mirror of
https://github.com/zebrajr/pytorch.git
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1e9c384afb
Summary: This PR fixes around 250 places in the codebase where we were making unnecessary copies of objects (some large, some small). ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/15026 Differential Revision: D13458784 Pulled By: goldsborough fbshipit-source-id: be5148b2ce09493588d70952e6f6d6ff5ec5199b
121 lines
3.6 KiB
C++
121 lines
3.6 KiB
C++
#include <torch/csrc/jit/assertions.h>
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#include <torch/csrc/jit/script/module.h>
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#include <torch/csrc/jit/script/compiler.h>
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#include <torch/csrc/jit/script/error_report.h>
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#include <torch/csrc/jit/export.h>
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#include <torch/csrc/jit/operator.h>
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namespace torch { namespace jit { namespace script {
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struct RecursiveMethodCallError : public std::exception {};
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void placeholderCreator(Method&) {
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throw RecursiveMethodCallError();
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}
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c10::optional<std::vector<Value*>> try_emit_call_to(
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Graph& graph,
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const SourceRange& loc,
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Method& callee,
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c10::optional<NamedValue> self,
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ArrayRef<NamedValue> args,
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ArrayRef<NamedValue> kwargs,
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std::stringstream& failure_messages,
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Method* caller,
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bool conv_tensors_to_nums) {
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try {
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callee.ensure_defined();
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} catch (RecursiveMethodCallError&) {
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throw ErrorReport(loc) << " method '" << callee.name()
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<< "' is called recursively involving this call site. Recursive calls are not supported";
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}
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auto fn = callee.graph();
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auto matched_schema = tryMatchSchema(
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callee.getSchema(),
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loc, graph, std::move(self), args, kwargs, failure_messages, conv_tensors_to_nums);
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if(!matched_schema)
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return c10::nullopt;
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// parameters to callee method (which become parameters to _this_ method
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// if they were not already)
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for(at::Tensor* member : callee.params()) {
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if(!caller) {
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throw ErrorReport(loc) << " attempting to call a method with parameters from a raw graph. File a bug report";
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}
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matched_schema->inputs.push_back(caller->get_or_add_parameter(member));
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}
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return inlineCallTo(graph, *callee.graph(), matched_schema->inputs);
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}
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std::vector<Value*> Method::emit_call_to(const SourceRange& loc, Method & callee, ArrayRef<NamedValue> args, ArrayRef<NamedValue> kwargs) {
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JIT_ASSERT(!executor);
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std::stringstream failure_messages;
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if (auto result = try_emit_call_to(
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*graph(),
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loc,
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callee,
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c10::nullopt,
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args,
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kwargs,
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failure_messages,
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this,
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/*conv_tensors_to_nums=*/true)) {
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return *result;
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}
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throw ErrorReport(loc) << failure_messages.str();
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}
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void Method::ensure_defined() {
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if(method_creator) {
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auto creator = method_creator;
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method_creator = placeholderCreator;
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creator(*this);
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method_creator = nullptr;
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}
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}
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void Module::to(at::Device device, at::ScalarType dtype, bool non_blocking) {
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to_impl(device, dtype, non_blocking);
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}
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void Module::to(at::ScalarType dtype, bool non_blocking) {
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to_impl(/*device=*/c10::nullopt, dtype, non_blocking);
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}
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void Module::to(at::Device device, bool non_blocking) {
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to_impl(device, /*dtype=*/c10::nullopt, non_blocking);
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}
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void Module::save(std::ostream& out) {
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ExportModule(*this, out);
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}
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void Module::save(const std::string& filename) {
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ExportModule(*this, filename);
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}
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void Module::to_impl(
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const c10::optional<at::Device>& device,
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const c10::optional<at::ScalarType>& dtype,
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bool non_blocking) {
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// First call `to()` on every child module.
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for (auto& child : modules) {
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child->module->to_impl(device, dtype, non_blocking);
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}
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// Then convert every of our parameters.
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for (auto& parameter : parameters) {
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// Need to access the `at::Tensor` as a `Variable` here.
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autograd::Variable variable = *parameter->slot();
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at::Tensor data = variable.data();
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// Use the data's original device or dtype if not supplied here.
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auto new_data = data.to(
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device.value_or(data.device()),
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dtype.value_or(data.scalar_type()),
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non_blocking);
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variable.set_data(new_data);
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}
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}
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}}}
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