!3098 IndexedSlices adapter of sparse optimizer
Merge pull request !3098 from wangnan39/sparse_optimizer_adapter_indexedslicepull/3098/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
10053f3d72
@ -1,174 +0,0 @@
|
|||||||
# 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 adam """
|
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
import mindspore.nn as nn
|
|
||||||
from mindspore import Tensor, Parameter, context
|
|
||||||
from mindspore.common.api import _executor
|
|
||||||
from mindspore.common import dtype as mstype
|
|
||||||
from mindspore.nn import TrainOneStepCell, WithLossCell
|
|
||||||
from mindspore.nn.optim import Optimizer
|
|
||||||
from mindspore.ops import operations as P
|
|
||||||
from mindspore.ops import composite as C
|
|
||||||
from mindspore.ops import functional as F
|
|
||||||
from mindspore._checkparam import Validator as validator
|
|
||||||
from mindspore._checkparam import Rel
|
|
||||||
|
|
||||||
context.set_context(enable_sparse=True)
|
|
||||||
|
|
||||||
adam_opt_for_map = C.MultitypeFuncGraph("adam_opt_for_map")
|
|
||||||
@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
|
|
||||||
"Tensor", "Tensor", "Tensor", "Bool")
|
|
||||||
def _update_run_op_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
|
|
||||||
op_mul = P.Mul()
|
|
||||||
op_square = P.Square()
|
|
||||||
op_sqrt = P.Sqrt()
|
|
||||||
op_cast = P.Cast()
|
|
||||||
op_reshape = P.Reshape()
|
|
||||||
op_shape = P.Shape()
|
|
||||||
|
|
||||||
param_fp32 = op_cast(param, mstype.float32)
|
|
||||||
m_fp32 = op_cast(m, mstype.float32)
|
|
||||||
v_fp32 = op_cast(v, mstype.float32)
|
|
||||||
gradient_fp32 = op_cast(gradient, mstype.float32)
|
|
||||||
|
|
||||||
next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
|
|
||||||
|
|
||||||
next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
|
|
||||||
- beta2, op_square(gradient_fp32))
|
|
||||||
|
|
||||||
update = next_m / (op_sqrt(next_v) + eps)
|
|
||||||
if decay_flag:
|
|
||||||
update = update + op_mul(weight_decay_tensor, param_fp32)
|
|
||||||
|
|
||||||
update_with_lr = op_mul(lr, update)
|
|
||||||
next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
|
|
||||||
|
|
||||||
next_v = F.depend(next_v, F.assign(param, next_param))
|
|
||||||
next_v = F.depend(next_v, F.assign(m, next_m))
|
|
||||||
next_v = F.depend(next_v, F.assign(v, next_v))
|
|
||||||
return next_v
|
|
||||||
|
|
||||||
|
|
||||||
@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
|
|
||||||
"Tensor", "Tensor", "Tuple", "Bool")
|
|
||||||
def _update_run_op_sparse_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
|
|
||||||
return gradient[2][2]
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
||||||
|
|
||||||
class AdamWeightDecaySparse(Optimizer):
|
|
||||||
"""
|
|
||||||
Implements Adam algorithm weight decay fix.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
|
|
||||||
should be class mindspore.Parameter.
|
|
||||||
learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
|
|
||||||
Iterable or a Tensor and the dims of the Tensor is 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 float or learning_rate is a Tensor
|
|
||||||
but the dims of the Tensor is 0, use fixed learning rate.
|
|
||||||
Other cases are not supported. 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.
|
|
||||||
Should be in range (0.0, 1.0).
|
|
||||||
eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
|
|
||||||
Should be greater than 0.
|
|
||||||
weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
|
|
||||||
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`,
|
|
||||||
and might be in sparse format.
|
|
||||||
|
|
||||||
Outputs:
|
|
||||||
tuple[Parameter], the updated velocity value, the shape is the same as `params`.
|
|
||||||
|
|
||||||
Examples:
|
|
||||||
>>> net = Net()
|
|
||||||
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
|
|
||||||
>>> optim = nn.AdamWeightDecay(params=net.trainable_params())
|
|
||||||
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
|
|
||||||
"""
|
|
||||||
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(AdamWeightDecaySparse, self).__init__(learning_rate, params)
|
|
||||||
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.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.map = C.Map()
|
|
||||||
|
|
||||||
def construct(self, gradients):
|
|
||||||
lr = self.get_lr()
|
|
||||||
updated_velocity = self.map(F.partial(adam_opt_for_map, self.beta1, self.beta2, self.eps, lr,
|
|
||||||
self.weight_decay_tensor),
|
|
||||||
self.params, self.moments1, self.moments2, gradients, self.decay_flag)
|
|
||||||
|
|
||||||
return updated_velocity
|
|
||||||
|
|
||||||
|
|
||||||
def test_AdamWeightDecaySparse():
|
|
||||||
""" test_AdamWeightDecaySparse """
|
|
||||||
context.set_context(mode=context.GRAPH_MODE)
|
|
||||||
class Loss(nn.Cell):
|
|
||||||
def __init__(self):
|
|
||||||
super(Loss, self).__init__()
|
|
||||||
def construct(self, base, target):
|
|
||||||
return base
|
|
||||||
class NetWithSparseGatherV2(nn.Cell):
|
|
||||||
def __init__(self):
|
|
||||||
super(NetWithSparseGatherV2, self).__init__()
|
|
||||||
self.w1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="w1")
|
|
||||||
self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2")
|
|
||||||
self.gatherv2 = P.SparseGatherV2()
|
|
||||||
self.axis = 0
|
|
||||||
def construct(self, indices):
|
|
||||||
return self.gatherv2(self.w1, indices, self.axis) * self.w2
|
|
||||||
|
|
||||||
inputs = Tensor(np.array([0, 1]).astype(np.int32))
|
|
||||||
label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
|
|
||||||
net = NetWithSparseGatherV2()
|
|
||||||
net.set_train()
|
|
||||||
loss = Loss()
|
|
||||||
optimizer = AdamWeightDecaySparse(net.trainable_params())
|
|
||||||
|
|
||||||
net_with_loss = WithLossCell(net, loss)
|
|
||||||
train_network = TrainOneStepCell(net_with_loss, optimizer)
|
|
||||||
_executor.compile(train_network, inputs, label)
|
|
||||||
Loading…
Reference in new issue