Files
composable_kernel/profiler/src/profile_gemm_multiply_add.cpp
emezh f628be2ed1 Verify HostTensorDescriptor when it is created (#2829)
* add proper GEMM layout verification

* Handle "auto" strides.

CalculateStrides only called when tensor's strides are empty or all of them are <=0 (auto strides).
CalculateStrides now supports GEMM::ColumnsMajor order. The assumption is still that it applies only to the inner two dims.
ValidateStrides throws if any of the tensor's strides is <=0.
profile_gemm_multiply_add updated to support "auto" strides for tensors.

Manual tests for profile_gemm_multiply_add (matrix B in Row and Col modes)
auto-strides
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 -1 -1 -1 -1 -1
Note, -1 should be deprecated (use 0 instead)

explicit strides (same as auto)
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 128
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 128 128 128 128 128

explicit strides (not the same as auto)
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138

mix of explicit and auto strides
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 0

invalid stride
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 64
	terminate called after throwing an instance of 'std::runtime_error'
	  what():  Invalid strides for RowMajor: mLens: 128 128 , mStrides: 64 1
	Aborted (core dumped)

* - add more names to ck::tensor_layout for easier namespace hierarchy checking
- updated convolutional layouts to use explicit ones or BaseConvolutionalLayout where it is not clear which layout to use (TBD) - see include/ck/library/utility/convolution_host_tensor_descriptor_helper.hpp

* added handling of partially initialized strides for GEMM. fixed more tests.

* clang-format and more fixes

* replace long dash by a simple hyphen - causes build failure in CK codegen.

* increase sizeof input, otherwise output size becomes zero or negative with large filter size

* select stride based on layout

* specify layout explicitly to avoid errors in HostTensorDescriptor creation

* add validation for higher GEMM tensor dimensions.; Add docstring to `HostTensorDescriptor`

* Not clear why permute test in test/permute_scale/test_permute_scale.cpp uses a lot of invalid strides. Setting layout to BypassLayoutVerification to avoid a lot of errors

* fix test (incl removing invalid config)

* fix moe examples:
- (in .cpp) add layout argument to non-2D tensors
- (in .hpp) fix asserts/failures that show up in Debug mode, specifically addressing 2D tensor by a single index (and 3D tensor by 2d index)

* fix moe_gemm2 example.

* fix profile and wmma examples

* clean-up early mods for ckprofile. verified with:
```
ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0
ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0
ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138
ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138
#
ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 1 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 2 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 3 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 128 128 128
#
ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 0 0 0 0
# ckProfiler gemm_add_relu 0 1 1 1 0 1 128 128 128 0 0 0 0    # not implemented
# ckProfiler gemm_add_relu 0 2 1 1 0 1 128 128 128 0 0 0 0    # not implemented
# ckProfiler gemm_add_relu 0 3 1 1 0 1 128 128 128 0 0 0 0    # not implemented
ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 128 128 128 128
#
ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 1 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 2 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 3 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 130 132 134 136 138
#
example_gemm_add_multiply_dl_fp16
example_gemm_add_multiply_xdl_fp16
#
ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 0 0 0
ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 128 128 128
```

* temporary skip first 8 test configs - they throw error

* temporary skip first 8 test configs in wmma too - they throw error

---------

Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>

[ROCm/composable_kernel commit: db2524be2d]
2025-09-25 18:22:13 -07:00

151 lines
6.1 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_gemm_multiply_add_impl.hpp"
#include "profiler_operation_registry.hpp"
#define OP_NAME "gemm_multiply_add"
#define OP_DESC "GEMM+MULTIPLY+ADD"
int profile_gemm_multiply_add(int argc, char* argv[])
{
enum struct MatrixLayout
{
MK_KN_MN_MN_MN, // 0
MK_NK_MN_MN_MN, // 1
};
enum struct MatrixDataType
{
F16_F16_F16_F16_F16, // 0
F16_F8_F32_F32_F16, // 1
};
if(argc != 16)
{
// clang-format off
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp16; 1: fp16Afp8B)\n");
printf("arg3: matrix layout (0: E[m, n] = Multiply_Add((A[m, k] * B[k, n]) x D1[m, n] + D0[m, n]);\n");
printf(" 1: E[m, n] = Multiply_Add((A[m, k] * B[n, k]) x D1[m, n] + 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 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;
#if defined CK_ENABLE_FP8
using F8 = ck::f8_t;
#endif
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);
bool pass = ck::profiler::profile_gemm_multiply_add_impl<ADataType,
BDataType,
AccDataType,
D0DataType,
D1DataType,
EDataType,
ALayout,
BLayout,
D0Layout,
D1Layout,
ELayout>(do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
StrideA,
StrideB,
StrideD0,
StrideD1,
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{});
}
#if defined CK_ENABLE_FP8
else if(data_type == MatrixDataType::F16_F8_F32_F32_F16 &&
layout == MatrixLayout::MK_KN_MN_MN_MN)
{
return profile(F16{}, F8{}, F32{}, F32{}, F32{}, F16{}, Row{}, Row{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::F16_F8_F32_F32_F16 &&
layout == MatrixLayout::MK_NK_MN_MN_MN)
{
return profile(F16{}, F8{}, F32{}, F32{}, F32{}, F16{}, Row{}, Col{}, Row{}, Row{}, Row{});
}
#endif
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_multiply_add);