Rename davinci to ascend in ops st test module

tests/st/ops/ascend/test_apply_momentum.py

@@ -1,44 +1,44 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.apply_momentum = P.ApplyMomentum(gradient_scale=1024.0)
-        self.variable = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='variable')
-        self.accumulation = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='accumulation')
-        self.learning_rate = Parameter(initializer(
-                                'normal', [1, ]), name='learning_rate')
-        self.gradient = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='gradient')
-        self.momentum = Parameter(initializer(
-                                'normal', [1, ]), name='momentum')
-    def construct(self):
-        return self.apply_momentum(self.variable, self.accumulation, self.learning_rate, self.gradient, self.momentum)
-
-def test_net():
-    apply_momentum = Net()
-    output = apply_momentum()
-    print(output.asnumpy())
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.apply_momentum = P.ApplyMomentum(gradient_scale=1024.0)
+        self.variable = Parameter(initializer(
+                                'normal', [2, 3, 3, 4]), name='variable')
+        self.accumulation = Parameter(initializer(
+                                'normal', [2, 3, 3, 4]), name='accumulation')
+        self.learning_rate = Parameter(initializer(
+                                'normal', [1, ]), name='learning_rate')
+        self.gradient = Parameter(initializer(
+                                'normal', [2, 3, 3, 4]), name='gradient')
+        self.momentum = Parameter(initializer(
+                                'normal', [1, ]), name='momentum')
+    def construct(self):
+        return self.apply_momentum(self.variable, self.accumulation, self.learning_rate, self.gradient, self.momentum)
+
+def test_net():
+    apply_momentum = Net()
+    output = apply_momentum()
+    print(output.asnumpy())

tests/st/ops/ascend/test_biasAddGrad.py

@@ -1,42 +1,42 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-from mindspore.ops.operations import _grad_ops as G
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
-context.set_context(device_target="Ascend")
-class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.bias_add_grad = G.BiasAddGrad()
-    #self.dout = Parameter(initializer(
-                #'normal', [2, 3, 3, 4]), name='dout')
-
-
-  @ms_function
-  def construct(self, dout):
-    return self.bias_add_grad(dout)
-
-dout = np.ones([2,3,4,4]).astype(np.float32)
-bias_add_grad = Net()
-output = bias_add_grad(Tensor(dout))
-expect_output = np.array([32.,32.,32.]).astype(np.float32)
-assert np.all(output.asnumpy()==expect_output), "bias_add_grad execute failed, please check current code commit"
-print(output.asnumpy())
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops.operations import _grad_ops as G
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+  def __init__(self):
+    super(Net, self).__init__()
+    self.bias_add_grad = G.BiasAddGrad()
+    #self.dout = Parameter(initializer(
+                #'normal', [2, 3, 3, 4]), name='dout')
+
+
+  @ms_function
+  def construct(self, dout):
+    return self.bias_add_grad(dout)
+
+dout = np.ones([2,3,4,4]).astype(np.float32)
+bias_add_grad = Net()
+output = bias_add_grad(Tensor(dout))
+expect_output = np.array([32.,32.,32.]).astype(np.float32)
+assert np.all(output.asnumpy()==expect_output), "bias_add_grad execute failed, please check current code commit"
+print(output.asnumpy())

tests/st/ops/ascend/test_bias_add_grad.py

@@ -1,39 +1,39 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-from mindspore.ops.operations import _grad_ops as G
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
-context.set_context(device_target="Ascend")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.bias_add_grad = G.BiasAddGrad()
-    
-
-    @ms_function
-    def construct(self, dout):
-        return self.bias_add_grad(dout)
-
-def test_net():
-    dout = np.random.rand(1, 1001).astype(np.float32)
-    bias_add_grad = Net()
-    output = bias_add_grad(dout)
-    print(output.asnumpy())
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops.operations import _grad_ops as G
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.bias_add_grad = G.BiasAddGrad()
+    
+
+    @ms_function
+    def construct(self, dout):
+        return self.bias_add_grad(dout)
+
+def test_net():
+    dout = np.random.rand(1, 1001).astype(np.float32)
+    bias_add_grad = Net()
+    output = bias_add_grad(dout)
+    print(output.asnumpy())

tests/st/ops/ascend/test_drop_out_gen_mask.py

@@ -1,44 +1,44 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.nn as nn
-import numpy as np
-import mindspore.context as context
-context.set_context(mode=context.GRAPH_MODE,
-                    device_target="Ascend")
-
-
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.mask = P.DropoutGenMask(10, 28)
-        self.shape = P.Shape()
-
-    def construct(self, x, y):
-        shape_x = self.shape(x)
-        return self.mask(shape_x, y)
-
-
-x = np.ones([2, 4, 2, 2]).astype(np.int32)
-y = np.array([1.0]).astype(np.float32)
-
-
-def test_net():
-    mask = Net()
-    tx, ty = Tensor(x), Tensor(y)
-    output = mask(tx, ty)
-    print(output.asnumpy())
-    assert ([255, 255, 255, 255] == output.asnumpy()).all()
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+import numpy as np
+import mindspore.context as context
+context.set_context(mode=context.GRAPH_MODE,
+                    device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.mask = P.DropoutGenMask(10, 28)
+        self.shape = P.Shape()
+
+    def construct(self, x, y):
+        shape_x = self.shape(x)
+        return self.mask(shape_x, y)
+
+
+x = np.ones([2, 4, 2, 2]).astype(np.int32)
+y = np.array([1.0]).astype(np.float32)
+
+
+def test_net():
+    mask = Net()
+    tx, ty = Tensor(x), Tensor(y)
+    output = mask(tx, ty)
+    print(output.asnumpy())
+    assert ([255, 255, 255, 255] == output.asnumpy()).all()

tests/st/ops/ascend/test_tbe_ops/test_greater.py

@@ -1,51 +1,51 @@
-# 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
-from mindspore.ops import operations as P
-from mindspore.nn import Cell
-from mindspore.common.tensor import Tensor
-from mindspore.train.model import Model
-from mindspore import log as logger
-from mindspore import context
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-class Greater(Cell):
-    def __init__(self):
-        super(Greater, self).__init__()
-        self.greater = P.Greater()
-
-    def construct(self, inputa, inputb):
-        return self.greater(inputa, inputb)
-
-def me_greater(inputa, inputb):
-    net = Greater()
-    net.set_train()
-    model = Model(net)
-
-    out = model.predict(inputa, inputb)
-    logger.info("Check input a: ")
-    logger.info(inputa)
-    logger.info("Check input b: ")
-    logger.info(inputb)
-    return out.asnumpy()
-
-@pytest.mark.ssd_tbe
-def test_greater_2d_scalar0():
-    a = np.random.randint(-5, 5, [8, 32]).astype(np.int32)
-    b = np.random.randint(-5, 5, [8, 32]).astype(np.int32)
-    out_me = me_greater(Tensor(a), Tensor(b))
-    logger.info("Check me result:")
+# 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
+from mindspore.ops import operations as P
+from mindspore.nn import Cell
+from mindspore.common.tensor import Tensor
+from mindspore.train.model import Model
+from mindspore import log as logger
+from mindspore import context
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Greater(Cell):
+    def __init__(self):
+        super(Greater, self).__init__()
+        self.greater = P.Greater()
+
+    def construct(self, inputa, inputb):
+        return self.greater(inputa, inputb)
+
+def me_greater(inputa, inputb):
+    net = Greater()
+    net.set_train()
+    model = Model(net)
+
+    out = model.predict(inputa, inputb)
+    logger.info("Check input a: ")
+    logger.info(inputa)
+    logger.info("Check input b: ")
+    logger.info(inputb)
+    return out.asnumpy()
+
+@pytest.mark.ssd_tbe
+def test_greater_2d_scalar0():
+    a = np.random.randint(-5, 5, [8, 32]).astype(np.int32)
+    b = np.random.randint(-5, 5, [8, 32]).astype(np.int32)
+    out_me = me_greater(Tensor(a), Tensor(b))
+    logger.info("Check me result:")
     logger.info(out_me)

tests/st/ops/ascend/test_tbe_ops/test_layernorm.py

@@ -1,55 +1,55 @@
-# 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
-from mindspore.nn import LayerNorm
-from mindspore.common.tensor import Tensor
-from mindspore.nn import Cell
-from mindspore.train.model import Model
-from mindspore import log as logger
-import pytest
-from mindspore import context
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-class Net(Cell):
-    def __init__(self, input_shape, begin_norm_axis, begin_params_axis, gamma, beta):
-        super(Net, self).__init__()
-        self.layernorm = LayerNorm(input_shape, begin_norm_axis, begin_params_axis, gamma, beta)
-
-    def construct(self, input):
-        x = self.layernorm(input)
-        return x
-
-def pt_me_layernorm(input_data, normalized_shape, gamma, beta, axis):
-    net = Net(normalized_shape, begin_norm_axis=axis,
-              begin_params_axis=axis,
-              gamma=Tensor(gamma),
-              beta=Tensor(beta))
-    net.set_train()
-    model = Model(net)
-    out_me = model.predict(Tensor(input_data))
-    logger.info("Check me result:")
-    logger.info(out_me.asnumpy())
-
-@pytest.mark.lower_bs
-def test_normal_layernorm_1_128_1024_axis_2():
-    """
-    2 input[1, 128, 1024],normalized_shape=[128, 1024]
-    """
-    input_data = np.random.randn(1, 128, 1024).astype(np.float32)
-    gamma = np.random.randn(1024).astype(np.float32)
-    gamma.fill(1.1)
-    beta = np.random.randn(1024).astype(np.float32)
-    beta.fill(0.1)
-    pt_me_layernorm(input_data, (1024, ), gamma, beta, 2)
+# 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
+from mindspore.nn import LayerNorm
+from mindspore.common.tensor import Tensor
+from mindspore.nn import Cell
+from mindspore.train.model import Model
+from mindspore import log as logger
+import pytest
+from mindspore import context
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Net(Cell):
+    def __init__(self, input_shape, begin_norm_axis, begin_params_axis, gamma, beta):
+        super(Net, self).__init__()
+        self.layernorm = LayerNorm(input_shape, begin_norm_axis, begin_params_axis, gamma, beta)
+
+    def construct(self, input):
+        x = self.layernorm(input)
+        return x
+
+def pt_me_layernorm(input_data, normalized_shape, gamma, beta, axis):
+    net = Net(normalized_shape, begin_norm_axis=axis,
+              begin_params_axis=axis,
+              gamma=Tensor(gamma),
+              beta=Tensor(beta))
+    net.set_train()
+    model = Model(net)
+    out_me = model.predict(Tensor(input_data))
+    logger.info("Check me result:")
+    logger.info(out_me.asnumpy())
+
+@pytest.mark.lower_bs
+def test_normal_layernorm_1_128_1024_axis_2():
+    """
+    2 input[1, 128, 1024],normalized_shape=[128, 1024]
+    """
+    input_data = np.random.randn(1, 128, 1024).astype(np.float32)
+    gamma = np.random.randn(1024).astype(np.float32)
+    gamma.fill(1.1)
+    beta = np.random.randn(1024).astype(np.float32)
+    beta.fill(0.1)
+    pt_me_layernorm(input_data, (1024, ), gamma, beta, 2)

tests/st/ops/ascend/test_tbe_ops/test_layernorm_grad.py

@@ -1,65 +1,65 @@
-# 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
-from mindspore.nn import LayerNorm
-from mindspore.common.tensor import Tensor
-from mindspore.nn import Cell
-from mindspore.ops.composite import GradOperation
-from mindspore import log as logger
-from mindspore import context
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-class Grad(Cell):
-    def __init__(self, network):
-        super(Grad, self).__init__()
-        self.grad = GradOperation(name="get_all", get_all=True, sens_param=True)
-        self.network = network
-
-    def construct(self, input, output_grad,):
-        gout = self.grad(self.network)(input, output_grad)
-        return gout
-
-class Net(Cell):
-    def __init__(self, input_shape, begin_norm_axis, begin_params_axis, gamma, beta):
-        super(Net, self).__init__()
-        self.layernorm = LayerNorm(input_shape, begin_norm_axis, begin_params_axis, gamma, beta)
-
-    def construct(self, input):
-        x = self.layernorm(input)
-        return x
-
-def py_me_layernorm_grad(input_data, normalized_shape, gamma, beta, axis, gradients):
-    input_me = Tensor(input_data)
-    net_me = Grad(Net(normalized_shape, begin_norm_axis=axis,
-                      begin_params_axis=axis,
-                      gamma=Tensor(gamma),
-                      beta=Tensor(beta)))
-    net_me.set_train()
-    out_pool_grad_me = Tensor(gradients)
-    out_grad = net_me(input_me, out_pool_grad_me)
-    logger.info("Check me result:")
-    logger.info(out_grad.asnumpy())
-
-def test_normal_layernorm_grad_normalize_2d():
-    """
-    1 input[1, 128, 1024],normalized_shape=[1024],element_affine=False
-    """
-    input_data = np.ones([1, 128, 1024]).astype(np.float32)
-    gradients = np.ones((1, 128, 1024)).astype(np.float32)
-    gamma = np.random.randn(1024).astype(np.float32)
-    gamma.fill(1.1)
-    beta = np.random.randn(1024).astype(np.float32)
-    beta.fill(0.1)
-    py_me_layernorm_grad(input_data, (1024,), gamma, beta, 2, gradients)
+# 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
+from mindspore.nn import LayerNorm
+from mindspore.common.tensor import Tensor
+from mindspore.nn import Cell
+from mindspore.ops.composite import GradOperation
+from mindspore import log as logger
+from mindspore import context
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Grad(Cell):
+    def __init__(self, network):
+        super(Grad, self).__init__()
+        self.grad = GradOperation(name="get_all", get_all=True, sens_param=True)
+        self.network = network
+
+    def construct(self, input, output_grad,):
+        gout = self.grad(self.network)(input, output_grad)
+        return gout
+
+class Net(Cell):
+    def __init__(self, input_shape, begin_norm_axis, begin_params_axis, gamma, beta):
+        super(Net, self).__init__()
+        self.layernorm = LayerNorm(input_shape, begin_norm_axis, begin_params_axis, gamma, beta)
+
+    def construct(self, input):
+        x = self.layernorm(input)
+        return x
+
+def py_me_layernorm_grad(input_data, normalized_shape, gamma, beta, axis, gradients):
+    input_me = Tensor(input_data)
+    net_me = Grad(Net(normalized_shape, begin_norm_axis=axis,
+                      begin_params_axis=axis,
+                      gamma=Tensor(gamma),
+                      beta=Tensor(beta)))
+    net_me.set_train()
+    out_pool_grad_me = Tensor(gradients)
+    out_grad = net_me(input_me, out_pool_grad_me)
+    logger.info("Check me result:")
+    logger.info(out_grad.asnumpy())
+
+def test_normal_layernorm_grad_normalize_2d():
+    """
+    1 input[1, 128, 1024],normalized_shape=[1024],element_affine=False
+    """
+    input_data = np.ones([1, 128, 1024]).astype(np.float32)
+    gradients = np.ones((1, 128, 1024)).astype(np.float32)
+    gamma = np.random.randn(1024).astype(np.float32)
+    gamma.fill(1.1)
+    beta = np.random.randn(1024).astype(np.float32)
+    beta.fill(0.1)
+    py_me_layernorm_grad(input_data, (1024,), gamma, beta, 2, gradients)

tests/st/ops/ascend/test_tbe_ops/test_logical_and.py

@@ -1,39 +1,39 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-context.set_context(device_target="Ascend")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.logical_and = P.LogicalAnd()
-
-    @ms_function
-    def construct(self, x1, x2):
-        return self.logical_and(x1, x2)
-
-x1 = [True, True, False, False, True, True, False, False]
-x2 = [True, False, False, True, True, False, False, True]
-def test_net():
-    logical_and = Net()
-    output = logical_and(Tensor(x1), Tensor(x2))
-    print(x1)
-    print(x2)
-    print(output.asnumpy())
-
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.logical_and = P.LogicalAnd()
+
+    @ms_function
+    def construct(self, x1, x2):
+        return self.logical_and(x1, x2)
+
+x1 = [True, True, False, False, True, True, False, False]
+x2 = [True, False, False, True, True, False, False, True]
+def test_net():
+    logical_and = Net()
+    output = logical_and(Tensor(x1), Tensor(x2))
+    print(x1)
+    print(x2)
+    print(output.asnumpy())
+

tests/st/ops/ascend/test_tbe_ops/test_logical_not.py

@@ -1,38 +1,38 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-context.set_context(device_target="Ascend")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.logical_not = P.LogicalNot()
-
-    @ms_function
-    def construct(self, x1):
-        return self.logical_not(x1)
-
-x1 = [True, True, False, False, True, True, False, False]
-
-def test_net():
-    logical_not = Net()
-    output = logical_not(Tensor(x1))
-    print(x1)
-    print(output.asnumpy())
-
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.logical_not = P.LogicalNot()
+
+    @ms_function
+    def construct(self, x1):
+        return self.logical_not(x1)
+
+x1 = [True, True, False, False, True, True, False, False]
+
+def test_net():
+    logical_not = Net()
+    output = logical_not(Tensor(x1))
+    print(x1)
+    print(output.asnumpy())
+

tests/st/ops/ascend/test_tbe_ops/test_logical_or.py

@@ -1,39 +1,39 @@
-# 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.
-# ============================================================================
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.nn as nn
-from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
-context.set_context(device_target="Ascend")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.logical_or = P.LogicalOr()
-
-    @ms_function
-    def construct(self, x1, x2):
-        return self.logical_or(x1, x2)
-
-x1 = [True, True, False, False, True, True, False, False]
-x2 = [True, False, False, True, True, False, False, True]
-def test_net():
-    logical_or = Net()
-    output = logical_or(Tensor(x1), Tensor(x2))
-    print(x1)
-    print(x2)
-    print(output.asnumpy())
-
+# 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+context.set_context(device_target="Ascend")
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.logical_or = P.LogicalOr()
+
+    @ms_function
+    def construct(self, x1, x2):
+        return self.logical_or(x1, x2)
+
+x1 = [True, True, False, False, True, True, False, False]
+x2 = [True, False, False, True, True, False, False, True]
+def test_net():
+    logical_or = Net()
+    output = logical_or(Tensor(x1), Tensor(x2))
+    print(x1)
+    print(x2)
+    print(output.asnumpy())
+