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batched_gemm + multiple_d + gemm + multiple_d (#394)

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* refactor

* start

* add device gemm file

* add BatchStrideD0

* add stridd0

* add gridwise file

* add d0 parameters to gridwise gemm

* add c layout transformer

* add d0 threadwise copy

* init kernel

* init kernel

* regular code

* nm desc put to out

* kernel parameter can not use reference

* host add bias+gelu

* run right for bias+gelu

* change AddFastGelu into another file

* interface add d1 bias parameters

* add d1 parameter to argument

* add d1 parameter to gridwise

* first all code,not verify

* gelu change to relu and GetElementSpaceSize bug

* add instance

* start add to ckprofiler

* ckprofiler finish code

* change input parameter for ckProfiler

* fix host bias+gelu bug

* show help for ckProfiler

* fix bug for lunch kernel ignore parametes

* add pad and fix about bug

* mutiple d0

* add dynamic d0_element_op

* change profiler and  instance to mutiple d0

* example have 2 d0

* remove some comments not using

* change 2 d0 have self  parameters

* change d element_op name

* change class name(multiple_d)

* fix bug

* fix bug that don't find file

* update profiler

* refactor

* update profiler

* clean

* revert example change

* add gon layout

* optimize parameter for gno

* add gon to gemm+gemm

* change helping input parameters

* change to GemmPadder_v2

* using ForEach

* fix gb_per_sec

Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: ltqin <letaoqin@amd.com>

[ROCm/composable_kernel commit: 370efa6c08]
This commit is contained in:
ltqin
2022-09-15 06:54:18 +08:00
committed by GitHub
parent 69b32e4446
commit 9d42129ff3
19 changed files with 4119 additions and 0 deletions
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1
example/37_batched_gemm_add_add_relu_gemm_add/CMakeLists.txt Normal file
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add_example_executable(example_batched_gemm_add_add_relu_gemm_add_xdl_fp16 batched_gemm_add_add_relu_gemm_add_xdl_fp16.cpp)

519
example/37_batched_gemm_add_add_relu_gemm_add/batched_gemm_add_add_relu_gemm_add_xdl_fp16.cpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
/*
Computes C_m_o = Relu(A0[m, k] * B0[n, k] + D00[m, n] + D01[mn]) * B1[n, o] + D1[m, o]
*/
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_multiple_d_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using A0DataType = F16;
using B0DataType = F16;
using Acc0DataType = F32;
using D00DataType = F16;
using D01DataType = F16;
using B1DataType = F16;
using Acc1DataType = F32;
using C1ShuffleDataType = F32;
using D1DataType = F16;
using E1DataType = F16;
using A0Layout = Row;
using B0Layout = Col;
using D00Layout = Row;
using D01Layout = Row;
using B1Layout = Row;
using D1Layout = Row;
using E1Layout = Row;
// E = Relu(C + D0 + D1)
struct AddAddRelu
{
__host__ __device__ void
operator()(ck::half_t& e, const ck::half_t& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const ck::half_t x = c + d0 + d1;
ck::tensor_operation::element_wise::Relu{}.template operator()<ck::half_t>(e, x);
}
__host__ __device__ void
operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const float x = c + (d0 + d1);
ck::tensor_operation::element_wise::Relu{}.template operator()<float>(e, x);
}
};
// E = Gelu(C + D0 + D1)
struct AddAddGelu
{
__host__ __device__ void
operator()(ck::half_t& e, const ck::half_t& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const ck::half_t x = c + d0 + d1;
ck::tensor_operation::element_wise::Gelu{}.template operator()<ck::half_t, ck::half_t>(e,
x);
}
__host__ __device__ void
operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const float x = c + (d0 + d1);
ck::tensor_operation::element_wise::Gelu{}.template operator()<float, float>(e, x);
}
};
// E = FastGelu(C + D0 + D1)
struct AddAddFastGelu
{
__host__ __device__ void
operator()(float& e, const float& c, const ck::half_t& d0, const ck::half_t& d1) const
{
const float x = c + (d0 + d1);
ck::tensor_operation::element_wise::FastGelu{}.template operator()<float, float>(e, x);
}
};
using A0ElementOp = PassThrough;
using B0ElementOp = PassThrough;
using CDE0ElementOp = AddAddRelu;
using A1ElementOp = PassThrough;
using B1ElementOp = PassThrough;
using CDE1ElementOp = ck::tensor_operation::element_wise::Add;
static constexpr bool PadGemm0M = false;
static constexpr bool PadGemm0N = false;
static constexpr bool PadGemm0K = false;
static constexpr bool PadGemm1N = false;
static constexpr bool PadGemm1K = false;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle<
A0Layout,
B0Layout,
ck::Tuple<D00Layout, D01Layout>,
B1Layout,
ck::Tuple<D1Layout>,
E1Layout,
A0DataType,
B0DataType,
Acc0DataType,
ck::Tuple<D00DataType, D01DataType>,
B1DataType,
Acc1DataType,
C1ShuffleDataType,
ck::Tuple<D1DataType>,
E1DataType,
A0ElementOp,
B0ElementOp,
CDE0ElementOp,
B1ElementOp,
CDE1ElementOp,
PadGemm0M,
PadGemm0N,
PadGemm0K,
PadGemm1N,
PadGemm1K,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8>; // CShuffleBlockTransferScalarPerVector_NPerBlock
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 64;
ck::index_t O = 128;
ck::index_t BatchCount = 4;
ck::index_t StrideA0 = -1;
ck::index_t StrideB0 = -1;
ck::index_t StrideD00 = -1;
ck::index_t StrideD01 = -1;
ck::index_t StrideB1 = -1;
ck::index_t StrideD1 = -1;
ck::index_t StrideE1 = -1;
ck::index_t BatchStrideA0 = -1;
ck::index_t BatchStrideB0 = -1;
ck::index_t BatchStrideD00 = -1;
ck::index_t BatchStrideD01 = -1;
ck::index_t BatchStrideB1 = -1;
ck::index_t BatchStrideD1 = -1;
ck::index_t BatchStrideE1 = -1;
if(argc == 1)
{
// use default case
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 9)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
BatchCount = std::stoi(argv[8]);
}
else if(argc == 23)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
BatchCount = std::stoi(argv[8]);
StrideA0 = std::stoi(argv[9]);
StrideB0 = std::stoi(argv[10]);
StrideD00 = std::stoi(argv[11]);
StrideD01 = std::stoi(argv[12]);
StrideB1 = std::stoi(argv[13]);
StrideD1 = std::stoi(argv[14]);
StrideE1 = std::stoi(argv[15]);
BatchStrideA0 = std::stoi(argv[16]);
BatchStrideB0 = std::stoi(argv[17]);
BatchStrideD00 = std::stoi(argv[18]);
BatchStrideD01 = std::stoi(argv[19]);
BatchStrideB1 = std::stoi(argv[20]);
BatchStrideD1 = std::stoi(argv[21]);
BatchStrideE1 = std::stoi(argv[22]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 8: M, N, K, O, Batch\n");
printf(
"arg9 to 15: StrideA0, StrideB0, StrideD00, StrideD01, StrideB1, StrideD1, StrideE1\n");
printf("arg16 to 22: BatchStrideA0, BatchStrideB0, BatchStrideD00, BatchStrideD01, "
"BatchStrideB1, BatchStrideD1, BatchStrideE1 \n");
exit(0);
}
const int DefaultStrideA0 = ck::is_same_v<A0Layout, Row> ? K : M;
const int DefaultStrideB0 = ck::is_same_v<B0Layout, Row> ? N : K;
const int DefaultStrideD00 = ck::is_same_v<D00Layout, Row> ? N : M;
const int DefaultStrideD01 = ck::is_same_v<D01Layout, Row> ? N : M;
const int DefaultStrideB1 = ck::is_same_v<B1Layout, Row> ? O : N;
const int DefaultStrideD1 = ck::is_same_v<D1Layout, Row> ? O : M;
const int DefaultStrideE1 = ck::is_same_v<E1Layout, Row> ? O : M;
StrideA0 = (StrideA0 < 0) ? DefaultStrideA0 : StrideA0;
StrideB0 = (StrideB0 < 0) ? DefaultStrideB0 : StrideB0;
StrideD00 = (StrideD00 < 0) ? DefaultStrideD00 : StrideD00;
StrideD01 = (StrideD01 < 0) ? DefaultStrideD01 : StrideD01;
StrideB1 = (StrideB1 < 0) ? DefaultStrideB1 : StrideB1;
StrideD1 = (StrideD1 < 0) ? DefaultStrideD1 : StrideD1;
StrideE1 = (StrideE1 < 0) ? DefaultStrideE1 : StrideE1;
const int DefaultBatchStrideA0 = (ck::is_same_v<A0Layout, Col> ? K : M) * StrideA0;
const int DefaultBatchStrideB0 = (ck::is_same_v<B0Layout, Col> ? N : K) * StrideB0;
const int DefaultBatchStrideD00 = (ck::is_same_v<D00Layout, Col> ? N : M) * StrideD00;
const int DefaultBatchStrideD01 = (ck::is_same_v<D01Layout, Col> ? N : M) * StrideD01;
const int DefaultBatchStrideB1 = (ck::is_same_v<B1Layout, Col> ? O : N) * StrideB1;
const int DefaultBatchStrideD1 = (ck::is_same_v<D1Layout, Col> ? O : M) * StrideD1;
const int DefaultBatchStrideE1 = (ck::is_same_v<E1Layout, Col> ? O : M) * StrideE1;
BatchStrideA0 = BatchStrideA0 < 0 ? DefaultBatchStrideA0 : BatchStrideA0;
BatchStrideB0 = BatchStrideB0 < 0 ? DefaultBatchStrideB0 : BatchStrideB0;
BatchStrideD00 = BatchStrideD00 < 0 ? DefaultBatchStrideD00 : BatchStrideD00;
BatchStrideD01 = BatchStrideD01 < 0 ? DefaultBatchStrideD01 : BatchStrideD01;
BatchStrideB1 = BatchStrideB1 < 0 ? DefaultBatchStrideB1 : BatchStrideB1;
BatchStrideD1 = BatchStrideD1 < 0 ? DefaultBatchStrideD1 : BatchStrideD1;
BatchStrideE1 = BatchStrideE1 < 0 ? DefaultBatchStrideE1 : BatchStrideE1;
auto f_host_tensor_descriptor = [](std::size_t batch_count,
std::size_t row,
std::size_t col,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
if(std::is_same<decltype(layout), Row>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, 1, stride}));
}
};
// E_m_o = A_m_k * B0_k_n * B1_n_o
Tensor<A0DataType> a0_g_m_k(
f_host_tensor_descriptor(BatchCount, M, K, StrideA0, BatchStrideA0, A0Layout{}));
Tensor<B0DataType> b0_g_k_n(
f_host_tensor_descriptor(BatchCount, K, N, StrideB0, BatchStrideB0, B0Layout{}));
Tensor<D00DataType> d00_g_m_n(
f_host_tensor_descriptor(BatchCount, M, N, StrideD00, BatchStrideD00, D00Layout{}));
Tensor<D01DataType> d01_g_m_n(
f_host_tensor_descriptor(BatchCount, M, N, StrideD01, BatchStrideD01, D01Layout{}));
Tensor<B1DataType> b1_g_n_o(
f_host_tensor_descriptor(BatchCount, N, O, StrideB1, BatchStrideB1, B1Layout{}));
Tensor<D1DataType> d1_g_m_o(
f_host_tensor_descriptor(BatchCount, M, O, StrideD1, BatchStrideD1, D1Layout{}));
Tensor<E1DataType> e1_g_m_o_host_result(
f_host_tensor_descriptor(BatchCount, M, O, StrideE1, BatchStrideE1, E1Layout{}));
Tensor<E1DataType> e1_g_m_o_device_result(
f_host_tensor_descriptor(BatchCount, M, O, StrideE1, BatchStrideE1, E1Layout{}));
std::cout << "a0_g_m_k: " << a0_g_m_k.mDesc << std::endl;
std::cout << "b0_g_k_n: " << b0_g_k_n.mDesc << std::endl;
std::cout << "d00_g_m_n: " << d00_g_m_n.mDesc
<< " size: " << d00_g_m_n.mDesc.GetElementSpaceSize() << std::endl;
std::cout << "d01_g_m_n: " << d01_g_m_n.mDesc
<< " size: " << d01_g_m_n.mDesc.GetElementSpaceSize() << std::endl;
std::cout << "b1_g_n_o: " << b1_g_n_o.mDesc << std::endl;
std::cout << "e1_g_m_o: " << e1_g_m_o_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a0_g_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 3});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 3});
d00_g_m_n.GenerateTensorValue(GeneratorTensor_2<D00DataType>{-2, 3});
d01_g_m_n.GenerateTensorValue(GeneratorTensor_2<D01DataType>{-2, 3});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 3});
d1_g_m_o.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 3});
break;
case 2:
a0_g_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{0.0, 1.0});
d00_g_m_n.GenerateTensorValue(GeneratorTensor_3<D00DataType>{0.0, 1.0});
d01_g_m_n.GenerateTensorValue(GeneratorTensor_3<D01DataType>{0.0, 1.0});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
d1_g_m_o.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
break;
default:
a0_g_m_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{1});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
d00_g_m_n.GenerateTensorValue(GeneratorTensor_1<D00DataType>{1});
d01_g_m_n.GenerateTensorValue(GeneratorTensor_1<D01DataType>{1});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
d1_g_m_o.GenerateTensorValue(GeneratorTensor_1<D1DataType>{1});
}
DeviceMem a0_g_m_k_device_buf(sizeof(A0DataType) * a0_g_m_k.mDesc.GetElementSize());
DeviceMem b0_g_k_n_device_buf(sizeof(B0DataType) * b0_g_k_n.mDesc.GetElementSize());
DeviceMem d00_g_m_n_device_buf(sizeof(D00DataType) * d00_g_m_n.mDesc.GetElementSpaceSize());
DeviceMem d01_g_m_n_device_buf(sizeof(D01DataType) * d01_g_m_n.mDesc.GetElementSpaceSize());
DeviceMem b1_g_n_o_device_buf(sizeof(B1DataType) * b1_g_n_o.mDesc.GetElementSize());
DeviceMem e1_g_m_o_device_buf(sizeof(E1DataType) *
e1_g_m_o_device_result.mDesc.GetElementSize());
DeviceMem d1_g_m_o_device_buf(sizeof(D1DataType) * d1_g_m_o.mDesc.GetElementSpaceSize());
a0_g_m_k_device_buf.ToDevice(a0_g_m_k.mData.data());
b0_g_k_n_device_buf.ToDevice(b0_g_k_n.mData.data());
d00_g_m_n_device_buf.ToDevice(d00_g_m_n.mData.data());
d01_g_m_n_device_buf.ToDevice(d01_g_m_n.mData.data());
b1_g_n_o_device_buf.ToDevice(b1_g_n_o.mData.data());
d1_g_m_o_device_buf.ToDevice(d1_g_m_o.mData.data());
auto a0_element_op = A0ElementOp{};
auto b0_element_op = B0ElementOp{};
auto cde0_element_op = CDE0ElementOp{};
auto b1_element_op = B1ElementOp{};
auto cde1_element_op = CDE1ElementOp{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument =
gemm.MakeArgument(static_cast<A0DataType*>(a0_g_m_k_device_buf.GetDeviceBuffer()),
static_cast<B0DataType*>(b0_g_k_n_device_buf.GetDeviceBuffer()),
std::array<const void*, 2>{d00_g_m_n_device_buf.GetDeviceBuffer(),
d01_g_m_n_device_buf.GetDeviceBuffer()},
static_cast<B1DataType*>(b1_g_n_o_device_buf.GetDeviceBuffer()),
std::array<const void*, 1>{d1_g_m_o_device_buf.GetDeviceBuffer()},
static_cast<E1DataType*>(e1_g_m_o_device_buf.GetDeviceBuffer()),
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
std::array<ck::index_t, 2>{StrideD00, StrideD01},
StrideB1,
std::array<ck::index_t, 1>{StrideD1},
StrideE1,
BatchStrideA0,
BatchStrideB0,
std::array<ck::index_t, 2>{BatchStrideD00, BatchStrideD01},
BatchStrideB1,
std::array<ck::index_t, 1>{BatchStrideD1},
BatchStrideE1,
a0_element_op,
b0_element_op,
cde0_element_op,
b1_element_op,
cde1_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
return 0;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
std::size_t num_btype =
(sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(D00DataType) * N +
sizeof(D01DataType) * N + sizeof(B1DataType) * N * O + sizeof(E1DataType) * M * O +
sizeof(D1DataType) * O) *
BatchCount;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
e1_g_m_o_device_buf.FromDevice(e1_g_m_o_device_result.mData.data());
if(do_verification)
{
using ReferenceGemm0Instance =
ck::tensor_operation::host::ReferenceBatchedGemm<A0DataType,
B0DataType,
Acc0DataType,
Acc0DataType,
A0ElementOp,
B0ElementOp,
PassThrough>;
using ReferenceGemm1Instance =
ck::tensor_operation::host::ReferenceBatchedGemm<Acc0DataType,
B1DataType,
Acc1DataType,
Acc1DataType,
PassThrough,
B1ElementOp,
PassThrough>;
// Output of Gemm0 is input A of Gemm1
Tensor<Acc0DataType> c0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
Tensor<Acc0DataType> e0_g_m_n(f_host_tensor_descriptor(BatchCount, M, N, N, M * N, Row{}));
Tensor<Acc1DataType> c1_g_m_o(f_host_tensor_descriptor(BatchCount, M, O, O, M * O, Row{}));
auto ref_gemm0 = ReferenceGemm0Instance{};
auto ref_gemm0_invoker = ref_gemm0.MakeInvoker();
auto ref_gemm0_argument = ref_gemm0.MakeArgument(
a0_g_m_k, b0_g_k_n, c0_g_m_n, a0_element_op, b0_element_op, PassThrough{});
ref_gemm0_invoker.Run(ref_gemm0_argument);
// bias+bias+relu
e0_g_m_n.ForEach([&](auto&, auto idx) {
cde0_element_op(e0_g_m_n(idx), c0_g_m_n(idx), d00_g_m_n(idx), d01_g_m_n(idx));
});
auto ref_gemm1 = ReferenceGemm1Instance{};
auto ref_gemm1_invoker = ref_gemm1.MakeInvoker();
auto ref_gemm1_argument = ref_gemm1.MakeArgument(
e0_g_m_n, b1_g_n_o, c1_g_m_o, PassThrough{}, b1_element_op, PassThrough{});
ref_gemm1_invoker.Run(ref_gemm1_argument);
// bias
e1_g_m_o_host_result.ForEach([&](auto&, auto idx) {
cde1_element_op(e1_g_m_o_host_result(idx), c1_g_m_o(idx), d1_g_m_o(idx));
});
return ck::utils::check_err(e1_g_m_o_device_result.mData, e1_g_m_o_host_result.mData) ? 0
: 1;
}
return 0;
}

1
example/CMakeLists.txt
View File

@@ -52,4 +52,5 @@ add_subdirectory(33_multiple_reduce)
add_subdirectory(34_batchnorm)
add_subdirectory(35_splitK_gemm)
add_subdirectory(36_sparse_embedding)
add_subdirectory(37_batched_gemm_add_add_relu_gemm_add)
add_subdirectory(41_grouped_conv_conv_fwd)