chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING
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import numpy as np
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import paddle
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from paddle import _C_ops
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from paddle._C_ops import imag, real # noqa: F401
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from paddle.utils.decorator_utils import param_one_alias
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from ..base.data_feeder import check_type, check_variable_and_dtype
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from ..base.framework import in_dynamic_or_pir_mode, use_pir_api
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from ..common_ops_import import Variable
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from ..framework import LayerHelper, core
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from .creation import assign
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if TYPE_CHECKING:
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from paddle import Tensor
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__all__ = []
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def rank(input: Tensor) -> Tensor:
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"""
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Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor.
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Args:
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input (Tensor): The input Tensor with shape of :math:`[N_1, N_2, ..., N_k]`, the data type is arbitrary.
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Returns:
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Tensor, the output data type is int32.: The 0-D tensor with the dimensions of the input Tensor.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> input = paddle.rand((3, 100, 100))
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>>> rank = paddle.rank(input)
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>>> print(rank.numpy())
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3
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"""
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check_type(input, 'input', (Variable, paddle.pir.Value), 'input')
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ndims = len(input.shape)
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out = assign(np.array(ndims, 'int32'))
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return out
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def shape(input: Tensor) -> Tensor:
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"""
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Get the shape of the input.
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.. code-block:: text
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Case1:
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Given N-D Tensor:
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input = [ [1, 2, 3, 4], [5, 6, 7, 8] ]
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Then:
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input.shape = [2, 4]
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Case2:
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Given SelectedRows:
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input.rows = [0, 4, 19]
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input.height = 20
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input.value = [ [1, 2], [3, 4], [5, 6] ] # inner tensor
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Then:
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input.shape = [3, 2]
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Args:
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input (Tensor): The input can be N-D Tensor or SelectedRows with data type bool, bfloat16, float16, float32, float64, int32, int64.
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If input variable is type of SelectedRows, returns the shape of it's inner tensor.
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Returns:
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Tensor: The shape of the input variable.
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Examples:
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.. code-block:: pycon
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>>> import numpy as np
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>>> import paddle
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>>> paddle.enable_static()
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>>> inputs = paddle.static.data(name="x", shape=[3, 100, 100], dtype="float32")
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>>> output = paddle.shape(inputs)
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>>> exe = paddle.static.Executor(paddle.CPUPlace())
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>>> exe.run(paddle.static.default_startup_program())
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>>> img = np.ones((3, 100, 100)).astype(np.float32)
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>>> res = exe.run(paddle.static.default_main_program(), feed={'x': img}, fetch_list=[output])
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>>> print(res)
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[array([ 3, 100, 100], dtype=int64)]
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"""
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if in_dynamic_or_pir_mode():
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out = _C_ops.shape64(input) # type: ignore
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out.stop_gradient = True
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return out
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else:
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check_variable_and_dtype(
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input,
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'input',
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[
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'bool',
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'uint16',
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'float16',
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'float32',
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'float64',
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'int32',
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'int64',
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'complex64',
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'complex128',
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'uint16',
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'float8_e4m3fn',
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'float8_e5m2',
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],
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'shape',
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)
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helper = LayerHelper('shape', **locals())
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out = helper.create_variable_for_type_inference(dtype='int32')
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helper.append_op(
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type='shape',
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inputs={'Input': input},
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outputs={'Out': out},
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stop_gradient=True,
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)
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out.stop_gradient = True
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return out
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@param_one_alias(["x", "input"])
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def is_complex(x: Tensor) -> bool:
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"""Return whether x is a tensor of complex data type(complex64 or complex128).
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.. note::
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Alias Support: The parameter name ``input`` can be used as an alias for ``x``.
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For example, ``input=tensor_x`` is equivalent to ``x=tensor_x``.
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Args:
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x (Tensor): The input tensor.
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input: An alias for ``x`` , with identical behavior.
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Returns:
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bool: True if the data type of the input is complex data type, otherwise false.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> x = paddle.to_tensor([1 + 2j, 3 + 4j])
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>>> print(paddle.is_complex(x))
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True
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>>> x = paddle.to_tensor([1.1, 1.2])
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>>> print(paddle.is_complex(x))
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False
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>>> x = paddle.to_tensor([1, 2, 3])
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>>> print(paddle.is_complex(x))
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False
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"""
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if not isinstance(
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x, (paddle.Tensor, paddle.static.Variable, paddle.pir.Value)
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):
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raise TypeError(f"Expected Tensor, but received type of x: {type(x)}")
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dtype = x.dtype
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is_complex_dtype = (
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dtype == core.VarDesc.VarType.COMPLEX64
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or dtype == core.VarDesc.VarType.COMPLEX128
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or dtype == core.DataType.COMPLEX64
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or dtype == core.DataType.COMPLEX128
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)
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return is_complex_dtype
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@param_one_alias(["x", "input"])
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def is_floating_point(x: Tensor) -> bool:
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"""
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Returns whether the dtype of `x` is one of paddle.float64, paddle.float32, paddle.float16, and paddle.bfloat16.
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.. note::
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Alias Support: The parameter name ``input`` can be used as an alias for ``x``.
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For example, ``is_floating_point(input=tensor_x)`` is equivalent to ``is_floating_point(x=tensor_x)``.
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Args:
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x (Tensor): The input tensor. alias: ``input``.
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Returns:
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bool: True if the dtype of `x` is floating type, otherwise false.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> x = paddle.arange(1.0, 5.0, dtype='float32')
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>>> y = paddle.arange(1, 5, dtype='int32')
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>>> print(paddle.is_floating_point(x))
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True
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>>> print(paddle.is_floating_point(y))
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False
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"""
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if not isinstance(
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x, (paddle.Tensor, paddle.static.Variable, paddle.pir.Value)
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):
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raise TypeError(f"Expected Tensor, but received type of x: {type(x)}")
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dtype = x.dtype
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is_fp_dtype = (
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dtype == core.VarDesc.VarType.FP32
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or dtype == core.VarDesc.VarType.FP64
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or dtype == core.VarDesc.VarType.FP16
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or dtype == core.VarDesc.VarType.BF16
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or dtype == core.DataType.FLOAT32
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or dtype == core.DataType.FLOAT64
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or dtype == core.DataType.FLOAT16
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or dtype == core.DataType.BFLOAT16
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)
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return is_fp_dtype
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def is_integer(x: Tensor) -> bool:
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"""Return whether x is a tensor of integral data type.
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Args:
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x (Tensor): The input tensor.
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Returns:
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bool: True if the data type of the input is integer data type, otherwise false.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> x = paddle.to_tensor([1 + 2j, 3 + 4j])
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>>> print(paddle.is_integer(x))
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False
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>>> x = paddle.to_tensor([1.1, 1.2])
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>>> print(paddle.is_integer(x))
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False
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>>> x = paddle.to_tensor([1, 2, 3])
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>>> print(paddle.is_integer(x))
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True
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"""
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if not isinstance(
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x, (paddle.Tensor, paddle.static.Variable, paddle.pir.Value)
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):
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raise TypeError(f"Expected Tensor, but received type of x: {type(x)}")
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dtype = x.dtype
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is_int_dtype = False
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if not use_pir_api():
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is_int_dtype = (
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dtype == core.VarDesc.VarType.UINT8
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or dtype == core.VarDesc.VarType.INT8
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or dtype == core.VarDesc.VarType.INT16
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or dtype == core.VarDesc.VarType.INT32
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or dtype == core.VarDesc.VarType.INT64
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)
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else:
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is_int_dtype = (
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dtype == core.DataType.UINT8
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or dtype == core.DataType.INT8
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or dtype == core.DataType.INT16
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or dtype == core.DataType.INT32
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or dtype == core.DataType.INT64
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)
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return is_int_dtype
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