!3098 IndexedSlices adapter of sparse optimizer
Merge pull request !3098 from wangnan39/sparse_optimizer_adapter_indexedslicepull/3098/MERGE
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5 years ago
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test adam """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter, context
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from mindspore.common.api import _executor
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from mindspore.common import dtype as mstype
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import Optimizer
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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context.set_context(enable_sparse=True)
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adam_opt_for_map = C.MultitypeFuncGraph("adam_opt_for_map")
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@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
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"Tensor", "Tensor", "Tensor", "Bool")
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def _update_run_op_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
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op_mul = P.Mul()
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op_square = P.Square()
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op_sqrt = P.Sqrt()
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op_cast = P.Cast()
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op_reshape = P.Reshape()
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op_shape = P.Shape()
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param_fp32 = op_cast(param, mstype.float32)
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m_fp32 = op_cast(m, mstype.float32)
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v_fp32 = op_cast(v, mstype.float32)
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gradient_fp32 = op_cast(gradient, mstype.float32)
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next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
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next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
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- beta2, op_square(gradient_fp32))
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update = next_m / (op_sqrt(next_v) + eps)
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if decay_flag:
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update = update + op_mul(weight_decay_tensor, param_fp32)
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update_with_lr = op_mul(lr, update)
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next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
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next_v = F.depend(next_v, F.assign(param, next_param))
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next_v = F.depend(next_v, F.assign(m, next_m))
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next_v = F.depend(next_v, F.assign(v, next_v))
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return next_v
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@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
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"Tensor", "Tensor", "Tuple", "Bool")
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def _update_run_op_sparse_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
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return gradient[2][2]
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def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
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"""Check the type of inputs."""
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validator.check_value_type("beta1", beta1, [float], prim_name)
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validator.check_value_type("beta2", beta2, [float], prim_name)
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validator.check_value_type("eps", eps, [float], prim_name)
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validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
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validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
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validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
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validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
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validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
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class AdamWeightDecaySparse(Optimizer):
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"""
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Implements Adam algorithm weight decay fix.
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Args:
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params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
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should be class mindspore.Parameter.
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learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
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Iterable or a Tensor and the dims of the Tensor is 1,
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use dynamic learning rate, then the i-th step will
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take the i-th value as the learning rate.
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When the learning_rate is float or learning_rate is a Tensor
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but the dims of the Tensor is 0, use fixed learning rate.
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Other cases are not supported. Default: 1e-3.
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beta1 (float): The exponential decay rate for the 1st moment estimates. Default: 0.9.
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Should be in range (0.0, 1.0).
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beta2 (float): The exponential decay rate for the 2nd moment estimates. Default: 0.999.
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Should be in range (0.0, 1.0).
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eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
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Should be greater than 0.
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weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
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decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
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lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name.
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Inputs:
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- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`,
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and might be in sparse format.
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Outputs:
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tuple[Parameter], the updated velocity value, the shape is the same as `params`.
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Examples:
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>>> net = Net()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits()
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>>> optim = nn.AdamWeightDecay(params=net.trainable_params())
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>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
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"""
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def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0,
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decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
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super(AdamWeightDecaySparse, self).__init__(learning_rate, params)
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if self.is_group:
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raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
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_check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
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self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
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self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
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self.eps = Tensor(np.array([eps]).astype(np.float32))
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self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
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self.params = self.parameters
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self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
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self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
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self.decay_flag = tuple(decay_filter(x) for x in self.params)
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self.map = C.Map()
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def construct(self, gradients):
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lr = self.get_lr()
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updated_velocity = self.map(F.partial(adam_opt_for_map, self.beta1, self.beta2, self.eps, lr,
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self.weight_decay_tensor),
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self.params, self.moments1, self.moments2, gradients, self.decay_flag)
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return updated_velocity
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def test_AdamWeightDecaySparse():
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""" test_AdamWeightDecaySparse """
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context.set_context(mode=context.GRAPH_MODE)
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class Loss(nn.Cell):
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def __init__(self):
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super(Loss, self).__init__()
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def construct(self, base, target):
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return base
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class NetWithSparseGatherV2(nn.Cell):
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def __init__(self):
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super(NetWithSparseGatherV2, self).__init__()
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self.w1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="w1")
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self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2")
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self.gatherv2 = P.SparseGatherV2()
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self.axis = 0
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def construct(self, indices):
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return self.gatherv2(self.w1, indices, self.axis) * self.w2
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inputs = Tensor(np.array([0, 1]).astype(np.int32))
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label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
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net = NetWithSparseGatherV2()
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net.set_train()
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loss = Loss()
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optimizer = AdamWeightDecaySparse(net.trainable_params())
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_executor.compile(train_network, inputs, label)
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