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Commit 1abaedd9 authored by Alan Turner's avatar Alan Turner
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Merge remote-tracking branch 'origin/develop' into gpu-invoker

parents bd2b3dd7 cb3fac4d
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example/09_convnd_fwd/convnd_fwd_dl_int8.cpp 0 → 100644
View file @ 1abaedd9
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_dl_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
using InDataType = int8_t;
using WeiDataType = int8_t;
using AccDataType = int32_t;
using DsDataType = ck::Tuple<int8_t>;
using OutDataType = int8_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::AddRelu;
static constexpr auto ConvSpec =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto GemmPadingSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
template <ck::index_t NDimSpatial, typename InLayout, typename WeiLayout, typename OutLayout>
// clang-format off
using DeviceGroupedConvNDFwdInstance = ck::tensor_operation::device::DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK
// ######| NDim| InData| WeiData| MultpleD| OutData| AccData| InLayout| WeiLayout| MultipleD| OutLayout| In| Wei| Out| Convolution| GEMM| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M11N11Thread| M11N11Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
// ######| Spatial| Type| Type| Type| Type| Type| | | Layout| | Elementwise| Elementwise| Elementwise| Forward| Spacialization| Size| Block| Block| Block| | ThreadM111| ThreadN111| Thread| ClusterM110Xs| ClusterN110Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccess| SrcVectorTensor| SrcVectorTensor| DstVectorTensor| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccess| SrcVectorTensor| SrcVectorTensor| DstVectorTensor| SrcDstAccess| SrcDstVectorDim| DstScalarPerVector|
// ######| | | | | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | | | | | K0_M0_M1_K1| K0_M0_M1_K1| ArrangeOrder| Order| Lengths_K0_M0_M1_K1| ContiguousDimOrder| Lengths_K0_M0_M1_K1| K0_N0_N1_K1| K0_N0_N1_K1| ArrangeOrder| Order| Lengths_K0_N0_N1_K1| ContiguousDimOrder| Lengths_K0_N0_N1_K1| Order| | |
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, InDataType, WeiDataType, DsDataType, OutDataType, AccDataType, InLayout, WeiLayout, ck::Tuple<OutLayout>, OutLayout, InElementOp, WeiElementOp, OutElementOp, ConvSpec, GemmPadingSpec, 256, 128, 128, 16, 4, 4, 4, 1, S<8, 2>, S<8, 2>, S<8, 1, 1, 4>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<4, 1, 1, 4>, S<1, 2, 0, 3>, S<1, 1, 1, 4>, S<8, 1, 1, 4>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<4, 1, 1, 4>, S<1, 2, 0, 3>, S<1, 1, 1, 4>, S<0, 1, 2, 3, 4, 5>, 5, 4>;
// clang-format on
#include "run_convnd_fwd_dl_example.inc"
int main(int argc, char* argv[]) { return run_convnd_fwd_dl_example(argc, argv) ? 0 : 1; }
example/09_convnd_fwd/convnd_fwd_xdl_bf16.cpp
View file @ 1abaedd9
......@@ -3,7 +3,7 @@
#include "convnd_fwd_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
......
example/09_convnd_fwd/convnd_fwd_xdl_fp16.cpp
View file @ 1abaedd9
......@@ -3,7 +3,7 @@
#include "convnd_fwd_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
......
example/09_convnd_fwd/convnd_fwd_xdl_fp32.cpp
View file @ 1abaedd9
......@@ -3,7 +3,7 @@
#include "convnd_fwd_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
......
example/09_convnd_fwd/convnd_fwd_xdl_fp64.cpp
View file @ 1abaedd9
......@@ -3,7 +3,7 @@
#include "convnd_fwd_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
......
example/09_convnd_fwd/convnd_fwd_xdl_int8.cpp
View file @ 1abaedd9
......@@ -3,7 +3,7 @@
#include "convnd_fwd_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
......
example/09_convnd_fwd/run_convnd_fwd_dl_example.inc 0 → 100644
View file @ 1abaedd9
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
bool run_convnd_fwd_dl_example(int argc, char* argv[])
{
print_helper_msg();
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
ck::utils::conv::ConvParam conv_param{
2, 1, 128, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};
if(argc == 1)
{
// use default
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
conv_param = ck::utils::conv::parse_conv_param(num_dim_spatial, 5, argv);
}
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
const auto run = [&](auto ndim_spatial, auto in_layout, auto wei_layout, auto out_layout) {
constexpr ck::index_t ndim_spatial_value = ndim_spatial.value;
std::cout << "ndim_spatial_value: " << ndim_spatial_value << std::endl;
using InLayout = decltype(in_layout);
using WeiLayout = decltype(wei_layout);
using OutLayout = decltype(out_layout);
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
return run_grouped_conv_fwd_dl<
ndim_spatial_value,
InDataType,
WeiDataType,
DsDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
DeviceGroupedConvNDFwdInstance<ndim_spatial_value, InLayout, WeiLayout, OutLayout>>(
do_verification,
init_method,
time_kernel,
conv_param,
in_g_n_c_wis_desc,
wei_g_k_c_xs_desc,
out_g_n_k_wos_desc,
in_element_op,
wei_element_op,
out_element_op);
};
namespace ctc = ck::tensor_layout::convolution;
if(conv_param.num_dim_spatial_ == 1)
{
return run(ck::Number<1>{}, ctc::GNWC{}, ctc::GKXC{}, ctc::GNWK{});
}
else if(conv_param.num_dim_spatial_ == 2)
{
return run(ck::Number<2>{}, ctc::GNHWC{}, ctc::GKYXC{}, ctc::GNHWK{});
}
else if(conv_param.num_dim_spatial_ == 3)
{
return run(ck::Number<3>{}, ctc::GNDHWC{}, ctc::GKZYXC{}, ctc::GNDHWK{});
}
return true;
}
example/10_convnd_fwd_multiple_d_multiple_reduce/common.hpp
View file @ 1abaedd9
......@@ -12,10 +12,11 @@
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d_multiple_r_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_multiple_r_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
......@@ -140,9 +141,7 @@ make_r0_host_tensor_descriptor(const ck::utils::conv::ConvParam& problem_size)
{
std::vector<ck::index_t> dimensions{problem_size.G_, problem_size.N_};
std::copy(begin(problem_size.output_spatial_lengths_),
end(problem_size.output_spatial_lengths_),
std::back_inserter(dimensions));
ck::ranges::copy(problem_size.output_spatial_lengths_, std::back_inserter(dimensions));
return HostTensorDescriptor(dimensions);
}
......@@ -158,10 +157,3 @@ void unpack_host_tensor_descriptor(const HostTensorDescriptor& descriptor,
assert(size(descriptor.GetStrides()) == size(strides));
std::copy_n(begin(descriptor.GetStrides()), size(descriptor.GetStrides()), begin(strides));
}
template <typename Range, typename OutputIterator>
auto copy(const Range& range, OutputIterator iter)
-> decltype(std::copy(std::begin(range), std::end(range), iter))
{
return std::copy(std::begin(range), std::end(range), iter);
}
example/10_convnd_fwd_multiple_d_multiple_reduce/run_convnd_fwd_max_example.inc
View file @ 1abaedd9
......@@ -77,15 +77,12 @@ bool run_convnd_fwd_max(const ck::utils::conv::ConvParam& problem_size,
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-8, 7}(conv_input.begin(),
conv_input.end());
ck::utils::FillUniformDistributionIntegerValue<BDataType>{-8, 7}(conv_weight.begin(),
conv_weight.end());
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-8, 7}(conv_input);
ck::utils::FillUniformDistributionIntegerValue<BDataType>{-8, 7}(conv_weight);
break;
default:
ck::utils::FillUniformDistribution<ADataType>{-5, 5}(conv_input.begin(), conv_input.end());
ck::utils::FillUniformDistribution<BDataType>{-5, 5}(conv_weight.begin(),
conv_weight.end());
ck::utils::FillUniformDistribution<ADataType>{-5, 5}(conv_input);
ck::utils::FillUniformDistribution<BDataType>{-5, 5}(conv_weight);
}
DeviceMem conv_input_device_buf(sizeof(ADataType) * conv_input.mDesc.GetElementSpaceSize());
......@@ -123,10 +120,10 @@ bool run_convnd_fwd_max(const ck::utils::conv::ConvParam& problem_size,
conv_output_g_n_k_wos_desc, conv_output_g_n_k_wos_lengths, conv_output_g_n_k_wos_strides);
unpack_host_tensor_descriptor(r0_desc, r0_lengths, r0_strides);
copy(problem_size.conv_filter_strides_, begin(conv_filter_strides));
copy(problem_size.conv_filter_dilations_, begin(conv_filter_dilations));
copy(problem_size.input_left_pads_, begin(input_left_pads));
copy(problem_size.input_right_pads_, begin(input_right_pads));
ck::ranges::copy(problem_size.conv_filter_strides_, begin(conv_filter_strides));
ck::ranges::copy(problem_size.conv_filter_dilations_, begin(conv_filter_dilations));
ck::ranges::copy(problem_size.input_left_pads_, begin(input_left_pads));
ck::ranges::copy(problem_size.input_right_pads_, begin(input_right_pads));
// run Conv + Reduction on device
auto conv = DeviceInstance<NDimSpatial>{};
......@@ -276,16 +273,13 @@ bool run_convnd_fwd_max(const ck::utils::conv::ConvParam& problem_size,
conv_output_device_buf.FromDevice(conv_output_device.mData.data());
r0_device_buf.FromDevice(r0_device.mData.data());
return ck::utils::check_err(conv_output_device.mData,
conv_output_host.mData,
return ck::utils::check_err(conv_output_device,
conv_output_host,
"Error: incorrect results! (Matrix E)",
1e-5f,
1e-4f) &&
ck::utils::check_err(r0_device.mData,
r0_host.mData,
"Error: incorrect results! (Matrix R0)",
1e-5f,
1e-4f);
ck::utils::check_err(
r0_device, r0_host, "Error: incorrect results! (Matrix R0)", 1e-5f, 1e-4f);
}
return true;
......
example/12_reduce/reduce_blockwise.cpp
View file @ 1abaedd9
......@@ -140,6 +140,10 @@ bool reduce_blockwise_test(bool do_verification,
if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
return;
std::array<int, ShapeType::NumReduceDim_> arrReduceDims;
ck::ranges::copy(reduceDims, arrReduceDims.begin());
result = reduce_blockwise_impl<InOutDataType,
AccDataType,
ReduceOpId,
......@@ -147,7 +151,7 @@ bool reduce_blockwise_test(bool do_verification,
ShapeType::NumReduceDim_,
PropagateNan,
OutputIndex>(
do_verification, init_method, time_kernel, inLengths, reduceDims, alpha, beta);
do_verification, init_method, time_kernel, inLengths, arrReduceDims, alpha, beta);
matched = true;
});
......
example/12_reduce/reduce_blockwise_impl.hpp
View file @ 1abaedd9
......@@ -8,14 +8,15 @@
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce_multiblock.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_reduce.hpp"
#include "ck/library/utility/algorithm.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/utility/host_common_util.hpp"
#include "ck/library/utility/host_reduction.hpp"
#include "reduce_example_common.hpp"
......@@ -30,7 +31,7 @@ int reduce_blockwise_impl(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
const std::array<int, NumReduceDim>& reduceDims,
float alpha,
float beta)
......@@ -38,6 +39,8 @@ int reduce_blockwise_impl(bool do_verification,
using namespace ck;
using namespace ck::tensor_operation::device;
constexpr index_t NumOutDim = (Rank - NumReduceDim == 0) ? 1 : Rank - NumReduceDim;
constexpr bool op_support_indices =
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
......@@ -143,7 +146,7 @@ int reduce_blockwise_impl(bool do_verification,
std::vector<size_t> outLengths;
std::vector<int> invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
auto invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
if(invariantDims.empty())
outLengths.push_back(1);
......@@ -233,48 +236,67 @@ int reduce_blockwise_impl(bool do_verification,
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
static_cast<int32_t>(reduce_total_length));
std::array<index_t, Rank> arrInLengths;
std::array<index_t, Rank> arrInStrides;
std::array<index_t, NumOutDim> arrOutLengths;
std::array<index_t, NumOutDim> arrOutStrides;
ck::ranges::copy(inLengths, arrInLengths.begin());
ck::ranges::copy(inStrides, arrInStrides.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
if(do_verification)
{
ReductionHost<InOutDataType,
AccDataType,
InOutDataType,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
Rank,
NumReduceDim,
PropagateNan,
OutputIndex>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run(alpha,
in.mData.data(),
beta,
out_ref.mData.data(),
out_indices_ref.mData.data(),
in_elementwise_op,
acc_elementwise_op);
};
using ReferenceReduceInstance =
ck::tensor_operation::host::ReferenceReduce<InOutDataType,
AccDataType,
InOutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
PropagateNan,
OutputIndex>;
auto reduce_ref = ReferenceReduceInstance{};
auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths,
arrInStrides,
arrOutLengths,
arrOutStrides,
reduceDims,
static_cast<double>(alpha),
static_cast<double>(beta),
in.mData.data(),
nullptr,
out_ref.mData.data(),
out_indices_ref.mData.data(),
in_elementwise_op,
acc_elementwise_op);
if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
<< std::endl;
return (false);
};
std::vector<ck::index_t> i_inLengths;
std::vector<ck::index_t> i_inStrides;
std::vector<ck::index_t> i_outLengths;
std::vector<ck::index_t> i_outStrides;
auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
i_inLengths.assign(inLengths.begin(), inLengths.end());
i_inStrides.assign(inStrides.begin(), inStrides.end());
i_outLengths.assign(outLengths.begin(), outLengths.end());
i_outStrides.assign(outStrides.begin(), outStrides.end());
invoker_ptr_ref->Run(argument_ptr_ref.get());
};
auto reduce = DeviceReduceInstance{};
auto argument_ptr = reduce.MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
auto argument_ptr = reduce.MakeArgumentPointer(arrInLengths,
arrInStrides,
arrOutLengths,
arrOutStrides,
reduceDims,
alpha,
beta,
static_cast<double>(alpha),
static_cast<double>(beta),
in_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
......@@ -284,9 +306,8 @@ int reduce_blockwise_impl(bool do_verification,
if(!reduce.IsSupportedArgument(argument_ptr.get()))
{
std::cerr
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
<< std::endl;
std::cerr << "The runtime parameters not supported by the DeviceReduce instance, exiting!"
<< std::endl;
return (-2);
};
......@@ -322,12 +343,12 @@ int reduce_blockwise_impl(bool do_verification,
#endif
out_dev.FromDevice(out.mData.data());
pass = pass && ck::utils::check_err(out.mData, out_ref.mData);
pass = pass && ck::utils::check_err(out, out_ref);
if(OutputIndex)
{
out_index_dev.FromDevice(out_indices.mData.data());
pass = pass && ck::utils::check_err(out_indices.mData, out_indices_ref.mData);
pass = pass && ck::utils::check_err(out_indices, out_indices_ref);
};
};
......
example/12_reduce/reduce_blockwise_two_call.cpp
View file @ 1abaedd9
......@@ -11,14 +11,14 @@
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce_multiblock.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_reduce.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/utility/host_common_util.hpp"
#include "ck/library/utility/host_reduction.hpp"
using namespace ck;
using namespace ck::tensor_operation::device;
......@@ -90,15 +90,15 @@ static bool time_kernel;
int main(int argc, char* argv[])
{
// used by the device reduction
const std::vector<int> reduceDims_1 = {4};
const std::vector<int> invariantDims_1 = {0, 1, 2, 3};
const std::array<int, 1> reduceDims_1 = {4};
// const std::array<int, 4> invariantDims_1 = {0, 1, 2, 3};
const std::vector<int> reduceDims_2 = {3};
const std::vector<int> invariantDims_2 = {0, 1, 2};
const std::array<int, 1> reduceDims_2 = {3};
// const std::array<int, 3> invariantDims_2 = {0, 1, 2};
// used by the host reduction
const std::vector<int> reduceDims = {3, 4};
const std::vector<int> invariantDims = {0, 1, 2};
const std::array<int, 2> reduceDims = {3, 4};
// const std::array<int, 3> invariantDims = {0, 1, 2};
const std::vector<size_t> inLengths_1 = {64, 320, 80, 4, 128};
......@@ -191,52 +191,71 @@ int main(int argc, char* argv[])
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
static_cast<int32_t>(reduce_total_length));
std::array<index_t, 5> arrInLengths_1;
std::array<index_t, 5> arrInStrides_1;
std::array<index_t, 4> arrInLengths_2;
std::array<index_t, 4> arrInStrides_2;
std::array<index_t, 3> arrOutLengths;
std::array<index_t, 3> arrOutStrides;
ck::ranges::copy(inLengths_1, arrInLengths_1.begin());
ck::ranges::copy(inStrides_1, arrInStrides_1.begin());
ck::ranges::copy(inLengths_2, arrInLengths_2.begin());
ck::ranges::copy(inStrides_2, arrInStrides_2.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
if(do_verify)
{
ReductionHost<InOutDataType,
AccDataType,
InOutDataType,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
5, // Rank
2, // NumReduceDim
PropagateNan,
OutputIndex>
hostReduce(in_1.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run(alpha,
in_1.mData.data(),
beta,
out_ref.mData.data(),
nullptr,
in_elementwise_op,
acc_elementwise_op);
};
using ReferenceReduceInstance =
ck::tensor_operation::host::ReferenceReduce<InOutDataType,
AccDataType,
InOutDataType,
5,
2,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
PropagateNan,
OutputIndex>;
auto reduce_ref = ReferenceReduceInstance{};
auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths_1,
arrInStrides_1,
arrOutLengths,
arrOutStrides,
reduceDims,
static_cast<double>(alpha),
static_cast<double>(beta),
in_1.mData.data(),
nullptr,
out_ref.mData.data(),
nullptr,
in_elementwise_op,
acc_elementwise_op);
if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
<< std::endl;
return (false);
};
std::vector<ck::index_t> i_inLengths_1;
std::vector<ck::index_t> i_inStrides_1;
std::vector<ck::index_t> i_inLengths_2;
std::vector<ck::index_t> i_inStrides_2;
std::vector<ck::index_t> i_outLengths;
std::vector<ck::index_t> i_outStrides;
auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
i_inLengths_1.assign(inLengths_1.begin(), inLengths_1.end());
i_inStrides_1.assign(inStrides_1.begin(), inStrides_1.end());
i_inLengths_2.assign(inLengths_2.begin(), inLengths_2.end());
i_inStrides_2.assign(inStrides_2.begin(), inStrides_2.end());
i_outLengths.assign(outLengths.begin(), outLengths.end());
i_outStrides.assign(outStrides.begin(), outStrides.end());
invoker_ptr_ref->Run(argument_ptr_ref.get());
};
auto reduce_1 = DeviceReduceInstance_1{};
auto argument_ptr_1 = reduce_1.MakeArgumentPointer(i_inLengths_1,
i_inStrides_1,
i_inLengths_2,
i_inStrides_2,
auto argument_ptr_1 = reduce_1.MakeArgumentPointer(arrInLengths_1,
arrInStrides_1,
arrInLengths_2,
arrInStrides_2,
reduceDims_1,
1.0f,
0.0f,
1.0,
0.0,
in_1_dev.GetDeviceBuffer(),
nullptr,
in_2_dev.GetDeviceBuffer(),
......@@ -246,22 +265,21 @@ int main(int argc, char* argv[])
if(!reduce_1.IsSupportedArgument(argument_ptr_1.get()))
{
std::cout
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
<< std::endl;
std::cout << "The runtime parameters seems supported by the DeviceReduce instance, exiting!"
<< std::endl;
};
auto invoker_ptr_1 = reduce_1.MakeInvokerPointer();
auto reduce_2 = DeviceReduceInstance_2{};
auto argument_ptr_2 = reduce_2.MakeArgumentPointer(i_inLengths_2,
i_inStrides_2,
i_outLengths,
i_outStrides,
auto argument_ptr_2 = reduce_2.MakeArgumentPointer(arrInLengths_2,
arrInStrides_2,
arrOutLengths,
arrOutStrides,
reduceDims_2,
alpha,
beta,
static_cast<double>(alpha),
static_cast<double>(beta),
in_2_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
......@@ -294,7 +312,7 @@ int main(int argc, char* argv[])
if(do_verify)
{
out_dev.FromDevice(out.mData.data());
pass = pass && ck::utils::check_err(out.mData, out_ref.mData);
pass = pass && ck::utils::check_err(out, out_ref);
};
return (pass ? 0 : 1);
......
example/12_reduce/reduce_example_common.hpp
View file @ 1abaedd9
......@@ -5,11 +5,10 @@
#include "ck/ck.hpp"
template <ck::index_t Rank, ck::index_t NumReduceDim>
std::vector<int> get_invariant_dims(const std::vector<int>& reduceDims)
template <int Rank, int NumReduceDim>
static inline std::array<int, Rank - NumReduceDim>
get_invariant_dims(const std::array<int, NumReduceDim>& reduceDims)
{
assert(NumReduceDim == reduceDims.size());
int reduceFlag = 0;
// flag the bits for the reduceDims
......@@ -18,13 +17,15 @@ std::vector<int> get_invariant_dims(const std::vector<int>& reduceDims)
reduceFlag |= 1 << reduceDims[i];
};
std::vector<int> invariantDims;
std::array<int, Rank - NumReduceDim> invariantDims;
// collect invariant dimensions
int dim = 0;
for(int i = 0; i < Rank; i++)
if((reduceFlag & (1 << i)) == 0)
{
invariantDims.push_back(i);
invariantDims[dim] = i;
dim++;
};
return invariantDims;
......
example/12_reduce/reduce_multiblock_atomic_add.cpp
View file @ 1abaedd9
......@@ -138,13 +138,17 @@ bool reduce_multiblock_atomic_add_test(bool do_verification,
if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
return;
std::array<int, ShapeType::NumReduceDim_> a_reduceDims;
ck::ranges::copy(reduceDims, a_reduceDims.begin());
result = reduce_multiblock_atomic_add_impl<InOutDataType,
AccDataType,
ReduceOpId,
ShapeType::Rank_,
ShapeType::NumReduceDim_,
PropagateNan>(
do_verification, init_method, time_kernel, inLengths, reduceDims, alpha, beta);
do_verification, init_method, time_kernel, inLengths, a_reduceDims, alpha, beta);
matched = true;
});
......
example/12_reduce/reduce_multiblock_atomic_add_impl.hpp
View file @ 1abaedd9
......@@ -8,14 +8,15 @@
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce_multiblock.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_multiblock.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_reduce.hpp"
#include "ck/library/utility/algorithm.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/utility/host_common_util.hpp"
#include "ck/library/utility/host_reduction.hpp"
#include "reduce_example_common.hpp"
......@@ -29,7 +30,7 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
const std::array<int, NumReduceDim>& reduceDims,
float alpha,
float beta)
......@@ -37,6 +38,8 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
using namespace ck;
using namespace ck::tensor_operation::device;
constexpr index_t NumOutDim = (Rank - NumReduceDim == 0) ? 1 : Rank - NumReduceDim;
constexpr bool op_support_atomic_add =
(ReduceOpId == ReduceTensorOp::ADD || ReduceOpId == ReduceTensorOp::AVG);
......@@ -84,7 +87,7 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
std::vector<size_t> outLengths;
std::vector<int> invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
auto invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
if(invariantDims.empty())
outLengths.push_back(1);
......@@ -146,48 +149,67 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
static_cast<int32_t>(reduce_total_length));
std::array<index_t, Rank> arrInLengths;
std::array<index_t, Rank> arrInStrides;
std::array<index_t, NumOutDim> arrOutLengths;
std::array<index_t, NumOutDim> arrOutStrides;
ck::ranges::copy(inLengths, arrInLengths.begin());
ck::ranges::copy(inStrides, arrInStrides.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
if(do_verification)
{
ReductionHost<InOutDataType,
AccDataType,
InOutDataType,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
Rank,
NumReduceDim,
PropagateNan,
false>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run(alpha,
in.mData.data(),
beta,
out_ref.mData.data(),
nullptr,
in_elementwise_op,
acc_elementwise_op);
};
using ReferenceReduceInstance =
ck::tensor_operation::host::ReferenceReduce<InOutDataType,
AccDataType,
InOutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
PropagateNan,
false>;
auto reduce_ref = ReferenceReduceInstance{};
auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths,
arrInStrides,
arrOutLengths,
arrOutStrides,
reduceDims,
static_cast<double>(alpha),
static_cast<double>(beta),
in.mData.data(),
nullptr,
out_ref.mData.data(),
nullptr,
in_elementwise_op,
acc_elementwise_op);
if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
<< std::endl;
return (false);
};
std::vector<ck::index_t> i_inLengths;
std::vector<ck::index_t> i_inStrides;
std::vector<ck::index_t> i_outLengths;
std::vector<ck::index_t> i_outStrides;
auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
i_inLengths.assign(inLengths.begin(), inLengths.end());
i_inStrides.assign(inStrides.begin(), inStrides.end());
i_outLengths.assign(outLengths.begin(), outLengths.end());
i_outStrides.assign(outStrides.begin(), outStrides.end());
invoker_ptr_ref->Run(argument_ptr_ref.get());
};
auto reduce = DeviceReduceInstance{};
auto argument_ptr = reduce.MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
auto argument_ptr = reduce.MakeArgumentPointer(arrInLengths,
arrInStrides,
arrOutLengths,
arrOutStrides,
reduceDims,
alpha,
beta,
static_cast<double>(alpha),
static_cast<double>(beta),
in_dev.GetDeviceBuffer(),
nullptr,
out_dev.GetDeviceBuffer(),
......@@ -197,9 +219,8 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
if(!reduce.IsSupportedArgument(argument_ptr.get()))
{
std::cerr
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
<< std::endl;
std::cerr << "The runtime parameters not supported by the DeviceReduce instance, exiting!"
<< std::endl;
return (-2);
};
......@@ -223,7 +244,7 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
if(do_verification)
{
out_dev.FromDevice(out.mData.data());
pass = pass && ck::utils::check_err(out.mData, out_ref.mData);
pass = pass && ck::utils::check_err(out, out_ref);
};
return (pass ? 0 : 1);
......
example/13_pool2d_fwd/pool2d_fwd_common.hpp
View file @ 1abaedd9
......@@ -9,13 +9,14 @@
#include "ck/utility/reduction_enums.hpp"
#include "ck/utility/reduction_functions_accumulate.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/device_pool2d_fwd_nhwc_nhwc.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_pool2d_fwd_nhwc_nhwc.hpp"
#include "ck/tensor_operation/gpu/element/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/utility/literals.hpp"
template <typename InDataType,
typename OutDataType,
......@@ -172,16 +173,16 @@ bool pool_test(bool do_verification,
// tensor layout
auto f_host_tensor_descriptor =
[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) {
using namespace ck::literals;
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, H * W, W, 1_uz});
}
else if constexpr(ck::is_same<decltype(layout),
ck::tensor_layout::convolution::NHWC>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, 1_uz, W * C_, C_});
}
};
......@@ -267,14 +268,14 @@ bool pool_test(bool do_verification,
out_device_buf.FromDevice(out_n_c_ho_wo_device.mData.data());
pass = pass && ck::utils::check_err(out_n_c_ho_wo_device.mData, out_n_c_ho_wo_host.mData);
pass = pass && ck::utils::check_err(out_n_c_ho_wo_device, out_n_c_ho_wo_host);
if constexpr(OutputIndex)
{
out_indices_device_buf.FromDevice(out_indices_n_c_ho_wo_device.mData.data());
pass = pass && ck::utils::check_err(out_indices_n_c_ho_wo_device.mData,
out_indices_n_c_ho_wo_host.mData);
pass = pass &&
ck::utils::check_err(out_indices_n_c_ho_wo_device, out_indices_n_c_ho_wo_host);
};
}
......
example/14_gemm_quantization/CMakeLists.txt 0 → 100644
View file @ 1abaedd9
add_example_executable(example_gemm_xdl_bias_relu_quantization_int8 gemm_xdl_bias_relu_quantization_int8.cpp)
add_example_executable(example_gemm_xdl_quantization_int8 gemm_xdl_quantization_int8.cpp)
\ No newline at end of file
example/14_gemm_xdl_requant_relu_requant/gemm_xdl_requant_relu_requant_int8.cpp → example/14_gemm_quantization/gemm_xdl_bias_relu_quantization_int8.cpp
View file @ 1abaedd9
......@@ -9,80 +9,74 @@
#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/device_gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_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 RequantReluRequant
{
// FIXME: We just need one scale for Relu / Leaky Relu / PRelu
RequantReluRequant(float scaleGemm, float scaleRelu)
: scaleGemm_(scaleGemm), scaleRelu_(scaleRelu)
{
}
__host__ __device__ constexpr void operator()(float& y, const float& x) const
{
float gemm_requant = scaleGemm_ * x;
float relu = gemm_requant > 0 ? gemm_requant : 0;
float relu_requant = scaleRelu_ * relu;
y = relu_requant > 127 ? 127 : relu_requant < -128 ? -128 : relu_requant;
}
float scaleGemm_;
float scaleRelu_;
};
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using AccDataType = int32_t;
using CShuffleDataType = float;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using I8 = int8_t;
using I32 = 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 ActivationOp = ck::tensor_operation::element_wise::Relu;
using CDEElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<ActivationOp>;
using ADataType = I8;
using BDataType = I8;
using AccDataType = I32;
using CShuffleDataType = I32;
using BiasDataType = I32;
using DsDataType = ck::Tuple<BiasDataType>;
using EDataType = I8;
using ALayout = Row;
using BLayout = Col;
using BiasLayout = Row;
using DsLayout = ck::Tuple<BiasLayout>;
using ELayout = Row;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle<
ALayout, // typename ALayout,
BLayout, // typename BLayout,
CLayout, // typename CLayout,
ADataType, // typename ADataType,
BDataType, // typename BDataType,
CDataType, // typename CDataType,
AccDataType, // typename GemmAccDataType,
CShuffleDataType, // typename CShuffleDataType,
PassThrough, // typename AElementwiseOperation,
PassThrough, // typename BElementwiseOperation,
RequantReluRequant, // typename CElementwiseOperation,
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<
ALayout,
BLayout,
DsLayout,
ELayout,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
PassThrough, // AElementwiseOperation,
PassThrough, // BElementwiseOperation,
CDEElementOp, // CDEElementwiseOperation,
GemmDefault, // GemmSpecialization GemmSpec,
1, // index_t NumGemmKPrefetchStage,
256, // index_t BlockSize,
256, // index_t MPerBlock,
128, // index_t NPerBlock,
64, // index_t KPerBlock,
16, // index_t AK1,
16, // index_t BK1,
32, // index_t MPerXDL,
32, // index_t NPerXDL,
4, // index_t MXdlPerWave,
2, // index_t NXdlPerWave,
S<4, 64, 1>, // typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
S<1, 0, 2>, // typename ABlockTransferThreadClusterArrangeOrder,
S<1, 0, 2>, // typename ABlockTransferSrcAccessOrder,
1, // NumGemmKPrefetchStage,
256, // BlockSize,
256, // MPerBlock,
128, // NPerBlock,
64, // KPerBlock,
16, // AK1,
16, // BK1,
32, // MPerXDL,
32, // NPerXDL,
4, // MXdlPerWave,
2, // NXdlPerWave,
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1,
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
S<1, 0, 2>, // ABlockTransferSrcAccessOrder,
2, // index_t ABlockTransferSrcVectorDim,
16, // index_t ABlockTransferSrcScalarPerVector,
16, // index_t ABlockTransferDstScalarPerVector_AK1,
......@@ -97,125 +91,100 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
1, // index_t CShuffleMXdlPerWavePerShuffle,
1, // index_t CShuffleNXdlPerWavePerShuffle,
S<1, 64, 1, 4>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
16>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
8>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
float,
AccDataType,
AccDataType,
PassThrough,
PassThrough,
RequantReluRequant>;
PassThrough>;
int main(int argc, char* argv[])
int main()
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// 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 StrideC = 4096;
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 1024;
float scale_gemm = 0.03;
float scale_relu = 1;
ck::index_t StrideA = 1024;
ck::index_t StrideB = 1024;
ck::index_t StrideBias = 0;
ck::index_t StrideE = 1024;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
float requant_scale = 0.03;
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]);
StrideC = std::stoi(argv[9]);
}
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=n0, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
auto f_host_tensor_descriptor =
auto f_host_tensor_descriptor2d =
[](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(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
std::vector<std::size_t>({stride, 1_uz}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
std::vector<std::size_t>({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<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({len}),
std::vector<std::size_t>({stride}));
};
Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
Tensor<BiasDataType> bias_n(f_host_tensor_descriptor1d(N, 1));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor2d(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 << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
std::cout << "bias_n: " << bias_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});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
}
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-128, 127});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-128, 127});
bias_n.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-128, 127});
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem bias_device_buf(sizeof(BiasDataType) * bias_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
bias_device_buf.ToDevice(bias_n.mData.data());
auto a_element_op = PassThrough{};
auto b_element_op = PassThrough{};
auto c_element_op = RequantReluRequant{scale_gemm, scale_relu};
auto a_element_op = PassThrough{};
auto b_element_op = PassThrough{};
auto cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
{bias_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
StrideC,
{StrideBias},
StrideE,
a_element_op,
b_element_op,
c_element_op);
cde_element_op);
if(!gemm.IsSupportedArgument(argument))
{
......@@ -228,7 +197,7 @@ int main(int argc, char* argv[])
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(CDataType) * M * N;
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
......@@ -237,19 +206,29 @@ int main(int argc, char* argv[])
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(do_verification)
{
Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});
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_host_result, a_element_op, b_element_op, c_element_op);
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);
return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
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), bias_n(n));
}
}
return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
}
return 0;
......
example/14_gemm_quantization/gemm_xdl_quantization_int8.cpp 0 → 100644
View file @ 1abaedd9
// 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/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_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"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using I8 = int8_t;
using I32 = 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 ActivationOp = PassThrough;
using CDEElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>;
using ADataType = I8;
using BDataType = I8;
using AccDataType = I32;
using CShuffleDataType = I32;
using DsDataType = ck::Tuple<>;
using EDataType = I8;
using ALayout = Row;
using BLayout = Col;
using DsLayout = ck::Tuple<>;
using ELayout = Row;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<
ALayout,
BLayout,
DsLayout,
ELayout,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
PassThrough, // AElementwiseOperation,
PassThrough, // BElementwiseOperation,
CDEElementOp, // CDEElementwiseOperation,
GemmDefault, // GemmSpecialization GemmSpec,
1, // NumGemmKPrefetchStage,
256, // BlockSize,
256, // MPerBlock,
128, // NPerBlock,
64, // KPerBlock,
16, // AK1,
16, // BK1,
32, // MPerXDL,
32, // NPerXDL,
4, // MXdlPerWave,
2, // NXdlPerWave,
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1,
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
S<1, 0, 2>, // ABlockTransferSrcAccessOrder,
2, // index_t ABlockTransferSrcVectorDim,
16, // index_t ABlockTransferSrcScalarPerVector,
16, // index_t ABlockTransferDstScalarPerVector_AK1,
1, // bool ABlockLdsExtraM,
S<4, 64, 1>, // typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
S<1, 0, 2>, // typename BBlockTransferThreadClusterArrangeOrder,
S<1, 0, 2>, // typename BBlockTransferSrcAccessOrder,
2, // index_t BBlockTransferSrcVectorDim,
8, // index_t BBlockTransferSrcScalarPerVector,
8, // index_t BBlockTransferDstScalarPerVector_BK1,
1, // bool BBlockLdsExtraN,
1, // index_t CShuffleMXdlPerWavePerShuffle,
1, // index_t CShuffleNXdlPerWavePerShuffle,
S<1, 64, 1, 4>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
16>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, EDataType, float, PassThrough, PassThrough, CDEElementOp>;
int main()
{
bool do_verification = true;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 1024;
ck::index_t StrideA = 1024;
ck::index_t StrideB = 1024;
ck::index_t StrideE = 1024;
float requant_scale = 0.03;
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(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1_uz}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({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<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 << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-128, 127});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-128, 127});
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_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());
auto a_element_op = PassThrough{};
auto b_element_op = PassThrough{};
auto cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
{},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
{},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
if(!gemm.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, "
<< gemm.GetTypeString() << std::endl;
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, e_m_n_host_result, a_element_op, b_element_op, cde_element_op);
ref_invoker.Run(ref_argument);
return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
}
return 0;
}
example/14_gemm_xdl_requant_relu_requant/CMakeLists.txt deleted 100644 → 0
View file @ bd2b3dd7
add_example_executable(example_gemm_xdl_requant_relu_requant_int8 gemm_xdl_requant_relu_requant_int8.cpp)
\ No newline at end of file
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