forked from mindspore-Ecosystem/mindspore
!11205 master_tensor_getitem_debug
From: @yepei6 Reviewed-by: @kingxian,@zh_qh Signed-off-by: @kingxian
This commit is contained in:
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commit
0018070b54
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@ -147,13 +147,15 @@ def judge_index_type(index_type, target_type):
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@constexpr
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def check_type_valid(dtype, target_type, op_name):
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if dtype != target_type and (isinstance(target_type, (list, tuple)) and dtype not in target_type):
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raise TypeError(f"The '{op_name}' doesn't supoort {dtype}' and expecte to receive {target_type}.")
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raise TypeError(
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f"The '{op_name}' doesn't supoort {dtype}' and expecte to receive {target_type}.")
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@constexpr
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def check_index_type_valid(dtype, target_type, op_name):
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if dtype != target_type and (isinstance(target_type, (list, tuple)) and dtype not in target_type):
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raise IndexError(f"The '{op_name}' doesn't supoort {dtype}' and expecte to receive {target_type}.")
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raise IndexError(
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f"The '{op_name}' doesn't supoort {dtype}' and expecte to receive {target_type}.")
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@constexpr
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@ -189,7 +191,8 @@ def get_pos_of_indexes_types(indexes_types, op_name):
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raise IndexError(f"For '{op_name}', the index elements only support "
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f"'Tensor', 'int32', 'int64', 'Slice', 'Ellipsis', but got {index_type}.")
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if len(ellipsis_positions) > 1:
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raise IndexError(f"For '{op_name}, an index can only have a single ellipsis('...')")
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raise IndexError(
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f"For '{op_name}, an index can only have a single ellipsis('...')")
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return slice_positions, ellipsis_positions, none_positions, int_positions, bool_positions, \
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tensor_positions, sequence_positions
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@ -260,7 +263,7 @@ def ellipsis2slice(input_, shape):
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return tuple(result)
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@ constexpr
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@constexpr
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def slice2indices(input_slices, shape):
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"""
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Converts slice to indices.
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@ -285,7 +288,7 @@ def slice2indices(input_slices, shape):
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return ravel
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@ constexpr
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@constexpr
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def check_indices(indices_size, index):
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"""Checks indices whether is empty."""
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if indices_size < 1:
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@ -294,7 +297,7 @@ def check_indices(indices_size, index):
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return indices_size
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@ constexpr
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@constexpr
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def check_indices_value_size(indices_size, value_size):
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"""Checks if the sizes are already matched."""
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if value_size < 1:
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@ -307,7 +310,7 @@ def check_indices_value_size(indices_size, value_size):
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return value_size
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@ constexpr
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@constexpr
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def integer_to_indices(index, shape):
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"""Converts int or tuple[int] to indices."""
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size = reduce(lambda x, y: x * y, shape)
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@ -317,7 +320,7 @@ def integer_to_indices(index, shape):
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return Tensor(value, dtype=mstype.int32)
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@ constexpr
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@constexpr
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def tuple_element_is_int(indexs):
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"""Judges tuple element type."""
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if not indexs:
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@ -330,18 +333,19 @@ def tuple_element_is_int(indexs):
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return False
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@ constexpr
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@constexpr
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def tuple_index_int_cnt(types, op_name):
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"""count the int type of types which contains the tuple elements' type."""
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int_cnt = sum(isinstance(ele, mstype.Int) for ele in types)
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return ALL_INT if int_cnt == len(types) else NO_INT if int_cnt == 0 else CONTAIN_INT
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@ constexpr
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@constexpr
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def tuple_index_type_cnt(types, op_name):
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"""count the tensor type of types which contains the tuple elements' type."""
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tensor_cnt = sum(isinstance(ele, mstype.tensor_type) for ele in types)
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basic_cnt = sum(isinstance(ele, (mstype.Int, mstype.Ellipsis_, mstype.Slice)) for ele in types)
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basic_cnt = sum(isinstance(
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ele, (mstype.Int, mstype.Ellipsis_, mstype.Slice)) for ele in types)
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if tensor_cnt == len(types):
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return ALL_TENSOR
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if basic_cnt == len(types):
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@ -349,7 +353,7 @@ def tuple_index_type_cnt(types, op_name):
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return MIXED
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@ constexpr
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@constexpr
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def check_value_elements(data_dtype, types):
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"""Judges the type of all elements of the tuple."""
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tensors_number = 0
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@ -377,10 +381,10 @@ def check_value_elements(data_dtype, types):
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# TODO to del
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@ constexpr
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@constexpr
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def get_index_tensor_dtype(dtype):
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"""Check a tuple of tensor data type."""
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if dtype == mstype.int32:
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if dtype in mstype.int_type:
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return INT_
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if dtype == mstype.bool_:
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return BOOL_
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@ -389,7 +393,7 @@ def get_index_tensor_dtype(dtype):
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# TODO to del
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@ constexpr
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@constexpr
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def check_index_tensors_dtype(indexes_types, op_name):
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"""Check a tuple of tensor data type."""
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for index_type in indexes_types:
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@ -400,7 +404,7 @@ def check_index_tensors_dtype(indexes_types, op_name):
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# TODO to del
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@ constexpr
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@constexpr
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def check_index_tensor_dtype(index_type, op_name):
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"""Check a tensor data type."""
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if index_type in (mstype.int32, mstype.int64):
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@ -410,7 +414,7 @@ def check_index_tensor_dtype(index_type, op_name):
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# TODO to del
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@ constexpr
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@constexpr
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def check_tensors_dtype_same(data_dtype, value_dtype, op_name):
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"""Check tensors data type same."""
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if value_dtype == data_dtype:
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@ -419,7 +423,7 @@ def check_tensors_dtype_same(data_dtype, value_dtype, op_name):
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f"is not consistent with assigned tensor data type {data_dtype}.")
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@ constexpr
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@constexpr
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def generate_broadcast_shape(shapes, op_name):
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"""Generate broadcast shape for a tuple of shape."""
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if not shapes:
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@ -428,13 +432,14 @@ def generate_broadcast_shape(shapes, op_name):
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for i, shape in enumerate(shapes):
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logger.debug(f"Broadcasts the {i}th tensor, the shape is {shape}.")
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try:
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broadcast_shape = op_utils.get_broadcast_shape(broadcast_shape, shape, op_name)
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broadcast_shape = op_utils.get_broadcast_shape(
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broadcast_shape, shape, op_name)
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except ValueError as ex:
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raise IndexError(ex)
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return tuple(broadcast_shape)
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@ constexpr
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@constexpr
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def check_two_shapes_need_broadcast(shape_x, shape_y):
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"""Check two shapes need broadcast."""
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error = ValueError(f"For 'tensor setitem with tensor', the value tensor shape "
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@ -451,14 +456,14 @@ def check_two_shapes_need_broadcast(shape_x, shape_y):
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return True
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@ constexpr
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@constexpr
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def compute_multiples(origin_shape, broadcast_shape):
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"""Compute multiples between origin shape with broadcast shape."""
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len_gap = len(broadcast_shape) - len(origin_shape)
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return broadcast_shape[0:len_gap] + tuple(map(lambda x, y: x // y, broadcast_shape[len_gap:], origin_shape))
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@ constexpr
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@constexpr
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def compute_new_shape(origin_shape, indexes_shapes_info):
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"""Compute new shape between origin shape with final shape."""
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new_shape = []
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@ -470,21 +475,22 @@ def compute_new_shape(origin_shape, indexes_shapes_info):
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return tuple(new_shape)
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@ constexpr
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@constexpr
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def check_sequence_index_type(sequence_index, op_name):
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"""check if the item's type of list_index is bool or int"""
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if not all([isinstance(index, (int, bool)) for index in sequence_index]):
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raise IndexError(f"In the {op_name} operation, only support 'integer' or 'boolean' array(list/tuple), "
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f"but got {type(index)} in array")
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for index in sequence_index:
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if not isinstance(index, int):
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raise IndexError(f"In the {op_name} operation, only support 'inter' or 'boolean' array(list/tuple), "
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f"but got {type(index)} in array.")
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@ constexpr
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@constexpr
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def convert_int_to_slice(tuple_index):
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tuple_index_new = tuple(slice(i, i+1, 1) for i in tuple_index)
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return tuple_index_new
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@ constexpr
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@constexpr
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def check_and_transform_int_index(index, shape, op_name):
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if index < -shape or index >= shape:
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raise IndexError(f"In the \"{op_name}\", the index should in the range [-{shape}, {shape-1}] to fit "
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@ -494,16 +500,20 @@ def check_and_transform_int_index(index, shape, op_name):
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return index
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@ constexpr
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@constexpr
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def transform_sequence_index(sequence_index, shape, op_name):
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"""transform list or tuple with integer and boolean to tuple with integer index"""
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bool_count = len(list(filter(lambda index: isinstance(index, bool), sequence_index)))
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int_count = len(list(filter(lambda index: isinstance(index, int), sequence_index)))-bool_count
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bool_count = len(
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list(filter(lambda index: isinstance(index, bool), sequence_index)))
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int_count = len(
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list(filter(lambda index: isinstance(index, int), sequence_index)))-bool_count
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if int_count == 0:
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if bool_count == shape:
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list_index = list(filter(lambda i: sequence_index[i], range(bool_count)))
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list_index = list(
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filter(lambda i: sequence_index[i], range(bool_count)))
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else:
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raise IndexError("The boolean array should have the same length with the corresponding dimensiton")
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raise IndexError(
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"The boolean array should have the same length with the corresponding dimensiton")
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else:
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list_index = [int(index) for index in sequence_index]
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for i, index in enumerate(list_index):
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@ -512,7 +522,7 @@ def transform_sequence_index(sequence_index, shape, op_name):
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return sub_tuple_index
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@ constexpr
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@constexpr
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def convert_slice_to_tensor(slice_number, final_shape, indexes_shapes_info, op_name):
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"""Convert a slice to a tensor."""
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shape = []
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@ -540,7 +550,7 @@ def convert_slice_to_tensor(slice_number, final_shape, indexes_shapes_info, op_n
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return tensor
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@ constexpr
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@constexpr
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def check_shapes_same(value_shapes, op_name):
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"""Check if the shapes in the tuple are consistent."""
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for i, shape in enumerate(value_shapes):
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@ -550,7 +560,7 @@ def check_shapes_same(value_shapes, op_name):
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return True
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@ constexpr
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@constexpr
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def convert_scalar_to_tensor(data_shape, data_dtype, indices_shape, value, op_type):
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"""Convert a scalar to a tensor."""
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if op_type == SET_ITEM_BY_ONE_TENSOR:
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@ -563,7 +573,7 @@ def convert_scalar_to_tensor(data_shape, data_dtype, indices_shape, value, op_ty
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f" is not consistent with the assigned tensor data type {data_dtype}.")
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@ constexpr
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@constexpr
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def convert_tuple_of_scalar_to_tensor(data_shape, data_dtype, index_shape, value, op_type):
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"""Convert a tuple of scalar to a tensor."""
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updates_shape = generate_updates_shape(data_shape, index_shape, op_type)
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@ -575,7 +585,7 @@ def convert_tuple_of_scalar_to_tensor(data_shape, data_dtype, index_shape, value
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return Tensor(np.tile(array, reps))
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@ constexpr
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@constexpr
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def generate_updates_shape(data_shape, index_shape, op_type):
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"""Generate updates shape for 'tensor setitem'."""
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if op_type == SET_ITEM_BY_ONE_TENSOR:
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@ -585,7 +595,7 @@ def generate_updates_shape(data_shape, index_shape, op_type):
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return updates_shape
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@ constexpr
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@constexpr
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def check_tuple_index_len(data_rank, tuple_index_len, op_name):
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"""Check if the number of index tensor exceeds the dimension of the operated tensor."""
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if tuple_index_len <= data_rank:
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@ -594,7 +604,7 @@ def check_tuple_index_len(data_rank, tuple_index_len, op_name):
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f"is greater than the dimension {data_rank} of the operated tensor.")
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@ constexpr
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@constexpr
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def generate_index_info_from_tuple_of_mixed_tensors(data_shape, indexes_types, tensor_indexes_shapes,
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tensor_indexes_dtypes, slice_indexes, op_name):
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"""
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@ -694,14 +704,14 @@ def scalar_in_sequence(x, y):
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return False
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@ constexpr
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@constexpr
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def get_np_eps(input_dtype):
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nptype = mstype.dtype_to_nptype(input_dtype)
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eps = np.finfo(nptype).eps
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return float(eps)
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@ constexpr
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@constexpr
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def check_number_index_type(number):
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"""Check if it is int or bool number"""
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if isinstance(number, bool):
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@ -712,7 +722,7 @@ def check_number_index_type(number):
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.format(number, type(number)))
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@ constexpr
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@constexpr
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def get_stride_info_from_slice(data_shape, slice_index):
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"""Get stride info from a python slice"""
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begin, end, step = get_slice_stride(data_shape[0], slice_index)
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@ -726,7 +736,7 @@ def get_stride_info_from_slice(data_shape, slice_index):
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return tuple(begin_strides), tuple(end_strides), tuple(step_strides)
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@ constexpr
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@constexpr
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def get_stride_info_from_integer(data_shape, number):
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"""Get stride info from a integer"""
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begin_strides = [number]
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@ -752,7 +762,7 @@ def get_slice_stride(dim_size, index_slice):
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return start, stop, step
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@ constexpr
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@constexpr
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def get_stride_info_from_tuple(data_shape, tuple_index):
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"""Get stride info from a tuple"""
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begin_strides, end_strides, step_strides = [], [], []
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@ -792,14 +802,14 @@ def get_stride_info_from_tuple(data_shape, tuple_index):
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return tuple(begin_strides), tuple(end_strides), tuple(step_strides), shrink_axis
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@ constexpr
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@constexpr
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def mstype_eq(x, y):
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if x == y:
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return True
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return False
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@ constexpr
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@constexpr
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def scalar_to_tensor(x):
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"""Convert a scalar to a tensor"""
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return Tensor(x)
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