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132 lines
4.4 KiB
132 lines
4.4 KiB
# Copyright 2019 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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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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import mindspore.common.dtype as mstype
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from mindspore.common.seed import _get_graph_seed
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from mindspore.common.api import _executor
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from mindspore._checkparam import Validator
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from mindspore.ops.primitive import constexpr
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y, b):
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predict = self.network(x, y, b)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y, b):
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return grad_all(self.network)(x, y, b)
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@constexpr
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def _is_float_dtype(dtype):
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if dtype in [mstype.float32, mstype.float16]:
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return True
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return False
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class Dropout(nn.Cell):
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def __init__(self, keep_prob=0.5, dtype=mstype.float32):
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super(Dropout, self).__init__()
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if keep_prob <= 0 or keep_prob > 1:
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raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
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Validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
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Validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
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self.keep_prob = keep_prob
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seed0, seed1 = _get_graph_seed(0, "dropout")
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self.seed0 = seed0
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self.seed1 = seed1
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self.dtype = dtype
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self.get_shape = P.Shape()
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self.dropout_gen_mask = P.DropoutGenMask(Seed0=self.seed0, Seed1=self.seed1)
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self.dropout_do_mask = P.DropoutDoMask()
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self.cast = P.Cast()
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self.is_gpu = context.get_context('device_target') in ["GPU"]
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self.dropout = P.Dropout(keep_prob)
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def construct(self, x):
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if not self.training:
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return x
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if self.is_gpu:
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out, _ = self.dropout(x)
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return out
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if self.keep_prob == 1:
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return x
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shape = self.get_shape(x)
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dtype = P.DType()(x)
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if _is_float_dtype(dtype):
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keep_prob = self.cast(self.keep_prob, dtype)
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else:
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keep_prob = self.cast(self.keep_prob, mstype.float16)
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output = self.dropout_gen_mask(shape, keep_prob)
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return self.dropout_do_mask(x, output, keep_prob)
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def extend_repr(self):
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return 'keep_prob={}, dtype={}'.format(self.keep_prob, self.dtype)
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# model_parallel test
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def test_two_matmul_dropout():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super().__init__()
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self.matmul1 = P.MatMul().shard(strategy1)
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self.dropout = Dropout()
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self.dropout.dropout_do_mask.shard(strategy2)
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self.dropout.dropout_gen_mask.shard(strategy2)
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self.matmul2 = P.MatMul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.matmul1(x, y)
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out = self.dropout(out)
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out = self.matmul2(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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strategy1 = ((4, 2), (2, 1))
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strategy2 = ((8, 1),)
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strategy3 = ((1, 8), (8, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net.set_train()
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_executor.compile(net, x, y, b)
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