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Summary: By default freeze_module pass, invoked from optimize_for_mobile, preserves only forward method. There is an option to specify a list of methods that can be preserved during freeze_module. This PR exposes that to optimize_for_module pass. Pull Request resolved: https://github.com/pytorch/pytorch/pull/40629 Test Plan: python test/test_mobile_optimizer.py Reviewed By: dreiss Differential Revision: D22260972 Pulled By: kimishpatel fbshipit-source-id: 452c653269da8bb865acfb58da2d28c23c66e326
78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
"""
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This module contains utility method for mobile model optimization and lint.
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"""
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import torch
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from enum import Enum
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from torch._C import MobileOptimizerType
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from typing import 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,
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optimization_blacklist: Set[MobileOptimizerType] = None,
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preserved_methods: List[AnyStr] = None):
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"""
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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_blacklist: A set with type of MobileOptimizerType.
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When set is not passed, 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_blacklist.
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Returns:
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script_module: 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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'Got {}, but ScriptModule is expected.'.format(type(script_module)))
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if optimization_blacklist is None:
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optimization_blacklist = set()
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if preserved_methods is None:
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preserved_methods = []
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optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile(script_module._c, optimization_blacklist, preserved_methods)
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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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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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'Got {}, but ScriptModule is expected.'.format(type(script_module)))
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lint_list = []
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if not hasattr(script_module, "_generate_bundled_inputs"):
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lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input, please add bundled inputs before "
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"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": "Param {} 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.".format(name)})
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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.".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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