forked from huawei/mindspore2022
207 lines
10 KiB
Python
207 lines
10 KiB
Python
# 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.
|
|
# ============================================================================
|
|
"""FTRL"""
|
|
from mindspore.ops import functional as F, composite as C, operations as P
|
|
from mindspore.common import Tensor
|
|
import mindspore.common.dtype as mstype
|
|
from mindspore.ops.operations import _inner_ops as inner
|
|
from mindspore._checkparam import Validator as validator
|
|
from mindspore._checkparam import Rel
|
|
from .optimizer import Optimizer, _apply_decay, _grad_scale
|
|
|
|
_ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
|
|
_ftrl_push_pull_opt = C.MultitypeFuncGraph("ftrl_opt")
|
|
|
|
|
|
@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tuple", "Tensor",
|
|
"Tensor", "Bool")
|
|
def _tensor_run_opt_with_sparse(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment,
|
|
ps_parameter):
|
|
"""Apply sparse ftrl optimizer to the weight parameter when the gradient is sparse."""
|
|
success = True
|
|
if ps_parameter:
|
|
op_shape = P.Shape()
|
|
_ps_pull = P.Pull()
|
|
_ps_push = P.Push("Ftrl", [0, 1, 2])
|
|
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(gradient[1]), op_shape(gradient[0]))
|
|
success = F.depend(success, _ps_pull(_ps_push((gradient[1], gradient[0]), shapes), weight))
|
|
else:
|
|
success = F.depend(success, spars_opt(weight, moment, linear, gradient[1], gradient[0]))
|
|
return success
|
|
|
|
|
|
@_ftrl_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
|
|
"Tensor", "Bool")
|
|
def _tensor_run_opt(opt, spars_opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment, ps_parameter):
|
|
"""Apply ftrl optimizer to the weight parameter."""
|
|
success = True
|
|
if ps_parameter:
|
|
op_shape = P.Shape()
|
|
_ps_pull = P.Pull()
|
|
_ps_push = P.Push("Ftrl", [0, 1, 2])
|
|
success = F.depend(success, _ps_pull(_ps_push((gradient, learning_rate, l1, l2, lr_power),
|
|
(op_shape(weight), op_shape(moment), op_shape(linear))), weight))
|
|
else:
|
|
success = F.depend(success, opt(weight, moment, linear, gradient, learning_rate, l1, l2, lr_power))
|
|
return success
|
|
|
|
|
|
@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tuple",
|
|
"Tensor", "Tensor")
|
|
def _tensor_run_push_pull_opt_with_sparse(push, pull, learning_rate, l1, l2, lr_power, linear, gradient,
|
|
weight, moment):
|
|
success = True
|
|
op_shape = P.Shape()
|
|
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(gradient[1]), op_shape(gradient[0]))
|
|
success = F.depend(success, pull(push((gradient[1], gradient[0]), shapes), weight))
|
|
return success
|
|
|
|
|
|
@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor",
|
|
"Tensor", "Tensor")
|
|
def _tensor_run_push_pull_opt_with_one_number(push, pull, learning_rate, l1, l2, lr_power, linear, gradient,
|
|
weight, moment):
|
|
success = True
|
|
op_shape = P.Shape()
|
|
success = F.depend(success, pull(push((gradient, learning_rate, l1, l2, lr_power),
|
|
(op_shape(weight), op_shape(moment), op_shape(linear))), weight))
|
|
return success
|
|
|
|
|
|
def _check_param(initial_accum, lr_power, l1, l2, use_locking, weight_decay=0.0, 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)
|
|
|
|
validator.check_value_type("lr_power", lr_power, [float], prim_name)
|
|
validator.check_number("lr_power", lr_power, 0.0, Rel.LE, prim_name)
|
|
|
|
validator.check_value_type("l1", l1, [float], prim_name)
|
|
validator.check_number("l1", l1, 0.0, Rel.GE, prim_name)
|
|
|
|
validator.check_value_type("l2", l2, [float], prim_name)
|
|
validator.check_number("l2", l2, 0.0, Rel.GE, prim_name)
|
|
|
|
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):
|
|
"""
|
|
Implement the FTRL algorithm with ApplyFtrl Operator.
|
|
|
|
FTRL is an online convex optimization algorithm that adaptively chooses its regularization function
|
|
based on the loss functions. Refer to paper `Adaptive Bound Optimization for Online Convex Optimization
|
|
<https://arxiv.org/abs/1002.4908>`_. Refer to paper `Ad Click Prediction: a View from the Trenches
|
|
<https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf>`_ for engineering document.
|
|
|
|
Note:
|
|
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`
|
|
should be Parameter.
|
|
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.
|
|
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.
|
|
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.
|
|
loss_scale (float): Value for the loss scale. It should be equal to or greater than 1.0. Default: 1.0.
|
|
wegith_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
|
|
|
|
Inputs:
|
|
- **grads** (tuple[Tensor]) - The gradients of `params` in optimizer, the shape is as same as the `params`
|
|
in optimizer.
|
|
|
|
Outputs:
|
|
tuple[Parameter], the updated parameters, the shape is the same as `params`.
|
|
|
|
Examples:
|
|
>>> net = Net()
|
|
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
|
>>> opt = nn.FTRL(net.trainable_params())
|
|
>>> 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)
|
|
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)
|
|
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
|
|
self.decay_tf = tuple((lambda: True)() for x in self.parameters)
|
|
self.hyper_map = C.HyperMap()
|
|
self.opt = P.ApplyFtrl(use_locking=use_locking)
|
|
self.sparse_opt = inner.SparseApplyFtrlNoReturn(learning_rate, l1, l2, lr_power, use_locking=use_locking)
|
|
|
|
def construct(self, grads):
|
|
params = self.parameters
|
|
moments = self.moments
|
|
linear = self.linear
|
|
lr = self.learning_rate
|
|
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),
|
|
linear, grads, params, moments, self.ps_parameters)
|
|
return success
|
|
|
|
|
|
class PSFTRL(Optimizer):
|
|
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(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)
|
|
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
|
|
self.decay_tf = tuple((lambda: True)() for x in self.parameters)
|
|
|
|
self.hyper_map = C.HyperMap()
|
|
self.push = P.Push("Ftrl", [0, 1, 2])
|
|
self.push.add_prim_attr("primitive_target", "CPU")
|
|
self.pull = P.Pull()
|
|
self.pull.add_prim_attr("primitive_target", "CPU")
|
|
|
|
def construct(self, grads):
|
|
params = self.parameters
|
|
moments = self.moments
|
|
linear = self.linear
|
|
lr = self.learning_rate
|
|
if self.weight_decay > 0.0:
|
|
grads = self.hyper_map(F.partial(_apply_decay, self.weight_decay), self.decay_tf, params, grads)
|
|
|
|
grads = self.scale_grad(grads)
|
|
success = self.map_(F.partial(_ftrl_push_pull_opt, self.push, self.pull, lr, self.l1, self.l2, self.lr_power),
|
|
linear, grads, params, moments)
|
|
return success
|