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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72951 Extend flatbuffer to support extra files map Flatbuffer schema has extra files. The users can write extra files by providing a `map<string, string>` which will be part of the flatbuffer model asset and and can be loaded back similar to pickle. ghstack-source-id: 149622799 Test Plan: fb: ```[pavithran@devvm5216.vll0 ~/fbsource/fbcode] cd ~/fbsource/fbcode/ && buck test caffe2/test/cpp/jit:jit -- FlatbufferTest.ExtraFiles Parsing buck files: finished in 0.7 sec Downloaded 0/8 artifacts, 0.00 bytes, 100.0% cache miss (for updated rules) Building: finished in 20.0 sec (100%) 22343/22343 jobs, 4/22343 updated Total time: 20.7 sec More details at https://www.internalfb.com/intern/buck/build/7dba5034-d623-4a1e-afa1-b0e809df7066 BUILD SUCCEEDED Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details. Running with tpx session id: 9c1ac1e0-a8c0-4a62-95df-8f49695aa7d1 Trace available for this run at /tmp/tpx-20220216-144630.207992/trace.log RemoteExecution session id: reSessionID-9c1ac1e0-a8c0-4a62-95df-8f49695aa7d1-tpx Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/7318349470518809 ✓ ListingSuccess: caffe2/test/cpp/jit:jit : 468 tests discovered (17.211) ✓ Pass: caffe2/test/cpp/jit:jit - FlatbufferTest.ExtraFiles (0.169) Summary Pass: 1 ListingSuccess: 1 If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users Finished test run: https://www.internalfb.com/intern/testinfra/testrun/7318349470518809```` Reviewed By: iseeyuan Differential Revision: D34286346 fbshipit-source-id: 4e09ab25b8ed6af6f8923db3aab046c255f13bb8 (cherry picked from commit ce8d88e22a360b25253d8a75f428d523fa88a79a)
124 lines
4.1 KiB
C++
124 lines
4.1 KiB
C++
#pragma once
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#include <ATen/core/ivalue.h>
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#include <caffe2/serialize/inline_container.h>
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#include <torch/csrc/jit/mobile/function.h>
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#include <torch/csrc/jit/mobile/interpreter.h>
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#include <torch/csrc/jit/mobile/module.h>
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#include <torch/csrc/jit/runtime/instruction.h>
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#include <torch/csrc/jit/serialization/mobile_bytecode_generated.h> // NOLINT
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#include <torch/custom_class.h>
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#include <string>
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#include <vector>
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namespace torch {
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namespace jit {
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using ExtraFilesMap = std::unordered_map<std::string, std::string>;
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// On high level, to produce a Module from a file on disk, we need to go
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// through the follow steps:
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// 1. Read: Read the file from disk -> memory
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// 2. Deserialize: Parse the bytes to produce some in memory manipulable
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// structure
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// 3. Module initialization: Produce mobile::Module out of the structure
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// produced in 2.
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// Under this context, the structure described in 2. is
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// mobile::serialization::Module
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// Parse a mobile::Module from flatbuffer's in-memory Module representation.
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// The caller is assumed to manage the lifetimes of Module.
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// This function does step 3 described above.
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TORCH_API mobile::Module initialize_mobile_module(
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mobile::serialization::Module* flatbuffer_module,
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c10::optional<at::Device> device = c10::nullopt);
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// Parse a mobile::Module from raw bytes.
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// ownership of data is shared to the returned Module.
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// (Feel free to pass in a unique_ptr too!)
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// This function does steps 2+3 described above
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TORCH_API mobile::Module parse_and_initialize_mobile_module(
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std::shared_ptr<char> data,
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size_t size,
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c10::optional<at::Device> device = c10::nullopt);
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// Load a mobile::Module from a filepath.
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// This function does steps 1+2+3 described above.
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// We need to have this as a convienience because Python
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// API will need to wrap this. C++ clients should use one
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// versions above.
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TORCH_API mobile::Module load_mobile_module_from_file(
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const std::string& filename,
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c10::optional<at::Device> device = c10::nullopt);
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TORCH_API void parseExtraFiles(
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mobile::serialization::Module* module,
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ExtraFilesMap& extra_files);
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class FlatbufferLoader {
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public:
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FlatbufferLoader();
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typedef IValue (
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*IValueParser)(FlatbufferLoader&, const mobile::serialization::IValue&);
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void registerIValueParser(
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mobile::serialization::IValueUnion ivalue_type,
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IValueParser parser);
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mobile::Module parseModule(mobile::serialization::Module* module);
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typedef TypePtr (*TypeResolver)(
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const std::string& type_str,
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std::shared_ptr<CompilationUnit> cu);
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void internal_registerTypeResolver(TypeResolver type_resolver);
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IValue& getIValue(uint32_t pos) {
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TORCH_CHECK(pos < all_ivalues_.size());
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return all_ivalues_[pos];
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}
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mobile::Function* getFunction(uint32_t pos) {
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return all_functions_[pos];
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}
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ClassTypePtr getType(uint32_t pos) {
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TORCH_CHECK(pos < all_ivalues_.size());
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return all_types_[pos];
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}
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c10::Storage getStorage(uint32_t index);
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TypePtr getOrCreateTypeAnnotations(const flatbuffers::String* offset);
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ClassTypePtr getOrCreateClassTypeForObject(
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const mobile::serialization::Object* object);
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const mobile::serialization::Module* getCurrentFlatbufferInput() {
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return module_;
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}
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std::shared_ptr<mobile::CompilationUnit> mcu_;
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std::shared_ptr<CompilationUnit> cu_;
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private:
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IValue parseIValue(const mobile::serialization::IValue* ivalue);
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std::unique_ptr<mobile::Function> parseFunction(
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const mobile::serialization::Function* method);
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std::unordered_map<uint32_t, mobile::Function*> all_functions_;
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std::vector<ClassTypePtr> all_types_;
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std::unordered_set<uint32_t> initialized_types_;
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std::unordered_map<const flatbuffers::String*, TypePtr> type_annotations_;
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std::vector<bool> storage_loaded_;
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std::vector<c10::Storage> storages_;
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std::vector<IValue> all_ivalues_;
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std::array<
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IValueParser,
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static_cast<uint8_t>(mobile::serialization::IValueUnion::MAX) + 1>
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ivalue_parsers_;
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TypeResolver type_resolver_ = nullptr;
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mobile::serialization::Module* module_ = nullptr;
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};
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} // namespace jit
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} // namespace torch
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