fary86
5 years ago
parent
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commit
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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 dynamic shape """
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from mindspore import Tensor, context, nn, Parameter
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from mindspore.ops import operations as P
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from mindspore import dtype as mstype
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import numpy as np
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context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
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def test_sparse_apply_proximal_ada_grad():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.sparse_apply_proximal_adagrad = P.SparseApplyProximalAdagrad()
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self.var = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="var")
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self.accum = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="accum")
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self.lr = 0.01
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self.l1 = 0.0
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self.l2 = 0.0
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def construct(self, grad, indices):
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out = self.sparse_apply_proximal_adagrad(self.var, self.accum, self.lr, self.l1, self.l2, grad, indices)
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return out[0]
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class NetWrapper(nn.Cell):
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def __init__(self):
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super(NetWrapper, self).__init__()
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self.unq = P.Unique()
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self.add = P.TensorAdd()
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self.expand_dims = P.ExpandDims()
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self.cast = P.Cast()
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self.net = Net()
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def construct(self, grad, inp):
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ids, _ = self.unq(inp)
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new_grad = self.expand_dims(ids, 1)
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new_grad = self.cast(new_grad, mstype.float32) + grad
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return self.net(new_grad, ids)
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net = NetWrapper()
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grad = Tensor(np.random.rand(1, 80).astype(np.float32))
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indices = Tensor(np.ones([7800]), mstype.int32)
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net(grad, indices)
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def test_sparse_apply_ftrl():
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class SparseApplyFtrlNet(nn.Cell):
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def __init__(self):
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super(SparseApplyFtrlNet, self).__init__()
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self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=0.01, l1=0.0, l2=0.0, lr_power=-0.5)
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self.var = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="var")
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self.accum = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="accum")
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self.linear = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="linear")
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def construct(self, grad, indices):
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out = self.sparse_apply_ftrl(self.var, self.accum, self.linear, grad, indices)
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return out[0]
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class NetWrapper(nn.Cell):
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def __init__(self):
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super(NetWrapper, self).__init__()
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self.unq = P.Unique()
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self.add = P.TensorAdd()
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self.expand_dims = P.ExpandDims()
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self.cast = P.Cast()
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self.net = SparseApplyFtrlNet()
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def construct(self, grad, inp):
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ids, _ = self.unq(inp)
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new_grad = self.expand_dims(ids, 1)
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new_grad = self.cast(new_grad, mstype.float32) + grad
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return self.net(new_grad, ids)
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net = NetWrapper()
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grad = Tensor(np.random.rand(1, 80).astype(np.float32))
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indices = Tensor(np.ones([7800]), mstype.int32)
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net(grad, indices)
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def test_gatherv2():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.unq = P.Unique()
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self.gather = P.GatherV2()
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def construct(self, x, y):
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u, _ = self.unq(y)
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z = self.gather(x, u, 0)
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return z
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x = Tensor(np.ones([20, 12], dtype=np.float32))
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y = Tensor(np.ones([8], dtype=np.int32))
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net = Net()
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net(x, y)
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