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Commit 06d2c7b1 authored by Jing Zhang's avatar Jing Zhang Committed by root
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clean

parents b27909a0 3b18f1e3
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library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_bias_2d.hpp deleted 100644 → 0
View file @ b27909a0
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/library/utility/host_tensor.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
template <typename ADataType,
typename BDataType,
typename C0DataType,
typename CDataType,
typename AccDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
struct ReferenceGemmBias2D : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<ADataType>& a_m_k,
const Tensor<BDataType>& b_k_n,
const Tensor<C0DataType>& c0_m_n,
Tensor<CDataType>& c_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
: a_m_k_{a_m_k},
b_k_n_{b_k_n},
c0_m_n_{c0_m_n},
c_m_n_{c_m_n},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
c_element_op_{c_element_op}
{
}
const Tensor<ADataType>& a_m_k_;
const Tensor<BDataType>& b_k_n_;
const Tensor<CDataType>& c0_m_n_;
Tensor<CDataType>& c_m_n_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CElementwiseOperation c_element_op_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceGemmBias2D::Argument;
float Run(const Argument& arg)
{
auto f_mk_kn_mn = [&](auto m, auto n) {
const int K = arg.a_m_k_.mDesc.GetLengths()[1];
AccDataType a = 0;
AccDataType b = 0;
AccDataType acc = 0;
for(int k = 0; k < K; ++k)
{
arg.a_element_op_(a, ck::type_convert<AccDataType>(arg.a_m_k_(m, k)));
arg.b_element_op_(b, ck::type_convert<AccDataType>(arg.b_k_n_(k, n)));
acc += a * b;
}
CDataType cast_acc = static_cast<CDataType>(acc);
arg.c_element_op_(arg.c_m_n_(m, n), cast_acc, arg.c0_m_n_(m, n));
};
make_ParallelTensorFunctor(
f_mk_kn_mn, arg.c_m_n_.mDesc.GetLengths()[0], arg.c_m_n_.mDesc.GetLengths()[1])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<ADataType>& a_m_k,
const Tensor<BDataType>& b_k_n,
const Tensor<C0DataType>& c0_m_n,
Tensor<CDataType>& c_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
{
return Argument{a_m_k, b_k_n, c0_m_n, c_m_n, a_element_op, b_element_op, c_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceGemmBias2D"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_bias_activation_add.hpp deleted 100644 → 0
View file @ b27909a0
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/library/utility/host_tensor.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
template <typename ADataType,
typename BDataType,
typename CDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
struct ReferenceGemmBiasActivationAdd : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<ADataType>& a_m_k,
const Tensor<BDataType>& b_k_n,
Tensor<CDataType>& c_m_n,
const Tensor<CDataType>& c0_n,
const Tensor<CDataType>& c1_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
: a_m_k_{a_m_k},
b_k_n_{b_k_n},
c_m_n_{c_m_n},
c0_n_{c0_n},
c1_m_n_{c1_m_n},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
c_element_op_{c_element_op}
{
}
const Tensor<ADataType>& a_m_k_;
const Tensor<BDataType>& b_k_n_;
Tensor<CDataType>& c_m_n_;
const Tensor<CDataType>& c0_n_;
const Tensor<CDataType>& c1_m_n_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CElementwiseOperation c_element_op_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceGemmBiasActivationAdd::Argument;
float Run(const Argument& arg)
{
auto f_mk_kn_mn = [&](auto m, auto n) {
const int K = arg.a_m_k_.mDesc.GetLengths()[1];
float v_acc = 0;
for(int k = 0; k < K; ++k)
{
float v_a;
float v_b;
arg.a_element_op_(v_a, static_cast<const float>(arg.a_m_k_(m, k)));
arg.b_element_op_(v_b, static_cast<const float>(arg.b_k_n_(k, n)));
v_acc += v_a * v_b;
}
float v_c;
arg.c_element_op_(v_c,
v_acc,
static_cast<float>(arg.c0_n_(n)),
static_cast<float>(arg.c1_m_n_(m, n)));
arg.c_m_n_(m, n) = v_c;
};
make_ParallelTensorFunctor(
f_mk_kn_mn, arg.c_m_n_.mDesc.GetLengths()[0], arg.c_m_n_.mDesc.GetLengths()[1])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<ADataType>& a_m_k,
const Tensor<BDataType>& b_k_n,
Tensor<CDataType>& c_m_n,
const Tensor<CDataType>& c0_n,
const Tensor<CDataType>& c1_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
{
return Argument{
a_m_k, b_k_n, c_m_n, c0_n, c1_m_n, a_element_op, b_element_op, c_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceGemmBiasActivationAdd"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_layernorm.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/reference_tensor_operation/cpu/reference_groupnorm.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_bias_activation.hpp → library/include/ck/library/reference_tensor_operation/cpu/reference_maxpool_bwd.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
#include <iostream> #include <iostream>
#include <sstream> #include <sstream>
#include <vector>
#include "ck/tensor_operation/gpu/device/device_base.hpp" #include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace host { namespace host {
using namespace std;
template <typename ADataType,
typename BDataType, template <typename DOutDataType,
typename CDataType, typename IndexDataType,
typename AElementwiseOperation, typename ConputeDataType,
typename BElementwiseOperation, typename DInDataType,
typename CElementwiseOperation> typename ElementwiseOperation>
struct ReferenceGemmBiasActivation : public device::BaseOperator struct ReferenceMaxPoolBwd : public device::BaseOperator
{ {
// Argument // Argument
struct Argument : public device::BaseArgument struct Argument : public device::BaseArgument
{ {
Argument(const Tensor<ADataType>& a_m_k, Argument(const Tensor<DOutDataType>& dout,
const Tensor<BDataType>& b_k_n, const Tensor<IndexDataType>& indices,
Tensor<CDataType>& c_m_n, Tensor<DInDataType>& din,
const Tensor<CDataType>& c0_n, ElementwiseOperation elementwise_op)
AElementwiseOperation a_element_op, : dout_(dout), indices_(indices), din_(din), elementwise_op_(elementwise_op)
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
: a_m_k_{a_m_k},
b_k_n_{b_k_n},
c_m_n_{c_m_n},
c0_n_{c0_n},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
c_element_op_{c_element_op}
{ {
} }
const Tensor<ADataType>& a_m_k_; const Tensor<DOutDataType>& dout_;
const Tensor<BDataType>& b_k_n_; const Tensor<IndexDataType>& indices_;
Tensor<CDataType>& c_m_n_; Tensor<DInDataType>& din_;
const Tensor<CDataType>& c0_n_; ElementwiseOperation elementwise_op_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CElementwiseOperation c_element_op_;
}; };
// Invoker // Invoker
struct Invoker : public device::BaseInvoker struct Invoker : public device::BaseInvoker
{ {
using Argument = ReferenceGemmBiasActivation::Argument;
float Run(const Argument& arg) float Run(const Argument& arg)
{ {
auto f_mk_kn_mn = [&](auto m, auto n) { int din_length = arg.din_.GetElementSpaceSize();
const int K = arg.a_m_k_.mDesc.GetLengths()[1]; int dout_length = arg.dout_.GetElementSpaceSize();
std::vector<ConputeDataType> buf(din_length, 0);
float v_acc = 0;
for(int i = 0; i < dout_length; ++i)
for(int k = 0; k < K; ++k) {
{ int index = arg.indices_.mData[i];
float v_a; if(index >= 0 && index < din_length)
float v_b; buf[index] += ck::type_convert<ConputeDataType>(arg.dout_.mData[i]);
}
arg.a_element_op_(v_a, static_cast<const float>(arg.a_m_k_(m, k)));
arg.b_element_op_(v_b, static_cast<const float>(arg.b_k_n_(k, n))); for(int i = 0; i < din_length; ++i)
arg.din_.mData[i] = ck::type_convert<DInDataType>(buf[i]);
v_acc += v_a * v_b;
}
float v_c;
arg.c_element_op_(v_c, v_acc, static_cast<float>(arg.c0_n_(n)));
arg.c_m_n_(m, n) = v_c;
};
make_ParallelTensorFunctor(
f_mk_kn_mn, arg.c_m_n_.mDesc.GetLengths()[0], arg.c_m_n_.mDesc.GetLengths()[1])(
std::thread::hardware_concurrency());
return 0; return 0;
} }
...@@ -96,23 +68,14 @@ struct ReferenceGemmBiasActivation : public device::BaseOperator ...@@ -96,23 +68,14 @@ struct ReferenceGemmBiasActivation : public device::BaseOperator
} }
}; };
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; } bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<ADataType>& a_m_k, static auto MakeArgument(const Tensor<DOutDataType>& dout,
const Tensor<BDataType>& b_k_n, const Tensor<IndexDataType>& indices,
Tensor<CDataType>& c_m_n, Tensor<DInDataType>& din,
const Tensor<CDataType>& c0_n, ElementwiseOperation elementwise_op)
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
{ {
return Argument{a_m_k, b_k_n, c_m_n, c0_n, a_element_op, b_element_op, c_element_op}; return Argument{dout, indices, din, elementwise_op};
} }
static auto MakeInvoker() { return Invoker{}; } static auto MakeInvoker() { return Invoker{}; }
...@@ -127,7 +90,7 @@ struct ReferenceGemmBiasActivation : public device::BaseOperator ...@@ -127,7 +90,7 @@ struct ReferenceGemmBiasActivation : public device::BaseOperator
auto str = std::stringstream(); auto str = std::stringstream();
// clang-format off // clang-format off
str << "ReferenceGemmBiasActivation" str << "ReferenceMaxPoolBwd"
<< std::endl; << std::endl;
// clang-format on // clang-format on
......
library/include/ck/library/reference_tensor_operation/cpu/reference_pool_fwd.hpp 0 → 100644
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include <vector>
#include <algorithm>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/utility/reduction_functions_accumulate.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
template <index_t InOutRank,
index_t WindowRank,
typename InDataType,
typename OutDataType,
typename ComputeDataType,
typename IndexDataType,
ck::ReduceTensorOp ReduceOpId,
bool PropagateNan,
bool OutputIndex>
struct ReferencePoolingFwd : public device::BaseOperator
{
using ReduceOperation = typename ck::reduce_binary_operator<ReduceOpId>::opType;
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<InDataType>& in,
Tensor<OutDataType>& out,
Tensor<IndexDataType>& out_indices,
const std::vector<ck::index_t>& window_spatial_lengths,
const std::vector<ck::index_t>& window_strides,
const std::vector<ck::index_t>& in_left_pads,
const std::vector<ck::index_t>& /*in_right_pads*/)
: in_(in),
out_(out),
out_indices_(out_indices),
window_spatial_lengths_(window_spatial_lengths),
window_strides_(window_strides),
in_left_pads_(in_left_pads),
reduceLength_(1)
{
static_for<0, WindowRank, 1>{}(
[&](auto I) { reduceLength_ *= window_spatial_lengths[I]; });
}
const Tensor<InDataType>& in_;
Tensor<OutDataType>& out_;
Tensor<IndexDataType>& out_indices_;
const std::vector<ck::index_t>& window_spatial_lengths_;
const std::vector<ck::index_t>& window_strides_;
const std::vector<ck::index_t>& in_left_pads_;
int reduceLength_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
float RunPooling3dFwd(const Argument& arg)
{
auto elementwise_ops =
ck::reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
arg.reduceLength_);
auto in_elementwise_op = std::get<0>(elementwise_ops);
auto acc_elementwise_op = std::get<1>(elementwise_ops);
if constexpr(!OutputIndex)
{
using Accumulation = ck::detail::
AccumulateWithNanCheck<PropagateNan, ReduceOperation, ComputeDataType>;
auto f_ncdhw = [&](auto n, auto c, auto do_, auto ho, auto wo) {
auto accuVal = ReduceOperation::template GetIdentityValue<ComputeDataType>();
for(ck::index_t z = 0; z < arg.window_spatial_lengths_[0]; ++z)
{
ck::index_t di = do_ * arg.window_strides_[0] + z - arg.in_left_pads_[0];
for(ck::index_t y = 0; y < arg.window_spatial_lengths_[1]; ++y)
{
ck::index_t hi = ho * arg.window_strides_[1] + y - arg.in_left_pads_[1];
for(ck::index_t x = 0; x < arg.window_spatial_lengths_[2]; ++x)
{
ck::index_t wi =
wo * arg.window_strides_[2] + x - arg.in_left_pads_[2];
if(di >= 0 &&
di < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[2]) &&
hi >= 0 &&
hi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[3]) &&
wi >= 0 &&
wi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[4]))
{
ComputeDataType currVal = ck::type_convert<ComputeDataType>(
arg.in_(n, c, di, hi, wi));
in_elementwise_op(currVal, currVal);
Accumulation::Calculate(accuVal, currVal);
}
}
}
}
acc_elementwise_op(accuVal, accuVal);
arg.out_(n, c, do_, ho, wo) = ck::type_convert<OutDataType>(accuVal);
};
make_ParallelTensorFunctor(f_ncdhw,
arg.out_.mDesc.GetLengths()[0],
arg.out_.mDesc.GetLengths()[1],
arg.out_.mDesc.GetLengths()[2],
arg.out_.mDesc.GetLengths()[3],
arg.out_.mDesc.GetLengths()[4])(
std::thread::hardware_concurrency());
}
else
{
using Accumulation = ck::detail::AccumulateWithIndexAndNanCheck<PropagateNan,
ReduceOperation,
ComputeDataType,
IndexDataType>;
auto f_ncdhw = [&](auto n, auto c, auto do_, auto ho, auto wo) {
auto accuVal = ReduceOperation::template GetIdentityValue<ComputeDataType>();
IndexDataType accuIndex = 0;
for(ck::index_t z = 0; z < arg.window_spatial_lengths_[0]; ++z)
{
ck::index_t di = do_ * arg.window_strides_[0] + z - arg.in_left_pads_[0];
for(ck::index_t y = 0; y < arg.window_spatial_lengths_[1]; ++y)
{
ck::index_t hi = ho * arg.window_strides_[1] + y - arg.in_left_pads_[1];
for(ck::index_t x = 0; x < arg.window_spatial_lengths_[2]; ++x)
{
ck::index_t wi =
wo * arg.window_strides_[2] + x - arg.in_left_pads_[2];
if(di >= 0 &&
di < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[2]) &&
hi >= 0 &&
hi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[3]) &&
wi >= 0 &&
wi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[4]))
{
ComputeDataType currVal = ck::type_convert<ComputeDataType>(
arg.in_(n, c, di, hi, wi));
IndexDataType currIndex =
arg.in_.GetOffsetFromMultiIndex(n, c, di, hi, wi);
in_elementwise_op(currVal, currVal);
Accumulation::Calculate(accuVal, currVal, accuIndex, currIndex);
}
}
}
}
acc_elementwise_op(accuVal, accuVal);
arg.out_(n, c, do_, ho, wo) = ck::type_convert<OutDataType>(accuVal);
arg.out_indices_(n, c, do_, ho, wo) = accuIndex;
};
make_ParallelTensorFunctor(f_ncdhw,
arg.out_.mDesc.GetLengths()[0],
arg.out_.mDesc.GetLengths()[1],
arg.out_.mDesc.GetLengths()[2],
arg.out_.mDesc.GetLengths()[3],
arg.out_.mDesc.GetLengths()[4])(
std::thread::hardware_concurrency());
};
return 0;
}
float RunPooling2dFwd(const Argument& arg)
{
auto elementwise_ops =
ck::reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
arg.reduceLength_);
auto in_elementwise_op = std::get<0>(elementwise_ops);
auto acc_elementwise_op = std::get<1>(elementwise_ops);
if constexpr(!OutputIndex)
{
using Accumulation = ck::detail::
AccumulateWithNanCheck<PropagateNan, ReduceOperation, ComputeDataType>;
auto f_nchw = [&](auto n, auto c, auto ho, auto wo) {
auto accuVal = ReduceOperation::template GetIdentityValue<ComputeDataType>();
for(ck::index_t y = 0; y < arg.window_spatial_lengths_[0]; ++y)
{
ck::index_t hi = ho * arg.window_strides_[0] + y - arg.in_left_pads_[0];
for(ck::index_t x = 0; x < arg.window_spatial_lengths_[1]; ++x)
{
ck::index_t wi = wo * arg.window_strides_[1] + x - arg.in_left_pads_[1];
if(hi >= 0 &&
hi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[2]) &&
wi >= 0 &&
wi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[3]))
{
ComputeDataType currVal =
ck::type_convert<ComputeDataType>(arg.in_(n, c, hi, wi));
in_elementwise_op(currVal, currVal);
Accumulation::Calculate(accuVal, currVal);
}
}
}
acc_elementwise_op(accuVal, accuVal);
arg.out_(n, c, ho, wo) = ck::type_convert<OutDataType>(accuVal);
};
make_ParallelTensorFunctor(f_nchw,
arg.out_.mDesc.GetLengths()[0],
arg.out_.mDesc.GetLengths()[1],
arg.out_.mDesc.GetLengths()[2],
arg.out_.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
}
else
{
using Accumulation = ck::detail::AccumulateWithIndexAndNanCheck<PropagateNan,
ReduceOperation,
ComputeDataType,
IndexDataType>;
auto f_nchw = [&](auto n, auto c, auto ho, auto wo) {
auto accuVal = ReduceOperation::template GetIdentityValue<ComputeDataType>();
IndexDataType accuIndex = 0;
for(ck::index_t y = 0; y < arg.window_spatial_lengths_[0]; ++y)
{
ck::index_t hi = ho * arg.window_strides_[0] + y - arg.in_left_pads_[0];
for(ck::index_t x = 0; x < arg.window_spatial_lengths_[1]; ++x)
{
ck::index_t wi = wo * arg.window_strides_[1] + x - arg.in_left_pads_[1];
if(hi >= 0 &&
hi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[2]) &&
wi >= 0 &&
wi < static_cast<ck::index_t>(arg.in_.mDesc.GetLengths()[3]))
{
ComputeDataType currVal =
ck::type_convert<ComputeDataType>(arg.in_(n, c, hi, wi));
IndexDataType currIndex =
arg.in_.GetOffsetFromMultiIndex(n, c, hi, wi);
in_elementwise_op(currVal, currVal);
Accumulation::Calculate(accuVal, currVal, accuIndex, currIndex);
}
}
}
acc_elementwise_op(accuVal, accuVal);
arg.out_(n, c, ho, wo) = ck::type_convert<OutDataType>(accuVal);
arg.out_indices_(n, c, ho, wo) = accuIndex;
};
make_ParallelTensorFunctor(f_nchw,
arg.out_.mDesc.GetLengths()[0],
arg.out_.mDesc.GetLengths()[1],
arg.out_.mDesc.GetLengths()[2],
arg.out_.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
};
return 0;
}
float Run(const Argument& arg)
{
// TODO - support generic pooling
if constexpr(InOutRank == 5 && WindowRank == 3)
return RunPooling3dFwd(arg);
else if constexpr(InOutRank == 4 && WindowRank == 2)
return RunPooling2dFwd(arg);
else
throw std::runtime_error("Only support pooling3d or pooling2d so far");
}
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<InDataType>& in,
Tensor<OutDataType>& out,
Tensor<IndexDataType>& out_indices,
const std::vector<ck::index_t>& window_spatial_lengths,
const std::vector<ck::index_t>& window_strides,
const std::vector<ck::index_t>& in_left_pads,
const std::vector<ck::index_t>& in_right_pads)
{
return Argument{in,
out,
out_indices,
window_spatial_lengths,
window_strides,
in_left_pads,
in_right_pads};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferencePoolingFwd"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/reference_tensor_operation/cpu/reference_reduce.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/reference_tensor_operation/cpu/reference_softmax.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/reference_tensor_operation/cpu/reference_sparse_embedding3_forward_layernorm.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/reference_tensor_operation/gpu/naive_conv_fwd.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#ifndef NAIVE_CONV_FWD_HPP #ifndef NAIVE_CONV_FWD_HPP
#define NAIVE_CONV_FWD_HPP #define NAIVE_CONV_FWD_HPP
......
library/include/ck/library/tensor_operation_instance/add_device_operation_instance.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
#include <cstdlib> #include <vector>
#include <memory>
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm.hpp" #include "ck/tensor_operation/gpu/device/device_batched_gemm.hpp"
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_add_relu_gemm_add.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
#include <cstdlib> #include <vector>
#include <memory>
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_multiple_d_gemm_multiple_d.hpp" #include "ck/tensor_operation/gpu/device/device_batched_gemm_multiple_d_gemm_multiple_d.hpp"
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_bias_permute.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_bias_softmax_gemm_permute.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
#include <cstdlib> #include <vector>
#include <memory>
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp" #include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp"
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_gemm.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
#include <cstdlib> #include <vector>
#include <memory>
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_gemm.hpp" #include "ck/tensor_operation/gpu/device/device_batched_gemm_gemm.hpp"
......
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_multi_d.hpp 0 → 100644
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <vector>
#include <memory>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_multi_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Row,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gnk_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Col,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gkn_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Row,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gnk_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Col,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gkn_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Row,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gnk_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Col,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gkn_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Row,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gnk_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Col,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gkn_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Row,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gnk_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Col,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gkn_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Row,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gnk_gmn_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Col,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gkn_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Row,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gnk_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
Col,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gkn_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Row,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gnk_gmn_irregular_instances(
std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Row,
Col,
Empty_Tuple,
Row,
int8_t,
int8_t,
Empty_Tuple,
int8_t,
PassThrough,
PassThrough,
PassThrough>>>& instances);
template <typename ALayout,
typename BLayout,
typename ELayout,
typename ADataType,
typename BDataType,
typename EDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatchedGemmMultiD<
ALayout,
BLayout,
Empty_Tuple,
ELayout,
ADataType,
BDataType,
Empty_Tuple,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>>
{
using DeviceOp = DeviceBatchedGemmMultiD<ALayout,
BLayout,
Empty_Tuple,
ELayout,
ADataType,
BDataType,
Empty_Tuple,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
is_same_v<EDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gkn_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gkn_gmn_irregular_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gnk_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gnk_gmn_irregular_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gkn_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gkn_gmn_irregular_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gnk_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gnk_gmn_irregular_instances(
op_ptrs);
}
}
else if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
is_same_v<EDataType, int8_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gkn_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gkn_gmn_irregular_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gnk_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gnk_gmn_irregular_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gkn_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gkn_gmn_irregular_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<ELayout, Row>)
{
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gnk_gmn_instances(op_ptrs);
add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gnk_gmn_irregular_instances(
op_ptrs);
}
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_softmax_gemm.hpp
View file @ 06d2c7b1
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
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