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https://github.com/zebrajr/pytorch.git
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a70573b589
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/12768 Note: DefaultTensorOptions no longer fits in 64-bits. I kept functions that take ScalarType as input to minimize changes for now. Reviewed By: ezyang Differential Revision: D10419671 fbshipit-source-id: 9cc8c5982fde9ff243e03d55c0c52c2aa2c7efd8
72 lines
2.1 KiB
C++
72 lines
2.1 KiB
C++
#ifndef CAFFE2_CORE_BLOB_H_
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#define CAFFE2_CORE_BLOB_H_
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#include <cstddef>
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#include <sstream>
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#include <typeinfo>
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#include <type_traits>
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#include <vector>
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#include "caffe2/core/common.h"
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#include <ATen/core/blob.h>
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#include <ATen/core/typeid.h>
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#include "caffe2/core/logging.h"
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#include "caffe2/core/tensor.h"
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namespace caffe2 {
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inline bool BlobIsTensorType(const Blob& blob, DeviceType device_type) {
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bool is_match = blob.meta().Match<Tensor>();
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if (!is_match) {
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return false;
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}
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const Tensor* tensor = &blob.Get<Tensor>();
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return tensor && *tensor && tensor->GetDeviceType() == device_type;
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}
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inline Tensor*
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BlobGetMutableTensor(Blob* blob, at::IntList dims, at::TensorOptions options) {
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if (blob->IsType<Tensor>()) {
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Tensor* tensor = blob->GetMutable<Tensor>();
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if (*tensor) {
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if (tensor->GetDevice() == options.device()) {
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if (tensor->sizes() != dims) {
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// Resize when the dims doesn't match
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tensor->Resize(dims);
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}
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if (tensor->meta() == options.dtype()) {
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tensor->raw_mutable_data();
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} else {
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// create a new Tensor when the data_type doesn't match
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return blob->Reset<Tensor>(new Tensor(caffe2::empty(dims, options)));
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}
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return tensor;
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}
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// create a new Tensor when device doesn't match
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}
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}
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VLOG(1) << "Create new mutable object " << TypeMeta::TypeName<Tensor>()
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<< " dims: " << dims;
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// << " options: " << options; (operator<< for Options is in at:: now)
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return blob->Reset<Tensor>(new Tensor(caffe2::empty(dims, options)));
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}
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inline Tensor* BlobGetMutableTensor(Blob* blob, DeviceType device_type) {
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if (blob->IsType<Tensor>()) {
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Tensor* tensor = blob->GetMutable<Tensor>();
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if (*tensor && tensor->GetDeviceType() == device_type) {
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return tensor;
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}
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}
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// if we're here, then either Blob didn't hold a Tensor
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// or that Tensor had the wrong DeviceType.
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VLOG(1) << "Create new mutable object " << TypeMeta::TypeName<Tensor>()
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<< " DeviceType:" << device_type;
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return blob->Reset<Tensor>(new Tensor(device_type));
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}
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} // namespace caffe2
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#endif // CAFFE2_CORE_BLOB_H_
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