forked from huawei/mindspore2022
235 lines
11 KiB
Python
Executable File
235 lines
11 KiB
Python
Executable File
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""lamb"""
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import numpy as np
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from mindspore.common import dtype as mstype
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from mindspore.common.initializer import initializer
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.common.parameter import Parameter
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from mindspore.common.tensor import Tensor
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from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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from .optimizer import Optimizer
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from .. import layer
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num_one = Tensor(np.ones([1]), mstype.float32)
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lamb_opt = C.MultitypeFuncGraph("lamb_opt")
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@lamb_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
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"Tensor", "Bool")
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def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, param, m, v,
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gradient, decay_flag):
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"""
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Update parameters.
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Args:
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beta1 (Tensor): The exponential decay rate for the 1st moment estimates. Should be in range (0.0, 1.0).
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beta2 (Tensor): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0).
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eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
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lr (Tensor): Learning rate.
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weight_decay_tensor (Tensor): Weight decay. Should be equal to or greater than 0.
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global_step (Tensor): Global step.
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param (Tensor): Parameters.
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m (Tensor): m value of parameters.
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v (Tensor): v value of parameters.
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gradient (Tensor): Gradient of parameters.
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decay_flag (bool): Specifies whether param update with weight decay.
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Returns:
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Tensor, the new value of v after updating.
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"""
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op_mul = P.Mul()
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op_sqrt = P.Sqrt()
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op_rsqrt = P.Rsqrt()
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op_square = P.Square()
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op_cast = P.Cast()
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op_reshape = P.Reshape()
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op_shape = P.Shape()
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op_pow = P.Pow()
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op_norm = layer.Norm()
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op_select = P.Select()
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op_greater = P.Greater()
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op_fill = P.Fill()
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op_dtype = P.DType()
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param = op_cast(param, mstype.float32)
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m = op_cast(m, mstype.float32)
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v = op_cast(v, mstype.float32)
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gradient = op_cast(gradient, mstype.float32)
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next_m = op_mul(beta1, m) + op_mul(op_cast(num_one, mstype.float32) - beta1, gradient)
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next_v = op_mul(beta2, v) + op_mul(op_cast(num_one, mstype.float32) - beta2, op_square(gradient))
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next_mm = next_m / (op_cast(num_one, mstype.float32)
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- op_pow(beta1, op_cast(global_step + num_one, mstype.float32)))
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next_vv = next_v / (op_cast(num_one, mstype.float32) -
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op_pow(beta2, op_cast(global_step + num_one, mstype.float32)))
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w_norm = op_norm(param)
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g_norm = op_norm(gradient)
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g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay_tensor * param)
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zeros = F.zeros_like_tensor(w_norm)
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ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
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trust_ratio = op_select(
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op_greater(w_norm, zeros),
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op_select(op_greater(g_norm, zeros), w_norm / g_norm_hat, ones),
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ones)
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tens = op_fill(op_dtype(trust_ratio), op_shape(trust_ratio), 10.0)
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trust_ratio = C.clip_by_value(trust_ratio, zeros, tens)
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update = next_mm / (op_sqrt(next_vv) + eps)
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if decay_flag:
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update = update + op_mul(weight_decay_tensor, param)
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update_with_lr = op_mul(op_mul(trust_ratio, lr), update)
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next_param = param - op_reshape(update_with_lr, op_shape(param))
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next_v = F.depend(next_v, F.assign(param, next_param))
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next_v = F.depend(next_v, F.assign(m, next_m))
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next_v = F.depend(next_v, F.assign(v, next_v))
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return next_v
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def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
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end_learning_rate, power, beta1, beta2, eps, weight_decay, prim_name):
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"""Check the type of inputs."""
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validator.check_float_positive('start_learning_rate', start_learning_rate, prim_name)
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validator.check_float_legal_value('start_learning_rate', start_learning_rate, prim_name)
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validator.check_float_positive('end_learning_rate', end_learning_rate, prim_name)
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validator.check_float_legal_value('end_learning_rate', end_learning_rate, prim_name)
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validator.check_float_positive('power', power, prim_name)
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validator.check_float_legal_value('power', power, prim_name)
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validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
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validator.check_integer('warmup_steps', decay_steps, 0, Rel.GT, prim_name)
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validator.check_value_type("beta1", beta1, [float], prim_name)
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validator.check_value_type("beta2", beta2, [float], prim_name)
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validator.check_value_type("eps", eps, [float], prim_name)
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validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
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validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
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validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
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validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
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validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
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class Lamb(Optimizer):
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"""
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Lamb Dynamic LR.
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LAMB is an optimization algorithm employing a layerwise adaptive large batch
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optimization technique. Refer to the paper `LARGE BATCH OPTIMIZATION FOR DEEP LEARNING: TRAINING BERT IN 76
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MINUTES <https://arxiv.org/abs/1904.00962>`_.
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Args:
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params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
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should be class mindspore.Parameter.
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decay_steps (int): The steps of the lr decay. Should be equal to or greater than 1.
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warmup_steps (int): The steps of lr warm up. Default: 0.
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start_learning_rate (float): A floating point value for the learning rate. Default: 0.1.
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end_learning_rate (float): A floating point value for the end learning rate. Default: 0.0001.
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power (float): The power of the polynomial. Default: 1.0.
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beta1 (float): The exponential decay rate for the 1st moment estimates. Default: 0.9.
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Should be in range (0.0, 1.0).
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beta2 (float): The exponential decay rate for the 2nd moment estimates. Default: 0.999.
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Should be in range (0.0, 1.0).
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eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
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Should be greater than 0.
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weight_decay (float): Weight decay (L2 penalty). Default: 0.0. Should be equal to or greater than 0.
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decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
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lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name.
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Inputs:
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- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
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Outputs:
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tuple[Parameter], the updated velocity value, the shape is the same as `params`.
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Examples:
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>>> net = Net()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits()
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>>> optim = nn.Lamb(params=net.trainable_params(), decay_steps=10)
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>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
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"""
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def __init__(self,
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params,
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decay_steps,
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warmup_steps=0,
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start_learning_rate=0.1,
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end_learning_rate=0.0001,
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power=1.0,
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beta1=0.9,
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beta2=0.999,
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eps=1e-6,
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weight_decay=0.0,
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decay_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name):
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super(Lamb, self).__init__(start_learning_rate, params)
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_check_param_value(decay_steps, warmup_steps, start_learning_rate, end_learning_rate,
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power, beta1, beta2, eps, weight_decay, self.cls_name)
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# turn them to scalar when me support scalar/tensor mix operations
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self.global_step = Parameter(initializer(0, [1]), name="global_step")
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self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
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self.warmup_flag = False
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if warmup_steps > 0:
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self.warmup_flag = True
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self.decay_steps = Tensor(np.array([decay_steps]).astype(np.float32))
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self.start_learning_rate = Tensor(np.array([start_learning_rate]).astype(np.float32))
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self.end_learning_rate = Tensor(np.array([end_learning_rate]).astype(np.float32))
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self.diff_learning_rate = Tensor(np.array([start_learning_rate - end_learning_rate]).astype(np.float32))
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self.power = power
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self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
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self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
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self.eps = Tensor(np.array([eps]).astype(np.float32))
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self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
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self.params = self.parameters
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self.moments1 = self.params.clone(prefix="lamb_m", init='zeros')
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self.moments2 = self.params.clone(prefix="lamb_v", init='zeros')
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self.decay_flag = tuple(decay_filter(x) for x in self.params)
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self.hyper_map = C.HyperMap()
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self.min = P.Minimum()
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self.pow = P.Pow()
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self.greater = P.Greater()
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self.one = Tensor(np.array([1.0]).astype(np.float32))
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self.cast = P.Cast()
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def construct(self, gradients):
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step = self.min(self.global_step, self.decay_steps)
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p = step / self.decay_steps
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lr = self.diff_learning_rate * self.pow(self.one - p, self.power) + self.end_learning_rate
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if self.warmup_flag:
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warmup_percent = self.global_step / self.warmup_steps
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warmup_lr = self.start_learning_rate * warmup_percent
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is_warmup = self.cast(self.greater(self.warmup_steps, self.global_step), mstype.float32)
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lr = (self.one - is_warmup) * lr + is_warmup * warmup_lr
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updated_velocity = self.hyper_map(F.partial(lamb_opt, self.beta1, self.beta2, self.eps, lr,
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self.weight_decay_tensor, self.global_step),
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self.params, self.moments1, self.moments2, gradients, self.decay_flag)
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added_global_step = self.global_step + self.one
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F.control_depend(lr, added_global_step)
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self.global_step = added_global_step
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return updated_velocity
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