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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "caffe2/core/hip/context_gpu.h"
#include "caffe2/core/hip/miopen_wrapper.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/types.h"
namespace caffe2 {
class MIOPEN_LRNOP final : public Operator<HIPContext> {
public:
USE_OPERATOR_FUNCTIONS(HIPContext);
MIOPEN_LRNOP(const OperatorDef& operator_def, Workspace* ws)
: Operator<HIPContext>(operator_def, ws),
miopen_wrapper_(&context_),
mode_(miopenLRNCrossChannel),
size_(OperatorBase::GetSingleArgument<int>("size", 0)),
alpha_(OperatorBase::GetSingleArgument<float>("alpha", 0)),
beta_(OperatorBase::GetSingleArgument<float>("beta", 0)),
bias_(OperatorBase::GetSingleArgument<float>("bias", 1)) {
MIOPEN_ENFORCE(miopenCreateTensorDescriptor(&data_desc_));
MIOPEN_ENFORCE(miopenCreateLRNDescriptor(&norm_desc_));
MIOPEN_ENFORCE(
miopenSetLRNDescriptor(norm_desc_, mode_, size_, alpha_, beta_, bias_));
}
~MIOPEN_LRNOP() {
MIOPEN_ENFORCE(miopenDestroyTensorDescriptor(data_desc_));
MIOPEN_ENFORCE(miopenDestroyLRNDescriptor(norm_desc_));
}
template <typename T, typename M>
bool DoRunWithType();
bool RunOnDevice() override;
protected:
MIOPENWrapper miopen_wrapper_;
miopenTensorDescriptor_t data_desc_;
miopenLRNDescriptor_t norm_desc_;
vector<int64_t> miopen_input_dims_;
const miopenLRNMode_t mode_;
const int size_;
const float alpha_;
const float beta_;
const float bias_;
// Input: X, Output: Y
};
class MIOPENLRNGradientOp final : public Operator<HIPContext> {
public:
USE_OPERATOR_FUNCTIONS(HIPContext);
MIOPENLRNGradientOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<HIPContext>(operator_def, ws),
miopen_wrapper_(&context_),
mode_(miopenLRNCrossChannel),
size_(OperatorBase::GetSingleArgument<int>("size", 0)),
alpha_(OperatorBase::GetSingleArgument<float>("alpha", 0)),
beta_(OperatorBase::GetSingleArgument<float>("beta", 0)),
bias_(OperatorBase::GetSingleArgument<float>("bias", 1)),
do_backward_(
OperatorBase::GetSingleArgument<bool>("do_backward", false)),
bwdLRNWs_(nullptr),
bwdLRNScratch_(nullptr) {
MIOPEN_ENFORCE(miopenCreateTensorDescriptor(&data_desc_));
MIOPEN_ENFORCE(miopenCreateLRNDescriptor(&norm_desc_));
MIOPEN_ENFORCE(
miopenSetLRNDescriptor(norm_desc_, mode_, size_, alpha_, beta_, bias_));
}
~MIOPENLRNGradientOp() {
MIOPEN_ENFORCE(miopenDestroyTensorDescriptor(data_desc_));
MIOPEN_ENFORCE(miopenDestroyLRNDescriptor(norm_desc_));
if (bwdLRNWs_) {
hipFree(bwdLRNWs_);
bwdLRNWs_ = nullptr;
}
if (bwdLRNScratch_) {
hipFree(bwdLRNScratch_);
bwdLRNScratch_ = nullptr;
}
}
template <typename T, typename M>
bool DoRunWithType();
bool RunOnDevice() override;
protected:
MIOPENWrapper miopen_wrapper_;
miopenTensorDescriptor_t data_desc_;
miopenLRNDescriptor_t norm_desc_;
vector<int64_t> miopen_input_dims_;
const miopenLRNMode_t mode_;
const int size_;
const float alpha_;
const float beta_;
const float bias_;
const bool do_backward_;
float* bwdLRNWs_;
float* bwdLRNScratch_;
// Input: X, Y, dY
// Output: dX
};
template <typename T, typename M>
bool MIOPEN_LRNOP::DoRunWithType() {
const auto& X = Input(0);
auto* Y = Output(0);
// Reshape tensor descriptors if necessary
if (X.sizes() != miopen_input_dims_) {
VLOG(1) << "Setting descriptors";
miopen_input_dims_ = X.sizes().vec();
int C = 1, H = 1, W = 1;
// Normal 4-dimensional tensors for images.
C = X.dim32(1);
H = X.dim32(2);
W = X.dim32(3);
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
data_desc_, miopenTypeWrapper<T>::type, X.dim32(0), C, H, W));
}
// now actually run the computation
MIOPEN_ENFORCE(miopenLRNForward(
miopen_wrapper_.inline_miopen_handle(),
norm_desc_,
&alpha_,
data_desc_,
X.template data<T>(),
&beta_,
data_desc_,
Y->template mutable_data<T>(),
false,
nullptr));
return true;
}
bool MIOPEN_LRNOP::RunOnDevice() {
// dispatch based on contents of tensor(s)
const auto& X = Input(0);
auto* Y = Output(0);
Y->ResizeLike(X);
if (X.IsType<float>()) {
return DoRunWithType<float, float>();
} else if (X.IsType<at::Half>()) {
return DoRunWithType<at::Half, float>();
} else {
CAFFE_THROW("Unsupported input type");
}
return false;
}
template <typename T, typename M>
bool MIOPENLRNGradientOp::DoRunWithType() {
const auto& X = Input(0);
const auto& Y = Input(1);
const auto& dY = Input(2);
auto* dX = Output(0);
if (dY.sizes() != miopen_input_dims_) {
VLOG(1) << "Setting descriptors";
miopen_input_dims_ = dY.sizes().vec();
int C = 1, H = 1, W = 1;
// Normal 4-dimensional tensors for images.
C = dY.dim32(1);
H = dY.dim32(2);
W = dY.dim32(3);
MIOPEN_ENFORCE(miopenSet4dTensorDescriptor(
data_desc_, miopenTypeWrapper<T>::type, dY.dim32(0), C, H, W));
}
size_t ws_size = 0;
MIOPEN_ENFORCE(miopenLRNGetWorkSpaceSize(data_desc_, &ws_size));
if (bwdLRNWs_ == nullptr) {
HIP_CHECK(hipMalloc(&bwdLRNWs_, ws_size));
}
// Run fwd pass to populate workspace
if (bwdLRNScratch_ == nullptr) {
HIP_CHECK(hipMalloc(&bwdLRNScratch_, X.size() * sizeof(float)));
}
MIOPEN_ENFORCE(miopenLRNForward(
miopen_wrapper_.inline_miopen_handle(),
norm_desc_,
&alpha_,
data_desc_,
X.template data<T>(),
&beta_,
data_desc_,
bwdLRNScratch_,
true,
bwdLRNWs_));
// Run the bwd computation
MIOPEN_ENFORCE(miopenLRNBackward(
miopen_wrapper_.inline_miopen_handle(),
norm_desc_,
&alpha_,
data_desc_,
Y.template data<T>(),
data_desc_,
dY.template data<T>(),
data_desc_,
X.template data<T>(),
&beta_,
data_desc_,
dX->template mutable_data<T>(),
bwdLRNWs_));
return true;
}
bool MIOPENLRNGradientOp::RunOnDevice() {
// dispatch based on contents of tensor(s)
const auto& X = Input(0);
const auto& Y = Input(1);
const auto& dY = Input(2);
auto* dX = Output(0);
dX->ResizeLike(dY);
if (dY.IsType<float>()) {
return DoRunWithType<float, float>();
} else if (dY.IsType<at::Half>()) {
return DoRunWithType<at::Half, float>();
} else {
CAFFE_THROW("Unsupported input type");
}
return false;
}
namespace {
REGISTER_MIOPEN_OPERATOR(LRN, MIOPEN_LRNOP);
REGISTER_MIOPEN_OPERATOR(LRNGradient, MIOPENLRNGradientOp);
} // namespace
}; // namespace caffe2
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