LambOptimizer

class paddle.fluid.optimizer.LambOptimizer(learning_rate=0.001, lamb_weight_decay=0.01, beta1=0.9, beta2=0.999, epsilon=1e-06, parameter_list=None, regularization=None, grad_clip=None, exclude_from_weight_decay_fn=None, name=None)[source]

LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer.

LAMB Optimizer is designed to scale up the batch size of training without losing accuracy, which supports adaptive element-wise updating and accurate layer-wise correction. For more information, please refer to Large Batch Optimization for Deep Learning: Training BERT in 76 minutes .

The updating of parameters follows:

\[ \begin{align}\begin{aligned}m_t &= \beta_1 m_{t - 1}+ (1 - \beta_1)g_t\\v_t &= \beta_2 v_{t - 1} + (1 - \beta_2)g_t^2\\r_t &= \frac{m_t}{\sqrt{v_t}+\epsilon}\\w_t &= w_{t-1} -\eta_t \frac{\left \| w_{t-1}\right \|}{\left \| r_t + \lambda w_{t-1}\right \|} (r_t + \lambda w_{t-1})\end{aligned}\end{align} \]

where \(m\) is the 1st moment, and \(v\) the 2nd moment, \(\eta\) the learning rate, \(\lambda\) the LAMB weight decay rate.

Parameters
  • learning_rate (float|Variable, optional) – the learning rate used to update parameters. Can be a float value or a Variable with data type float32. Default 0.001.

  • lamb_weight_decay (float, optional) – The LAMB weight decay rate. Default 0.01.

  • beta1 (float, optional) – The exponential decay rate for the 1st moment estimates. Default 0.9.

  • beta2 (float, optional) – The exponential decay rate for the 2nd moment estimates. Default 0.999.

  • epsilon (float, optional) – A small float value for numerical stability. Default 1e-6.

  • parameter_list (list, optional) – List of Variable names 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.

  • regularization (WeightDecayRegularizer, optional) – The strategy of regularization. There are two method: L1Decay , L2Decay . If a parameter has set regularizer using ParamAttr already, the regularization setting here in optimizer will be ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. Default None, meaning there is no regularization.

  • grad_clip (GradientClipBase, optional) – Gradient cliping strategy, it’s an instance of some derived class of GradientClipBase . There are three cliping strategies ( GradientClipByGlobalNorm , GradientClipByNorm , GradientClipByValue ). Default None, meaning there is no gradient clipping.

  • exclude_from_weight_decay_fn (function|None) – Exclude a parameter from weight decay when exclude_from_weight_decay_fn(parameter) returns true. Default None.

  • name (str|None) – For detailed information, please refer to Name . Usually name is no need to set and None by default.

Examples

import paddle.fluid as fluid

data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
cost = fluid.layers.mean(hidden)

def exclude_fn(param):
    return param.name.endswith('.b_0')

optimizer = fluid.optimizer.Lamb(learning_rate=0.002,
                                 exclude_from_weight_decay_fn=exclude_fn)
optimizer.minimize(cost)
clear_gradients()

Clear the gradients of all optimized parameters for model.

Returns

None

Examples

import paddle.fluid as fluid
import numpy as np

with fluid.dygraph.guard():
    value = np.arange(26).reshape(2, 13).astype("float32")
    a = fluid.dygraph.to_variable(value)
    linear = fluid.Linear(13, 5, dtype="float32")
    # This can be any optimizer supported by dygraph.
    adam = fluid.optimizer.Adam(learning_rate = 0.01,
                                parameter_list = linear.parameters())
    out = linear(a)
    out.backward()
    adam.minimize(out)
    adam.clear_gradients()
current_step_lr()

Note

This API is ONLY available in Dygraph mode

Get current step learning rate. The return value is all the same When LearningRateDecay is not used, otherwise return the step learning rate.

Returns

The learning rate of the current step.

Return type

float

Examples

import paddle.fluid as fluid
import numpy as np

# example1: LearningRateDecay is not used, return value is all the same
with fluid.dygraph.guard():
    emb = fluid.dygraph.Embedding([10, 10])
    adam = fluid.optimizer.Adam(0.001, parameter_list = emb.parameters())
    lr = adam.current_step_lr()
    print(lr) # 0.001

# example2: PiecewiseDecay is used, return the step learning rate
with fluid.dygraph.guard():
    inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
    linear = fluid.dygraph.nn.Linear(10, 10)
    inp = fluid.dygraph.to_variable(inp)
    out = linear(inp)
    loss = fluid.layers.reduce_mean(out)

    bd = [2, 4, 6, 8]
    value = [0.2, 0.4, 0.6, 0.8, 1.0]
    adam = fluid.optimizer.Adam(fluid.dygraph.PiecewiseDecay(bd, value, 0),
                           parameter_list=linear.parameters())

    # first step: learning rate is 0.2
    np.allclose(adam.current_step_lr(), 0.2, rtol=1e-06, atol=0.0) # True

    # learning rate for different steps
    ret = [0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0]
    for i in range(12):
        adam.minimize(loss)
        lr = adam.current_step_lr()
        np.allclose(lr, ret[i], rtol=1e-06, atol=0.0) # True
minimize(loss, startup_program=None, parameter_list=None, no_grad_set=None)

Add operations to minimize loss by updating parameter_list.

Parameters
  • loss (Variable) – A Variable containing the value to minimize.

  • startup_program (Program, optional) – Program for initializing parameters in parameter_list. The default value is None, at this time default_startup_program will be used.

  • parameter_list (list, optional) – List of Variable or Variable.name to update to minimize loss. The default value is None, at this time all parameters will be updated.

  • no_grad_set (set, optional) – Set of Variable or Variable.name that don’t need to be updated. The default value is None.

Returns

tuple (optimize_ops, params_grads), A list of operators appended by minimize and a list of (param, grad) variable pairs, param is Parameter, grad is the gradient value corresponding to the parameter. The returned tuple can be passed to fetch_list in Executor.run() to indicate program pruning. If so, the program will be pruned by feed and fetch_list before run, see details in Executor.

Return type

tuple

Examples

Please refer to the example of current Optimizer.

set_dict(state_dict)

Load optimizer state dict. For Adam optimizer, contains beta1, beta2, momentum etc. If LearningRateDecay have been used, global_step will be changed.

Parameters

state_dict (dict) – Dict contains all the Variable needed by optimizer

Returns

None

Examples

with fluid.dygraph.guard():
    emb = fluid.dygraph.Embedding([10, 10])

    state_dict = emb.state_dict()
    fluid.save_dygraph(state_dict, "paddle_dy")

    adam = fluid.optimizer.Adam(learning_rate=fluid.layers.noam_decay( 100, 10000),
                                parameter_list=emb.parameters())
    state_dict = adam.state_dict()
    fluid.save_dygraph(state_dict, "paddle_dy")

    para_state_dict, opti_state_dict = fluid.load_dygraph( "paddle_dy")

    adam.set_dict(opti_state_dict)
state_dict()

Get state dict information from optimizer. It contain all the variable used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. If LearningRateDecay have been used, global_step will be include in state dict. If the optimizer never be called(minimize function), the state_dict is empty.

Args: None :returns: dict contains all the variable used by optimizer :rtype: state_dict(dict)

Examples

import paddle.fluid as fluid

with fluid.dygraph.guard():
    emb = fluid.dygraph.Embedding([10, 10])

    adam = fluid.optimizer.Adam(0.001, parameter_list=emb.parameters())
    state_dict = adam.state_dict()