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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9939 Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13 Pull Request resolved: https://github.com/pytorch/translate/pull/166 Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125 Closes https://github.com/pytorch/pytorch/pull/9125 Use inheritance for polymorphism, and remove template parameter This is to change the templating in call sites, the core implementations will change later Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are: 1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)), 2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided. 3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type 4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s. Reviewed By: ezyang, houseroad Differential Revision: D9024330 fbshipit-source-id: e0b8295d2dc6ebe2963383ded5af799ad17164ba
84 lines
2.4 KiB
C++
84 lines
2.4 KiB
C++
#pragma once
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#include "rebatching_queue.h"
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namespace caffe2 {
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using RebatchingQueuePtr = std::unique_ptr<RebatchingQueue>;
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class CreateRebatchingQueueOp : public Operator<CPUContext> {
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public:
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CreateRebatchingQueueOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator(operator_def, ws) {}
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bool RunOnDevice() override {
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*OperatorBase::Output<RebatchingQueuePtr>(0) =
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RebatchingQueuePtr(new RebatchingQueue(
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OperatorBase::GetSingleArgument<int>("capacity", 1),
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OperatorBase::GetSingleArgument<int>("num_blobs", 1)));
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return true;
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}
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};
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class EnqueueRebatchingQueueOp : public Operator<CPUContext> {
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public:
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EnqueueRebatchingQueueOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator(operator_def, ws),
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enqueueBatch_(
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OperatorBase::GetSingleArgument<bool>("enqueue_batch", false)) {}
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bool RunOnDevice() override {
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auto& queue = Inputs()[0]->template Get<RebatchingQueuePtr>();
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CHECK(queue);
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CAFFE_ENFORCE_EQ(InputSize(), queue->numBlobs() + 1);
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std::vector<const Tensor*> inputTensors;
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inputTensors.reserve(InputSize() - 1);
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for (int i = 1; i < InputSize(); ++i) {
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inputTensors.push_back(&Input(i));
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}
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return enqueueBatch_ ? queue->enqueueMany(context_, inputTensors)
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: queue->enqueueOne(context_, inputTensors);
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}
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private:
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const bool enqueueBatch_;
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};
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class DequeueRebatchingQueueOp : public Operator<CPUContext> {
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public:
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DequeueRebatchingQueueOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator(operator_def, ws),
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numElements_(OperatorBase::GetSingleArgument<int>("num_elements", 1)) {}
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bool RunOnDevice() override {
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auto& queue = Inputs()[0]->template Get<RebatchingQueuePtr>();
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CHECK(queue);
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std::vector<Tensor*> outputTensors;
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outputTensors.reserve(OutputSize());
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for (int i = 0; i < OutputSize(); ++i) {
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outputTensors.push_back(Output(i));
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}
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return queue->dequeue(context_, numElements_, outputTensors);
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}
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private:
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int numElements_;
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};
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class CloseRebatchingQueueOp : public Operator<CPUContext> {
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public:
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CloseRebatchingQueueOp(const OperatorDef& operator_def, Workspace* ws)
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: Operator(operator_def, ws) {}
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bool RunOnDevice() override {
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CAFFE_ENFORCE_EQ(InputSize(), 1);
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auto& queue = Inputs()[0]->template Get<RebatchingQueuePtr>();
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CAFFE_ENFORCE(queue);
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queue->close();
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return true;
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}
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};
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} // caffe2
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