fix pylint warnings

tests/st/auto_parallel/test_expand_loss.py

@@ -1,26 +1,26 @@
-# 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 os
-import pytest
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_single
-def test_expand_loss():
-    sh_path = os.path.split(os.path.realpath(__file__))[0]
-    ret = os.system(f"sh {sh_path}/run_auto_parallel_loss_expand.sh")
-    assert (ret == 0)
+# 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 os
+import pytest
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_single
+def test_expand_loss():
+    sh_path = os.path.split(os.path.realpath(__file__))[0]
+    ret = os.system(f"sh {sh_path}/run_auto_parallel_loss_expand.sh")
+    assert ret == 0

tests/st/auto_parallel/test_model_parallel_onehot.py

@@ -1,22 +1,21 @@
-# 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 os
-import pytest
-
-
-def test_expand_loss():
-    ret = os.system("sh run_onehot_model_parallel.sh")
-    assert (ret == 0)
+# 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 os
+
+
+def test_expand_loss():
+    ret = os.system("sh run_onehot_model_parallel.sh")
+    assert ret == 0

tests/st/auto_parallel/test_resnet50_expand_loss_2p.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 # ============================================================================
 
-import numpy as np
 import os
+import numpy as np
 import pytest
 
 import mindspore.common.dtype as mstype
@@ -37,31 +37,29 @@ init()
 context.set_auto_parallel_context(mirror_mean=True, parallel_mode=ParallelMode.AUTO_PARALLEL)
 
 
-def weight_variable(shape, factor=0.1):
+def weight_variable():
     return One()
 
 
 def _conv3x3(in_channels, out_channels, stride=1, padding=0, pad_mode='same'):
-    init_value = weight_variable((out_channels, in_channels, 3, 3))
+    init_value = weight_variable()
     return nn.Conv2d(in_channels, out_channels,
                      kernel_size=3, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value)
 
 
 def _conv1x1(in_channels, out_channels, stride=1, padding=0, pad_mode='same'):
-    init_value = weight_variable((out_channels, in_channels, 1, 1))
+    init_value = weight_variable()
     return nn.Conv2d(in_channels, out_channels,
                      kernel_size=1, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value)
 
 
 def _conv7x7(in_channels, out_channels, stride=1, padding=0, pad_mode='same'):
-    init_value = weight_variable((out_channels, in_channels, 7, 7))
+    init_value = weight_variable()
     return nn.Conv2d(in_channels, out_channels,
                      kernel_size=7, stride=stride, padding=padding, pad_mode=pad_mode, weight_init=init_value)
 
 
 def _fused_bn(channels, momentum=0.9):
-    init_weight = weight_variable((channels,))
-    init_bias = weight_variable((channels,))
     return nn.BatchNorm2d(channels, momentum=momentum)
 
 
@@ -210,8 +208,8 @@ class ResNet(nn.Cell):
 
         self.mean = P.ReduceMean(keep_dims=True)
         self.end_point = nn.Dense(2048, num_classes, has_bias=True,
-                                  weight_init=weight_variable((num_classes, 2048)),
-                                  bias_init=weight_variable((num_classes,)))
+                                  weight_init=weight_variable(),
+                                  bias_init=weight_variable())
         self.squeeze = P.Squeeze()
         self.cast = P.Cast()
 
@@ -345,9 +343,8 @@ class Dataset():
             raise StopIteration
         self.index += 1
         if self.input_num == 2:
-            return self.predict, self.label
-        else:
-            return self.predict,
+            return (self.predict, self.label)
+        return (self.predict,)
 
     def reset(self):
         self.index = 0
@@ -364,7 +361,7 @@ class ModelCallback(Callback):
         super(ModelCallback, self).__init__()
         self.loss_list = []
 
-    def epoch_end(self, run_context, *args):
+    def epoch_end(self, run_context):
         cb_params = run_context.original_args()
         result = cb_params.net_outputs
         self.loss_list.append(result.asnumpy().mean())
@@ -376,9 +373,9 @@ class ModelCallback(Callback):
 def test_train_feed(num_classes=8192):
     set_algo_parameters(elementwise_op_strategy_follow=True)
     parallel_callback = ModelCallback()
-    dataGen = DataGenerator()
-    input_full, input_part = dataGen.input_data((32 * 2, 3, 224, 224))
-    label_full, label_part = dataGen.label_data((32 * 2,))
+    data_gen = DataGenerator()
+    _, input_part = data_gen.input_data((32 * 2, 3, 224, 224))
+    _, label_part = data_gen.label_data((32 * 2,))
     dataset = Dataset(input_part, label_part)
     net = resnet50(num_classes)
     loss = SoftmaxCrossEntropyExpand(sparse=True)
@@ -396,9 +393,9 @@ def test_train_feed(num_classes=8192):
 def test_train_feed2(num_classes=1001):
     set_algo_parameters(elementwise_op_strategy_follow=True)
     parallel_callback = ModelCallback()
-    dataGen = DataGenerator()
-    input_full, input_part = dataGen.input_data((32 * 2, 3, 224, 224))
-    label_full, label_part = dataGen.label_data((32 * 2,))
+    data_gen = DataGenerator()
+    _, input_part = data_gen.input_data((32 * 2, 3, 224, 224))
+    _, label_part = data_gen.label_data((32 * 2,))
     dataset = Dataset(input_part, label_part)
     net = resnet50(num_classes)
     loss = SoftmaxCrossEntropyExpand(sparse=True)

tests/ut/python/communication/__init__.py

@@ -1,17 +1,17 @@
-# 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 sys
-
-sys.path.append("../../..")
+# 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 sys
+
+sys.path.append("../../..")

tests/ut/python/communication/test_comm.py

@@ -25,7 +25,6 @@ from mindspore.nn import Dense
 from mindspore.nn import Momentum
 from mindspore.nn import ReLU
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.ops.operations import Split
 from mindspore.ops.operations.comm_ops import AllReduce, AllGather, _AlltoAll, ReduceOp, ReduceScatter
 from mindspore.ops.operations.comm_ops import Broadcast
 

tests/ut/python/communication/test_data_parallel_lenet.py

@@ -16,8 +16,8 @@
 @File   : test_data_parallel_lenet.py
 @Desc   : test data parallel lenet
 """
-import numpy as np
 import os
+import numpy as np
 
 import mindspore.context as context
 import mindspore.nn as nn
@@ -80,7 +80,6 @@ def test_lenet5_train_step_training_pynative():
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
                                       device_num=8, mirror_mean=True)
-    size = 3
     predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
     label = Tensor(np.zeros([1, 10]).astype(np.float32))
     DatasetLenet(predict, label, 2)

tests/ut/python/parallel/__init__.py

@@ -19,7 +19,7 @@ from mindspore.parallel._utils import _reset_op_id
 from mindspore.parallel.algo_parameter_config import reset_algo_parameters
 
 
-def setup_module(module):
+def setup_module():
     auto_parallel_context().set_enable_all_reduce_fusion(enable_all_reduce_fusion=True)
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
     reset_cost_model_context()

tests/ut/python/parallel/parallel_end_to_end/dropout/_test_dropout_parallel_4p.py

@@ -1,120 +1,120 @@
-# Copyright 2019 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 os
-
-import mindspore as ms
-import mindspore.communication.management as distributedTool
-from mindspore import context
-from mindspore.common.tensor import Tensor
-from mindspore.nn import Cell
-from mindspore.nn import Dropout
-
-device_num = 4
-device_id = int(os.environ["RANK_ID"])
-path = "./output/"
-
-
-def setup_module():
-    print("~~~~~~~~~~~set up~~~~~~~~~~~~~")
-    context.set_context(mode=context.GRAPH_MODE)
-    context.set_auto_parallel_context(device_num=device_num, global_rank=device_id)
-    distributedTool.init()
-    distributedTool.create_group("0-3", [0, 1, 2, 3])
-    print("~~~~~~~~~~~set up finished~~~~~~~~~~~~~")
-
-
-def teardown_module():
-    print("~~~~~~~~~~~~tear down~~~~~~~~~~")
-
-
-class Net(Cell):
-    def __init__(self, keep_prob, seed0, seed1, strategy=None):
-        super(Net, self).__init__()
-        self.drop = Dropout(keep_prob, seed0, seed1, dtype=ms.float32, strategy=strategy)
-
-    def construct(self, input):
-        x = self.drop(input)
-        return x
-
-
-# pylint: disable=comparison-with-itself
-class DropoutFactory:
-    def __init__(self, input_shape, keep_prob, seed0, seed1, strategy0=None):
-        size = 1
-        prefix = ""
-        for s in input_shape:
-            prefix = prefix + str(s)
-            size = size * s
-        self.prefix = prefix
-        number_range = min(10, size)
-        self.input_np = np.reshape(np.arange(0, size) % number_range, input_shape).astype(np.float32)
-        self.keep_prob = keep_prob
-        self.seed0 = seed0
-        self.seed1 = seed1
-        self.strategy0 = strategy0
-        need_dev_num = 1
-        for s in strategy0[1]:
-            need_dev_num = need_dev_num * s
-        self.x_id = device_id % need_dev_num
-        self.out_id = device_id % need_dev_num
-
-    def get_parallel_blocks(self, input_, strategy):
-        blocks = [input_]
-        i = 0
-        for stra in strategy:
-            temp = []
-            while len(blocks) > 0:
-                block = blocks.pop(0)
-                temp.extend(np.split(block, stra, axis=i))
-            blocks.extend(temp)
-            i += 1
-        return blocks
-
-    def d4_tensor_compare(self, input, out_me):
-        [a, b, c, d] = input.shape
-        for i in range(a):
-            for j in range(b):
-                for k in range(c):
-                    for e in range(d):
-                        if out_me[i, j, k, e] == 0:
-                            assert True == True
-                        else:
-                            assert np.allclose(out_me[i, j, k, e], input[i, j, k, e] * (1 / 0.4), 0.0001, 0.0001)
-
-    def forward_mindspore_parallel_impl(self):
-        x = Tensor(self.input_np)
-        inputs_x = self.get_parallel_blocks(self.input_np, self.strategy0[1])
-        x1 = Tensor(inputs_x[self.x_id])
-        net = Net(0.4, 0, 0, strategy=self.strategy0)
-        context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
-        net.set_auto_parallel()
-        out = net(x, parallel_inputs_compile=[x], parallel_inputs_run=[x1])
-        return out.asnumpy()
-
-    def forward_cmp(self):
-        out_mindspore_parallel = self.forward_mindspore_parallel_impl()
-        input_blocks = self.get_parallel_blocks(self.input_np, self.strategy0[1])
-        self.d4_tensor_compare(input_blocks[self.out_id], out_mindspore_parallel)
-
-
-def test_reid_dropout_forward_seed_F32_64_512_8_8():
-    fact = DropoutFactory(input_shape=(64, 512, 8, 8), keep_prob=0.4, seed0=0, seed1=0, strategy0=(0, (4, 1, 1, 1)))
-    fact.forward_cmp()
-
-
-def test_reid_dropout_forward_seed_F32_64_512_8_8_repeat():
-    fact = DropoutFactory(input_shape=(64, 512, 8, 8), keep_prob=0.4, seed0=0, seed1=0, strategy0=(0, (2, 1, 1, 1)))
-    fact.forward_cmp()
+# Copyright 2019 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 os
+import numpy as np
+
+import mindspore as ms
+import mindspore.communication.management as distributedTool
+from mindspore import context
+from mindspore.common.tensor import Tensor
+from mindspore.nn import Cell
+from mindspore.nn import Dropout
+
+device_num = 4
+device_id = int(os.environ["RANK_ID"])
+path = "./output/"
+
+
+def setup_module():
+    print("~~~~~~~~~~~set up~~~~~~~~~~~~~")
+    context.set_context(mode=context.GRAPH_MODE)
+    context.set_auto_parallel_context(device_num=device_num, global_rank=device_id)
+    distributedTool.init()
+    distributedTool.create_group("0-3", [0, 1, 2, 3])
+    print("~~~~~~~~~~~set up finished~~~~~~~~~~~~~")
+
+
+def teardown_module():
+    print("~~~~~~~~~~~~tear down~~~~~~~~~~")
+
+
+class Net(Cell):
+    def __init__(self, keep_prob, seed0, seed1, strategy=None):
+        super(Net, self).__init__()
+        self.drop = Dropout(keep_prob, seed0, seed1, dtype=ms.float32, strategy=strategy)
+
+    def construct(self, input_):
+        x = self.drop(input_)
+        return x
+
+
+# pylint: disable=comparison-with-itself
+class DropoutFactory:
+    def __init__(self, input_shape, keep_prob, seed0, seed1, strategy0=None):
+        size = 1
+        prefix = ""
+        for s in input_shape:
+            prefix = prefix + str(s)
+            size = size * s
+        self.prefix = prefix
+        number_range = min(10, size)
+        self.input_np = np.reshape(np.arange(0, size) % number_range, input_shape).astype(np.float32)
+        self.keep_prob = keep_prob
+        self.seed0 = seed0
+        self.seed1 = seed1
+        self.strategy0 = strategy0
+        need_dev_num = 1
+        for s in strategy0[1]:
+            need_dev_num = need_dev_num * s
+        self.x_id = device_id % need_dev_num
+        self.out_id = device_id % need_dev_num
+
+    def get_parallel_blocks(self, input_, strategy):
+        blocks = [input_]
+        i = 0
+        for stra in strategy:
+            temp = []
+            while len(blocks) > 0:
+                block = blocks.pop(0)
+                temp.extend(np.split(block, stra, axis=i))
+            blocks.extend(temp)
+            i += 1
+        return blocks
+
+    def d4_tensor_compare(self, input_, out_me):
+        [a, b, c, d] = input_.shape
+        for i in range(a):
+            for j in range(b):
+                for k in range(c):
+                    for e in range(d):
+                        if out_me[i, j, k, e] == 0:
+                            assert True
+                        else:
+                            assert np.allclose(out_me[i, j, k, e], input_[i, j, k, e] * (1 / 0.4), 0.0001, 0.0001)
+
+    def forward_mindspore_parallel_impl(self):
+        x = Tensor(self.input_np)
+        inputs_x = self.get_parallel_blocks(self.input_np, self.strategy0[1])
+        x1 = Tensor(inputs_x[self.x_id])
+        net = Net(0.4, 0, 0, strategy=self.strategy0)
+        context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+        net.set_auto_parallel()
+        out = net(x, parallel_inputs_compile=[x], parallel_inputs_run=[x1])
+        return out.asnumpy()
+
+    def forward_cmp(self):
+        out_mindspore_parallel = self.forward_mindspore_parallel_impl()
+        input_blocks = self.get_parallel_blocks(self.input_np, self.strategy0[1])
+        self.d4_tensor_compare(input_blocks[self.out_id], out_mindspore_parallel)
+
+
+def test_reid_dropout_forward_seed_F32_64_512_8_8():
+    fact = DropoutFactory(input_shape=(64, 512, 8, 8), keep_prob=0.4, seed0=0, seed1=0, strategy0=(0, (4, 1, 1, 1)))
+    fact.forward_cmp()
+
+
+def test_reid_dropout_forward_seed_F32_64_512_8_8_repeat():
+    fact = DropoutFactory(input_shape=(64, 512, 8, 8), keep_prob=0.4, seed0=0, seed1=0, strategy0=(0, (2, 1, 1, 1)))
+    fact.forward_cmp()

tests/ut/python/parallel/test_add_relu_redistribution.py

@@ -54,7 +54,7 @@ class Grad(nn.Cell):
         return C.grad_all(self.network)(x, y)
 
 
-def compile(net, x, y):
+def compile_net(net, x, y):
     net.set_auto_parallel()
     _executor.compile(net, x, y)
 
@@ -69,7 +69,7 @@ def test_add_relu_stride_slice():
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    compile(net, x, y)
+    compile_net(net, x, y)
 
 
 def test_add_relu_all_gather():
@@ -82,4 +82,4 @@ def test_add_relu_all_gather():
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    compile(net, x, y)
+    compile_net(net, x, y)