mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-23 14:59:34 +08:00
f636dc9276
Summary: The PR clang-formats everything in `torch/csrc/jit/` and adds it to the pre-commit hook. Here is a list of non-mechanical changes: - I went over each file and fixed up whenever I could tell that clang-format was clobbering comment formatting. - Made the macros in register_prim_ops a little more clang-format friendly by omitting trailing commas - Refactored autodiff.cpp to use a helper class with explicit state rather than a bunch of capturing lambdas - Small improvements to the precommit hook clang-format Pull Request resolved: https://github.com/pytorch/pytorch/pull/15524 Differential Revision: D13547989 Pulled By: suo fbshipit-source-id: 3ff1541bb06433ccfe6de6e33f29227a2b5bb493
138 lines
3.7 KiB
C++
138 lines
3.7 KiB
C++
#include <torch/csrc/jit/assertions.h>
|
|
#include <torch/csrc/jit/export.h>
|
|
#include <torch/csrc/jit/operator.h>
|
|
#include <torch/csrc/jit/script/compiler.h>
|
|
#include <torch/csrc/jit/script/error_report.h>
|
|
#include <torch/csrc/jit/script/module.h>
|
|
#include <torch/csrc/jit/script/schema_matching.h>
|
|
|
|
namespace torch {
|
|
namespace jit {
|
|
namespace script {
|
|
|
|
struct RecursiveMethodCallError : public std::exception {};
|
|
void placeholderCreator(Method&) {
|
|
throw RecursiveMethodCallError();
|
|
}
|
|
|
|
Value* try_emit_call_to(
|
|
Graph& graph,
|
|
const SourceRange& loc,
|
|
Method& callee,
|
|
c10::optional<NamedValue> self,
|
|
ArrayRef<NamedValue> args,
|
|
ArrayRef<NamedValue> kwargs,
|
|
std::stringstream& failure_messages,
|
|
Method* caller,
|
|
bool conv_tensors_to_nums) {
|
|
try {
|
|
callee.ensure_defined();
|
|
} catch (RecursiveMethodCallError&) {
|
|
throw ErrorReport(loc)
|
|
<< " method '" << callee.name()
|
|
<< "' is called recursively involving this call site. Recursive calls are not supported";
|
|
}
|
|
auto fn = callee.graph();
|
|
|
|
auto matched_schema = tryMatchSchema(
|
|
callee.getSchema(),
|
|
loc,
|
|
graph,
|
|
std::move(self),
|
|
args,
|
|
kwargs,
|
|
failure_messages,
|
|
conv_tensors_to_nums);
|
|
if (!matched_schema)
|
|
return nullptr;
|
|
|
|
// parameters to callee method (which become parameters to _this_ method
|
|
// if they were not already)
|
|
for (at::Tensor* member : callee.params()) {
|
|
if (!caller) {
|
|
throw ErrorReport(loc)
|
|
<< " attempting to call a method with parameters from a raw graph. File a bug report";
|
|
}
|
|
matched_schema->inputs.push_back(caller->get_or_add_parameter(member));
|
|
}
|
|
callee.check_single_output();
|
|
return inlineCallTo(graph, *callee.graph(), matched_schema->inputs).at(0);
|
|
}
|
|
|
|
Value* Method::emit_call_to(
|
|
const SourceRange& loc,
|
|
Method& callee,
|
|
ArrayRef<NamedValue> args,
|
|
ArrayRef<NamedValue> kwargs) {
|
|
JIT_ASSERT(!executor);
|
|
std::stringstream failure_messages;
|
|
if (auto result = try_emit_call_to(
|
|
*graph(),
|
|
loc,
|
|
callee,
|
|
c10::nullopt,
|
|
args,
|
|
kwargs,
|
|
failure_messages,
|
|
this,
|
|
/*conv_tensors_to_nums=*/true)) {
|
|
return result;
|
|
}
|
|
throw ErrorReport(loc) << failure_messages.str();
|
|
}
|
|
|
|
void Method::ensure_defined() {
|
|
if (method_creator) {
|
|
auto creator = method_creator;
|
|
method_creator = placeholderCreator;
|
|
creator(*this);
|
|
method_creator = nullptr;
|
|
}
|
|
}
|
|
|
|
void Module::to(at::Device device, at::ScalarType dtype, bool non_blocking) {
|
|
to_impl(device, dtype, non_blocking);
|
|
}
|
|
|
|
void Module::to(at::ScalarType dtype, bool non_blocking) {
|
|
to_impl(/*device=*/c10::nullopt, dtype, non_blocking);
|
|
}
|
|
|
|
void Module::to(at::Device device, bool non_blocking) {
|
|
to_impl(device, /*dtype=*/c10::nullopt, non_blocking);
|
|
}
|
|
|
|
void Module::save(std::ostream& out) {
|
|
ExportModule(*this, out);
|
|
}
|
|
|
|
void Module::save(const std::string& filename) {
|
|
ExportModule(*this, filename);
|
|
}
|
|
|
|
void Module::to_impl(
|
|
const c10::optional<at::Device>& device,
|
|
const c10::optional<at::ScalarType>& dtype,
|
|
bool non_blocking) {
|
|
// First call `to()` on every child module.
|
|
for (auto& child : modules) {
|
|
child->module->to_impl(device, dtype, non_blocking);
|
|
}
|
|
// Then convert every of our parameters.
|
|
for (auto& parameter : parameters) {
|
|
// Need to access the `at::Tensor` as a `Variable` here.
|
|
autograd::Variable variable = *parameter->slot();
|
|
at::Tensor data = variable.data();
|
|
// Use the data's original device or dtype if not supplied here.
|
|
auto new_data = data.to(
|
|
device.value_or(data.device()),
|
|
dtype.value_or(data.scalar_type()),
|
|
non_blocking);
|
|
variable.set_data(new_data);
|
|
}
|
|
}
|
|
|
|
} // namespace script
|
|
} // namespace jit
|
|
} // namespace torch
|