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Unverified Commit ac9e01e2 authored by Illia Silin's avatar Illia Silin Committed by GitHub
Browse files

Clean-up the headers (#713) 



* fix headers for gpu instances

* remove unused headers

---------
Co-authored-by: default avatarzjing14 <zhangjing14@gmail.com>
parent 76ec0089
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library/include/ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp
View file @ ac9e01e2
...@@ -3,8 +3,8 @@ ...@@ -3,8 +3,8 @@
#pragma once #pragma once
#include <cstdlib> #include <vector>
#include <memory>
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp" #include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
......
library/include/ck/library/tensor_operation_instance/gpu/normalization.hpp
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...@@ -3,8 +3,8 @@ ...@@ -3,8 +3,8 @@
#pragma once #pragma once
#include <cstdlib> #include <vector>
#include <memory>
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_normalization.hpp" #include "ck/tensor_operation/gpu/device/device_normalization.hpp"
......
library/include/ck/library/utility/host_conv.hpp deleted 100644 → 0
View file @ 76ec0089
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "host_tensor.hpp"
#include "conv_common.hpp"
template <typename TIn,
typename TWei,
typename TOut,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void host_conv_nchw_kcyx_nkhw(const Tensor<TIn>& in,
const Tensor<TWei>& wei,
Tensor<TOut>& out,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads&)
{
constexpr auto I0 = ck::Number<0>{};
constexpr auto I1 = ck::Number<1>{};
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
float v = 0;
for(int c = 0; c < wei.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * conv_strides[I0] + y * conv_dilations[I0] - in_left_pads[I0];
for(int x = 0; x < wei.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * conv_strides[I1] + x * conv_dilations[I1] - in_left_pads[I1];
if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in.mDesc.GetLengths()[3])
{
v += ck::type_convert<float>(in(n, c, hi, wi)) *
ck::type_convert<float>(wei(k, c, y, x));
}
}
}
}
out(n, k, ho, wo) = ck::type_convert<TOut>(v);
};
make_ParallelTensorFunctor(f_nchw,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
}
template <typename TIn,
typename TWei,
typename TOut,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void host_conv3d_ndhwc_kzyxc_ndhwk(const Tensor<TIn>& in,
const Tensor<TWei>& wei,
Tensor<TOut>& out,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads&)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
const auto Di = in.mDesc.GetLengths()[1];
const auto Hi = in.mDesc.GetLengths()[2];
const auto Wi = in.mDesc.GetLengths()[3];
const auto Z = wei.mDesc.GetLengths()[1];
const auto Y = wei.mDesc.GetLengths()[2];
const auto X = wei.mDesc.GetLengths()[3];
const auto C = wei.mDesc.GetLengths()[4];
auto f_ndhwc = [&](auto n, auto do_tmp, auto ho_tmp, auto wo_tmp, auto k) {
// do__ must be converted to signed integer, otherwise zmin might be wrong in cases
// negative values.
const int do_ = static_cast<int>(do_tmp);
const int ho = static_cast<int>(ho_tmp);
const int wo = static_cast<int>(wo_tmp);
const int zmin =
std::max(0,
(in_left_pads[I0] - do_ * conv_strides[I0] + conv_dilations[I0] - 1) /
conv_dilations[I0]);
const int ymin =
std::max(0,
(in_left_pads[I1] - ho * conv_strides[I1] + conv_dilations[I1] - 1) /
conv_dilations[I1]);
const int xmin =
std::max(0,
(in_left_pads[I2] - wo * conv_strides[I2] + conv_dilations[I2] - 1) /
conv_dilations[I2]);
const int zmax =
std::min(Z, (in_left_pads[I0] - do_ * conv_strides[I0] + Di) / conv_dilations[I0]);
const int ymax =
std::min(Y, (in_left_pads[I1] - ho * conv_strides[I1] + Hi) / conv_dilations[I1]);
const int xmax =
std::min(X, (in_left_pads[I2] - wo * conv_strides[I2] + Wi) / conv_dilations[I2]);
const int di_min = do_ * conv_strides[I0] + zmin * conv_dilations[I0] - in_left_pads[I0];
const int hi_min = ho * conv_strides[I1] + ymin * conv_dilations[I1] - in_left_pads[I1];
const int wi_min = wo * conv_strides[I2] + xmin * conv_dilations[I2] - in_left_pads[I2];
double v = 0;
const TIn* in_n = in.mData.data() + n * Di * Hi * Wi * C;
const TWei* wei_k = wei.mData.data() + k * Z * Y * X * C;
int di = di_min;
for(int z = zmin; z < zmax; ++z, di += conv_dilations[I0])
{
const TIn* in_n_di = in_n + di * Hi * Wi * C;
const TWei* wei_k_z = wei_k + z * Y * X * C;
int hi = hi_min;
for(int y = ymin; y < ymax; ++y, hi += conv_dilations[I1])
{
const TIn* in_n_di_hi = in_n_di + hi * Wi * C;
const TWei* wei_k_z_y = wei_k_z + y * X * C;
int wi = wi_min;
for(int x = xmin; x < xmax; ++x, wi += conv_dilations[I2])
{
const TIn* in_n_di_hi_wi = in_n_di_hi + wi * C;
const TWei* wei_k_z_y_x = wei_k_z_y + x * C;
for(int c = 0; c < C; ++c)
{
v += static_cast<const double>(in_n_di_hi_wi[c]) *
static_cast<const double>(wei_k_z_y_x[c]);
}
}
}
}
out(n, do_, ho, wo, k) = v;
};
make_ParallelTensorFunctor(f_ndhwc,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3],
out.mDesc.GetLengths()[4])(std::thread::hardware_concurrency() - 4);
}
library/include/ck/library/utility/op_instance_engine.hpp deleted 100644 → 0
View file @ 76ec0089
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <iostream>
#include <limits>
#include <memory>
#include <stdexcept>
#include <tuple>
#include <utility>
#include <vector>
#include "ck/utility/functional2.hpp"
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
namespace ck {
namespace utils {
struct ProfileBestConfig
{
std::string best_op_name;
float best_avg_time = std::numeric_limits<float>::max();
float best_tflops = std::numeric_limits<float>::max();
float best_gb_per_sec = std::numeric_limits<float>::max();
};
/**
* @brief This class describes an operation instance(s).
*
* Op instance defines a particular specializations of operator
* template. Thanks to this specific input/output data types, data
* layouts and modifying elementwise operations it is able to create
* it's input/output tensors, provide pointers to instances which
* can execute it and all operation specific parameters.
*/
template <typename OutDataType, typename... InArgTypes>
class OpInstance
{
public:
template <typename T>
using TensorPtr = std::unique_ptr<Tensor<T>>;
using InTensorsTuple = std::tuple<TensorPtr<InArgTypes>...>;
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
using DeviceBuffers = std::vector<DeviceMemPtr>;
OpInstance() = default;
OpInstance(const OpInstance&) = default;
OpInstance& operator=(const OpInstance&) = default;
virtual ~OpInstance(){};
virtual InTensorsTuple GetInputTensors() const = 0;
virtual TensorPtr<OutDataType> GetOutputTensor() const = 0;
virtual std::unique_ptr<tensor_operation::device::BaseInvoker>
MakeInvokerPointer(tensor_operation::device::BaseOperator*) const = 0;
virtual std::unique_ptr<tensor_operation::device::BaseArgument>
MakeArgumentPointer(tensor_operation::device::BaseOperator*,
const DeviceBuffers&,
const DeviceMemPtr&) const = 0;
virtual std::size_t GetFlops() const = 0;
virtual std::size_t GetBtype() const = 0;
};
/**
* @brief A generic operation instance run engine.
*/
template <typename OutDataType, typename... InArgTypes>
class OpInstanceRunEngine
{
public:
using OpInstanceT = OpInstance<InArgTypes..., OutDataType>;
template <typename T>
using TensorPtr = std::unique_ptr<Tensor<T>>;
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
using InTensorsTuple = std::tuple<TensorPtr<InArgTypes>...>;
using DeviceBuffers = std::vector<DeviceMemPtr>;
using InArgsTypesTuple = std::tuple<InArgTypes...>;
OpInstanceRunEngine() = delete;
template <typename ReferenceOp = std::function<void()>>
OpInstanceRunEngine(const OpInstanceT& op_instance,
const ReferenceOp& reference_op = ReferenceOp{},
bool do_verification = true)
: op_instance_{op_instance}
{
in_tensors_ = op_instance_.GetInputTensors();
out_tensor_ = op_instance_.GetOutputTensor();
if constexpr(std::is_invocable_v<ReferenceOp,
const Tensor<InArgTypes>&...,
Tensor<OutDataType>&>)
{
if(do_verification)
{
ref_output_ = op_instance_.GetOutputTensor();
CallRefOpUnpackArgs(reference_op, std::make_index_sequence<kNInArgs_>{});
}
}
AllocateDeviceInputTensors(std::make_index_sequence<kNInArgs_>{});
out_device_buffer_ = std::make_unique<DeviceMem>(sizeof(OutDataType) *
out_tensor_->mDesc.GetElementSpaceSize());
out_device_buffer_->SetZero();
}
virtual ~OpInstanceRunEngine(){};
template <typename OpInstancePtr>
bool Test(const std::vector<OpInstancePtr>& op_ptrs)
{
bool res{true};
for(auto& op_ptr : op_ptrs)
{
auto invoker = op_instance_.MakeInvokerPointer(op_ptr.get());
auto argument = op_instance_.MakeArgumentPointer(
op_ptr.get(), in_device_buffers_, out_device_buffer_);
if(op_ptr->IsSupportedArgument(argument.get()))
{
std::cout << "Testing instance: " << op_ptr->GetTypeString() << std::endl;
invoker->Run(argument.get());
out_device_buffer_->FromDevice(out_tensor_->mData.data());
if(!ref_output_)
{
throw std::runtime_error(
"OpInstanceRunEngine::Test: Reference value not availabe."
" You have to provide reference function.");
}
// TODO: enable flexible use of custom check_error functions
bool inst_res = CheckErr(out_tensor_->mData, ref_output_->mData);
std::cout << (inst_res ? "SUCCESS" : "FAILURE") << std::endl;
res = res && inst_res;
out_device_buffer_->SetZero();
}
else
{
std::cout << "Given conv problem is not supported by instance: \n\t>>>>"
<< op_ptr->GetTypeString() << std::endl;
}
}
return res;
}
template <typename OpInstancePtr>
ProfileBestConfig Profile(const std::vector<OpInstancePtr>& op_ptrs,
bool time_kernel = false,
bool do_verification = false,
bool do_log = false)
{
ProfileBestConfig best_config;
for(auto& op_ptr : op_ptrs)
{
auto invoker = op_instance_.MakeInvokerPointer(op_ptr.get());
auto argument = op_instance_.MakeArgumentPointer(
op_ptr.get(), in_device_buffers_, out_device_buffer_);
if(op_ptr->IsSupportedArgument(argument.get()))
{
std::string op_name = op_ptr->GetTypeString();
float avg_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flops = op_instance_.GetFlops();
std::size_t num_btype = op_instance_.GetBtype();
float tflops = static_cast<float>(flops) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << op_name << std::endl;
if(avg_time < best_config.best_avg_time)
{
best_config.best_op_name = op_name;
best_config.best_tflops = tflops;
best_config.best_gb_per_sec = gb_per_sec;
best_config.best_avg_time = avg_time;
}
if(do_verification)
{
out_device_buffer_->FromDevice(out_tensor_->mData.data());
if(!ref_output_)
{
throw std::runtime_error(
"OpInstanceRunEngine::Profile: Reference value not availabe."
" You have to provide reference function.");
}
// TODO: enable flexible use of custom check_error functions
CheckErr(out_tensor_->mData, ref_output_->mData);
if(do_log) {}
}
out_device_buffer_->SetZero();
}
}
return best_config;
}
void SetAtol(double a) { atol_ = a; }
void SetRtol(double r) { rtol_ = r; }
private:
template <typename F, std::size_t... Is>
void CallRefOpUnpackArgs(const F& f, std::index_sequence<Is...>) const
{
f(*std::get<Is>(in_tensors_)..., *ref_output_);
}
template <std::size_t... Is>
void AllocateDeviceInputTensors(std::index_sequence<Is...>)
{
(AllocateDeviceInputTensorsImpl<Is>(), ...);
}
template <std::size_t Index>
void AllocateDeviceInputTensorsImpl()
{
const auto& ts = std::get<Index>(in_tensors_);
in_device_buffers_
.emplace_back(
std::make_unique<DeviceMem>(sizeof(std::tuple_element_t<Index, InArgsTypesTuple>) *
ts->mDesc.GetElementSpaceSize()))
->ToDevice(ts->mData.data());
}
static constexpr std::size_t kNInArgs_ = std::tuple_size_v<InTensorsTuple>;
const OpInstanceT& op_instance_;
double rtol_{1e-5};
double atol_{1e-8};
InTensorsTuple in_tensors_;
TensorPtr<OutDataType> out_tensor_;
TensorPtr<OutDataType> ref_output_;
DeviceBuffers in_device_buffers_;
DeviceMemPtr out_device_buffer_;
template <typename T>
bool CheckErr(const std::vector<T>& dev_out, const std::vector<T>& ref_out) const
{
return ck::utils::check_err(dev_out, ref_out, "Error: incorrect results!", rtol_, atol_);
}
};
} // namespace utils
} // namespace ck
profiler/include/profiler/data_type_enum_helper.hpp deleted 100644 → 0
View file @ 76ec0089
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma
#include "ck/utility/data_type.hpp"
#include "profiler/data_type_enum.hpp"
namespace ck {
template <DataTypeEnum DataTypeEnum>
struct get_datatype_from_enum;
template <>
struct get_datatype_from_enum<DataTypeEnum::Int8>
{
using type = int8_t;
};
template <>
struct get_datatype_from_enum<DataTypeEnum::Int32>
{
using type = int32_t;
};
template <>
struct get_datatype_from_enum<DataTypeEnum::Half>
{
using type = half_t;
};
template <>
struct get_datatype_from_enum<DataTypeEnum::Float>
{
using type = float;
};
template <>
struct get_datatype_from_enum<DataTypeEnum::Double>
{
using type = double;
};
template <typename T>
struct get_datatype_enum_from_type;
template <>
struct get_datatype_enum_from_type<int8_t>
{
static constexpr DataTypeEnum value = DataTypeEnum::Int8;
};
template <>
struct get_datatype_enum_from_type<int32_t>
{
static constexpr DataTypeEnum value = DataTypeEnum::Int32;
};
template <>
struct get_datatype_enum_from_type<half_t>
{
static constexpr DataTypeEnum value = DataTypeEnum::Half;
};
template <>
struct get_datatype_enum_from_type<float>
{
static constexpr DataTypeEnum value = DataTypeEnum::Float;
};
template <>
struct get_datatype_enum_from_type<double>
{
static constexpr DataTypeEnum value = DataTypeEnum::Double;
};
} // namespace ck
profiler/include/profiler/profile_convnd_bwd_data_impl.hpp deleted 100644 → 0
View file @ 76ec0089
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_bwd_data.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp"
using F16 = ck::half_t;
using F32 = float;
using BF16 = ck::bhalf_t;
using INT8 = int8_t;
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using DeviceConvBwdDataNoOpPtr =
DeviceConvBwdDataPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instances(
std::vector<DeviceConvBwdDataNoOpPtr>&);
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
namespace ck {
namespace profiler {
using DeviceConvBwdDataNoOpPtr = ck::tensor_operation::device::instance::DeviceConvBwdDataNoOpPtr;
template <typename InLayout>
HostTensorDescriptor get_input_host_tensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
template <typename WeiLayout>
HostTensorDescriptor get_filters_host_tensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
template <typename OutLayout>
HostTensorDescriptor get_output_host_ensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
template <typename InDataType, typename WeiDataType, typename OutDataType>
void get_device_conv_bwd_data_op_ptr(
InDataType, WeiDataType, OutDataType, std::vector<DeviceConvBwdDataNoOpPtr>&, int)
{
std::cout << "can not find device conv bwd data" << std::endl;
exit(1);
}
template <>
void get_device_conv_bwd_data_op_ptr(
F32, F32, F32, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 1:
ck::tensor_operation::device::instance::
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(conv_ptrs);
break;
case 2:
ck::tensor_operation::device::instance::
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
break;
case 3:
ck::tensor_operation::device::instance::
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(conv_ptrs);
break;
default: break;
}
}
template <>
void get_device_conv_bwd_data_op_ptr(
F16, F16, F16, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 1:
ck::tensor_operation::device::instance::
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(conv_ptrs);
break;
case 2:
ck::tensor_operation::device::instance::
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
break;
case 3:
ck::tensor_operation::device::instance::
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instances(conv_ptrs);
break;
default: break;
}
}
template <>
void get_device_conv_bwd_data_op_ptr(
BF16, BF16, BF16, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 1:
ck::tensor_operation::device::instance::
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(conv_ptrs);
break;
case 2:
ck::tensor_operation::device::instance::
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(conv_ptrs);
break;
case 3:
ck::tensor_operation::device::instance::
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(conv_ptrs);
break;
default: break;
}
}
template <>
void get_device_conv_bwd_data_op_ptr(
INT8, INT8, INT8, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 1:
ck::tensor_operation::device::instance::
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instances(conv_ptrs);
break;
case 2:
ck::tensor_operation::device::instance::
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(conv_ptrs);
break;
case 3:
ck::tensor_operation::device::instance::
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instances(conv_ptrs);
break;
default: break;
}
}
template <typename T>
static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
{
float max_diff = 1e-6;
for(std::size_t i = 0; i < ref.mData.size(); ++i)
{
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
if(max_diff < diff)
{
return false;
}
}
return true;
}
template <typename DataType>
void show_data_nhwc_layout(Tensor<DataType>& nhwc)
{
std::cout << "[";
for(int n = 0; n < ck::type_convert<int>(nhwc.mDesc.GetLengths()[0]); n++)
{
std::cout << "[";
for(int hi = 0; hi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[2]); hi++)
{
std::cout << "[";
for(int wi = 0; wi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[3]); wi++)
{
std::cout << "[";
for(int c = 0; c < ck::type_convert<int>(nhwc.mDesc.GetLengths()[1]); c++)
{
std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << " ";
}
std::cout << "]";
}
std::cout << "]";
}
std::cout << "]";
}
std::cout << "]";
}
template <int NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename AccDataType,
typename InLayout,
typename WeiLayout,
typename OutLayout>
bool profile_convnd_bwd_data_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
const std::vector<ck::index_t>& input_spatial_lengths,
const std::vector<ck::index_t>& filter_spatial_lengths,
const std::vector<ck::index_t>& output_spatial_lengths,
const std::vector<ck::index_t>& conv_filter_strides,
const std::vector<ck::index_t>& conv_filter_dilations,
const std::vector<ck::index_t>& input_left_pads,
const std::vector<ck::index_t>& input_right_pads)
{
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
std::vector<std::size_t> input_dims{static_cast<std::size_t>(N), static_cast<std::size_t>(C)};
input_dims.insert(
std::end(input_dims), std::begin(input_spatial_lengths), std::end(input_spatial_lengths));
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(K), static_cast<std::size_t>(C)};
filter_dims.insert(std::end(filter_dims),
std::begin(filter_spatial_lengths),
std::end(filter_spatial_lengths));
std::vector<std::size_t> output_dims{static_cast<std::size_t>(N), static_cast<std::size_t>(K)};
output_dims.insert(std::end(output_dims),
std::begin(output_spatial_lengths),
std::end(output_spatial_lengths));
Tensor<InDataType> input_host_result(
get_input_host_tensor_descriptor<InLayout>(input_dims, NDimSpatial));
Tensor<InDataType> input_device_result(
get_input_host_tensor_descriptor<InLayout>(input_dims, NDimSpatial));
Tensor<WeiDataType> weights(
get_filters_host_tensor_descriptor<WeiLayout>(filter_dims, NDimSpatial));
Tensor<OutDataType> output(
get_output_host_ensor_descriptor<OutLayout>(output_dims, NDimSpatial));
std::cout << "input: " << input_host_result.mDesc << std::endl;
std::cout << "weights: " << weights.mDesc << std::endl;
std::cout << "output: " << output.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
output.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
weights.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
break;
default:
output.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
weights.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
}
DeviceMem in_device_buf(sizeof(InDataType) * input_device_result.mDesc.GetElementSpace());
DeviceMem wei_device_buf(sizeof(WeiDataType) * weights.mDesc.GetElementSpace());
DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpace());
out_device_buf.ToDevice(output.mData.data());
wei_device_buf.ToDevice(weights.mData.data());
// reset input to zero
in_device_buf.SetZero();
if(do_verification)
{
auto RunReference = [&](auto& ref_conv) {
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(input_host_result,
weights,
output,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
ref_invoker.Run(ref_argument);
};
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
WeiDataType,
OutDataType,
AccDataType,
InElementOp,
WeiElementOp,
OutElementOp,
NDimSpatial>();
RunReference(ref_conv);
}
// add device Conv instances
std::vector<DeviceConvBwdDataNoOpPtr> conv_ptrs;
get_device_conv_bwd_data_op_ptr(
InDataType{}, WeiDataType{}, OutDataType{}, conv_ptrs, NDimSpatial);
if(conv_ptrs.size() <= 0)
{
throw std::runtime_error("wrong! no device Conv instance found");
}
std::string best_conv_name;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device Conv instances
bool success = true;
for(auto& conv_ptr : conv_ptrs)
{
auto argument_ptr = conv_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
N,
K,
C,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op);
auto invoker_ptr = conv_ptr->MakeInvokerPointer();
if(conv_ptr->IsSupportedArgument(argument_ptr.get()))
{
std::string conv_name = conv_ptr->GetTypeString();
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop =
ck::utils::conv::get_flops(N, C, K, filter_spatial_lengths, output_spatial_lengths);
std::size_t num_btype =
ck::utils::conv::get_btype<InDataType, WeiDataType, OutDataType>(
N, C, K, input_spatial_lengths, filter_spatial_lengths, output_spatial_lengths);
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s" << std::endl;
if(tflops > best_tflops)
{
best_conv_name = conv_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
in_device_buf.FromDevice(input_device_result.mData.data());
if(!check_out(input_host_result, input_device_result))
{
std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl;
success = false;
}
else
{
std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl;
}
success = ck::utils::check_err(input_host_result, input_device_result);
if(do_log)
{
std::cout << "in : ";
show_data_nhwc_layout(output);
std::cout << std::endl;
std::cout << "wei: ";
show_data_nhwc_layout(weights);
std::cout << std::endl;
std::cout << "out_host : ";
show_data_nhwc_layout(input_host_result);
std::cout << std::endl;
std::cout << "out_device: ";
show_data_nhwc_layout(input_device_result);
std::cout << std::endl;
}
}
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_conv_name << std::endl;
return success;
}
} // namespace profiler
} // namespace ck
profiler/include/profiler/profile_convnd_bwd_weight_impl.hpp deleted 100644 → 0
View file @ 76ec0089
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_backward_weight.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_backward_weight.hpp"
using F16 = ck::half_t;
using F32 = float;
using BF16 = ck::bhalf_t;
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using DeviceConvndBwdWeightNoOpPtr =
DeviceConvBwdWeightPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
void add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f32_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_convnd_bwd_weight_xdl_nhwc_kyxc_nhwk_f32_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f32_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f16_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_convnd_bwd_weight_xdl_nhwc_kyxc_nhwk_f16_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f16_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_bf16_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_bf16_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
void add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(
std::vector<DeviceConvndBwdWeightNoOpPtr>&);
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
namespace ck {
namespace profiler {
using DeviceConvndBwdWeightNoOpPtr =
ck::tensor_operation::device::instance::DeviceConvndBwdWeightNoOpPtr;
template <typename InLayout>
HostTensorDescriptor get_input_host_tensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
template <typename WeiLayout>
HostTensorDescriptor get_filters_host_tensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
template <typename OutLayout>
HostTensorDescriptor get_output_host_ensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
template <typename InDataType, typename WeiDataType, typename OutDataType>
void get_device_conv_bwd_weight_op_ptr(
InDataType, WeiDataType, OutDataType, std::vector<DeviceConvndBwdWeightNoOpPtr>&, int)
{
std::cout << "can not find device conv bwd weight" << std::endl;
exit(1);
}
template <>
void get_device_conv_bwd_weight_op_ptr(
F32, F32, F32, std::vector<DeviceConvndBwdWeightNoOpPtr>& conv_ptrs, int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 1:
ck::tensor_operation::device::instance::
add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f32_instances(conv_ptrs);
break;
case 2:
ck::tensor_operation::device::instance::
add_device_convnd_bwd_weight_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
break;
case 3:
ck::tensor_operation::device::instance::
add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f32_instances(conv_ptrs);
break;
default: break;
}
}
template <>
void get_device_conv_bwd_weight_op_ptr(
F16, F16, F16, std::vector<DeviceConvndBwdWeightNoOpPtr>& conv_ptrs, int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 1:
ck::tensor_operation::device::instance::
add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f16_instances(conv_ptrs);
break;
case 2:
ck::tensor_operation::device::instance::
add_device_convnd_bwd_weight_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
break;
case 3:
ck::tensor_operation::device::instance::
add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f16_instances(conv_ptrs);
break;
default: break;
}
}
template <>
void get_device_conv_bwd_weight_op_ptr(
BF16, BF16, BF16, std::vector<DeviceConvndBwdWeightNoOpPtr>& conv_ptrs, int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 1:
ck::tensor_operation::device::instance::
add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_bf16_instances(conv_ptrs);
break;
case 2:
ck::tensor_operation::device::instance::
add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_bf16_instances(conv_ptrs);
break;
case 3:
ck::tensor_operation::device::instance::
add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(conv_ptrs);
break;
default: break;
}
}
template <typename DataType>
void show_data_nhwc_layout(Tensor<DataType>& nhwc)
{
std::cout << "[";
for(int n = 0; n < ck::type_convert<int>(nhwc.mDesc.GetLengths()[0]); n++)
{
std::cout << "[";
for(int hi = 0; hi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[2]); hi++)
{
std::cout << "[";
for(int wi = 0; wi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[3]); wi++)
{
std::cout << "[";
for(int c = 0; c < ck::type_convert<int>(nhwc.mDesc.GetLengths()[1]); c++)
{
std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << " ";
}
std::cout << "]";
}
std::cout << "]";
}
std::cout << "]";
}
std::cout << "]";
}
template <int NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename InLayout,
typename WeiLayout,
typename OutLayout>
bool profile_convnd_bwd_weight_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
ck::index_t N,
ck::index_t K,
ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
ck::index_t split_k)
{
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
std::vector<std::size_t> input_dims{static_cast<std::size_t>(N), static_cast<std::size_t>(C)};
input_dims.insert(
std::end(input_dims), std::begin(input_spatial_lengths), std::end(input_spatial_lengths));
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(K), static_cast<std::size_t>(C)};
filter_dims.insert(std::end(filter_dims),
std::begin(filter_spatial_lengths),
std::end(filter_spatial_lengths));
std::vector<std::size_t> output_dims{static_cast<std::size_t>(N), static_cast<std::size_t>(K)};
output_dims.insert(std::end(output_dims),
std::begin(output_spatial_lengths),
std::end(output_spatial_lengths));
Tensor<InDataType> input(get_input_host_tensor_descriptor<InLayout>(input_dims, NDimSpatial));
Tensor<WeiDataType> weights_host_result(
get_filters_host_tensor_descriptor<WeiLayout>(filter_dims, NDimSpatial));
Tensor<WeiDataType> weights_device_result(
get_filters_host_tensor_descriptor<WeiLayout>(filter_dims, NDimSpatial));
Tensor<OutDataType> output(
get_output_host_ensor_descriptor<OutLayout>(output_dims, NDimSpatial));
std::cout << "input: " << input.mDesc << std::endl;
std::cout << "weights: " << weights_host_result.mDesc << std::endl;
std::cout << "output: " << output.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
input.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-2, 2});
output.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-2, 2});
break;
default:
input.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
output.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
}
DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpace());
DeviceMem wei_device_buf(sizeof(WeiDataType) * weights_device_result.mDesc.GetElementSpace());
DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpace());
in_device_buf.ToDevice(input.mData.data());
out_device_buf.ToDevice(output.mData.data());
// reset input to zero
wei_device_buf.SetZero();
if(do_verification)
{
auto RunReference = [&](auto& ref_conv) {
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(input,
weights_host_result,
output,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
ref_invoker.Run(ref_argument);
};
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdWeight<InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
NDimSpatial>();
RunReference(ref_conv);
}
// add device Conv instances
std::vector<DeviceConvndBwdWeightNoOpPtr> conv_ptrs;
get_device_conv_bwd_weight_op_ptr(
InDataType{}, WeiDataType{}, OutDataType{}, conv_ptrs, NDimSpatial);
if(conv_ptrs.size() <= 0)
{
throw std::runtime_error("wrong! no device Conv instance found");
}
std::string best_conv_name;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device Conv instances
bool success = true;
for(auto& conv_ptr : conv_ptrs)
{
// using atomic, so need to reset input, setzero is done in invoker
// if(split_k > 1)
//{
// wei_device_buf.SetZero();
//}
auto argument_ptr = conv_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
N,
K,
C,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op,
split_k);
if(!conv_ptr->IsSupportedArgument(argument_ptr.get()))
{
std::cout << "wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem"
<< std::endl;
continue;
}
auto invoker_ptr = conv_ptr->MakeInvokerPointer();
std::string conv_name = conv_ptr->GetTypeString();
float ave_time = 0;
if(std::is_same<InDataType, ck::bhalf_t>::value && split_k > 1)
{
// alloc work space
size_t bwd_weight_workspace_size = conv_ptr->GetWorkSpaceSize(argument_ptr.get());
if(bwd_weight_workspace_size <= 0)
{
printf("wrong work space size\n");
exit(1);
}
DeviceMem wei_work_space_device_buf(bwd_weight_workspace_size);
wei_work_space_device_buf.SetZero();
conv_ptr->SetWorkSpacePointer(argument_ptr.get(),
wei_work_space_device_buf.GetDeviceBuffer());
ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
}
else
{
ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
}
std::size_t flop =
ck::utils::conv::get_flops(N, C, K, filter_spatial_lengths, output_spatial_lengths);
std::size_t num_btype = ck::utils::conv::get_btype<InDataType, WeiDataType, OutDataType>(
N, C, K, input_spatial_lengths, filter_spatial_lengths, output_spatial_lengths);
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s" << std::endl;
if(tflops > best_tflops)
{
best_conv_name = conv_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
wei_device_buf.FromDevice(weights_device_result.mData.data());
success = ck::utils::check_err(weights_host_result, weights_device_result);
if(success == false)
{
std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl;
}
else
{
std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl;
}
if(do_log)
{
std::cout << "in : ";
show_data_nhwc_layout(output);
std::cout << std::endl;
std::cout << "wei: ";
show_data_nhwc_layout(weights_host_result);
std::cout << std::endl;
std::cout << "out : ";
show_data_nhwc_layout(input);
std::cout << std::endl;
std::cout << "wei_device: ";
show_data_nhwc_layout(weights_device_result);
std::cout << std::endl;
}
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_conv_name << std::endl;
return success;
}
} // namespace profiler
} // namespace ck
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