vm for splitv

mindspore/ops/_op_impl/tbe/__init__.py

@@ -260,4 +260,5 @@ from .data_format_dim_map import _data_format_dim_map_tbe
 from .histogram_fixed_width import _histogram_fixed_width_tbe
 from .tensor_scatter_update import _tensor_scatter_update_tbe
 from .inplace_update import _inplace_update_tbe
+from .splitv import _split_v_tbe
 from .in_top_k import _in_top_k_tbe

mindspore/ops/_op_impl/tbe/splitv.py

+# 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.
+# ============================================================================
+
+"""SplitV op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+split_v_op_info = TBERegOp("SplitV") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("split_v_d.so") \
+    .compute_cost(10) \
+    .kernel_name("split_v_d") \
+    .partial_flag(True) \
+    .attr("size_splits", "required", "listInt", "all") \
+    .attr("split_dim", "required", "int", "all") \
+    .attr("num_split", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "dynamic", "all") \
+    .op_pattern("dynamicFormat") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_NHWC, DataType.BOOL_NHWC) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_NHWC, DataType.I8_NHWC) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_NHWC, DataType.U8_NHWC) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I16_NHWC, DataType.I16_NHWC) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U16_NHWC, DataType.U16_NHWC) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_NHWC, DataType.I32_NHWC) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U32_NHWC, DataType.U32_NHWC) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_NHWC, DataType.I64_NHWC) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.U64_NHWC, DataType.U64_NHWC) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .get_op_info()
+
+
+@op_info_register(split_v_op_info)
+def _split_v_tbe():
+    """SplitV TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -309,8 +309,7 @@ __all__ = [
     "DataFormatDimMap",
     "ApproximateEqual",
     "InplaceUpdate",
-    "InTopK",
-    "DataFormatDimMap"
+    "InTopK"
 ]
 
 __all__.extend(_quant_ops.__all__)

mindspore/ops/operations/math_ops.py

@@ -200,14 +200,14 @@ class AssignSub(PrimitiveWithInfer):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.AssignSub = P.AssignSub()
-        >>>         self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int64), name="global_step")
+        >>>         self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int32), name="global_step")
         >>>
         >>>     def construct(self, x):
         >>>         self.AssignSub(self.variable, x)
         >>>         return self.variable
         >>>
         >>> net = Net()
-        >>> value = Tensor(np.ones([1]).astype(np.int64)*100)
+        >>> value = Tensor(np.ones([1]).astype(np.int32)*100)
         >>> net(value)
     """