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Commit fdddc8f4 authored by Bartlomiej Kocot's avatar Bartlomiej Kocot
Browse files

Merge branch 'develop' of github.com:ROCmSoftwarePlatform/composable_kernel... 

Merge branch 'develop' of github.com:ROCmSoftwarePlatform/composable_kernel into barkocot/fix-cmake-tensor-op-instance
parents 8f48018d f7331c60
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Showing with 299 additions and 241 deletions
example/20_grouped_conv_bwd_weight/run_grouped_conv_bwd_weight_example.inc
View file @ fdddc8f4
...@@ -5,7 +5,7 @@ template <ck::index_t NDimSpatial> ...@@ -5,7 +5,7 @@ template <ck::index_t NDimSpatial>
bool run_grouped_conv_bwd_weight(const ExecutionConfig& config, bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
const ck::utils::conv::ConvParam& conv_param) const ck::utils::conv::ConvParam& conv_param)
{ {
// Dl op doesn't support split_k > 1 // Dl and WMMA ops don't support split_k > 1
constexpr ck::index_t split_k = 1; constexpr ck::index_t split_k = 1;
const auto in_g_n_c_wis_desc = const auto in_g_n_c_wis_desc =
...@@ -143,23 +143,3 @@ bool run_grouped_conv_bwd_weight(const ExecutionConfig& config, ...@@ -143,23 +143,3 @@ bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
return true; return true;
} }
bool run_grouped_conv_bwd_weight_example(int argc, char* argv[])
{
ExecutionConfig config;
ck::utils::conv::ConvParam conv_param = DefaultConvParam;
if(!parse_cmd_args(argc, argv, config, conv_param))
{
return false;
}
switch(conv_param.num_dim_spatial_)
{
case 1: return run_grouped_conv_bwd_weight<1>(config, conv_param);
case 2: return run_grouped_conv_bwd_weight<2>(config, conv_param);
case 3: return run_grouped_conv_bwd_weight<3>(config, conv_param);
}
return false;
}
example/21_gemm_layernorm/gemm_bias_relu_add_layernorm_xdl_welford_fp16.cpp
View file @ fdddc8f4
...@@ -114,12 +114,15 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n, ...@@ -114,12 +114,15 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n,
BetaDataType, BetaDataType,
HDataType, HDataType,
AccDataType, AccDataType,
AccDataType,
HElementOp, HElementOp,
2, 2,
1>; 1>;
Tensor<EMeanVarDataType> e_m_n(HostTensorDescriptor{M, N}); Tensor<EMeanVarDataType> e_m_n(HostTensorDescriptor{M, N});
Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N}); Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});
Tensor<AccDataType> save_mean({M});
Tensor<AccDataType> save_inv_std({M});
auto ref_gemm = ReferenceGemm{}; auto ref_gemm = ReferenceGemm{};
auto ref_gemm_invoker = ref_gemm.MakeInvoker(); auto ref_gemm_invoker = ref_gemm.MakeInvoker();
...@@ -145,7 +148,7 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n, ...@@ -145,7 +148,7 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n,
auto ref_layernorm_invoker = ref_layernorm.MakeInvoker(); auto ref_layernorm_invoker = ref_layernorm.MakeInvoker();
auto ref_layernorm_argument = ref_layernorm.MakeArgument( auto ref_layernorm_argument = ref_layernorm.MakeArgument(
e_m_n, gamma_n, beta_n, h_m_n, h_element_op, {M, N}, {1}, epsilon); e_m_n, gamma_n, beta_n, h_m_n, save_mean, save_inv_std, h_element_op, {M, N}, {1}, epsilon);
ref_layernorm_invoker.Run(ref_layernorm_argument); ref_layernorm_invoker.Run(ref_layernorm_argument);
} }
......
example/22_cgemm/CMakeLists.txt
View file @ fdddc8f4
add_custom_target(example_cgemm_xdl) add_custom_target(example_cgemm_xdl)
add_example_executable(example_cgemm_xdl_bf16 cgemm_xdl_bf16.cpp) add_example_executable(example_cgemm_xdl_bf16 cgemm_xdl_bf16.cpp)
if(result EQUAL 0) add_example_dependencies(example_cgemm_xdl example_cgemm_xdl_bf16)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_bf16)
endif()
add_example_executable(example_cgemm_xdl_fp16 cgemm_xdl_fp16.cpp) add_example_executable(example_cgemm_xdl_fp16 cgemm_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_cgemm_xdl example_cgemm_xdl_fp16)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_fp16)
endif()
add_example_executable(example_cgemm_xdl_fp32 cgemm_xdl_fp32.cpp) add_example_executable(example_cgemm_xdl_fp32 cgemm_xdl_fp32.cpp)
if(result EQUAL 0) add_example_dependencies(example_cgemm_xdl example_cgemm_xdl_fp32)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_fp32)
endif()
add_example_executable(example_cgemm_xdl_int8 cgemm_xdl_int8.cpp) add_example_executable(example_cgemm_xdl_int8 cgemm_xdl_int8.cpp)
if(result EQUAL 0) add_example_dependencies(example_cgemm_xdl example_cgemm_xdl_int8)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_int8)
endif()
if(USE_BITINT_EXTENSION_INT4) if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_cgemm_xdl_int4 cgemm_xdl_int4.cpp) add_example_executable(example_cgemm_xdl_int4 cgemm_xdl_int4.cpp)
add_dependencies(example_cgemm_xdl example_cgemm_xdl_int4) add_example_dependencies(example_cgemm_xdl example_cgemm_xdl_int4)
endif() endif()
example/24_batched_gemm/CMakeLists.txt
View file @ fdddc8f4
add_custom_target(example_batched_gemm_xdl) add_custom_target(example_batched_gemm_xdl)
add_example_executable(example_batched_gemm_xdl_fp32 batched_gemm_xdl_fp32.cpp) add_example_executable(example_batched_gemm_xdl_fp32 batched_gemm_xdl_fp32.cpp)
if(result EQUAL 0) add_example_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_fp32)
add_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_fp32)
endif()
add_example_executable(example_batched_gemm_xdl_fp16 batched_gemm_xdl_fp16.cpp) add_example_executable(example_batched_gemm_xdl_fp16 batched_gemm_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_fp16)
add_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_fp16)
endif()
add_example_executable(example_batched_gemm_xdl_bf16 batched_gemm_xdl_bf16.cpp) add_example_executable(example_batched_gemm_xdl_bf16 batched_gemm_xdl_bf16.cpp)
if(result EQUAL 0) add_example_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_bf16)
add_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_bf16)
endif()
add_example_executable(example_batched_gemm_xdl_int8 batched_gemm_xdl_int8.cpp) add_example_executable(example_batched_gemm_xdl_int8 batched_gemm_xdl_int8.cpp)
if(result EQUAL 0) add_example_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_int8)
add_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_int8)
endif()
if(USE_BITINT_EXTENSION_INT4) if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_batched_gemm_xdl_int4 batched_gemm_xdl_int4.cpp) add_example_executable(example_batched_gemm_xdl_int4 batched_gemm_xdl_int4.cpp)
if(result EQUAL 0) add_example_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_int4)
add_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_int4)
endif()
endif() endif()
example/27_layernorm/layernorm_fp16.cpp
View file @ fdddc8f4
...@@ -3,12 +3,15 @@ ...@@ -3,12 +3,15 @@
#include "common.hpp" #include "common.hpp"
using XDataType = ck::half_t; using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t; using BetaDataType = ck::half_t;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using ComputeDataType = float; using SaveMeanInvStdDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using ComputeDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
#define SAVE_MEAN_INV_STD
constexpr int Rank = 2; constexpr int Rank = 2;
constexpr int NumReduceDim = 1; constexpr int NumReduceDim = 1;
...@@ -19,6 +22,7 @@ using DeviceInstance = ...@@ -19,6 +22,7 @@ using DeviceInstance =
BetaDataType, BetaDataType,
ComputeDataType, ComputeDataType,
YDataType, YDataType,
SaveMeanInvStdDataType,
PassThrough, PassThrough,
Rank, Rank,
NumReduceDim, NumReduceDim,
...@@ -33,7 +37,8 @@ using DeviceInstance = ...@@ -33,7 +37,8 @@ using DeviceInstance =
8, // GammaScalarPerVector 8, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K) 1, // BetaVecDim (0=M, 1=K)
8, // BetaScalarPerVector 8, // BetaScalarPerVector
8>; // OutScalarPerVector 8, // YScalarPerVector
1>; // SaveMeanInvStdScalarPerVector
#include "run_layernorm_example.inc" #include "run_layernorm_example.inc"
int main() { return run_groupnorm_example<DeviceInstance>(); } int main() { return run_groupnorm_example<DeviceInstance>(); }
example/27_layernorm/layernorm_splitk_fp16.cpp
View file @ fdddc8f4
...@@ -3,12 +3,15 @@ ...@@ -3,12 +3,15 @@
#include "common.hpp" #include "common.hpp"
using XDataType = ck::half_t; using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t; using BetaDataType = ck::half_t;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using ComputeDataType = float; using SaveMeanInvStdDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using ComputeDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
#define SAVE_MEAN_INV_STD
constexpr int Rank = 2; constexpr int Rank = 2;
constexpr int NumReduceDim = 1; constexpr int NumReduceDim = 1;
...@@ -19,6 +22,7 @@ using DeviceInstance = ...@@ -19,6 +22,7 @@ using DeviceInstance =
BetaDataType, BetaDataType,
ComputeDataType, ComputeDataType,
YDataType, YDataType,
SaveMeanInvStdDataType,
PassThrough, PassThrough,
Rank, Rank,
NumReduceDim, NumReduceDim,
...@@ -33,7 +37,8 @@ using DeviceInstance = ...@@ -33,7 +37,8 @@ using DeviceInstance =
8, // GammaScalarPerVector 8, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K) 1, // BetaVecDim (0=M, 1=K)
8, // BetaScalarPerVector 8, // BetaScalarPerVector
8>; // YScalarPerVector 8, // YScalarPerVector
1>; // SaveMeanInvStdScalarPerVector
#include "run_layernorm_example.inc" #include "run_layernorm_example.inc"
......
example/27_layernorm/run_layernorm_example.inc
View file @ fdddc8f4
...@@ -10,22 +10,13 @@ int run_groupnorm_example() ...@@ -10,22 +10,13 @@ int run_groupnorm_example()
ck::index_t M = 1024; ck::index_t M = 1024;
ck::index_t N = 1024; ck::index_t N = 1024;
ck::index_t Stride = N;
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) { Tensor<XDataType> x({M, N});
return HostTensorDescriptor({len}, {stride}); Tensor<GammaDataType> gamma({N});
}; Tensor<BetaDataType> beta({N});
Tensor<YDataType> y({M, N});
auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) { Tensor<SaveMeanInvStdDataType> save_mean({M});
using namespace ck::literals; Tensor<SaveMeanInvStdDataType> save_inv_std({M});
return HostTensorDescriptor({row, col}, {stride, 1_uz});
};
Tensor<XDataType> x(f_host_tensor_descriptor2d(M, N, Stride));
Tensor<GammaDataType> gamma(f_host_tensor_descriptor1d(N, 1));
Tensor<BetaDataType> beta(f_host_tensor_descriptor1d(N, 1));
Tensor<YDataType> y(f_host_tensor_descriptor2d(M, N, Stride));
x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0.0, 1.0}); x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0.0, 1.0});
gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0}); gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
...@@ -35,6 +26,11 @@ int run_groupnorm_example() ...@@ -35,6 +26,11 @@ int run_groupnorm_example()
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize()); DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize()); DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize()); DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
#ifdef SAVE_MEAN_INV_STD
DeviceMem save_mean_dev(sizeof(SaveMeanInvStdDataType) * save_mean.mDesc.GetElementSpaceSize());
DeviceMem save_inv_std_dev(sizeof(SaveMeanInvStdDataType) *
save_inv_std.mDesc.GetElementSpaceSize());
#endif
x_dev.ToDevice(x.mData.data()); x_dev.ToDevice(x.mData.data());
gamma_dev.ToDevice(gamma.mData.data()); gamma_dev.ToDevice(gamma.mData.data());
...@@ -47,14 +43,23 @@ int run_groupnorm_example() ...@@ -47,14 +43,23 @@ int run_groupnorm_example()
{0, 1}, {0, 1},
{0, 1}, {0, 1},
std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()}, std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
save_mean.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
save_mean.mDesc.GetStrides().end()},
{1}, {1},
1e-4, 1e-4,
x_dev.GetDeviceBuffer(), x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(), gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(), beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(), y_dev.GetDeviceBuffer(),
#ifdef SAVE_MEAN_INV_STD
save_mean_dev.GetDeviceBuffer(),
save_inv_std_dev.GetDeviceBuffer(),
#else
nullptr, nullptr,
nullptr, nullptr,
#endif
PassThrough{}); PassThrough{});
if(!device_instance.IsSupportedArgument(argument_ptr.get())) if(!device_instance.IsSupportedArgument(argument_ptr.get()))
...@@ -72,24 +77,45 @@ int run_groupnorm_example() ...@@ -72,24 +77,45 @@ int run_groupnorm_example()
bool pass = true; bool pass = true;
{ {
Tensor<YDataType> host_y(f_host_tensor_descriptor2d(M, N, Stride)); Tensor<YDataType> host_y({M, N});
using ReferenceInstance = ck::tensor_operation::host::ReferenceLayernorm<XDataType, Tensor<SaveMeanInvStdDataType> host_save_mean({M});
GammaDataType, Tensor<SaveMeanInvStdDataType> host_save_inv_std({M});
BetaDataType,
YDataType, using ReferenceInstance =
ComputeDataType, ck::tensor_operation::host::ReferenceLayernorm<XDataType,
PassThrough, GammaDataType,
Rank, BetaDataType,
NumReduceDim>; YDataType,
SaveMeanInvStdDataType,
ComputeDataType,
PassThrough,
Rank,
NumReduceDim>;
ReferenceInstance ref; ReferenceInstance ref;
auto ref_argument = auto ref_argument = ref.MakeArgument(x,
ref.MakeArgument(x, gamma, beta, host_y, PassThrough{}, {M, N}, {1}, 1e-4); gamma,
auto ref_invoker = ref.MakeInvoker(); beta,
host_y,
host_save_mean,
host_save_inv_std,
PassThrough{},
{M, N},
{1},
1e-4);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
y_dev.FromDevice(y.mData.data()); y_dev.FromDevice(y.mData.data());
pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3); pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results (y)", 1e-3, 1e-3);
#ifdef SAVE_MEAN_INV_STD
save_mean_dev.FromDevice(save_mean.mData.data());
save_inv_std_dev.FromDevice(save_inv_std.mData.data());
pass &= ck::utils::check_err(
save_mean, host_save_mean, "Error: Incorrect results (mean)", 1e-3, 1e-3);
pass &= ck::utils::check_err(
save_inv_std, host_save_inv_std, "Error: Incorrect results (inv_std)", 1e-3, 1e-3);
#endif
} }
return (pass ? 0 : 1); return (pass ? 0 : 1);
......
example/30_grouped_conv_fwd_multiple_d/CMakeLists.txt
View file @ fdddc8f4
...@@ -3,44 +3,38 @@ list(APPEND gpu_list2 gfx1100 gfx1101 gfx1102) ...@@ -3,44 +3,38 @@ list(APPEND gpu_list2 gfx1100 gfx1101 gfx1102)
set(target 0) set(target 0)
foreach(gpu IN LISTS GPU_TARGETS) foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list1 AND target EQUAL 0) if(gpu IN_LIST gpu_list1 AND target EQUAL 0)
add_custom_target(example_grouped_conv_fwd_multiple_d) add_custom_target(example_grouped_conv_fwd_multiple_d)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_fp16 grouped_conv_fwd_bias_relu_add_xdl_fp16.cpp)
if(result EQUAL 0) add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_fp16 grouped_conv_fwd_bias_relu_add_xdl_fp16.cpp)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_fp16) add_example_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_fp16)
endif()
add_example_executable(example_grouped_conv_fwd_xdl_fp16 grouped_conv_fwd_xdl_fp16.cpp) add_example_executable(example_grouped_conv_fwd_xdl_fp16 grouped_conv_fwd_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_xdl_fp16)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_xdl_fp16)
endif() add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_fp32 grouped_conv_fwd_bias_relu_add_xdl_fp32.cpp)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_fp32 grouped_conv_fwd_bias_relu_add_xdl_fp32.cpp) add_example_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_fp32)
if(result EQUAL 0)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_fp32) add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_bf16 grouped_conv_fwd_bias_relu_add_xdl_bf16.cpp)
endif() add_example_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_bf16)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_bf16 grouped_conv_fwd_bias_relu_add_xdl_bf16.cpp)
if(result EQUAL 0) add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_int8 grouped_conv_fwd_bias_relu_add_xdl_int8.cpp)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_bf16) add_example_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int8)
endif()
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_int8 grouped_conv_fwd_bias_relu_add_xdl_int8.cpp) if(USE_BITINT_EXTENSION_INT4)
if(result EQUAL 0) add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_int4 grouped_conv_fwd_bias_relu_add_xdl_int4.cpp)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int8) add_example_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int4)
endif() endif() # USE_BITINT_EXTENSION_INT4
if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_int4 grouped_conv_fwd_bias_relu_add_xdl_int4.cpp) set(target 1)
if(result EQUAL 0) endif()
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int4)
endif()
endif() # USE_BITINT_EXTENSION_INT4
set(target 1)
endif()
endforeach() endforeach()
set(target 0) set(target 0)
foreach(gpu IN LISTS GPU_TARGETS) foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list2 AND target EQUAL 0) if(gpu IN_LIST gpu_list2 AND target EQUAL 0)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_wmma_fp16 grouped_conv_fwd_bias_relu_add_wmma_fp16.cpp) add_example_executable(example_grouped_conv_fwd_bias_relu_add_wmma_fp16 grouped_conv_fwd_bias_relu_add_wmma_fp16.cpp)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_wmma_int8 grouped_conv_fwd_bias_relu_add_wmma_int8.cpp) add_example_executable(example_grouped_conv_fwd_bias_relu_add_wmma_int8 grouped_conv_fwd_bias_relu_add_wmma_int8.cpp)
set(target 1) set(target 1)
endif() endif()
endforeach() endforeach()
example/30_grouped_conv_fwd_multiple_d/README.md
View file @ fdddc8f4
...@@ -4,7 +4,7 @@ arg1: verification (0=no, 1=yes) ...@@ -4,7 +4,7 @@ arg1: verification (0=no, 1=yes)
arg2: initialization (0=no init, 1=integer value, 2=decimal value) arg2: initialization (0=no init, 1=integer value, 2=decimal value)
arg3: time kernel (0=no, 1=yes) arg3: time kernel (0=no, 1=yes)
Following arguments (depending on number of spatial dims): Following arguments (depending on number of spatial dims):
Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d) Number of spatial dimensions (1=Conv1D, 2=Conv2D, 3=Conv3D)
G, N, K, C, G, N, K, C,
<filter spatial dimensions>, (ie Y, X for 2D) <filter spatial dimensions>, (ie Y, X for 2D)
<input image spatial dimensions>, (ie Hi, Wi for 2D) <input image spatial dimensions>, (ie Hi, Wi for 2D)
......
example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt
View file @ fdddc8f4
add_custom_target(example_gemm_scale_softmax_gemm) add_custom_target(example_gemm_scale_softmax_gemm)
add_example_executable(example_batched_gemm_scale_softmax_gemm_xdl_fp16 batched_gemm_scale_softmax_gemm_xdl_fp16.cpp) add_example_executable(example_batched_gemm_scale_softmax_gemm_xdl_fp16 batched_gemm_scale_softmax_gemm_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_xdl_fp16)
add_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_xdl_fp16)
endif()
add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_xdl_fp16 batched_gemm_scale_softmax_gemm_permute_xdl_fp16.cpp) add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_xdl_fp16 batched_gemm_scale_softmax_gemm_permute_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_permute_xdl_fp16)
add_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_permute_xdl_fp16)
endif()
add_example_executable(example_grouped_gemm_scale_softmax_gemm_permute_xdl_fp16 grouped_gemm_scale_softmax_gemm_permute_xdl_fp16.cpp) add_example_executable(example_grouped_gemm_scale_softmax_gemm_permute_xdl_fp16 grouped_gemm_scale_softmax_gemm_permute_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_gemm_scale_softmax_gemm example_grouped_gemm_scale_softmax_gemm_permute_xdl_fp16)
add_dependencies(example_gemm_scale_softmax_gemm example_grouped_gemm_scale_softmax_gemm_permute_xdl_fp16)
endif()
add_example_executable(example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16 batched_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16.cpp) add_example_executable(example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16 batched_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16)
add_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16)
endif()
add_example_executable(example_grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16 grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16.cpp) add_example_executable(example_grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16 grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_gemm_scale_softmax_gemm example_grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16)
add_dependencies(example_gemm_scale_softmax_gemm example_grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16)
endif()
add_example_executable(example_batched_gemm_scale_softmax_gemm_xdl_bf16 batched_gemm_scale_softmax_gemm_xdl_bf16.cpp) add_example_executable(example_batched_gemm_scale_softmax_gemm_xdl_bf16 batched_gemm_scale_softmax_gemm_xdl_bf16.cpp)
if(result EQUAL 0) add_example_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_xdl_bf16)
add_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_xdl_bf16)
endif()
add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_xdl_bf16 batched_gemm_scale_softmax_gemm_permute_xdl_bf16.cpp) add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_xdl_bf16 batched_gemm_scale_softmax_gemm_permute_xdl_bf16.cpp)
if(result EQUAL 0) add_example_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_permute_xdl_bf16)
add_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_permute_xdl_bf16)
endif()
example/35_splitK_gemm/CMakeLists.txt
View file @ fdddc8f4
...@@ -4,28 +4,23 @@ foreach(gpu IN LISTS GPU_TARGETS) ...@@ -4,28 +4,23 @@ foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0) if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_custom_target(example_splitK_gemm_xdl) add_custom_target(example_splitK_gemm_xdl)
add_example_executable(example_splitK_gemm_xdl_fp32 splitK_gemm_xdl_fp32.cpp) add_example_executable(example_splitK_gemm_xdl_fp32 splitK_gemm_xdl_fp32.cpp)
if(result EQUAL 0) add_example_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_fp32)
add_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_fp32)
endif() add_example_executable(example_splitK_gemm_xdl_fp16 splitK_gemm_xdl_fp16.cpp)
add_example_executable(example_splitK_gemm_xdl_fp16 splitK_gemm_xdl_fp16.cpp) add_example_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_fp16)
if(result EQUAL 0)
add_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_fp16) add_example_executable(example_splitK_gemm_xdl_bf16 splitK_gemm_xdl_bf16.cpp)
endif() add_example_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_bf16)
add_example_executable(example_splitK_gemm_xdl_bf16 splitK_gemm_xdl_bf16.cpp)
if(result EQUAL 0) add_example_executable(example_splitK_gemm_xdl_int8 splitK_gemm_xdl_int8.cpp)
add_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_bf16) add_example_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_int8)
endif()
add_example_executable(example_splitK_gemm_xdl_int8 splitK_gemm_xdl_int8.cpp)
if(result EQUAL 0)
add_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_int8)
endif()
if(USE_BITINT_EXTENSION_INT4) if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_splitK_gemm_xdl_int4 splitK_gemm_xdl_int4.cpp) add_example_executable(example_splitK_gemm_xdl_int4 splitK_gemm_xdl_int4.cpp)
if(result EQUAL 0) add_example_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_int4)
add_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_int4)
endif()
endif() endif()
set(target 1) set(target 1)
endif() endif()
endforeach() endforeach()
example/38_grouped_conv_bwd_data_multiple_d/CMakeLists.txt
View file @ fdddc8f4
...@@ -2,27 +2,26 @@ list(APPEND gpu_list_xdl gfx908 gfx90a gfx940 gfx941 gfx942) ...@@ -2,27 +2,26 @@ list(APPEND gpu_list_xdl gfx908 gfx90a gfx940 gfx941 gfx942)
list(APPEND gpu_list_wmma gfx1100 gfx1101 gfx1102) list(APPEND gpu_list_wmma gfx1100 gfx1101 gfx1102)
set(target 0) set(target 0)
foreach(gpu IN LISTS GPU_TARGETS) foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list_xdl AND target EQUAL 0) if(gpu IN_LIST gpu_list_xdl AND target EQUAL 0)
add_custom_target(example_grouped_conv_bwd_data) add_custom_target(example_grouped_conv_bwd_data)
add_example_executable(example_grouped_conv_bwd_data_xdl_fp16 grouped_conv_bwd_data_xdl_fp16.cpp)
if(result EQUAL 0) add_example_executable(example_grouped_conv_bwd_data_xdl_fp16 grouped_conv_bwd_data_xdl_fp16.cpp)
add_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_xdl_fp16) add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_xdl_fp16)
endif()
add_example_executable(example_grouped_conv_bwd_data_bias_relu_xdl_fp16 grouped_conv_bwd_data_bias_relu_xdl_fp16.cpp) add_example_executable(example_grouped_conv_bwd_data_bias_relu_xdl_fp16 grouped_conv_bwd_data_bias_relu_xdl_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_bias_relu_xdl_fp16)
add_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_bias_relu_xdl_fp16)
endif() set(target 1)
set(target 1) endif()
endif()
endforeach() endforeach()
foreach(gpu IN LISTS GPU_TARGETS) foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list_wmma AND target EQUAL 0) if(gpu IN_LIST gpu_list_wmma AND target EQUAL 0)
add_custom_target(example_grouped_conv_bwd_data) add_custom_target(example_grouped_conv_bwd_data)
add_example_executable(example_grouped_conv_bwd_data_wmma_fp16 grouped_conv_bwd_data_wmma_fp16.cpp)
if(result EQUAL 0) add_example_executable(example_grouped_conv_bwd_data_wmma_fp16 grouped_conv_bwd_data_wmma_fp16.cpp)
add_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_wmma_fp16) add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_wmma_fp16)
endif()
set(target 1) set(target 1)
endif() endif()
endforeach() endforeach()
example/39_permute/CMakeLists.txt
View file @ fdddc8f4
add_custom_target(example_permute) add_custom_target(example_permute)
add_example_executable(example_permute_1xHxW_fp16 permute_1xHxW_fp16.cpp) add_example_executable(example_permute_1xHxW_fp16 permute_1xHxW_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_permute example_permute_1xHxW_fp16)
add_dependencies(example_permute example_permute_1xHxW_fp16)
endif()
add_example_executable(example_permute_NxHxW_fp16 permute_NxHxW_fp16.cpp) add_example_executable(example_permute_NxHxW_fp16 permute_NxHxW_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_permute example_permute_NxHxW_fp16)
add_dependencies(example_permute example_permute_NxHxW_fp16)
endif()
add_example_executable(example_permute_HxWx4_fp16 permute_HxWx4_fp16.cpp) add_example_executable(example_permute_HxWx4_fp16 permute_HxWx4_fp16.cpp)
if(result EQUAL 0) add_example_dependencies(example_permute example_permute_HxWx4_fp16)
add_dependencies(example_permute example_permute_HxWx4_fp16)
endif()
example/42_groupnorm/groupnorm_sigmoid_mul_fp16.cpp
View file @ fdddc8f4
...@@ -6,11 +6,14 @@ ...@@ -6,11 +6,14 @@
constexpr int Rank = 5; constexpr int Rank = 5;
constexpr int NumReduceDim = 3; constexpr int NumReduceDim = 3;
using XDataType = ck::half_t; using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t; using BetaDataType = ck::half_t;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using ComputeDataType = float; using SaveMeanInvStdDataType = float;
using ComputeDataType = float;
#define SAVE_MEAN_INV_STD
struct YElementOp struct YElementOp
{ {
...@@ -39,6 +42,7 @@ using DeviceInstance = ...@@ -39,6 +42,7 @@ using DeviceInstance =
BetaDataType, BetaDataType,
ComputeDataType, ComputeDataType,
YDataType, YDataType,
SaveMeanInvStdDataType,
YElementOp, YElementOp,
Rank, Rank,
NumReduceDim, NumReduceDim,
...@@ -53,7 +57,8 @@ using DeviceInstance = ...@@ -53,7 +57,8 @@ using DeviceInstance =
2, // GammaScalarPerVector 2, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K) 1, // BetaVecDim (0=M, 1=K)
2, // BetaScalarPerVector 2, // BetaScalarPerVector
2>; // OutScalarPerVector 2, // YScalarPerVector
1>; // SaveMeanInvStdScalarPerVector
#include "run_groupnorm_example.inc" #include "run_groupnorm_example.inc"
......
example/42_groupnorm/groupnorm_splitk_fp16.cpp
View file @ fdddc8f4
...@@ -6,12 +6,15 @@ ...@@ -6,12 +6,15 @@
constexpr int Rank = 5; constexpr int Rank = 5;
constexpr int NumReduceDim = 3; constexpr int NumReduceDim = 3;
using XDataType = ck::half_t; using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t; using BetaDataType = ck::half_t;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using ComputeDataType = float; using SaveMeanInvStdDataType = float;
using YElementOp = ck::tensor_operation::element_wise::Swish; using ComputeDataType = float;
using YElementOp = ck::tensor_operation::element_wise::Swish;
#define SAVE_MEAN_INV_STD
using DeviceInstance = using DeviceInstance =
ck::tensor_operation::device::DeviceNormalizationSplitKImpl<XDataType, ck::tensor_operation::device::DeviceNormalizationSplitKImpl<XDataType,
...@@ -19,6 +22,7 @@ using DeviceInstance = ...@@ -19,6 +22,7 @@ using DeviceInstance =
BetaDataType, BetaDataType,
ComputeDataType, ComputeDataType,
YDataType, YDataType,
SaveMeanInvStdDataType,
YElementOp, YElementOp,
Rank, Rank,
NumReduceDim, NumReduceDim,
...@@ -33,7 +37,8 @@ using DeviceInstance = ...@@ -33,7 +37,8 @@ using DeviceInstance =
2, // GammaScalarPerVector 2, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K) 1, // BetaVecDim (0=M, 1=K)
2, // BetaScalarPerVector 2, // BetaScalarPerVector
2>; // OutScalarPerVector 2, // YScalarPerVector
1>; // SaveMeanInvStdScalarPerVector
#include "run_groupnorm_example.inc" #include "run_groupnorm_example.inc"
......
example/42_groupnorm/groupnorm_swish_fp16.cpp
View file @ fdddc8f4
...@@ -6,12 +6,15 @@ ...@@ -6,12 +6,15 @@
constexpr int Rank = 5; constexpr int Rank = 5;
constexpr int NumReduceDim = 3; constexpr int NumReduceDim = 3;
using XDataType = ck::half_t; using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t; using BetaDataType = ck::half_t;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using ComputeDataType = float; using SaveMeanInvStdDataType = float;
using YElementOp = ck::tensor_operation::element_wise::Swish; using ComputeDataType = float;
using YElementOp = ck::tensor_operation::element_wise::Swish;
#define SAVE_MEAN_INV_STD
using DeviceInstance = using DeviceInstance =
ck::tensor_operation::device::DeviceNormalizationImpl<XDataType, ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
...@@ -19,6 +22,7 @@ using DeviceInstance = ...@@ -19,6 +22,7 @@ using DeviceInstance =
BetaDataType, BetaDataType,
ComputeDataType, ComputeDataType,
YDataType, YDataType,
SaveMeanInvStdDataType,
YElementOp, YElementOp,
Rank, Rank,
NumReduceDim, NumReduceDim,
...@@ -33,7 +37,8 @@ using DeviceInstance = ...@@ -33,7 +37,8 @@ using DeviceInstance =
2, // GammaScalarPerVector 2, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K) 1, // BetaVecDim (0=M, 1=K)
2, // BetaScalarPerVector 2, // BetaScalarPerVector
2>; // OutScalarPerVector 2, // YScalarPerVector
1>; // SaveMeanInvStdScalarPerVector
#include "run_groupnorm_example.inc" #include "run_groupnorm_example.inc"
......
example/42_groupnorm/run_groupnorm_example.inc
View file @ fdddc8f4
...@@ -34,6 +34,8 @@ int run_groupnorm_example(int argc, char* argv[]) ...@@ -34,6 +34,8 @@ int run_groupnorm_example(int argc, char* argv[])
Tensor<YDataType> y({N, H, W, G, C}); Tensor<YDataType> y({N, H, W, G, C});
Tensor<GammaDataType> gamma({G, C}); Tensor<GammaDataType> gamma({G, C});
Tensor<BetaDataType> beta({G, C}); Tensor<BetaDataType> beta({G, C});
Tensor<SaveMeanInvStdDataType> save_mean({N, G});
Tensor<SaveMeanInvStdDataType> save_inv_std({N, G});
ck::utils::FillUniformDistribution<XDataType>{0.f, 1.f}(x); ck::utils::FillUniformDistribution<XDataType>{0.f, 1.f}(x);
ck::utils::FillUniformDistribution<GammaDataType>{0.f, 1.f}(gamma); ck::utils::FillUniformDistribution<GammaDataType>{0.f, 1.f}(gamma);
...@@ -43,6 +45,11 @@ int run_groupnorm_example(int argc, char* argv[]) ...@@ -43,6 +45,11 @@ int run_groupnorm_example(int argc, char* argv[])
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize()); DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize()); DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize()); DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
#ifdef SAVE_MEAN_INV_STD
DeviceMem save_mean_dev(sizeof(SaveMeanInvStdDataType) * save_mean.mDesc.GetElementSpaceSize());
DeviceMem save_inv_std_dev(sizeof(SaveMeanInvStdDataType) *
save_inv_std.mDesc.GetElementSpaceSize());
#endif
x_dev.ToDevice(x.mData.data()); x_dev.ToDevice(x.mData.data());
gamma_dev.ToDevice(gamma.mData.data()); gamma_dev.ToDevice(gamma.mData.data());
...@@ -57,14 +64,23 @@ int run_groupnorm_example(int argc, char* argv[]) ...@@ -57,14 +64,23 @@ int run_groupnorm_example(int argc, char* argv[])
{0, 0, 0, C, 1}, {0, 0, 0, C, 1},
{0, 0, 0, C, 1}, {0, 0, 0, C, 1},
std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()}, std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
save_mean.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
save_mean.mDesc.GetStrides().end()},
{1, 2, 4}, // reduction dimension: [H, W, C] {1, 2, 4}, // reduction dimension: [H, W, C]
1e-6, 1e-6,
x_dev.GetDeviceBuffer(), x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(), gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(), beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(), y_dev.GetDeviceBuffer(),
#ifdef SAVE_MEAN_INV_STD
save_mean_dev.GetDeviceBuffer(),
save_inv_std_dev.GetDeviceBuffer(),
#else
nullptr, nullptr,
nullptr, nullptr,
#endif
y_element_op); y_element_op);
if(!device_instance.IsSupportedArgument(argument_ptr.get())) if(!device_instance.IsSupportedArgument(argument_ptr.get()))
...@@ -92,21 +108,40 @@ int run_groupnorm_example(int argc, char* argv[]) ...@@ -92,21 +108,40 @@ int run_groupnorm_example(int argc, char* argv[])
bool pass = true; bool pass = true;
{ {
Tensor<YDataType> host_y({N, H, W, G, C}); Tensor<YDataType> host_y({N, H, W, G, C});
using ReferenceInstance = ck::tensor_operation::host::ReferenceGroupnorm<XDataType, Tensor<SaveMeanInvStdDataType> host_save_mean(HostTensorDescriptor{N, G});
GammaDataType, Tensor<SaveMeanInvStdDataType> host_save_inv_std(HostTensorDescriptor{N, G});
BetaDataType, using ReferenceInstance =
YDataType, ck::tensor_operation::host::ReferenceGroupnorm<XDataType,
ComputeDataType, GammaDataType,
YElementOp>; BetaDataType,
YDataType,
SaveMeanInvStdDataType,
ComputeDataType,
YElementOp>;
ReferenceInstance ref; ReferenceInstance ref;
auto ref_argument = auto ref_argument = ref.MakeArgument(x,
ref.MakeArgument(x, gamma, beta, host_y, y_element_op, {N, H, W, G, C}, 1e-6); gamma,
auto ref_invoker = ref.MakeInvoker(); beta,
host_y,
host_save_mean,
host_save_inv_std,
y_element_op,
{N, H, W, G, C},
1e-6);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
y_dev.FromDevice(y.mData.data()); y_dev.FromDevice(y.mData.data());
pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3); pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3);
#ifdef SAVE_MEAN_INV_STD
save_mean_dev.FromDevice(save_mean.mData.data());
save_inv_std_dev.FromDevice(save_inv_std.mData.data());
pass &= ck::utils::check_err(
save_mean, host_save_mean, "Error: Incorrect results (mean)", 1e-3, 1e-3);
pass &= ck::utils::check_err(
save_inv_std, host_save_inv_std, "Error: Incorrect results (inv_std)", 1e-3, 1e-3);
#endif
} }
return (pass ? 0 : 1); return (pass ? 0 : 1);
......
example/45_elementwise_normalization/elementwise_layernorm_blockwise.cpp
View file @ fdddc8f4
...@@ -167,20 +167,31 @@ int main() ...@@ -167,20 +167,31 @@ int main()
XElementwiseOperation>(x, a, b, mn, XElementwiseOperation{}); XElementwiseOperation>(x, a, b, mn, XElementwiseOperation{});
Tensor<YDataType> host_y(f_host_tensor_descriptor2d(M, N, Stride)); Tensor<YDataType> host_y(f_host_tensor_descriptor2d(M, N, Stride));
Tensor<AccDataType> host_save_mean({M});
Tensor<AccDataType> host_save_inv_std({M});
using ReferenceInstance = using ReferenceInstance =
ck::tensor_operation::host::ReferenceLayernorm<XDataType, ck::tensor_operation::host::ReferenceLayernorm<XDataType,
GammaDataType, GammaDataType,
BetaDataType, BetaDataType,
YDataType, YDataType,
AccDataType, AccDataType,
AccDataType,
YElementwiseOperation, YElementwiseOperation,
Rank, Rank,
NumReduceDim>; NumReduceDim>;
ReferenceInstance ref; ReferenceInstance ref;
auto ref_argument = auto ref_argument = ref.MakeArgument(x,
ref.MakeArgument(x, gamma, beta, host_y, YElementwiseOperation{}, {M, N}, {1}, 1e-4); gamma,
auto ref_invoker = ref.MakeInvoker(); beta,
host_y,
host_save_mean,
host_save_inv_std,
YElementwiseOperation{},
{M, N},
{1},
1e-4);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
y_dev.FromDevice(y.mData.data()); y_dev.FromDevice(y.mData.data());
......
example/52_im2col_col2im/CMakeLists.txt
View file @ fdddc8f4
list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942) list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0) set(target 0)
foreach(gpu IN LISTS GPU_TARGETS) foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0) if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_custom_target(example_im2col_col2im) add_custom_target(example_im2col_col2im)
add_example_executable(example_image_to_column_f32 image_to_column_f32.cpp)
add_dependencies(example_im2col_col2im example_image_to_column_f32) add_example_executable(example_image_to_column_f32 image_to_column_f32.cpp)
add_example_executable(example_column_to_image_f32 column_to_image_f32.cpp) add_example_dependencies(example_im2col_col2im example_image_to_column_f32)
add_dependencies(example_im2col_col2im example_column_to_image_f32)
set(target 1) add_example_executable(example_column_to_image_f32 column_to_image_f32.cpp)
endif() add_example_dependencies(example_im2col_col2im example_column_to_image_f32)
set(target 1)
endif()
endforeach() endforeach()
example/60_gemm_multi_ABD/CMakeLists.txt 0 → 100644
View file @ fdddc8f4
list(APPEND gpu_list2 gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list2 AND target EQUAL 0)
add_example_executable(example_gemm_multi_ABD_xdl_fp16 gemm_multi_ABD_xdl_fp16.cpp)
set(target 1)
endif()
endforeach()
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