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Source code for torch.utils.mobile_optimizer

# mypy: allow-untyped-defs
"""This module contains utility method for mobile model optimization and lint."""

import torch
from enum import Enum
from torch._C import _MobileOptimizerType as MobileOptimizerType
from typing import Optional, Set, List, AnyStr

class LintCode(Enum):
    BUNDLED_INPUT = 1
    REQUIRES_GRAD = 2
    DROPOUT = 3
    BATCHNORM = 4

[docs]def optimize_for_mobile( script_module: torch.jit.ScriptModule, optimization_blocklist: Optional[Set[MobileOptimizerType]] = None, preserved_methods: Optional[List[AnyStr]] = None, backend: str = 'CPU') -> torch.jit.RecursiveScriptModule: """ Optimize a torch script module for mobile deployment. Args: script_module: An instance of torch script module with type of ScriptModule. optimization_blocklist: A set with type of MobileOptimizerType. When set is not passed, optimization method will run all the optimizer pass; otherwise, optimizer method will run the optimization pass that is not included inside optimization_blocklist. preserved_methods: A list of methods that needed to be preserved when freeze_module pass is invoked backend: Device type to use for running the result model ('CPU'(default), 'Vulkan' or 'Metal'). Returns: A new optimized torch script module """ if not isinstance(script_module, torch.jit.ScriptModule): raise TypeError( f'Got {type(script_module)}, but ScriptModule is expected.') if optimization_blocklist is None: optimization_blocklist = set() if preserved_methods is None: preserved_methods = [] # Convert potential byte arrays into strings (if there is any) to pass type checking # Here we use a new name as assigning it back to preserved_methods will invoke # mypy errors (i.e. List[AnyStr] = List[str]) preserved_methods_str: List[str] = [str(method) for method in preserved_methods] bundled_inputs_attributes = _get_bundled_inputs_preserved_attributes(script_module, preserved_methods_str) if all(hasattr(script_module, method) for method in bundled_inputs_attributes): preserved_methods_str = list(set(preserved_methods_str + bundled_inputs_attributes)) non_exist_methods = [] for method in preserved_methods_str: if not hasattr(script_module, method): non_exist_methods.append(method) if non_exist_methods: raise AttributeError( f"The following methods to preserve do not exist in script_module: {', '.join(non_exist_methods)}") backend = backend.lower() if backend == 'cpu': optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile( script_module._c, optimization_blocklist, preserved_methods_str) elif backend == 'vulkan': optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile( script_module._c, optimization_blocklist, preserved_methods_str) elif backend == 'metal': optimized_cpp_module = torch._C._jit_pass_metal_optimize_for_mobile(script_module._c, preserved_methods_str) else: raise TypeError("Unknown backend, must be one of 'CPU', 'Vulkan' or 'Metal'") return torch.jit._recursive.wrap_cpp_module(optimized_cpp_module)
def generate_mobile_module_lints(script_module: torch.jit.ScriptModule): """ Generate a list of lints for a given torch script module. Args: script_module: An instance of torch script module with type of ScriptModule. Returns: lint_map: A list of dictionary that contains modules lints """ if not isinstance(script_module, torch.jit.ScriptModule): raise TypeError( f'Got {type(script_module)}, but ScriptModule is expected.') lint_list = [] if not hasattr(script_module, "_generate_bundled_inputs_for_forward"): lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input for forward, please add bundled inputs " "before saving the module using torch.utils.bundled_inputs.augment_model_with_bundled_inputs."}) for name, param in script_module.named_parameters(): if param.requires_grad: lint_list.append({"name": LintCode.REQUIRES_GRAD.name, "message": f"Param {name} requires grad, " "please set torch.no_grad() to reduce memory usage and improve computation speed during " "inference phase."}) op_names = torch.jit.export_opnames(script_module) for op_name in op_names: if "dropout" in op_name: lint_list.append({"name": LintCode.DROPOUT.name, "message": f"Operator {op_name} exists, remember to call eval() before " "saving the module.and call torch.utils.mobile_optimizer.optimize_for_mobile to drop dropout " "operator."}) if "batch_norm" in op_name: lint_list.append({"name": LintCode.BATCHNORM.name, "message": f"Operator {op_name} exists, remember to call eval() before " "saving the module and call torch.utils.mobile_optimizer.optimize_for_mobile to drop batch_norm " "operator."}) return lint_list def _get_bundled_inputs_preserved_attributes(script_module: torch.jit.ScriptModule, preserved_methods: List[str]) -> List[str]: bundled_inputs_attributes = [] # Has bundled inputs for forward if hasattr(script_module, 'get_all_bundled_inputs'): bundled_inputs_attributes.append('get_all_bundled_inputs') bundled_inputs_attributes.append('get_num_bundled_inputs') # Bundled inputs in module after the change that introduced bundled inputs for multiple functions if hasattr(script_module, 'get_bundled_inputs_functions_and_info'): bundled_inputs_attributes.append('get_bundled_inputs_functions_and_info') all_info = script_module.get_bundled_inputs_functions_and_info() for function_name in all_info: if function_name not in preserved_methods: bundled_inputs_attributes.append(function_name) bundled_inputs_attributes.append("get_all_bundled_inputs_for_" + function_name) bundled_inputs_attributes.append("_bundled_inputs_deflated_" + function_name) return bundled_inputs_attributes

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