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!23584 Optimize the exception info of tensor index 

Merge pull request !23584 from zhangzhaoju/master_syntax
This commit is contained in:
i-robot 2021-09-17 01:09:02 +00:00 committed by Gitee
parent 5919315f5e 753dd2aeb8
commit e30715d4e5
3 changed files with 55 additions and 17 deletions
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7
mindspore/_extends/parse/standard_method.py
View File

@ -1603,20 +1603,21 @@ def check_is_tensor_bool_cond(shp):
"""check if tensor is a bool condition"""
if shp in ((), (1,)):
return True
raise ValueError("The truth value of an array with several elements is ambiguous.")
raise ValueError(f"Only tensor which shape is () or (1,) can be converted to bool, but got tensor shape is {shp}")
@constexpr
def const_tensor_to_bool(x):
"""convert bool tensor to bool condition"""
if x is None:
raise ValueError("Only constant tensor bool can be converted to bool")
raise ValueError("Only tensor which shape is () or (1,) can be converted to bool, but got None")
x = x.asnumpy()
if x.shape == ():
return bool(x)
if x.shape == (1,):
return bool(x[0])
raise ValueError("The truth value of an array with several elements is ambiguous.")
raise ValueError(
f"Only tensor which shape is () or (1,) can be converted to bool, but got tensor shape is {x.shape}")
@constexpr

50
mindspore/ops/composite/multitype_ops/_compile_utils.py
View File

@ -164,7 +164,7 @@ def tensor_item(data, *args):
def tensor_itemset(data, *args):
"""Tensor setitem by index and value."""
if not args:
const_utils.raise_value_error("itemset must have at least one argument")
const_utils.raise_value_error("'Tensor.itemset()' must have at least one argument, but got None.")
if len(args) == 2:
if const_utils.judge_index_type(F.typeof(args[0]), mstype.int64):
return tensor_itemset_by_number_with_number(data, args[0], args[1])
@ -172,7 +172,9 @@ def tensor_itemset(data, *args):
return tensor_itemset_by_tuple_with_number(data, args[0], args[1])
const_utils.raise_type_error("The index object cannot be interpreted as an integer")
if len(args) > 2:
const_utils.raise_value_error("incorrect number of indices for array")
exp_msg = const_utils.gen_exception_msg("'Tensor.itemset()' must have at most 2 argument, but got {}.",
len(args))
const_utils.raise_value_error(exp_msg)
return tensor_itemset_with_number(data, args[0])
@ -181,10 +183,16 @@ tensor_operator_registry.register("itemset", tensor_itemset)
def tensor_itemset_with_number(data, number_value):
"""set value of tensor whose shape is (1,)"""
if not const_utils.judge_index_type(F.typeof(number_value), mstype.number_type):
const_utils.raise_index_error("The Tensor could only use the number value for itemset")
exp_msg = const_utils.gen_exception_msg(
"'Tensor.itemset()' only support number input, but got {}", number_value)
const_utils.raise_index_error(exp_msg)
if data.shape != (1,):
const_utils.raise_index_error("The Tensor without shape (1,) could not use the itemset api with only one args")
exp_msg = const_utils.gen_exception_msg(
"Only tensor which shape is (1,) support 1 arg that means omit index, "
"but the tensor shape is {} and got 1 input.", data.shape)
const_utils.raise_index_error(exp_msg)
return const_utils.make_tensor((number_value,), F.dtype(data))
@ -197,7 +205,9 @@ def tensor_itemset_by_number_with_number(data, int_index, number_value):
def tensor_itemset_by_tuple_with_number(data, tuple_index, nubmer_value):
if len(tuple_index) != data.ndim:
const_utils.raise_value_error("incorrect number of indices for array")
exp_msg = const_utils.gen_exception_msg(
"Tuple index len({}) is not same to tensor dimension({})", len(tuple_index), data.ndim)
const_utils.raise_index_error(exp_msg)
return tensor_setitem_by_tuple_with_number(data, tuple_index, nubmer_value)
@ -234,7 +244,9 @@ def _transform_ellipsis_to_slice(data, tuple_index, op_name):
ellipsis_cnt = len(ellipsis_positions)
# pylint: disable=chained-comparison
if ellipsis_occupy_dims < 0 and ellipsis_cnt >= 0:
const_utils.raise_index_error("too many indices for array")
exp_msg = const_utils.gen_exception_msg(
"Tuple index {} out rang of tensor shape {}.", tuple_index, data_shape)
const_utils.raise_index_error(exp_msg)
tuple_index_new = ()
for i, index in enumerate(tuple_index):
@ -257,7 +269,7 @@ def _expand_data_dims(data, tuple_index):
expand_positions += (i,)
elif const_utils.judge_index_type(index_type, mstype.bool_):
if not index:
const_utils.raise_index_error("Dose not support 'False'.")
const_utils.raise_index_error("Bool element of tuple index must be 'True', but got 'False'.")
tuple_index_new += (const_utils.make_tensor([0], mstype.int64),)
expand_positions += (i,)
else:
@ -284,7 +296,9 @@ def tensor_index_by_number(data, number_index):
return _tensor_index_by_bool(data, number_index)
if isinstance(number_index, int):
return _tensor_index_by_integer(data, number_index)
return const_utils.raise_index_error("Only support integers, slices(`:`), ellipsis(`...`), None and bool.")
exp_msg = const_utils.gen_exception_msg(
"Number index of tensor must be int or bool, but got {}.", number_index)
return const_utils.raise_index_error(exp_msg)
def _tensor_index_by_bool(data, bool_value):
@ -326,7 +340,7 @@ def tensor_index_by_list(data, list_index):
if const_utils.judge_indexes_types(indexes_types, mstype.int_type + (mstype.bool_,)):
tensor_index = const_utils.sequence_to_index(list_index, data_shape[0])
if tensor_index is False:
const_utils.raise_index_error("Getitem does not support empty list, this will reference shape '0'.")
const_utils.raise_index_error("When tensor is indexed by list, the list can't be empty.")
return F.gather(data, tensor_index, 0)
tuple_index_new = ()
@ -408,12 +422,17 @@ def _tensor_getitem_by_tuple(data, tuple_index, op_name):
elif i in sequence_positions:
tensor_index = const_utils.sequence_to_index(index, dim_size)
if tensor_index is False:
const_utils.raise_index_error('Tensor with size 0 is not supported')
const_utils.raise_index_error("The sequence element(tuple/list) in tuple index can't be empty.")
tuple_index_new += (tensor_index,)
tensor_indexes.append(tensor_index)
tensor_positions += (i,)
elif i in tensor_positions:
const_utils.check_type_valid(F.dtype(index), mstype.int_type, op_name)
invalid = const_utils.check_type_invalid(F.dtype(index), mstype.int_type)
if invalid:
exp_msg = const_utils.gen_exception_msg(
"The tensor element in tuple index must be int type, but got {}.", F.dtype(index))
const_utils.raise_index_error(exp_msg)
tensor_index = F.cast(index, mstype.int64)
tuple_index_new += (tensor_index,)
tensor_indexes.append(tensor_index)
@ -488,7 +507,11 @@ def _generate_indices_from_tuple(data, tuple_index, op_name, fancy_position):
tensor_indexes.append(tensor_index)
tensor_positions += (i,)
elif i in tensor_positions:
const_utils.check_type_valid(F.dtype(index), mstype.int_type, op_name)
invalid = const_utils.check_type_invalid(F.dtype(index), mstype.int_type)
if invalid:
exp_msg = const_utils.gen_exception_msg(
"The tensor element in tuple index must be int type, but got {}.", F.dtype(index))
const_utils.raise_index_error(exp_msg)
tensor_index = F.cast(index, mstype.int64)
tuple_index_new += (tensor_index,)
tensor_indexes.append(tensor_index)
@ -645,7 +668,10 @@ def tensor_setitem_by_tensor_with_number(data, index, value):
def tensor_setitem_by_tensor_with_sequence(data, index, value):
"""Assigns the tensor by tensor with tuple value."""
index_dtype = F.dtype(index)
const_utils.check_type_valid(index_dtype, (mstype.int32, mstype.int64), const_utils.TENSOR_SETITEM)
invalid = const_utils.check_type_invalid(index_dtype, (mstype.int32, mstype.int64))
if invalid:
exp_msg = const_utils.gen_exception_msg("The tensor index must be int type, but got {}.", index_dtype)
const_utils.raise_index_error(exp_msg)
return _tensor_setitem_by_tensor_with_sequence(data, index, value)

15
mindspore/ops/composite/multitype_ops/_constexpr_utils.py
View File

@ -276,15 +276,21 @@ def judge_indexes_types(dtypes, target_type):
return True
@constexpr
def check_type_invalid(dtype, target_type):
"""Checks whether the dtype is valid."""
return dtype != target_type and (isinstance(target_type, (list, tuple)) and dtype not in target_type)
@constexpr
def check_type_valid(dtype, target_type, op_name):
"""Checks whether the dtype is valid."""
if dtype != target_type and (isinstance(target_type, (list, tuple)) and dtype not in target_type):
if op_name in (TENSOR_GETITEM, TENSOR_SETITEM):
raise IndexError(
f"The '{op_name}' doesn't supoort {dtype}' and expect to receive {target_type}.")
f"The '{op_name}' doesn't support {dtype}' and expect to receive {target_type}.")
raise TypeError(
f"The '{op_name}' doesn't supoort {dtype}' and expect to receive {target_type}.")
f"The '{op_name}' doesn't support {dtype}' and expect to receive {target_type}.")
@constexpr
@ -834,3 +840,8 @@ def use_copy_slice(tuple_index):
isinstance(tuple_index[1], slice) and tuple_index[1].step in (1, None) and
all(x == slice(None, None, None) for x in tuple_index[2:]))
return False
@constexpr
def gen_exception_msg(msg_format, *args):
return msg_format.format(args)