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100 lines
3.0 KiB
100 lines
3.0 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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# ============================================================================
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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.common.api import _executor
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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 import Tensor, context
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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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):
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return C.grad_all(self.network)(x, y)
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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):
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predict = self.network(x, y)
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return self.loss(predict)
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class Net(nn.Cell):
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def __init__(self, shape, offset):
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super().__init__()
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self.index = Tensor(np.ones(shape), dtype=ms.int32)
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self.offset = offset
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self.elu = P.EmbeddingLookup()
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self.mm = P.BatchMatMul()
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def construct(self, x, y):
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out = self.elu(x, self.index, self.offset)
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out = self.mm(out, y)
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return out
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def test_embeddinglookup_reducescatter_false():
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shape = [8, 8]
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offset = 8
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net = NetWithLoss(Net(shape, offset))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_embeddinglookup_reducescatter_true():
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shape = [8, 8]
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offset = 8
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net = NetWithLoss(Net(shape, offset))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_embeddinglookup_reducescatter_false_grad():
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shape = [8, 8]
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offset = 8
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net = GradWrap(NetWithLoss(Net(shape, offset)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_embeddinglookup_reducescatter_true_grad():
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context.set_context(save_graphs=True)
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shape = [8, 8]
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offset = 8
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net = GradWrap(NetWithLoss(Net(shape, offset)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
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_executor.compile(net, x, y)
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