paddlenlp.ops.optimizer.adamwdl 源代码

# 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 functools import partial

import paddle
from paddle.optimizer import AdamW

__all__ = ["AdamWDL", "layerwise_lr_decay"]


# Layerwise decay
[文档]def layerwise_lr_decay(decay_rate, name_dict, n_layers, param): """ Args: decay_rate (float): The layer-wise decay ratio. name_dict (dict): The keys of name_dict is dynamic name of model while the value of name_dict is static name. Use model.named_parameters() to get name_dict. n_layers (int): Total number of layers in the transformer encoder. """ ratio = 1.0 static_name = name_dict[param.name] if "encoder.layers" in static_name: idx = static_name.find("encoder.layers.") layer = int(static_name[idx:].split(".")[2]) ratio = decay_rate ** (n_layers - layer) elif "embedding" in static_name: ratio = decay_rate ** (n_layers + 1) return ratio
[文档]class AdamWDL(AdamW): r""" The AdamWDL optimizer is implemented based on the AdamW Optimization with dynamic lr setting. Generally it's used for transformer model. We use "layerwise_lr_decay" as default dynamic lr setting method of AdamWDL. “Layer-wise decay” means exponentially decaying the learning rates of individual layers in a top-down manner. For example, suppose the 24-th layer uses a learning rate l, and the Layer-wise decay rate is α, then the learning rate of layer m is lα^(24-m). See more details on: https://arxiv.org/abs/1906.08237. .. math:: & t = t + 1 & moment\_1\_out = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad & moment\_2\_out = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad & learning\_rate = learning\_rate * \frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t} & param\_out = param - learning\_rate * (\frac{moment\_1}{\sqrt{moment\_2} + \epsilon} + \lambda * param) 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, optional): The exponential decay rate for the 1st moment estimates. It should be a float number or a Tensor with shape [1] and data type as float32. The default value is 0.9. beta2 (float, optional): The exponential decay rate for the 2nd moment estimates. It should be a float number or a Tensor with shape [1] and data type as float32. The default value is 0.999. epsilon (float, optional): A small float value for numerical stability. It should be a float number or a Tensor with shape [1] and data type as float32. The default value is 1e-08. parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \ This parameter is required in dygraph mode. \ The default value is None in static mode, at this time all parameters will be updated. weight_decay (float, optional): The weight decay coefficient, it can be float or Tensor. The default value is 0.01. apply_decay_param_fun (function|None, optional): If it is not None, only tensors that makes apply_decay_param_fun(Tensor.name)==True will be updated. It only works when we want to specify tensors. Default: None. grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of some derived class of ``GradientClipBase`` . There are three cliping strategies ( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` , :ref:`api_fluid_clip_GradientClipByValue` ). 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. layerwise_decay (float, optional): The layer-wise decay ratio. Defaults to 1.0. n_layers (int, optional): The total number of encoder layers. Defaults to 12. set_param_lr_fun (function|None, optional): If it's not None, set_param_lr_fun() will set the the parameter learning rate before it executes Adam Operator. Defaults to :ref:`layerwise_lr_decay`. name_dict (dict, optional): The keys of name_dict is dynamic name of model while the value of name_dict is static name. Use model.named_parameters() to get name_dict. name (str, 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. Examples: .. code-block:: python import paddle from paddlenlp.ops.optimizer import AdamWDL def simple_lr_setting(decay_rate, name_dict, n_layers, param): ratio = 1.0 static_name = name_dict[param.name] if "weight" in static_name: ratio = decay_rate**0.5 param.optimize_attr["learning_rate"] *= ratio linear = paddle.nn.Linear(10, 10) name_dict = dict() for n, p in linear.named_parameters(): name_dict[p.name] = n inp = paddle.rand([10,10], dtype="float32") out = linear(inp) loss = paddle.mean(out) adamwdl = AdamWDL( learning_rate=1e-4, parameters=linear.parameters(), set_param_lr_fun=simple_lr_setting, layerwise_decay=0.8, name_dict=name_dict) loss.backward() adamwdl.step() adamwdl.clear_grad() """ def __init__( self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, parameters=None, weight_decay=0.01, apply_decay_param_fun=None, grad_clip=None, lazy_mode=False, multi_precision=False, layerwise_decay=1.0, n_layers=12, set_param_lr_fun=layerwise_lr_decay, name_dict=None, name=None, ): if not isinstance(layerwise_decay, float) and not isinstance(layerwise_decay, paddle.framework.Variable): raise TypeError("coeff should be float or Tensor.") self.layerwise_decay = layerwise_decay self.n_layers = n_layers self.set_param_lr_fun = partial(set_param_lr_fun, layerwise_decay, name_dict, n_layers) coeff = weight_decay self._coeff = coeff self._lr_to_coeff = dict() super(AdamWDL, self).__init__( learning_rate=learning_rate, parameters=parameters, beta1=beta1, beta2=beta2, epsilon=epsilon, grad_clip=grad_clip, name=name, apply_decay_param_fun=apply_decay_param_fun, weight_decay=weight_decay, lazy_mode=lazy_mode, multi_precision=multi_precision, ) 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 _append_optimize_op(self, block, param_and_grad): if self.set_param_lr_fun is None: return super(AdamWDL, self)._append_optimize_op(block, param_and_grad) self._append_decoupled_weight_decay(block, param_and_grad) prev_lr = param_and_grad[0].optimize_attr["learning_rate"] ratio = self.set_param_lr_fun(param_and_grad[0]) param_and_grad[0].optimize_attr["learning_rate"] *= ratio # excute Adam op res = super(AdamWDL, self)._append_optimize_op(block, param_and_grad) param_and_grad[0].optimize_attr["learning_rate"] = prev_lr return res def _append_decoupled_weight_decay(self, block, param_and_grad): """ Add decoupled weight decay op. parameter = parameter - parameter * coeff * lr Args: block: block in which variable is to be created param_and_grad: (parameters, gradients) pairs, the parameters need to decay. Raises: Exception: The type of coeff and parameter is not consistent. """ if isinstance(param_and_grad, dict): param_and_grad = self._update_param_group(param_and_grad) param, grad = param_and_grad if self._apply_decay_param_fun is not None and not self._apply_decay_param_fun(param.name): return if isinstance(self._learning_rate, float): learning_rate = self._learning_rate else: # NOTE. We add this function to the _append_optimize_op(), # for we must make sure _create_param_lr() be called after # optimizer._create_global_learning_rate(). learning_rate = self._create_param_lr(param_and_grad) with block.program._optimized_guard([param, grad]), paddle.static.name_scope("weight decay"): self._params_name.add(param.name) # If it has been calculated, the result will be reused. # NOTE(wangxi): In dygraph mode, apply_gradient will be executed # every step, so need clear _lr_to_coeff every step, # we do this in _create_optimization_pass decay_coeff = self._lr_to_coeff.get(learning_rate, None) if decay_coeff is None: # NOTE(wangxi): for pipeline to set device:all with paddle.static.device_guard(None): decay_coeff = 1.0 - learning_rate * self._coeff self._lr_to_coeff[learning_rate] = decay_coeff find_master = self._multi_precision and param.dtype == paddle.float16 if find_master: master_weight = self._master_weights[param.name] scaled_param = master_weight * decay_coeff paddle.fluid.layers.assign(input=scaled_param, output=master_weight) else: scaled_param = param * decay_coeff paddle.fluid.layers.assign(input=scaled_param, output=param) def _create_optimization_pass(self, parameters_and_grads): optimize_ops = super(AdamWDL, self)._create_optimization_pass(parameters_and_grads) # In dygraph mode, clear _lr_to_coeff after applied gradient self._lr_to_coeff = dict() return optimize_ops def __str__(self): return " ".join(["Weight Decay, params:", ",".join(self._params_name)]) def _update_param_group(self, parameters): self._coeff = parameters.get("coeff", self._default_dict["coeff"]) parameters = parameters.get("params") return parameters