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https://github.com/zebrajr/pytorch.git
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aebf3b47ae
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
63 lines
2.1 KiB
C++
63 lines
2.1 KiB
C++
/**
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* Copyright (c) 2016-present, Facebook, Inc.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// Note(jiayq): the import_array function is done inside
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// caffe2_python.cc. Read
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// http://docs.scipy.org/doc/numpy-1.10.1/reference/c-api.array.html#miscellaneous
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// for more details.
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#define NO_IMPORT_ARRAY
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#include "caffe2/python/pybind_state.h"
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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include "caffe2/core/tensor_int8.h"
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namespace caffe2 {
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namespace python {
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class Int8TensorFetcher : public BlobFetcherBase {
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public:
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pybind11::object Fetch(const Blob& blob) override {
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const caffe2::int8::Int8TensorCPU& src =
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blob.template Get<caffe2::int8::Int8TensorCPU>();
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const int numpy_type = CaffeToNumpyType(src.t.meta());
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CAFFE_ENFORCE(numpy_type != -1, "Int8Tensor contains unknown type data");
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std::vector<npy_intp> npy_dims;
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for (const auto dim : src.t.dims()) {
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npy_dims.push_back(dim);
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}
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auto data_array = pybind11::reinterpret_steal<pybind11::object>(
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PyArray_SimpleNew(src.t.dims().size(), npy_dims.data(), numpy_type));
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void* ptr = static_cast<void*>(
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PyArray_DATA(reinterpret_cast<PyArrayObject*>(data_array.ptr())));
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CPUContext context;
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context.CopyBytesSameDevice(src.t.nbytes(), src.t.raw_data(), ptr);
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context.FinishDeviceComputation();
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auto result = pybind11::cast<pybind11::object>(
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pybind11::make_tuple(data_array, src.scale, src.zero_point));
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return result;
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}
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};
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REGISTER_BLOB_FETCHER(
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(TypeMeta::Id<caffe2::int8::Int8TensorCPU>()),
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caffe2::python::Int8TensorFetcher);
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} // namespace python
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} // namespace caffe2
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