!1652 support vm for SoftmaxGradExt

Merge pull request !1652 from jiangjinsheng/vm_SoftmaxGradExt

mindspore/ops/_op_impl/tbe/__init__.py

@@ -104,6 +104,7 @@ from .tanh_grad import _tanh_grad_tbe
 from .softmax import _softmax_tbe
 from .softplus import _softplus_tbe
 from .softplus_grad import _softplus_grad_tbe
+from .softmax_grad_ext import _softmax_grad_ext_tbe
 from .square import _square_tbe
 from .sqrt import _sqrt_tbe
 from .transpose_d import _transpose_d_tbe

mindspore/ops/_op_impl/tbe/softmax_grad_ext.py

@@ -0,0 +1,47 @@
+# 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.
+# ============================================================================
+
+"""SoftmaxGradExt op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+softmax_grad_ext_op_info = TBERegOp("SoftmaxGradExt") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("softmax_grad_ext.so") \
+    .compute_cost(10) \
+    .kernel_name("softmax_grad_ext") \
+    .partial_flag(True) \
+    .dynamic_format(True) \
+    .attr("axes", "required", "listInt", "all") \
+    .attr("keep_dims", "required", "bool", "all") \
+    .input(0, "grad", False, "required", "all") \
+    .input(1, "x1", False, "required", "all") \
+    .input(2, "x2", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(softmax_grad_ext_op_info)
+def _softmax_grad_ext_tbe():
+    """SoftmaxGradExt TBE register"""
+    return

mindspore/ops/operations/inner_ops.py

@@ -15,6 +15,9 @@
 
 """inner_ops"""
 
+import numbers
+from ..._checkparam import Validator as validator
+from ..._checkparam import Rel
 from ...common.dtype import tensor, dtype_to_pytype
 from ..primitive import prim_attr_register, PrimitiveWithInfer
 
@@ -40,8 +43,10 @@ class ScalarCast(PrimitiveWithInfer):
         pass
 
     def __infer__(self, x, t):
+        validator.check_integer('x shape', len(x['shape']), 0, Rel.EQ, self.name)
         value, to = x['value'], t['value']
         if value is not None:
+            validator.check_value_type("value", value, [numbers.Number, bool], self.name)
             if isinstance(to, type(tensor)):
                 to = to.element_type()
             np_type = dtype_to_pytype(to)