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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33294 1. Serialize bytecode of __setstate__ and run it when loading the model. 2. One use case is quantization. To test this use case a few operators are registered temporarily for lite interpreter. The "_" prefix registration will be removed when the operators are all migrated to mobile. Test Plan: Imported from OSS Differential Revision: D20162898 Pulled By: iseeyuan fbshipit-source-id: 7a3180807bf38fbce594d86993896861f12bb58c
161 lines
4.5 KiB
C++
161 lines
4.5 KiB
C++
#include "interpreter.h"
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#include <torch/csrc/jit/mobile/function.h>
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#include <ATen/core/operator_name.h>
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#include <torch/csrc/jit/runtime/vararg_functions.h>
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#if defined(PYTORCH_MOBILE_OPERATOR_OBSERVER)
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#include <torch/csrc/autograd/record_function.h>
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#include <torch/csrc/jit/mobile/observer.h>
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#endif
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namespace torch{
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namespace jit{
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char const * toString(OpCode op);
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std::ostream& operator<<(std::ostream& out, Instruction inst);
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namespace mobile {
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InterpreterState::InterpreterState(std::shared_ptr<Code> code) : code_(std::move(code)) {
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registers_.resize(code_->register_size_);
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}
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bool InterpreterState::run(Stack& stack) {
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size_t pc = 0;
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while (true) {
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Instruction inst = code_->instructions_[pc];
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// std::cout << "RUNNING " << pc << " " << code_->instructions_[pc];
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// if (inst.op == OP) {
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// std::cout << ", " << code_->op_names_[inst.X].name << "." <<
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// code_->op_names_[inst.X].overload_name;
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// }
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// std::cout << std::endl;
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// for (auto val : stack) {
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// if (val.isTensor()) {
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// std::cout << val.toTensor().sizes() << std::endl;
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// } else {
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// std::cout << val << std::endl;
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// }
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// }
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switch (inst.op) {
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case OP: {
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#if defined(PYTORCH_MOBILE_OPERATOR_OBSERVER)
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if (auto debug_info = at::getThreadLocalDebugInfo()) {
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if (auto* mobile_debug_info = dynamic_cast<MobileDebugInfo*>(
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debug_info.get())) {
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mobile_debug_info->setOpIdx(pc);
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}
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}
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RECORD_FUNCTION(code_->op_names_[inst.X].name, stack);
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#endif
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code_->operators_[inst.X](stack);
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++pc;
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} break;
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case OPN: {
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stack.push_back(inst.N);
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code_->operators_[inst.X](stack);
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++pc;
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} break;
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case LOAD:
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stack.emplace_back(reg(inst.X));
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++pc;
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break;
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case MOVE:
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stack.emplace_back(std::move(reg(inst.X)));
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++pc;
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break;
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case STORE:
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reg(inst.X) = pop(stack);
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++pc;
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break;
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case STOREN:
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for (size_t i = inst.N; i > 0; --i) {
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reg(inst.X + i - 1) = pop(stack);
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}
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++pc;
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break;
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case DROP:
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pop(stack);
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++pc;
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break;
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case DROPR:
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reg(inst.X) = IValue();
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++pc;
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break;
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case LOADC:
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stack.emplace_back(code_->constants_[inst.X]);
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++pc;
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break;
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case GET_ATTR: {
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auto userObj = pop(stack).toObject();
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auto value = userObj->getSlot(inst.X);
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push(stack, std::move(value));
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++pc;
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} break;
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case SET_ATTR: {
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auto v = pop(stack);
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auto userObj = pop(stack).toObject();
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// Mobile only: since the number of slots is not known, resize the numAttributes
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// before setSlot.
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while (userObj->type()->numAttributes() <= inst.X) {
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std::stringstream ss;
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ss << userObj->type()->numAttributes();
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userObj->type()->addAttribute(ss.str(), c10::NoneType::create());
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}
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userObj->setSlot(inst.X, std::move(v));
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++pc;
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} break;
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case JF:
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pc += (pop(stack).toBool()) ? 1 : inst.X;
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break;
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case JMP:
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pc += inst.X;
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break;
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case LOOP: {
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// stack: iteration_count, max_iter, cond, loop_carried_deps...
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auto frame = stack.end() - (inst.N + 1);
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int64_t trip_count = frame[0].toInt();
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int64_t max_trip_count = frame[1].toInt();
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bool cond = frame[2].toBool();
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if (trip_count < max_trip_count && cond) {
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frame[2] = trip_count;
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frame[0] = trip_count + 1;
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++pc;
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} else {
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size_t n_loop_carried = inst.N - 2;
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for (size_t i = 0; i < n_loop_carried; ++i) {
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frame[i] = std::move(frame[i + 3]);
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}
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drop(stack, 3); // iteration_count, max_iter, cond
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pc += inst.X;
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}
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} break;
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case RET:
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return false;
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case LIST_CONSTRUCT: {
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auto type = code_->types_[inst.X]->expect<at::ListType>();
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listConstruct(stack, type, inst.N);
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++pc;
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} break;
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case TUPLE_CONSTRUCT: {
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tupleConstruct(stack, inst.X);
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++pc;
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} break;
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case WARN: {
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drop(stack, 1);
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AT_WARN(pop(stack).toStringRef());
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++pc;
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} break;
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default:
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AT_ERROR(toString(inst.op), " is invalid.");
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}
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}
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return false;
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}
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IValue& InterpreterState::reg(size_t reg) {
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return *(registers_.end() - reg);
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}
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} // namespace mobile
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} // namespace torch
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} // namespace jit
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