How to use __deepcopy__ method in tavern

Best Python code snippet using tavern

WaveNetWrapper.py

Source:WaveNetWrapper.py Github

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...119 def init_hidden(self, batch_size=1):120 return None121 def parameters(self):122 return self.model.parameters()123# def __deepcopy__(self, memo):124# """125# Fix the deepcopy operation with WeigthNorm layers by removing all126# during copying. The code was posted as a solution at127# https://github.com/pytorch/pytorch/issues/28594128# """129# # save and delete all weightnorm weights on self130# weights = {}131# for hook in self._forward_pre_hooks.values():132# if isinstance(hook, WeightNorm):133# weights[hook.name] = getattr(self, hook.name)134# delattr(self, hook.name)135# # remove this deepcopy method, restoring the object's original one if necessary136# __deepcopy__ = self.__deepcopy__137# if orig_deepcopy:138# self.__deepcopy__ = orig_deepcopy139# else:140# del self.__deepcopy__141# # actually do the copy142# result = copy.deepcopy(self)143# # restore weights and method on self144# for name, value in weights.items():145# setattr(self, name, value)146# self.__deepcopy__ = __deepcopy__147# return result148for layer in [Conv1d, ConvTranspose2d]:149 orig_deepcopy = getattr(layer, '__deepcopy__', None)150 def __deepcopy__(self, memo):151 """152 Fix the deepcopy operation with WeigthNorm layers by removing all153 during copying. The code was posted as a solution at154 https://github.com/pytorch/pytorch/issues/28594155 """156 # save and delete all weightnorm weights on self157 weights = {}158 for hook in self._forward_pre_hooks.values():159 if isinstance(hook, WeightNorm):160 weights[hook.name] = getattr(self, hook.name)161 delattr(self, hook.name)162 # remove this deepcopy method, restoring the object's original one if necessary163 __deepcopy__ = self.__deepcopy__164 if orig_deepcopy:...

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tcnlib.py

Source:tcnlib.py Github

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...3from torch.nn.utils import weight_norm4from torch.nn.utils.weight_norm import WeightNorm5import copy6orig_deepcopy = getattr(nn.Conv1d, '__deepcopy__', None)7def __deepcopy__(self, memo):8 # save and delete all weightnorm weights on self9 weights = {}10 for hook in self._forward_pre_hooks.values():11 if isinstance(hook, WeightNorm):12 weights[hook.name] = getattr(self, hook.name)13 delattr(self, hook.name)14 # remove this deepcopy method, restoring the object's original one if necessary15 __deepcopy__ = self.__deepcopy__16 if orig_deepcopy:17 self.__deepcopy__ = orig_deepcopy18 else:19 del self.__deepcopy__20 # actually do the copy21 result = copy.deepcopy(self)...

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utils.py

Source:utils.py Github

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1#!/usr/bin/env python32# Copyright (c) Meta Platforms, Inc. and affiliates.3# All rights reserved.4#5# This source code is licensed under the BSD-style license found in the6# LICENSE file in the root directory of this source tree.7from collections import OrderedDict8from typing import Optional, List, Set, Union9import torch10from torchrec.distributed.types import ShardedModule11def append_prefix(prefix: str, name: str) -> str:12 """13 Appends provided prefix to provided name.14 """15 if prefix != "" and name != "":16 return prefix + "." + name17 else:18 return prefix + name19def filter_state_dict(20 state_dict: "OrderedDict[str, torch.Tensor]", name: str21) -> "OrderedDict[str, torch.Tensor]":22 """23 Filters state dict for keys that start with provided name.24 Strips provided name from beginning of key in the resulting state dict.25 Args:26 state_dict (OrderedDict[str, torch.Tensor]): input state dict to filter.27 name (str): name to filter from state dict keys.28 Returns:29 OrderedDict[str, torch.Tensor]: filtered state dict.30 """31 filtered_state_dict = OrderedDict()32 for key, value in state_dict.items():33 if key.startswith(name):34 # + 1 to length is to remove the '.' after the key35 filtered_state_dict[key[len(name) + 1 :]] = value36 return filtered_state_dict37def _get_unsharded_module_names_helper(38 model: torch.nn.Module,39 path: str,40 unsharded_module_names: Set[str],41) -> bool:42 sharded_children = set()43 for name, child in model.named_children():44 curr_path = path + name45 if isinstance(child, ShardedModule):46 sharded_children.add(name)47 else:48 child_sharded = _get_unsharded_module_names_helper(49 child,50 curr_path + ".",51 unsharded_module_names,52 )53 if child_sharded:54 sharded_children.add(name)55 if len(sharded_children) > 0:56 for name, _ in model.named_children():57 if name not in sharded_children:58 unsharded_module_names.add(path + name)59 return len(sharded_children) > 060def get_unsharded_module_names(model: torch.nn.Module) -> List[str]:61 """62 Retrieves names of top level modules that do not contain any sharded sub-modules.63 Args:64 model (torch.nn.Module): model to retrieve unsharded module names from.65 Returns:66 List[str]: list of names of modules that don't have sharded sub-modules.67 """68 unsharded_module_names: Set[str] = set()69 _get_unsharded_module_names_helper(70 model,71 "",72 unsharded_module_names,73 )74 return list(unsharded_module_names)75class sharded_model_copy:76 """77 Allows copying of DistributedModelParallel module to a target device.78 Example::79 # Copying model to CPU.80 m = DistributedModelParallel(m)81 with sharded_model_copy("cpu"):82 m_cpu = copy.deepcopy(m)83 """84 def __init__(self, device: Optional[Union[str, int, torch.device]]) -> None:85 self.device = device86 def __enter__(self) -> None:87 # pyre-ignore [16]88 self.t_copy_save_ = torch.Tensor.__deepcopy__89 # pyre-ignore [16]90 self.p_copy_save_ = torch.nn.Parameter.__deepcopy__91 device = self.device92 # pyre-ignore [2, 3, 53]93 def _tensor_copy(tensor, memo):94 if tensor.device != device:95 return tensor.detach().to(device)96 else:97 return tensor.detach().clone()98 # pyre-ignore [2, 3]99 def _no_copy(obj, memo):100 return obj101 _copy_or_not = _tensor_copy if self.device is not None else _no_copy102 # pyre-ignore [2, 3, 53]103 def _param_copy(param, memo):104 return torch.nn.Parameter(105 _copy_or_not(param, memo), requires_grad=param.requires_grad106 )107 # pyre-ignore [16]108 torch.Tensor.__deepcopy__ = _copy_or_not109 torch.nn.Parameter.__deepcopy__ = _param_copy110 torch._C._distributed_c10d.ProcessGroupNCCL.__deepcopy__ = _no_copy111 torch._C._distributed_c10d.ProcessGroupGloo.__deepcopy__ = _no_copy112 torch._C._distributed_c10d.Work.__deepcopy__ = _no_copy113 # pyre-ignore [16]114 torch.cuda.streams.Stream.__deepcopy__ = _no_copy115 # pyre-ignore [2]116 def __exit__(self, exc_type, exc_val, exc_tb) -> None:117 # pyre-ignore [16]118 torch.Tensor.__deepcopy__ = self.t_copy_save_119 # pyre-ignore [16]120 torch.nn.Parameter.__deepcopy__ = self.p_copy_save_121 torch._C._distributed_c10d.ProcessGroupNCCL.__deepcopy__ = None122 torch._C._distributed_c10d.ProcessGroupGloo.__deepcopy__ = None123 torch._C._distributed_c10d.Work.__deepcopy__ = None124 # pyre-ignore [16]...

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copy.py

Source:copy.py Github

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1from copy import deepcopy2from .deco import accepts3def empty_copy(obj):4 """Create empty copy of object.5 Parameters6 ----------7 obj : some python object8 Returns9 -------10 obj :11 Empty copy of obj.12 """13 class Empty(obj.__class__):14 def __init__(self):15 pass16 newcopy = Empty()17 newcopy.__class__ = obj.__class__18 return newcopy19@accepts("s", (list, tuple))20def deepcopy_with_sharing(obj, shared_attributes, memo=None):21 """Deepcopy an object, except for a given list of attributes.22 Those atttributes are shared between the original object and its copy.From:23 https://stackoverflow.com/q/150071824 Parameters25 ----------26 obj: some object27 shared_attributes : list28 A list of strings identifying the attributes that sould be shared29 between the original and its copy.30 memo : dict31 The dictionary passed into __deepcopy__. Ignore this argument if32 not calling from within __deepcopy__.33 """34 shared_attributes = {k: getattr(obj, k) for k in shared_attributes}35 if hasattr(obj, '__deepcopy__'):36 # Do hack to prevent infinite recursion in call to deepcopy37 deepcopy_method = obj.__deepcopy__38 obj.__deepcopy__ = None39 for attr in shared_attributes:40 try:41 del obj.__dict__[attr]42 except KeyError:43 pass44 clone = deepcopy(obj)45 for attr, val in shared_attributes.items():46 setattr(obj, attr, val)47 setattr(clone, attr, val)48 if hasattr(obj, '__deepcopy__'):49 # Undo hack50 obj.__deepcopy__ = deepcopy_method51 del clone.__deepcopy__52 return clone53@accepts("s", list)54def create_clone(obj, keep_attributes, sharing=False):55 """Create clone of object.56 Attributes in keep_attributes are deepcopied, all other discarded.57 """58 del_attributes = [item for item in obj.__dict__.keys() if item not in59 keep_attributes]60 clone = deepcopy_with_sharing(obj, shared_attributes=del_attributes,61 memo=None)62 if sharing is False:63 for attr in del_attributes:64 if attr in obj.__dict__.keys():65 clone.__dict__[attr] = None...

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