mirror of
https://github.com/zebrajr/pytorch.git
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ced2c7e2b2
Summary: Reason for this change: (1) Setting/Getting default gpu id doesn't seem to be used at all. (2) It actually is confusing compared to the CUDA_VISIBLE_DEVICES options etc. (3) When setting cuda_gpu_id=-1 in the CUDAContext arg, it used to use the default gpu id but probably we should use the current gpu - so that the caller will be able to control the device placement. One use case is for TensorRT - if we have a custom callback layer, then it would be easier for TRT or whatever caller to set the running device. Reviewed By: dzhulgakov Differential Revision: D6740357 fbshipit-source-id: 2ea710e434b10220d5a198e31c93847304636863
113 lines
3.5 KiB
C++
113 lines
3.5 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 "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/common_cudnn.h"
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#include "caffe2/core/context_gpu.h"
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#include "caffe2/operators/operator_fallback_gpu.h"
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namespace caffe2 {
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namespace python {
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REGISTER_CUDA_OPERATOR(Python, GPUFallbackOp<PythonOp<CPUContext, false>>);
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REGISTER_CUDA_OPERATOR(
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PythonGradient,
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GPUFallbackOp<PythonGradientOp<CPUContext, false>>);
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REGISTER_CUDA_OPERATOR(PythonDLPack, PythonOp<CUDAContext, true>);
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REGISTER_CUDA_OPERATOR(
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PythonDLPackGradient,
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PythonGradientOp<CUDAContext, true>);
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REGISTER_BLOB_FETCHER((TypeMeta::Id<TensorCUDA>()), TensorFetcher<CUDAContext>);
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REGISTER_BLOB_FEEDER(CUDA, TensorFeeder<CUDAContext>);
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namespace py = pybind11;
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void addCUDAGlobalMethods(py::module& m) {
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m.def("num_cuda_devices", &NumCudaDevices);
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m.def("get_cuda_version", &CudaVersion);
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m.def("get_cudnn_version", &cudnnCompiledVersion);
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m.def("get_cuda_peer_access_pattern", []() {
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std::vector<std::vector<bool>> pattern;
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CAFFE_ENFORCE(caffe2::GetCudaPeerAccessPattern(&pattern));
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return pattern;
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});
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m.def("get_device_properties", [](int deviceid) {
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auto& prop = GetDeviceProperty(deviceid);
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std::map<std::string, py::object> obj;
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obj["name"] = py::cast(prop.name);
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obj["major"] = py::cast(prop.major);
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obj["minor"] = py::cast(prop.minor);
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return obj;
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});
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};
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void addCUDAObjectMethods(py::module& m) {
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py::class_<DLPackWrapper<CUDAContext>>(m, "DLPackTensorCUDA")
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.def_property_readonly(
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"data",
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[](DLPackWrapper<CUDAContext>* t) -> py::object {
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CAFFE_ENFORCE_EQ(
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t->device_option.device_type(),
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CUDA,
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"Expected CUDA device option for CUDA tensor");
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return t->data();
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},
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"Return DLPack tensor with tensor's data.")
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.def(
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"feed",
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[](DLPackWrapper<CUDAContext>* t, py::object obj) {
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CAFFE_ENFORCE_EQ(
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t->device_option.device_type(),
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CUDA,
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"Expected CUDA device option for CUDA tensor");
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t->feed(obj);
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},
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"Copy data from given DLPack tensor into this tensor.")
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.def_property_readonly(
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"_shape",
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[](const DLPackWrapper<CUDAContext>& t) { return t.tensor->dims(); })
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.def(
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"_reshape",
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[](DLPackWrapper<CUDAContext>* t, std::vector<TIndex> dims) {
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t->tensor->Resize(dims);
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});
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}
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PYBIND11_MODULE(caffe2_pybind11_state_gpu, m) {
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m.doc() = "pybind11 stateful interface to Caffe2 workspaces - GPU edition";
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addGlobalMethods(m);
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addCUDAGlobalMethods(m);
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addObjectMethods(m);
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addCUDAObjectMethods(m);
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}
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} // namespace python
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} // namespace caffe2
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