uniform learning_rate behavior of optimizers

mindspore/nn/dynamic_lr.py

@@ -231,8 +231,9 @@ def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
         >>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
         [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
     """
-    validator.check_float_positive('min_lr', min_lr, None)
+    if not isinstance(min_lr, float):
-    validator.check_float_legal_value('min_lr', min_lr, None)
+        raise TypeError("min_lr must be float.")
+    validator.check_number_range("min_lr", min_lr, 0.0, float("inf"), Rel.INC_LEFT, None)
     validator.check_float_positive('max_lr', max_lr, None)
     validator.check_float_legal_value('max_lr', max_lr, None)
     validator.check_integer('total_step', total_step, 0, Rel.GT, None)
@@ -288,8 +289,9 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
     """
     validator.check_float_positive('learning_rate', learning_rate, None)
     validator.check_float_legal_value('learning_rate', learning_rate, None)
-    validator.check_float_positive('end_learning_rate', end_learning_rate, None)
+    if not isinstance(end_learning_rate, float):
-    validator.check_float_legal_value('end_learning_rate', end_learning_rate, None)
+        raise TypeError("end_learning_rate must be float.")
+    validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT, None)
     validator.check_float_positive('power', power, None)
     validator.check_float_legal_value('power', power, None)
     validator.check_integer('total_step', total_step, 0, Rel.GT, None)
@@ -311,11 +313,58 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e
     return lr
 
 
+def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch):
+    r"""
+    Get learning rate warming up.
+
+    For the i-th step, the formula of computing warmup_learning_rate[i] is:
+
+    .. math::
+        warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / tmp\_warmup\_epoch
+
+    Where :math:`tmp\_epoch=min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        warmup_steps (int): The warm up steps of learning rate.
+
+    Inputs:
+        Tensor. The current step number.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> total_step = 6
+        >>> step_per_epoch = 2
+        >>> warmup_epoch = 2
+        >>> warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
+        [0.0, 0.0, 0.05, 0.05, 0.1, 0.1]
+    """
+    if not isinstance(learning_rate, float):
+        raise TypeError("learning_rate must be float.")
+    validator.check_number_range("learning_rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, None)
+    validator.check_integer('warmup_epoch', warmup_epoch, 0, Rel.GT, None)
+    validator.check_integer('total_step', total_step, 0, Rel.GT, None)
+    validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
+
+    function = lambda x, y: (x, min(x, y))
+
+    lr = []
+    for i in range(total_step):
+        current_epoch = math.floor(i / step_per_epoch)
+        warmup_epoch, tmp_epoch = function(warmup_epoch, current_epoch)
+        lr.append(learning_rate * tmp_epoch/ warmup_epoch)
+    return lr
+
+
 __all__ = [
     'piecewise_constant_lr',
     'exponential_decay_lr',
     'natural_exp_decay_lr',
     'inverse_decay_lr',
     'cosine_decay_lr',
-    'polynomial_decay_lr'
+    'polynomial_decay_lr',
+    'warmup_lr'
 ]

mindspore/nn/learning_rate_schedule.py

@@ -0,0 +1,368 @@
+# 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.
+# ============================================================================
+"""Learning rate schedule."""
+
+import math
+
+from ..common import dtype as mstype
+from ..ops import operations as P
+from .cell import Cell
+from .._checkparam import Validator as validator
+from .._checkparam import Rel
+
+
+class LearningRateSchedule(Cell):
+    def __init__(self):
+        super(LearningRateSchedule, self).__init__()
+
+    def construct(self, global_step):
+        raise NotImplementedError
+
+
+def _check_inputs(learning_rate, decay_rate, decay_steps, is_stair, cls_name):
+    validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, cls_name)
+    validator.check_float_positive('learning_rate', learning_rate, cls_name)
+    validator.check_float_legal_value('learning_rate', learning_rate, cls_name)
+    validator.check_float_positive('decay_rate', decay_rate, cls_name)
+    validator.check_float_legal_value('decay_rate', decay_rate, cls_name)
+    validator.check_value_type('is_stair', is_stair, [bool], cls_name)
+
+
+class ExponentialDecayLR(LearningRateSchedule):
+    r"""
+    Calculate learning rate base on exponential decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{p}}
+
+    Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
+    is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        decay_rate (float): The decay rate.
+        decay_steps (int): A value used to calculate decayed learning rate.
+        is_stair (bool): If true, learning rate decay once every `decay_steps` times. Default: False.
+
+    Inputs:
+        Tensor. The current step number.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> decay_rate = 0.9
+        >>> decay_steps = 4
+        >>> global_step = Tenosr(2, mstype.int32)
+        >>> exponential_decay_lr = ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
+        >>> exponential_decay_lr(global_step)
+    """
+    def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
+        super(ExponentialDecayLR, self).__init__()
+        _check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
+        self.learning_rate = learning_rate
+        self.decay_rate = decay_rate
+        self.decay_steps = decay_steps
+        self.is_stair = is_stair
+        self.pow = P.Pow()
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        p = self.cast(global_step, mstype.float32) / self.decay_steps
+        if self.is_stair:
+            p = P.Floor()(p)
+        return self.learning_rate * self.pow(self.decay_rate, p)
+
+
+class NaturalExpDecayLR(LearningRateSchedule):
+    r"""
+    Calculate learning rate base on natural exponential decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * p}
+
+    Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
+    is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        decay_rate (float): The decay rate.
+        decay_steps (int): A value used to calculate decayed learning rate.
+        is_stair (bool): If true, learning rate decay once every `decay_steps` times. Default: False.
+
+    Inputs:
+        Tensor. The current step number.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> decay_rate = 0.9
+        >>> decay_steps = 4
+        >>> global_step = Tenosr(2, mstype.int32)
+        >>> natural_exp_decay_lr = NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
+        >>> natural_exp_decay_lr(global_step)
+    """
+    def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
+        super(NaturalExpDecayLR, self).__init__()
+        _check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
+        self.learning_rate = learning_rate
+        self.decay_rate = decay_rate
+        self.decay_steps = decay_steps
+        self.is_stair = is_stair
+        self.math_e = math.e
+        self.pow = P.Pow()
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        p = self.cast(global_step, mstype.float32)
+        if self.is_stair:
+            p = P.FloorDiv()(p, self.decay_steps) * self.decay_steps
+        return self.learning_rate * self.pow(self.math_e, -self.decay_rate * p)
+
+
+class InverseDecayLR(LearningRateSchedule):
+    r"""
+    Calculate learning rate base on inverse-time decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * p}
+
+    Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
+    is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        decay_rate (float): The decay rate.
+        decay_epoch (int): A value used to calculate decayed learning rate.
+        is_stair (bool): If true, learning rate decay once every `decay_steps` times. Default: False.
+
+    Inputs:
+        Tensor. The current step number.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> decay_rate = 0.9
+        >>> decay_steps = 4
+        >>> global_step = Tenosr(2, mstype.int32)
+        >>> inverse_decay_lr = InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
+        >>> inverse_decay_lr(global_step)
+    """
+    def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
+        super(InverseDecayLR, self).__init__()
+        _check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
+        self.learning_rate = learning_rate
+        self.decay_rate = decay_rate
+        self.decay_steps = decay_steps
+        self.is_stair = is_stair
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        p = self.cast(global_step, mstype.float32) / self.decay_steps
+        if self.is_stair:
+            p = P.Floor()(p)
+        return self.learning_rate / (1 + self.decay_rate * p)
+
+
+class CosineDecayLR(LearningRateSchedule):
+    r"""
+    Calculate learning rate base on cosine decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
+        (1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))
+
+    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
+
+    Args:
+        min_lr (float): The minimum value of learning rate.
+        max_lr (float): The maximum value of learning rate.
+        decay_steps (int): A value used to calculate decayed learning rate.
+
+    Inputs:
+        Tensor. The current step number.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+
+    Examples:
+        >>> min_lr = 0.01
+        >>> max_lr = 0.1
+        >>> decay_steps = 4
+        >>> global_step = Tenosr(2, mstype.int32)
+        >>> cosine_decay_lr = CosineDecayLR(min_lr, max_lr, decay_steps)
+        >>> cosine_decay_lr(global_steps)
+    """
+    def __init__(self, min_lr, max_lr, decay_steps):
+        super(CosineDecayLR, self).__init__()
+        if not isinstance(min_lr, float):
+            raise TypeError("min_lr must be float.")
+        validator.check_number_range("min_lr", min_lr, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
+        validator.check_float_positive('max_lr', max_lr, self.cls_name)
+        validator.check_float_legal_value('max_lr', max_lr, self.cls_name)
+        validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, self.cls_name)
+        if min_lr >= max_lr:
+            raise ValueError('`max_lr` should be greater than `min_lr`.')
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.decay_steps = decay_steps
+        self.math_pi = math.pi
+        self.delta = 0.5 * (max_lr - min_lr)
+        self.cos = P.Cos()
+        self.min = P.Minimum()
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        p = self.cast(self.min(global_step, self.decay_steps), mstype.float32)
+        return self.min_lr + self.delta * (1.0 + self.cos(self.math_pi * p / self.decay_steps))
+
+
+class PolynomialDecayLR(LearningRateSchedule):
+    r"""
+    Calculate learning rate base on polynomial decay function.
+
+    For the i-th step, the formula of computing decayed_learning_rate[i] is:
+
+    .. math::
+        decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
+        (1 - tmp\_step / tmp\_decay\_step)^{power} + end\_learning\_rate
+
+    Where :math:`tmp\_step=min(global\_step, decay\_step).
+    If `update_decay_steps` is true, update the value of `tmp_decay_step` every `decay_steps`. The formula
+    is :math:`tmp\_decay\_step = decay\_step * ceil(global\_step / decay\_steps)`
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        end_learning_rate (float): The end value of learning rate.
+        decay_steps (int): A value used to calculate decayed learning rate.
+        power (float): A value used to calculate decayed learning rate. This parameter should be greater than 0.
+        update_decay_steps (bool): If true, learning rate decay once every `decay_steps` times. Default: False.
+
+    Inputs:
+        Tensor. The current step number.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> end_learning_rate = 0.01
+        >>> decay_steps = 4
+        >>> power = 0.5
+        >>> global_step = Tenosr(2, mstype.int32)
+        >>> polynomial_decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        >>> polynomial_decay_lr(global_step)
+    """
+    def __init__(self, learning_rate, end_learning_rate, decay_steps, power, update_decay_steps=False):
+        super(PolynomialDecayLR, self).__init__()
+        validator.check_float_positive('learning_rate', learning_rate, None)
+        validator.check_float_legal_value('learning_rate', learning_rate, None)
+        if not isinstance(end_learning_rate, float):
+            raise TypeError("end_learning_rate must be float.")
+        validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT,
+                                     self.cls_name)
+        validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, self.cls_name)
+        validator.check_value_type('update_decay_steps', update_decay_steps, [bool], self.cls_name)
+        validator.check_float_positive('power', power, self.cls_name)
+        validator.check_float_legal_value('power', power, self.cls_name)
+
+        self.decay_steps = decay_steps
+        self.start_learning_rate = learning_rate
+        self.end_learning_rate = end_learning_rate
+        self.diff_learning_rate = learning_rate - end_learning_rate
+        self.power = power
+        self.update_decay_steps = update_decay_steps
+        self.pow = P.Pow()
+        self.ceil = P.Ceil()
+        self.min = P.Minimum()
+        self.max = P.Maximum()
+
+    def construct(self, global_step):
+        tmp_global_step = P.Cast()(global_step, mstype.float32)
+        tmp_decay_step = self.decay_steps
+        if self.update_decay_steps:
+            tmp_decay_step = tmp_decay_step * self.max(self.ceil(tmp_global_step / tmp_decay_step), 1)
+        else:
+            tmp_global_step = self.min(tmp_global_step, tmp_decay_step)
+        p = tmp_global_step / tmp_decay_step
+        lr = self.diff_learning_rate * self.pow(1.0 - p, self.power) + self.end_learning_rate
+        return lr
+
+
+class WarmUpLR(LearningRateSchedule):
+    r"""
+    Get learning rate warming up.
+
+    For the i-th step, the formula of computing warmup_learning_rate[i] is:
+
+    .. math::
+        warmup\_learning\_rate[i] = learning\_rate * tmp\_step / warmup\_steps
+
+    Where :math:`tmp\_step=min(global\_step, warmup\_steps).
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        warmup_steps (int): The warm up steps of learning rate.
+
+    Inputs:
+        Tensor. The current step number.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+
+    Examples:
+        >>> learning_rate = 0.1
+        >>> warmup_steps = 2
+        >>> global_step = Tenosr(2, mstype.int32)
+        >>> warmup_lr = WarmUpLR(learning_rate, warmup_steps)
+        >>> warmup_lr(global_step)
+    """
+    def __init__(self, learning_rate, warmup_steps):
+        super(WarmUpLR, self).__init__()
+        if not isinstance(learning_rate, float):
+            raise TypeError("learning_rate must be float.")
+        validator.check_number_range("learning_rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
+        validator.check_integer('warmup_steps', warmup_steps, 0, Rel.GT, self.cls_name)
+        self.warmup_steps = warmup_steps
+        self.learning_rate = learning_rate
+        self.min = P.Minimum()
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        warmup_percent = self.cast(self.min(global_step, self.warmup_steps), mstype.float32)/ self.warmup_steps
+        return self.learning_rate * warmup_percent
+
+
+__all__ = [
+    'ExponentialDecayLR',
+    'NaturalExpDecayLR',
+    'InverseDecayLR',
+    'CosineDecayLR',
+    'PolynomialDecayLR',
+    'WarmUpLR'
+]

mindspore/nn/optim/__init__.py

@@ -20,7 +20,7 @@ The optimizer is used to calculate and update the gradients.
 """
 from .optimizer import Optimizer
 from .momentum import Momentum
-from .adam import Adam, PSAdam, AdamWeightDecay, AdamWeightDecayDynamicLR
+from .adam import Adam, PSAdam, AdamWeightDecay
 from .lamb import Lamb
 from .sgd import SGD
 from .lars import LARS
@@ -30,4 +30,4 @@ from .proximal_ada_grad import ProximalAdagrad
 from .lazyadam import LazyAdam
 
 __all__ = ['Optimizer', 'Momentum', 'LARS', 'Adam', 'PSAdam', 'AdamWeightDecay', 'LazyAdam',
-           'AdamWeightDecayDynamicLR', 'Lamb', 'SGD', 'FTRL', 'PSFTRL', 'RMSProp', 'ProximalAdagrad']
+           'Lamb', 'SGD', 'FTRL', 'PSFTRL', 'RMSProp', 'ProximalAdagrad']

mindspore/nn/optim/adam.py

@@ -30,9 +30,9 @@ _adam_opt = C.MultitypeFuncGraph("adam_opt")
 _adam_push_pull_opt = C.MultitypeFuncGraph("_adam_push_pull_opt")
 
 
-@_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
+@_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
                     "Tensor", "Bool", "Bool")
-def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag, optim_filter):
+def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, decay_flag, optim_filter):
     """
     Update parameters.
 
@@ -41,7 +41,7 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, grad
         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 in range [0.0, 1.0].
+        weight_decay (Number): Weight decay. Should be in range [0.0, 1.0].
         param (Tensor): Parameters.
         m (Tensor): m value of parameters.
         v (Tensor): v value of parameters.
@@ -73,7 +73,7 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, grad
 
         update = next_m / (eps + op_sqrt(next_v))
         if decay_flag:
-            update = op_mul(weight_decay_tensor, param_fp32) + update
+            update = op_mul(weight_decay, param_fp32) + update
 
         update_with_lr = op_mul(lr, update)
         next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
@@ -85,29 +85,6 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, grad
     return gradient
 
 
-def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
-    """Check the type of inputs."""
-    validator.check_value_type("beta1", beta1, [float], prim_name)
-    validator.check_value_type("beta2", beta2, [float], prim_name)
-    validator.check_value_type("eps", eps, [float], prim_name)
-    validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
-    validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
-    validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
-
-
-def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, prim_name):
-    """Check the type of inputs."""
-    validator.check_value_type("learning_rate", learning_rate, [float], prim_name)
-    validator.check_number_range("learning_rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
-    validator.check_value_type("end_learning_rate", end_learning_rate, [float], prim_name)
-    validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
-    validator.check_float_positive('power', power, prim_name)
-    validator.check_float_legal_value('power', power, prim_name)
-    validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
-
-
 @_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "IndexedSlices",
                     "Tensor", "Tensor", "Tensor", "Bool")
 def _run_opt_with_sparse(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
@@ -179,6 +156,16 @@ def _run_push_pull_opt_with_one_number(push, pull, beta1_power, beta2_power, bet
     return success
 
 
+def _check_param_value(beta1, beta2, eps, prim_name):
+    """Check the type of inputs."""
+    validator.check_value_type("beta1", beta1, [float], prim_name)
+    validator.check_value_type("beta2", beta2, [float], prim_name)
+    validator.check_value_type("eps", eps, [float], prim_name)
+    validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
+    validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
+    validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
+
+
 class Adam(Optimizer):
     r"""
     Updates gradients by Adaptive Moment Estimation (Adam) algorithm.
@@ -202,12 +189,9 @@ class Adam(Optimizer):
     :math:`\epsilon` represents `eps`.
 
     Note:
-        The Adam optimizer supports separating parameter groups. Different parameter groups can set different
-        `learning_rate` and `weight_decay`.
-
         When separating parameter groups, the weight decay in each group will be applied on the parameters if the
-        value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
-        applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters.
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
 
         To improve parameter groups performance, the customized order of parameters can be supported.
 
@@ -232,14 +216,14 @@ class Adam(Optimizer):
               the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
               in the value of 'order_params' should be in one of group parameters.
 
-        learning_rate (Union[int, float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
-                                                             Iterable or a Tensor and the dims of the Tensor is 1,
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
-                                                             use dynamic learning rate, then the i-th step will
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
-                                                             take the i-th value as the learning rate.
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
-                                                             When the learning_rate is float or learning_rate is a
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
-                                                             Tensor but the dims of the Tensor is 0, use fixed learning
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
-                                                             rate. Other cases are not supported. It should be equal to
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
-                                                             or greater than 0. Default: 1e-3.
+            Default: 1e-3.
         beta1 (float): The exponential decay rate for the 1st moment estimates. Should be in range (0.0, 1.0). Default:
                        0.9.
         beta2 (float): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0). Default:
@@ -272,9 +256,9 @@ class Adam(Optimizer):
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         >>>                 {'params': no_conv_params, 'lr': 0.01},
         >>>                 {'order_params': net.trainable_params()}]
-        >>> optim = nn.Adam(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> optm = nn.Adam(group_params, learning_rate=0.1, weight_decay=0.0)
-        >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
+        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
-        >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
+        >>> # The no_conv_params's parameters will use learning rate of 0.01 and defaule weight decay of 0.0.
         >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
         >>>
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
@@ -284,7 +268,7 @@ class Adam(Optimizer):
     def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
                  use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
         super(Adam, self).__init__(learning_rate, params, weight_decay, loss_scale)
-        _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
+        _check_param_value(beta1, beta2, eps, self.cls_name)
         validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
         validator.check_value_type("use_nesterov", use_nesterov, [bool], self.cls_name)
 
@@ -329,7 +313,7 @@ class PSAdam(Optimizer):
     def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
                  use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
         super(PSAdam, self).__init__(learning_rate, params, weight_decay, loss_scale)
-        _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
+        _check_param_value(beta1, beta2, eps, self.cls_name)
         validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
         validator.check_value_type("use_nesterov", use_nesterov, [bool], self.cls_name)
 
@@ -375,17 +359,38 @@ class AdamWeightDecay(Optimizer):
     """
     Implements Adam algorithm weight decay fix.
 
+    Note:
+        When separating parameter groups, the weight decay in each group will be applied on the parameters if the
+        weight decay is posigive. When not separating parameter groups, the `weight_decay` in the API will be applied
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
+
+        To improve parameter groups performance, the customized order of parameters can be supported.
+
     Args:
-        params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
+        params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
-                                  should be class mindspore.Parameter.
+            the element in `params` should be class `Parameter`. When the `params` is a list of `dict`, the "params",
-        learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+            "lr", "weight_decay" and "order_params" are the keys can be parsed.
-                                                        Iterable or a Tensor and the dims of the Tensor is 1,
+
-                                                        use dynamic learning rate, then the i-th step will
+            - params: Required. The value should be a list of `Parameter`.
-                                                        take the i-th value as the learning rate.
+
-                                                        When the learning_rate is float or learning_rate is a Tensor
+            - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
-                                                        but the dims of the Tensor is 0, use fixed learning rate.
+              If not, the `learning_rate` in the API will be used.
-                                                        Other cases are not supported. It should be equal to or
+
-                                                        greater than 0. Default: 1e-3.
+            - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
+              will be used. If not, the `weight_decay` in the API will be used.
+
+            - order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and
+              the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
+              in the value of 'order_params' should be in one of group parameters.
+
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
+            Default: 1e-3.
         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.
@@ -404,136 +409,48 @@ class AdamWeightDecay(Optimizer):
 
     Examples:
         >>> net = Net()
-        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> #1) All parameters use the same learning rate and weight decay
         >>> optim = nn.AdamWeightDecay(params=net.trainable_params())
-        >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
+        >>>
+        >>> #2) Use parameter groups and set different values
+        >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
+        >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
+        >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
+        >>>                 {'params': no_conv_params, 'lr': 0.01},
+        >>>                 {'order_params': net.trainable_params()}]
+        >>> optim = nn.AdamWeightDecay(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
+        >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0.
+        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
+        >>>
+        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> model = Model(net, loss_fn=loss, optimizer=optim)
    """
-    def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0,
+    def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0):
-                 decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
+        super(AdamWeightDecay, self).__init__(learning_rate, params, weight_decay)
-        super(AdamWeightDecay, self).__init__(learning_rate, params)
+        _check_param_value(beta1, beta2, eps, self.cls_name)
-        if self.is_group:
-            raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
-        _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
         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.moments1 = self.parameters.clone(prefix="adam_m", init='zeros')
-
+        self.moments2 = self.parameters.clone(prefix="adam_v", init='zeros')
-        self.params = self.parameters
-        self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
-        self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
-        self.decay_flag = tuple(decay_filter(x) for x in self.params)
-
         self.hyper_map = C.HyperMap()
 
     def construct(self, gradients):
         lr = self.get_lr()
-        optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr,
-                                                self.weight_decay_tensor),
-                                      self.params, self.moments1, self.moments2, gradients,
-                                      self.decay_flag, self.optim_filter)
-        if self.use_parallel:
-            optim_result = self.broadcast_params(optim_result)
-        return optim_result
-
-
-class AdamWeightDecayDynamicLR(Optimizer):
-    """
-    Adam Weight Decay Dynamic Learning Rate (LR).
-
-    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 decay. It must be int and positive.
-        warmup_steps (int): The steps of lr warm up. Default: 0.
-        learning_rate (float): A floating point value for the learning rate. It should be equal to or
-            greater than 0. Default: 0.001.
-        end_learning_rate (float): A floating point value for the end learning rate. It should be equal
-            to or greater than 0. Default: 0.0001.
-        power (float): The Power of the polynomial. It must be positive. Default: 10.0.
-        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). It should be in range [0.0, 1.0]. Default: 0.0.
-        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[bool], all elements are True.
-
-    Examples:
-        >>> net = Net()
-        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = nn.AdamWeightDecayDynamicLR(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,
-                 learning_rate=0.001,
-                 end_learning_rate=0.0001,
-                 power=10.0,
-                 beta1=0.9,
-                 beta2=0.999,
-                 eps=1e-6,
-                 weight_decay=0.0,
-                 decay_filter=lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()):
-        super(AdamWeightDecayDynamicLR, self).__init__(0.0, params)
         if self.is_group:
-            raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
+            if self.is_group_lr:
-        _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
+                optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps),
-        _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, self.cls_name)
+                                              lr, self.weight_decay, self.parameters, self.moments1, self.moments2,
-        validator.check_integer('warmup_steps', warmup_steps, 0, Rel.GE, self.cls_name)
+                                              gradients, self.decay_flags, self.optim_filter)
-        # turn them to scalar when me support scalar/tensor mix operations
+            else:
-        self.global_step = Parameter(initializer(0, [1]), name="global_step")
+                optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr),
-        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+                                              self.weight_decay, self.parameters, self.moments1, self.moments2,
-        self.warmup_flag = False
+                                              gradients, self.decay_flags, self.optim_filter)
-        if warmup_steps > 0:
+        else:
-            self.warmup_flag = True
+            optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr, self.weight_decay),
-        self.decay_steps = Tensor(np.array([decay_steps]).astype(np.float32))
+                                          self.parameters, self.moments1, self.moments2,
-        self.end_learning_rate = Tensor(np.array([end_learning_rate]).astype(np.float32))
+                                          gradients, self.decay_flags, self.optim_filter)
-        self.diff_learning_rate = Tensor(np.array([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="adam_m", init='zeros')
-        self.moments2 = self.params.clone(prefix="adam_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()
-        self.start_learning_rate = Tensor(np.array([learning_rate]).astype(np.float32))
-
-    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
-        optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr,
-                                                self.weight_decay_tensor),
-                                      self.params, self.moments1, self.moments2, gradients,
-                                      self.decay_flag, self.optim_filter)
         if self.use_parallel:
             optim_result = self.broadcast_params(optim_result)
-        added_global_step = self.global_step + self.one
-        F.control_depend(lr, added_global_step)
-        self.global_step = added_global_step
-
         return optim_result

mindspore/nn/optim/ftrl.py

@@ -24,9 +24,9 @@ _ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
 _ftrl_push_pull_opt = C.MultitypeFuncGraph("ftrl_opt")
 
 
-@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "IndexedSlices", "Tensor",
+@_ftrl_opt.register("Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor", "IndexedSlices", "Tensor",
                     "Tensor", "Bool")
-def _tensor_run_opt_with_sparse(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment,
+def _tensor_run_opt_with_sparse(opt, spars_opt, l1, l2, lr_power, learning_rate, linear, gradient, weight, moment,
                                 ps_parameter):
     """Apply sparse ftrl optimizer to the weight parameter when the gradient is sparse."""
     success = True
@@ -43,9 +43,9 @@ def _tensor_run_opt_with_sparse(opt, spars_opt, learning_rate, l1, l2, lr_power,
     return success
 
 
-@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
+@_ftrl_opt.register("Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor",
                     "Tensor", "Bool")
-def _tensor_run_opt(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment, ps_parameter):
+def _tensor_run_opt(opt, spars_opt, l1, l2, lr_power, learning_rate, linear, gradient, weight, moment, ps_parameter):
     """Apply ftrl optimizer to the weight parameter."""
     success = True
     if ps_parameter:
@@ -83,7 +83,7 @@ def _tensor_run_push_pull_opt_with_one_number(push, pull, learning_rate, l1, l2,
     return success
 
 
-def _check_param(initial_accum, lr_power, l1, l2, use_locking, weight_decay=0.0, prim_name=None):
+def _check_param(initial_accum, lr_power, l1, l2, use_locking, prim_name=None):
     """Check param."""
     validator.check_value_type("initial_accum", initial_accum, [float], prim_name)
     validator.check_number("initial_accum", initial_accum, 0.0, Rel.GE, prim_name)
@@ -99,9 +99,6 @@ def _check_param(initial_accum, lr_power, l1, l2, use_locking, weight_decay=0.0,
 
     validator.check_value_type("use_locking", use_locking, [bool], prim_name)
 
-    validator.check_value_type("weight_decay", weight_decay, [float], prim_name)
-    validator.check_number("weight_decay", weight_decay, 0.0, Rel.GE, prim_name)
-
 
 class FTRL(Optimizer):
     """
@@ -113,15 +110,34 @@ class FTRL(Optimizer):
     <https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf>`_ for engineering document.
 
     Note:
+        When separating parameter groups, the weight decay in each group will be applied on the parameters if the
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
+        on all of the parameters.
+
+        To improve parameter groups performance, the customized order of parameters can be supported.
+
         The sparse strategy is applied while the SparseGatherV2 operator being used for forward network.
-        The sparse feature is under continuous development. The sparse
+        The sparse feature is under continuous development. The sparse behavior is currently performed on the CPU.
-        behavior is currently performed on the CPU.
 
     Args:
-        params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
+        params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
-            should be Parameter.
+            the element in `params` should be class `Parameter`. When the `params` is a list of `dict`, the "params",
+            "lr", "weight_decay" and "order_params" are the keys can be parsed.
+
+            - params: Required. The value should be a list of `Parameter`.
+
+            - lr: Using different learning rate by separating parameters is currently not supported.
+
+            - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
+              will be used. If not, the `weight_decay` in the API will be used.
+
+            - order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and
+              the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
+              in the value of 'order_params' should be in one of group parameters.
+
         initial_accum (float): The starting value for accumulators, must be zero or positive values. Default: 0.1.
-        learning_rate (float): The learning rate value, should be positive. Default: 0.001.
+        learning_rate (float): The learning rate value, should be zero or positive, dynamic learning rate is currently
+            not supported. Default: 0.001.
         lr_power (float): Learning rate power controls how the learning rate decreases during training, must be less
             than or equal to zero. Use fixed learning rate if lr_power is zero. Default: -0.5.
         l1 (float): l1 regularization strength, must be greater than or equal to zero. Default: 0.0.
@@ -139,23 +155,36 @@ class FTRL(Optimizer):
 
     Examples:
         >>> net = Net()
+        >>> #1) All parameters use the same learning rate and weight decay
+        >>> optim = nn.FTRL(params=net.trainable_params())
+        >>>
+        >>> #2) Use parameter groups and set different values
+        >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
+        >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
+        >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
+        >>>                 {'params': no_conv_params},
+        >>>                 {'order_params': net.trainable_params()}]
+        >>> optim = nn.FTRL(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> # The conv_params's parameters will use weight decay of 0.01.
+        >>> # The no_conv_params's parameters will use default weight decay of 0.0.
+        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
+        >>>
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = nn.FTRL(net.trainable_params())
+        >>> model = Model(net, loss_fn=loss, optimizer=optim)
-        >>> model = Model(net, loss_fn=loss, optimizer=opt, metrics=None)
     """
     def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
                  use_locking=False, loss_scale=1.0, weight_decay=0.0):
-        super(FTRL, self).__init__(learning_rate, params, loss_scale=loss_scale)
+        super(FTRL, self).__init__(learning_rate, params, weight_decay, loss_scale=loss_scale)
-        if self.is_group:
+        if self.dynamic_lr or self.is_group_lr:
-            raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
+            raise ValueError('Dynamic learning rate or group learning rate is currently not supported.')
-        _check_param(initial_accum, lr_power, l1, l2, use_locking, weight_decay, self.cls_name)
+        _check_param(initial_accum, lr_power, l1, l2, use_locking, self.cls_name)
         self.moments = self.parameters.clone(prefix="moments", init=initial_accum)
         self.linear = self.parameters.clone(prefix="linear", init='zeros')
         self.l1 = l1
         self.l2 = l2
         self.lr_power = lr_power
-        self.weight_decay = weight_decay
+        if not self.is_group:
-        self.decay_tf = tuple((lambda: True)() for x in self.parameters)
+            self.decay_flags = tuple((lambda: True)() for x in self.parameters)
         self.hyper_map = C.HyperMap()
         self.opt = P.ApplyFtrl(use_locking=use_locking)
         self.sparse_opt = P.FusedSparseFtrl(learning_rate, l1, l2, lr_power, use_locking=use_locking)
@@ -164,12 +193,11 @@ class FTRL(Optimizer):
         params = self.parameters
         moments = self.moments
         linear = self.linear
-        lr = self.learning_rate
+        grads = self.decay_weight(grads)
-        if self.weight_decay > 0.0:
-            grads = self.map_(F.partial(_apply_decay, self.weight_decay), self.decay_tf, params, grads)
-
         grads = self.scale_grad(grads)
-        success = self.map_(F.partial(_ftrl_opt, self.opt, self.sparse_opt, lr, self.l1, self.l2, self.lr_power),
+        lr = self.get_lr()
+
+        success = self.map_(F.partial(_ftrl_opt, self.opt, self.sparse_opt, self.l1, self.l2, self.lr_power, lr),
                             linear, grads, params, moments, self.ps_parameters)
         return success
 
@@ -180,7 +208,7 @@ class PSFTRL(Optimizer):
         super(PSFTRL, self).__init__(learning_rate, params, loss_scale=loss_scale)
         if self.is_group:
             raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
-        _check_param(initial_accum, lr_power, l1, l2, use_locking, weight_decay, self.cls_name)
+        _check_param(initial_accum, lr_power, l1, l2, use_locking, self.cls_name)
         self.moments = self.parameters.clone(prefix="moments", init=initial_accum)
         self.linear = self.parameters.clone(prefix="linear", init='zeros')
         self.l1 = l1

mindspore/nn/optim/lamb.py

@@ -32,10 +32,9 @@ 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",
+@_lamb_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
                     "Tensor", "Bool", "Bool")
-def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, param, m, v,
+def _update_run_op(beta1, beta2, eps, global_step, lr, weight_decay, param, m, v, gradient, decay_flag, optim_filter):
-                   gradient, decay_flag, optim_filter):
     """
     Update parameters.
 
@@ -44,7 +43,7 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
         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 in range [0.0, 1.0].
+        weight_decay (Number): Weight decay. Should be in range [0.0, 1.0].
         global_step (Tensor): Global step.
         param (Tensor): Parameters.
         m (Tensor): m value of parameters.
@@ -87,7 +86,7 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
         w_norm = op_norm(param_fp32)
         g_norm = op_norm(gradient_fp32)
 
-        g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay_tensor * param_fp32)
+        g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay * param_fp32)
         zeros = F.zeros_like(w_norm)
         ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
         trust_ratio = op_select(
@@ -99,7 +98,7 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
         update = next_mm / (op_sqrt(next_vv) + eps)
 
         if decay_flag:
-            update = update + op_mul(weight_decay_tensor, param_fp32)
+            update = update + op_mul(weight_decay, param_fp32)
 
         update_with_lr = op_mul(op_mul(trust_ratio, lr), update)
 
@@ -116,10 +115,9 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
 lamb_opt_graph_kernel = C.MultitypeFuncGraph("lamb_opt_graph_kernel")
 
 
-@lamb_opt_graph_kernel.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
+@lamb_opt_graph_kernel.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Number",
                                 "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
-def _update_run_op_graph_kernel(beta1, beta2, eps, lr, weight_decay_tensor,
+def _update_run_op_graph_kernel(beta1, beta2, eps, global_step, lr, weight_decay, param, m, v, gradient, decay_flag):
-                                global_step, param, m, v, gradient, decay_flag):
     """
     Update parameters.
 
@@ -128,7 +126,7 @@ def _update_run_op_graph_kernel(beta1, beta2, eps, lr, weight_decay_tensor,
         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 in range [0.0, 1.0].
+        weight_decay (Number): Weight decay. Should be in range [0.0, 1.0].
         global_step (Tensor): Global step.
         param (Tensor): Parameters.
         m (Tensor): m value of parameters.
@@ -157,11 +155,10 @@ def _update_run_op_graph_kernel(beta1, beta2, eps, lr, weight_decay_tensor,
     i6 = op_cast(num_one, mstype.float32) - op_pow(beta1, i6_ex)
     i3 = op_cast(num_one, mstype.float32) - op_pow(beta2, i6_ex)
     i1 = op_square(gradient_fp32)
-    add3, update = G.LambNextMV()(i1, v, i3, gradient, m, i6, param, beta1,
+    add3, update = G.LambNextMV()(i1, v, i3, gradient, m, i6, param, beta1, i9, beta2, x1, weight_decay, eps)
-                                  i9, beta2, x1, weight_decay_tensor, eps)
 
     if decay_flag:
-        update = update + op_mul(weight_decay_tensor, param_fp32)
+        update = update + op_mul(weight_decay, param_fp32)
 
     w_norm = op_norm(param_fp32)
     g_norm = op_norm(gradient_fp32)
@@ -171,38 +168,18 @@ def _update_run_op_graph_kernel(beta1, beta2, eps, lr, weight_decay_tensor,
     ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
     tens = op_fill(op_dtype(w_norm), op_shape(w_norm), 10.0)
 
-    next_param = G.LambUpdateWithLR()(g_norm, w_norm, g_norm_hat, lr, update,
+    next_param = G.LambUpdateWithLR()(g_norm, w_norm, g_norm_hat, lr, update, param, zeros, ones, tens)
-                                      param, zeros, ones, tens)
     next_v = F.control_depend(add3, next_param)
     return next_v
 
 
-def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
+def _check_param_value(beta1, beta2, eps, prim_name):
-                       end_learning_rate, power, beta1, beta2, eps, weight_decay, prim_name):
-    """Check the type of inputs."""
-    validator.check_value_type("start_learning_rate", start_learning_rate, [float], prim_name)
-    validator.check_number_range("start_learning_rate rate", start_learning_rate, 0.0, float("inf"), Rel.INC_LEFT,
-                                 prim_name)
-    validator.check_value_type("end_learning_rate", end_learning_rate, [float], prim_name)
-    validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT,
-                                 prim_name)
-    validator.check_float_positive('power', power, prim_name)
-    validator.check_float_legal_value('power', power, prim_name)
-    validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
-    validator.check_integer('warmup_steps', warmup_steps, 0, Rel.GE, prim_name)
     validator.check_value_type("beta1", beta1, [float], prim_name)
     validator.check_value_type("beta2", beta2, [float], prim_name)
     validator.check_value_type("eps", eps, [float], prim_name)
-    validator.check_value_type(
+    validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-        "weight_dacay", weight_decay, [float], prim_name)
+    validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    validator.check_number_range(
+    validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
-        "beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    validator.check_number_range(
-        "beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    validator.check_number_range(
-        "eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
-    validator.check_number_range(
-        "weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
 
 
 class Lamb(Optimizer):
@@ -213,16 +190,37 @@ class Lamb(Optimizer):
     optimization technique. Refer to the paper `LARGE BATCH OPTIMIZATION FOR DEEP LEARNING: TRAINING BERT IN 76
     MINUTES <https://arxiv.org/abs/1904.00962>`_.
 
+    Note:
+        When separating parameter groups, the weight decay in each group will be applied on the parameters if the
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
+
+        To improve parameter groups performance, the customized order of parameters can be supported.
+
     Args:
-        params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
+        params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
-                                  should be class mindspore.Parameter.
+            the element in `params` should be class `Parameter`. When the `params` is a list of `dict`, the "params",
-        decay_steps (int): The steps of the lr decay. Should be equal to or greater than 1.
+            "lr", "weight_decay" and "order_params" are the keys can be parsed.
-        warmup_steps (int): The steps of lr warm up. Should be equal to or greater than 0. Default: 0.
+
-        start_learning_rate (float): A floating point value for the learning rate. Should be equal to
+            - params: Required. The value should be a list of `Parameter`.
-            or greater than 0. Default: 0.1.
+
-        end_learning_rate (float): A floating point value for the end learning rate. Should be equal to
+            - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
-            or greater than 0. Default: 0.0001.
+              If not, the `learning_rate` in the API will be used.
-        power (float): The power of the polynomial. It must be positive. Default: 1.0.
+
+            - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
+              will be used. If not, the `weight_decay` in the API will be used.
+
+            - order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and
+              the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
+              in the value of 'order_params' should be in one of group parameters.
+
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
         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.
@@ -241,90 +239,84 @@ class Lamb(Optimizer):
 
     Examples:
         >>> net = Net()
+        >>> #1) All parameters use the same learning rate and weight decay
+        >>> optim = nn.Lamb(params=net.trainable_params())
+        >>>
+        >>> #2) Use parameter groups and set different values
+        >>> poly_decay_lr = learning_rate_schedule.PolynomialDecayLR()
+        >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
+        >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
+        >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
+        >>>                 {'params': no_conv_params, 'lr': poly_decay_lr},
+        >>>                 {'order_params': net.trainable_params(0.01, 0.0001, 10, 0.5)}]
+        >>> optim = nn.Lamb(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
+        >>> # The no_conv_params's parameters will use dynamic learning rate of poly decay learning rate and default
+        >>> # weight decay of 0.0.
+        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
+        >>>
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = nn.Lamb(params=net.trainable_params(), decay_steps=10)
+        >>> model = Model(net, loss_fn=loss, optimizer=optim)
-        >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
     """
 
-    def __init__(self,
+    def __init__(self, params, learning_rate, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0):
-                 params,
+        super(Lamb, self).__init__(learning_rate, params, weight_decay)
-                 decay_steps,
+        _check_param_value(beta1, beta2, eps, self.cls_name)
-                 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.lower() and 'bias' not in x.name.lower()):
-        super(Lamb, self).__init__(0.0, params)
-        if self.is_group:
-            raise RuntimeError(
-                f"The {self.cls_name} optimizer cannot support group setting.")
-        _check_param_value(decay_steps, warmup_steps, start_learning_rate, end_learning_rate,
-                           power, beta1, beta2, eps, weight_decay, self.cls_name)
 
         # 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)
 
+        if not self.dynamic_lr:
+            self.global_step = Parameter(initializer(0, [1]), name='global_step')
+            self.assignadd = P.AssignAdd()
         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()
         self.enable_graph_kernel = context.get_context("enable_graph_kernel")
 
     def construct(self, gradients):
-        step = self.min(self.global_step, self.decay_steps)
+        lr = self.get_lr()
-        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
         if self.enable_graph_kernel:
-            optim_result = self.hyper_map(F.partial(lamb_opt_graph_kernel,
+            if self.is_group:
-                                                    self.beta1, self.beta2, self.eps, lr,
+                if self.is_group_lr:
-                                                    self.weight_decay_tensor, self.global_step),
+                    optim_result = self.hyper_map(F.partial(lamb_opt_graph_kernel, self.beta1, self.beta2, self.eps,
-                                          self.params, self.moments1, self.moments2, gradients, self.decay_flag)
+                                                            self.global_step),
+                                                  lr, self.weight_decay, self.params, self.moments1, self.moments2,
+                                                  gradients, self.decay_flags)
+                else:
+                    optim_result = self.hyper_map(F.partial(lamb_opt_graph_kernel, self.beta1, self.beta2, self.eps,
+                                                            self.global_step, lr),
+                                                  self.weight_decay, self.params, self.moments1, self.moments2,
+                                                  gradients, self.decay_flags)
+            else:
+                optim_result = self.hyper_map(F.partial(lamb_opt_graph_kernel, self.beta1, self.beta2, self.eps,
+                                                        self.global_step, lr, self.weight_decay),
+                                              self.params, self.moments1, self.moments2, gradients, self.decay_flags)
         else:
-            optim_result = self.hyper_map(F.partial(_lamb_opt,
+            if self.is_group:
-                                                    self.beta1, self.beta2, self.eps, lr,
+                if self.is_group_lr:
-                                                    self.weight_decay_tensor, self.global_step),
+                    optim_result = self.hyper_map(F.partial(_lamb_opt, self.beta1, self.beta2, self.eps,
-                                          self.params, self.moments1, self.moments2, gradients,
+                                                            self.global_step),
-                                          self.decay_flag, self.optim_filter)
+                                                  lr, self.weight_decay, self.params, self.moments1, self.moments2,
+                                                  gradients, self.decay_flags, self.optim_filter)
+                else:
+                    optim_result = self.hyper_map(F.partial(_lamb_opt, self.beta1, self.beta2, self.eps,
+                                                            self.global_step, lr),
+                                                  self.weight_decay, self.params, self.moments1, self.moments2,
+                                                  gradients, self.decay_flags, self.optim_filter)
+            else:
+                optim_result = self.hyper_map(F.partial(_lamb_opt, self.beta1, self.beta2, self.eps,
+                                                        self.global_step, lr, self.weight_decay),
+                                              self.params, self.moments1, self.moments2, gradients,
+                                              self.decay_flags, self.optim_filter)
+
         if self.use_parallel:
             optim_result = self.broadcast_params(optim_result)
 
-        added_global_step = self.global_step + self.one
+        if not self.dynamic_lr:
-        F.control_depend(lr, added_global_step)
+            F.control_depend(lr, self.assignadd(self.global_step, 1))
-        self.global_step = added_global_step
 
         return optim_result

mindspore/nn/optim/lars.py

@@ -38,14 +38,14 @@ def _tensor_run_opt(lars, learning_rate, weight_decay, gradient, weight, decay_f
 
     return gradient
 
+
 def _check_param_value(optimizer, epsilon, coefficient, use_clip, prim_name):
     validator.check_value_type("optimizer", optimizer, Optimizer, prim_name)
-    if "Adam" in optimizer.cls_name or "Lamb" in optimizer.cls_name:
-        raise TypeError("LARS can not be used with ", optimizer.cls_name)
     validator.check_value_type("epsilon", epsilon, [float], prim_name)
     validator.check_value_type("coefficient", coefficient, [float], prim_name)
     validator.check_value_type("use_clip", use_clip, [bool], prim_name)
 
+
 class LARS(Optimizer):
     """
     Implements the LARS algorithm with LARSUpdate Operator.
@@ -81,45 +81,71 @@ class LARS(Optimizer):
         super(LARS, self).__init__(0.0, [Parameter(Tensor(0.0), name="fake_param")])
         _check_param_value(optimizer, epsilon, coefficient, use_clip, self.cls_name)
         self.opt = optimizer
+        self.parameters = optimizer.parameters
+        self.use_clip = use_clip
+        self.lars_flag = tuple(lars_filter(x) for x in self.parameters)
+        self.is_group = optimizer.is_group
+        self.learning_rate = Parameter(Tensor(0.0, dtype=mstype.float32), name="fake_lr")
+        self.decay_flags = optimizer.decay_flags
+        self.reciprocal_scale = optimizer.reciprocal_scale
+        self.hyper_map = C.HyperMap()
         self.lars = P.LARSUpdate(epsilon, coefficient, use_clip)
         self.cast = P.Cast()
-        self.parameters = optimizer.parameters
+
-        if use_clip is True:
+        if use_clip:
-            self.learning_rate = optimizer.learning_rate
+            self.is_group_lr = optimizer.is_group_lr
             self.dynamic_lr = optimizer.dynamic_lr
-            self.gather = optimizer.gather
+            self.origin_learning_rate = optimizer.learning_rate
-            self.assignadd = optimizer.assignadd
             self.global_step = optimizer.global_step
-        else:
+            if self.is_group_lr and self.dynamic_lr:
-            self.learning_rate = Parameter(Tensor(0.0, dtype=mstype.float32), name="fake_lr")
+                raise ValueError('Grouped dynamic learning rate is currently not supported for the inputs optimizer ' \
-        self.reciprocal_scale = optimizer.reciprocal_scale
+                                 'of lars.')
-        optimizer.reciprocal_scale = 1.0
+
-        self.is_group = optimizer.is_group
         if self.is_group:
             self.weight_decay = tuple(map(lambda x: x / optimizer.loss_scale, optimizer.weight_decay))
+            optimizer.weight_decay = tuple(map(lambda x: 0.0, optimizer.weight_decay))
         else:
             self.weight_decay = optimizer.weight_decay / optimizer.loss_scale
+            optimizer.weight_decay = 0.0
+
+        optimizer.decay_flags = tuple(map(lambda x: False, self.decay_flags))
+        optimizer.reciprocal_scale = 1.0
         optimizer.exec_weight_decay = False
-        optimizer.weight_decay = 0.0
+
-        self.decay_flags = optimizer.decay_flags
+    def _get_lr(self):
-        self.lars_flag = tuple(lars_filter(x) for x in self.parameters)
+        """Get the learning rate of current step."""
-        self.hyper_map = C.HyperMap()
+        lr = self.origin_learning_rate
+        if self.dynamic_lr:
+            if self.is_group_lr:
+                lr = ()
+                for learning_rate in self.origin_learning_rate:
+                    current_dynamic_lr = learning_rate(self.global_step)
+                    lr += (current_dynamic_lr,)
+            else:
+                lr = self.origin_learning_rate(self.global_step)
+
+        return lr
 
     def construct(self, gradients):
         params = self.parameters
-        if self.dynamic_lr:
+        if self.use_clip:
-            lr = self.gather(self.learning_rate, self.global_step, 0)
+            lr = self._get_lr()
-            F.control_depend(lr, self.assignadd(self.global_step, 1))
         else:
             lr = self.learning_rate
+
         if self.reciprocal_scale != 1.0:
             gradients = self.hyper_map(F.partial(_grad_scale, self.reciprocal_scale), gradients)
+
         if self.is_group:
-            grad_t = self.hyper_map(F.partial(_lars_opt, self.lars, lr), self.weight_decay,
+            if self.is_group_lr:
-                                    gradients, params, self.decay_flags, self.lars_flag)
+                gradients = self.hyper_map(F.partial(_lars_opt, self.lars), lr, self.weight_decay,
+                                           gradients, params, self.decay_flags, self.lars_flag)
+            else:
+                gradients = self.hyper_map(F.partial(_lars_opt, self.lars, lr), self.weight_decay,
+                                           gradients, params, self.decay_flags, self.lars_flag)
         else:
-            grad_t = self.hyper_map(F.partial(_lars_opt, self.lars, lr, self.weight_decay),
+            gradients = self.hyper_map(F.partial(_lars_opt, self.lars, lr, self.weight_decay),
-                                    gradients, params, self.decay_flags, self.lars_flag)
+                                       gradients, params, self.decay_flags, self.lars_flag)
-        success = self.opt(grad_t)
+        success = self.opt(gradients)
 
         return success

mindspore/nn/optim/lazyadam.py

@@ -84,12 +84,11 @@ class LazyAdam(Optimizer):
     :math:`\epsilon` represents `eps`.
 
     Note:
-        The LazyAdam optimizer supports separating parameter groups. Different parameter groups can set different
-        `learning_rate` and `weight_decay`.
-
         When separating parameter groups, the weight decay in each group will be applied on the parameters if the
-        value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
-        applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters.
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
+
+        To improve parameter groups performance, the customized order of parameters can be supported.
 
         The sparse strategy is applied while the SparseGatherV2 operator being used for forward network.
         The sparse behavior, to be notice, is not equivalent to the
@@ -113,13 +112,14 @@ class LazyAdam(Optimizer):
               the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
               in the value of 'order_params' should be in one of group parameters.
 
-        learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
-                                                        Iterable or a Tensor and the dims of the Tensor is 1,
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
-                                                        use dynamic learning rate, then the i-th step will
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
-                                                        take the i-th value as the learning rate.
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
-                                                        When the learning_rate is float or learning_rate is a Tensor
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
-                                                        but the dims of the Tensor is 0, use fixed learning rate.
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
-                                                        Other cases are not supported. Default: 1e-3.
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
+            Default: 1e-3.
         beta1 (float): The exponential decay rate for the 1st moment estimates. Should be in range (0.0, 1.0). Default:
                        0.9.
         beta2 (float): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0). Default:
@@ -153,9 +153,9 @@ class LazyAdam(Optimizer):
         >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
         >>>                 {'params': no_conv_params, 'lr': 0.01},
         >>>                 {'order_params': net.trainable_params()}]
-        >>> optim = nn.LazyAdam(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> opt = nn.LazyAdam(group_params, learning_rate=0.1, weight_decay=0.0)
-        >>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
+        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
-        >>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
+        >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0.
         >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
         >>>
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()

mindspore/nn/optim/momentum.py

@@ -47,12 +47,9 @@ class Momentum(Optimizer):
     Refer to the paper on the importance of initialization and momentum in deep learning for more details.
 
     Note:
-        The Momentum optimizer supports separating parameter groups. Different parameter groups can set different
-        `learning_rate` and `weight_decay`.
-
         When separating parameter groups, the weight decay in each group will be applied on the parameters if the
-        value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
-        applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters.
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
 
         To improve parameter groups performance, the customized order of parameters can be supported.
 
@@ -73,14 +70,13 @@ class Momentum(Optimizer):
               the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
               in the value of 'order_params' should be in one of group parameters.
 
-        learning_rate (Union[int, float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
-                                                             Iterable or a Tensor and the dims of the Tensor is 1,
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
-                                                             use dynamic learning rate, then the i-th step will
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
-                                                             take the i-th value as the learning rate.
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
-                                                             When the learning_rate is float or learning_rate is a
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
-                                                             Tensor but the dims of the Tensor is 0, use fixed learning
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
-                                                             rate. Other cases are not supported. It should be equal to
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
-                                                             or greater than 0.0.
         momentum (float): Hyperparameter of type float, means momentum for the moving average.
             It should be at least 0.0.
         weight_decay (int, float): Weight decay (L2 penalty). It should be in range [0.0, 1.0]. Default: 0.0.

mindspore/nn/optim/optimizer.py

@@ -20,6 +20,7 @@ import numpy as np
 import mindspore
 from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore.nn.cell import Cell
+from mindspore.nn.layer.container import CellList
 from mindspore.common.parameter import Parameter, ParameterTuple
 from mindspore.common.initializer import initializer
 from mindspore.common.tensor import Tensor, IndexedSlices
@@ -30,6 +31,7 @@ from mindspore import log as logger
 from mindspore.parallel._utils import _get_global_rank, _get_device_num, _get_parallel_mode
 from mindspore.train.parallel_utils import ParallelMode
 from mindspore import context
+from mindspore.nn.learning_rate_schedule import LearningRateSchedule
 
 __all__ = ['Optimizer']
 
@@ -44,25 +46,22 @@ class Optimizer(Cell):
         This class defines the API to add Ops to train a model. Never use
         this class directly, but instead instantiate one of its subclasses.
 
-        Some optimizers support separating parameter groups. Different parameter groups can set different
+        Different parameter groups can set different `learning_rate` and `weight_decay`.
-        `learning_rate` and `weight_decay`.
 
         When separating parameter groups, the weight decay in each group will be applied on the parameters if the
-        value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be
+        weight_decay is positive. For most optimizer, when not separating parameters, the `weight_decay` in the API will
-        applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters.
+        be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
 
         To improve parameter groups performance, the customized order of parameters can be supported.
 
     Args:
-        learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning
-                                                        Iterable or a Tensor and the dims of the Tensor is 1,
+            rate. When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
-                                                        use dynamic learning rate, then the i-th step will
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
-                                                        take the i-th value as the learning rate.
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
-                                                        When the learning_rate is float or learning_rate is a Tensor
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
-                                                        but the dims of the Tensor is 0, use fixed learning rate.
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
-                                                        Other cases are not supported. It should be equal to or greater
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
-                                                        than 0. If the type of `learning_rate` input is int, it will be
-                                                        converted to float.
         parameters (Union[list[Parameter], list[dict]]): When the `parameters` is a list of `Parameter` which will be
             updated, the element in `parameters` should be class `Parameter`. When the `parameters` is a list of `dict`,
             the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed.
@@ -104,32 +103,17 @@ class Optimizer(Cell):
             loss_scale = float(loss_scale)
         validator.check_value_type("loss_scale", loss_scale, [float], self.cls_name)
         validator.check_number_range("loss_scale", loss_scale, 1.0, float("inf"), Rel.INC_LEFT, self.cls_name)
+        self.loss_scale = loss_scale
 
-        if isinstance(weight_decay, int):
+        weight_decay = self._preprocess_weight_decay(weight_decay)
-            weight_decay = float(weight_decay)
-        validator.check_value_type("weight_decay", weight_decay, [float], self.cls_name)
-        validator.check_number_range("weight_decay", weight_decay, 0.0, 1.0, Rel.INC_BOTH, self.cls_name)
 
+        self.dynamic_lr = False
+        self.assignadd = None
+        self.global_step = None
         self.is_group = False
         self.is_group_lr = False
         self.is_group_params_ordered = False
-        self.loss_scale = loss_scale
+        learning_rate = self._preprocess_single_lr(learning_rate)
-        if isinstance(learning_rate, int):
-            learning_rate = float(learning_rate)
-        if isinstance(learning_rate, float):
-            self.dynamic_lr = False
-            self.gather = None
-            self.assignadd = None
-            self.global_step = None
-            self.scalar_lr = learning_rate
-        else:
-            self.dynamic_lr = True
-            self.gather = P.GatherV2()
-            self.assignadd = P.AssignAdd()
-            self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')
-            self.scalar_lr = None
-
-        learning_rate = self._get_single_lr(learning_rate)
         if isinstance(parameters[0], dict):
             self.is_group = True
             self.group_params = []
@@ -137,32 +121,40 @@ class Optimizer(Cell):
             self.group_weight_decay = []
             self._init_group_params(parameters, learning_rate, weight_decay)
 
-        if self.is_group_lr:
+        # The final value of dynamic_lr can be determined after the process of parse_single_lr and init_group_params
-            self.learning_rate = ParameterTuple(self.group_lr)
+        if self.dynamic_lr:
-        else:
+            self.assignadd = P.AssignAdd()
-            self.learning_rate = Parameter(Tensor(learning_rate, mstype.float32), name="learning_rate")
+            self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')
 
+        if self.is_group_lr:
+            if self.dynamic_lr:
+                self.learning_rate = CellList(self.group_lr)
+            else:
+                self.learning_rate = tuple(self.group_lr)
+        else:
+            self.learning_rate = self._build_single_lr(learning_rate, 'learning_rate')
         if self.is_group:
             self.parameters = ParameterTuple(self.group_params)
             self.weight_decay = tuple(self.group_weight_decay)
             decay_filter = lambda x: x > 0
             self.decay_flags = tuple(decay_filter(x) for x in self.weight_decay)
+            self.exec_weight_decay = any(self.decay_flags)
         else:
             self.parameters = ParameterTuple(parameters)
             self.weight_decay = weight_decay * loss_scale
             decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
             self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
+            self.exec_weight_decay = self.weight_decay > 0
         ps_filter = lambda x: x.is_param_ps
         self.ps_parameters = tuple(ps_filter(x) for x in self.parameters)
         self.reciprocal_scale = 1.0 / loss_scale
-        self.exec_weight_decay = any(self.decay_flags)
         self.param_length = len(self.parameters)
         self.map_ = C.Map()
 
         use_parallel = context.get_auto_parallel_context("enable_parallel_optimizer")
         self.use_parallel = use_parallel
         if use_parallel:
-            if self.cls_name not in ["Lamb", "AdamWeightDecayDynamicLR", "AdamWeightDecay"]:
+            if self.cls_name not in ["Lamb", "AdamWeightDecay"]:
                 raise RuntimeError("Optimizer segmentation does not support optimizer {}".format(self.cls_name))
             if _get_parallel_mode() != ParallelMode.DATA_PARALLEL:
                 raise RuntimeError("Optimizer segmentation does not support parallel mode {}".format
@@ -193,13 +185,12 @@ class Optimizer(Cell):
         Returns:
             tuple[Tensor], The gradients after weight decay.
         """
-        params = self.parameters
+        if self.exec_weight_decay:
-        if self.is_group:
+            params = self.parameters
-            if self.exec_weight_decay:
+            if self.is_group:
                 gradients = self.map_(F.partial(_apply_decay), self.weight_decay, self.decay_flags,
                                       params, gradients)
-        else:
+            else:
-            if self.weight_decay > 0:
                 gradients = self.map_(F.partial(_apply_decay, self.weight_decay), self.decay_flags,
                                       params, gradients)
 
@@ -225,24 +216,53 @@ class Optimizer(Cell):
 
         return gradients
 
-    def _get_single_lr(self, learning_rate):
+    def _preprocess_weight_decay(self, weight_decay):
-        """Get learning rate in Tensor type."""
+        """Check weight decay, and convert int to float."""
-        if isinstance(learning_rate, float):
+        if isinstance(weight_decay, (float, int)):
+            weight_decay = float(weight_decay)
+            validator.check_number_range("weight_decay", weight_decay, 0.0, 1.0, Rel.INC_BOTH, self.cls_name)
+            return weight_decay
+        raise TypeError("Weight decay should be int or float.")
+
+    def _preprocess_single_lr(self, learning_rate):
+        """Check lr value, and convert lr to a float, a Tensor or a LearningRateSchedule."""
+        if isinstance(learning_rate, (float, int)):
+            learning_rate = float(learning_rate)
             validator.check_number_range("learning rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
-            lr = Tensor(learning_rate, mstype.float32)
+            return learning_rate
-        elif isinstance(learning_rate, Iterable):
+        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 0:
-            lr = Tensor(np.array(list(learning_rate)).astype(np.float32))
+            return learning_rate
-        elif isinstance(learning_rate, Tensor):
+
+        self.dynamic_lr = True
+        if isinstance(learning_rate, Iterable):
+            return Tensor(np.array(list(learning_rate)).astype(np.float32))
+        if isinstance(learning_rate, Tensor):
             if learning_rate.dim() > 1:
-                raise ValueError("Learning rate should be a 0 or 1 dim `Tensor`,"
+                raise ValueError("The dim of `Tensor` type Learning rate should be a 0 or 1,"
                                  f"but got {learning_rate.dim()}.")
             if learning_rate.dim() == 1 and learning_rate.size() < 2:
-                logger.warning("If want to use the dynamic learning rate, please make sure that the number "
+                logger.warning("If use `Tensor` type dynamic learning rate, please make sure that the number"
-                               "of elements in the list, tuple or tensor passed is greater than 1.")
+                               "of elements in the tensor passed is greater than 1.")
-            lr = learning_rate
+            return learning_rate
-        else:
+        if isinstance(learning_rate, LearningRateSchedule):
-            raise TypeError("Learning rate should be float, Tensor or Iterable.")
+            return learning_rate
-        return lr
+        raise TypeError("Learning rate should be int, float, Tensor, Iterable or LearningRateSchedule.")
+
+    def _build_single_lr(self, learning_rate, name):
+        """Build learning rate value, convert learning rate to a Parameter or a LearningRateSchedule."""
+        if isinstance(learning_rate, float):
+            learning_rate = Parameter(Tensor(learning_rate, mstype.float32), name)
+            if self.is_group_lr and self.dynamic_lr:
+                learning_rate = _ConvertToCell(learning_rate)
+            return learning_rate
+        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 0:
+            learning_rate = Parameter(learning_rate, name)
+            if self.is_group_lr and self.dynamic_lr:
+                learning_rate = _ConvertToCell(learning_rate)
+            return learning_rate
+        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 1:
+            return _IteratorLearningRate(learning_rate, name)
+        return learning_rate
 
     def _check_group_params(self, parameters):
         """Check group params."""
@@ -270,13 +290,12 @@ class Optimizer(Cell):
     def _parse_group_params(self, parameters, learning_rate):
         """Parse group params."""
         self._check_group_params(parameters)
-        if self.dynamic_lr:
+        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 1:
-            dynamic_lr_length = learning_rate.size()
+            tensor_lr_length = learning_rate.size()
         else:
-            dynamic_lr_length = 0
+            tensor_lr_length = 0
 
         for group_param in parameters:
-            lr_length = dynamic_lr_length
             if 'order_params' in group_param.keys():
                 if len(group_param.keys()) > 1:
                     raise ValueError("The order params dict in group parameters should "
@@ -288,53 +307,38 @@ class Optimizer(Cell):
 
             if 'lr' in group_param.keys():
                 self.is_group_lr = True
-                self._get_single_lr(group_param['lr'])
+                group_lr = self._preprocess_single_lr(group_param['lr'])
-                if isinstance(group_param['lr'], Iterable):
-                    lr_length = len(group_param['lr'])
-                    self.dynamic_lr = True
-                elif isinstance(group_param['lr'], Tensor):
-                    lr_length = group_param['lr'].size()
-                    self.dynamic_lr = True
 
-            if dynamic_lr_length not in (lr_length, 0):
+                if isinstance(group_lr, Tensor) and group_lr.dim() == 1:
-                raise ValueError("The dynamic learning rate in group should be the same size.")
+                    group_lr_length = group_lr.size()
-
+                    if tensor_lr_length == 0:
-            dynamic_lr_length = lr_length
+                        tensor_lr_length = group_lr_length
-        self.dynamic_lr_length = dynamic_lr_length
+                    elif group_lr_length != tensor_lr_length:
+                        raise ValueError("The Tensor type dynamic learning rate in group should be the same size.")
 
     def _init_group_params(self, parameters, learning_rate, weight_decay):
         """Init learning rate or weight decay in group params."""
-        origin_dynamic_lr = self.dynamic_lr
         self._parse_group_params(parameters, learning_rate)
-        if self.dynamic_lr and not origin_dynamic_lr:
+        default_lr = self._build_single_lr(learning_rate, 'learning_rate')
-            self.gather = P.GatherV2()
-            self.assignadd = P.AssignAdd()
-            self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')
 
         params_store = []
-        for group_param in parameters:
+        for group_num, group_param in enumerate(parameters):
             if 'order_params' in group_param.keys():
                 ordered_parameters = group_param['order_params']
                 continue
 
             self.group_params += group_param['params']
+
             if 'lr' in group_param.keys():
-                params_dynamic_lr = isinstance(group_param['lr'], (Iterable, Tensor))
+                lr_param_name = 'learning_rate_group_' + str(group_num)
-                if self.dynamic_lr and not params_dynamic_lr:
+                lr = self._preprocess_single_lr(group_param['lr'])
-                    lr = Tensor(np.array([group_param['lr']] * self.dynamic_lr_length).astype(np.float32))
+                lr = self._build_single_lr(lr, lr_param_name)
-                else:
-                    lr = self._get_single_lr(group_param['lr'])
             else:
-                if self.dynamic_lr and not origin_dynamic_lr:
+                lr = default_lr
-                    lr = Tensor(np.array([self.scalar_lr] * self.dynamic_lr_length).astype(np.float32))
-                else:
-                    lr = learning_rate
 
             if 'weight_decay' in group_param.keys():
-                validator.check_float_legal_value('weight_decay', group_param['weight_decay'], None)
+                cur_weight_decay = self._preprocess_weight_decay(group_param['weight_decay'])
-                validator.check_number_range('weight_decay', group_param['weight_decay'], 0.0, 1.0,
+                weight_decay_ = cur_weight_decay * self.loss_scale
-                                             Rel.INC_BOTH, self.cls_name)
-                weight_decay_ = group_param['weight_decay'] * self.loss_scale
             else:
                 weight_decay_ = weight_decay * self.loss_scale
 
@@ -348,7 +352,7 @@ class Optimizer(Cell):
                     raise RuntimeError(f"The {param.name} parameter has appeared in parameter groups.")
 
                 params_store.append(param.name)
-                self.group_lr.append(Parameter(lr, name="lr_" + param.name))
+                self.group_lr.append(lr)
                 self.group_weight_decay.append(weight_decay_)
 
         if self.is_group_params_ordered:
@@ -384,19 +388,17 @@ class Optimizer(Cell):
         Returns:
             float, the learning rate of current step.
         """
-        if self.is_group_lr:
+        lr = self.learning_rate
-            lr = self.learning_rate
+        if self.dynamic_lr:
-            if self.dynamic_lr:
+            if self.is_group_lr:
                 lr = ()
-                for i in range(self.param_length):
+                for learning_rate in self.learning_rate:
-                    current_dynamic_lr = self.gather(self.learning_rate[i], self.global_step, 0)
+                    current_dynamic_lr = learning_rate(self.global_step)
                     lr += (current_dynamic_lr,)
-                F.control_depend(lr, self.assignadd(self.global_step, 1))
+            else:
-        else:
+                lr = self.learning_rate(self.global_step)
-            lr = self.learning_rate
+
-            if self.dynamic_lr:
+            F.control_depend(lr, self.assignadd(self.global_step, 1))
-                lr = self.gather(self.learning_rate, self.global_step, 0)
-                F.control_depend(lr, self.assignadd(self.global_step, 1))
         return lr
 
     def get_lr_parameter(self, param):
@@ -409,29 +411,31 @@ class Optimizer(Cell):
         Returns:
             Parameter, single `Parameter` or `list[Parameter]` according to the input type.
         """
-        if not isinstance(param, (Parameter, list)):
+        def get_lr_value(learning_rate):
+            if isinstance(learning_rate, (_ConvertToCell, _IteratorLearningRate)):
+                return learning_rate.learning_rate
+
+            return learning_rate
+
+        if isinstance(param, Parameter):
+            param_list = [param]
+        elif isinstance(param, list):
+            param_list = param
+        else:
             raise TypeError(f"The parameter only support 'Parameter' or 'list' type.")
 
-        if isinstance(param, list):
+        lr = []
-            lr = []
+        for p in param_list:
-            for p in param:
+            validator.check_value_type("parameter", p, [Parameter], self.cls_name)
-                validator.check_value_type("parameter", p, [Parameter], self.cls_name)
+            if p not in self.parameters:
-                if p not in self.parameters:
+                raise ValueError(f"The parameter {p.name} is not in optimizer.")
-                    raise ValueError(f"The parameter {p.name} is not in optimizer.")
-                if self.is_group_lr:
-                    index = self.parameters.index(p)
-                    lr.append(self.learning_rate[index])
-                else:
-                    lr.append(self.learning_rate)
-        else:
-            if param not in self.parameters:
-                raise ValueError(f"The parameter {param.name} is not in optimizer.")
             if self.is_group_lr:
-                index = self.parameters.index(param)
+                index = self.parameters.index(p)
-                lr = self.learning_rate[index]
+                lr.append(get_lr_value(self.learning_rate[index]))
             else:
-                lr = self.learning_rate
+                lr.append(get_lr_value(self.learning_rate))
-        return lr
+
+        return lr if isinstance(param, list) else lr[0]
 
     def _get_parameter_group_id(self):
         """
@@ -524,3 +528,33 @@ def tensor_grad_scale_with_sparse(scale, grad):
     if scale == 1.0:
         return grad
     return IndexedSlices(grad.indices(), grad.values() * scale, grad.dense_shape())
+
+
+class _ConvertToCell(LearningRateSchedule):
+    """Inner api, convert learning rate of scalar to LearningRateSchedule."""
+    def __init__(self, learning_rate):
+        super(_ConvertToCell, self).__init__()
+        if not isinstance(learning_rate, Parameter):
+            raise TypeError('Learning rate must be Parameter.')
+        self.learning_rate = learning_rate
+
+    def construct(self, global_step):
+        return self.learning_rate + 1.0 - 1.0
+
+
+class _IteratorLearningRate(LearningRateSchedule):
+    """Inner api, convert learning rate of Tensor(list) to LearningRateSchedule."""
+    def __init__(self, learning_rate, name):
+        super(_IteratorLearningRate, self).__init__()
+        if isinstance(learning_rate, Tensor):
+            if learning_rate.dim() != 1:
+                raise ValueError("The dim of `Tensor` type dynamic learning rate should be a 1,"
+                                 f"but got {learning_rate.dim()}.")
+        else:
+            raise TypeError("Learning rate should be Tensor.")
+
+        self.learning_rate = Parameter(learning_rate, name)
+        self.gather = P.GatherV2()
+
+    def construct(self, global_step):
+        return self.gather(self.learning_rate, global_step, 0)

mindspore/nn/optim/proximal_ada_grad.py

@@ -32,7 +32,7 @@ def _tensor_run_opt_with_sparse(opt, sparse_opt, learning_rate, l1, l2, gradient
 
 
 @_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
-def _tensor_run_opt(opt, sparse_opt, learning_rate, l1, l2, gradient, weight, accum):
+def _tensor_run_opt(opt, sparse_opt, l1, l2, learning_rate, gradient, weight, accum):
     """Apply proximal_ada_grad optimizer to the weight parameter."""
     success = True
     success = F.depend(success, opt(weight, accum, learning_rate, l1, l2, gradient))
@@ -59,15 +59,42 @@ class ProximalAdagrad(Optimizer):
     <http://papers.nips.cc//paper/3793-efficient-learning-using-forward-backward-splitting.pdf>`_.
 
     Note:
+        When separating parameter groups, the weight decay in each group will be applied on the parameters if the
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
+
+        To improve parameter groups performance, the customized order of parameters can be supported.
+
         The sparse strategy is applied while the SparseGatherV2 operator being used for forward network.
         The sparse feature is under continuous development. The sparse
         behavior is currently performed on the CPU.
 
     Args:
-        params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
+        params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
-            should be Parameter.
+            the element in `params` should be class `Parameter`. When the `params` is a list of `dict`, the "params",
+            "lr", "weight_decay" and "order_params" are the keys can be parsed.
+
+            - params: Required. The value should be a list of `Parameter`.
+
+            - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
+              If not, the `learning_rate` in the API will be used.
+
+            - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
+              will be used. If not, the `weight_decay` in the API will be used.
+
+            - order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and
+              the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
+              in the value of 'order_params' should be in one of group parameters.
+
         accum (float): The starting value for accumulators, must be zero or positive values. Default: 0.1.
-        learning_rate (float): The learning rate value, must be greater than or equal to zero. Default: 0.001.
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
+            Default: 0.001.
         l1 (float): l1 regularization strength, must be greater than or equal to zero. Default: 0.0.
         l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
         use_locking (bool): If True use locks for update operation. Default: False.
@@ -83,21 +110,31 @@ class ProximalAdagrad(Optimizer):
 
     Examples:
         >>> net = Net()
+        >>> #1) All parameters use the same learning rate and weight decay
+        >>> optim = nn.ProximalAdagrad(params=net.trainable_params())
+        >>>
+        >>> #2) Use parameter groups and set different values
+        >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
+        >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
+        >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
+        >>>                 {'params': no_conv_params, 'lr': 0.01},
+        >>>                 {'order_params': net.trainable_params()}]
+        >>> optim = nn.ProximalAdagrad(group_params, learning_rate=0.1, weight_decay=0.0)
+        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
+        >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0.
+        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
+        >>>
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-        >>> opt = nn.ProximalAdagrad(net.trainable_params())
+        >>> model = Model(net, loss_fn=loss, optimizer=optim)
-        >>> model = Model(net, loss_fn=loss, optimizer=opt, metrics=None)
     """
 
     def __init__(self, params, accum=0.1, learning_rate=0.001, l1=0.0, l2=0.0,
                  use_locking=False, loss_scale=1.0, weight_decay=0.0):
         super(ProximalAdagrad, self).__init__(learning_rate, params, weight_decay, loss_scale)
-        if self.is_group:
-            raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
         _check_param_value(accum, l1, l2, use_locking, self.cls_name)
         self.accum = self.parameters.clone(prefix="accum", init=accum)
         self.l1 = Tensor(l1, mstype.float32)
         self.l2 = Tensor(l2, mstype.float32)
-        self.weight_decay = weight_decay
         self.hyper_map = C.HyperMap()
         self.opt = P.ApplyProximalAdagrad(use_locking=use_locking)
         self.sparse_opt = P.FusedSparseProximalAdagrad(use_locking=use_locking)
@@ -107,7 +144,11 @@ class ProximalAdagrad(Optimizer):
         accum = self.accum
         grads = self.decay_weight(grads)
         grads = self.scale_grad(grads)
-        lr = self.learning_rate
+        lr = self.get_lr()
-        success = self.map_(F.partial(_proximal_ada_grad_opt, self.opt, self.sparse_opt, lr, self.l1, self.l2),
+        if self.is_group_lr:
-                            grads, params, accum)
+            success = self.map_(F.partial(_proximal_ada_grad_opt, self.opt, self.sparse_opt, self.l1, self.l2), lr,
+                                grads, params, accum)
+        else:
+            success = self.map_(F.partial(_proximal_ada_grad_opt, self.opt, self.sparse_opt, self.l1, self.l2, lr),
+                                grads, params, accum)
         return success

mindspore/nn/optim/rmsprop.py

@@ -44,12 +44,9 @@ class RMSProp(Optimizer):
     Implements Root Mean Squared Propagation (RMSProp) algorithm.
 
     Note:
-        The RMSProp optimizer supports separating parameter groups. Different parameter groups can set different
-        `learning_rate` and `weight_decay`.
-
         When separating parameter groups, the weight decay in each group will be applied on the parameters if the
-        value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
-        applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters.
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
 
         To improve parameter groups performance, the customized order of parameters can be supported.
 
@@ -109,13 +106,14 @@ class RMSProp(Optimizer):
               the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
               in the value of 'order_params' should be in one of group parameters.
 
-        learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
-                                                        Iterable or a Tensor and the dims of the Tensor is 1,
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
-                                                        use dynamic learning rate, then the i-th step will
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
-                                                        take the i-th value as the learning rate.
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
-                                                        When the learning_rate is float or learning_rate is a Tensor
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
-                                                        but the dims of the Tensor is 0, use fixed learning rate.
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
-                                                        Other cases are not supported. Default: 0.1.
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
+            Default: 0.1.
         decay (float): Decay rate. Should be equal to or greater than 0. Default: 0.9.
         momentum (float): Hyperparameter of type float, means momentum for the moving average. Should be equal to or
                           greater than 0. Default: 0.0.

mindspore/nn/optim/sgd.py

@@ -40,14 +40,11 @@ class SGD(Optimizer):
     momentum in deep learning <http://proceedings.mlr.press/v28/sutskever13.html>`_.
 
     Note:
-        The SGD optimizer supports separating parameter groups. Different parameter groups can set different
-        `learning_rate` and `weight_decay`.
-
         When separating parameter groups, the weight decay in each group will be applied on the parameters if the
-        value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be
+        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
-        applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters.
+        on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
 
-       To improve parameter groups performance, the customized order of parameters can be supported.
+        To improve parameter groups performance, the customized order of parameters can be supported.
 
     Args:
         params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
@@ -66,14 +63,14 @@ class SGD(Optimizer):
               the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
               in the value of 'order_params' should be in one of group parameters.
 
-        learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
+        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate.
-                                                        Iterable or a Tensor and the dims of the Tensor is 1,
+            When the learning_rate is a Iterable or a Tensor with dimension of 1, use dynamic learning rate, then
-                                                        use dynamic learning rate, then the i-th step will
+            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
-                                                        take the i-th value as the learning rate.
+            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
-                                                        When the learning_rate is float or learning_rate is a Tensor
+            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor with
-                                                        but the dims of the Tensor is 0, use fixed learning rate.
+            dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should be
-                                                        Other cases are not supported. It should be equal to or
+            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
-                                                        greater than 0. Default: 0.1.
+            Default: 0.1.
         momentum (float): A floating point value the momentum. should be at least 0.0. Default: 0.0.
         dampening (float): A floating point value of dampening for momentum. should be at least 0.0. Default: 0.0.
         weight_decay (float): Weight decay (L2 penalty). It should be in range [0.0, 1.0]. Default: 0.0.

model_zoo/mass/src/utils/lr_scheduler.py

@@ -14,9 +14,10 @@
 # ============================================================================
 """Learning scheduler."""
 from math import ceil
-
 import numpy as np
 
+import mindspore.nn.learning_rate_schedule as lr_schedules
+
 
 def square_root_schedule(lr, update_num, decay_start_step,
                          warmup_steps=2000,
@@ -105,3 +106,35 @@ def polynomial_decay_scheduler(lr, min_lr, decay_steps, total_update_num, warmup
         lrs[step] = (lr - min_lr) * pow(1 - _step / _decay_steps, power) + min_lr
 
     return lrs
+
+
+class BertLearningRate(lr_schedules.LearningRateSchedule):
+    """
+    Implements of warmup-polydecay learning rate scheduler.
+
+    Args:
+        learning_rate (float): The initial value of learning rate.
+        end_learning_rate (float): The end value of learning rate.
+        warmup_steps (int): The warm up steps of learning rate.
+        decay_steps (int): A value used to calculate decayed learning rate.
+        power (float): A value used to calculate decayed learning rate.
+
+    Returns:
+        Tensor. The learning rate value for the current step.
+    """
+    def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
+        super(BertLearningRate, self).__init__()
+        self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
+        self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+
+        self.greater = P.Greater()
+        self.one = Tensor(np.array([1.0]).astype(np.float32))
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+        warmup_lr = self.warmup_lr(global_step)
+        decay_lr = self.decay_lr(global_step)
+        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        return lr

model_zoo/mass/train.py

@@ -37,7 +37,7 @@ from src.transformer.infer_mass import infer
 from src.utils import LossCallBack
 from src.utils import one_weight, zero_weight, weight_variable
 from src.utils import square_root_schedule
-from src.utils.lr_scheduler import polynomial_decay_scheduler
+from src.utils.lr_scheduler import polynomial_decay_scheduler, BertLearningRate
 
 parser = argparse.ArgumentParser(description='MASS train entry point.')
 parser.add_argument("--config", type=str, required=True, help="model config json file path.")
@@ -178,10 +178,16 @@ def _build_training_pipeline(config: TransformerConfig,
     if config.optimizer.lower() == "adam":
         optimizer = Adam(net_with_loss.trainable_params(), lr, beta1=0.9, beta2=0.98)
     elif config.optimizer.lower() == "lamb":
-        optimizer = Lamb(net_with_loss.trainable_params(), decay_steps=12000,
+        lr = BertLearningRate(decay_steps=12000, learning_rate=config.lr, end_learning_rate=config.min_lr,
-                         start_learning_rate=config.lr, end_learning_rate=config.min_lr,
+                              power=10.0, warmup_steps=config.warmup_steps)
-                         power=10.0, warmup_steps=config.warmup_steps, weight_decay=0.01,
+        decay_params = list(filter(lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower(),
-                         eps=1e-6)
+                                   net_with_loss.trainable_params()))
+        other_params = list(filter(lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower(),
+                                   net_with_loss.trainable_params()))
+        group_params = [{'params': decay_params, 'weight_decay': 0.01},
+                        {'params': other_params}]
+
+        optimizer = Lamb(group_params, lr, eps=1e-6)
     elif config.optimizer.lower() == "momentum":
         optimizer = Momentum(net_with_loss.trainable_params(), lr, momentum=0.9)
     else:

model_zoo/official/nlp/bert/README.md

@@ -147,7 +147,7 @@ Parameters for dataset and network (Pre-Training/Fine-Tuning/Evaluation):
     compute_type                    compute type in BertTransformer: mstype.float16 | mstype.float32, default is mstype.float16
 
 Parameters for optimizer:
-    AdamWeightDecayDynamicLR:
+    AdamWeightDecay:
     decay_steps                     steps of the learning rate decay: N
     learning_rate                   value of learning rate: Q
     end_learning_rate               value of end learning rate: Q, must be positive

model_zoo/official/nlp/bert/run_classifier.py

@@ -23,12 +23,12 @@ from src.bert_for_finetune import BertFinetuneCell, BertCLS
 from src.finetune_eval_config import optimizer_cfg, bert_net_cfg
 from src.dataset import create_classification_dataset
 from src.assessment_method import Accuracy, F1, MCC, Spearman_Correlation
-from src.utils import make_directory, LossCallBack, LoadNewestCkpt
+from src.utils import make_directory, LossCallBack, LoadNewestCkpt, BertLearningRate
 import mindspore.common.dtype as mstype
 from mindspore import context
 from mindspore import log as logger
 from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
-from mindspore.nn.optim import AdamWeightDecayDynamicLR, Lamb, Momentum
+from mindspore.nn.optim import AdamWeightDecay, Lamb, Momentum
 from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
@@ -42,27 +42,31 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
         raise ValueError("Pretrain model missed, finetune task must load pretrain model!")
     steps_per_epoch = dataset.get_dataset_size()
     # optimizer
-    if optimizer_cfg.optimizer == 'AdamWeightDecayDynamicLR':
+    if optimizer_cfg.optimizer == 'AdamWeightDecay':
-        optimizer = AdamWeightDecayDynamicLR(network.trainable_params(),
+        lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.AdamWeightDecay.learning_rate,
-                                             decay_steps=steps_per_epoch * epoch_num,
+                                       end_learning_rate=optimizer_cfg.AdamWeightDecay.end_learning_rate,
-                                             learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.learning_rate,
+                                       warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
-                                             end_learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.end_learning_rate,
+                                       decay_steps=steps_per_epoch * epoch_num,
-                                             power=optimizer_cfg.AdamWeightDecayDynamicLR.power,
+                                       power=optimizer_cfg.AdamWeightDecay.power)
-                                             warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
+        params = net_with_loss.trainable_params()
-                                             weight_decay=optimizer_cfg.AdamWeightDecayDynamicLR.weight_decay,
+        decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-                                             eps=optimizer_cfg.AdamWeightDecayDynamicLR.eps)
+        other_params = list(filter(lambda x: x not in decay_params, params))
+        group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
+                        {'params': other_params, 'weight_decay': 0.0}]
+
+        optimizer = AdamWeightDecay(group_params, lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)
     elif optimizer_cfg.optimizer == 'Lamb':
-        optimizer = Lamb(network.trainable_params(), decay_steps=steps_per_epoch * epoch_num,
+        lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.Lamb.learning_rate,
-                         start_learning_rate=optimizer_cfg.Lamb.start_learning_rate,
+                                       end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
-                         end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
+                                       warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
-                         power=optimizer_cfg.Lamb.power, weight_decay=optimizer_cfg.Lamb.weight_decay,
+                                       decay_steps=steps_per_epoch * epoch_num,
-                         warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
+                                       power=optimizer_cfg.Lamb.power)
-                         decay_filter=optimizer_cfg.Lamb.decay_filter)
+        optimizer = Lamb(network.trainable_params(), learning_rate=lr_schedule)
     elif optimizer_cfg.optimizer == 'Momentum':
         optimizer = Momentum(network.trainable_params(), learning_rate=optimizer_cfg.Momentum.learning_rate,
                              momentum=optimizer_cfg.Momentum.momentum)
     else:
-        raise Exception("Optimizer not supported. support: [AdamWeightDecayDynamicLR, Lamb, Momentum]")
+        raise Exception("Optimizer not supported. support: [AdamWeightDecay, Lamb, Momentum]")
 
     # load checkpoint into network
     ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)

model_zoo/official/nlp/bert/run_ner.py

@@ -23,13 +23,13 @@ import argparse
 from src.bert_for_finetune import BertFinetuneCell, BertNER
 from src.finetune_eval_config import optimizer_cfg, bert_net_cfg
 from src.dataset import create_ner_dataset
-from src.utils import make_directory, LossCallBack, LoadNewestCkpt
+from src.utils import make_directory, LossCallBack, LoadNewestCkpt, BertLearningRate
 from src.assessment_method import Accuracy, F1, MCC, Spearman_Correlation
 import mindspore.common.dtype as mstype
 from mindspore import context
 from mindspore import log as logger
 from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
-from mindspore.nn.optim import AdamWeightDecayDynamicLR, Lamb, Momentum
+from mindspore.nn.optim import AdamWeightDecay, Lamb, Momentum
 from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
@@ -44,27 +44,30 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
         raise ValueError("Pretrain model missed, finetune task must load pretrain model!")
     steps_per_epoch = dataset.get_dataset_size()
     # optimizer
-    if optimizer_cfg.optimizer == 'AdamWeightDecayDynamicLR':
+    if optimizer_cfg.optimizer == 'AdamWeightDecay':
-        optimizer = AdamWeightDecayDynamicLR(network.trainable_params(),
+        lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.AdamWeightDecay.learning_rate,
-                                             decay_steps=steps_per_epoch * epoch_num,
+                                       end_learning_rate=optimizer_cfg.AdamWeightDecay.end_learning_rate,
-                                             learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.learning_rate,
+                                       warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
-                                             end_learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.end_learning_rate,
+                                       decay_steps=steps_per_epoch * epoch_num,
-                                             power=optimizer_cfg.AdamWeightDecayDynamicLR.power,
+                                       power=optimizer_cfg.AdamWeightDecay.power)
-                                             warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
+        params = network.trainable_params()
-                                             weight_decay=optimizer_cfg.AdamWeightDecayDynamicLR.weight_decay,
+        decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-                                             eps=optimizer_cfg.AdamWeightDecayDynamicLR.eps)
+        other_params = list(filter(lambda x: x not in decay_params, params))
+        group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
+                        {'params': other_params, 'weight_decay': 0.0}]
+        optimizer = AdamWeightDecay(group_params, lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)
     elif optimizer_cfg.optimizer == 'Lamb':
-        optimizer = Lamb(network.trainable_params(), decay_steps=steps_per_epoch * epoch_num,
+        lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.Lamb.learning_rate,
-                         start_learning_rate=optimizer_cfg.Lamb.start_learning_rate,
+                                       end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
-                         end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
+                                       warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
-                         power=optimizer_cfg.Lamb.power, weight_decay=optimizer_cfg.Lamb.weight_decay,
+                                       decay_steps=steps_per_epoch * epoch_num,
-                         warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
+                                       power=optimizer_cfg.Lamb.power)
-                         decay_filter=optimizer_cfg.Lamb.decay_filter)
+        optimizer = Lamb(network.trainable_params(), learning_rate=lr_schedule)
     elif optimizer_cfg.optimizer == 'Momentum':
         optimizer = Momentum(network.trainable_params(), learning_rate=optimizer_cfg.Momentum.learning_rate,
                              momentum=optimizer_cfg.Momentum.momentum)
     else:
-        raise Exception("Optimizer not supported. support: [AdamWeightDecayDynamicLR, Lamb, Momentum]")
+        raise Exception("Optimizer not supported. support: [AdamWeightDecay, Lamb, Momentum]")
 
     # load checkpoint into network
     ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)

model_zoo/official/nlp/bert/run_pretrain.py

@@ -28,12 +28,12 @@ from mindspore.train.parallel_utils import ParallelMode
 from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecayDynamicLR
+from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecay
 from mindspore import log as logger
 from src import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
 from src.dataset import create_bert_dataset
 from src.config import cfg, bert_net_cfg
-from src.utils import LossCallBack
+from src.utils import LossCallBack, BertLearningRate
 _current_dir = os.path.dirname(os.path.realpath(__file__))
 
 
@@ -109,24 +109,35 @@ def run_pretrain():
     netwithloss = BertNetworkWithLoss(bert_net_cfg, True)
 
     if cfg.optimizer == 'Lamb':
-        optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size() * new_repeat_count,
+        lr_schedule = BertLearningRate(learning_rate=cfg.Lamb.learning_rate,
-                         start_learning_rate=cfg.Lamb.start_learning_rate, end_learning_rate=cfg.Lamb.end_learning_rate,
+                                       end_learning_rate=cfg.Lamb.end_learning_rate,
-                         power=cfg.Lamb.power, warmup_steps=cfg.Lamb.warmup_steps, weight_decay=cfg.Lamb.weight_decay,
+                                       warmup_steps=cfg.Lamb.warmup_steps,
-                         eps=cfg.Lamb.eps)
+                                       decay_steps=ds.get_dataset_size() * new_repeat_count,
+                                       power=cfg.Lamb.power)
+        params = net_with_loss.trainable_params()
+        decay_params = list(filter(cfg.Lamb.decay_filter, params))
+        other_params = list(filter(lambda x: x not in decay_params, params))
+        group_params = [{'params': decay_params, 'weight_decay': cfg.Lamb.weight_decay},
+                        {'params': other_params}]
+        optimizer = Lamb(group_params, learning_rate=lr_schedule, eps=cfg.Lamb.eps)
     elif cfg.optimizer == 'Momentum':
         optimizer = Momentum(netwithloss.trainable_params(), learning_rate=cfg.Momentum.learning_rate,
                              momentum=cfg.Momentum.momentum)
-    elif cfg.optimizer == 'AdamWeightDecayDynamicLR':
+    elif cfg.optimizer == 'AdamWeightDecay':
-        optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(),
+        lr_schedule = BertLearningRate(learning_rate=cfg.AdamWeightDecay.learning_rate,
-                                             decay_steps=ds.get_dataset_size() * new_repeat_count,
+                                       end_learning_rate=cfg.AdamWeightDecay.end_learning_rate,
-                                             learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
+                                       warmup_steps=cfg.AdamWeightDecay.warmup_steps,
-                                             end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
+                                       decay_steps=ds.get_dataset_size() * new_repeat_count,
-                                             power=cfg.AdamWeightDecayDynamicLR.power,
+                                       power=cfg.AdamWeightDecay.power)
-                                             weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
+        params = net_with_loss.trainable_params()
-                                             eps=cfg.AdamWeightDecayDynamicLR.eps,
+        decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
-                                             warmup_steps=cfg.AdamWeightDecayDynamicLR.warmup_steps)
+        other_params = list(filter(lambda x: x not in decay_params, params))
+        group_params = [{'params': decay_params, 'weight_decay': cfg.AdamWeightDecay.weight_decay},
+                        {'params': other_params, 'weight_decay': 0.0}]
+
+        optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=cfg.AdamWeightDecay.eps)
     else:
-        raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecayDynamicLR]".
+        raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecay]".
                          format(cfg.optimizer))
     callback = [TimeMonitor(ds.get_dataset_size()), LossCallBack()]
     if args_opt.enable_save_ckpt == "true":

model_zoo/official/nlp/bert/run_squad.py

@@ -25,12 +25,12 @@ from src.dataset import create_squad_dataset
 from src import tokenization
 from src.create_squad_data import read_squad_examples, convert_examples_to_features
 from src.run_squad import write_predictions
-from src.utils import make_directory, LossCallBack, LoadNewestCkpt
+from src.utils import make_directory, LossCallBack, LoadNewestCkpt, BertLearningRate
 import mindspore.common.dtype as mstype
 from mindspore import context
 from mindspore import log as logger
 from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
-from mindspore.nn.optim import AdamWeightDecayDynamicLR, Lamb, Momentum
+from mindspore.nn.optim import AdamWeightDecay, Lamb, Momentum
 from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
@@ -44,27 +44,31 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
         raise ValueError("Pretrain model missed, finetune task must load pretrain model!")
     steps_per_epoch = dataset.get_dataset_size()
     # optimizer
-    if optimizer_cfg.optimizer == 'AdamWeightDecayDynamicLR':
+    if optimizer_cfg.optimizer == 'AdamWeightDecay':
-        optimizer = AdamWeightDecayDynamicLR(network.trainable_params(),
+        lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.AdamWeightDecay.learning_rate,
-                                             decay_steps=steps_per_epoch * epoch_num,
+                                       end_learning_rate=optimizer_cfg.AdamWeightDecay.end_learning_rate,
-                                             learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.learning_rate,
+                                       warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
-                                             end_learning_rate=optimizer_cfg.AdamWeightDecayDynamicLR.end_learning_rate,
+                                       decay_steps=steps_per_epoch * epoch_num,
-                                             power=optimizer_cfg.AdamWeightDecayDynamicLR.power,
+                                       power=optimizer_cfg.AdamWeightDecay.power)
-                                             warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
+        params = network.trainable_params()
-                                             weight_decay=optimizer_cfg.AdamWeightDecayDynamicLR.weight_decay,
+        decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-                                             eps=optimizer_cfg.AdamWeightDecayDynamicLR.eps)
+        other_params = list(filter(lambda x: x not in decay_params, params))
+        group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
+                        {'params': other_params, 'weight_decay': 0.0}]
+
+        optimizer = AdamWeightDecay(group_params, lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)
     elif optimizer_cfg.optimizer == 'Lamb':
-        optimizer = Lamb(network.trainable_params(), decay_steps=steps_per_epoch * epoch_num,
+        lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.Lamb.learning_rate,
-                         start_learning_rate=optimizer_cfg.Lamb.start_learning_rate,
+                                       end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
-                         end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
+                                       warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
-                         power=optimizer_cfg.Lamb.power, weight_decay=optimizer_cfg.Lamb.weight_decay,
+                                       decay_steps=steps_per_epoch * epoch_num,
-                         warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
+                                       power=optimizer_cfg.Lamb.power)
-                         decay_filter=optimizer_cfg.Lamb.decay_filter)
+        optimizer = Lamb(network.trainable_params(), learning_rate=lr_schedule)
     elif optimizer_cfg.optimizer == 'Momentum':
         optimizer = Momentum(network.trainable_params(), learning_rate=optimizer_cfg.Momentum.learning_rate,
                              momentum=optimizer_cfg.Momentum.momentum)
     else:
-        raise Exception("Optimizer not supported. support: [AdamWeightDecayDynamicLR, Lamb, Momentum]")
+        raise Exception("Optimizer not supported. support: [AdamWeightDecay, Lamb, Momentum]")
 
     # load checkpoint into network
     ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)

model_zoo/official/nlp/bert/src/config.py

@@ -24,20 +24,22 @@ cfg = edict({
     'scale_factor': 2,
     'scale_window': 1000,
     'optimizer': 'Lamb',
-    'AdamWeightDecayDynamicLR': edict({
+    'AdamWeightDecay': edict({
         'learning_rate': 3e-5,
         'end_learning_rate': 1e-10,
         'power': 5.0,
         'weight_decay': 1e-5,
+        'decay_filter': lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower(),
         'eps': 1e-6,
         'warmup_steps': 10000,
     }),
     'Lamb': edict({
-        'start_learning_rate': 3e-5,
+        'learning_rate': 3e-5,
         'end_learning_rate': 1e-10,
         'power': 10.0,
         'warmup_steps': 10000,
         'weight_decay': 0.01,
+        'decay_filter': lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower(),
         'eps': 1e-6,
     }),
     'Momentum': edict({

model_zoo/official/nlp/bert/src/finetune_eval_config.py

@@ -23,19 +23,20 @@ from .bert_model import BertConfig
 
 optimizer_cfg = edict({
     'optimizer': 'Lamb',
-    'AdamWeightDecayDynamicLR': edict({
+    'AdamWeightDecay': edict({
         'learning_rate': 2e-5,
         'end_learning_rate': 1e-7,
         'power': 1.0,
         'weight_decay': 1e-5,
+        'decay_filter': lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower(),
         'eps': 1e-6,
     }),
     'Lamb': edict({
-        'start_learning_rate': 2e-5,
+        'learning_rate': 2e-5,
         'end_learning_rate': 1e-7,
         'power': 1.0,
         'weight_decay': 0.01,
-        'decay_filter': lambda x: False,
+        'decay_filter': lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower(),
     }),
     'Momentum': edict({
         'learning_rate': 2e-5,

model_zoo/official/nlp/bert/src/utils.py

@@ -23,6 +23,7 @@ from mindspore.ops import operations as P
 from mindspore.common.tensor import Tensor
 from mindspore.common import dtype as mstype
 from mindspore.train.callback import Callback
+from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR
 
 
 class CrossEntropyCalculation(nn.Cell):
@@ -123,3 +124,25 @@ def LoadNewestCkpt(load_finetune_checkpoint_dir, steps_per_epoch, epoch_num, pre
                     max_num = int(num)
                     load_finetune_checkpoint_path = os.path.join(load_finetune_checkpoint_dir, filename)
     return load_finetune_checkpoint_path
+
+
+class BertLearningRate(LearningRateSchedule):
+    """
+    Warmup-decay learning rate for Bert network.
+    """
+    def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
+        super(BertLearningRate, self).__init__()
+        self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
+        self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+
+        self.greater = P.Greater()
+        self.one = Tensor(np.array([1.0]).astype(np.float32))
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+        warmup_lr = self.warmup_lr(global_step)
+        decay_lr = self.decay_lr(global_step)
+        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        return lr

tests/mindspore_test_framework/apps/test_lamb_check_loss.py

@@ -30,7 +30,7 @@ verification_set = [
         'block': {
             'model': network,
             'loss': SquaredLoss(),
-            'opt': Lamb(network.trainable_params(), decay_steps=num_epochs, warmup_steps=10, weight_decay=0.01),
+            'opt': Lamb(network.trainable_params(), 0.02, weight_decay=0.01),
             'num_epochs': num_epochs,
             'loss_upper_bound': 0.3,
         },

tests/mindspore_test_framework/pipeline/gradient/check_training.py

@@ -31,7 +31,7 @@ Example:
             'block': {
                 'model': network,
                 'loss': SquaredLoss(),
-                'opt': Lamb(network.trainable_params(), decay_steps=num_epochs, warmup_steps=10, weight_decay=0.01),
+                'opt': Lamb(network.trainable_params(), lr=0.02, weight_decay=0.01),
                 'num_epochs': num_epochs,
                 'loss_upper_bound': 0.3,
             },

tests/perf_test/bert/test_bert_train.py

@@ -22,8 +22,9 @@ import os
 import mindspore.common.dtype as mstype
 import mindspore.context as context
 from mindspore import Tensor
-from mindspore.nn.optim import AdamWeightDecayDynamicLR
+from mindspore.nn.optim import AdamWeightDecay
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
+from mindspore.nn import learning_rate_schedule as lr_schedules
 from model_zoo.bert.src import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
 from ...dataset_mock import MindData
 from ...ops_common import nn, np, batch_tuple_tensor, build_construct_graph
@@ -98,6 +99,25 @@ def get_config(version='base', batch_size=1):
     return BertConfig(batch_size=batch_size)
 
 
+class BertLearningRate(lr_schedules.LearningRateSchedule):
+    def __init__(self, decay_steps, warmup_steps=0, learning_rate=0.1, end_learning_rate=0.0001, power=1.0):
+        super(BertLearningRate, self).__init__()
+        self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
+        self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+
+        self.greater = P.Greater()
+        self.one = Tensor(np.array([1.0]).astype(np.float32))
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+        warmup_lr = self.warmup_lr(global_step)
+        decay_lr = self.decay_lr(global_step)
+        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        return lr
+
+
 def test_bert_train():
     """
     the main function
@@ -123,7 +143,8 @@ def test_bert_train():
 
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(), 10)
+    lr = BertLearningRate(10)
+    optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)
     net = ModelBert(netwithloss, optimizer=optimizer)
     net.set_train()
     build_construct_graph(net, *inputs, execute=False)
@@ -147,7 +168,8 @@ def test_bert_withlossscale_train():
 
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(), 10)
+    lr = BertLearningRate(10)
+    optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)
     net = ModelBert(netwithloss, optimizer=optimizer)
     net.set_train()
     build_construct_graph(net, *inputs, execute=True)
@@ -173,7 +195,8 @@ def bert_withlossscale_manager_train():
 
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(), 10)
+    lr = BertLearningRate(10)
+    optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)
     net = ModelBert(netwithloss, optimizer=optimizer)
     net.set_train()
     build_construct_graph(net, *inputs, execute=True)
@@ -200,7 +223,8 @@ def bert_withlossscale_manager_train_feed():
 
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(), 10)
+    lr = BertLearningRate(10)
+    optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)
     net = ModelBert(netwithloss, optimizer=optimizer)
     net.set_train()
     build_construct_graph(net, *inputs, execute=True)

tests/st/networks/models/bert/src/config.py

@@ -24,7 +24,7 @@ cfg = edict({
     'scale_factor': 2,
     'scale_window': 1000,
     'optimizer': 'Lamb',
-    'AdamWeightDecayDynamicLR': edict({
+    'AdamWeightDecay': edict({
         'learning_rate': 3e-5,
         'end_learning_rate': 1e-10,
         'power': 5.0,
@@ -33,7 +33,7 @@ cfg = edict({
         'warmup_steps': 10000,
     }),
     'Lamb': edict({
-        'start_learning_rate': 3e-5,
+        'learning_rate': 3e-5,
         'end_learning_rate': 1e-10,
         'power': 10.0,
         'warmup_steps': 10000,

tests/st/networks/models/bert/src/finetune_config.py

@@ -32,7 +32,7 @@ cfg = edict({
     'pre_training_ckpt': '/your/path/pre_training.ckpt',
     'use_crf': False,
     'optimizer': 'Lamb',
-    'AdamWeightDecayDynamicLR': edict({
+    'AdamWeightDecay': edict({
         'learning_rate': 2e-5,
         'end_learning_rate': 1e-7,
         'power': 1.0,
@@ -40,7 +40,7 @@ cfg = edict({
         'eps': 1e-6,
     }),
     'Lamb': edict({
-        'start_learning_rate': 2e-5,
+        'learning_rate': 2e-5,
         'end_learning_rate': 1e-7,
         'power': 1.0,
         'decay_filter': lambda x: False,

tests/st/networks/models/bert/test_bert_graph_kernel.py

@@ -29,9 +29,11 @@ from mindspore.nn.optim import Lamb
 from mindspore.train.callback import Callback
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
 from mindspore.train.model import Model
+from mindspore.nn import learning_rate_schedule as lr_schedules
 from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
 from src.bert_model import BertConfig
 
+
 DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
 SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"
 
@@ -111,6 +113,25 @@ def weight_variable(shape):
     return Tensor(ones)
 
 
+class BertLearningRate(lr_schedules.LearningRateSchedule):
+    def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
+        super(BertLearningRate, self).__init__()
+        self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
+        self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+
+        self.greater = P.Greater()
+        self.one = Tensor(np.array([1.0]).astype(np.float32))
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+        warmup_lr = self.warmup_lr(global_step)
+        decay_lr = self.decay_lr(global_step)
+        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        return lr
+
+
 class ModelCallback(Callback):
     def __init__(self):
         super(ModelCallback, self).__init__()
@@ -134,9 +155,15 @@ def test_bert_tdt():
     ds = me_de_train_dataset()
     config = get_config(version='large', batch_size=16)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*ds.get_repeat_count(),
+    lr = BertLearningRate(decay_steps=ds.get_dataset_size()*ds.get_repeat_count(), learning_rate=5e-5,
-                     start_learning_rate=5e-5, end_learning_rate=1e-9,
+                          end_learning_rate=1e-9, power=10.0, warmup_steps=0)
-                     power=10.0, warmup_steps=0, weight_decay=0.01)
+    decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
+    no_decay_filter = lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower()
+    decay_params = list(filter(decay_filter, net_with_loss.trainable_params()))
+    other_params = list(filter(no_decay_filter, net_with_loss.trainable_params()))
+    group_params = [{'params': decay_params, 'weight_decay': 0.01},
+                    {'params': other_params}]
+    optimizer = Lamb(group_params, lr)
     scale_window = 3
     scale_manager = DynamicLossScaleManager(262144, 2, scale_window)
     netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,

tests/st/networks/models/bert/test_bert_tdt_lossscale.py

@@ -33,6 +33,7 @@ from mindspore.nn.optim import Lamb
 from mindspore.train.callback import Callback
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
 from mindspore.train.model import Model
+import mindspore.nn.learning_rate_schedule as lr_schedules
 
 _current_dir = os.path.dirname(os.path.realpath(__file__))
 DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
@@ -125,6 +126,25 @@ def weight_variable(shape):
     return Tensor(ones)
 
 
+class BertLearningRate(lr_schedules.LearningRateSchedule):
+    def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
+        super(BertLearningRate, self).__init__()
+        self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
+        self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+
+        self.greater = P.Greater()
+        self.one = Tensor(np.array([1.0]).astype(np.float32))
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+        warmup_lr = self.warmup_lr(global_step)
+        decay_lr = self.decay_lr(global_step)
+        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        return lr
+
+
 class ModelCallback(Callback):
     def __init__(self):
         super(ModelCallback, self).__init__()
@@ -162,9 +182,16 @@ def test_bert_percision():
     batch_size = 16
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*new_repeat_count,
+    lr = BertLearningRate(decay_steps=ds.get_dataset_size()*new_repeat_count,
-                     start_learning_rate=5e-5, end_learning_rate=1e-9,
+                          learning_rate=5e-5, end_learning_rate=1e-9,
-                     power=10.0, warmup_steps=0, weight_decay=0.01)
+                          power=10.0, warmup_steps=0)
+    decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
+    no_decay_filter = lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower()
+    decay_params = list(filter(decay_filter, net_with_loss.trainable_params()))
+    other_params = list(filter(no_decay_filter, net_with_loss.trainable_params()))
+    group_params = [{'params': decay_params, 'weight_decay': 0.01},
+                    {'params': other_params}]
+    optimizer = Lamb(group_params, lr)
     scale_window = 3
     scale_manager = DynamicLossScaleManager(2 ** 16, 2, scale_window)
     netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
@@ -220,9 +247,18 @@ def test_bert_performance():
     batch_size = 16
     config = get_config(version=version, batch_size=batch_size)
     netwithloss = BertNetworkWithLoss(config, True)
-    optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*new_repeat_count,
+
-                     start_learning_rate=5e-5, end_learning_rate=1e-9,
+    lr = BertLearningRate(decay_steps=ds.get_dataset_size()*new_repeat_count,
-                     power=10.0, warmup_steps=0, weight_decay=0.01)
+                          learning_rate=5e-5, end_learning_rate=1e-9,
+                          power=10.0, warmup_steps=0)
+    decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
+    no_decay_filter = lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower()
+    decay_params = list(filter(decay_filter, net_with_loss.trainable_params()))
+    other_params = list(filter(no_decay_filter, net_with_loss.trainable_params()))
+    group_params = [{'params': decay_params, 'weight_decay': 0.01},
+                    {'params': other_params}]
+    optimizer = Lamb(group_params, lr)
+
     scale_window = 3
     scale_manager = DynamicLossScaleManager(2 ** 16, 2, scale_window)
     netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,

tests/ut/python/nn/optim/test_adam.py

@@ -20,8 +20,10 @@ import mindspore.nn as nn
 from mindspore import Tensor, Parameter, context
 from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.nn.optim import Adam, AdamWeightDecay, AdamWeightDecayDynamicLR
+from mindspore.nn.optim import Adam, AdamWeightDecay
 from mindspore.ops import operations as P
+import mindspore.nn.learning_rate_schedule as lr_schedules
+from mindspore.nn.dynamic_lr import polynomial_decay_lr
 
 context.set_context(enable_sparse=True)
 
@@ -112,6 +114,62 @@ def test_sparse_adam_compile():
     _executor.compile(train_network, indices, label)
 
 
+def test_adam_group1():
+    """ test_adam_group_lr_and_weight_decay """
+    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
+    label = Tensor(np.zeros([1, 10]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    net_with_loss = WithLossCell(net, loss)
+    all_params = net.trainable_params()
+
+    poly_decay_lr = polynomial_decay_lr(0.01, 0.0001, total_step=10, step_per_epoch=1, decay_epoch=3, power=1.0)
+
+    group_params = [{'params': [all_params[0]], 'lr': poly_decay_lr, 'weight_decay': 0.9},
+                    {'params': [all_params[1]]}]
+    optimizer = nn.Adam(group_params, learning_rate=0.1)
+
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
+
+
+def test_adam_group2():
+    """ test_adam_group_lr_and_weight_decay """
+    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
+    label = Tensor(np.zeros([1, 10]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    net_with_loss = WithLossCell(net, loss)
+    all_params = net.trainable_params()
+
+    schedule_lr = lr_schedules.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
+    group_params = [{'params': [all_params[0]], 'lr': 0.02, 'weight_decay': 0.9},
+                    {'params': [all_params[1]]}]
+    optimizer = nn.Adam(group_params, learning_rate=schedule_lr)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
+
+
+def test_adamweightdecay_group():
+    """ test_adam_group_lr_and_weight_decay """
+    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
+    label = Tensor(np.zeros([1, 10]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    net_with_loss = WithLossCell(net, loss)
+    all_params = net.trainable_params()
+
+    schedule_lr = lr_schedules.PolynomialDecayLR(0.01, 0.0001, 3, power=1.0)
+    group_params = [{'params': [all_params[0]], 'lr': 0.02, 'weight_decay': 0.9},
+                    {'params': [all_params[1]]}]
+    optimizer = nn.AdamWeightDecay(group_params, learning_rate=schedule_lr)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
+
+
 def test_AdamWeightDecay_beta1():
     net = Net()
     print("**********", net.get_parameters())
@@ -131,20 +189,6 @@ def test_AdamWeightDecay_e():
         AdamWeightDecay(net.get_parameters(), eps=-0.1, learning_rate=0.1)
 
 
-def test_AdamWeightDecayDynamicLR():
-    """ test_AdamWeightDecayDynamicLR """
-    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
-    label = Tensor(np.zeros([1, 10]).astype(np.float32))
-    net = Net()
-    net.set_train()
-    loss = nn.SoftmaxCrossEntropyWithLogits()
-    optimizer = AdamWeightDecayDynamicLR(net.trainable_params(), decay_steps=20, learning_rate=0.1)
-
-    net_with_loss = WithLossCell(net, loss)
-    train_network = TrainOneStepCell(net_with_loss, optimizer)
-    _executor.compile(train_network, inputs, label)
-
-
 def test_adam_mindspore_with_empty_params():
     net = nn.Flatten()
     with pytest.raises(ValueError, match=r"Optimizer got an empty parameter list"):

tests/ut/python/nn/optim/test_lamb.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """ test lamb """
 import numpy as np
-import pytest
 
 import mindspore.nn as nn
 from mindspore import Tensor, Parameter
@@ -22,6 +21,27 @@ from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn.optim import Lamb
 from mindspore.ops import operations as P
+import mindspore.common.dtype as mstype
+from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR
+
+
+class LambLearningRate(LearningRateSchedule):
+    def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
+        super(LambLearningRate, self).__init__()
+        self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
+        self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+
+        self.greater = P.Greater()
+        self.one = Tensor(np.array([1.0]).astype(np.float32))
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+        warmup_lr = self.warmup_lr(global_step)
+        decay_lr = self.decay_lr(global_step)
+        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        return lr
 
 
 class Net(nn.Cell):
@@ -51,6 +71,21 @@ class NetWithoutWeight(nn.Cell):
         return x
 
 
+def test_lamb_compile_dynamic_lr():
+    """ test_Lamb_compile """
+    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
+    label = Tensor(np.zeros([1, 10]).astype(np.float32))
+    net = Net()
+    net.set_train()
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    warmup_decay_lr = LambLearningRate(0.01, 0.0001, 10, 20, 1.0)
+    optimizer = Lamb(net.trainable_params(), warmup_decay_lr)
+
+    net_with_loss = WithLossCell(net, loss)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+    _executor.compile(train_network, inputs, label)
+
+
 def test_lamb_compile():
     """ test_Lamb_compile """
     inputs = Tensor(np.ones([1, 64]).astype(np.float32))
@@ -58,20 +93,27 @@ def test_lamb_compile():
     net = Net()
     net.set_train()
     loss = nn.SoftmaxCrossEntropyWithLogits()
-    optimizer = Lamb(net.trainable_params(), decay_steps=10)
+
+    optimizer = Lamb(net.trainable_params(), 0.02, 0.9)
 
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
     _executor.compile(train_network, inputs, label)
 
 
-def test_lamb_error():
+def test_lamb_group():
+    """ test_Lamb_group_compile """
+    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
+    label = Tensor(np.zeros([1, 10]).astype(np.float32))
     net = Net()
-    with pytest.raises(TypeError):
+    net.set_train()
-        Lamb(net.get_parameters(), decay_steps=6, warmup_steps=5.0)
+    loss = nn.SoftmaxCrossEntropyWithLogits()
+    warmup_decay_lr = LambLearningRate(0.01, 0.0001, 10, 20, 1.0)
+    all_params = net.trainable_params()
+    group_params = [{'params': [all_params[0]], 'lr': warmup_decay_lr, 'weight_decay': 0.9},
+                    {'params': [all_params[1]]}]
+    optimizer = Lamb(group_params, 0.02)
 
-    with pytest.raises(TypeError):
+    net_with_loss = WithLossCell(net, loss)
-        Lamb(net.get_parameters(), decay_steps=1.0)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
-
+    _executor.compile(train_network, inputs, label)
-    with pytest.raises(ValueError):
-        Lamb(net.get_parameters(), decay_steps=0)

tests/ut/python/nn/optim/test_optimizer.py

@@ -18,7 +18,7 @@ import pytest
 
 from mindspore import Tensor
 from mindspore.common.parameter import Parameter
-from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay, AdamWeightDecayDynamicLR
+from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay
 
 
 class IterableObjc:
@@ -81,10 +81,6 @@ class TestNullParam():
         with pytest.raises(ValueError):
             AdamWeightDecay(None)
 
-    def test_AdamWeightDecayDynamicLR_init(self):
-        with pytest.raises(ValueError):
-            AdamWeightDecayDynamicLR(None, 10)
-
     def test_Sgd_init(self):
         with pytest.raises(ValueError):
             SGD(None)
@@ -101,10 +97,6 @@ class TestUnsupportParam():
         with pytest.raises(TypeError):
             AdamWeightDecay(9)
 
-    def test_AdamWeightDecayDynamicLR_init(self):
-        with pytest.raises(TypeError):
-            AdamWeightDecayDynamicLR(0.5, 10)
-
     def test_Sgd_init(self):
         with pytest.raises(TypeError):
             paramsTensor = Parameter(Tensor(np.zeros([1, 2, 3])), "x")

tests/ut/python/nn/optim/test_proximal_ada_grad.py

@@ -37,6 +37,7 @@ class Net(nn.Cell):
         x = self.biasAdd(self.matmul(x, self.weight), self.bias)
         return x
 
+
 class NetWithSparseGatherV2(nn.Cell):
     """ NetWithSparseGatherV2 definition """
     def __init__(self):

tests/ut/python/nn/test_dynamic_lr.py

@@ -28,7 +28,7 @@ decay_epoch = 2
 min_lr = 0.01
 max_lr = 0.1
 power = 0.5
-
+warmup_epoch = 2
 
 class TestInputs:
     def test_milestone1(self):
@@ -234,3 +234,8 @@ def test_polynomial_decay():
     lr2 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
                                  True)
     assert len(lr2) == total_step
+
+
+def test_warmup():
+    lr1 = dr.warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
+    assert len(lr1) == total_step

tests/ut/python/nn/test_learning_rate_schedule.py

@@ -0,0 +1,157 @@
+# 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.
+# ============================================================================
+""" Test Dynamic Learning Rate """
+import pytest
+
+from mindspore import Tensor, Parameter
+from mindspore.nn import learning_rate_schedule as lr_schedules
+from mindspore.common.api import _executor
+import mindspore.common.dtype as mstype
+
+
+learning_rate = 0.1
+end_learning_rate = 0.01
+decay_rate = 0.9
+decay_steps = 4
+warmup_steps = 2
+min_lr = 0.01
+max_lr = 0.1
+power = 0.5
+global_step = Parameter(Tensor(2, mstype.int32), 'global_step')
+
+
+class TestInit:
+    def test_learning_rate_type(self):
+        lr = True
+        with pytest.raises(TypeError):
+            lr_schedules.ExponentialDecayLR(lr, decay_rate, decay_steps)
+
+        with pytest.raises(TypeError):
+            lr_schedules.PolynomialDecayLR(lr, end_learning_rate, decay_steps, power)
+
+    def test_learning_rate_value(self):
+        lr = -1.0
+        with pytest.raises(ValueError):
+            lr_schedules.ExponentialDecayLR(lr, decay_rate, decay_steps)
+
+        with pytest.raises(ValueError):
+            lr_schedules.PolynomialDecayLR(lr, end_learning_rate, decay_steps, power)
+
+    def test_end_learning_rate_type(self):
+        lr = True
+        with pytest.raises(TypeError):
+            lr_schedules.PolynomialDecayLR(learning_rate, lr, decay_steps, power)
+
+    def test_end_learning_rate_value(self):
+        lr = -1.0
+        with pytest.raises(ValueError):
+            lr_schedules.PolynomialDecayLR(learning_rate, lr, decay_steps, power)
+
+    def test_decay_rate_type(self):
+        rate = 'a'
+        with pytest.raises(TypeError):
+            lr_schedules.ExponentialDecayLR(learning_rate, rate, decay_steps)
+
+    def test_decay_rate_value(self):
+        rate = -1.0
+        with pytest.raises(ValueError):
+            lr_schedules.ExponentialDecayLR(learning_rate, rate, decay_steps)
+
+    def test_decay_steps_type(self):
+        decay_steps_e = 'm'
+        with pytest.raises(TypeError):
+            lr_schedules.ExponentialDecayLR(learning_rate, decay_rate, decay_steps_e)
+
+        with pytest.raises(TypeError):
+            lr_schedules.CosineDecayLR(min_lr, max_lr, decay_steps_e)
+
+        with pytest.raises(TypeError):
+            lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps_e, power)
+
+    def test_decay_steps_value(self):
+        decay_steps_e = -2
+        with pytest.raises(ValueError):
+            lr_schedules.ExponentialDecayLR(learning_rate, decay_rate, decay_steps_e)
+
+        with pytest.raises(ValueError):
+            lr_schedules.CosineDecayLR(min_lr, max_lr, decay_steps_e)
+
+        with pytest.raises(ValueError):
+            lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps_e, power)
+
+    def test_is_stair(self):
+        is_stair = 1
+        with pytest.raises(TypeError):
+            lr_schedules.ExponentialDecayLR(learning_rate, decay_rate, decay_steps, is_stair)
+
+    def test_min_lr_type(self):
+        min_lr1 = True
+        with pytest.raises(TypeError):
+            lr_schedules.CosineDecayLR(min_lr1, max_lr, decay_steps)
+
+    def test_min_lr_value(self):
+        min_lr1 = -1.0
+        with pytest.raises(ValueError):
+            lr_schedules.CosineDecayLR(min_lr1, max_lr, decay_steps)
+
+    def test_max_lr_type(self):
+        max_lr1 = 'a'
+        with pytest.raises(TypeError):
+            lr_schedules.CosineDecayLR(min_lr, max_lr1, decay_steps)
+
+    def test_max_lr_value(self):
+        max_lr1 = -1.0
+        with pytest.raises(ValueError):
+            lr_schedules.CosineDecayLR(min_lr, max_lr1, decay_steps)
+
+    def test_power(self):
+        power1 = True
+        with pytest.raises(TypeError):
+            lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power1)
+
+
+def test_exponential_decay():
+    lr_schedule = lr_schedules.ExponentialDecayLR(learning_rate, decay_rate, decay_steps, True)
+    _executor.compile(lr_schedule, global_step)
+
+
+def test_enatural_exp_decay():
+    lr_schedule = lr_schedules.NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
+    _executor.compile(lr_schedule, global_step)
+
+
+def test_inverse_decay():
+    lr_schedule = lr_schedules.InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
+    _executor.compile(lr_schedule, global_step)
+
+
+def test_cosine_decay():
+    lr_schedule = lr_schedules.CosineDecayLR(min_lr, max_lr, decay_steps)
+    _executor.compile(lr_schedule, global_step)
+
+
+def test_polynomial_decay():
+    lr_schedule = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+    _executor.compile(lr_schedule, global_step)
+
+
+def test_polynomial_decay2():
+    lr_schedule = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power, True)
+    _executor.compile(lr_schedule, global_step)
+
+
+def test_warmup():
+    lr_schedule = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
+    _executor.compile(lr_schedule, global_step)

tests/ut/python/optimizer/test_optimizer_with_loss_scale.py

@@ -152,7 +152,7 @@ def test_compile_fp16_overflow():
     net = NetFP16(16, 16)
 
     loss = MSELoss()
-    optimizer = Lamb(net.trainable_params(), decay_steps=10, warmup_steps=5)
+    optimizer = Lamb(net.trainable_params(), learning_rate=0.01)
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer)
     train_network.set_train()

tests/ut/python/optimizer/test_optimizer_with_parameter_groups.py

@@ -104,9 +104,11 @@ def test_group_dynamic_1():
     assert opt.is_group_params_ordered is True
     for lr, param, order_param in zip(opt.learning_rate, opt.parameters, net.trainable_params()):
         if param in conv_params:
-            assert np.all(lr.data.asnumpy() == Tensor(np.array([conv_lr] * 3).astype(np.float32)).asnumpy())
+            assert np.all(lr.learning_rate.data.asnumpy() == \
+                          Tensor(np.array([conv_lr] * 3).astype(np.float32)).asnumpy())
         else:
-            assert np.all(lr.data.asnumpy() == Tensor(np.array(list(default_lr)).astype(np.float32)).asnumpy())
+            assert np.all(lr.learning_rate.data.asnumpy() == \
+                          Tensor(np.array(list(default_lr)).astype(np.float32)).asnumpy())
 
         assert param.name == order_param.name
 
@@ -134,9 +136,11 @@ def test_group_dynamic_2():
     assert opt.dynamic_lr is True
     for lr, param in zip(opt.learning_rate, opt.parameters):
         if param in conv_params:
-            assert np.all(lr.data.asnumpy() == Tensor(np.array(list(conv_lr)).astype(np.float32)).asnumpy())
+            assert np.all(lr.learning_rate.data.asnumpy() == \
+                          Tensor(np.array(list(conv_lr)).astype(np.float32)).asnumpy())
         else:
-            assert np.all(lr.data.asnumpy() == Tensor(np.array([default_lr] * 3).astype(np.float32)).asnumpy())
+            assert np.all(lr.learning_rate.data.asnumpy() == \
+                          Tensor(np.array([default_lr] * 3).astype(np.float32)).asnumpy())
 
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, opt)
@@ -157,7 +161,7 @@ def test_group_dynamic_no_same_size():
 
 def test_group_not_float_lr():
     net = LeNet5()
-    conv_lr = 1
+    conv_lr = np.array(1)
     default_lr = 0.3
     conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
     no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
@@ -169,7 +173,7 @@ def test_group_not_float_lr():
 
 def test_group_not_float_weight_decay():
     net = LeNet5()
-    conv_weight_decay = 1
+    conv_weight_decay = np.array(1)
     conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
     no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
     group_params = [{'params': conv_params, 'weight_decay': conv_weight_decay},
@@ -238,11 +242,15 @@ def test_get_lr_parameter_with_group():
     assert opt.is_group_lr is True
     for param in opt.parameters:
         lr = opt.get_lr_parameter(param)
-        assert lr.name == 'lr_' + param.name
+        if 'conv' in param.name:
+            cur_name = 'learning_rate_group_' + '0'
+        else:
+            cur_name = 'learning_rate_group_' + '1'
+        assert lr.name == cur_name
 
     lr_list = opt.get_lr_parameter(conv_params)
     for lr, param in zip(lr_list, conv_params):
-        assert lr.name == 'lr_' + param.name
+        assert lr.name == 'learning_rate_group_' + '0'
 
 
 def test_get_lr_parameter_with_order_group():
@@ -256,7 +264,11 @@ def test_get_lr_parameter_with_order_group():
     assert opt.is_group_lr is True
     for param in opt.parameters:
         lr = opt.get_lr_parameter(param)
-        assert lr.name == 'lr_' + param.name
+        if 'conv' in param.name:
+            cur_name = 'learning_rate_group_' + '0'
+        else:
+            cur_name = 'learning_rate'
+        assert lr.name == cur_name
 
 
 def test_get_lr_parameter_with_no_group():
@@ -271,7 +283,7 @@ def test_get_lr_parameter_with_no_group():
     assert opt.is_group_lr is False
     for param in opt.parameters:
         lr = opt.get_lr_parameter(param)
-        assert lr.name == opt.learning_rate.name
+        assert lr.name == 'learning_rate'
 
     params_error = [1, 2, 3]
     with pytest.raises(TypeError):
@@ -305,7 +317,11 @@ def test_order_params_1():
             assert decay_flags is False
 
         assert param.name == order_param.name
-        assert lr.name == 'lr_' + param.name
+        if 'conv' in param.name:
+            assert lr.name == 'learning_rate'
+        elif 'bias' in param.name:
+            assert lr.name == 'learning_rate_group_' + '1'
+
 
 
 def test_order_params_2():
@@ -323,8 +339,9 @@ def test_order_params_2():
     assert opt.is_group is True
     assert opt.is_group_lr is True
     assert opt.is_group_params_ordered is True
+    all_lr = opt.get_lr_parameter(fc1_params+conv_params)
     for weight_decay, decay_flags, lr, param, order_param in zip(
-            opt.weight_decay, opt.decay_flags, opt.learning_rate, opt.parameters, fc1_params+conv_params):
+            opt.weight_decay, opt.decay_flags, all_lr, opt.parameters, fc1_params+conv_params):
         if param in conv_params:
             assert np.all(lr.data.asnumpy() == Tensor(np.array([default_lr] * 3), mstype.float32).asnumpy())
             assert weight_decay == conv_weight_decay
@@ -339,8 +356,10 @@ def test_order_params_2():
             assert decay_flags is False
 
         assert param.name == order_param.name
-        assert lr.name == 'lr_' + param.name
+        if 'conv' in param.name:
-
+            assert lr.name == 'learning_rate'
+        elif 'fc1' in param.name:
+            assert lr.name == 'learning_rate_group_' + '0'
 
 def test_get_order_params_with_not_same():
     net = LeNet5()

tests/ut/python/parallel/test_parallel_optimizer.py

@@ -20,7 +20,7 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.nn.optim import Adam, AdamWeightDecay, AdamWeightDecayDynamicLR, Lamb
+from mindspore.nn.optim import Adam, AdamWeightDecay, Lamb
 from mindspore.ops import operations as P
 from mindspore import context
 
@@ -51,23 +51,8 @@ class Net(nn.Cell):
         return s
 
 
-def test_AdamWeightDecayDynamicLR():
-    """ test_AdamWeightDecayDynamicLR """
-    context.set_auto_parallel_context(parallel_mode="data_parallel", device_num=2, enable_parallel_optimizer=True)
-    inputs = Tensor(np.ones([32, 128]).astype(np.float32))
-    label = Tensor(np.zeros([32, 768]).astype(np.float32))
-    net = Net()
-    net.set_train()
-    loss = nn.SoftmaxCrossEntropyWithLogits()
-    optimizer = AdamWeightDecayDynamicLR(net.trainable_params(), decay_steps=20, learning_rate=0.1)
-
-    net_with_loss = WithLossCell(net, loss)
-    train_network = TrainOneStepCell(net_with_loss, optimizer)
-    _executor.compile(train_network, inputs, label)
-
-
 def test_AdamWeightDecay():
-    """ test_AdamWeightDecayDynamicLR """
+    """ test_AdamWeightDecay """
     context.set_auto_parallel_context(parallel_mode="data_parallel", device_num=2, enable_parallel_optimizer=True)
     inputs = Tensor(np.ones([32, 128]).astype(np.float32))
     label = Tensor(np.zeros([32, 768]).astype(np.float32))
@@ -89,7 +74,7 @@ def test_lamb_compile():
     net = Net()
     net.set_train()
     loss = nn.SoftmaxCrossEntropyWithLogits()
-    optimizer = Lamb(net.trainable_params(), decay_steps=10)
+    optimizer = Lamb(net.trainable_params(), learning_rate=0.1)
 
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
@@ -102,9 +87,9 @@ def test_edge_case():
     net = Net()
     with pytest.raises(RuntimeError):
         context.set_auto_parallel_context(parallel_mode="stand_alone")
-        Lamb(net.trainable_params(), decay_steps=10)
+        Lamb(net.trainable_params(), learning_rate=0.1)
     with pytest.raises(RuntimeError):
         Adam(net.trainable_params(), learning_rate=0.1)
     with pytest.raises(RuntimeError):
         context.set_auto_parallel_context(device_num=16)
-        Lamb(net.trainable_params(), decay_steps=10)
+        Lamb(net.trainable_params(), learning_rate=0.1)