mindspore.ops.TBERegOp

class mindspore.ops.TBERegOp(op_name)

Class for TBE operator information register.

Parameters

op_name (str) – kernel name.

Examples

>>> import mindspore.ops as ops
>>> op_name_op_info = ops.TBERegOp("OpName") \
...    .fusion_type("ELEMWISE") \
...    .async_flag(False) \
...    .binfile_name("op_name.so") \
...    .compute_cost(10) \
...    .kernel_name("op_name") \
...    .partial_flag(True) \
...    .op_pattern("formatAgnostic") \
...    .need_check_supported(True) \
...    .dynamic_shape(True) \
...    .dynamic_rank_support(True) \
...    .dynamic_compile_static(True) \
...    .attr("format", "required", "str", "all") \
...    .input(0, "x1", None, "required", None) \
...    .input(0, "x2", None, "required", None) \
...    .input(1, "axis", None, "required", None) \
...    .output(0, "y", True, "required", "all") \
...    .real_input_index([1, 0])
...    .input_to_attr_index([2])
...    .unknown_shape_formats(["ND", "ND", "ND", "ND"])
...    .reshape_type("NC") \
...    .is_dynamic_format(True) \
...    .dtype_format(DataType.F16_None, DataType.F16_None) \
...    .dtype_format(DataType.F32_None, DataType.F32_None) \
...    .dtype_format(DataType.I32_None, DataType.I32_None) \
...    .get_op_info()
>>>

async_flag(async_flag=False)

Define the calculation efficiency of the operator, whether the asynchronous calculation is supported.

Parameters

async_flag (bool) – Value of async flag. Default: False .

attr(name=None, param_type=None, value_type=None, value=None, default_value=None, **kwargs)

Register TBE op attribute information.

Parameters

• name (str) – Name of the attribute. Default: None .

• param_type (str) – Param type of the attribute. Default: None .

• value_type (str) – Type of the attribute. Default: None .

• value (str) – Value of the attribute. Default: None .

• default_value (str) – Default value of attribute. Default: None .

• kwargs (dict) – Other information of the attribute.

binfile_name(binfile_name)

Set the binary file name of the operator, it is optional.

Parameters

binfile_name (str) – The binary file name of the operator.

compute_cost(compute_cost=10)

Define the calculation efficiency of operator, which refers to the value of the cost model in the tiling module.

Parameters

compute_cost (int) – Value of compute cost. Default: 10 .

dynamic_compile_static(dynamic_compile_static=False)

Whether the operator supports dynamic compile static.

Parameters

dynamic_compile_static (bool) – Value of dynamic compile static. Default: False .

dynamic_rank_support(dynamic_rank_support)

Description whether the operator supports dynamic rank (dynamic dimension).

Parameters

dynamic_rank_support (bool) – Description whether the operator supports dynamic rank (dynamic dimension). True: indicates that dynamic rank is supported, and the operator supports shape (- 2), which is used to determine whether dynamic is performed. False: indicates that the operator does not support dynamic rank. Default: False .

dynamic_shape(dynamic_shape=False)

Whether the operator supports dynamic shape.

Parameters

dynamic_shape (bool) – Value of dynamic shape. Default: False .

input(index=None, name=None, need_compile=None, param_type=None, shape=None, value_depend=None, **kwargs)

Register TBE op input information.

Parameters

• index (int) – Order of the input. Default: None .

• name (str) – Name of the input. Default: None .

• need_compile (bool) – Whether the input needs to be compiled or not. Default: None .

• param_type (str) – Type of the input. Default: None .

• shape (str) – Shape of the input. Default: None .

• value_depend (str) – Whether the input is constant value depend. Default: None .

• kwargs (dict) – Other information of the input.

input_to_attr_index(input_to_attr_index)

Description the index of input need to cast to attr.

Parameters

input_to_attr_index (list) – Value of input_to_attr_index. Default: () .

is_dynamic_format(is_dynamic_format=False)

Whether the operator needs calop_select_format api.

Parameters

is_dynamic_format (bool) – Value of is_dynamic_format. Default: False .

kernel_name(kernel_name)

The name of operator kernel.

Parameters

kernel_name (str) – Name of operator kernel.

need_check_supported(need_check_supported=False)

Whether the operator needs check supports.

Parameters

need_check_supported (bool) – Value of need_check_supported. Default: False .

op_pattern(pattern=None)

The behavior type of operator, such as broadcast, reduce and so on.

Parameters

pattern (str) – Value of op pattern, e.g. “broadcast”, “reduce”. Default: None .

output(index=None, name=None, need_compile=None, param_type=None, shape=None, **kwargs)

Register TBE op output information.

Parameters

• index (int) – Order of the output. Default: None .

• name (str) – Name of the output. Default: None .

• need_compile (bool) – Whether the output needs to be compiled or not. Default: None .

• param_type (str) – Type of the output. Default: None .

• shape (str) – Shape of the output. Default: None .

• kwargs (dict) – Other information of the output.

partial_flag(partial_flag=True)

Define the calculation efficiency of operator, whether the partial calculation is supported.

Parameters

partial_flag (bool) – Value of partial flag. Default: True .

real_input_index(real_input_index)

Description operator front end and tbe operator input mapping.

Parameters

real_input_index (list) – Value of real_input_index. Default: () .

reshape_type(reshape_type)

Reshape type of operator.

Parameters

reshape_type (str) – Value of reshape type. For example, if the input shape is \((2, 3)\) and reshape_type is set to “CH”, then the new shape is \((1, 2, 3, 1)\). “CH” means the C and H dimensions are kept and new dimensions are added for N and W dimension.

unknown_shape_formats(unknown_shape_formats)

Description data arrangement of operator input / output tensor in dynamic shape scene.

Parameters

unknown_shape_formats (list) – Description data arrangement of operator input / output tensor in dynamic shape scene.