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Unverified Commit c50c0fb4 authored by zjing14's avatar zjing14 Committed by GitHub
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Merge branch 'develop' into fixed_fp8_gemm_init

parents bbb5d943 562b4cec
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Showing with 1194 additions and 44 deletions
example/02_gemm_bilinear/CMakeLists.txt
View file @ c50c0fb4
...@@ -5,6 +5,9 @@ set(target 0) ...@@ -5,6 +5,9 @@ set(target 0)
foreach(gpu IN LISTS GPU_TARGETS) foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list1 AND target EQUAL 0) if(gpu IN_LIST gpu_list1 AND target EQUAL 0)
add_example_executable(example_gemm_bilinear_wmma_fp16 gemm_bilinear_wmma_fp16.cpp) add_example_executable(example_gemm_bilinear_wmma_fp16 gemm_bilinear_wmma_fp16.cpp)
add_example_executable(example_gemm_bilinear_wmma_int8 gemm_bilinear_wmma_int8.cpp)
endif()
if(GPU_TARGETS MATCHES "gfx908" OR GPU_TARGETS MATCHES "gfx90a" OR GPU_TARGETS MATCHES "gfx940")
set(target 1) set(target 1)
endif() endif()
endforeach() endforeach()
......
example/02_gemm_bilinear/gemm_bilinear_wmma_int8.cpp 0 → 100644
View file @ c50c0fb4
// 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 "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.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/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/check_err.hpp"
struct AlphaBetaAdd
{
AlphaBetaAdd(int alpha, int beta) : alpha_(alpha), beta_(beta){};
template <typename E, typename C, typename D>
__host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
template <>
__host__ __device__ constexpr void operator()<std::int8_t, std::int32_t, std::int8_t>(
std::int8_t& e, const std::int32_t& c, const std::int8_t& d) const
{
e = ck::type_convert<std::int8_t>(alpha_ * c + beta_ * ck::type_convert<std::int32_t>(d));
};
int alpha_;
int beta_;
};
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using I8 = std::int8_t;
using I32 = std::int32_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = I8;
using BDataType = I8;
using AccDataType = I32;
using CShuffleDataType = I32;
using DDataType = I8;
using EDataType = I8;
using ALayout = Row;
using BLayout = Row;
using DLayout = Row;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AlphaBetaAdd;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceOpInstance =
ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffle<ALayout,
BLayout,
ck::Tuple<DLayout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<DDataType>,
EDataType,
AccDataType,
CShuffleDataType,
AElementOp,
BElementOp,
CDEElementOp,
GemmSpec,
32,
16,
16,
4,
16,
16,
16,
1,
1,
S<2, 16, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
16,
16,
1,
S<4, 1, 8>,
S<0, 2, 1>,
S<0, 2, 1>,
1,
16,
2,
1,
1,
1,
S<1, 16, 1, 2>,
8>;
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = true;
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideD = 4096;
ck::index_t StrideE = 4096;
int alpha = 1;
int beta = 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 == 6)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
alpha = std::stof(argv[4]);
beta = std::stof(argv[5]);
}
else if(argc == 13)
{
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]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideD = std::stoi(argv[9]);
StrideE = std::stoi(argv[10]);
alpha = std::stof(argv[11]);
beta = std::stof(argv[12]);
}
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 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, alpha, "
"beta\n");
exit(0);
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DLayout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
}
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
d_device_buf.ToDevice(d_m_n.mData.data());
e_device_buf.ToDevice(e_m_n_device_result.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{alpha, beta};
// do GEMM
auto device_op = DeviceOpInstance{};
auto invoker = device_op.MakeInvoker();
auto argument =
device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 1>{StrideD},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
if(!device_op.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
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"
<< std::endl;
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(do_verification)
{
Tensor<CShuffleDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CShuffleDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument =
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
}
}
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
}
return 0;
}
include/ck/tensor_operation/gpu/block/blockwise_gemm_wmma.hpp
View file @ c50c0fb4
...@@ -221,49 +221,102 @@ struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle ...@@ -221,49 +221,102 @@ struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatB>( auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatB>(
b_thread_desc_.GetElementSpaceSize()); b_thread_desc_.GetElementSpaceSize());
static_for<0, KPerBlock / WmmaK, 1>{}([&](auto k) { // k=0,1,2 instead of k=0,kpack*1, ... // basic intrinsic to determine loopover direction
static_for<0, MRepeat, 1>{}([&](auto m0) { if constexpr(MRepeat < NRepeat)
// read A {
a_thread_copy_.Run(a_block_desc_k0_m0_m1_m2_k1, static_for<0, KPerBlock / WmmaK, 1>{}(
make_tuple(Number<k * WmmaK / A_K1>{}, m0, I0, I0, I0), [&](auto k) { // k=0,1,2 instead of k=0,kpack*1, ...
a_block_buf, static_for<0, MRepeat, 1>{}([&](auto m0) {
a_thread_desc_, // read A
make_tuple(I0, m0, I0, I0, I0), a_thread_copy_.Run(a_block_desc_k0_m0_m1_m2_k1,
a_thread_buf); make_tuple(Number<k * WmmaK / A_K1>{}, m0, I0, I0, I0),
a_block_buf,
static_for<0, NRepeat, 1>{}([&](auto n0) { a_thread_desc_,
// read B make_tuple(I0, m0, I0, I0, I0),
b_thread_copy_.Run(b_block_desc_k0_n0_n1_n2_k1, a_thread_buf);
make_tuple(Number<k * WmmaK / B_K1>{}, n0, I0, I0, I0),
b_block_buf, static_for<0, NRepeat, 1>{}([&](auto n0) {
b_thread_desc_, // read B
make_tuple(I0, n0, I0, I0, I0), b_thread_copy_.Run(
b_thread_buf); b_block_desc_k0_n0_n1_n2_k1,
vector_type<FloatA, WmmaK> a_thread_vec; make_tuple(Number<k * WmmaK / B_K1>{}, n0, I0, I0, I0),
vector_type<FloatB, WmmaK> b_thread_vec; b_block_buf,
b_thread_desc_,
static_for<0, WmmaK, 1>{}([&](auto i) { make_tuple(I0, n0, I0, I0, I0),
a_thread_vec.template AsType<FloatA>()(i) = b_thread_buf);
a_thread_buf[Number<a_thread_desc_.CalculateOffset( vector_type<FloatA, WmmaK> a_thread_vec;
make_tuple(i / A_K1, m0, 0, 0, i % A_K1))>{}]; vector_type<FloatB, WmmaK> b_thread_vec;
b_thread_vec.template AsType<FloatB>()(i) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset( static_for<0, WmmaK, 1>{}([&](auto i) {
make_tuple(i / B_K1, n0, 0, 0, i % B_K1))>{}]; a_thread_vec.template AsType<FloatA>()(i) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(i / A_K1, m0, 0, 0, i % A_K1))>{}];
b_thread_vec.template AsType<FloatB>()(i) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(i / B_K1, n0, 0, 0, i % B_K1))>{}];
});
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
wmma_gemm.template Run(
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
});
}); });
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
wmma_gemm.template Run(
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
}); });
}); }
}); else
{
static_for<0, KPerBlock / WmmaK, 1>{}(
[&](auto k) { // k=0,1,2 instead of k=0,kpack*1, ...
static_for<0, NRepeat, 1>{}([&](auto n0) {
// read B
b_thread_copy_.Run(b_block_desc_k0_n0_n1_n2_k1,
make_tuple(Number<k * WmmaK / B_K1>{}, n0, I0, I0, I0),
b_block_buf,
b_thread_desc_,
make_tuple(I0, n0, I0, I0, I0),
b_thread_buf);
static_for<0, MRepeat, 1>{}([&](auto m0) {
// read A
a_thread_copy_.Run(
a_block_desc_k0_m0_m1_m2_k1,
make_tuple(Number<k * WmmaK / A_K1>{}, m0, I0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(I0, m0, I0, I0, I0),
a_thread_buf);
vector_type<FloatA, WmmaK> a_thread_vec;
vector_type<FloatB, WmmaK> b_thread_vec;
static_for<0, WmmaK, 1>{}([&](auto i) {
a_thread_vec.template AsType<FloatA>()(i) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(i / A_K1, m0, 0, 0, i % A_K1))>{}];
b_thread_vec.template AsType<FloatB>()(i) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(i / B_K1, n0, 0, 0, i % B_K1))>{}];
});
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
wmma_gemm.template Run(
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
});
});
});
}
} }
protected: protected:
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_wmma_cshuffle.hpp
View file @ c50c0fb4
...@@ -599,7 +599,7 @@ struct DeviceGroupedConvFwdMultipleD_Wmma_CShuffle ...@@ -599,7 +599,7 @@ struct DeviceGroupedConvFwdMultipleD_Wmma_CShuffle
// check if it's 1x1, stride=1 conv // check if it's 1x1, stride=1 conv
for(index_t i = 0; i < NDimSpatial; ++i) for(index_t i = 0; i < NDimSpatial; ++i)
{ {
const index_t X = arg.b_g_k_c_xs_lengths_[i + 2]; const index_t X = arg.b_g_k_c_xs_lengths_[i + 3];
const index_t ConvStride = arg.conv_filter_strides_[i]; const index_t ConvStride = arg.conv_filter_strides_[i];
const index_t LeftPad = arg.input_left_pads_[i]; const index_t LeftPad = arg.input_left_pads_[i];
const index_t RightPad = arg.input_right_pads_[i]; const index_t RightPad = arg.input_right_pads_[i];
...@@ -616,7 +616,7 @@ struct DeviceGroupedConvFwdMultipleD_Wmma_CShuffle ...@@ -616,7 +616,7 @@ struct DeviceGroupedConvFwdMultipleD_Wmma_CShuffle
// check if it's 1x1 conv // check if it's 1x1 conv
for(index_t i = 0; i < NDimSpatial; ++i) for(index_t i = 0; i < NDimSpatial; ++i)
{ {
const index_t X = arg.b_g_k_c_xs_lengths_[i + 2]; const index_t X = arg.b_g_k_c_xs_lengths_[i + 3];
const index_t LeftPad = arg.input_left_pads_[i]; const index_t LeftPad = arg.input_left_pads_[i];
const index_t RightPad = arg.input_right_pads_[i]; const index_t RightPad = arg.input_right_pads_[i];
......
include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp
View file @ c50c0fb4
...@@ -186,6 +186,13 @@ struct Bilinear ...@@ -186,6 +186,13 @@ struct Bilinear
y = type_convert<half_t>(alpha_ * x0 + beta_ * ck::type_convert<float>(x1)); y = type_convert<half_t>(alpha_ * x0 + beta_ * ck::type_convert<float>(x1));
}; };
template <>
__host__ __device__ constexpr void operator()<std::int8_t, std::int32_t, std::int8_t>(
std::int8_t& y, const std::int32_t& x0, const std::int8_t& x1) const
{
y = type_convert<std::int8_t>(x0 + ck::type_convert<std::int32_t>(x1));
};
float alpha_; float alpha_;
float beta_; float beta_;
}; };
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ c50c0fb4
...@@ -31,6 +31,7 @@ using F64_Tuple = ck::Tuple<F64>; ...@@ -31,6 +31,7 @@ using F64_Tuple = ck::Tuple<F64>;
using F32_Tuple = ck::Tuple<F32>; using F32_Tuple = ck::Tuple<F32>;
using I32_Tuple = ck::Tuple<I32>; using I32_Tuple = ck::Tuple<I32>;
using I32_F32_Tuple = ck::Tuple<I32, F32>; using I32_F32_Tuple = ck::Tuple<I32, F32>;
using I8_Tuple = ck::Tuple<I8>;
using F32_F32_Tuple = ck::Tuple<F32, F32>; using F32_F32_Tuple = ck::Tuple<F32, F32>;
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_bilinear.hpp
View file @ c50c0fb4
...@@ -69,6 +69,58 @@ void add_device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_mk_nk_mn_mn_instance ...@@ -69,6 +69,58 @@ void add_device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_mk_nk_mn_mn_instance
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear>>>& instances);
void add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_kn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Row,
Row_Tuple,
Row,
I8,
I8,
I8_Tuple,
I8,
PassThrough,
PassThrough,
Bilinear>>>& instances);
void add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_nk_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Col,
Row_Tuple,
Row,
I8,
I8,
I8_Tuple,
I8,
PassThrough,
PassThrough,
Bilinear>>>& instances);
void add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_kn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Col,
Row,
Row_Tuple,
Row,
I8,
I8,
I8_Tuple,
I8,
PassThrough,
PassThrough,
Bilinear>>>& instances);
void add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_nk_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Col,
Col,
Row_Tuple,
Row,
I8,
I8,
I8_Tuple,
I8,
PassThrough,
PassThrough,
Bilinear>>>& instances);
// GEMM + Bilinear // GEMM + Bilinear
template <typename ALayout, template <typename ALayout,
typename BLayout, typename BLayout,
...@@ -135,6 +187,30 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu ...@@ -135,6 +187,30 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu
op_ptrs); op_ptrs);
} }
} }
else if constexpr(is_same_v<ADataType, std::int8_t> && is_same_v<BDataType, std::int8_t> &&
is_same_v<DDataType, std::int8_t> && is_same_v<EDataType, std::int8_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<DLayout, Row> && is_same_v<ELayout, Row>)
{
add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_kn_mn_mn_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<DLayout, Row> && is_same_v<ELayout, Row>)
{
add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_nk_mn_mn_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<DLayout, Row> && is_same_v<ELayout, Row>)
{
add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_kn_mn_mn_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<DLayout, Row> && is_same_v<ELayout, Row>)
{
add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_nk_mn_mn_instances(op_ptrs);
}
}
return op_ptrs; return op_ptrs;
} }
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp
View file @ c50c0fb4
...@@ -145,6 +145,19 @@ void add_device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f32_instances( ...@@ -145,6 +145,19 @@ void add_device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f32_instances(
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2,
GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f16_instances( void add_device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2, std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2,
NHWGC, NHWGC,
...@@ -159,6 +172,19 @@ void add_device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f16_instances( ...@@ -159,6 +172,19 @@ void add_device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f16_instances(
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_i8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2,
GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f32_instances( void add_device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2, std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2,
NHWGC, NHWGC,
...@@ -407,6 +433,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -407,6 +433,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{ {
add_device_grouped_conv2d_fwd_xdl_gnhwc_gkyxc_gnhwk_f16_instances(op_ptrs); add_device_grouped_conv2d_fwd_xdl_gnhwc_gkyxc_gnhwk_f16_instances(op_ptrs);
add_device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f16_instances(op_ptrs); add_device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f16_instances(op_ptrs);
add_device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_f16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> && is_same_v<WeiDataType, ck::bhalf_t> &&
...@@ -414,6 +441,11 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -414,6 +441,11 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{ {
add_device_grouped_conv1d_fwd_xdl_gnhwc_gkyxc_gnhwk_bf16_instances(op_ptrs); add_device_grouped_conv1d_fwd_xdl_gnhwc_gkyxc_gnhwk_bf16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t>)
{
add_device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_i8_instances(op_ptrs);
}
} }
else if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWGC> && else if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWGC> &&
is_same_v<WeiLayout, GKYXC> && is_same_v<OutLayout, NHWGK>) is_same_v<WeiLayout, GKYXC> && is_same_v<OutLayout, NHWGK>)
......
library/src/tensor_operation_instance/gpu/gemm_bilinear/CMakeLists.txt
View file @ c50c0fb4
...@@ -4,5 +4,9 @@ add_instance_library(device_gemm_bilinear_instance ...@@ -4,5 +4,9 @@ add_instance_library(device_gemm_bilinear_instance
device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_km_nk_mn_mn_instance.cpp device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_km_nk_mn_mn_instance.cpp
device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instance.cpp device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_mk_kn_mn_mn_instance.cpp
device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_mk_nk_mn_mn_instance.cpp device_gemm_bilinear_xdl_c_shuffle_f16_f16_f16_f16_mk_nk_mn_mn_instance.cpp
device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_kn_mn_mn_instance.cpp
device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_nk_mn_mn_instance.cpp
device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_kn_mn_mn_instance.cpp
device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_nk_mn_mn_instance.cpp
) )
endif() endif()
library/src/tensor_operation_instance/gpu/gemm_bilinear/device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_kn_mn_mn_instance.cpp 0 → 100644
View file @ c50c0fb4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using I8 = std::int8_t;
using I32 = std::int32_t;
using I8_Tuple = ck::Tuple<std::int8_t>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Row_Tuple = ck::Tuple<Row>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// e[m, n] = bilinear(a[m, k] * b[k, n], d[m, n])
using device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_kn_mn_mn_instances = std::tuple<
// clang-format off
//################################| A| B| Ds| E| AData| BData| DsData| EData| AccData| CShuffle| A| B| CDE| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//################################| Layout| Layout| Layout| Layout| Type| Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 256, 128, 128, 4, 16, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 8>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 128, 64, 64, 4, 16, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 4>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 64, 32, 32, 4, 16, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 2>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 32, 16, 16, 4, 16, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
// M/N/K padding
//################################| A| B| Ds| E| AData| BData| DsData| EData| AccData| CShuffle| A| B| CDE| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//################################| Layout| Layout| Layout| Layout| Type| Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 4, 16, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 8>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 4, 16, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 4>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 4, 16, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 2>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 4, 16, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 8, 8, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 2, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 8, 8, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 2, S<1, 32, 1, 4>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 8, 8, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 2, S<1, 32, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 8, 8, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 8, 4, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 2, S<1, 32, 1, 8>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 8, 4, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 2, S<1, 32, 1, 4>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 8, 4, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 2, S<1, 32, 1, 2>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 8, 4, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 2>, 4>
// clang-format on
>;
void add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_kn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Col,
Row,
Row_Tuple,
Row,
I8,
I8,
I8_Tuple,
I8,
PassThrough,
PassThrough,
Bilinear>>>& instances)
{
add_device_operation_instances(
instances, device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_kn_mn_mn_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_bilinear/device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_nk_mn_mn_instance.cpp 0 → 100644
View file @ c50c0fb4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using I8 = std::int8_t;
using I32 = std::int32_t;
using I8_Tuple = ck::Tuple<std::int8_t>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Row_Tuple = ck::Tuple<Row>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// e[m, n] = bilinear(a[m, k] * b[k, n], d[m, n])
using device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_nk_mn_mn_instances = std::tuple<
// clang-format off
//################################| A| B| Ds| E| AData| BData| DsData| EData| AccData| CShuffle| A| B| CDE| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//################################| Layout| Layout| Layout| Layout| Type| Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 256, 128, 128, 4, 16, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 2, S<1, 32, 1, 8>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 128, 64, 64, 4, 16, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 2, S<1, 32, 1, 4>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 64, 32, 32, 4, 16, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 2, S<1, 32, 1, 2>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 32, 16, 16, 4, 16, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
// M/N/K padding
//################################| A| B| Ds| E| AData| BData| DsData| EData| AccData| CShuffle| A| B| CDE| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//################################| Layout| Layout| Layout| Layout| Type| Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 4, 16, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 2, S<1, 32, 1, 8>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 4, 16, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 2, S<1, 32, 1, 4>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 4, 16, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 2, S<1, 32, 1, 2>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 4, 16, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 8, 8, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 2, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 8, 8, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 2, S<1, 32, 1, 4>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 8, 8, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 2, S<1, 32, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 8, 8, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 8, 4, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 2, S<1, 32, 1, 8>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 8, 4, 16, 16, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 2, S<1, 32, 1, 4>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 8, 4, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 2, S<1, 32, 1, 2>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Col, Col, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 8, 4, 16, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 2>, 4>
// clang-format on
>;
void add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_nk_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Col,
Col,
Row_Tuple,
Row,
I8,
I8,
I8_Tuple,
I8,
PassThrough,
PassThrough,
Bilinear>>>& instances)
{
add_device_operation_instances(
instances, device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_km_nk_mn_mn_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_bilinear/device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_kn_mn_mn_instance.cpp 0 → 100644
View file @ c50c0fb4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using I8 = std::int8_t;
using I32 = std::int32_t;
using I8_Tuple = ck::Tuple<std::int8_t>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Row_Tuple = ck::Tuple<Row>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// e[m, n] = bilinear(a[m, k] * b[k, n], d[m, n])
using device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_kn_mn_mn_instances = std::tuple<
// clang-format off
//################################| A| B| Ds| E| AData| BData| DsData| EData| AccData| CShuffle| A| B| CDE| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//################################| Layout| Layout| Layout| Layout| Type| Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 256, 128, 128, 4, 16, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 8>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 128, 64, 64, 4, 16, 16, 16, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 4>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 64, 32, 32, 4, 16, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 2>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmDefault, 32, 16, 16, 4, 16, 16, 16, 1, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
// M/N/K padding
//################################| A| B| Ds| E| AData| BData| DsData| EData| AccData| CShuffle| A| B| CDE| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//################################| Layout| Layout| Layout| Layout| Type| Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 4, 16, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 8>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 4, 16, 16, 16, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 4>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 4, 16, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 2, S<1, 32, 1, 2>, 16>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 4, 16, 16, 16, 1, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 8, 8, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 2, S<1, 32, 1, 8>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 8, 8, 16, 16, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 2, S<1, 32, 1, 4>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 8, 8, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 2, S<1, 32, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 8, 8, 16, 16, 1, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 256, 128, 128, 8, 4, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 2, S<1, 32, 1, 8>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 128, 64, 64, 8, 4, 16, 16, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 2, S<1, 32, 1, 4>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 64, 32, 32, 8, 4, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 2, S<1, 32, 1, 2>, 4>,
DeviceGemmMultipleD_Wmma_CShuffle< Row, Row, Row_Tuple, Row, I8, I8, I8_Tuple, I8, I32, I32, PassThrough, PassThrough, Bilinear, GemmMNKPadding, 32, 16, 16, 8, 4, 16, 16, 1, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 2>, 4>
// clang-format on
>;
void add_device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_kn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Row,
Row_Tuple,
Row,
I8,
I8,
I8_Tuple,
I8,
PassThrough,
PassThrough,
Bilinear>>>& instances)
{
add_device_operation_instances(
instances, device_gemm_bilinear_wmma_c_shuffle_i8_i8_i8_i8_mk_kn_mn_mn_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/CMakeLists.txt
View file @ c50c0fb4
...@@ -12,6 +12,9 @@ add_instance_library(device_grouped_conv2d_fwd_instance ...@@ -12,6 +12,9 @@ add_instance_library(device_grouped_conv2d_fwd_instance
# GNHWC, GKYXC, GNHWK # GNHWC, GKYXC, GNHWK
device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f16_instance.cpp device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f16_instance.cpp
device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f32_instance.cpp device_grouped_conv2d_fwd_dl_gnhwc_gkyxc_gnhwk_f32_instance.cpp
# WMMA
device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_f16_instance.cpp
device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_i8_instance.cpp
# NHWGC, GKYXC, NHWGK # NHWGC, GKYXC, NHWGK
device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f16_instance.cpp device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f16_instance.cpp
device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f32_instance.cpp device_grouped_conv2d_fwd_dl_nhwgc_gkyxc_nhwgk_f32_instance.cpp
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_f16_instance.cpp 0 → 100644
View file @ c50c0fb4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv2d_fwd_wmma_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[g, n, hi ,wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
void add_device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2,
GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_f16_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwdDefault>{});
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_f16_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwd1x1P0>{});
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_f16_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwd1x1S1P0>{});
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_f16_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwdOddC>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_i8_instance.cpp 0 → 100644
View file @ c50c0fb4
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv2d_fwd_wmma_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[g, n, hi ,wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
void add_device_grouped_conv2d_fwd_wmma_gnhwc_gkyxc_gnhwk_i8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<2,
GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_i8_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwdDefault>{});
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_i8_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwd1x1P0>{});
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_i8_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwd1x1S1P0>{});
add_device_operation_instances(instances,
device_grouped_conv2d_fwd_wmma_i8_instances<GNHWC,
GKYXC,
Empty_Tuple,
GNHWK,
Empty_Tuple,
PassThrough,
ConvFwdOddC>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/include/profiler/profile_grouped_conv_fwd_impl.hpp
View file @ c50c0fb4
...@@ -215,7 +215,7 @@ bool profile_grouped_conv_fwd_impl(int do_verification, ...@@ -215,7 +215,7 @@ bool profile_grouped_conv_fwd_impl(int do_verification,
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory< const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances(); DeviceOp>::GetInstances();
std::cout << "xdl found " << op_ptrs.size() << " instances" << std::endl; std::cout << "ckProfiler found " << op_ptrs.size() << " instances" << std::endl;
for(auto& op_ptr : op_ptrs) for(auto& op_ptr : op_ptrs)
{ {
......
profiler/src/profile_gemm_bilinear.cpp
View file @ c50c0fb4
...@@ -71,6 +71,9 @@ int profile_gemm_bilinear(int argc, char* argv[]) ...@@ -71,6 +71,9 @@ int profile_gemm_bilinear(int argc, char* argv[])
using F16 = ck::half_t; using F16 = ck::half_t;
using F32 = float; using F32 = float;
using I8 = std::int8_t;
using I32 = std::int32_t;
using Row = ck::tensor_layout::gemm::RowMajor; using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor; using Col = ck::tensor_layout::gemm::ColumnMajor;
...@@ -141,6 +144,22 @@ int profile_gemm_bilinear(int argc, char* argv[]) ...@@ -141,6 +144,22 @@ int profile_gemm_bilinear(int argc, char* argv[])
{ {
return profile(F16{}, F16{}, F32{}, F16{}, F16{}, Col{}, Col{}, Row{}, Row{}); return profile(F16{}, F16{}, F32{}, F16{}, F16{}, Col{}, Col{}, Row{}, Row{});
} }
else if(data_type == MatrixDataType::INT8_INT8_INT8_INT8 && layout == MatrixLayout::MK_KN_MN_MN)
{
return profile(I8{}, I8{}, I32{}, I8{}, I8{}, Row{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::INT8_INT8_INT8_INT8 && layout == MatrixLayout::MK_NK_MN_MN)
{
return profile(I8{}, I8{}, I32{}, I8{}, I8{}, Row{}, Col{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::INT8_INT8_INT8_INT8 && layout == MatrixLayout::KM_KN_MN_MN)
{
return profile(I8{}, I8{}, I32{}, I8{}, I8{}, Col{}, Row{}, Row{}, Row{});
}
else if(data_type == MatrixDataType::INT8_INT8_INT8_INT8 && layout == MatrixLayout::KM_NK_MN_MN)
{
return profile(I8{}, I8{}, I32{}, I8{}, I8{}, Col{}, Col{}, Row{}, Row{});
}
else else
{ {
std::cout << "this data_type & layout is not implemented" << std::endl; std::cout << "this data_type & layout is not implemented" << std::endl;
......
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