You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
151 lines
5.1 KiB
151 lines
5.1 KiB
# 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 dynamic shape """
|
|
import numpy as np
|
|
|
|
from mindspore import Tensor, context, nn, Parameter
|
|
from mindspore import dtype as mstype
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
|
|
|
|
|
|
def test_sparse_apply_proximal_ada_grad():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.sparse_apply_proximal_adagrad = P.SparseApplyProximalAdagrad()
|
|
self.var = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="var")
|
|
self.accum = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="accum")
|
|
self.lr = 0.01
|
|
self.l1 = 0.0
|
|
self.l2 = 0.0
|
|
|
|
def construct(self, grad, indices):
|
|
out = self.sparse_apply_proximal_adagrad(self.var, self.accum, self.lr, self.l1, self.l2, grad, indices)
|
|
return out[0]
|
|
|
|
class NetWrapper(nn.Cell):
|
|
def __init__(self):
|
|
super(NetWrapper, self).__init__()
|
|
self.unq = P.Unique()
|
|
self.add = P.Add()
|
|
self.expand_dims = P.ExpandDims()
|
|
self.cast = P.Cast()
|
|
self.net = Net()
|
|
|
|
def construct(self, grad, inp):
|
|
ids, _ = self.unq(inp)
|
|
new_grad = self.expand_dims(ids, 1)
|
|
new_grad = self.cast(new_grad, mstype.float32) + grad
|
|
return self.net(new_grad, ids)
|
|
|
|
net = NetWrapper()
|
|
grad = Tensor(np.random.rand(1, 80).astype(np.float32))
|
|
indices = Tensor(np.ones([7800]), mstype.int32)
|
|
net(grad, indices)
|
|
|
|
|
|
def test_sparse_apply_ftrl():
|
|
class SparseApplyFtrlNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SparseApplyFtrlNet, self).__init__()
|
|
self.sparse_apply_ftrl = P.SparseApplyFtrl(lr=0.01, l1=0.0, l2=0.0, lr_power=-0.5)
|
|
self.var = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="var")
|
|
self.accum = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="accum")
|
|
self.linear = Parameter(Tensor(np.random.rand(7800, 80).astype(np.float32)), name="linear")
|
|
|
|
def construct(self, grad, indices):
|
|
out = self.sparse_apply_ftrl(self.var, self.accum, self.linear, grad, indices)
|
|
return out[0]
|
|
|
|
class NetWrapper(nn.Cell):
|
|
def __init__(self):
|
|
super(NetWrapper, self).__init__()
|
|
self.unq = P.Unique()
|
|
self.add = P.Add()
|
|
self.expand_dims = P.ExpandDims()
|
|
self.cast = P.Cast()
|
|
self.net = SparseApplyFtrlNet()
|
|
|
|
def construct(self, grad, inp):
|
|
ids, _ = self.unq(inp)
|
|
new_grad = self.expand_dims(ids, 1)
|
|
new_grad = self.cast(new_grad, mstype.float32) + grad
|
|
return self.net(new_grad, ids)
|
|
|
|
net = NetWrapper()
|
|
grad = Tensor(np.random.rand(1, 80).astype(np.float32))
|
|
indices = Tensor(np.ones([7800]), mstype.int32)
|
|
net(grad, indices)
|
|
|
|
|
|
def test_gatherv2():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.unq = P.Unique()
|
|
self.gather = P.Gather()
|
|
self.yy = Tensor(np.ones([8], dtype=np.int32))
|
|
|
|
def construct(self, x, y):
|
|
shp = P.Shape()(self.yy)
|
|
y = P.Reshape()(y, shp)
|
|
u, _ = self.unq(y)
|
|
u_shp = P.DynamicShape()(u)
|
|
z = self.gather(x, u, 0)
|
|
return z, u_shp
|
|
|
|
x = Tensor(np.ones([20, 12], dtype=np.float32))
|
|
y = Tensor(np.ones([2, 4], dtype=np.int32))
|
|
net = Net()
|
|
net(x, y)
|
|
|
|
|
|
def test_segmentsum():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.unq = P.Unique()
|
|
self.segment_ids = Tensor([0, 0, 1, 2, 1, 1, 1, 1], mstype.int32)
|
|
self.sum = P.UnsortedSegmentSum()
|
|
def construct(self, x):
|
|
u, _ = self.unq(x)
|
|
shp = P.DynamicShape()(u)
|
|
z = self.sum(x, self.segment_ids, shp[0])
|
|
return z, shp[0]
|
|
|
|
x = Tensor(np.ones([8], dtype=np.int32))
|
|
net = Net()
|
|
net(x)
|
|
|
|
|
|
def test_addn():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.unq = P.Unique()
|
|
self.addn = P.AddN()
|
|
|
|
def construct(self, x):
|
|
u, _ = self.unq(x)
|
|
u = self.addn((u, u, u))
|
|
z = self.addn([u, u])
|
|
return z
|
|
|
|
y = Tensor(np.ones([8], dtype=np.int32))
|
|
net = Net()
|
|
net(y)
|