# 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.
# ============================================================================
"""inner_ops"""
import numbers
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ...common.dtype import tensor, dtype_to_pytype
from ..primitive import prim_attr_register, PrimitiveWithInfer
[docs]class ScalarCast(PrimitiveWithInfer):
"""
Casts the input scalar to another type.
Inputs:
- **input_x** (scalar) - The input scalar. Only constant value is allowed.
- **input_y** (mindspore.dtype) - The type to be cast. Only constant value is allowed.
Outputs:
Scalar. The type is the same as the python type corresponding to `input_y`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> scalar_cast = ops.ScalarCast()
>>> output = scalar_cast(255.0, mindspore.int32)
>>> print(output)
255
"""
@prim_attr_register
def __init__(self):
pass
def __infer__(self, x, t):
validator.check_equal_int(len(x['shape']), 0, 'x shape', 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)
value = np_type(value)
out = {'shape': x['shape'],
'dtype': t['value'],
'value': value}
return out
[docs]class Randperm(PrimitiveWithInfer):
"""
Generates random samples from 0 to n-1.
Args:
max_length (int): Number of items expected to get and the number must be greater than 0. Default: 1.
pad (int): The pad value to be filled. Default: -1.
dtype (mindspore.dtype): The type of output. Default: mindspore.int32.
Inputs:
- **n** (Tensor[int]) - The input tensor with shape: (1,) and the number must be in (0, `max_length`].
Default: 1.
Outputs:
- **output** (Tensor) - The output Tensor with shape: (`max_length`,) and type: `dtype`.
Supported Platforms:
``Ascend``
Examples:
>>> randperm = ops.Randperm(max_length=30, pad=-1)
>>> n = Tensor([20], dtype=mindspore.int32)
>>> output = randperm(n)
>>> print(output)
[15 6 11 19 14 16 9 5 13 18 4 10 8 0 17 2 14 1 12 3 7
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1]
"""
@prim_attr_register
def __init__(self, max_length=1, pad=-1, dtype=mstype.int32):
"""Initialize Randperm"""
validator.check_value_type("pad", pad, [int], self.name)
validator.check_value_type("max_length", max_length, [int], self.name)
validator.check_int(max_length, 1, Rel.GE, "1", self.name)
self.dtype = dtype
self.max_length = max_length
self.init_prim_io_names(inputs=[], outputs=['output'])
def infer_shape(self, n_shape):
validator.check_int(len(n_shape), 1, Rel.EQ, "rank_of_n", self.name)
validator.check_int(n_shape[0], 1, Rel.EQ, "length_of_n", self.name)
return [self.max_length]
def infer_dtype(self, n_type):
validator.check_type_name("n_type", n_type, mstype.int32, self.name)
valid_values = (mstype.int8, mstype.int16, mstype.int32, mstype.int64,
mstype.uint8, mstype.uint16, mstype.uint32, mstype.uint64)
validator.check_type_name("dtype", self.dtype, valid_values, self.name)
return self.dtype
[docs]class NoRepeatNGram(PrimitiveWithInfer):
"""
Update log_probs with repeat n-grams.
Args:
ngram_size (int): Size of n-grams, must be greater than 0. Default: 1.
Inputs:
- **state_seq** (Tensor) - A 3-D tensor with shape: (batch_size, beam_width, m).
- **log_probs** (Tensor) - A 3-D tensor with shape: (batch_size, beam_width, vocab_size).
The value of log_probs will be replaced with -FLOAT_MAX when n-grams repeated.
Outputs:
- **log_probs** (Tensor) - The output Tensor with same shape and type as original `log_probs`.
Supported Platforms:
``Ascend``
Examples:
>>> no_repeat_ngram = ops.NoRepeatNGram(ngram_size=3)
>>> state_seq = Tensor([[[1, 2, 1, 2, 5, 1, 2],
[9, 3, 9, 5, 4, 1, 5]],
[[4, 8, 6, 4, 5, 6, 4],
[4, 8, 8, 4, 3, 4, 8]]], dtype=mindspore.int32)
>>> log_probs = Tensor([[[0.75858542, 0.8437121 , 0.69025469, 0.79379992, 0.27400691,
0.84709179, 0.78771346, 0.68587179, 0.22943851, 0.17682976]],
[[0.99401879, 0.77239773, 0.81973878, 0.32085208, 0.59944118,
0.3125177, 0.52604189, 0.77111461, 0.98443699, 0.71532898]]], dtype=mindspore.float32)
>>> output = no_repeat_ngram(state_seq, log_probs)
>>> print(output)
[[[0.75858542 -3.4028235e+38 0.69025469 0.79379992 0.27400691
-3.4028235e+38 0.78771346 0.68587179 0.22943851 0.17682976]]
[[0.99401879 0.77239773 0.81973878 0.32085208 0.59944118
-3.4028235e+38 0.52604189 0.77111461 0.98443699 0.71532898]]]
"""
@prim_attr_register
def __init__(self, ngram_size=1):
"""NoRepeatNGram Randperm"""
validator.check_value_type("ngram_size", ngram_size, [int], self.name)
validator.check_int(ngram_size, 1, Rel.GE, "ngram_size", self.name)
self.ngram_size = ngram_size
self.init_prim_io_names(inputs=['state_seq', 'log_probs'], outputs=['log_probs'])
def infer_shape(self, seq_shape, log_shape):
validator.check_int(len(seq_shape), 3, Rel.EQ, "rank_of_seq", self.name)
validator.check_int(len(log_shape), 3, Rel.EQ, "rank_of_log", self.name)
validator.check_int(seq_shape[0], log_shape[0], Rel.EQ, "seq_shape shape[0]", self.name)
validator.check_int(seq_shape[1], log_shape[1], Rel.EQ, "seq_shape shape[1]", self.name)
validator.check_int(self.ngram_size, seq_shape[2] + 1, Rel.LE, "ngram_size", self.name)
return log_shape
def infer_dtype(self, seq_type, log_type):
validator.check_type_name("seq_type", seq_type, mstype.int32, self.name)
valid_values = (mstype.float16, mstype.float32, mstype.float64)
validator.check_type_name("log_type", log_type, valid_values, self.name)
return log_type