How to use deepcopy method in autotest

Best Python code snippet using autotest_python

WaveNetWrapper.py

Source:WaveNetWrapper.py

...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:...

tcnlib.py

Source:tcnlib.py

...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)...

utils.py

Source:utils.py

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]...

copy.py

Source:copy.py

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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