chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 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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import collections
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import typing
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import paddle
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import paddle.framework.dtype as dtypes
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from paddle.base import framework
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from .phi_ops_map import op_info, op_map
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class PrimOption:
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def __init__(self):
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self.enable_prim = False
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def get_status(self):
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return self.enable_prim
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def set_status(self, flag):
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self.enable_prim = flag
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prim_option = PrimOption()
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@framework.static_only
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def prim_enabled() -> bool:
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"""
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Note:
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**ONLY available in the static graph mode.**
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Shows whether the automatic differentiation mechanism based on
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automatic differential basic operators is ON. Defaults to OFF.
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Returns:
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flag(bool): Whether the automatic differentiation mechanism based on automatic differential basic operators is ON.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.incubate.autograd import enable_prim, disable_prim, prim_enabled
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>>> paddle.enable_static()
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>>> enable_prim()
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>>> print(prim_enabled())
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True
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>>> disable_prim()
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>>> print(prim_enabled())
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False
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"""
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return prim_option.get_status()
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@framework.static_only
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def enable_prim() -> None:
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"""
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Note:
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**ONLY available in the static graph mode.**
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Turns ON automatic differentiation mechanism based on automatic
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differential basic operators.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.incubate.autograd import enable_prim, prim_enabled
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>>> paddle.enable_static()
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>>> enable_prim()
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>>> print(prim_enabled())
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True
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"""
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prim_option.set_status(True)
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@framework.static_only
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def disable_prim() -> None:
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"""
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Note:
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**ONLY available in the static graph mode.**
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Turns OFF automatic differentiation mechanism based on automatic
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differential basic operators.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.incubate.autograd import enable_prim, disable_prim, prim_enabled
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>>> paddle.enable_static()
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>>> enable_prim()
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>>> print(prim_enabled())
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True
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>>> disable_prim()
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>>> print(prim_enabled())
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False
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"""
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prim_option.set_status(False)
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INT_DTYPE_2_STRING = {
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0: 'bool',
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1: 'int16',
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2: 'int32',
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3: 'int64',
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4: 'float16',
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5: 'float32',
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6: 'float64',
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20: 'uint8',
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21: 'int8',
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23: 'complex64',
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24: 'complex128',
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}
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def get_var_block(block, names, is_tensor_list=None):
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assert isinstance(names, list)
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if len(names) == 0:
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return None
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elif len(names) == 1:
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if is_tensor_list:
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return [block.var(names[0])]
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return block.var(names[0])
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else:
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return [block.var(name) for name in names]
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def get_input_var_list(op):
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if op.input_names is None:
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return []
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else:
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return [
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get_var_block(op.block, op.input(n)) for n in sorted(op.input_names)
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]
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def _solve_arg(item):
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if "=" not in item:
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res = item
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else:
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res = item.split('=')[0]
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[arg_type, arg_name] = res.strip().split()
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return arg_type.strip(), arg_name.strip()
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def _get_attr_value(op, arg_type, arg_name):
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op_content = op_map[op.type]
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if "attrs" in op_content.keys() and arg_name in op_content["attrs"].keys():
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arg_name = op_content["attrs"][arg_name]
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# Note: in some cases, attrs may be optional , thus assign None. Such case must be recorded.
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if arg_name not in op.attr_names:
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return None
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else:
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if arg_type == "DataType":
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return dtypes.dtype(op.attr(arg_name))
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return op.attr(arg_name)
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def _get_args_values(op, phi_name):
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"get attrs' values for api args' values"
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args = op_info[phi_name]
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args_list = args["args"].split(",")
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inputs = collections.OrderedDict()
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attrs = []
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for item in args_list:
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arg_type, arg_name = _solve_arg(item)
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op_content = op_map[op.type]
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# IntArray and Scalar are special cases which may cause dynamic shape. In these case, tensor-relative types are removed in composite op.
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if arg_type in ("IntArray", "Scalar"):
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tensor_key = "int_array" if arg_type == "IntArray" else "scalar"
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if op_content.get(tensor_key):
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tensor_content = op_content[tensor_key].get(arg_name)
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if not tensor_content:
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raise ValueError(
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f'No value found for {arg_name} of {arg_type} type for operator {op.type}.'
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)
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for item in ("tensor_name", "tensors_name"):
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# name of intarray may differ from operator arg_name
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arg_name_new = tensor_content.get(item)
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if (
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arg_name_new is not None
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and arg_name_new in op.input_names
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and get_var_block(op.block, op.input(arg_name_new))
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):
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raise ValueError(
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f"Tensor type of {arg_type} is not supported in composite op. Please set other type value of input arg {arg_name_new} for operator {op.type}."
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)
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if arg_type in ("Tensor", "Tensor[]"):
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# assume Tensor type must belong to inputs
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if (
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"inputs" in op_content.keys()
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and arg_name in op_content["inputs"].keys()
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):
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inputs[op_content["inputs"][arg_name]] = arg_type
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else:
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inputs[arg_name] = arg_type
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else:
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attr_value = _get_attr_value(op, arg_type, arg_name)
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attrs.append(attr_value)
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return inputs, attrs
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def prepare_python_api_arguments(op):
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"""
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Generate all args inputs of composite op. Because inputs of composite op is
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the same as phi op described in ops.yaml. So we need to map origin op to phi op
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and then push input data and attrs of origin op to corresponding phi op.
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"""
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if op.input_names is None:
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return []
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else:
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if op.type in op_map:
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phi_name = op_map[op.type]["phi_name"]
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else:
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phi_name = op.type
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inputs, attrs = _get_args_values(op, phi_name)
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res = []
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for item, tensor_type in inputs.items():
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if item in op.input_names:
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if tensor_type == "Tensor[]":
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res.append(
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get_var_block(
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op.block, op.input(item), is_tensor_list=True
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)
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)
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else:
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res.append(get_var_block(op.block, op.input(item)))
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else:
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# Note: in some cases, inputs may be optional, thus assign None. Such case must be recorded.
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res.append(None)
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if attrs:
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res.extend(attrs)
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return res
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def get_output_var_list(op):
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if op.output_names is None:
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return []
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else:
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return [
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get_var_block(op.block, op.output(n))
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for n in sorted(op.output_names)
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]
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def map_output_for_composite(op):
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"""origin op outputs must be mapped into outputs of composite rule. map info has been defined in op_compat.yaml"""
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origin_output_names = op.output_names
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if origin_output_names is None:
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return []
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else:
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name = op.type
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res = []
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if op_map[name].get("outputs"):
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for item in op_map[name]["outputs"].keys():
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origin_output_name = op_map[name]["outputs"][item]
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if origin_output_name not in origin_output_names:
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res.append(None)
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# Note: in some cases, some output of origin op is optional, so op name may not be in origin_output_names
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continue
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origin_output_var = get_var_block(
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op.block, op.output(origin_output_name)
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)
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res.append(origin_output_var)
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elif len(origin_output_names) == 1:
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# When origin output num is 1, map info is not needed.
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origin_output_var = get_var_block(
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op.block, op.output(origin_output_names[0])
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)
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res.append(origin_output_var)
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else:
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raise ValueError(
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"When replace op with composite rule, there must exist output map info from origin op to composite rule."
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)
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return res
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def flatten(inp):
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if inp is None or isinstance(
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inp, (paddle.base.framework.Variable, paddle.pir.Value)
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):
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return [inp]
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flattened = []
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for part in inp:
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flattened += flatten(part)
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return flattened
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def flatten_and_remove_none(inp):
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flattened = flatten(inp)
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return [var for var in flattened if var is not None]
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def as_tensors(xs):
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if isinstance(xs, (framework.Variable, paddle.pir.Value)):
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return (xs,)
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elif isinstance(xs, typing.Sequence):
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return tuple(xs)
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else:
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return xs
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