136 lines
6.3 KiB
Python
136 lines
6.3 KiB
Python
"""This module contains utility method for mobile model optimization and lint."""
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import torch
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from enum import Enum
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from torch._C import _MobileOptimizerType as MobileOptimizerType
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from typing import Optional, Set, List, AnyStr
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class LintCode(Enum):
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BUNDLED_INPUT = 1
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REQUIRES_GRAD = 2
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DROPOUT = 3
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BATCHNORM = 4
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def optimize_for_mobile(
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script_module: torch.jit.ScriptModule,
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optimization_blocklist: Optional[Set[MobileOptimizerType]] = None,
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preserved_methods: Optional[List[AnyStr]] = None,
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backend: str = 'CPU') -> torch.jit.RecursiveScriptModule:
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"""
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Optimize a torch script module for mobile deployment.
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Args:
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script_module: An instance of torch script module with type of ScriptModule.
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optimization_blocklist: A set with type of MobileOptimizerType. When set is not passed,
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optimization method will run all the optimizer pass; otherwise, optimizer
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method will run the optimization pass that is not included inside optimization_blocklist.
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preserved_methods: A list of methods that needed to be preserved when freeze_module pass is invoked
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backend: Device type to use for running the result model ('CPU'(default), 'Vulkan' or 'Metal').
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Returns:
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A new optimized torch script module
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"""
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if not isinstance(script_module, torch.jit.ScriptModule):
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raise TypeError(
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f'Got {type(script_module)}, but ScriptModule is expected.')
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if optimization_blocklist is None:
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optimization_blocklist = set()
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if preserved_methods is None:
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preserved_methods = []
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# Convert potential byte arrays into strings (if there is any) to pass type checking
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# Here we use a new name as assigning it back to preserved_methods will invoke
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# mypy errors (i.e. List[AnyStr] = List[str])
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preserved_methods_str: List[str] = [str(method) for method in preserved_methods]
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bundled_inputs_attributes = _get_bundled_inputs_preserved_attributes(script_module, preserved_methods_str)
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if all(hasattr(script_module, method) for method in bundled_inputs_attributes):
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preserved_methods_str = list(set(preserved_methods_str + bundled_inputs_attributes))
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non_exist_methods = []
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for method in preserved_methods_str:
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if not hasattr(script_module, method):
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non_exist_methods.append(method)
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if non_exist_methods:
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raise AttributeError(
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f"The following methods to preserve do not exist in script_module: {', '.join(non_exist_methods)}")
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backend = backend.lower()
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if backend == 'cpu':
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optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile(
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script_module._c,
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optimization_blocklist,
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preserved_methods_str)
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elif backend == 'vulkan':
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optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(
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script_module._c,
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optimization_blocklist,
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preserved_methods_str)
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elif backend == 'metal':
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optimized_cpp_module = torch._C._jit_pass_metal_optimize_for_mobile(script_module._c, preserved_methods_str)
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else:
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raise TypeError("Unknown backend, must be one of 'CPU', 'Vulkan' or 'Metal'")
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return torch.jit._recursive.wrap_cpp_module(optimized_cpp_module)
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def generate_mobile_module_lints(script_module: torch.jit.ScriptModule):
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"""
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Generate a list of lints for a given torch script module.
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Args:
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script_module: An instance of torch script module with type of ScriptModule.
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Returns:
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lint_map: A list of dictionary that contains modules lints
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"""
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if not isinstance(script_module, torch.jit.ScriptModule):
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raise TypeError(
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f'Got {type(script_module)}, but ScriptModule is expected.')
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lint_list = []
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if not hasattr(script_module, "_generate_bundled_inputs_for_forward"):
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lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input for forward, please add bundled inputs "
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"before saving the module using torch.utils.bundled_inputs.augment_model_with_bundled_inputs."})
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for name, param in script_module.named_parameters():
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if param.requires_grad:
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lint_list.append({"name": LintCode.REQUIRES_GRAD.name, "message": f"Param {name} requires grad, "
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"please set torch.no_grad() to reduce memory usage and improve computation speed during "
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"inference phase."})
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op_names = torch.jit.export_opnames(script_module)
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for op_name in op_names:
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if "dropout" in op_name:
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lint_list.append({"name": LintCode.DROPOUT.name, "message": "Operator {} exists, remember to call eval() before "
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"saving the module.and call torch.utils.mobile_optimizer.optimize_for_mobile to drop dropout "
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"operator.".format(op_name)})
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if "batch_norm" in op_name:
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lint_list.append({"name": LintCode.BATCHNORM.name, "message": "Operator {} exists, remember to call eval() before "
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"saving the module and call torch.utils.mobile_optimizer.optimize_for_mobile to drop batch_norm "
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"operator.".format(op_name)})
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return lint_list
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def _get_bundled_inputs_preserved_attributes(script_module: torch.jit.ScriptModule, preserved_methods: List[str]) -> List[str]:
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bundled_inputs_attributes = []
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# Has bundled inputs for forward
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if hasattr(script_module, 'get_all_bundled_inputs'):
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bundled_inputs_attributes.append('get_all_bundled_inputs')
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bundled_inputs_attributes.append('get_num_bundled_inputs')
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# Bundled inputs in module after the change that introduced bundled inputs for multiple functions
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if hasattr(script_module, 'get_bundled_inputs_functions_and_info'):
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bundled_inputs_attributes.append('get_bundled_inputs_functions_and_info')
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all_info = script_module.get_bundled_inputs_functions_and_info()
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for function_name in all_info:
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if function_name not in preserved_methods:
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bundled_inputs_attributes.append(function_name)
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bundled_inputs_attributes.append("get_all_bundled_inputs_for_" + function_name)
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bundled_inputs_attributes.append("_bundled_inputs_deflated_" + function_name)
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return bundled_inputs_attributes
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