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Commit dc0bae32 authored by Adam Osewski's avatar Adam Osewski
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Merge branch 'develop' into aosewski/wavelet_omniperf

parents 68474822 ba40c2ce
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Showing with 576 additions and 43 deletions
profiler/src/profile_conv_bwd_data.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_conv_bwd_data_impl.hpp"
#include "profiler/profile_conv_bwd_data_impl.hpp"
#include "profiler_operation_registry.hpp"
namespace {
......@@ -24,10 +25,13 @@ enum struct ConvDataType
INT8_INT8_INT8, // 3
};
#define OP_NAME "conv_bwd_data"
#define OP_DESC "Convolution Backward Data"
static void print_helper_msg()
{
std::cout
<< "arg1: tensor operation (conv_bwd_data: Convolution Backward Data)\n"
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
<< " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight bf16, Output bf16\n"
......@@ -182,3 +186,5 @@ int profile_conv_bwd_data(int argc, char* argv[])
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_conv_bwd_data);
profiler/src/profile_conv_fwd.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_conv_fwd_impl.hpp"
#include "profiler/profile_conv_fwd_impl.hpp"
#include "profiler_operation_registry.hpp"
namespace {
......@@ -24,11 +25,14 @@ enum struct ConvDataType
INT8_INT8_INT8, // 3
};
#define OP_NAME "conv_fwd"
#define OP_DESC "Convolution Forward"
static void print_helper_msg()
{
std::cout
// clang-format-off
<< "arg1: tensor operation (conv_fwd: Convolution Forward)\n"
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
<< " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight bf16, Output bf16\n"
......@@ -184,3 +188,5 @@ int profile_conv_fwd(int argc, char* argv[])
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_conv_fwd);
profiler/src/profile_conv_fwd_bias_relu.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_conv_fwd_bias_relu_impl.hpp"
#include "profiler/profile_conv_fwd_bias_relu_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct ConvDataType
{
......@@ -32,11 +33,14 @@ enum struct ConvOutputLayout
NHWK, // 1
};
#define OP_NAME "conv_fwd_bias_relu"
#define OP_DESC "Convolution Forward+Bias+ReLU"
int profile_conv_fwd_bias_relu(int argc, char* argv[])
{
if(argc != 25)
{
printf("arg1: tensor operation (conv_fwd_bias_relu: ForwardConvolution+Bias+ReLu)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\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");
......@@ -114,3 +118,5 @@ int profile_conv_fwd_bias_relu(int argc, char* argv[])
return 0;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_conv_fwd_bias_relu);
profiler/src/profile_conv_fwd_bias_relu_add.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_conv_fwd_bias_relu_add_impl.hpp"
#include "profiler/profile_conv_fwd_bias_relu_add_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct ConvDataType
{
......@@ -32,12 +33,14 @@ enum struct ConvOutputLayout
NHWK, // 1
};
#define OP_NAME "conv_fwd_bias_relu_add"
#define OP_DESC "Convolution Forward+Bias+ReLU+Add"
int profile_conv_fwd_bias_relu_add(int argc, char* argv[])
{
if(argc != 25)
{
printf(
"arg1: tensor operation (conv_fwd_bias_relu_add: ForwardConvolution+Bias+ReLu+Add)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\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");
......@@ -115,3 +118,5 @@ int profile_conv_fwd_bias_relu_add(int argc, char* argv[])
return 0;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_conv_fwd_bias_relu_add);
profiler/src/profile_gemm.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_gemm_impl.hpp"
#include "profiler/profile_gemm_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct GemmMatrixLayout
{
......@@ -24,9 +25,12 @@ enum struct GemmDataType
INT8_INT8_INT8, // 3
};
#define OP_NAME "gemm"
#define OP_DESC "GEMM"
static void print_helper_msg()
{
std::cout << "arg1: tensor operation (gemm: GEMM)\n"
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n"
<< "arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n"
<< " 1: A[m, k] * B[n, k] = C[m, n];\n"
......@@ -184,3 +188,5 @@ int profile_gemm(int argc, char* argv[])
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm);
profiler/src/profile_gemm_add_add_fastgelu.cpp
View file @ dc0bae32
......@@ -6,7 +6,11 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_gemm_add_add_fastgelu_impl.hpp"
#include "profiler/profile_gemm_add_add_fastgelu_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_add_add_fastgelu"
#define OP_DESC "GEMM+Add+Add+FastGeLU"
int profile_gemm_add_add_fastgelu(int argc, char* argv[])
{
......@@ -29,7 +33,7 @@ int profile_gemm_add_add_fastgelu(int argc, char* argv[])
if(argc != 16)
{
// clang-format off
printf("arg1: tensor operation (gemm_add_add_fastgelu: GEMM+Add+Add+FastGeLU)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
printf("arg3: matrix layout (0: E[m, n] = FastGeLU(A[m, k] * B[k, n] + D0[m, n] + D1[m, n]);\n");
printf(" 1: E[m, n] = FastGeLU(A[m, k] * B[n, k] + D0[m, n] + D1[m, n]);\n");
......@@ -150,3 +154,5 @@ int profile_gemm_add_add_fastgelu(int argc, char* argv[])
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_add_add_fastgelu);
profiler/src/profile_gemm_add_fastgelu.cpp 0 → 100644
View file @ dc0bae32
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_gemm_add_fastgelu_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_add_fastgelu"
#define OP_DESC "GEMM+Add+FastGeLU"
int profile_gemm_add_fastgelu(int argc, char* argv[])
{
enum struct MatrixLayout
{
MK_KN_MN_MN, // 0
MK_NK_MN_MN, // 1
KM_KN_MN_MN, // 2
KM_NK_MN_MN, // 3
};
enum struct MatrixDataType
{
F32_F32_F32_F32, // 0
F16_F16_F16_F16, // 1
BF16_BF16_BF16_BF16, // 2
INT8_INT8_INT8_INT8, // 3
};
if(argc != 15)
{
// clang-format off
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
printf("arg3: matrix layout (0: E[m, n] = FastGeLU(A[m, k] * B[k, n] + D0[m, n]);\n");
printf(" 1: E[m, n] = FastGeLU(A[m, k] * B[n, k] + D0[m, n]);\n");
printf(" 2: E[m, n] = FastGeLU(A[k, m] * B[k, n] + D0[m, n]);\n");
printf(" 3: E[m, n] = FastGeLU(A[k, m] * B[n, k] + D0[m, n]))\n");
printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=no, 1=yes)\n");
printf("arg8 to 14: M, N, K, StrideA, StrideB, StrideD0, StrideE\n");
// clang-format on
exit(1);
}
const auto data_type = static_cast<MatrixDataType>(std::stoi(argv[2]));
const auto layout = static_cast<MatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]);
const int K = std::stoi(argv[10]);
const int StrideA = std::stoi(argv[11]);
const int StrideB = std::stoi(argv[12]);
const int StrideD0 = std::stoi(argv[13]);
const int StrideE = std::stoi(argv[14]);
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
auto profile = [&](auto a_type,
auto b_type,
auto acc_type,
auto d0_type,
auto e_type,
auto a_layout,
auto b_layout,
auto d0_layout,
auto e_layout) {
using ADataType = decltype(a_type);
using BDataType = decltype(b_type);
using AccDataType = decltype(acc_type);
using D0DataType = decltype(d0_type);
using EDataType = decltype(e_type);
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using D0Layout = decltype(d0_layout);
using ELayout = decltype(e_layout);
const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
const int DefaultStrideD0 = ck::is_same_v<D0Layout, Row> ? N : M;
const int DefaultStrideE = ck::is_same_v<ELayout, Row> ? N : M;
bool pass = ck::profiler::profile_gemm_add_fastgelu_impl<ADataType,
BDataType,
AccDataType,
D0DataType,
EDataType,
ALayout,
BLayout,
D0Layout,
ELayout>(
do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
(StrideA < 0) ? DefaultStrideA : StrideA,
(StrideB < 0) ? DefaultStrideB : StrideB,
(StrideD0 < 0) ? DefaultStrideD0 : StrideD0,
(StrideE < 0) ? DefaultStrideE : StrideE);
return pass ? 0 : 1;
};
if(data_type == MatrixDataType::F16_F16_F16_F16 && layout == MatrixLayout::MK_KN_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, Row{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16_F16 && layout == MatrixLayout::MK_NK_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, Row{}, Col{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16_F16 && layout == MatrixLayout::KM_KN_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, Col{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16_F16 && layout == MatrixLayout::KM_NK_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, Col{}, Col{}, Row{}, Row{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_add_fastgelu);
profiler/src/profile_gemm_add_multiply.cpp 0 → 100644
View file @ dc0bae32
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_gemm_add_multiply_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_add_multiply"
#define OP_DESC "GEMM+Add+MULTIPLY"
int profile_gemm_add_multiply(int argc, char* argv[])
{
enum struct MatrixLayout
{
MK_KN_MN_MN_MN, // 0
MK_NK_MN_MN_MN, // 1
KM_KN_MN_MN_MN, // 2
KM_NK_MN_MN_MN, // 3
};
enum struct MatrixDataType
{
F32_F32_F32_F32_F32, // 0
F16_F16_F16_F16_F16, // 1
BF16_BF16_BF16_BF16_BF16, // 2
INT8_INT8_INT8_INT8_INT8, // 3
};
if(argc != 16)
{
// clang-format off
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
printf("arg3: matrix layout (0: E[m, n] = AddMultiply((A[m, k] * B[k, n] + D0[m, n]) x D1[m, n]);\n");
printf(" 1: E[m, n] = AddMultiply((A[m, k] * B[k, n] + D0[m, n]) x D1[m, n]);\n");
printf(" 2: E[m, n] = AddMultiply((A[m, k] * B[k, n] + D0[m, n]) x D1[m, n]);\n");
printf(" 3: E[m, n] = AddMultiply((A[m, k] * B[k, n] + D0[m, n]) x D1[m, n]))\n");
printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=no, 1=yes)\n");
printf("arg8 to 15: M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideE\n");
// clang-format on
exit(1);
}
const auto data_type = static_cast<MatrixDataType>(std::stoi(argv[2]));
const auto layout = static_cast<MatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]);
const int K = std::stoi(argv[10]);
const int StrideA = std::stoi(argv[11]);
const int StrideB = std::stoi(argv[12]);
const int StrideD0 = std::stoi(argv[13]);
const int StrideD1 = std::stoi(argv[14]);
const int StrideE = std::stoi(argv[15]);
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
auto profile = [&](auto a_type,
auto b_type,
auto acc_type,
auto d0_type,
auto d1_type,
auto e_type,
auto a_layout,
auto b_layout,
auto d0_layout,
auto d1_layout,
auto e_layout) {
using ADataType = decltype(a_type);
using BDataType = decltype(b_type);
using AccDataType = decltype(acc_type);
using D0DataType = decltype(d0_type);
using D1DataType = decltype(d1_type);
using EDataType = decltype(e_type);
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using D0Layout = decltype(d0_layout);
using D1Layout = decltype(d1_layout);
using ELayout = decltype(e_layout);
const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
const int DefaultStrideD0 = ck::is_same_v<D0Layout, Row> ? N : M;
const int DefaultStrideD1 = ck::is_same_v<D1Layout, Row> ? N : M;
const int DefaultStrideE = ck::is_same_v<ELayout, Row> ? N : M;
bool pass = ck::profiler::profile_gemm_add_multiply_impl<ADataType,
BDataType,
AccDataType,
D0DataType,
D1DataType,
EDataType,
ALayout,
BLayout,
D0Layout,
D1Layout,
ELayout>(
do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
(StrideA < 0) ? DefaultStrideA : StrideA,
(StrideB < 0) ? DefaultStrideB : StrideB,
(StrideD0 < 0) ? DefaultStrideD0 : StrideD0,
(StrideD1 < 0) ? DefaultStrideD1 : StrideD1,
(StrideE < 0) ? DefaultStrideE : StrideE);
return pass ? 0 : 1;
};
if(data_type == MatrixDataType::F16_F16_F16_F16_F16 && layout == MatrixLayout::MK_KN_MN_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, F16{}, Row{}, Row{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16_F16_F16 &&
layout == MatrixLayout::MK_NK_MN_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, F16{}, Row{}, Col{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16_F16_F16 &&
layout == MatrixLayout::KM_KN_MN_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, F16{}, Col{}, Row{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16_F16_F16 &&
layout == MatrixLayout::KM_NK_MN_MN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, F16{}, Col{}, Col{}, Row{}, Row{}, Row{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_add_multiply);
profiler/src/profile_gemm_bias_add_reduce.cpp
View file @ dc0bae32
......@@ -6,7 +6,11 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_gemm_bias_add_reduce_impl.hpp"
#include "profiler/profile_gemm_bias_add_reduce_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_bias_add_reduce"
#define OP_DESC "GEMM+Bias+Add+Reduce"
int profile_gemm_bias_add_reduce(int argc, char* argv[])
{
......@@ -26,7 +30,7 @@ int profile_gemm_bias_add_reduce(int argc, char* argv[])
if(!(argc == 14 || argc == 15))
{
printf("arg1: tensor operation (gemm: GEMM+bias+add+Reduce)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16)\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
......@@ -159,3 +163,5 @@ int profile_gemm_bias_add_reduce(int argc, char* argv[])
return 0;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_bias_add_reduce);
profiler/src/profile_gemm_bilinear.cpp
View file @ dc0bae32
......@@ -6,7 +6,11 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_gemm_bilinear_impl.hpp"
#include "profiler/profile_gemm_bilinear_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_bilinear"
#define OP_DESC "GEMM+Bilinear"
int profile_gemm_bilinear(int argc, char* argv[])
{
......@@ -29,7 +33,7 @@ int profile_gemm_bilinear(int argc, char* argv[])
if(argc != 17)
{
// clang-format off
printf("arg1: tensor operation (gemm_bilinear: GEMM+Bilinear)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
printf("arg3: matrix layout (0: E[m, n] = alpha * A[m, k] * B[k, n] + beta * D[m, n];\n");
printf(" 1: E[m, n] = alpha * A[m, k] * B[n, k] + beta * D[m, n];\n");
......@@ -144,3 +148,5 @@ int profile_gemm_bilinear(int argc, char* argv[])
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_bilinear);
profiler/src/profile_gemm_fastgelu.cpp 0 → 100644
View file @ dc0bae32
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_gemm_fastgelu_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_fastgelu"
#define OP_DESC "GEMM+FastGeLU"
int profile_gemm_fastgelu(int argc, char* argv[])
{
enum struct MatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
KM_KN_MN, // 2
KM_NK_MN, // 3
};
enum struct MatrixDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
};
if(argc != 14)
{
// clang-format off
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
printf("arg3: matrix layout (0: E[m, n] = FastGeLU(A[m, k] * B[k, n]);\n");
printf(" 1: E[m, n] = FastGeLU(A[m, k] * B[n, k]);\n");
printf(" 2: E[m, n] = FastGeLU(A[k, m] * B[k, n]);\n");
printf(" 3: E[m, n] = FastGeLU(A[k, m] * B[n, k]))\n");
printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=no, 1=yes)\n");
printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideE\n");
// clang-format on
exit(1);
}
const auto data_type = static_cast<MatrixDataType>(std::stoi(argv[2]));
const auto layout = static_cast<MatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]);
const int K = std::stoi(argv[10]);
const int StrideA = std::stoi(argv[11]);
const int StrideB = std::stoi(argv[12]);
const int StrideE = std::stoi(argv[13]);
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
auto profile = [&](auto a_type,
auto b_type,
auto acc_type,
auto e_type,
auto a_layout,
auto b_layout,
auto e_layout) {
using ADataType = decltype(a_type);
using BDataType = decltype(b_type);
using AccDataType = decltype(acc_type);
using EDataType = decltype(e_type);
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using ELayout = decltype(e_layout);
const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
const int DefaultStrideE = ck::is_same_v<ELayout, Row> ? N : M;
bool pass = ck::profiler::profile_gemm_fastgelu_impl<ADataType,
BDataType,
AccDataType,
EDataType,
ALayout,
BLayout,
ELayout>(
do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
(StrideA < 0) ? DefaultStrideA : StrideA,
(StrideB < 0) ? DefaultStrideB : StrideB,
(StrideE < 0) ? DefaultStrideE : StrideE);
return pass ? 0 : 1;
};
if(data_type == MatrixDataType::F16_F16_F16 && layout == MatrixLayout::MK_KN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16 && layout == MatrixLayout::MK_NK_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16 && layout == MatrixLayout::KM_KN_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, Col{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F16_F16 && layout == MatrixLayout::KM_NK_MN)
{
return profile(F16{}, F16{}, F32{}, F16{}, Col{}, Col{}, Row{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_fastgelu);
profiler/src/profile_gemm_reduce.cpp
View file @ dc0bae32
......@@ -6,7 +6,11 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_gemm_reduce_impl.hpp"
#include "profiler/profile_gemm_reduce_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_reduce"
#define OP_DESC "GEMM+Reduce"
int profile_gemm_reduce(int argc, char* argv[])
{
......@@ -26,7 +30,7 @@ int profile_gemm_reduce(int argc, char* argv[])
if(!(argc == 14 || argc == 15))
{
printf("arg1: tensor operation (gemm: GEMM+Reduce)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16)\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
......@@ -146,3 +150,5 @@ int profile_gemm_reduce(int argc, char* argv[])
return 0;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_reduce);
profiler/src/profile_gemm_splitk.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_gemm_splitk_impl.hpp"
#include "profiler/profile_gemm_splitk_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct GemmMatrixLayout
{
......@@ -24,11 +25,14 @@ enum struct GemmDataType
INT8_INT8_INT8, // 3
};
#define OP_NAME "gemm_splitk"
#define OP_DESC "Split-K GEMM"
int profile_gemm_splitk(int argc, char* argv[])
{
if(argc != 15)
{
printf("arg1: tensor operation (gemm_splitk: Split-K GEMM)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
......@@ -146,3 +150,5 @@ int profile_gemm_splitk(int argc, char* argv[])
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_splitk);
profiler/src/profile_grouped_conv_bwd_weight.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <iostream>
#include <numeric>
#include "profiler/include/profile_grouped_conv_bwd_weight_impl.hpp"
#include "profiler/profile_grouped_conv_bwd_weight_impl.hpp"
#include "profiler_operation_registry.hpp"
namespace {
......@@ -23,9 +24,12 @@ enum struct ConvDataType
BF16_F32_BF16, // 2
};
#define OP_NAME "grouped_conv_bwd_weight"
#define OP_DESC "Grouped Convolution Backward Weight"
static void print_helper_msg()
{
std::cout << "arg1: tensor operation (conv_bwd_weight: Convolution Backward Weight\n"
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
<< " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight fp32, Output bf16)\n"
......@@ -174,3 +178,5 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_grouped_conv_bwd_weight);
profiler/src/profile_grouped_conv_fwd.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_grouped_conv_fwd_impl.hpp"
#include "profiler/profile_grouped_conv_fwd_impl.hpp"
#include "profiler_operation_registry.hpp"
namespace {
......@@ -24,11 +25,14 @@ enum struct ConvDataType
INT8_INT8_INT8, // 3
};
#define OP_NAME "grouped_conv_fwd"
#define OP_DESC "Grouped Convolution Forward"
static void print_helper_msg()
{
std::cout
// clang-format off
<< "arg1: tensor operation (grouped_conv_fwd: Grouped Convolution Forward)\n"
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
<< " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight bf16, Output bf16\n"
......@@ -252,3 +256,5 @@ int profile_grouped_conv_fwd(int argc, char* argv[])
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_grouped_conv_fwd);
profiler/src/profile_grouped_gemm.cpp
View file @ dc0bae32
......@@ -6,7 +6,8 @@
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_grouped_gemm_impl.hpp"
#include "profiler/profile_grouped_gemm_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct GemmMatrixLayout
{
......@@ -44,11 +45,14 @@ std::vector<int> argToIntArray(char* input)
return out;
}
#define OP_NAME "grouped_gemm"
#define OP_DESC "Grouped GEMM"
int profile_grouped_gemm(int argc, char* argv[])
{
if(!(argc == 14))
{
printf("arg1: tensor operation (grouped_gemm: Grouped GEMM)\n");
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
......@@ -161,3 +165,5 @@ int profile_grouped_gemm(int argc, char* argv[])
return 0;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_grouped_gemm);
profiler/src/profile_groupnorm.cpp
View file @ dc0bae32
......@@ -5,8 +5,9 @@
#include <vector>
#include <unordered_map>
#include "profiler/include/data_type_enum.hpp"
#include "profiler/include/profile_groupnorm_impl.hpp"
#include "profiler/data_type_enum.hpp"
#include "profiler/profile_groupnorm_impl.hpp"
#include "profiler_operation_registry.hpp"
using ck::index_t;
......@@ -43,9 +44,12 @@ struct GroupnormArgParser
}
};
#define OP_NAME "groupnorm"
#define OP_DESC "Group Normalization"
void print_help_groupnorm()
{
std::cout << "arg1: tensor operation (groupnorm: Group normalization)\n"
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp16; 1: fp32)\n"
<< "arg3: verification (0: no; 1: yes)\n"
<< "arg4: initialization (0: no init; 1: integer value; 2: decimal value)\n"
......@@ -104,3 +108,5 @@ int profile_groupnorm(int argc, char* argv[])
return 0;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_groupnorm);
profiler/src/profile_layernorm.cpp
View file @ dc0bae32
......@@ -5,8 +5,9 @@
#include <vector>
#include <unordered_map>
#include "profiler/include/data_type_enum.hpp"
#include "profiler/include/profile_layernorm_impl.hpp"
#include "profiler/data_type_enum.hpp"
#include "profiler/profile_layernorm_impl.hpp"
#include "profiler_operation_registry.hpp"
using ck::index_t;
......@@ -96,3 +97,5 @@ int profile_layernorm(int argc, char* argv[])
return 0;
}
REGISTER_PROFILER_OPERATION("layernorm", "Layer Normalization", profile_layernorm);
profiler/src/profile_reduce.cpp
View file @ dc0bae32
......@@ -13,8 +13,9 @@
#include "ck/library/utility/host_common_util.hpp"
#include "profiler/include/profile_reduce_impl.hpp"
#include "profiler/include/data_type_enum.hpp"
#include "profiler/profile_reduce_impl.hpp"
#include "profiler/data_type_enum.hpp"
#include "profiler_operation_registry.hpp"
using namespace std;
......@@ -429,3 +430,5 @@ int profile_reduce(int argc, char* argv[])
return (0);
};
REGISTER_PROFILER_OPERATION("reduce", "Reduce", profile_reduce);
profiler/src/profile_softmax.cpp
View file @ dc0bae32
......@@ -5,7 +5,8 @@
#include <vector>
#include <unordered_map>
#include "profiler/include/profile_softmax_impl.hpp"
#include "profiler/profile_softmax_impl.hpp"
#include "profiler_operation_registry.hpp"
using ck::index_t;
using ck::profiler::SoftmaxDataType;
......@@ -98,8 +99,8 @@ int profile_softmax(int argc, char* argv[])
length,
stride,
reduce,
float(alpha),
float(beta));
double(alpha),
double(beta));
}
else if(data_type == SoftmaxDataType::F32_F32)
{
......@@ -110,8 +111,8 @@ int profile_softmax(int argc, char* argv[])
length,
stride,
reduce,
float(alpha),
float(beta));
double(alpha),
double(beta));
}
else
{
......@@ -130,8 +131,8 @@ int profile_softmax(int argc, char* argv[])
length,
stride,
reduce,
float(alpha),
float(beta));
double(alpha),
double(beta));
}
else if(data_type == SoftmaxDataType::F32_F32)
{
......@@ -142,8 +143,8 @@ int profile_softmax(int argc, char* argv[])
length,
stride,
reduce,
float(alpha),
float(beta));
double(alpha),
double(beta));
}
else
{
......@@ -164,3 +165,5 @@ int profile_softmax(int argc, char* argv[])
// profile_normalization(argc, argv);
// return 0;
// }
REGISTER_PROFILER_OPERATION("softmax", "Softmax", profile_softmax);
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