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Commit d92fb7e8 authored by rocking's avatar rocking
Browse files

Merge commit 'a3c910ac' into gemm_softmax

parents bfc80764 a3c910ac
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Showing with 1167 additions and 1143 deletions
profiler/include/profile_convnd_fwd.hpp 0 → 100644
View file @ d92fb7e8
#pragma once
namespace ck {
namespace profiler {
int profile_convnd_fwd(int argc, char* argv[]);
} // namespace profiler
} // namespace ck
profiler/include/profile_gemm_reduce_impl.hpp
View file @ d92fb7e8
......@@ -7,7 +7,7 @@
#include "tensor_layout.hpp"
#include "device_tensor.hpp"
#include "element_wise_operation.hpp"
#include "element_wise_reduce_operation.hpp"
#include "reduction_operator.hpp"
#include "device_gemm_reduce.hpp"
#include "reference_gemm.hpp"
......@@ -20,8 +20,7 @@ using DeviceGemmReduceNoOpPtr = ck::tensor_operation::device::DeviceGemmReducePt
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::ReduceSum,
ck::tensor_operation::element_wise::ReduceSquareSum>;
ck::tensor_operation::element_wise::UnarySquare<float, float, false>>;
void add_device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instances(
std::vector<DeviceGemmReduceNoOpPtr>&);
......@@ -116,14 +115,16 @@ bool profile_gemm_reduce_impl(int do_verification,
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
using D0ReduceOp = ck::tensor_operation::element_wise::ReduceSum;
using D1ReduceOp = ck::tensor_operation::element_wise::ReduceSquareSum;
using D0ReduceOp = ck::reduce::Add<float>;
using D1ReduceOp = ck::reduce::Add<float>;
using D1ElementOp = ck::tensor_operation::element_wise::UnarySquare<float, float, false>;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
const auto d0_reduce_op = D0ReduceOp{};
const auto d1_reduce_op = D1ReduceOp{};
const auto d1_element_op = D1ElementOp{};
if(do_verification)
{
......@@ -140,17 +141,21 @@ bool profile_gemm_reduce_impl(int do_verification,
for(int m = 0; m < M; ++m)
{
float d0_acc = d0_reduce_op.GetReduceZeroValue();
float d1_acc = d1_reduce_op.GetReduceZeroValue();
float d0_acc = d0_reduce_op.GetReductionZeroVal();
float d1_acc = d1_reduce_op.GetReductionZeroVal();
for(int n = 0; n < N; ++n)
{
d0_reduce_op.Reduce(d0_acc, c_m_n_host_result(m, n));
d1_reduce_op.Reduce(d1_acc, c_m_n_host_result(m, n));
float d0_val = ck::type_convert<float>(c_m_n_host_result(m, n));
float d1_val;
d1_element_op(d1_val, d0_val);
d0_reduce_op(d0_acc, d0_val);
d1_reduce_op(d1_acc, d1_val);
}
d0_m_host_result(m) = d0_acc;
d1_m_host_result(m) = d1_acc;
d0_m_host_result(m) = ck::type_convert<DDataType>(d0_acc);
d1_m_host_result(m) = ck::type_convert<DDataType>(d1_acc);
}
}
......@@ -232,8 +237,7 @@ bool profile_gemm_reduce_impl(int do_verification,
a_element_op,
b_element_op,
c_element_op,
d0_reduce_op,
d1_reduce_op);
d1_element_op);
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
......
profiler/src/profile_conv_fwd.cpp deleted 100644 → 0
View file @ bfc80764
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "profile_conv_fwd_impl.hpp"
enum struct ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
};
enum struct ConvInputLayout
{
NCHW, // 0
NHWC, // 1
};
enum struct ConvWeightLayout
{
KCYX, // 0
KYXC, // 1
};
enum struct ConvOutputLayout
{
NKHW, // 0
NHWK, // 1
};
int profile_conv_fwd(int argc, char* argv[])
{
if(argc != 25)
{
printf("arg1: tensor operation (conv_fwd: ForwardConvolution)\n");
printf("arg2: data type (0: fp32; 1: fp16)\n");
printf("arg3: input tensor layout (0: NCHW; 1: NHWC)\n");
printf("arg4: weight tensor layout (0: KCYX; 1: KYXC)\n");
printf("arg5: output tensor layout (0: NKHW; 1: NHWK)\n");
printf("arg6: verification (0: no; 1: yes)\n");
printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg9: run kernel # of times (>1)\n");
printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(1);
}
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const auto in_layout = static_cast<ConvInputLayout>(std::stoi(argv[3]));
const auto wei_layout = static_cast<ConvWeightLayout>(std::stoi(argv[4]));
const auto out_layout = static_cast<ConvOutputLayout>(std::stoi(argv[5]));
const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]);
const int nrepeat = std::stoi(argv[9]);
const ck::index_t N = std::stoi(argv[10]);
const ck::index_t K = std::stoi(argv[11]);
const ck::index_t C = std::stoi(argv[12]);
const ck::index_t Y = std::stoi(argv[13]);
const ck::index_t X = std::stoi(argv[14]);
const ck::index_t Hi = std::stoi(argv[15]);
const ck::index_t Wi = std::stoi(argv[16]);
const ck::index_t conv_stride_h = std::stoi(argv[17]);
const ck::index_t conv_stride_w = std::stoi(argv[18]);
const ck::index_t conv_dilation_h = std::stoi(argv[19]);
const ck::index_t conv_dilation_w = std::stoi(argv[20]);
const ck::index_t in_left_pad_h = std::stoi(argv[21]);
const ck::index_t in_left_pad_w = std::stoi(argv[22]);
const ck::index_t in_right_pad_h = std::stoi(argv[23]);
const ck::index_t in_right_pad_w = std::stoi(argv[24]);
const ck::index_t YEff = (Y - 1) * conv_dilation_h + 1;
const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
if(data_type == ConvDataType::F32_F32_F32 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_fwd_impl<2,
float,
float,
float,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else if(data_type == ConvDataType::F16_F16_F16 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_fwd_impl<2,
ck::half_t,
ck::half_t,
ck::half_t,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else if(data_type == ConvDataType::BF16_BF16_BF16 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_fwd_impl<2,
uint16_t,
uint16_t,
uint16_t,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else if(data_type == ConvDataType::INT8_INT8_INT8 && in_layout == ConvInputLayout::NHWC &&
wei_layout == ConvWeightLayout::KYXC && out_layout == ConvOutputLayout::NHWK)
{
ck::profiler::profile_conv_fwd_impl<2,
int8_t,
int8_t,
int8_t,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
do_verification,
init_method,
do_log,
nrepeat,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
}
else
{
throw std::runtime_error("wrong! this Conv data_type & layout is not implemented");
}
return 1;
}
profiler/src/profile_convnd_bwd_data.cpp
View file @ d92fb7e8
......@@ -7,6 +7,8 @@
#include "profile_convnd_bwd_data_impl.hpp"
namespace {
enum struct ConvDataType
{
F32_F32_F32, // 0
......@@ -76,6 +78,8 @@ ck::utils::conv::ConvParams parse_conv_params(int num_dim_spatial, char* argv[],
return params;
}
} // namespace
int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
{
const int preParams = 10;
......
profiler/src/profile_convnd_fwd.cpp 0 → 100644
View file @ d92fb7e8
#include <cstdlib>
#include <iostream>
#include <memory>
#include <string>
#include <vector>
#include <half.hpp>
#include "conv_fwd_util.hpp"
#include "element_wise_operation.hpp"
#include "fill.hpp"
#include "profile_convnd_fwd.hpp"
#include "tensor_layout.hpp"
namespace {
enum struct ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
};
enum struct ConvDataLayout
{
NCHW, // 0
NHWC, // 1
};
namespace ctl = ck::tensor_layout::convolution;
template <int NDim, ConvDataLayout DataLayout>
struct ConvolutionLayouts;
template <>
struct ConvolutionLayouts<1, ConvDataLayout::NHWC>
{
typedef ctl::NWC Input;
typedef ctl::KXC Weight;
typedef ctl::NWK Output;
};
template <>
struct ConvolutionLayouts<2, ConvDataLayout::NHWC>
{
typedef ctl::NHWC Input;
typedef ctl::KYXC Weight;
typedef ctl::NHWK Output;
};
template <>
struct ConvolutionLayouts<3, ConvDataLayout::NHWC>
{
typedef ctl::NDHWC Input;
typedef ctl::KZYXC Weight;
typedef ctl::NDHWK Output;
};
template <>
struct ConvolutionLayouts<1, ConvDataLayout::NCHW>
{
typedef ctl::NCW Input;
typedef ctl::KCX Weight;
typedef ctl::NKW Output;
};
template <>
struct ConvolutionLayouts<2, ConvDataLayout::NCHW>
{
typedef ctl::NCHW Input;
typedef ctl::KCYX Weight;
typedef ctl::NKHW Output;
};
template <>
struct ConvolutionLayouts<3, ConvDataLayout::NCHW>
{
typedef ctl::NCDHW Input;
typedef ctl::KCZYX Weight;
typedef ctl::NKDHW Output;
};
void print_use_msg()
{
std::cout << "arg1: tensor operation (conv_fwd: ForwardConvolution)\n"
<< "arg2: data type (0: fp32; 1: fp16, 2: bf16, 3: int8)\n"
<< "arg3: data layout (0: NCHW; 1: NHWC)\n"
<< "arg4: verification (0=no, 1=yes)\n"
<< "arg5: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg7: run kernel # of times (>1)\n"
<< "arg8: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
<< " <filter spatial dimensions>, (ie Y, X for 2D)\n"
<< " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
<< " <strides>, (ie Sy, Sx for 2D)\n"
<< " <dilations>, (ie Dy, Dx for 2D)\n"
<< " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
<< " <right padding>, (ie RightPy, RightPx for 2D)\n"
<< std::endl;
}
ck::utils::conv::ConvParams parse_params(int num_dim_spatial, int argc, char* argv[])
{
// (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
int conv_args = 3 + num_dim_spatial * 6;
int cmdline_nargs = conv_args + 9;
if(cmdline_nargs != argc)
{
print_use_msg();
exit(1);
}
int arg_idx = 9;
return ck::utils::conv::parse_conv_params(num_dim_spatial, arg_idx, argv);
}
template <int NDim,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename ConvLayouts>
void profile_convnd_instances_impl(const ck::utils::conv::ConvParams& params,
bool do_verification,
bool do_log,
int nrepeat,
int init_method,
ConvLayouts)
{
using namespace std::placeholders;
using namespace ck::utils;
std::unique_ptr<OpInstance<OutDataType, InDataType, WeiDataType>> conv_instance;
switch(init_method)
{
case 0:
conv_instance =
std::make_unique<conv::ConvFwdOpInstance<InDataType,
WeiDataType,
OutDataType,
typename ConvLayouts::Input,
typename ConvLayouts::Weight,
typename ConvLayouts::Output>>(params, false);
break;
case 1:
conv_instance = std::make_unique<
conv::ConvFwdOpInstance<InDataType,
WeiDataType,
OutDataType,
typename ConvLayouts::Input,
typename ConvLayouts::Weight,
typename ConvLayouts::Output,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::utils::FillUniform<int>,
ck::utils::FillUniform<int>>>(
params, true, ck::utils::FillUniform<int>{}, ck::utils::FillUniform<int>{});
break;
case 2:
conv_instance = std::make_unique<
conv::ConvFwdOpInstance<InDataType,
WeiDataType,
OutDataType,
typename ConvLayouts::Input,
typename ConvLayouts::Weight,
typename ConvLayouts::Output,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::utils::FillUniform<InDataType>,
ck::utils::FillUniform<WeiDataType>>>(
params,
true,
ck::utils::FillUniform<InDataType>{},
ck::utils::FillUniform<WeiDataType>{});
break;
default: throw std::runtime_error("Unsupported init method!");
}
auto reference_conv_fwd_fun = std::bind(
conv::run_reference_convolution_forward<NDim, InDataType, WeiDataType, OutDataType>,
params,
_1,
_2,
_3);
OpInstanceRunEngine<InDataType, WeiDataType, OutDataType> run_engine(*conv_instance,
reference_conv_fwd_fun);
auto best_conf = run_engine.Profile(
conv::ConvolutionFwdInstances<InDataType, WeiDataType, OutDataType>::template Get<NDim>(),
nrepeat,
do_verification,
do_log);
std::cout << "Best configuration parameters:"
<< "\nname: " << best_conf.best_op_name << "\navg_time: " << best_conf.best_avg_time
<< "\ntflops: " << best_conf.best_tflops << "\nGB/s: " << best_conf.best_gb_per_sec
<< std::endl;
}
template <int NDim>
void profile_convnd_instances(ConvDataType data_type,
ConvDataLayout data_layout,
const ck::utils::conv::ConvParams& params,
bool do_verification,
bool do_log,
int nrepeat,
int init_method)
{
switch(data_layout)
{
case ConvDataLayout::NHWC: {
switch(data_type)
{
case ConvDataType::F32_F32_F32:
profile_convnd_instances_impl<NDim, float, float, float>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break;
case ConvDataType::F16_F16_F16:
profile_convnd_instances_impl<NDim, ck::half_t, ck::half_t, ck::half_t>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break;
case ConvDataType::BF16_BF16_BF16:
profile_convnd_instances_impl<NDim, ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break;
case ConvDataType::INT8_INT8_INT8:
profile_convnd_instances_impl<NDim, int8_t, int8_t, int8_t>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NHWC>{});
break;
}
break;
}
case ConvDataLayout::NCHW: {
switch(data_type)
{
case ConvDataType::F32_F32_F32:
profile_convnd_instances_impl<NDim, float, float, float>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break;
case ConvDataType::F16_F16_F16:
profile_convnd_instances_impl<NDim, ck::half_t, ck::half_t, ck::half_t>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break;
case ConvDataType::BF16_BF16_BF16:
profile_convnd_instances_impl<NDim, ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break;
case ConvDataType::INT8_INT8_INT8:
profile_convnd_instances_impl<NDim, int8_t, int8_t, int8_t>(
params,
do_verification,
do_log,
nrepeat,
init_method,
ConvolutionLayouts<NDim, ConvDataLayout::NCHW>{});
break;
}
break;
}
}
}
} // namespace
int ck::profiler::profile_convnd_fwd(int argc, char* argv[])
{
using namespace ck::utils::conv;
ConvDataType data_type{ConvDataType::F32_F32_F32};
ConvDataLayout data_layout{ConvDataLayout::NHWC};
bool do_verification{true};
int init_method{2};
bool do_log{false};
int nrepeat{100};
int num_dim_spatial{2};
ConvParams params;
if(argc >= 4)
{
data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
data_layout = static_cast<ConvDataLayout>(std::stoi(argv[3]));
}
if(argc >= 9)
{
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
nrepeat = std::stoi(argv[7]);
num_dim_spatial = std::stoi(argv[8]);
}
if(argc >= 10)
{
params = parse_params(num_dim_spatial, argc, argv);
}
// TODO Print nice message what is being profiled.
switch(num_dim_spatial)
{
case 1:
profile_convnd_instances<1>(
data_type, data_layout, params, do_verification, do_log, nrepeat, init_method);
break;
case 2:
profile_convnd_instances<2>(
data_type, data_layout, params, do_verification, do_log, nrepeat, init_method);
break;
case 3:
profile_convnd_instances<3>(
data_type, data_layout, params, do_verification, do_log, nrepeat, init_method);
break;
default:
throw std::runtime_error("profile_conv_fwd: unsupported num_dim_spatial value: " +
std::to_string(num_dim_spatial));
}
return 1;
}
profiler/src/profiler.cpp
View file @ d92fb7e8
......@@ -4,6 +4,8 @@
#include <cstdlib>
#include <cstring>
#include "profile_convnd_fwd.hpp"
int profile_gemm(int, char*[]);
int profile_gemm_bias_2d(int, char*[]);
int profile_gemm_bias_relu(int, char*[]);
......@@ -11,7 +13,6 @@ int profile_gemm_bias_relu_add(int, char*[]);
int profile_gemm_reduce(int, char*[]);
int profile_batched_gemm(int, char*[]);
int profile_grouped_gemm(int, char*[]);
int profile_conv_fwd(int, char*[]);
int profile_conv_fwd_bias_relu(int, char*[]);
int profile_conv_fwd_bias_relu_add(int, char*[]);
int profile_conv_fwd_bias_relu_atomic_add(int, char*[]);
......@@ -56,7 +57,7 @@ int main(int argc, char* argv[])
}
else if(strcmp(argv[1], "conv_fwd") == 0)
{
return profile_conv_fwd(argc, argv);
return ck::profiler::profile_convnd_fwd(argc, argv);
}
else if(strcmp(argv[1], "conv_fwd_bias_relu") == 0)
{
......
script/clang-format-overwrite.sh
View file @ d92fb7e8
find . -name deps -prune -o -name build -prune -o -iname '*.h' -o -iname '*.hpp' -o -iname '*.cpp' -o -iname '*.h.in' -o -iname '*.hpp.in' -o -iname '*.cpp.in' -o -iname '*.cl' -o -iname '*.cuh' -o -iname '*.cu' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
#find . -name deps -prune -o -name build -prune -o -iname '*.h' -o -iname '*.hpp' -o -iname '*.cpp' -o -iname '*.h.in' -o -iname '*.hpp.in' -o -iname '*.cpp.in' -o -iname '*.cl' -o -iname '*.cuh' -o -iname '*.cu' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
git status --porcelain | awk '$1 != "D" && (match($2, "\\.cpp|hpp")) {print $2}' | xargs -n 1 -P 16 -I{} -t sh -c 'clang-format-10 -i -style=file {}'
script/parse_perf_data.py 0 → 100644
View file @ d92fb7e8
#!/usr/bin/env python3
import os, io
import argparse
def print_to_string(*args, **kwargs):
output = io.StringIO()
print(*args, file=output, **kwargs)
contents = output.getvalue()
output.close()
return contents
def parse_args():
parser = argparse.ArgumentParser(description='Parse results from tf benchmark runs')
parser.add_argument('filename', type=str, help='Log file to prase or directory containing log files')
args = parser.parse_args()
files = []
if os.path.isdir(args.filename):
all_files = os.listdir(args.filename)
for name in all_files:
if not 'log' in name:
continue
files.append(os.path.join(args.filename, name))
else:
files = [args.filename]
args.files = files
return args
def main():
args = parse_args()
results = []
#parse results
glue=""
for filename in args.files:
for line in open(filename):
if 'Best Perf' in line:
lst=line.split()
results.append(print_to_string(glue.join(lst[8:]),lst[4]))
#sort results
#read baseline results for the latest develop branch
#write new results to the db
#compare the results to the baseline
#return 0 if performance criteria met, otherwise return 1
print(results)
return 0
if __name__ == '__main__':
main()
\ No newline at end of file
script/profile_gemm.sh
View file @ d92fb7e8
#!/bin/bash
## GPU visibility
export HIP_VISIBLE_DEVICES=0
make -j ckProfiler
DRIVER="./profiler/ckProfiler"
export HIP_VISIBLE_DEVICES=0
#make -j ckProfiler
DRIVER="../build/bin/ckProfiler"
echo $DRIVER
OP=$1
DATATYPE=$2
LAYOUT=$3
......@@ -43,3 +41,13 @@ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1024 1024 1024 1088 1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2048 2048 2048 2112 2112 2112
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 4096 4096 4096 4160 4160 4160
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 8192 8192 8192 8256 8256 8256
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 6656 8192 8192 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 3328 4096 4096 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1664 2048 2048 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 832 1024 1024 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 7040 8192 8192 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 5120 5632 4096 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 2560 2816 2048 -1 -1 -1
$DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $REPEAT 1280 1408 1024 -1 -1 -1
test/CMakeLists.txt
View file @ d92fb7e8
......@@ -24,6 +24,7 @@ include_directories(BEFORE
add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR})
add_custom_target(tests)
function(add_test_executable TEST_NAME)
message("adding test ${TEST_NAME}")
add_executable(${TEST_NAME} ${ARGN})
......@@ -32,6 +33,20 @@ function(add_test_executable TEST_NAME)
add_dependencies(check ${TEST_NAME})
endfunction(add_test_executable TEST_NAME)
include(GoogleTest)
function(add_gtest_executable TEST_NAME)
message("adding gtest ${TEST_NAME}")
add_executable(${TEST_NAME} ${ARGN})
add_dependencies(tests ${TEST_NAME})
add_dependencies(check ${TEST_NAME})
# suppress gtest warnings
target_compile_options(${TEST_NAME} PRIVATE -Wno-global-constructors)
target_link_libraries(${TEST_NAME} PRIVATE gtest_main)
gtest_discover_tests(${TEST_NAME})
endfunction(add_gtest_executable TEST_NAME)
add_subdirectory(magic_number_division)
add_subdirectory(space_filling_curve)
add_subdirectory(conv_util)
......
test/conv2d_bwd_weight/CMakeLists.txt
View file @ d92fb7e8
......@@ -4,5 +4,4 @@ include_directories(BEFORE
)
add_test_executable(test_conv2d_bwd_weight conv2d_bwd_weight.cpp)
target_link_libraries(test_conv2d_bwd_weight PRIVATE host_tensor)
target_link_libraries(test_conv2d_bwd_weight PRIVATE device_conv2d_bwd_weight_instance)
target_link_libraries(test_conv2d_bwd_weight PRIVATE host_tensor device_conv2d_bwd_weight_instance conv_fwd_util)
test/conv_util/CMakeLists.txt
View file @ d92fb7e8
add_test_executable(test_conv_util conv_util.cpp)
target_link_libraries(test_conv_util PRIVATE host_tensor)
add_gtest_executable(test_conv_util conv_util.cpp)
target_link_libraries(test_conv_util PRIVATE host_tensor conv_fwd_util)
test/conv_util/conv_util.cpp
View file @ d92fb7e8
#include <iostream>
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "config.hpp"
#include "conv_fwd_util.hpp"
......@@ -9,196 +10,194 @@
namespace {
bool test_conv_params_get_output_spatial_lengths()
class TestConvUtil : public ::testing::Test
{
bool res{true};
// -------------------------- default 2D ------------------------------------
public:
void SetNDParams(std::size_t ndims)
{
conv_params.num_dim_spatial = ndims;
conv_params.filter_spatial_lengths = std::vector<ck::index_t>(ndims, 3);
conv_params.input_spatial_lengths = std::vector<ck::index_t>(ndims, 71);
conv_params.conv_filter_strides = std::vector<ck::index_t>(ndims, 2);
conv_params.conv_filter_dilations = std::vector<ck::index_t>(ndims, 1);
conv_params.input_left_pads = std::vector<ck::index_t>(ndims, 1);
conv_params.input_right_pads = std::vector<ck::index_t>(ndims, 1);
}
protected:
// ------- default 2D -------
// input NCHW {128,192,71,71},
// weights KCYX {256,192,3,3},
// stride {2,2},
// dilations {1,1},
// padding {{1,1}, {1,1}}
ck::utils::conv::ConvParams conv_params;
};
} // namespace
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths2D)
{
ck::utils::conv::ConvParams conv_params;
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len,
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36},
"Error: ConvParams 2D default constructor.");
"Error: ConvParams 2D default constructor."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71, 71}, "Error: ConvParams 2D stride {1,1}.");
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71, 71}, "Error: ConvParams 2D stride {1,1}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2};
conv_params.input_left_pads = std::vector<ck::index_t>{2, 2};
conv_params.input_right_pads = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len,
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37},
"Error: ConvParams 2D padding left/right {2,2}.");
"Error: ConvParams 2D padding left/right {2,2}."));
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36}, "Error: ConvParams 2D dilation {2,2}.");
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36}, "Error: ConvParams 2D dilation {2,2}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{3, 3};
conv_params.input_left_pads = std::vector<ck::index_t>{1, 1};
conv_params.input_right_pads = std::vector<ck::index_t>{1, 1};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res =
EXPECT_TRUE(
ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{23, 23},
"Error: ConvParams 2D strides{3,3}, padding {1,1}, dilations {2,2}.");
"Error: ConvParams 2D strides{3,3}, padding {1,1}, dilations {2,2}."));
}
// -------------------------- 1D ------------------------------------
conv_params.num_dim_spatial = 1;
conv_params.filter_spatial_lengths = std::vector<ck::index_t>{3};
conv_params.input_spatial_lengths = std::vector<ck::index_t>{71};
conv_params.conv_filter_strides = std::vector<ck::index_t>{2};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{1};
conv_params.input_left_pads = std::vector<ck::index_t>{1};
conv_params.input_right_pads = std::vector<ck::index_t>{1};
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths1D)
{
SetNDParams(1);
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D.");
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71}, "Error: ConvParams 1D stride {1}.");
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71}, "Error: ConvParams 1D stride {1}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{2};
conv_params.input_left_pads = std::vector<ck::index_t>{2};
conv_params.input_right_pads = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len,
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37},
"Error: ConvParams 1D padding left/right {2}.");
"Error: ConvParams 1D padding left/right {2}."));
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D dilation {2}.");
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D dilation {2}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{3};
conv_params.input_left_pads = std::vector<ck::index_t>{1};
conv_params.input_right_pads = std::vector<ck::index_t>{1};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len,
EXPECT_TRUE(
ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{23},
"Error: ConvParams 1D strides{3}, padding {1}, dilations {2}.");
"Error: ConvParams 1D strides{3}, padding {1}, dilations {2}."));
}
// -------------------------- 3D ------------------------------------
conv_params.num_dim_spatial = 3;
conv_params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3, 3};
conv_params.input_spatial_lengths = std::vector<ck::index_t>{71, 71, 71};
conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2, 2};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{1, 1, 1};
conv_params.input_left_pads = std::vector<ck::index_t>{1, 1, 1};
conv_params.input_right_pads = std::vector<ck::index_t>{1, 1, 1};
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths3D)
{
SetNDParams(3);
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36, 36}, "Error: ConvParams 3D.");
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36, 36}, "Error: ConvParams 3D."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{1, 1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len,
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{71, 71, 71},
"Error: ConvParams 3D stride {1, 1, 1}.");
"Error: ConvParams 3D stride {1, 1, 1}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_left_pads = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_right_pads = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len,
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37, 37},
"Error: ConvParams 3D padding left/right {2, 2, 2}.");
"Error: ConvParams 3D padding left/right {2, 2, 2}."));
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(out_spatial_len,
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36, 36},
"Error: ConvParams 3D dilation {2, 2, 2}.");
"Error: ConvParams 3D dilation {2, 2, 2}."));
conv_params.conv_filter_strides = std::vector<ck::index_t>{3, 3, 3};
conv_params.input_left_pads = std::vector<ck::index_t>{1, 1, 1};
conv_params.input_right_pads = std::vector<ck::index_t>{1, 1, 1};
conv_params.conv_filter_dilations = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
res = ck::utils::check_err(
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len,
std::vector<ck::index_t>{23, 23, 23},
"Error: ConvParams 3D strides{3, 3, 3}, padding {1, 1, 1}, dilations {2, 2, 2}.");
return res;
"Error: ConvParams 3D strides{3, 3, 3}, padding {1, 1, 1}, dilations {2, 2, 2}."));
}
bool test_get_host_tensor_descriptor()
TEST(ConvUtil, GetHostTensorDescriptor)
{
bool res{true};
namespace tl = ck::tensor_layout::convolution;
std::vector<std::size_t> dims{2, 3, 4, 5};
HostTensorDescriptor h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWC{});
res =
ck::utils::check_err(h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NHWC dimensions lengths!");
res = ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 1, 3 * 5, 3}, "Error: wrong NHWC dimensions strides!");
EXPECT_TRUE(ck::utils::check_err(
h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NHWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 1, 3 * 5, 3}, "Error: wrong NHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCHW{});
res =
ck::utils::check_err(h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NCHW dimensions lengths!");
res = ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 4 * 5, 5, 1}, "Error: wrong NCHW dimensions strides!");
EXPECT_TRUE(ck::utils::check_err(
h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NCHW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 4 * 5, 5, 1}, "Error: wrong NCHW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWC{});
res = ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NWC dimensions lengths!");
res =
ck::utils::check_err(h.GetStrides(), {3 * 4, 1, 3}, "Error: wrong NWC dimensions strides!");
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 1, 3}, "Error: wrong NWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCW{});
res = ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NCW dimensions lengths!");
res =
ck::utils::check_err(h.GetStrides(), {3 * 4, 4, 1}, "Error: wrong NCW dimensions strides!");
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NCW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 4, 1}, "Error: wrong NCW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4, 5, 6};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWC{});
res = ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NDHWC dimensions lengths!");
res = ck::utils::check_err(h.GetStrides(),
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NDHWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
{3 * 4 * 5 * 6, // N
1, // C
3 * 5 * 6, // D
3 * 6, // H
3}, // W
"Error: wrong NDHWC dimensions strides!");
"Error: wrong NDHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCDHW{});
res = ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NCDHW dimensions lengths!");
res = ck::utils::check_err(h.GetStrides(),
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NCDHW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
{3 * 4 * 5 * 6, // N
4 * 5 * 6, // C
5 * 6, // D
6, // H
1}, // W
"Error: wrong NCDHW dimensions strides!");
return res;
}
} // namespace
int main(void)
{
bool res = test_conv_params_get_output_spatial_lengths();
std::cout << "test_conv_params_get_output_spatial_lengths ..... "
<< (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_get_host_tensor_descriptor();
std::cout << "test_get_host_tensor_descriptor ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
return res ? 0 : 1;
"Error: wrong NCDHW dimensions strides!"));
}
test/convnd_bwd_data/CMakeLists.txt
View file @ d92fb7e8
......@@ -4,5 +4,4 @@ include_directories(BEFORE
)
add_test_executable(test_convnd_bwd_data convnd_bwd_data.cpp)
target_link_libraries(test_convnd_bwd_data PRIVATE host_tensor)
target_link_libraries(test_convnd_bwd_data PRIVATE device_convnd_bwd_data_instance)
target_link_libraries(test_convnd_bwd_data PRIVATE host_tensor device_convnd_bwd_data_instance conv_fwd_util)
test/convnd_fwd/CMakeLists.txt
View file @ d92fb7e8
add_custom_target(test_convnd_fwd)
add_test_executable(test_conv1d_fwd conv1d_fwd.cpp)
target_link_libraries(test_conv1d_fwd PRIVATE host_tensor)
target_link_libraries(test_conv1d_fwd PRIVATE device_conv1d_fwd_instance)
add_gtest_executable(test_conv1d_fwd conv1d_fwd.cpp)
target_link_libraries(test_conv1d_fwd PRIVATE host_tensor device_conv1d_fwd_instance conv_fwd_util)
add_dependencies(test_convnd_fwd test_conv1d_fwd)
add_test_executable(test_conv2d_fwd conv2d_fwd.cpp)
target_link_libraries(test_conv2d_fwd PRIVATE host_tensor)
target_link_libraries(test_conv2d_fwd PRIVATE device_conv2d_fwd_instance)
add_gtest_executable(test_conv2d_fwd conv2d_fwd.cpp)
target_link_libraries(test_conv2d_fwd PRIVATE host_tensor device_conv2d_fwd_instance conv_fwd_util)
add_dependencies(test_convnd_fwd test_conv2d_fwd)
add_test_executable(test_conv3d_fwd conv3d_fwd.cpp)
target_link_libraries(test_conv3d_fwd PRIVATE host_tensor)
target_link_libraries(test_conv3d_fwd PRIVATE device_conv3d_fwd_instance)
add_gtest_executable(test_conv3d_fwd conv3d_fwd.cpp)
target_link_libraries(test_conv3d_fwd PRIVATE host_tensor device_conv3d_fwd_instance conv_fwd_util)
add_dependencies(test_convnd_fwd test_conv3d_fwd)
test/convnd_fwd/conv1d_fwd.cpp
View file @ d92fb7e8
......@@ -2,155 +2,92 @@
#include <stdexcept>
#include <tuple>
#include <vector>
#include "gtest/gtest.h"
#include "data_type.hpp"
#include "element_wise_operation.hpp"
#include "conv_fwd_util.hpp"
#include "conv_util.hpp"
#include "host_tensor.hpp"
#include "tensor_layout.hpp"
#include "check_err.hpp"
// Forward declarations for conv instances.
using DeviceConvFwdNoOpPtr =
ck::tensor_operation::device::DeviceConvFwdPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_conv1d_fwd_instance {
void add_device_conv1d_fwd_xdl_nwc_kxc_nwk_bf16_instances(std::vector<DeviceConvFwdNoOpPtr>&);
void add_device_conv1d_fwd_xdl_nwc_kxc_nwk_f16_instances(std::vector<DeviceConvFwdNoOpPtr>&);
void add_device_conv1d_fwd_xdl_nwc_kxc_nwk_f32_instances(std::vector<DeviceConvFwdNoOpPtr>&);
void add_device_conv1d_fwd_xdl_nwc_kxc_nwk_int8_instances(std::vector<DeviceConvFwdNoOpPtr>&);
} // namespace device_conv1d_fwd_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
namespace {
bool test_conv1D_nwc()
template <typename T>
bool test_conv1d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs)
{
bool res{true};
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
ck::utils::conv::ConvParams params;
params.num_dim_spatial = 1;
params.N = 2;
params.K = 16;
params.C = 4;
params.filter_spatial_lengths = std::vector<ck::index_t>{3};
params.input_spatial_lengths = std::vector<ck::index_t>{16};
params.conv_filter_strides = std::vector<ck::index_t>{1};
params.input_spatial_lengths = std::vector<ck::index_t>{71};
params.conv_filter_strides = std::vector<ck::index_t>{2};
params.conv_filter_dilations = std::vector<ck::index_t>{1};
params.input_left_pads = std::vector<ck::index_t>{1};
params.input_right_pads = std::vector<ck::index_t>{1};
auto host_tensors =
ck::utils::conv::get_host_tensors<float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(params);
const Tensor<float>& input = std::get<0>(host_tensors);
const Tensor<float>& weights = std::get<1>(host_tensors);
Tensor<float>& host_output = std::get<2>(host_tensors);
Tensor<float>& device_output = std::get<3>(host_tensors);
ck::utils::conv::run_reference_convolution_forward<1>(params, input, weights, host_output);
test::conv::RunConv<1>(params, input, weights, device_output);
res = res &&
ck::utils::check_err(
device_output.mData, host_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
conv::ConvFwdOpInstance<T, T, T, ctl::NWC, ctl::KCX, ctl::NWK> conv_instance(params);
return res;
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
return run_engine.Test(conv_ptrs);
}
template <typename T>
bool test_conv1d_nwc_instances(const std::vector<DeviceConvFwdNoOpPtr>& conv_ptrs)
} // anonymous namespace
TEST(Conv1DFwdNWC, TestConv1D)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
ck::utils::conv::ConvParams params;
params.num_dim_spatial = 1;
params.N = 2;
params.K = 16;
params.C = 4;
params.filter_spatial_lengths = std::vector<ck::index_t>{3};
params.input_spatial_lengths = std::vector<ck::index_t>{71};
params.conv_filter_strides = std::vector<ck::index_t>{2};
params.input_spatial_lengths = std::vector<ck::index_t>{16};
params.conv_filter_strides = std::vector<ck::index_t>{1};
params.conv_filter_dilations = std::vector<ck::index_t>{1};
params.input_left_pads = std::vector<ck::index_t>{1};
params.input_right_pads = std::vector<ck::index_t>{1};
auto host_tensors =
ck::utils::conv::get_host_tensors<T,
T,
T,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(params);
const Tensor<T>& input = std::get<0>(host_tensors);
const Tensor<T>& weights = std::get<1>(host_tensors);
Tensor<T>& host_output = std::get<2>(host_tensors);
Tensor<T>& device_output = std::get<3>(host_tensors);
ck::utils::conv::run_reference_convolution_forward<1>(params, input, weights, host_output);
return ck::utils::conv::run_convolution_forward_instances<1>(
params, conv_ptrs, input, weights, device_output, host_output);
}
bool test_conv1d_nwc_bf16_instances()
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv1d_fwd_instance::
add_device_conv1d_fwd_xdl_nwc_kxc_nwk_bf16_instances(conv_ptrs);
return test_conv1d_nwc_instances<ck::bhalf_t>(conv_ptrs);
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<1>(conv_ptrs);
conv::ConvFwdOpInstance<float, float, float, ctl::NWC, ctl::KCX, ctl::NWK> conv_instance(
params);
auto reference_conv_fwd_fun = std::bind(
conv::run_reference_convolution_forward<1, float, float, float>, params, _1, _2, _3);
OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
bool test_conv1d_nwc_f16_instances()
TEST(Conv1DFwdNWC, Bf16Iinstances)
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv1d_fwd_instance::
add_device_conv1d_fwd_xdl_nwc_kxc_nwk_f16_instances(conv_ptrs);
return test_conv1d_nwc_instances<ck::half_t>(conv_ptrs);
EXPECT_TRUE(test_conv1d_nwc_instances<ck::bhalf_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<1>()));
}
bool test_conv1d_nwc_f32_instances()
TEST(Conv1DFwdNWC, F16Instances)
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv1d_fwd_instance::
add_device_conv1d_fwd_xdl_nwc_kxc_nwk_f32_instances(conv_ptrs);
return test_conv1d_nwc_instances<float>(conv_ptrs);
EXPECT_TRUE(test_conv1d_nwc_instances<ck::half_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<1>()));
}
bool test_conv1d_nwc_int8_instances()
TEST(Conv1DFwdNWC, F32Instances)
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv1d_fwd_instance::
add_device_conv1d_fwd_xdl_nwc_kxc_nwk_int8_instances(conv_ptrs);
return test_conv1d_nwc_instances<int8_t>(conv_ptrs);
EXPECT_TRUE(test_conv1d_nwc_instances<float>(
ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<1>()));
}
} // anonymous namespace
int main()
TEST(Conv1DFwdNWC, Int8Instances)
{
bool res{true};
res = test_conv1D_nwc();
std::cout << "test_conv1D_nwc ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_conv1d_nwc_bf16_instances();
std::cout << "\nTestConv1DNWCBF16Instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv1d_nwc_f16_instances();
std::cout << "\ntest_conv1d_nwc_f16_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv1d_nwc_f32_instances();
std::cout << "\ntest_conv1d_nwc_f32_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv1d_nwc_int8_instances();
std::cout << "\ntes_tconv1_dnw_cint_8instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
return res ? 0 : 1;
EXPECT_TRUE(test_conv1d_nwc_instances<int8_t>(
ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<1>()));
}
test/convnd_fwd/conv2d_fwd.cpp
View file @ d92fb7e8
#include <half.hpp>
#include <iostream>
#include <stdexcept>
#include <tuple>
#include <vector>
#include "gtest/gtest.h"
#include "data_type.hpp"
#include "element_wise_operation.hpp"
#include "conv_fwd_util.hpp"
#include "conv_util.hpp"
#include "host_tensor.hpp"
#include "tensor_layout.hpp"
#include "check_err.hpp"
// Forward declarations for conv instances.
using DeviceConvFwdNoOpPtr =
ck::tensor_operation::device::DeviceConvFwdPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_conv2d_fwd_instance {
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(std::vector<DeviceConvFwdNoOpPtr>&);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(std::vector<DeviceConvFwdNoOpPtr>&);
void add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(
std::vector<DeviceConvFwdNoOpPtr>&);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(std::vector<DeviceConvFwdNoOpPtr>&);
void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(std::vector<DeviceConvFwdNoOpPtr>&);
} // namespace device_conv2d_fwd_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
namespace {
bool test_conv2d_nhwc()
{
bool res{true};
ck::utils::conv::ConvParams params;
params.N = 2;
params.K = 16;
params.C = 4;
params.input_spatial_lengths = std::vector<ck::index_t>{16, 16};
params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
auto host_tensors = ck::utils::conv::get_host_tensors(params);
const Tensor<float>& input = std::get<0>(host_tensors);
const Tensor<float>& weights = std::get<1>(host_tensors);
Tensor<float>& host_output = std::get<2>(host_tensors);
Tensor<float>& device_output = std::get<3>(host_tensors);
ck::utils::conv::run_reference_convolution_forward<2>(params, input, weights, host_output);
test::conv::RunConv<2>(params, input, weights, device_output);
res = res &&
ck::utils::check_err(
device_output.mData, host_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
return res;
}
template <typename T>
bool test_conv2d_nhwc_instances(const std::vector<DeviceConvFwdNoOpPtr>& conv_ptrs)
bool test_conv2d_nhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs)
{
ck::utils::conv::ConvParams params;
using namespace std::placeholders;
using namespace ck::utils;
conv::ConvParams params;
params.num_dim_spatial = 2;
params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3};
params.input_spatial_lengths = std::vector<ck::index_t>{71, 71};
......@@ -74,77 +26,66 @@ bool test_conv2d_nhwc_instances(const std::vector<DeviceConvFwdNoOpPtr>& conv_pt
params.input_left_pads = std::vector<ck::index_t>{1, 1};
params.input_right_pads = std::vector<ck::index_t>{1, 1};
auto host_tensors =
ck::utils::conv::get_host_tensors<T,
T,
T,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(params);
const Tensor<T>& input = std::get<0>(host_tensors);
const Tensor<T>& weights = std::get<1>(host_tensors);
Tensor<T>& host_output = std::get<2>(host_tensors);
Tensor<T>& device_output = std::get<3>(host_tensors);
ck::utils::conv::run_reference_convolution_forward<2>(params, input, weights, host_output);
return ck::utils::conv::run_convolution_forward_instances<2>(
params, conv_ptrs, input, weights, device_output, host_output);
conv::ConvFwdOpInstance<T, T, T> conv_instance(params);
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<2, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
return run_engine.Test(conv_ptrs);
}
bool test_conv2d_nhwc_bf16_instances()
} // anonymous namespace
TEST(Conv2DFwdNHWC, TestConv2D)
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv2d_fwd_instance::
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(conv_ptrs);
return test_conv2d_nhwc_instances<ck::bhalf_t>(conv_ptrs);
using namespace std::placeholders;
using namespace ck::utils;
ck::utils::conv::ConvParams params;
params.N = 2;
params.K = 16;
params.C = 4;
params.input_spatial_lengths = std::vector<ck::index_t>{16, 16};
params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<2>(conv_ptrs);
conv::ConvFwdOpInstance<float, float, float> conv_instance(params);
auto reference_conv_fwd_fun = std::bind(
conv::run_reference_convolution_forward<2, float, float, float>, params, _1, _2, _3);
OpInstanceRunEngine<float, float, float> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
bool test_conv2d_nhwc_f16_instances()
TEST(Conv2DFwdNHWC, Bf16Instances)
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv2d_fwd_instance::
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
ck::tensor_operation::device::device_conv2d_fwd_instance::
add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
return test_conv2d_nhwc_instances<ck::half_t>(conv_ptrs);
EXPECT_TRUE(test_conv2d_nhwc_instances<ck::bhalf_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::bhalf_t, ck::bhalf_t, ck::bhalf_t>::Get<2>()));
}
bool test_conv2d_nhwc_f32_instances()
TEST(Conv2DFwdNHWC, F16Instances)
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv2d_fwd_instance::
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
return test_conv2d_nhwc_instances<float>(conv_ptrs);
EXPECT_TRUE(test_conv2d_nhwc_instances<ck::half_t>(
ck::utils::conv::ConvolutionFwdInstances<ck::half_t, ck::half_t, ck::half_t>::Get<2>()));
}
bool test_conv2d_nhwc_int8_instances()
TEST(Conv2DFwdNHWC, BF32Instances)
{
std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
ck::tensor_operation::device::device_conv2d_fwd_instance::
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(conv_ptrs);
return test_conv2d_nhwc_instances<int8_t>(conv_ptrs);
EXPECT_TRUE(test_conv2d_nhwc_instances<float>(
ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<2>()));
}
} // anonymous namespace
TEST(Conv2DFwdNHWC, F32Instances)
{
EXPECT_TRUE(test_conv2d_nhwc_instances<float>(
ck::utils::conv::ConvolutionFwdInstances<float, float, float>::Get<2>()));
}
int main()
TEST(Conv2DFwdNHWC, Int8Instances)
{
bool res{true};
res = test_conv2d_nhwc();
std::cout << "test_conv2d_nhwc ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
res = test_conv2d_nhwc_bf16_instances();
std::cout << "\ntest_conv2d_nhwc_bf16_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv2d_nhwc_f16_instances();
std::cout << "\ntest_conv2d_nhwc_f16_instances ....." << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv2d_nhwc_f32_instances();
std::cout << "\ntest_conv2d_nhwc_f32_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
res = test_conv2d_nhwc_int8_instances();
std::cout << "\ntest_conv2d_nhwc_int8_instances ..... " << (res ? "SUCCESS" : "FAILURE")
<< std::endl;
return res ? 0 : 1;
EXPECT_TRUE(test_conv2d_nhwc_instances<int8_t>(
ck::utils::conv::ConvolutionFwdInstances<int8_t, int8_t, int8_t>::Get<2>()));
}
test/convnd_fwd/conv3d_fwd.cpp
View file @ d92fb7e8
This diff is collapsed.
test/convnd_fwd/conv_util.hpp
View file @ d92fb7e8
This diff is collapsed.
test/gemm/gemm_util.hpp
View file @ d92fb7e8
......@@ -139,17 +139,10 @@ struct TestGemm
Tensor<CDataType> c_m_n_device_result(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
auto f_generate_tensor_value = [](auto desc, auto type) {
auto f_generate_tensor_value = [](auto& tensor, auto type) {
using dataType = decltype(type);
if(std::is_same<dataType, int8_t>::value)
{
desc.GenerateTensorValue(GeneratorTensor_2<int8_t>{-5, 5});
}
else
{
desc.GenerateTensorValue(GeneratorTensor_3<dataType>{-0.5, 0.5});
}
tensor.GenerateTensorValue(GeneratorTensor_2<dataType>{-5, 5});
};
f_generate_tensor_value(a_m_k, ADataType{});
......
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