pytorch

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#include <vector>
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#include <torch/csrc/jit/backends/backend.h>
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#include <torch/csrc/jit/mobile/nnc/context.h>
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namespace torch {
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namespace jit {
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namespace mobile {
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namespace nnc {
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class NNCBackend : public PyTorchBackendInterface {
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 public:
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  explicit NNCBackend() = default;
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  ~NNCBackend() override = default;
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  bool is_available() override {
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    return true;
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  }
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  c10::impl::GenericDict compile(
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      c10::IValue processed,
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      c10::impl::GenericDict method_compile_spec) override {
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    cu_ = std::make_shared<CompilationUnit>(processed);
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    // Input method_compile_spec:
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    //   Key: method name
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    //   Value: compile spec for each method
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    // Output:
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    //   Key: method name
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    //   Value: a backend handle for each method
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    auto spec =
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        c10::impl::toTypedDict<std::string, at::IValue>(method_compile_spec);
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    auto handles = c10::Dict<std::string, std::string>();
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    for (const auto& it : spec) {
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      // The handle for each method is the key (method name) itself.
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      handles.insert(it.key(), it.key());
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    }
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    return c10::impl::toGenericDict(handles);
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  }
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  c10::impl::GenericList execute(
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      c10::IValue handle,
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      c10::impl::GenericList inputs) override {
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    const std::string& method_name = handle.toStringRef();
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    auto function_name = c10::QualifiedName(method_name);
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    return cu_->run(function_name, inputs);
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  }
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 private:
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  std::shared_ptr<CompilationUnit> cu_;
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};
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namespace {
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// TODO(mvz): temporarily disable NNC backend in mobile builds.
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// static const auto cls = torch::jit::backend<NNCBackend>("nnc");
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} // namespace
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} // namespace nnc
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} // namespace mobile
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} // namespace jit
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} // namespace torch
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