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Use torch:: instead of at:: in all C++ APIs (#13523)

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Summary:
In TorchScript and C++ extensions we currently advocate a mix of `torch::` and `at::` namespace usage. In the C++ frontend I had instead exported all symbols from `at::` and some from `c10::` into the `torch::` namespace. This is far, far easier for users to understand, and also avoid bugs around creating tensors vs. variables. The same should from now on be true for the TorchScript C++ API (for running and loading models) and all C++ extensions.

Note that since we're just talking about typedefs, this change does not break any existing code.

Once this lands I will update stuff in `pytorch/tutorials` too.

zdevito ezyang gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13523

Differential Revision: D12942787

Pulled By: goldsborough

fbshipit-source-id: 76058936bd8707b33d9e5bbc2d0705fc3d820763
This commit is contained in:
Peter Goldsborough
2018-11-06 14:28:20 -08:00
committed by Facebook Github Bot
parent be424de869
commit 393ad6582d
90 changed files with 158 additions and 164 deletions
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26
test/custom_operator/test_custom_ops.cpp
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@ -33,7 +33,7 @@ void get_operator_from_registry_and_execute() {
torch::jit::Stack stack;
torch::jit::push(stack, torch::ones(5), 2.0, 3);
op->getOperation()(stack);
std::vector<at::Tensor> output;
std::vector<torch::Tensor> output;
torch::jit::pop(stack, output);
const auto manual = custom_op(torch::ones(5), 2.0, 3);
@ -99,19 +99,19 @@ void test_move_to_device(const std::string& path_to_exported_script_module) {
torch::jit::load(path_to_exported_script_module);
AT_ASSERT(module != nullptr);
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
return tensor.device().is_cpu();
});
module->to(at::kCUDA);
module->to(torch::kCUDA);
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
return tensor.device().is_cuda();
});
module->to(at::kCPU);
module->to(torch::kCPU);
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
return tensor.device().is_cpu();
});
}
@ -121,16 +121,16 @@ void test_move_to_dtype(const std::string& path_to_exported_script_module) {
torch::jit::load(path_to_exported_script_module);
AT_ASSERT(module != nullptr);
module->to(at::kInt);
module->to(torch::kInt);
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
return tensor.dtype() == at::kInt;
helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
return tensor.dtype() == torch::kInt;
});
module->to(at::kDouble);
module->to(torch::kDouble);
helpers::check_all_parameters(*module, [](const at::Tensor& tensor) {
return tensor.dtype() == at::kDouble;
helpers::check_all_parameters(*module, [](const torch::Tensor& tensor) {
return tensor.dtype() == torch::kDouble;
});
}
@ -147,7 +147,7 @@ int main(int argc, const char* argv[]) {
test_argument_checking_for_serialized_modules(path_to_exported_script_module);
test_move_to_dtype(path_to_exported_script_module);
if (at::globalContext().getNumGPUs() > 0) {
if (torch::globalContext().getNumGPUs() > 0) {
test_move_to_device(path_to_exported_script_module);
}