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