!6108 add model with loss test case

Merge pull request !6108 from hanyang/master

tests/ut/python/parallel/test_model_with_loss.py

@@ -0,0 +1,121 @@
+# 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 mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, Momentum
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+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, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().shard(strategy1)
+        self.neg = P.Neg().shard(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x):
+        out = self.mul(x, self.mul_weight)
+        out = self.neg(out)
+        return out
+
+
+_x = Tensor(np.ones([32, 128]), dtype=ms.float32)
+_b = Tensor(np.ones([32]), dtype=ms.int32)
+_w1 = Tensor(np.ones([512, 128]), 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)
+    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    opt = Momentum(net.trainable_params(), learning_rate, momentum)
+    model = Model(net, loss, optimizer=opt)
+    model.train(epoch_size, dataset, dataset_sink_mode=False)
+    context.reset_auto_parallel_context()
+
+
+def test_neg_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1), (16, 1))
+    strategy2 = ((16, 1),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_model_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((1, 16), (1, 16))
+    strategy2 = ((1, 16),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_hybrid_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 4), (4, 4))
+    strategy2 = ((4, 4),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
+    net = Net(_w1)
+    compile_net(net)
+
+
+def test_neg_repeat_calc():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 4), (4, 4))
+    strategy2 = ((2, 2),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_neg_repeat_calc2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((4, 2), (4, 2))
+    strategy2 = ((4, 4),)
+    net = Net(_w1, strategy1, strategy2)
+    compile_net(net)

tests/ut/python/parallel/test_model_without_loss.py

@@ -0,0 +1,191 @@
+# 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 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, weight, weight2, strategy1=None, strategy2=None, is_parameter=True):
+        super().__init__()
+        self.concat = P.Concat(axis=0).shard(strategy1)
+        if is_parameter:
+            self.weight = Parameter(weight, "w1")
+        else:
+            self.weight = weight
+        self.mul = P.Mul().shard(strategy2)
+        self.weight2 = Parameter(weight2, "w2")
+
+    def construct(self, x, b):
+        out = self.concat((self.weight, self.weight2))
+        out = self.mul(x, out)
+        return out
+
+
+class Net2(Cell):
+    def __init__(self, weight, strategy1=None, strategy2=None, axis=0):
+        super().__init__()
+        self.mul = P.Mul().shard(strategy1)
+        self.concat = P.Concat(axis=axis).shard(strategy2)
+        self.weight = Parameter(weight, "w")
+
+    def construct(self, x, b):
+        out = self.mul(x, x)
+        out = self.concat((out, self.weight))
+        return out
+
+
+class Net3(Cell):
+    def __init__(self, weight, weight2, weight3, strategy1=None, strategy2=None, is_parameter=True):
+        super().__init__()
+        self.concat = P.Concat(axis=0).shard(strategy1)
+        if is_parameter:
+            self.weight = Parameter(weight, "w1")
+        else:
+            self.weight = weight
+        self.mul = P.Mul().shard(strategy2)
+        self.weight2 = Parameter(weight2, "w2")
+        self.weight3 = Parameter(weight3, "w3")
+
+    def construct(self, x, b):
+        out = self.concat((self.weight, self.weight2, self.weight3))
+        out = self.mul(x, out)
+        return out
+
+
+_x = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([16, 64, 32, 32]), dtype=ms.int32)
+_w1 = Tensor(np.ones([96, 64, 32]), dtype=ms.float32)
+_w2 = Tensor(np.ones([32, 64, 32]), dtype=ms.float32)
+_w3 = Tensor(np.ones([128, 16, 32]), dtype=ms.float32)
+
+w1 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
+w2 = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
+w3 = Tensor(np.ones([64, 64, 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, amp_level="O2")
+    model.train(epoch_size, dataset, dataset_sink_mode=False)
+    context.reset_auto_parallel_context()
+
+
+def test_concat_parameter():
+    context.set_auto_parallel_context(
+        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 2), (1, 4, 2))
+    strategy2 = ((1, 4, 2), (1, 4, 2))
+    net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
+    compile_net(net)
+
+
+def test_concat_parameter_no_full_split():
+    context.set_auto_parallel_context(
+        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 2, 2), (1, 2, 2))
+    strategy2 = ((1, 4, 2), (1, 4, 2))
+    net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
+    compile_net(net)
+
+
+def test_concat_tensor_and_parameter():
+    context.set_auto_parallel_context(
+        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 2, 2), (1, 2, 2))
+    strategy2 = ((1, 4, 2), (1, 4, 2))
+    net = Net(_w1, _w2, strategy1, strategy2, is_parameter=False)
+    compile_net(net)
+
+
+def test_concat_output():
+    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, 4, 2), (1, 4, 2))
+    net = Net2(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_concat_output_no_full_split():
+    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), (1, 2, 2))
+    net = Net2(_w1, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_concat_no_strategy():
+    context.set_auto_parallel_context(
+        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 2), (2, 2, 2))
+    strategy2 = None
+    net = Net2(_w3, strategy1, strategy2, axis=1)
+    compile_net(net)
+
+
+def test_concat_auto_parallel():
+    context.set_auto_parallel_context(
+        parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net2(_w2)
+    compile_net(net)
+
+
+def test_concat_auto_parallel2():
+    context.set_auto_parallel_context(
+        parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    strategy1 = None
+    strategy2 = None
+    net = Net2(_w3, strategy1, strategy2, axis=1)
+    compile_net(net)
+
+
+def test_concat_auto_parallel_3_tensor():
+    context.set_auto_parallel_context(
+        parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net3(w1, w2, w3)
+    compile_net(net)

tests/ut/python/parallel/test_o2_level.py

@@ -0,0 +1,208 @@
+# 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.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, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True, mask=0):
+        super().__init__()
+        self.mul = P.Mul().shard(strategy1)
+        self.strided_slice = P.StridedSlice(begin_mask=mask).shard(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().shard(strategy1)
+        self.strided_slice = P.StridedSlice().shard(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([16, 64, 1]), dtype=ms.float32)
+_b = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32)
+_w2 = Tensor(np.ones([128, 64, 1]), 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, amp_level="O2")
+    model.train(epoch_size, dataset, dataset_sink_mode=False)
+    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)