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/**
* Copyright (c) 2018-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.
*/
#pragma once
#include <algorithm>
#include "caffe2/contrib/gloo/common.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/math.h"
#include <gloo/algorithm.h>
#include <gloo/common/error.h>
#include <gloo/context.h>
namespace caffe2 {
namespace gloo {
template <class Context>
class ReduceScatterOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
ReduceScatterOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
ws_(ws),
status_blob_(
OperatorBase::GetSingleArgument<std::string>("status_blob", "")) {
if (status_blob_ != "") {
ws_->CreateBlob(status_blob_);
}
}
virtual ~ReduceScatterOp() {}
bool RunOnDevice() override {
std::call_once(once_, [&] { initialize(); });
// If any parameter has changed in between runs, the initialized
// algorithm is invalid and cannot be used.
update(current_);
CAFFE_ENFORCE(current_ == init_, "Inputs/outputs have changed");
try {
algorithm_->run();
} catch (::gloo::IoException& ioe) {
LOG(ERROR) << "Caught gloo IO exception: " << ioe.what();
if (status_blob_ != "") {
signalFailure(ws_->GetBlob(status_blob_), ioe);
return false;
} else {
throw;
}
}
return true;
}
protected:
void initialize() {
// Store which inputs/outputs this instance initialized with
update(init_);
// Verify inputs == outputs
CAFFE_ENFORCE_EQ(init_.inputs.size(), init_.outputs.size());
for (const auto i : c10::irange(init_.inputs.size())) {
CAFFE_ENFORCE_EQ(init_.inputs[i], init_.outputs[i]);
}
// Verify tensors all have same size
size_t size = Input(1).numel();
for (auto i = 2; i < InputSize() - 1; i++) {
CAFFE_ENFORCE_EQ(Input(i).numel(), size);
}
// Verify tensors all have same type
TypeMeta meta = Input(1).dtype();
for (auto i = 2; i < InputSize() - 1; i++) {
CAFFE_ENFORCE(Input(i).dtype() == meta);
}
initializeHalvingDoubling();
}
void initializeHalvingDoubling();
std::once_flag once_;
std::unique_ptr<::gloo::Algorithm> algorithm_;
// Captures the parameters passed to Gloo when first initialized.
// An instance is updated every time this op runs and is compared
// to the reference instance for equality. If any parameter has
// changed from run to run, the initialized algorithm is invalid.
void update(GlooParameters& params) {
params.context = OperatorBase::Input<std::shared_ptr<::gloo::Context>>(0);
params.inputs.resize(InputSize() - 2);
params.outputs.resize(OutputSize() - 1);
for (const auto i : c10::irange(params.inputs.size())) {
params.inputs[i] = Input(i + 1).raw_data();
params.outputs[i] = Output(i)->raw_mutable_data();
}
params.size = Output(0)->numel();
params.meta = Output(0)->dtype();
// Verify recvCountsSize == comm_size
CAFFE_ENFORCE_EQ(Input(InputSize() - 1).numel(), params.context->size);
int* recvCounts = (int*)Input(InputSize() - 1).raw_data();
recvCounts_.assign(recvCounts, recvCounts + Input(InputSize() - 1).numel());
}
GlooParameters init_;
GlooParameters current_;
Workspace* ws_;
std::string status_blob_;
std::vector<int> recvCounts_;
};
} // namespace gloo
} // namespace caffe2
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