData| BetaData| HData| A| B| CDE| H| 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| PostShuffle| PostShuffle| Layernorm| Layernorm|
//######| | | | | Type| Type| Type| DataType| Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Elementwise| Spacialization| 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| ThreadClusterLengths| ScalarPerVector| ThreadClusterLengths| ThreadSliceSize|
//######| | | | | | | | | | | | | | Operation| Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _M_N| _M_N| _M_N| _M|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, DsLayout, HLayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EMeanVarDataType, GammaDataType, BetaDataType, HDataType, AElementOp, BElementOp, CDEElementOp, HElementOp, GemmDefault, 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<32, 8>, 8, S<8, 32>, 8>;
// clang-format on
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({len}),
std::vector<std::size_t>({stride}));
};
auto f_host_tensor_descriptor2d =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};
void host_gemm_layernorm(Tensor<HDataType>& h_m_n,
const Tensor<ADataType>& a_m_k,
const Tensor<BDataType>& b_k_n,
const Tensor<D0DataType>& bias_n,
const Tensor<D1DataType>& d1_m_n,
const Tensor<GammaDataType>& gamma_n,
const Tensor<BetaDataType>& beta_n,
AElementOp a_element_op,
BElementOp b_element_op,
CDEElementOp cde_element_op,
int M,
int N,
AccDataType epsilon = 1e-5)
{
using ReferenceGemm = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
AccDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
using ReferenceLayernorm = ck::tensor_operation::host::ReferenceLayernorm<EMeanVarDataType,
GammaDataType,
BetaDataType,
HDataType,
AccDataType,
HElementOp,
2,
1>;
Tensor<EMeanVarDataType> e_m_n(HostTensorDescriptor{M, N});
Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});
auto ref_gemm = ReferenceGemm{};
auto ref_gemm_invoker = ref_gemm.MakeInvoker();
auto ref_gemm_argument =
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
ref_gemm_invoker.Run(ref_gemm_argument);
for(int n = 0; n < N; ++n)
{
AccDataType bias = static_cast<AccDataType>(bias_n(n));
for(int m = 0; m < M; ++m)
{
AccDataType e = static_cast<AccDataType>(e_m_n(m, n));
AccDataType d1 = static_cast<AccDataType>(d1_m_n(m, n));
cde_element_op(e, c_m_n(m, n), bias, d1);
e_m_n(m, n) = static_cast<EMeanVarDataType>(e);
}
}
ReferenceLayernorm ref_layernorm;
auto ref_layernorm_invoker = ref_layernorm.MakeInvoker();
auto ref_layernorm_argument = ref_layernorm.MakeArgument(
e_m_n, gamma_n, beta_n, h_m_n, HElementOp{}, {M, N}, {1}, epsilon);
ref_layernorm_invoker.Run(ref_layernorm_argument);
}
int main()
{
bool do_verification = true;
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 1024;
ck::index_t StrideA = K;
ck::index_t StrideB = K;
ck::index_t StrideD0 = 0;
ck::index_t StrideD1 = N;
ck::index_t StrideH = N;
float epsilon = 1e-5;
Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
Tensor<D0DataType> d0_n(f_host_tensor_descriptor1d(N, 1));
Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor2d(M, N, StrideD1, D1Layout{}));
Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
Tensor<HDataType> h_m_n(f_host_tensor_descriptor2d(M, N, StrideH, HLayout{}));
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
d0_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-1, 1});
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{-1, 1});
gamma_n.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-1, 1});
beta_n.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-1, 1});
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem d0_device_buf(sizeof(D0DataType) * d0_n.mDesc.GetElementSpaceSize());
DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpaceSize());
DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpaceSize());
DeviceMem h_device_buf(sizeof(HDataType) * h_m_n.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
d0_device_buf.ToDevice(d0_n.mData.data());
d1_device_buf.ToDevice(d1_m_n.mData.data());
gamma_device_buf.ToDevice(gamma_n.mData.data());
beta_device_buf.ToDevice(beta_n.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto h_element_op = HElementOp{};
auto device_op = DeviceOpInstance{};
auto invoker = device_op.MakeInvoker();
auto argument =
device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
{d0_device_buf.GetDeviceBuffer(), d1_device_buf.GetDeviceBuffer()},
gamma_device_buf.GetDeviceBuffer(),
beta_device_buf.GetDeviceBuffer(),
h_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
{StrideD0, StrideD1},
StrideH,
epsilon,
a_element_op,
b_element_op,
cde_element_op,
h_element_op);
if(!device_op.IsSupportedArgument(argument))
{
throw std::runtime_error("wrong! this device_op instance does not support this problem");
}
size_t workspace_sz = device_op.GetWorkSpaceSize(&argument);
DeviceMem workspace_dev(workspace_sz);
device_op.SetWorkSpacePointer(&argument, workspace_dev.GetDeviceBuffer());
invoker.Run(argument, StreamConfig{nullptr, false});
bool pass = true;
if(do_verification)
{
Tensor<HDataType> h_m_n_host(HostTensorDescriptor{M, N});
host_gemm_layernorm(h_m_n_host,
a_m_k,
b_k_n,
d0_n,
d1_m_n,
gamma_n,
beta_n,
a_element_op,
b_element_op,
cde_element_op,
M,
N,
epsilon);
h_device_buf.FromDevice(h_m_n.mData.data());
pass &=
ck::utils::check_err(h_m_n, h_m_n_host, "Error: Incorrect results h_m_n", 1e-2, 1e-2);
}
return pass ? 0 : 1;
}
example/21_gemm_layernorm/gemm_layernorm_xdl_fp16.cpp → example/21_gemm_layernorm/gemm_layernorm_xdl_naive_fp16.cpp
View file @ f1b53d78
......@@ -10,7 +10,7 @@
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/device_memory.hpp"
......@@ -92,7 +92,7 @@ using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataTyp
using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
// A:x, B:E[x], C:E[x^2], D:Gamma, E:Beta , F:y
using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwise<
using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwiseImpl<
ck::Tuple<EDataType,
R0DataType,
R1DataType,
......
example/23_softmax/softmax_blockwise.cpp
View file @ f1b53d78
......@@ -56,8 +56,8 @@ class SimpleAppArgs
int option_index = 0;
public:
std::vector<size_t> inLengths = {8, 128, 2048};
std::vector<AccDataType> scales = {2.0f, 2.0f};
std::vector<size_t> inLengths = {8, 128, 2048};
std::vector<double> scales = {2.0, 2.0};
bool do_verification = true;
int init_method = 2;
......@@ -151,8 +151,8 @@ int main(int argc, char* argv[])
auto inStrides = in.mDesc.GetStrides();
auto outStrides = out.mDesc.GetStrides();
AccDataType alpha = args.scales[0];
AccDataType beta = args.scales[1];
double alpha = args.scales[0];
double beta = args.scales[1];
std::cout << "in: " << in.mDesc << std::endl;
std::cout << "out: " << out.mDesc << std::endl;
......@@ -221,8 +221,8 @@ int main(int argc, char* argv[])
auto argument_ptr = device_instance.MakeArgumentPointer(i_inLengths,
i_inStrides,
reduceDims,
&alpha,
&beta,
alpha,
beta,
in_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer(),
PassThrough{},
......
example/33_multiple_reduce/dual_reduce_common.hpp
View file @ f1b53d78
......@@ -217,8 +217,8 @@ int mean_meansquare_dual_reduce_test(size_t n,
size_t invariant_total_length = n;
size_t reduce_total_length = h * w * c;
const AccDataType alpha = ck::type_convert<AccDataType>(1.0f);
const AccDataType beta = ck::type_convert<AccDataType>(0.0f);
const double alpha = 1.0f;
const double beta = 0.0f;
std::size_t num_thread = 1;
......@@ -267,8 +267,8 @@ int mean_meansquare_dual_reduce_test(size_t n,
i_outLengths,
{i_outStrides, i_outStrides},
reduceDims,
{&alpha, &alpha},
{&beta, &beta},
{alpha, alpha},
{beta, beta},
in_dev.GetDeviceBuffer(),
{mean_dev.GetDeviceBuffer(), meansquare_dev.GetDeviceBuffer()},
ck::make_tuple(InElementwiseOperation_Mean{}, InElementwiseOperation_Meansquare{}),
......
example/34_batchnorm/batchnorm_infer_impl.hpp
View file @ f1b53d78
......@@ -10,7 +10,7 @@
#include "ck/utility/sequence.hpp"
#include "ck/utility/tuple.hpp"
#include "ck/utility/reduction_operator.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "batchnorm_common.hpp"
......@@ -46,7 +46,7 @@ int bnorm_infer(
static_assert(NumBatchNormReduceDim < Rank,
"Invalid number of reduced dimensions for batchnorm!");
using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwise<
using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwiseImpl<
ck::Tuple<XDataType, AccDataType, AccDataType, AccDataType, AccDataType>, // x, mean,
// variance,
// scale,
......
example/36_sparse_embedding/sparse_embedding3_forward_layernorm.cpp
View file @ f1b53d78
......@@ -9,7 +9,8 @@
#include <ctime>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_sparse_embedding3_forward_layernorm.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_sparse_embeddings_forward_layernorm.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"
......@@ -18,53 +19,26 @@
#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;
// clang-format off
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;
using EmbElementwiseOperation = ck::tensor_operation::element_wise::AddAdd;
// 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>;
using DeviceInstance_fp16_e256 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 256, 1, 1, 3>;
using DeviceInstance_fp16_e512 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 512, 1, 2, 3>;
using DeviceInstance_fp16_e768 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 768, 1, 1, 3>;
using DeviceInstance_fp16_e1024 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 1024, 1, 2, 3>;
using DeviceInstance_fp16_e1536 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 1536, 1, 2, 3>;
using DeviceInstance_fp16_e2048 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 2048, 1, 2, 3>;
using DeviceInstance_fp16_e4096 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 4096, 1, 8, 3>;
using DeviceInstance_fp16_e8192 = ck::tensor_operation::device::DeviceSparseEmbeddingsForwardLayernorm<EmbType, IndexType, GammaDataType, BetaDataType, AccDataType, OutType, EmbElementwiseOperation, 256, 1, 256, 1, 8192, 1, 8, 3>;
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; };
......@@ -152,19 +126,20 @@ int main()
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);
auto argument_ptr = device_instance.MakeArgumentPointer(
out_dev.GetDeviceBuffer(),
{ck::type_convert<EmbType*>(emb_a_dev.GetDeviceBuffer()),
ck::type_convert<EmbType*>(emb_b_dev.GetDeviceBuffer()),
ck::type_convert<EmbType*>(emb_c_dev.GetDeviceBuffer())},
{ck::type_convert<IndexType*>(index_a_dev.GetDeviceBuffer()),
ck::type_convert<IndexType*>(index_b_dev.GetDeviceBuffer()),
ck::type_convert<IndexType*>(index_c_dev.GetDeviceBuffer())},
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
current_dim,
index_length,
epsilon,
EmbElementwiseOperation{});
std::cout << "Dim:" << current_dim << ", kernel:" << device_instance.GetTypeString()
<< std::endl
<< std::flush;
......
example/44_elementwise_permute/elementwise_permute_4D_fp16.cpp
View file @ f1b53d78
......@@ -3,7 +3,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
......@@ -19,13 +19,13 @@ using BDataType = F16;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceElementwisePermuteInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ADataType>,
ck::Tuple<BDataType>,
PassThrough,
4,
8,
ck::Sequence<8>,
ck::Sequence<1>>;
ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>,
ck::Tuple<BDataType>,
PassThrough,
4,
8,
ck::Sequence<8>,
ck::Sequence<1>>;
template <typename HostTensorA, typename HostTensorB, typename Functor>
void host_elementwise4D(HostTensorB& B_nhwc, const HostTensorA& A_nchw, Functor functor)
......
example/44_elementwise_permute/elementwise_permute_4D_fp16_2d.cpp
View file @ f1b53d78
......@@ -3,7 +3,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise_2d.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_2d_impl.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
......@@ -17,15 +17,15 @@ using BDataType = F16;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceElementwisePermuteInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ADataType>,
ck::Tuple<BDataType>,
PassThrough,
3, // NumDim_M
1, // NumDim_N
8,
8,
ck::Sequence<8>,
ck::Sequence<8>>;
ck::tensor_operation::device::DeviceElementwise2dImpl<ck::Tuple<ADataType>,
ck::Tuple<BDataType>,
PassThrough,
3, // NumDim_M
1, // NumDim_N
8,
8,
ck::Sequence<8>,
ck::Sequence<8>>;
template <typename HostTensorA, typename HostTensorB, typename Functor>
void host_elementwise4D(HostTensorB& B_nhwc,
......
example/46_gemm_add_multiply/CMakeLists.txt 0 → 100644
View file @ f1b53d78
add_example_executable(example_gemm_add_multiply_dl_fp16 gemm_add_multiply_dl_fp16.cpp)
add_example_executable(example_gemm_add_multiply_xdl_fp16 gemm_add_multiply_xdl_fp16.cpp)
example/46_gemm_add_multiply/README.md 0 → 100644
View file @ f1b53d78
# Instructions for ```example_gemm_add_multiply_dl_fp16```
## Run ```example_gemm_add_multiply_dl_fp16```
```bash
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: time kernel (0=no, 1=yes)
#arg4 to 11: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, StrideE"
./bin/example_gemm_add_multiply_dl_fp16 1 1 1
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
```
a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
b_k_n: dim 2, lengths {4096, 4096}, strides {4096, 1}
d0_m_n: dim 2, lengths {3840, 4096}, strides {0, 1}
d1_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
e_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
arg.a_grid_desc_k0_m0_m1_k1_{2048, 3840, 2}
arg.b_grid_desc_k0_n0_n1_k1_{2048, 4096, 2}
arg.e_grid_desc_m_n_{ 3840, 4096}
launch_and_time_kernel: grid_dim {960, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 3.99904 ms, 32.22 TFlops, 31.9913 GB/s, DeviceGemmMultipleD_Dl<256, 128, 128, 16, 2, 4, 4, 1>
```
example/46_gemm_add_multiply/common.hpp 0 → 100644
View file @ f1b53d78
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <algorithm>
#include <cstddef>
#include <iostream>
#include <stdexcept>
#include <string>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.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"
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 AddMultiply = ck::tensor_operation::element_wise::AddMultiply;
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using I8 = int8_t;
using I32 = int32_t;
struct ProblemSize final
{
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideD0 = 0;
ck::index_t StrideD1 = 4096;
ck::index_t StrideE = 4096;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
};
inline bool
parse_cmd_args(int argc, char* argv[], ProblemSize& problem_size, ExecutionConfig& config)
{
if(argc == 1)
{
// use default case
}
else if(argc == 4)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
else if(argc == 12)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
problem_size.M = std::stoi(argv[4]);
problem_size.N = std::stoi(argv[5]);
problem_size.K = std::stoi(argv[6]);
problem_size.StrideA = std::stoi(argv[7]);
problem_size.StrideB = std::stoi(argv[8]);
problem_size.StrideD0 = std::stoi(argv[9]);
problem_size.StrideD1 = std::stoi(argv[10]);
problem_size.StrideE = std::stoi(argv[11]);
}
else
{
std::cerr << "arg1: verification (0=no, 1=yes)" << std::endl
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)"
<< std::endl
<< "arg3: time kernel (0=no, 1=yes)" << std::endl
<< "arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, "
"StrideE"
<< std::endl;
return false;
}
return true;
}
example/46_gemm_add_multiply/gemm_add_multiply_dl_fp16.cpp 0 → 100644
View file @ f1b53d78
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_dl.hpp"
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using D0DataType = F16;
using D1DataType = F16;
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
using EDataType = F16;
using ALayout = Row;
using BLayout = Row;
using D0Layout = Row;
using D1Layout = Row;
using DsLayout = ck::Tuple<D0Layout, D1Layout>;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AddMultiply;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// clang-format off
using DeviceOpInstance = ck::tensor_operation::device::
// ##################| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| DsData| EData| A| B| CDE| GEMM| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M11N11Thread| M11N11Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
// ##################| | | | | Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | ThreadM111| ThreadN111| Thread| ClusterM110Xs| ClusterN110Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccess| SrcVectorTensor| SrcVectorTensor| DstVectorTensor| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccess| SrcVectorTensor| SrcVectorTensor| DstVectorTensor| SrcDstAccess| SrcDstVectorDim| DstScalarPerVector|
// ##################| | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | K0_M0_M1_K1| K0_M0_M1_K1| ArrangeOrder| Order| Lengths_K0_M0_M1_K1| ContiguousDimOrder| Lengths_K0_M0_M1_K1| K0_N0_N1_K1| K0_N0_N1_K1| ArrangeOrder| Order| Lengths_K0_N0_N1_K1| ContiguousDimOrder| Lengths_K0_N0_N1_K1| Order| | |
// ##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Dl< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 256, 128, 128, 16, 2, 4, 4, 1, S<8, 2>, S<8, 2>, S<8, 1, 1, 2>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<4, 1, 1, 2>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<2, 1, 4, 2>, S<8, 1, 32, 1>, S<0, 3, 1, 2>, S<0, 3, 1, 2>, S<1, 1, 4, 1>, S<0, 3, 1, 2>, S<1, 1, 4, 2>, S<0, 1, 2, 3, 4, 5>, 5, 4>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
AccDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
#include "run_gemm_add_multiply_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_add_multiply_example(argc, argv); }
example/46_gemm_add_multiply/gemm_add_multiply_xdl_fp16.cpp 0 → 100644
View file @ f1b53d78
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using D0DataType = F16;
using D1DataType = F16;
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
using EDataType = F16;
using ALayout = Row;
using BLayout = Row;
using D0Layout = Row;
using D1Layout = Row;
using DsLayout = ck::Tuple<D0Layout, D1Layout>;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AddMultiply;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// clang-format off
using DeviceOpInstance = ck::tensor_operation::device::
//##############################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| 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|
//##############################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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|
//##############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Xdl_CShuffle< Row, Row, DsLayout, Row, F16, F16, F32, F16, DsDataType, F16, PassThrough, PassThrough, CDEElementOp, GemmDefault, 1, 128, 128, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
AccDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
#include "run_gemm_add_multiply_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_add_multiply_example(argc, argv); }
example/46_gemm_add_multiply/run_gemm_add_multiply_example.inc 0 → 100644
View file @ f1b53d78
#pragma once
bool run_gemm_add_multiply(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideE] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD0, D0Layout{}));
Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD1, D1Layout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
switch(config.init_method)
{
case 0: break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-1, 1});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
}
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
d0_device_buf.ToDevice(d0_m_n.mData.data());
d1_device_buf.ToDevice(d1_m_n.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
// do GEMM
auto device_op = DeviceOpInstance{};
auto invoker = device_op.MakeInvoker();
auto argument =
device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
{d0_device_buf.GetDeviceBuffer(), d1_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
{StrideD0, StrideD1},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
if(!device_op.IsSupportedArgument(argument))
{
std::cout << "wrong! this device_op instance does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(D0DataType) * N + sizeof(D1DataType) * M * N +
sizeof(EDataType) * M * N;
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, "
<< device_op.GetTypeString() << std::endl;
if(config.do_verification)
{
Tensor<AccDataType> c_m_n({M, N});
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument =
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
}
}
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
}
return true;
}
bool run_gemm_add_multiply_example(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) ||
run_gemm_add_multiply(problem_size, config);
}
include/ck/ck.hpp
View file @ f1b53d78
......@@ -18,8 +18,13 @@
#define CK_USE_LAUNCH_BOUNDS 1
#ifdef CK_USE_LAUNCH_BOUNDS
// for most kernels
#define CK_MAX_THREAD_PER_BLOCK 256
#define CK_MIN_BLOCK_PER_CU 2
// for wavelet GEMM kernel
#define CK_WAVELET_MAX_THREAD_PER_BLOCK 512
#define CK_WAVELET_MIN_BLOCK_PER_CU 2
#endif
// check GPU target
......
include/ck/tensor_operation/gpu/device/device_elementwise_base.hpp → include/ck/tensor_operation/gpu/device/device_elementwise.hpp
View file @ f1b53d78
......@@ -17,7 +17,7 @@ template <typename InDataTypeTuple,
typename OutDataTypeTuple,
typename ElementwiseOperation,
index_t NumDim>
struct DeviceElementwiseBase : public BaseOperator
struct DeviceElementwise : public BaseOperator
{
static constexpr int NumInput = InDataTypeTuple::Size();
static constexpr int NumOutput = OutDataTypeTuple::Size();
......@@ -37,8 +37,8 @@ template <typename InDataTypeTuple,
typename OutDataTypeTuple,
typename ElementwiseOperation,
index_t NumDim>
using DeviceElementwiseBasePtr = std::unique_ptr<
DeviceElementwiseBase<InDataTypeTuple, OutDataTypeTuple, ElementwiseOperation, NumDim>>;
using DeviceElementwisePtr = std::unique_ptr<
DeviceElementwise<InDataTypeTuple, OutDataTypeTuple, ElementwiseOperation, NumDim>>;
} // namespace device
} // namespace tensor_operation
......
include/ck/tensor_operation/gpu/device/device_elementwise_normalization.hpp
View file @ f1b53d78
......@@ -32,7 +32,7 @@ struct DeviceElementwiseNormalization : public BaseOperator
const std::vector<index_t> betaStrides,
const std::vector<index_t> yStrides,
const std::vector<index_t> reduceDims,
AccDataType epsilon,
double epsilon,
const std::array<const void*, NumInput> in_dev_buffers,
const void* p_gamma,
const void* p_beta,
......
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