forked from mindspore-Ecosystem/mindspore
165 lines
6.3 KiB
Python
165 lines
6.3 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.
|
|
# ============================================================================
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore as ms
|
|
from mindspore import context, Tensor, Parameter
|
|
from mindspore.common.api import _executor
|
|
from mindspore.nn import Cell, TrainOneStepCell, Momentum
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class Net(Cell):
|
|
def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True, mask=0):
|
|
super().__init__()
|
|
self.mul = P.Mul().set_strategy(strategy1)
|
|
self.strided_slice = P.StridedSlice(begin_mask=mask).set_strategy(strategy2)
|
|
if is_parameter:
|
|
self.weight = Parameter(weight, "w1")
|
|
else:
|
|
self.weight = weight
|
|
self.mul2 = P.Mul()
|
|
self.weight2 = Parameter(w2, "w2")
|
|
self.begin = begin
|
|
self.end = end
|
|
self.strides = strides
|
|
|
|
def construct(self, x, b):
|
|
out = self.strided_slice(self.weight, self.begin, self.end, self.strides)
|
|
out = self.mul(x, out)
|
|
out = self.mul2(out, self.weight2)
|
|
return out
|
|
|
|
|
|
class Net2(Cell):
|
|
def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None):
|
|
super().__init__()
|
|
self.mul = P.Mul().set_strategy(strategy1)
|
|
self.strided_slice = P.StridedSlice().set_strategy(strategy2)
|
|
self.weight2 = Parameter(weight2, "w2")
|
|
self.begin = begin
|
|
self.end = end
|
|
self.strides = strides
|
|
|
|
def construct(self, x, b):
|
|
out = self.mul(x, self.weight2)
|
|
out = self.strided_slice(out, self.begin, self.end, self.strides)
|
|
return out
|
|
|
|
|
|
_x = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
|
|
_w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32)
|
|
_w2 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
|
|
_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
|
|
|
|
|
|
def compile_net(net):
|
|
context.set_context(save_graphs=True)
|
|
optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
|
train_net = TrainOneStepCell(net, optimizer)
|
|
train_net.set_auto_parallel()
|
|
_executor.compile(train_net, _x, _b)
|
|
context.reset_auto_parallel_context()
|
|
|
|
|
|
def test_stridedslice_no_fully_fetch_split_error():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((2, 2, 2), (2, 2, 2))
|
|
strategy2 = ((2, 2, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_strides_no_1_split_error():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((2, 2, 2), (2, 2, 2))
|
|
strategy2 = ((1, 2, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2), strategy1, strategy2, is_parameter=True)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_mask_no_0_split_error():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((2, 2, 2), (2, 2, 2))
|
|
strategy2 = ((1, 2, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, mask=1)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_begin_size_smaller():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 4, 2),)
|
|
net = Net(_w1, _w2, (0, 0), (128, 64), (1, 1), strategy1, strategy2, is_parameter=True)
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_parameter():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 4, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_tensor():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 4, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False)
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_parameter_no_full_split():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 1), (1, 4, 2))
|
|
strategy2 = ((1, 2, 2),)
|
|
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_output():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 8, 1),)
|
|
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_output_no_full_split():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 4, 1),)
|
|
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_no_strategy():
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = None
|
|
net = Net2(_w2, (0, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_stridedslice_auto_parallel():
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
|
|
net = Net2(_w2, (0, 0, 0), (32, 64, 1), (1, 1, 1))
|
|
compile_net(net)
|