fix bugs and add register info

mindspore/ops/_op_impl/tbe/__init__.py

@@ -304,3 +304,5 @@ from .max_pool_grad_grad_with_argmax import _max_pool_grad_grad_with_argmax_tbe
 from .tensor_move import _tensor_move_tbe
 from .population_count import _population_count_tbe
 from .parallel_concat import _parallel_concat_tbe
+from .adam_apply_one_assign import _adam_apply_one_assign_tbe
+from .adam_apply_one_with_decay_assign import _adam_apply_one_with_decay_assign_tbe

mindspore/ops/_op_impl/tbe/adam_apply_one_assign.py

@@ -0,0 +1,53 @@
+# 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.
+# ============================================================================
+
+"""AdamApplyOneAssign op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+adam_apply_one_assign_op_info = TBERegOp("AdamApplyOneAssign") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("adam_apply_one_assign.so") \
+    .compute_cost(10) \
+    .kernel_name("adam_apply_one_assign") \
+    .partial_flag(True) \
+    .input(0, "input0", False, "required", "all") \
+    .input(1, "input1", False, "required", "all") \
+    .input(2, "input2", False, "required", "all") \
+    .input(3, "input3", False, "required", "all") \
+    .input(4, "input4", False, "required", "all") \
+    .input(5, "mul0_x", False, "required", "all") \
+    .input(6, "mul1_x", False, "required", "all") \
+    .input(7, "mul2_x", False, "required", "all") \
+    .input(8, "mul3_x", False, "required", "all") \
+    .input(9, "add2_y", False, "required", "all") \
+    .output(0, "output0", True, "required", "all") \
+    .output(1, "output1", True, "required", "all") \
+    .output(2, "output2", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(adam_apply_one_assign_op_info)
+def _adam_apply_one_assign_tbe():
+    """AdamApplyOneAssign TBE register"""
+    return

mindspore/ops/_op_impl/tbe/adam_apply_one_with_decay_assign.py

@@ -0,0 +1,54 @@
+# 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.
+# ============================================================================
+
+"""AdamApplyOneWithDecayAssign op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+adam_apply_one_with_decay_assign_op_info = TBERegOp("AdamApplyOneWithDecayAssign") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("adam_apply_one_with_decay_assign.so") \
+    .compute_cost(10) \
+    .kernel_name("adam_apply_one_with_decay_assign") \
+    .partial_flag(True) \
+    .input(0, "input0", False, "required", "all") \
+    .input(1, "input1", False, "required", "all") \
+    .input(2, "input2", False, "required", "all") \
+    .input(3, "input3", False, "required", "all") \
+    .input(4, "input4", False, "required", "all") \
+    .input(5, "mul0_x", False, "required", "all") \
+    .input(6, "mul1_x", False, "required", "all") \
+    .input(7, "mul2_x", False, "required", "all") \
+    .input(8, "mul3_x", False, "required", "all") \
+    .input(9, "mul4_x", False, "required", "all") \
+    .input(10, "add2_y", False, "required", "all") \
+    .output(0, "output0", True, "required", "all") \
+    .output(1, "output1", True, "required", "all") \
+    .output(2, "output2", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(adam_apply_one_with_decay_assign_op_info)
+def _adam_apply_one_with_decay_assign_tbe():
+    """AdamApplyOneWithDecayAssign TBE register"""
+    return

mindspore/ops/operations/array_ops.py

@@ -2257,10 +2257,10 @@ class Diag(PrimitiveWithInfer):
     :math:`output[i_1,..., i_k, i_1,..., i_k] = input_x[i_1,..., i_k]` and 0 everywhere else.
 
     Inputs:
-        - **input_x** (Tensor) - The input tensor.
+        - **input_x** (Tensor) - The input tensor. The input shape should be less than 5d.
 
     Outputs:
-        Tensor.
+        Tensor, has the same dtype as the 'input_x'.
 
     Examples:
         >>> input_x = Tensor([1, 2, 3, 4])

mindspore/ops/operations/nn_ops.py

@@ -1001,14 +1001,14 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
     :math:`\text{in_channels} * \text{channel_multiplier}` channels.
 
     Args:
-        channel_multiplier (int): The multipiler for the original output conv.
+        channel_multiplier (int): The multipiler for the original output conv. Its value must be greater than 0.
         kernel_size (Union[int, tuple[int]]): The size of the conv kernel.
         mode (int): 0 Math convolution, 1 cross-correlation convolution ,
                        2 deconvolution, 3 depthwise convolution. Default: 3.
         pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid".
-        pad (Union[int, tuple[int]]): The pad value to fill. Default: 0. If `pad` is one integer, the padding of
+        pad (Union[int, tuple[int]]): The pad value to fill. If `pad` is one integer, the padding of
             top, bottom, left and right is same, equal to pad. If `pad` is tuple with four integer, the padding
-            of top, bottom, left and right equal to pad[0], pad[1], pad[2], pad[3] with corresponding.
+            of top, bottom, left and right equal to pad[0], pad[1], pad[2], pad[3] with corresponding. Default: 0.
         stride (Union[int, tuple[int]]): The stride to apply conv filter. Default: 1.
         dilation (Union[int, tuple[int]]): Specifies the dilation rate to use for dilated convolution. Default: 1.
         group (int): Splits input into groups. Default: 1.
@@ -1026,7 +1026,7 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
         >>> weight = Tensor(np.ones([1, 32, 3, 3]), mindspore.float32)
         >>> depthwise_conv2d = P.DepthwiseConv2dNative(channel_multiplier = 3, kernel_size = (3, 3))
         >>> output = depthwise_conv2d(input, weight)
-        >>> assert output.shape == (10, 96, 30, 30)
+        >>> output.shape == (10, 96, 30, 30)
     """
 
     @prim_attr_register
@@ -1850,7 +1850,7 @@ class DataFormatDimMap(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Tensor) - A Tensor with each element as a dimension index in source data format.
-          Must be in the range [-4, 4). It's type is int32.
+          The suggested values is in the range [-4, 4). It's type is int32.
 
     Outputs:
         Tensor, has the same type as the `input_x`.