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Unverified Commit 48c85879 authored by rocking5566's avatar rocking5566 Committed by GitHub
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Merge branch 'develop' into conv_quant_int8

parents aa71a478 1b62bfaa
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client_example/05_layernorm/layernorm2d.cpp
View file @ 48c85879
......@@ -10,7 +10,7 @@
#include "ck/tensor_operation/gpu/device/device_normalization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/layernorm.hpp"
#include "ck/library/tensor_operation_instance/gpu/normalization.hpp"
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
......@@ -51,14 +51,14 @@ int main(int argc, char* argv[])
SimpleDeviceMem beta_device_buf(sizeof(BetaDataType) * N);
SimpleDeviceMem y_device_buf(sizeof(YDataType) * xy_size);
using DeviceOp = ck::tensor_operation::device::DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim>;
using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
......
client_example/07_conv2d_fwd/CMakeLists.txt 0 → 100644
View file @ 48c85879
add_executable(client_conv2d_fwd conv2d_fwd.cpp)
target_link_libraries(client_conv2d_fwd PRIVATE composable_kernel::device_operations)
client_example/07_conv2d_fwd/conv2d_fwd.cpp 0 → 100644
View file @ 48c85879
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iomanip>
#include <iostream>
#include <vector>
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/convolution_forward.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
using OutDataType = ck::half_t;
using InLayout = ck::tensor_layout::convolution::NHWC;
using WeiLayout = ck::tensor_layout::convolution::KYXC;
using OutLayout = ck::tensor_layout::convolution::NHWK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t N = 16;
static constexpr ck::index_t K = 32;
static constexpr ck::index_t C = 3;
static constexpr ck::index_t Y = 3;
static constexpr ck::index_t X = 3;
static constexpr ck::index_t Hi = 224;
static constexpr ck::index_t Wi = 224;
static constexpr ck::index_t Ho = 113;
static constexpr ck::index_t Wo = 113;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
std::vector<ck::index_t> in_spatial_lengths{Hi, Wi};
std::vector<ck::index_t> filter_spatial_lengths{Y, X};
std::vector<ck::index_t> out_spatial_lengths{Ho, Wo};
std::vector<ck::index_t> filter_strides{2, 2};
std::vector<ck::index_t> filter_dilations{1, 1};
std::vector<ck::index_t> input_left_pads{2, 2};
std::vector<ck::index_t> input_right_pads{2, 2};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
using DeviceOp = ck::tensor_operation::device::DeviceConvFwd<NumDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
PassThrough,
PassThrough,
PassThrough>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
int best_op_id = -1;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
float best_tflops = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
out.GetDeviceBuffer(),
N,
K,
C,
in_spatial_lengths,
filter_spatial_lengths,
out_spatial_lengths,
filter_strides,
filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = 2 * N * K * C * Ho * Wo * Y * X;
std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * C +
sizeof(WeiDataType) * K * Y * X * C +
sizeof(OutDataType) * N * Ho * Wo * K;
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_bytes / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_op_id = i;
best_op_name = op_name;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
best_tflops = tflops;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
out.GetDeviceBuffer(),
N,
K,
C,
in_spatial_lengths,
filter_spatial_lengths,
out_spatial_lengths,
filter_strides,
filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
\ No newline at end of file
example/27_layernorm/layernorm_blockwise.cpp
View file @ 48c85879
......@@ -9,7 +9,7 @@
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm_impl.hpp"
#include "ck/tensor_operation/gpu/device/device_normalization_impl.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/library/utility/check_err.hpp"
......@@ -30,26 +30,26 @@ constexpr int Rank = 2;
constexpr int NumReduceDim = 1;
using DeviceInstance =
ck::tensor_operation::device::DeviceLayernormImpl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim,
256, // BlockSize
8, // ClusterM
32, // ClusterK
1, // SliceM
8, // SliceK
1, // SrcVecDim (0=M, 1=K)
8, // SrcScalarPerVector
1, // GammaVecDim (0=M, 1=K)
8, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K)
8, // BetaScalarPerVector
8>; // OutScalarPerVector
ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim,
256, // BlockSize
8, // ClusterM
32, // ClusterK
1, // SliceM
8, // SliceK
1, // SrcVecDim (0=M, 1=K)
8, // SrcScalarPerVector
1, // GammaVecDim (0=M, 1=K)
8, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K)
8, // BetaScalarPerVector
8>; // OutScalarPerVector
int main()
{
......
example/42_groupnorm/groupnorm_sigmoid_fp16.cpp
View file @ 48c85879
......@@ -9,7 +9,7 @@
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm_impl.hpp"
#include "ck/tensor_operation/gpu/device/device_normalization_impl.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/library/utility/fill.hpp"
......@@ -47,26 +47,26 @@ struct YElementOp
};
using DeviceInstance =
ck::tensor_operation::device::DeviceLayernormImpl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
YElementOp,
Rank,
NumReduceDim,
1024, // BlockSize
1, // ClusterM
1024, // ClusterK
1, // SliceM
32, // SliceK
1, // SrcVecDim (0=M, 1=K)
2, // SrcScalarPerVector
1, // GammaVecDim (0=M, 1=K)
2, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K)
2, // BetaScalarPerVector
2>; // OutScalarPerVector
ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
YElementOp,
Rank,
NumReduceDim,
1024, // BlockSize
1, // ClusterM
1024, // ClusterK
1, // SliceM
32, // SliceK
1, // SrcVecDim (0=M, 1=K)
2, // SrcScalarPerVector
1, // GammaVecDim (0=M, 1=K)
2, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K)
2, // BetaScalarPerVector
2>; // OutScalarPerVector
int main(int argc, char* argv[])
{
......
include/ck/tensor_operation/gpu/device/device_normalization.hpp
View file @ 48c85879
......@@ -11,33 +11,6 @@
namespace ck {
namespace tensor_operation {
namespace device {
struct DeviceNormalization : public BaseOperator
{
// inLengths: input tensor extent(s) from high to low dimension
// inStrides: input tensor stride(s) from high to low dimension
// reduceDims: the dimension(s) the normalization operation is applied
// alpha: typeless pointer in host memory storing the alpha scaling value of type AccDataType
// beta: typeless pointer in host memory storing the beta scaling value of type AccDataType
// in_dev: typeless const pointer in device memory storing the input tensor
// out_dev: typeless pointer in device memory storing the output tensor
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> inLengths,
const std::vector<index_t> inStrides,
const std::vector<int> reduceDims,
const void* alpha,
const void* beta,
const void* in_dev,
void* out_dev) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
virtual index_t GetRank() const = 0;
virtual index_t GetNumReduceDim() const = 0;
};
using DeviceNormalizationPtr = std::unique_ptr<DeviceNormalization>;
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
......@@ -46,7 +19,7 @@ template <typename XDataType,
typename AccElementwiseOperation,
index_t Rank,
index_t NumReduceDim>
struct DeviceLayernorm : public BaseOperator
struct DeviceNormalization : public BaseOperator
{
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const std::vector<index_t> lengths,
......@@ -73,14 +46,14 @@ template <typename XDataType,
typename AccElementwiseOperation,
index_t Rank,
index_t NumReduceDim>
using DeviceLayernormPtr = std::unique_ptr<DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
AccElementwiseOperation,
Rank,
NumReduceDim>>;
using DeviceNormalizationPtr = std::unique_ptr<DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
AccElementwiseOperation,
Rank,
NumReduceDim>>;
} // namespace device
} // namespace tensor_operation
......
include/ck/tensor_operation/gpu/device/device_layernorm_impl.hpp → include/ck/tensor_operation/gpu/device/device_normalization_impl.hpp
View file @ 48c85879
......@@ -75,14 +75,14 @@ template <typename XDataType,
index_t BetaSrcVectorDim,
index_t BetaSrcVectorSize,
index_t YDstVectorSize>
struct DeviceLayernormImpl : public DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
AccElementwiseOperation,
Rank,
NumReduceDim>
struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
AccElementwiseOperation,
Rank,
NumReduceDim>
{
static_assert(
((GammaSrcVectorDim == 0 && MThreadSliceSize % GammaSrcVectorSize == 0) ||
......@@ -452,7 +452,7 @@ struct DeviceLayernormImpl : public DeviceLayernorm<XDataType,
auto str = std::stringstream();
// clang-format off
str << "DeviceLayernormImpl<" << BlockSize << ",";
str << "DeviceNormalizationImpl<" << BlockSize << ",";
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
str << "XYSrcVectorDim_" << XYSrcVectorDim << ",";
......
library/include/ck/library/tensor_operation_instance/gpu/layernorm.hpp → library/include/ck/library/tensor_operation_instance/gpu/normalization.hpp
View file @ 48c85879
......@@ -18,24 +18,24 @@ namespace device {
namespace instance {
// FP16
void add_device_layernorm_rank_2_1_f16_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, PassThrough, 2, 1>>>&);
void add_device_normalization_rank_2_1_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 2, 1>>>&);
void add_device_layernorm_rank_4_3_f16_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, PassThrough, 4, 3>>>&);
void add_device_normalization_rank_4_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 4, 3>>>&);
void add_device_layernorm_rank_5_3_f16_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, PassThrough, 5, 3>>>&);
void add_device_normalization_rank_5_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 5, 3>>>&);
// FP32
void add_device_layernorm_rank_2_1_f32_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, PassThrough, 2, 1>>>&);
void add_device_normalization_rank_2_1_f32_instances(
std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, PassThrough, 2, 1>>>&);
void add_device_layernorm_rank_4_3_f32_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, PassThrough, 4, 3>>>&);
void add_device_normalization_rank_4_3_f32_instances(
std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, PassThrough, 4, 3>>>&);
void add_device_layernorm_rank_5_3_f32_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, PassThrough, 5, 3>>>&);
void add_device_normalization_rank_5_3_f32_instances(
std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, PassThrough, 5, 3>>>&);
template <typename XDataType,
typename GammaDataType,
......@@ -43,24 +43,24 @@ template <typename XDataType,
typename YDataType,
index_t Rank,
index_t NumReduceDim>
struct DeviceOperationInstanceFactory<
ck::tensor_operation::device::DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
F32,
YDataType,
ck::tensor_operation::element_wise::PassThrough,
Rank,
NumReduceDim>>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormalization<
XDataType,
GammaDataType,
BetaDataType,
F32,
YDataType,
ck::tensor_operation::element_wise::PassThrough,
Rank,
NumReduceDim>>
{
using DeviceOp = DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
F32,
YDataType,
ck::tensor_operation::element_wise::PassThrough,
Rank,
NumReduceDim>;
using DeviceOp = DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
F32,
YDataType,
ck::tensor_operation::element_wise::PassThrough,
Rank,
NumReduceDim>;
static auto GetInstances()
{
......@@ -71,15 +71,15 @@ struct DeviceOperationInstanceFactory<
{
if constexpr(Rank == 2 && NumReduceDim == 1)
{
add_device_layernorm_rank_2_1_f16_instances(op_ptrs);
add_device_normalization_rank_2_1_f16_instances(op_ptrs);
}
else if constexpr(Rank == 4 && NumReduceDim == 3)
{
add_device_layernorm_rank_4_3_f16_instances(op_ptrs);
add_device_normalization_rank_4_3_f16_instances(op_ptrs);
}
else if constexpr(Rank == 5 && NumReduceDim == 3)
{
add_device_layernorm_rank_5_3_f16_instances(op_ptrs);
add_device_normalization_rank_5_3_f16_instances(op_ptrs);
}
}
else if constexpr(is_same_v<XDataType, F32> && is_same_v<GammaDataType, F32> &&
......@@ -87,15 +87,15 @@ struct DeviceOperationInstanceFactory<
{
if constexpr(Rank == 2 && NumReduceDim == 1)
{
add_device_layernorm_rank_2_1_f32_instances(op_ptrs);
add_device_normalization_rank_2_1_f32_instances(op_ptrs);
}
else if constexpr(Rank == 4 && NumReduceDim == 3)
{
add_device_layernorm_rank_4_3_f32_instances(op_ptrs);
add_device_normalization_rank_4_3_f32_instances(op_ptrs);
}
else if constexpr(Rank == 5 && NumReduceDim == 3)
{
add_device_layernorm_rank_5_3_f32_instances(op_ptrs);
add_device_normalization_rank_5_3_f32_instances(op_ptrs);
}
}
......
library/src/tensor_operation_instance/gpu/CMakeLists.txt
View file @ 48c85879
......@@ -17,7 +17,6 @@ IF(IS_DIRECTORY "${subdir_path}")
ENDIF()
ENDFOREACH()
add_library(device_operations STATIC ${CK_DEVICE_INSTANCES})
add_library(composablekernels::device_operations ALIAS device_operations)
......
library/src/tensor_operation_instance/gpu/normalization/CMakeLists.txt
View file @ 48c85879
add_instance_library(device_normalization_instance
device_layernorm_f16_instance.cpp
device_layernorm_f32_instance.cpp
device_softmax_f32_f32_instance.cpp
device_softmax_f16_f16_instance.cpp
device_normalization_f16_instance.cpp
device_normalization_f32_instance.cpp
)
library/src/tensor_operation_instance/gpu/normalization/device_layernorm_f16_instance.cpp deleted 100644 → 0
View file @ aa71a478
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm_impl.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Pass = ck::tensor_operation::element_wise::PassThrough;
template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_layernorm_f16_instances = std::tuple<
// clang-format off
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize>
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 1, 1, 1, 1, 1, 1>, // fallback kernel
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 2, 1, 2, 1, 2, 2>, // fallback kernel
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 4, 1, 4, 1, 4, 4>, // fallback kernel
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 4, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 32, 1, 8, 1, 8, 1, 8, 8>,
DeviceLayernormImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 2, 1, 2, 1, 2, 2>
// clang-format on
>;
void add_device_layernorm_rank_2_1_f16_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, Pass, 2, 1>>>& instances)
{
add_device_operation_instances(instances, device_layernorm_f16_instances<Pass, 2, 1>{});
}
void add_device_layernorm_rank_4_3_f16_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, Pass, 4, 3>>>& instances)
{
add_device_operation_instances(instances, device_layernorm_f16_instances<Pass, 4, 3>{});
}
void add_device_layernorm_rank_5_3_f16_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F16, F16, F16, F32, F16, Pass, 5, 3>>>& instances)
{
add_device_operation_instances(instances, device_layernorm_f16_instances<Pass, 5, 3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/normalization/device_normalization_f16_instance.cpp 0 → 100644
View file @ 48c85879
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_normalization_impl.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Pass = ck::tensor_operation::element_wise::PassThrough;
template <typename OutElementwise, index_t Rank, index_t Reduce>
// clang-format off
using device_normalization_f16_instances =
std::tuple <
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize>
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 1, 1, 1, 1, 1, 1>, // fallback kernel
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 2, 1, 2, 1, 2, 2>, // fallback kernel
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 4, 1, 4, 1, 4, 4>, // fallback kernel
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 4, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 32, 1, 8, 1, 8, 1, 8, 8>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 2, 1, 2, 1, 2, 2>
>;
// clang-format on
void add_device_normalization_rank_2_1_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Pass, 2, 1>>>&
instances)
{
add_device_operation_instances(instances, device_normalization_f16_instances<Pass, 2, 1>{});
}
void add_device_normalization_rank_4_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Pass, 4, 3>>>&
instances)
{
add_device_operation_instances(instances, device_normalization_f16_instances<Pass, 4, 3>{});
}
void add_device_normalization_rank_5_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Pass, 5, 3>>>&
instances)
{
add_device_operation_instances(instances, device_normalization_f16_instances<Pass, 5, 3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/normalization/device_layernorm_f32_instance.cpp → library/src/tensor_operation_instance/gpu/normalization/device_normalization_f32_instance.cpp
View file @ 48c85879
......@@ -2,7 +2,7 @@
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm_impl.hpp"
#include "ck/tensor_operation/gpu/device/device_normalization_impl.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
......@@ -20,33 +20,36 @@ template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_layernorm_f32_instances = std::tuple<
// clang-format off
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize>
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 1, 1, 1, 1, 1, 1>, // fallback kernel
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 2, 1, 2, 1, 2, 2>, // fallback kernel
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 4, 64, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4>,
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4>,
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4>,
DeviceLayernormImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4>
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 1, 1, 1, 1, 1, 1>, // fallback kernel
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 2, 1, 2, 1, 2, 2>, // fallback kernel
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 8, 32, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 4, 64, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 2, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4>
// clang-format on
>;
void add_device_layernorm_rank_2_1_f32_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, Pass, 2, 1>>>& instances)
void add_device_normalization_rank_2_1_f32_instances(
std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, Pass, 2, 1>>>&
instances)
{
add_device_operation_instances(instances, device_layernorm_f32_instances<Pass, 2, 1>{});
}
void add_device_layernorm_rank_4_3_f32_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, Pass, 4, 3>>>& instances)
void add_device_normalization_rank_4_3_f32_instances(
std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, Pass, 4, 3>>>&
instances)
{
add_device_operation_instances(instances, device_layernorm_f32_instances<Pass, 4, 3>{});
}
void add_device_layernorm_rank_5_3_f32_instances(
std::vector<std::unique_ptr<DeviceLayernorm<F32, F32, F32, F32, F32, Pass, 5, 3>>>& instances)
void add_device_normalization_rank_5_3_f32_instances(
std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, Pass, 5, 3>>>&
instances)
{
add_device_operation_instances(instances, device_layernorm_f32_instances<Pass, 5, 3>{});
}
......
library/src/tensor_operation_instance/gpu/softmax/CMakeLists.txt 0 → 100644
View file @ 48c85879
add_instance_library(device_softmax_instance
device_softmax_f16_f16_instance.cpp
device_softmax_f32_f32_instance.cpp
)
profiler/CMakeLists.txt
View file @ 48c85879
......@@ -25,7 +25,7 @@ set(PROFILER_SOURCE
src/profile_reduce.cpp
src/profile_groupnorm.cpp
src/profile_layernorm.cpp
src/profile_normalization.cpp
src/profile_softmax.cpp
)
add_executable(ckProfiler ${PROFILER_SOURCE})
......@@ -55,4 +55,5 @@ target_link_libraries(ckProfiler PRIVATE device_conv3d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance)
target_link_libraries(ckProfiler PRIVATE device_normalization_instance)
target_link_libraries(ckProfiler PRIVATE device_softmax_instance)
target_link_libraries(ckProfiler PRIVATE device_reduce_instance)
profiler/include/profile_groupnorm_impl.hpp
View file @ 48c85879
......@@ -7,7 +7,7 @@
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/layernorm.hpp"
#include "ck/library/tensor_operation_instance/gpu/normalization.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
......@@ -75,14 +75,14 @@ bool profile_groupnorm_impl(int do_verification,
beta_dev.ToDevice(beta.mData.data());
// add device normalization instances
using DeviceOp = ck::tensor_operation::device::DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
5,
3>;
using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
5,
3>;
// get device op instances
const auto instance_ptrs =
......
profiler/include/profile_layernorm_impl.hpp
View file @ 48c85879
......@@ -7,7 +7,7 @@
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/layernorm.hpp"
#include "ck/library/tensor_operation_instance/gpu/normalization.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
......@@ -28,27 +28,29 @@ void profile_layernorm_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
std::vector<index_t> length,
std::vector<index_t> strideXY,
std::vector<index_t> strideGamma,
std::vector<index_t> strideBeta)
std::vector<index_t> length)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
if(length.size() < 2)
return;
// Assume normalize dimension except for first dimension
// Assume normalize dimension except for batch (first) dimension
std::vector<index_t> reduce_length{length.begin() + 1, length.end()};
std::vector<index_t> reduce_dim;
for(int i = 1; i < Rank; ++i)
reduce_dim.push_back(i);
Tensor<XDataType> x(length);
Tensor<GammaDataType> gamma(reduce_length, strideGamma);
Tensor<BetaDataType> beta(reduce_length, strideBeta);
Tensor<YDataType> y(length, strideXY);
Tensor<YDataType> host_y(length, strideXY);
Tensor<GammaDataType> gamma(reduce_length);
Tensor<BetaDataType> beta(reduce_length);
Tensor<YDataType> y(length);
Tensor<YDataType> host_y(length);
std::vector<index_t> strideXY =
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()};
std::vector<index_t> strideGammaBeta = strideXY;
strideGammaBeta[0] = 0;
switch(init_method)
{
......@@ -84,14 +86,14 @@ void profile_layernorm_impl(int do_verification,
constexpr int NumReduceDim = Rank - 1;
// add device normalization instances
using DeviceOp = ck::tensor_operation::device::DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim>;
using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim>;
// get device op instances
const auto instance_ptrs =
......@@ -126,8 +128,8 @@ void profile_layernorm_impl(int do_verification,
{
auto argument_ptr = inst_ptr->MakeArgumentPointer(length,
strideXY,
strideGamma,
strideBeta,
strideGammaBeta,
strideGammaBeta,
strideXY,
reduce_dim,
1e-4,
......
profiler/include/profile_normalization_impl.hpp → profiler/include/profile_softmax_impl.hpp
View file @ 48c85879
......@@ -69,16 +69,16 @@ template <> std::string type_to_string<int32_t>() { return "int32"; }
// clang-format on
template <typename InDataType, typename AccDataType, typename OutDataType, index_t Rank>
void profile_normalization_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
std::vector<index_t> in_length,
std::vector<index_t> in_strides,
std::vector<index_t> reduce_dims,
AccDataType alpha,
AccDataType beta,
NormType norm_type)
void profile_softmax_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
std::vector<index_t> in_length,
std::vector<index_t> in_strides,
std::vector<index_t> reduce_dims,
AccDataType alpha,
AccDataType beta,
NormType norm_type)
{
if(Rank != in_length.size())
{
......
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