# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""lamb"""
import numpy as np
from mindspore.common import dtype as mstype
from mindspore.common.initializer import initializer
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore._checkparam import ParamValidator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer
from .. import layer
num_one = Tensor(np.ones([1]), mstype.float32)
lamb_opt = C.MultitypeFuncGraph("lamb_opt")
@lamb_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Bool")
def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, param, m, v,
gradient, decay_flag):
"""
Update parameters.
Args:
beta1 (Tensor): The exponential decay rate for the 1st moment estimates. Should be in range (0.0, 1.0).
beta2 (Tensor): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0).
eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
lr (Tensor): Learning rate.
weight_decay_tensor (Tensor): Weight decay. Should be equal to or greater than 0.
global_step (Tensor): Global step.
param (Tensor): Parameters.
m (Tensor): m value of parameters.
v (Tensor): v value of parameters.
gradient (Tensor): Gradient of parameters.
decay_flag (bool): Specifies whether param update with weight decay.
Returns:
Tensor, the new value of v after updating.
"""
op_mul = P.Mul()
op_sqrt = P.Sqrt()
op_rsqrt = P.Rsqrt()
op_square = P.Square()
op_cast = P.Cast()
op_reshape = P.Reshape()
op_shape = P.Shape()
op_pow = P.Pow()
op_norm = layer.Norm()
op_select = P.Select()
op_greater = P.Greater()
op_fill = P.Fill()
op_dtype = P.DType()
param = op_cast(param, mstype.float32)
m = op_cast(m, mstype.float32)
v = op_cast(v, mstype.float32)
gradient = op_cast(gradient, mstype.float32)
next_m = op_mul(beta1, m) + op_mul(op_cast(num_one, mstype.float32) - beta1, gradient)
next_v = op_mul(beta2, v) + op_mul(op_cast(num_one, mstype.float32) - beta2, op_square(gradient))
next_mm = next_m / (op_cast(num_one, mstype.float32)
- op_pow(beta1, op_cast(global_step + num_one, mstype.float32)))
next_vv = next_v / (op_cast(num_one, mstype.float32) -
op_pow(beta2, op_cast(global_step + num_one, mstype.float32)))
w_norm = op_norm(param)
g_norm = op_norm(gradient)
g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay_tensor * param)
zeros = F.zeros_like_tensor(w_norm)
ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
trust_ratio = op_select(
op_greater(w_norm, zeros),
op_select(op_greater(g_norm, zeros), w_norm / g_norm_hat, ones),
ones)
tens = op_fill(op_dtype(trust_ratio), op_shape(trust_ratio), 10.0)
trust_ratio = C.clip_by_value(trust_ratio, zeros, tens)
update = next_mm / (op_sqrt(next_vv) + eps)
if decay_flag:
update = update + op_mul(weight_decay_tensor, param)
update_with_lr = op_mul(op_mul(trust_ratio, lr), update)
next_param = param - op_reshape(update_with_lr, op_shape(param))
next_v = F.depend(next_v, F.assign(param, next_param))
next_v = F.depend(next_v, F.assign(m, next_m))
next_v = F.depend(next_v, F.assign(v, next_v))
return next_v
def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
end_learning_rate, power, beta1, beta2, eps, weight_decay):
"""Check the type of inputs."""
validator.check_type("decay_steps", decay_steps, [int])
validator.check_type("warmup_steps", warmup_steps, [int])
validator.check_type("start_learning_rate", start_learning_rate, [float])
validator.check_type("end_learning_rate", end_learning_rate, [float])
validator.check_type("power", power, [float])
validator.check_type("beta1", beta1, [float])
validator.check_type("beta2", beta2, [float])
validator.check_type("eps", eps, [float])
validator.check_type("weight_dacay", weight_decay, [float])
validator.check_number_range("decay_steps", decay_steps, 1, float("inf"), Rel.INC_LEFT)
validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER)
validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER)
validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER)
validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT)
[docs]class Lamb(Optimizer):
"""
Lamb Dynamic LR.
LAMB is an optimization algorithm employing a layerwise adaptive large batch
optimization technique. Refer to the paper `LARGE BATCH OPTIMIZATION FOR DEEP LEARNING: TRAINING BERT IN 76
MINUTES <https://arxiv.org/abs/1904.00962>`_.
Args:
params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
should be class mindspore.Parameter.
decay_steps (int): The steps of the lr decay. Should be equal to or greater than 1.
warmup_steps (int): The steps of lr warm up. Default: 0.
start_learning_rate (float): A floating point value for the learning rate. Default: 0.1.
end_learning_rate (float): A floating point value for the end learning rate. Default: 0.0001.
power (float): The power of the polynomial. Default: 1.0.
beta1 (float): The exponential decay rate for the 1st moment estimates. Default: 0.9.
Should be in range (0.0, 1.0).
beta2 (float): The exponential decay rate for the 2nd moment estimates. Default: 0.999.
Should be in range (0.0, 1.0).
eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
Should be greater than 0.
weight_decay (float): Weight decay (L2 penalty). Default: 0.0. Should be equal to or greater than 0.
decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
Outputs:
tuple[Parameter], the updated velocity value, the shape is the same as `params`.
Examples:
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> optim = nn.Lamb(params=net.trainable_params(), decay_steps=10)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
"""
def __init__(self,
params,
decay_steps,
warmup_steps=0,
start_learning_rate=0.1,
end_learning_rate=0.0001,
power=1.0,
beta1=0.9,
beta2=0.999,
eps=1e-6,
weight_decay=0.0,
decay_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name):
super(Lamb, self).__init__(start_learning_rate, params)
_check_param_value(decay_steps, warmup_steps, start_learning_rate, end_learning_rate,
power, beta1, beta2, eps, weight_decay)
# turn them to scalar when me support scalar/tensor mix operations
self.global_step = Parameter(initializer(0, [1]), name="global_step")
self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
self.warmup_flag = False
if warmup_steps > 0:
self.warmup_flag = True
self.decay_steps = Tensor(np.array([decay_steps]).astype(np.float32))
self.start_learning_rate = Tensor(np.array([start_learning_rate]).astype(np.float32))
self.end_learning_rate = Tensor(np.array([end_learning_rate]).astype(np.float32))
self.diff_learning_rate = Tensor(np.array([start_learning_rate - end_learning_rate]).astype(np.float32))
self.power = power
self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
self.eps = Tensor(np.array([eps]).astype(np.float32))
self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
self.params = self.parameters
self.moments1 = self.params.clone(prefix="lamb_m", init='zeros')
self.moments2 = self.params.clone(prefix="lamb_v", init='zeros')
self.decay_flag = tuple(decay_filter(x) for x in self.params)
self.hyper_map = C.HyperMap()
self.min = P.Minimum()
self.pow = P.Pow()
self.greater = P.Greater()
self.one = Tensor(np.array([1.0]).astype(np.float32))
self.cast = P.Cast()
def construct(self, gradients):
step = self.min(self.global_step, self.decay_steps)
p = step / self.decay_steps
lr = self.diff_learning_rate * self.pow(self.one - p, self.power) + self.end_learning_rate
if self.warmup_flag:
warmup_percent = self.global_step / self.warmup_steps
warmup_lr = self.start_learning_rate * warmup_percent
is_warmup = self.cast(self.greater(self.warmup_steps, self.global_step), mstype.float32)
lr = (self.one - is_warmup) * lr + is_warmup * warmup_lr
updated_velocity = self.hyper_map(F.partial(lamb_opt, self.beta1, self.beta2, self.eps, lr,
self.weight_decay_tensor, self.global_step),
self.params, self.moments1, self.moments2, gradients, self.decay_flag)
added_global_step = self.global_step + self.one
F.control_depend(lr, added_global_step)
self.global_step = added_global_step
return updated_velocity