forked from mindspore-Ecosystem/mindspore
250 lines
8.1 KiB
Python
250 lines
8.1 KiB
Python
# 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_mix_precision"""
|
|
import numpy as np
|
|
|
|
import mindspore.common.dtype as mstype
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, context
|
|
from mindspore.common import ParameterTuple
|
|
from mindspore.common.api import _executor
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.nn import Momentum
|
|
from mindspore.nn import TrainOneStepCell, WithLossCell
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops import functional as F
|
|
from mindspore.train.parallel_utils import ParallelMode
|
|
from tests.ops_common import convert
|
|
from ....train_step_wrap import train_step_with_loss_warp
|
|
|
|
|
|
class LeNet5(nn.Cell):
|
|
"""LeNet5"""
|
|
|
|
def __init__(self):
|
|
super(LeNet5, self).__init__()
|
|
self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
|
|
self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
|
|
self.fc1 = nn.Dense(16 * 5 * 5, 120)
|
|
self.fc2 = nn.Dense(120, 84)
|
|
self.fc3 = nn.Dense(84, 10)
|
|
self.relu = nn.ReLU()
|
|
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
|
|
self.flatten = P.Flatten()
|
|
|
|
def construct(self, x):
|
|
x = self.max_pool2d(self.relu(self.conv1(x)))
|
|
x = self.max_pool2d(self.relu(self.conv2(x)))
|
|
x = self.flatten(x)
|
|
x = self.relu(self.fc1(x))
|
|
x = self.relu(self.fc2(x))
|
|
x = self.fc3(x)
|
|
return x
|
|
|
|
|
|
class NetForConcat(nn.Cell):
|
|
def __init__(self):
|
|
super(NetForConcat, self).__init__()
|
|
self.concat = P.Concat()
|
|
self.x1 = Tensor(np.zeros([1, 10]).astype(np.float32))
|
|
self.x2 = Parameter(Tensor(np.zeros([1, 10]).astype(np.float32)), name='x2')
|
|
|
|
def construct(self, x0):
|
|
return self.concat((x0, self.x1, self.x2))
|
|
|
|
|
|
def test_add_cast_flag():
|
|
predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.zeros([1, 10]).astype(np.float32))
|
|
net = LeNet5()
|
|
net.to_float(mstype.float16)
|
|
net.fc3.to_float(mstype.float32)
|
|
net = train_step_with_loss_warp(net)
|
|
net.set_train()
|
|
_executor.compile(net, predict, label)
|
|
|
|
|
|
def test_add_cast_flag_tensor():
|
|
x1 = Tensor(np.zeros([1, 10]).astype(np.float32))
|
|
net = NetForConcat()
|
|
net.add_flags_recursive(fp16=True)
|
|
net.set_train()
|
|
_executor.compile(net, x1)
|
|
|
|
|
|
def test_on_momentum():
|
|
predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.zeros([1, 10]).astype(np.float32))
|
|
net = LeNet5()
|
|
net = train_step_with_loss_warp(net).to_float(mstype.float16)
|
|
net.set_train()
|
|
_executor.compile(net, predict, label)
|
|
|
|
|
|
def test_data_parallel_with_cast():
|
|
"""test_data_parallel_with_cast"""
|
|
predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.zeros([1, 10]).astype(np.float32))
|
|
net = LeNet5()
|
|
net.to_float(mstype.float16)
|
|
net.fc3.to_float(mstype.float32)
|
|
loss_fn = nn.SoftmaxCrossEntropyWithLogits()
|
|
|
|
optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
|
|
learning_rate=0.1,
|
|
momentum=0.9)
|
|
net = WithLossCell(net, loss_fn)
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True, device_num=8)
|
|
net = TrainOneStepCell(net, optimizer)
|
|
|
|
_executor.compile(net, predict, label)
|
|
context.reset_auto_parallel_context()
|
|
|
|
|
|
class NetForPReLU(nn.Cell):
|
|
def __init__(self):
|
|
super(NetForPReLU, self).__init__()
|
|
self.prelu = nn.PReLU()
|
|
|
|
def construct(self, x):
|
|
return self.prelu(x)
|
|
|
|
|
|
def test_nn_prelu():
|
|
x = Tensor(np.ones([1, 16, 10, 10]).astype(np.float32) * 0.01)
|
|
net = NetForPReLU().set_train()
|
|
net.add_flags_recursive(fp16=True)
|
|
_executor.compile(net, x)
|
|
|
|
|
|
class NetForCast(nn.Cell):
|
|
def __init__(self):
|
|
super(NetForCast, self).__init__()
|
|
self.concat = P.Concat()
|
|
self.x1 = Tensor(1.0, mstype.float32)
|
|
|
|
def construct(self, x0):
|
|
x = self.x1 * x0
|
|
return x
|
|
|
|
|
|
def test_cast():
|
|
x = Tensor(np.ones([1, 16, 10, 10]).astype(np.float32) * 0.01)
|
|
net = NetForCast()
|
|
net.add_flags_recursive(fp16=True)
|
|
_executor.compile(net, x)
|
|
|
|
|
|
class IRBlockZ(nn.Cell):
|
|
def __init__(self, inplanes, planes):
|
|
super(IRBlockZ, self).__init__()
|
|
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=1, pad_mode="same", group=1, has_bias=False,
|
|
dilation=1)
|
|
self.act_layer = nn.PReLU(planes)
|
|
|
|
def construct(self, x):
|
|
out = self.conv1(x)
|
|
return self.act_layer(out)
|
|
|
|
|
|
class GetParamGrad(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GetParamGrad, self).__init__(auto_prefix=False)
|
|
self.network = network
|
|
self.weights = ParameterTuple(network.trainable_params())
|
|
self.grad = C.GradOperation('grad',
|
|
get_by_list=True,
|
|
sens_param=True)
|
|
|
|
def construct(self, data, sens):
|
|
weights = self.weights
|
|
return self.grad(self.network, weights)(data, sens)
|
|
|
|
|
|
def test_grad_conv_prelu():
|
|
shapes = [[64, 64, 112, 112]]
|
|
outshape = [[64, 64, 112, 112]]
|
|
net = IRBlockZ(inplanes=64, planes=64).add_flags_recursive(fp16=True)
|
|
inputs = [convert(shp, dtype=np.float16) for shp in shapes]
|
|
sens_shape = outshape[0]
|
|
sens = convert(sens_shape, dtype=np.float16)
|
|
all_inputs = inputs + [sens]
|
|
net = GetParamGrad(net)
|
|
net.set_train()
|
|
net(*all_inputs)
|
|
|
|
|
|
def test_dict_cast():
|
|
class FirstNet(nn.Cell):
|
|
def __init__(self):
|
|
super(FirstNet, self).__init__()
|
|
self.net = SecondNet()
|
|
self.sub = P.Sub()
|
|
|
|
def construct(self, tensor_a, tensor_b):
|
|
a = F.mixed_precision_cast(mstype.float16, tensor_a)
|
|
b = F.mixed_precision_cast(mstype.float16, tensor_b)
|
|
c = self.sub(a, b)
|
|
dictionary = {"key": a}
|
|
result = self.net(c, key1=a, key2=dictionary)
|
|
return result
|
|
|
|
class SecondNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SecondNet, self).__init__()
|
|
self.add = P.TensorAdd()
|
|
|
|
def construct(self, tensor_c, **kwargs):
|
|
d = F.mixed_precision_cast(mstype.float16, tensor_c)
|
|
dict_cast = F.mixed_precision_cast(mstype.float16, kwargs)
|
|
e = self.add(d, dict_cast["key1"])
|
|
f = self.add(e, dict_cast["key2"]["key"])
|
|
return f
|
|
|
|
x = Tensor(np.array([1, 2.5, 3.5]), mstype.float32)
|
|
y = Tensor(np.array([4, 5.5, 6.5]), mstype.float32)
|
|
net = FirstNet()
|
|
net(x, y)
|
|
|
|
|
|
def test_kwarg_cast():
|
|
class FirstNet(nn.Cell):
|
|
def __init__(self):
|
|
super(FirstNet, self).__init__()
|
|
self.net = SecondNet().add_flags_recursive(fp16=True)
|
|
self.add = P.TensorAdd()
|
|
|
|
def construct(self, tensor_a, tensor_b):
|
|
tensor_c = self.add(tensor_a, tensor_b)
|
|
dictionary = {"key": tensor_a}
|
|
result = self.net(key1=tensor_c, key2=dictionary)
|
|
return result
|
|
|
|
class SecondNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SecondNet, self).__init__()
|
|
self.add = P.TensorAdd()
|
|
|
|
def construct(self, key1=1, key2=2):
|
|
tensor_d = self.add(key1, key2["key"])
|
|
return tensor_d
|
|
|
|
x = Tensor(np.array([1, 2.5, 3.5]), mstype.float32)
|
|
y = Tensor(np.array([4, 5.5, 6.5]), mstype.float32)
|
|
net = FirstNet()
|
|
net(x, y)
|