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!17239 add parallel gathernd test case 

From: @hanyang001
Reviewed-by: @stsuteng,@yangzhenzhang
Signed-off-by: @stsuteng
This commit is contained in:
mindspore-ci-bot 2021-05-29 09:11:25 +08:00 committed by Gitee
parent 5e87097d15 c51dff2634
commit b45b63fc58
1 changed files with 376 additions and 0 deletions
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tests/ut/python/parallel/test_gathernd_further.py Normal file
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# Copyright 2021 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.nn import Cell, Momentum
from mindspore.ops import operations as P
from mindspore.train import Model
from tests.dataset_mock import MindData
class Dataset(MindData):
def __init__(self, predict, label, length=3):
super(Dataset, self).__init__(size=length)
self.predict = predict
self.label = label
self.index = 0
self.length = length
def __iter__(self):
return self
def __next__(self):
if self.index >= self.length:
raise StopIteration
self.index += 1
return self.predict, self.label
def reset(self):
self.index = 0
class Net(Cell):
def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None):
super().__init__()
self.mul = P.Mul().shard(strategy1)
self.w1 = Parameter(Tensor(np.ones(w1_shape), dtype=ms.float32), "w1")
self.indices = Tensor(np.ones(indices_shape), dtype=ms.int32)
self.gathernd = P.GatherNd().shard(strategy2)
self.relu = P.ReLU().shard(strategy3)
def construct(self, x, b):
out = self.mul(x, self.w1)
out = self.gathernd(out, self.indices)
out = self.relu(out)
return out
class Net2(Cell):
def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None):
super().__init__()
self.mul = P.Mul().shard(strategy1)
self.w1 = Parameter(Tensor(np.ones(w1_shape), dtype=ms.float32), "w1")
self.indices = Tensor(np.ones(indices_shape), dtype=ms.int32)
self.gathernd = P.GatherNd().shard(strategy2)
self.relu = P.ReLU().shard(strategy3)
def construct(self, x, b):
out = self.mul(x, self.w1)
out = self.gathernd(out, self.indices)
return out
class Net3(Cell):
def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None):
super().__init__()
self.mul = P.Mul().shard(strategy1)
self.w1 = Parameter(Tensor(np.ones(w1_shape), dtype=ms.float32), "w1")
self.indices = Tensor(np.ones(indices_shape), dtype=ms.int32)
self.gathernd = P.GatherNd().shard(strategy2)
self.relu = P.ReLU().shard(strategy3)
def construct(self, x, b):
out = self.gathernd(x, self.indices)
out = self.relu(out)
out = self.mul(out, self.w1)
return out
# full_batch = false
_x = Tensor(np.ones([1, 16, 32]), dtype=ms.float32)
_b = Tensor(np.ones([1, 16, 32]), dtype=ms.float32)
def compile_net(net):
context.set_context(save_graphs=True)
learning_rate = 0.1
momentum = 0.9
epoch_size = 2
dataset = Dataset(_x, _b)
opt = Momentum(net.trainable_params(), learning_rate, momentum)
model = Model(net, optimizer=opt)
model.train(epoch_size, dataset, dataset_sink_mode=False)
context.reset_auto_parallel_context()
def test_gathernd_data_parallel():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 1]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel2():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 2]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel3():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel4():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 1]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1, 1, 1),)
net = Net2(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel5():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 2]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1, 1),)
net = Net2(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel6():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1),)
net = Net2(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel7():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 4, 2, 16, 32]
indices_shape = [8, 4, 2, 1]
strategy1 = ((8, 1, 1, 1, 1), (8, 1, 1, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1, 1, 1),)
net = Net3(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel8():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 4, 2, 32]
indices_shape = [8, 4, 2, 2]
strategy1 = ((8, 1, 1, 1), (8, 1, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1, 1),)
net = Net3(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_data_parallel9():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 4, 2]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (8, 1, 1, 1))
strategy3 = ((8, 1, 1),)
net = Net3(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 1]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel2():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 2]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel3():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel4():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 1]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1, 1, 1),)
net = Net2(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel5():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 2]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1, 1),)
net = Net2(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel6():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1),)
net = Net2(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel7():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 4, 2, 16, 32]
indices_shape = [8, 4, 2, 1]
strategy1 = ((8, 1, 1, 1, 1), (8, 1, 1, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1, 1, 1),)
net = Net3(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel8():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 4, 2, 32]
indices_shape = [8, 4, 2, 2]
strategy1 = ((8, 1, 1, 1), (8, 1, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1, 1),)
net = Net3(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_model_parallel9():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 4, 2]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (2, 2, 2, 1))
strategy3 = ((8, 1, 1),)
net = Net3(w1_shape, indices_shape, strategy1, strategy2, strategy3)
compile_net(net)
def test_gathernd_auto_parallel():
context.set_auto_parallel_context(
parallel_mode="auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 1]
net = Net(w1_shape, indices_shape)
compile_net(net)
def test_gathernd_auto_parallel2():
context.set_auto_parallel_context(
parallel_mode="auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 2]
net = Net(w1_shape, indices_shape)
compile_net(net)
def test_gathernd_auto_parallel3():
context.set_auto_parallel_context(
parallel_mode="auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 3]
net = Net(w1_shape, indices_shape)
compile_net(net)
def test_gathernd_strategy_error():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((2, 1, 1), (1, 2, 2, 1))
strategy3 = ((8, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
with pytest.raises(RuntimeError):
compile_net(net)
def test_gathernd_strategy_error2():
context.set_auto_parallel_context(
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
w1_shape = [8, 16, 32]
indices_shape = [8, 4, 2, 3]
strategy1 = ((8, 1, 1), (8, 1, 1))
strategy2 = ((1, 1, 1), (1, 2, 2, 2))
strategy3 = ((8, 1, 1),)
net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
with pytest.raises(RuntimeError):
compile_net(net)