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https://github.com/zebrajr/pytorch.git
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1a4473bbd7
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13125 Previously, it returned a vector of THCStream*, which we eventually turned into CUDAStream. No need to spatter the conversion code everywhere: just do it correctly to begin with. An important side effect of doing it this way is that we no longer pass nullptr to CUDAStream; instead, we create the default stream. I will rely on this in a later patch. Reviewed By: gchanan Differential Revision: D10853224 fbshipit-source-id: f6bd6594eba4626eb41a4a5e67fc64c9bbb46a1a
69 lines
2.2 KiB
C++
69 lines
2.2 KiB
C++
#include "torch/csrc/utils/pybind.h"
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#include "torch/csrc/cuda/comm.h"
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#include "torch/csrc/cuda/Stream.h"
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#include "torch/csrc/cuda/THCP.h"
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#include "torch/csrc/utils/auto_gil.h"
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#include "torch/csrc/utils/functional.h"
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#include <ATen/ATen.h>
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#include <THC/THC.h>
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#include <cstddef>
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#include <vector>
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namespace torch { namespace cuda { namespace python {
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void initCommMethods(PyObject *module) {
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auto m = py::cast<py::module>(module);
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m.def(
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"_broadcast_coalesced",
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[](std::vector<at::Tensor>& tensors,
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std::vector<int64_t> devices,
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size_t buffer_size) {
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return broadcast_coalesced(tensors, devices, buffer_size);
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},
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py::arg("tensors"),
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py::arg("devices"),
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py::arg("buffer_size"),
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py::call_guard<py::gil_scoped_release>())
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.def(
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"_broadcast",
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[](at::Tensor& tensor, std::vector<int64_t> devices) {
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return broadcast(tensor, devices);
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},
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py::call_guard<py::gil_scoped_release>())
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.def(
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"_scatter",
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[](at::Tensor& tensor,
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std::vector<int64_t>& devices,
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c10::optional<std::vector<int64_t>> chunk_sizes,
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int64_t dim,
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c10::optional<py::object> py_streams) {
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c10::optional<std::vector<c10::optional<at::cuda::CUDAStream>>> streams;
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if (py_streams) {
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py::handle handle = *py_streams;
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streams = THPUtils_PySequence_to_CUDAStreamList(handle.ptr());
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}
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// Note: We're holding the GIL up to here.
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AutoNoGIL no_gil;
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return scatter(tensor, devices, chunk_sizes, dim, streams);
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},
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py::arg("tensor"),
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py::arg("devices"),
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py::arg("chunk_sizes"),
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py::arg("dim"),
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py::arg("streams"))
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.def(
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"_gather",
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[](std::vector<at::Tensor>& tensors,
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int64_t dim,
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c10::optional<int32_t> destination_index) {
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return gather(tensors, dim, destination_index);
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},
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py::arg("tensors"),
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py::arg("dim"),
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py::arg("destination_index"),
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py::call_guard<py::gil_scoped_release>());
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}
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}}}
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