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Source code for torch.utils.mobile_optimizer

"""
This module contains utility method for mobile model optimization and lint.
"""

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

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

[docs]def optimize_for_mobile( script_module, optimization_blocklist: Set[MobileOptimizerType] = None, preserved_methods: List[AnyStr] = None, backend: str = 'CPU'): """ 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. perserved_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( 'Got {}, but ScriptModule is expected.'.format(type(script_module))) if optimization_blocklist is None: optimization_blocklist = set() if preserved_methods is None: preserved_methods = [] backend = backend.lower() if backend == 'cpu': optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile(script_module._c, optimization_blocklist, preserved_methods) elif backend == 'vulkan': optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(script_module._c, preserved_methods) elif backend == 'metal': optimized_cpp_module = torch._C._jit_pass_metal_optimize_for_mobile(script_module._c, preserved_methods) 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): """ 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( 'Got {}, but ScriptModule is expected.'.format(type(script_module))) lint_list = [] if not hasattr(script_module, "_generate_bundled_inputs"): lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input, 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": "Param {} requires grad, " "please set torch.no_grad() to reduce memory usage and improve computation speed during " "inference phase.".format(name)}) 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": "Operator {} exists, remember to call eval() before " "saving the module.and call torch.utils.mobile_optimizer.optimize_for_mobile to drop dropout " "operator.".format(op_name)}) if "batch_norm" in op_name: lint_list.append({"name": LintCode.BATCHNORM.name, "message": "Operator {} exists, remember to call eval() before " "saving the module and call torch.utils.mobile_optimizer.optimize_for_mobile to drop batch_norm " "operator.".format(op_name)}) return lint_list

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