PhnddlmZmZmZmZmZddlmZddlm Z ddl m Z GddejZ y))ListUnionMappingDictAnyN)Tensor) ShardedTensorczeZdZdeeeeefffdZd de fdZ ddZ de ee ffdZd eee ffd Zd e fd Zy)ShardedOptimizer named_paramscrg}|jD]T}t|tr1|jD]}|j |j D|j |V||_||g|i||_|jj|_|jj|_ y)a ShardedOptimizer collects all tensors and local shard tensors of ShardedTensor, then use these tensors as ``params`` for optimizers Args: named_params (Dict[str, Union[Tensor, ShardedTensor]]) : a Dict of parameters, where key is the parameter key, value is either Tensor or ShardedTensor parameter. optimizer_class (torch.optim.Optimizer): the Optimizer to use locally, i.e. torch.optim.SGD, torch.optim.Adagrad, etc. *optimizer_args: the arguments to initialize the optimizer. **optimizer_kwargs: the key-word arguments to initialize the optimizer. N) values isinstancer local_shardsappendtensorr _optim param_groupsstate)selfr optimizer_classoptimizer_argsoptimizer_kwargstensorsvalue local_shards uC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\torch/distributed/_shard/sharded_optim/api.py__init__zShardedOptimizer.__init__ s*!#!((*E%/#(#5#5#7KNN;#5#56$8u% +)%gSSBRS  KK44[[&& set_to_nonec:|jj|y)aResets the gradients of all optimized :class:`torch.Tensor` s. Args: set_to_none (bool): instead of setting to zero, set the grads to None. This will in general have lower memory footprint, and can modestly improve performance. However, it changes certain behaviors. For example: 1. When the user tries to access a gradient and perform manual ops on it, a None attribute or a Tensor full of 0s will behave differently. 2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s are guaranteed to be None for params that did not receive a gradient. 3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None (in one case it does the step with a gradient of 0 and in the other it skips the step altogether). N)r zero_grad)rr s rr"zShardedOptimizer.zero_grad+s k*rNc:|jj|y)afPerforms a single optimization step (parameter update). Args: closure (Callable): A closure that reevaluates the model and returns the loss. Optional for most optimizers. .. note:: Unless otherwise specified, this function should not modify the ``.grad`` field of the parameters. N)rstep)rclosures rr$zShardedOptimizer.step<s !rreturnctd)z Returned state and param_groups will contain parameter keys instead of parameter indices like torch.optim.Optimizer. This allows for advanced functionality like optimizer re-sharding to be implemented. z0ShardedOptimizer state_dict not implemented yet!NotImplementedError)rs r state_dictzShardedOptimizer.state_dictIs""TUUrr*ctd)zLoads the ShardedOptimizer state. Args: state_dict (dict): ShardedOptimizer state. Should be an object returned from a call to :meth:`state_dict`. z5ShardedOptimizer load_state_dict not implemented yet!r()rr*s rload_state_dictz ShardedOptimizer.load_state_dictSs""YZZr param_groupctd)zAdd a new param group z5ShardedOptimizer add_param_group not implemented yet!r()rr-s radd_param_groupz ShardedOptimizer.add_param_group]s""YZZr)T)N)__name__ __module__ __qualname__rstrrrr rboolr"r$rrr*r,r/rrr r sp 'c5)>#??@ 'D+T+" "VDcNV['#s(*;[[3[rr )typingrrrrr torch.optimoptimtorchr'torch.distributed._shard.sharded_tensorr Optimizerr r5rrr<s(22AY[uY[r