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/* ************************************************************************
* Copyright (C) 2020-2024 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell cop-
* ies of the Software, and to permit persons to whom the Software is furnished
* to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IM-
* PLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
* FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
* COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
* IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNE-
* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
* ************************************************************************ */
#include "client_utility.hpp"
#include <hip/hip_runtime.h>
#include <rocblas/rocblas.h>
#include <cstdint>
#include <iostream>
#include <limits>
#include <memory>
template <typename T, typename MathOpT = T>
void mat_mat_mult(T alpha,
T beta,
int M,
int N,
int K,
T* A,
int As1,
int As2,
T* B,
int Bs1,
int Bs2,
T* C,
int Cs1,
int Cs2,
T* D,
int Ds1,
int Ds2)
{
for(int i1 = 0; i1 < M; i1++)
{
for(int i2 = 0; i2 < N; i2++)
{
T t = 0.0;
for(int i3 = 0; i3 < K; i3++)
{
t += T(MathOpT(A[i1 * As1 + i3 * As2])) * T(MathOpT(B[i3 * Bs1 + i2 * Bs2]));
}
D[i1 * Ds1 + i2 * Ds2] = beta * C[i1 * Cs1 + i2 * Cs2] + alpha * t;
}
}
}
int main()
{
rocblas_datatype a_type = rocblas_datatype_f32_r;
rocblas_datatype b_type = rocblas_datatype_f32_r;
rocblas_datatype c_type = rocblas_datatype_f32_r;
rocblas_datatype d_type = rocblas_datatype_f32_r;
rocblas_datatype compute_type = rocblas_datatype_f32_r;
using a_t = float;
using b_t = float;
using c_t = float;
using d_t = float;
rocblas_operation transa = rocblas_operation_none, transb = rocblas_operation_transpose;
//rocblas_int m = 960;
//rocblas_int n = 1024;
//rocblas_int k = 1024;
rocblas_int m = 127;
rocblas_int n = 126;
rocblas_int k = 126;
float alpha = 1.0;
float beta = 1.0;
int batch_count = 2;
rocblas_stride stride_a, stride_b, stride_c, stride_d;
size_t lda, ldb, ldc = m, ldd = m;
size_t size_a1, size_b1, size_c1, size_d1;
size_c1 = size_d1 = m * n;
int a_stride_1, a_stride_2, b_stride_1, b_stride_2;
if(transa == rocblas_operation_none)
{
lda = m;
size_a1 = k * lda;
a_stride_1 = 1;
a_stride_2 = lda;
std::cout << "N";
}
else
{
lda = k;
size_a1 = m * lda;
a_stride_1 = lda;
a_stride_2 = 1;
std::cout << "T";
}
if(transb == rocblas_operation_none)
{
ldb = k;
size_b1 = n * ldb;
b_stride_1 = 1;
b_stride_2 = ldb;
std::cout << "N: ";
}
else
{
ldb = n;
size_b1 = k * ldb;
b_stride_1 = ldb;
b_stride_2 = 1;
std::cout << "T: ";
}
stride_a = size_a1;
stride_b = size_b1;
stride_c = size_c1;
stride_d = size_d1;
std::cout << m << ", " << n << ", " << k << ", " << lda << ", " << ldb << ", " << ldc << ", "
<< ldd << ", " << stride_a << ", " << stride_b << ", " << stride_c << ", " << stride_d
<< ", " << batch_count << ", " << alpha << ", " << beta << ", ";
size_t size_a = batch_count == 0 ? size_a1 : size_a1 + stride_a * (batch_count - 1);
size_t size_b = batch_count == 0 ? size_b1 : size_b1 + stride_b * (batch_count - 1);
size_t size_c = batch_count == 0 ? size_c1 : size_c1 + stride_c * (batch_count - 1);
size_t size_d = batch_count == 0 ? size_d1 : size_d1 + stride_d * (batch_count - 1);
std::cout << "gemm_ex example" << std::endl;
// Naming: da is in GPU (device) memory. ha is in CPU (host) memory
auto ha = std::make_unique<a_t[]>(size_a);
auto hb = std::make_unique<b_t[]>(size_b);
auto hc = std::make_unique<c_t[]>(size_c);
auto hd = std::make_unique<d_t[]>(size_d);
auto hd_gold = std::make_unique<d_t[]>(size_d);
// initial data on host
srand(1);
for(size_t i = 0; i < size_a; ++i)
ha[i] = rand() % 17;
for(size_t i = 0; i < size_b; ++i)
hb[i] = rand() % 17;
for(size_t i = 0; i < size_c; ++i)
hc[i] = rand() % 17;
for(size_t i = 0; i < size_d; ++i)
hd[i] = std::numeric_limits<d_t>::signaling_NaN();
// allocate memory on device
a_t* da;
b_t* db;
c_t* dc;
d_t* dd;
CHECK_HIP_ERROR(hipMalloc(&da, size_a * sizeof(a_t)));
CHECK_HIP_ERROR(hipMalloc(&db, size_b * sizeof(b_t)));
CHECK_HIP_ERROR(hipMalloc(&dc, size_c * sizeof(c_t)));
CHECK_HIP_ERROR(hipMalloc(&dd, size_d * sizeof(d_t)));
// copy matrices from host to device
CHECK_HIP_ERROR(hipMemcpy(da, &ha[0], sizeof(a_t) * size_a, hipMemcpyHostToDevice));
CHECK_HIP_ERROR(hipMemcpy(db, &hb[0], sizeof(b_t) * size_b, hipMemcpyHostToDevice));
CHECK_HIP_ERROR(hipMemcpy(dc, &hc[0], sizeof(c_t) * size_c, hipMemcpyHostToDevice));
CHECK_HIP_ERROR(hipMemcpy(dd, &hd[0], sizeof(d_t) * size_d, hipMemcpyHostToDevice));
rocblas_handle handle;
CHECK_ROCBLAS_ERROR(rocblas_create_handle(&handle));
auto algo = rocblas_gemm_algo_standard;
int32_t solution_index = 0;
uint32_t flags = 0;
CHECK_ROCBLAS_ERROR(rocblas_set_math_mode(handle, rocblas_xf32_xdl_math_op));
rocblas_math_mode math_mode;
CHECK_ROCBLAS_ERROR(rocblas_get_math_mode(handle, &math_mode));
CHECK_ROCBLAS_ERROR(rocblas_gemm_strided_batched_ex(handle,
transa,
transb,
m,
n,
k,
&alpha,
da,
a_type,
lda,
stride_a,
db,
b_type,
ldb,
stride_b,
&beta,
dc,
c_type,
ldc,
stride_c,
dd,
d_type,
ldd,
stride_d,
batch_count,
compute_type,
algo,
solution_index,
flags));
// copy output from device to CPU
CHECK_HIP_ERROR(hipMemcpy(&hd[0], dd, sizeof(d_t) * size_d, hipMemcpyDeviceToHost));
// calculate golden or correct result
for(int i = 0; i < batch_count; i++)
{
a_t* a_ptr = &ha[i * stride_a];
b_t* b_ptr = &hb[i * stride_b];
c_t* c_ptr = &hc[i * stride_d];
d_t* d_ptr = &hd_gold[i * stride_d];
if(math_mode == rocblas_xf32_xdl_math_op)
mat_mat_mult<a_t, rocblas_xfloat32>(alpha,
beta,
m,
n,
k,
a_ptr,
a_stride_1,
a_stride_2,
b_ptr,
b_stride_1,
b_stride_2,
c_ptr,
1,
ldc,
d_ptr,
1,
ldd);
else
mat_mat_mult<a_t>(alpha,
beta,
m,
n,
k,
a_ptr,
a_stride_1,
a_stride_2,
b_ptr,
b_stride_1,
b_stride_2,
c_ptr,
1,
ldc,
d_ptr,
1,
ldd);
}
double max_relative_error = 0;
for(size_t i = 0; i < size_d; i++)
{
auto relative_error = fabs(double(hd_gold[i] - hd[i]) / hd_gold[i]);
if(relative_error > max_relative_error)
max_relative_error = relative_error;
}
auto eps = std::numeric_limits<d_t>::epsilon();
auto tolerance = 10.0;
if(max_relative_error != max_relative_error || max_relative_error > eps * tolerance)
std::cout << "FAIL: ";
else
std::cout << "PASS: ";
std::cout << "max_relative_error = " << max_relative_error << std::endl;
CHECK_HIP_ERROR(hipFree(da));
CHECK_HIP_ERROR(hipFree(db));
CHECK_HIP_ERROR(hipFree(dc));
CHECK_ROCBLAS_ERROR(rocblas_destroy_handle(handle));
return EXIT_SUCCESS;
}
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