# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import warnings from collections import defaultdict from collections.abc import Callable from typing import TYPE_CHECKING import paddle from paddle import pir from paddle.base.libpaddle import DataType from paddle.distributed.flex_checkpoint.dcp.sharded_weight import ( ShardedStateDict, ShardedWeight, create_sharded_weight_with_new_local, ) from paddle.pir import Value from .. import _C_ops from ..base import core, framework from ..base.dygraph import base as imperative_base from ..base.framework import ( Parameter, Variable, in_dynamic_or_pir_mode, in_pir_mode, ) from ..nn.clip import GradientClipBase from .lr import LRScheduler from .optimizer import Optimizer if TYPE_CHECKING: from collections.abc import Sequence from paddle import Tensor from .adam import _AdamParameterConfig __all__ = [] class AdamW(Optimizer): r""" The AdamW optimizer is implemented based on the AdamW Optimization in paper `DECOUPLED WEIGHT DECAY REGULARIZATION `_. it can resolves the problem of L2 regularization failure in the Adam optimizer. .. math:: \begin{aligned} &\hspace{5mm} t = t + 1 \\ &\hspace{5mm} moment\_1\_out = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad \\ &\hspace{5mm} moment\_2\_out = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad \\ &\hspace{5mm} learning\_rate = learning\_rate * \frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t} \\ &\hspace{5mm}\textbf{if} \: \textit{amsgrad}: \\ &\hspace{15mm} moment\_2\_max\_out = max(moment\_2\_out, moment\_2\_max) \\ &\hspace{15mm} param\_out = param - learning\_rate * (\frac{moment\_1\_out}{\sqrt{moment\_2\_max\_out} + \epsilon} + \lambda * param) \\ &\hspace{5mm}\textbf{else}: \: \\ &\hspace{15mm} param\_out = param - learning\_rate * (\frac{moment\_1\_out}{\sqrt{moment\_2\_out} + \epsilon} + \lambda * param) \\ \end{aligned} Args: learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``. It can be a float value or a LRScheduler. The default value is 0.001. beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates. It should be a float number or a 0-D Tensor with shape [] and data type as float32. The default value is 0.9. beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates. It should be a float number or a 0-D Tensor with shape [] and data type as float32. The default value is 0.999. epsilon (float|Tensor, optional): A small float value for numerical stability. The default value is 1e-08. parameters (list|tuple|None, optional): List/Tuple of ``Tensor`` names to update to minimize ``loss``. This parameter is required in dygraph mode. And you can specify different options for different parameter groups such as the learning rate, weight decay, etc, then the parameters are list of dict. Note that the learning_rate in parameter groups represents the scale of base learning_rate. The default value is None in static graph mode, at this time all parameters will be updated. weight_decay (int|float|Tensor, optional): The weight decay coefficient, it can be int, float or Tensor. The default value is 0.01. lr_ratio (Callable|None, optional): If it is not None, the learning rate will be updated with layer-wise learning rate ratio. Otherwise, the learning rate is the original. Default: None. apply_decay_param_fun (Callable|None, optional): If it is not None, only tensors that makes apply_decay_param_fun(Tensor.name)==True will be updated with weight decay. It only works when we want to specify tensors. Default: None. grad_clip (GradientClipBase|None, optional): Gradient clipping strategy, it's an instance of some derived class of ``GradientClipBase`` . There are three clipping strategies ( :ref:`api_paddle_nn_ClipGradByGlobalNorm` , :ref:`api_paddle_nn_ClipGradByNorm` , :ref:`api_paddle_nn_ClipGradByValue` ). Default None, meaning there is no gradient clipping. lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators. The accumulators are updated at every step. Every element of the two moving-average is updated in both dense mode and sparse mode. If the size of parameter is very large, then the update may be very slow. The lazy mode only update the element that has gradient in current mini-batch, so it will be much more faster. But this mode has different semantics with the original Adam algorithm and may lead to different result. The default value is False. multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false. amsgrad (bool, optional): Whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond `_. Default is false. name (str|None, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. The default value is None. Notes: **Currently, AdamW doesn't support sparse parameter optimization.** Examples: .. code-block:: python >>> import paddle >>> linear = paddle.nn.Linear(10, 10) >>> inp = paddle.rand([10,10], dtype="float32") >>> out = linear(inp) >>> loss = paddle.mean(out) >>> beta1 = paddle.to_tensor([0.9], dtype="float32") >>> beta2 = paddle.to_tensor([0.99], dtype="float32") >>> opt = paddle.optimizer.AdamW( ... learning_rate=0.1, ... parameters=linear.parameters(), ... beta1=beta1, ... beta2=beta2, ... weight_decay=0.01 ... ) >>> loss.backward() >>> opt.step() >>> opt.clear_grad() >>> # Note that the learning_rate of linear_2 is 0.01. >>> linear_1 = paddle.nn.Linear(10, 10) >>> linear_2 = paddle.nn.Linear(10, 10) >>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1) >>> out = linear_1(inp) >>> out = linear_2(out) >>> loss = paddle.mean(out) >>> opt = paddle.optimizer.AdamW( ... learning_rate=0.1, ... parameters=[{ # type: ignore ... 'params': linear_1.parameters() ... }, { ... 'params': linear_2.parameters(), ... 'weight_decay': 0.001, ... 'learning_rate': 0.1, ... 'beta1': 0.8 ... }], ... weight_decay=0.01, ... beta1=0.9 ... ) >>> loss.backward() >>> opt.step() >>> opt.clear_grad() """ helper: None type: str _moment1_acc_str = "moment1" _moment2_acc_str = "moment2" _moment2_acc_max_str = "moment2_max" _beta1_pow_acc_str = "beta1_pow_acc" _beta2_pow_acc_str = "beta2_pow_acc" def __init__( self, learning_rate: float | LRScheduler = 0.001, beta1: float | Tensor = 0.9, beta2: float | Tensor = 0.999, epsilon: float | Tensor = 1e-8, parameters: ( Sequence[Tensor] | Sequence[_AdamParameterConfig] | None ) = None, weight_decay: float | Tensor = 0.01, use_lowprecision_moment: bool = False, lr_ratio: Callable[[Tensor], float] | None = None, apply_decay_param_fun: Callable[[str], bool] | None = None, grad_clip: GradientClipBase | None = None, lazy_mode: bool = False, multi_precision: bool = False, amsgrad: bool = False, name: str | None = None, ) -> None: assert learning_rate is not None assert beta1 is not None assert beta2 is not None assert epsilon is not None if not isinstance(beta1, Value) and not 0 <= beta1 < 1: raise ValueError("Invalid value of beta1, expect beta1 in [0,1).") if not isinstance(beta2, Value) and not 0 <= beta2 < 1: raise ValueError("Invalid value of beta2, expect beta2 in [0,1).") if not isinstance(epsilon, Value) and not 0 <= epsilon: raise ValueError("Invalid value of epsilon, expect epsilon >= 0.") if not isinstance(weight_decay, (int, float)) and not isinstance( weight_decay, (framework.Variable, Value) ): raise TypeError("weight_decay should be int, float or Tensor.") if lr_ratio is not None: assert isinstance(lr_ratio, Callable) if ( not core.is_compiled_with_cuda() and not core.is_compiled_with_xpu() and paddle.device.get_device().split(":")[0] not in paddle.device.get_all_custom_device_type() ): raise NotImplementedError("'lr_ratio' is unimplemented in CPU.") if parameters is not None: # paddle.Tensor is also iterable, so here we don't check whether # the input is iterable, if the input is paddle.Tensor, the # list(paddle.Tensor) will be a error value if isinstance(parameters, paddle.Tensor): raise TypeError( "`parameters` argument given to the optimizer should be " f"an iterable of paddle Tensors, but got argument type is `{type(parameters)}`." ) if isinstance(parameters, dict): raise TypeError( "`parameters` argument should not get dict type, " "if parameter groups is needed, please set `parameters`" " as list of dict" ) self._parameter_list = list(parameters) else: self._parameter_list = None self._name = name if framework.in_dygraph_mode(): if self._parameter_list is None: raise AttributeError( "parameters argument given to the Optimizer should not be None in dygraph mode." ) if not isinstance(learning_rate, (float, LRScheduler)): raise TypeError( f"learning rate should be float or LRScheduler, got {type(learning_rate)} here" ) if grad_clip is not None: if not isinstance(grad_clip, GradientClipBase): raise TypeError( "'grad_clip' should be an instance of GradientClipBase's derived class" ) self._dtype = None # Infer the dtype form parameter if self._parameter_list: if isinstance(self._parameter_list[0], dict): for param_group in self._parameter_list: assert 'params' in param_group, ( 'params should be set in parameters if parameter groups are optimized in different options' ) self._dtype = self._parameter_list[0]['params'][0].dtype else: self._dtype = self._parameter_list[0].dtype # each program should have a independent learning rate # program -> tensor(learning_rate) self._learning_rate_map = {} # Dictionary of accumulators. Some optimizer subclasses need to # allocate and manage extra tensors associated with the parameters # to train. These tensors are called accumulators. # {accum_name : { parameter_name : accumulator_for_parameter, ...}, ...} self._accumulators = defaultdict(lambda: {}) self.helper = None self._opti_name_list = [] self._accumulators_holder = {} self._param_device_map = {} self.clear_gradients = self.clear_grad self.type = "adamw" self._learning_rate = learning_rate self._params_name = set() self._apply_decay_param_fun = apply_decay_param_fun self._weight_decay = float(weight_decay) self._use_lowprecision_moment = use_lowprecision_moment self._grad_clip = grad_clip self._lr_ratio = lr_ratio self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon self._lazy_mode = lazy_mode self._multi_precision = multi_precision self._master_weights = {} # whether to use AMSGrad self._amsgrad = amsgrad self._default_dict = { 'weight_decay': float(weight_decay), 'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon, 'lazy_mode': lazy_mode, 'grad_clip': grad_clip, } self._param_groups = [] if self._parameter_list and isinstance(self._parameter_list[0], dict): for param_group in self._parameter_list: self._add_param_group(param_group.copy()) else: self._param_groups = self._parameter_list self._use_multi_tensor = None self.regularization = None self._auxiliary_vars = {} self._already_create_accumulator = set() self._create_master_grad_states() self._use_fusion_storage = False self._need_refuse = False self.fusion_storage = None self._fuse_buffer_version = 0 self.merged_model_params = None def _set_auxiliary_var(self, key, val): self._auxiliary_vars[key] = val def _get_auxiliary_var(self, key): if key in self._auxiliary_vars: return self._auxiliary_vars[key] else: return None def _add_param_group(self, param_group): """ Add a param group to parameter_list. Args: param_group (dict): The group of Tensors to be optimized with different optimization options. """ params = param_group['params'] if isinstance(params, (Parameter, pir.core.ParameterMeta)): param_group['params'] = [params] elif isinstance(params, set): raise TypeError( "optimizer parameters should be in ordered collections," "but received set, please use list instead." ) else: param_group['params'] = list(params) # Update optimization options for each groups for k, v in self._default_dict.items(): param_group.setdefault(k, v) param_set = set() for group in self._param_groups: param_set.update(set(group['params'])) if not param_set.isdisjoint(set(param_group['params'])): raise ValueError( "some parameters appear in more than one parameter group" ) for param in param_group['params']: param.optimize_attr['learning_rate'] = param_group.get( 'learning_rate', 1.0 ) self._param_groups.append(param_group) def _add_moments_pows(self, p): acc_dtype = p.dtype if ( self._is_dtype_fp16_or_bf16(acc_dtype) and not self._use_lowprecision_moment ): acc_dtype = ( DataType.FLOAT32 if in_pir_mode() else core.VarDesc.VarType.FP32 ) if core.is_compiled_with_xpu(): import os xpu_adamw_moment_dtype = os.getenv( "xpu_adamw_moment_dtype", default="fp32" ) if xpu_adamw_moment_dtype == "fp16": self._add_accumulator( self._moment1_acc_str, p, dtype=core.VarDesc.VarType.FP16 ) self._add_accumulator( self._moment2_acc_str, p, dtype=core.VarDesc.VarType.FP16 ) if self._amsgrad: self._add_accumulator( self._moment2_acc_max_str, p, dtype=core.VarDesc.VarType.FP16, ) else: self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype) self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype) if self._amsgrad: self._add_accumulator( self._moment2_acc_max_str, p, dtype=acc_dtype ) else: self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype) self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype) if self._amsgrad: self._add_accumulator( self._moment2_acc_max_str, p, dtype=acc_dtype ) self._add_accumulator( name=self._beta1_pow_acc_str, param=p, dtype=acc_dtype, fill_value=( 0.9 if isinstance(self._beta1, (Variable, Value)) else self._beta1 ), shape=[1], type=core.VarDesc.VarType.DENSE_TENSOR, device='cpu', ) self._add_accumulator( name=self._beta2_pow_acc_str, param=p, dtype=acc_dtype, fill_value=( 0.999 if isinstance(self._beta2, (Variable, Value)) else self._beta2 ), shape=[1], type=core.VarDesc.VarType.DENSE_TENSOR, device='cpu', ) def _create_accumulators(self, block, parameters): assert isinstance(block, (framework.Block, pir.Block)) if isinstance(parameters, dict): parameters = self._update_param_group(parameters) # Create accumulator tensors for first and second moments for p in parameters: if p.name in self._already_create_accumulator: continue if self._multi_precision and self._is_dtype_fp16_or_bf16(p.dtype): master_p = self._create_master_weight(p) self._add_moments_pows(master_p) self._already_create_accumulator.add(p.name) continue if ( self._is_dtype_fp16_or_bf16(p.dtype) and not self._multi_precision ): warnings.warn( "Accumulating with FP16 or BF16 in optimizer can lead to poor accuracy or slow convergence." "Consider using multi_precision=True option of the Adam optimizer." ) self._add_moments_pows(p) self._already_create_accumulator.add(p.name) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, (framework.Block, pir.Block)) if isinstance(param_and_grad, dict): param_and_grad = self._update_param_group(param_and_grad) param, grad = param_and_grad # Whether we should do weight decay for the parameter. with_decay = True if ( self._apply_decay_param_fun is not None and not self._apply_decay_param_fun(param.name) ): with_decay = False moment1 = self._get_accumulator_master( self._moment1_acc_str, param_and_grad[0] ) moment2 = self._get_accumulator_master( self._moment2_acc_str, param_and_grad[0] ) moment2_max = ( self._get_accumulator_master( self._moment2_acc_max_str, param_and_grad[0] ) if self._amsgrad else None ) beta1_pow_acc = self._get_accumulator_master( self._beta1_pow_acc_str, param_and_grad[0] ) beta2_pow_acc = self._get_accumulator_master( self._beta2_pow_acc_str, param_and_grad[0] ) find_master = self._multi_precision and self._is_dtype_fp16_or_bf16( param_and_grad[0].dtype ) master_weight = ( self._master_weights[param_and_grad[0].name] if find_master else None ) lr = self._create_param_lr(param_and_grad) # create the adamw optimize op if in_dynamic_or_pir_mode(): lr_ratio_ = ( 1.0 if self._lr_ratio is None else self._lr_ratio(param_and_grad[0]) ) _beta1 = ( self._beta1 if not isinstance(self._beta1, Variable) else self._beta1.item(0) ) _beta2 = ( self._beta2 if not isinstance(self._beta2, Variable) else self._beta2.item(0) ) found_inf = ( self._get_auxiliary_var('found_inf') if in_pir_mode() else None ) _, _, _, _, _, _, _ = _C_ops.adamw_( param_and_grad[0], param_and_grad[1], lr, moment1, moment2, moment2_max, beta1_pow_acc, beta2_pow_acc, master_weight, found_inf, _beta1, _beta2, self._epsilon, lr_ratio_, self._weight_decay, with_decay, self._lazy_mode, 1000, find_master, False, self._amsgrad, ) return None else: inputs = { "Param": [param_and_grad[0]], "Grad": [param_and_grad[1]], "LearningRate": [lr], "Moment1": [moment1], "Moment2": [moment2], "Beta1Pow": [beta1_pow_acc], "Beta2Pow": [beta2_pow_acc], } # Pass found_inf to adamw, to skip update for not only param, but also momentum and beta_pow found_inf = self._get_auxiliary_var('found_inf') if found_inf: inputs['SkipUpdate'] = found_inf outputs = { "ParamOut": [param_and_grad[0]], "Moment1Out": [moment1], "Moment2Out": [moment2], "Beta1PowOut": [beta1_pow_acc], "Beta2PowOut": [beta2_pow_acc], } attrs = { "lazy_mode": self._lazy_mode, "min_row_size_to_use_multithread": 1000, "multi_precision": find_master, "with_decay": with_decay, "coeff": self._weight_decay, "lr_ratio": ( 1.0 if self._lr_ratio is None else self._lr_ratio(param_and_grad[0]) ), "amsgrad": self._amsgrad, } if isinstance(self._beta1, Variable): inputs['Beta1Tensor'] = self._beta1 else: attrs['beta1'] = self._beta1 if isinstance(self._beta2, Variable): inputs['Beta2Tensor'] = self._beta2 else: attrs['beta2'] = self._beta2 if isinstance(self._epsilon, Variable): inputs['EpsilonTensor'] = self._epsilon else: attrs['epsilon'] = self._epsilon if self._amsgrad: inputs["Moment2Max"] = [moment2_max] outputs["Moment2MaxOut"] = [moment2_max] if find_master: inputs["MasterParam"] = master_weight outputs["MasterParamOut"] = master_weight adamw_op = block.append_op( type=self.type, inputs=inputs, outputs=outputs, attrs=attrs, stop_gradient=True, ) return adamw_op def __str__(self): return " ".join(["Weight Decay, params:", ",".join(self._params_name)]) @imperative_base.no_grad @framework.non_static_only def step(self) -> None: """ Execute the optimizer and update parameters once. Returns: None Examples: .. code-block:: python >>> import paddle >>> a = paddle.rand([2,13], dtype="float32") >>> linear = paddle.nn.Linear(13, 5) >>> # This can be any optimizer supported by dygraph. >>> opt = paddle.optimizer.AdamW(learning_rate = 0.01, ... parameters = linear.parameters()) >>> out = linear(a) >>> out.backward() >>> opt.step() >>> opt.clear_grad() """ if paddle.base.dygraph.base.in_to_static_mode(): self._declarative_step() return if not isinstance(self._parameter_list[0], dict): params_grads = [] for param in self._parameter_list: if param.stop_gradient: continue if param._grad_ivar() is not None: grad_var = param._grad_ivar() if framework.in_dygraph_mode(): if ( hasattr(grad_var, "is_selected_rows") and grad_var.is_selected_rows() and self.regularization is not None ): raise RuntimeError( "AdamW don't support weight_decay with sparse parameters, please set it to None." ) else: if ( hasattr(grad_var, "_is_sparse") and grad_var._is_sparse() and self.regularization is not None ): raise RuntimeError( "AdamW don't support weight_decay with sparse parameters, please set it to None." ) params_grads.append((param, grad_var)) optimize_ops = self._apply_optimize( loss=None, startup_program=None, params_grads=params_grads ) else: # optimize parameters in groups for param_group in self._param_groups: params_grads = defaultdict(lambda: []) for param in param_group['params']: if param.stop_gradient: continue if param._grad_ivar() is not None: grad_var = param._grad_ivar() if framework.in_dygraph_mode(): if ( hasattr(grad_var, "is_selected_rows") and grad_var.is_selected_rows() and self.regularization is not None ): raise RuntimeError( "AdamW don't support weight_decay with sparse parameters, please set it to None." ) else: if ( hasattr(grad_var, "_is_sparse") and grad_var._is_sparse() and self.regularization is not None ): raise RuntimeError( "AdamW don't support weight_decay with sparse parameters, please set it to None." ) params_grads['params'].append((param, grad_var)) params_grads.update( {k: v for k, v in param_group.items() if k != 'params'} ) self._apply_optimize( loss=None, startup_program=None, params_grads=params_grads ) def _update_param_group(self, parameters): self._beta1 = parameters.get('beta1', self._default_dict['beta1']) self._beta2 = parameters.get('beta2', self._default_dict['beta2']) self._epsilon = parameters.get('epsilon', self._default_dict['epsilon']) self._lazy_mode = parameters.get( 'lazy_mode', self._default_dict['lazy_mode'] ) self._weight_decay = parameters.get( 'weight_decay', self._default_dict['weight_decay'] ) parameters = parameters.get('params') return parameters def sharded_state_dict( self, model_sharded_state_dict: ShardedStateDict, ) -> ShardedStateDict: """ Convert optimizer state dict to a sharded state dict based on model sharding information. Args: model_sharded_state_dict (dict): Sharded state dict of the model, containing tensor metadata. Returns: dict: A new optimizer state dict where weights are wrapped as ShardedWeight. """ _FP32_MASTER = "fp32_master_0" _MOMENT_NAME = "moment" _optimizer_scalar_name = [ "beta1_pow_acc_0", "beta2_pow_acc_0", ] _optimizer_non_scaler_name = [ "moment1_0", "moment2_0", "velocity_0", ] def _generate_base_static_name(vname): if _FP32_MASTER in vname: return tuple(vname.split("_" + _FP32_MASTER + "_", 1)) for name in _optimizer_scalar_name + _optimizer_non_scaler_name: if vname.endswith(name): return vname[: -(len(name) + 1)], name raise ValueError(f"Cannot split variable name: {vname}.") optimizer_sharded_state_dict = {} optimizer_state_dict = self.state_dict() # Build name mapping and remove non-tensor entries from optimizer state static_to_struct_mapping = {} model_sharded_state_dict = dict( sorted(model_sharded_state_dict.items()) ) for k, v in model_sharded_state_dict.items(): # When shared weights exist, the v.local_tensor.name of shared parameters are identical, but only the first parameter has optimizer states. Therefore, only the key-value pairs of the first occurrence in the shared parameter group need to be retained. if v.local_tensor.name not in static_to_struct_mapping: static_to_struct_mapping[v.local_tensor.name] = k master_weights = optimizer_state_dict.pop("master_weights", None) optimizer_state_dict.pop("LR_Scheduler", None) # Process main optimizer states for key, tensor in optimizer_state_dict.items(): static_name, optim_state_type = _generate_base_static_name(key) struct_name = static_to_struct_mapping[static_name] sharded_weight = model_sharded_state_dict[struct_name] unified_name = f"{struct_name}.{optim_state_type}" # Determine tensor partitioning scheme if _MOMENT_NAME in optim_state_type: optimizer_sharded_state_dict[unified_name] = ( create_sharded_weight_with_new_local( unified_name, tensor, sharded_weight ) ) else: # Non-momentum parameters optimizer_sharded_state_dict[unified_name] = ShardedWeight( key=unified_name, local_tensor=tensor, local_shape=(1,), global_shape=(1,), global_offset=(0,), ) # Process master weights if using mixed precision if master_weights is not None: for key, tensor in master_weights.items(): struct_name = static_to_struct_mapping[key] sharded_weight = model_sharded_state_dict[struct_name] unified_name = f"{struct_name}.w_0" optimizer_sharded_state_dict[unified_name] = ( create_sharded_weight_with_new_local( unified_name, tensor, sharded_weight ) ) return optimizer_sharded_state_dict