dhruvbird

optimized_model._save_for_lite_interpreter("AddTensorsModelOptimized.ptl")

dhruvbird

#include <iostream>
#include <vector>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/module.h>

int main() {
  auto model = torch::jit::_load_for_mobile("AddTensorsModelOptimized.ptl"));

  std::vector<at::IValue> inputs, ret;
  inputs.push_back(at::zeros({3}));

dhruvbird

graph(%self : __torch__.___torch_mangle_41.AddtensorsModel,
%x.1 : Tensor,
%y.1 : Tensor):
%5 : Tensor = prim::CallMethod[name="helper"](%self, %x.1, %y.1) # <ipython-input-98-b27af2abc3c5>:12:11
return (%5)

dhruvbird

graph(%self : __torch__.___torch_mangle_41.AddtensorsModel,
%x.1 : Tensor,
%y.1 : Tensor):
%6 : int = prim::Constant[value=1]()
%z.1 : Tensor = prim::GetAttr[name="t1"](%self)
%7 : Tensor = aten::add(%z.1, %x.1, %6) # <ipython-input-98-b27af2abc3c5>:8:8
%z.5 : Tensor = aten::add(%7, %y.1, %6) # <ipython-input-98-b27af2abc3c5>:8:8
return (%z.5)

dhruvbird

graph(%self : __torch__.___torch_mangle_47.AddtensorsModel,
%x.1 : Tensor,
%y.1 : Tensor):
%3 : int = prim::Constant[value=1]()
%self.t1 : Tensor = prim::Constant[value= 0 5 10 [ CPUFloatType{3} ]]()
%5 : Tensor = aten::add(%self.t1, %x.1, %3) # <ipython-input-98-b27af2abc3c5>:8:8
%z.5 : Tensor = aten::add(%5, %y.1, %3) # <ipython-input-98-b27af2abc3c5>:8:8
return (%z.5)

dhruvbird

import zipfile
bytecode_pkl = None
with zipfile.ZipFile('AddTensorsModelOptimized.ptl', 'r') as myzip:
  bytecode_pkl = myzip.open("AddTensorsModelOptimized/bytecode.pkl")
 
from torch.utils import show_pickle
show_pickle.DumpUnpickler.dump(bytecode_pkl, None)

(5,
 ('__torch__.___torch_mangle_12.AddtensorsModel.forward',

dhruvbird

def fill_inorder_recursive_bst_array(bst, idx, val):
    """Recursively fill an array "bst" with values so that values
    increment by 1 every time. "idx" is the index in "bst" that
    the method should look up to write out the next value in sequence.

    The first invocation is expected to pass idx == 0, with enough
    space in the BST array (bst) to hold a complete and balanced
    binary tree.

    This method returns the next value in sequence that need to be

dhruvbird

import torch
import zipfile

class AddTensorsModel(torch.nn.Module):
  def __init__(self):
    super().__init__();

  def forward(self, x, y):
    return x + y

dhruvbird

zf = zipfile.ZipFile("AddTensorsModel.ptl")
zf.infolist()


[<ZipInfo filename='AddTensorsModel/extra/model_info.txt' file_size=70>,
 <ZipInfo filename='AddTensorsModel/data.pkl' file_size=86>,
 <ZipInfo filename='AddTensorsModel/code/__torch__.py' compress_type=deflate file_size=247 compress_size=166>,
 <ZipInfo filename='AddTensorsModel/code/__torch__.py.debug_pkl' file_size=145>,
 <ZipInfo filename='AddTensorsModel/constants.pkl' file_size=4>,
 <ZipInfo filename='AddTensorsModel/bytecode.pkl' file_size=452>,

dhruvbird

print(zf.read("AddTensorsModel/extra/model_info.txt"))

b"This model's forward() method accepts 2 tensors, and returns their sum"