You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
356 lines
11 KiB
356 lines
11 KiB
# Copyright (c) 2021 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 typing import Any
|
|
from typing import List
|
|
from typing import Tuple
|
|
from typing import Union
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
from paddle.fluid import core
|
|
from paddle.nn import functional as F
|
|
|
|
from deepspeech.utils.log import Log
|
|
|
|
#TODO(Hui Zhang): remove fluid import
|
|
logger = Log(__name__).getlog()
|
|
|
|
########### hcak logging #############
|
|
logger.warn = logger.warning
|
|
|
|
########### hcak paddle #############
|
|
paddle.half = 'float16'
|
|
paddle.float = 'float32'
|
|
paddle.double = 'float64'
|
|
paddle.short = 'int16'
|
|
paddle.int = 'int32'
|
|
paddle.long = 'int64'
|
|
paddle.uint16 = 'uint16'
|
|
paddle.cdouble = 'complex128'
|
|
|
|
|
|
def convert_dtype_to_string(tensor_dtype):
|
|
"""
|
|
Convert the data type in numpy to the data type in Paddle
|
|
Args:
|
|
tensor_dtype(core.VarDesc.VarType): the data type in numpy.
|
|
Returns:
|
|
core.VarDesc.VarType: the data type in Paddle.
|
|
"""
|
|
dtype = tensor_dtype
|
|
if dtype == core.VarDesc.VarType.FP32:
|
|
return paddle.float32
|
|
elif dtype == core.VarDesc.VarType.FP64:
|
|
return paddle.float64
|
|
elif dtype == core.VarDesc.VarType.FP16:
|
|
return paddle.float16
|
|
elif dtype == core.VarDesc.VarType.INT32:
|
|
return paddle.int32
|
|
elif dtype == core.VarDesc.VarType.INT16:
|
|
return paddle.int16
|
|
elif dtype == core.VarDesc.VarType.INT64:
|
|
return paddle.int64
|
|
elif dtype == core.VarDesc.VarType.BOOL:
|
|
return paddle.bool
|
|
elif dtype == core.VarDesc.VarType.BF16:
|
|
# since there is still no support for bfloat16 in NumPy,
|
|
# uint16 is used for casting bfloat16
|
|
return paddle.uint16
|
|
elif dtype == core.VarDesc.VarType.UINT8:
|
|
return paddle.uint8
|
|
elif dtype == core.VarDesc.VarType.INT8:
|
|
return paddle.int8
|
|
elif dtype == core.VarDesc.VarType.COMPLEX64:
|
|
return paddle.complex64
|
|
elif dtype == core.VarDesc.VarType.COMPLEX128:
|
|
return paddle.complex128
|
|
else:
|
|
raise ValueError("Not supported tensor dtype %s" % dtype)
|
|
|
|
|
|
if not hasattr(paddle, 'softmax'):
|
|
logger.debug("register user softmax to paddle, remove this when fixed!")
|
|
setattr(paddle, 'softmax', paddle.nn.functional.softmax)
|
|
|
|
if not hasattr(paddle, 'log_softmax'):
|
|
logger.debug("register user log_softmax to paddle, remove this when fixed!")
|
|
setattr(paddle, 'log_softmax', paddle.nn.functional.log_softmax)
|
|
|
|
if not hasattr(paddle, 'sigmoid'):
|
|
logger.debug("register user sigmoid to paddle, remove this when fixed!")
|
|
setattr(paddle, 'sigmoid', paddle.nn.functional.sigmoid)
|
|
|
|
if not hasattr(paddle, 'log_sigmoid'):
|
|
logger.debug("register user log_sigmoid to paddle, remove this when fixed!")
|
|
setattr(paddle, 'log_sigmoid', paddle.nn.functional.log_sigmoid)
|
|
|
|
if not hasattr(paddle, 'relu'):
|
|
logger.debug("register user relu to paddle, remove this when fixed!")
|
|
setattr(paddle, 'relu', paddle.nn.functional.relu)
|
|
|
|
|
|
def cat(xs, dim=0):
|
|
return paddle.concat(xs, axis=dim)
|
|
|
|
|
|
if not hasattr(paddle, 'cat'):
|
|
logger.debug(
|
|
"override cat of paddle if exists or register, remove this when fixed!")
|
|
paddle.cat = cat
|
|
|
|
|
|
########### hcak paddle.Tensor #############
|
|
def item(x: paddle.Tensor):
|
|
return x.numpy().item()
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'item'):
|
|
logger.debug(
|
|
"override item of paddle.Tensor if exists or register, remove this when fixed!"
|
|
)
|
|
paddle.Tensor.item = item
|
|
|
|
|
|
def func_long(x: paddle.Tensor):
|
|
return paddle.cast(x, paddle.long)
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'long'):
|
|
logger.debug(
|
|
"override long of paddle.Tensor if exists or register, remove this when fixed!"
|
|
)
|
|
paddle.Tensor.long = func_long
|
|
|
|
if not hasattr(paddle.Tensor, 'numel'):
|
|
logger.debug(
|
|
"override numel of paddle.Tensor if exists or register, remove this when fixed!"
|
|
)
|
|
paddle.Tensor.numel = paddle.numel
|
|
|
|
|
|
def new_full(x: paddle.Tensor,
|
|
size: Union[List[int], Tuple[int], paddle.Tensor],
|
|
fill_value: Union[float, int, bool, paddle.Tensor],
|
|
dtype=None):
|
|
return paddle.full(size, fill_value, dtype=x.dtype)
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'new_full'):
|
|
logger.debug(
|
|
"override new_full of paddle.Tensor if exists or register, remove this when fixed!"
|
|
)
|
|
paddle.Tensor.new_full = new_full
|
|
|
|
|
|
def eq(xs: paddle.Tensor, ys: Union[paddle.Tensor, float]) -> paddle.Tensor:
|
|
if convert_dtype_to_string(xs.dtype) == paddle.bool:
|
|
xs = xs.astype(paddle.int)
|
|
return xs.equal(
|
|
paddle.to_tensor(
|
|
ys, dtype=convert_dtype_to_string(xs.dtype), place=xs.place))
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'eq'):
|
|
logger.debug(
|
|
"override eq of paddle.Tensor if exists or register, remove this when fixed!"
|
|
)
|
|
paddle.Tensor.eq = eq
|
|
|
|
if not hasattr(paddle, 'eq'):
|
|
logger.debug(
|
|
"override eq of paddle if exists or register, remove this when fixed!")
|
|
paddle.eq = eq
|
|
|
|
|
|
def contiguous(xs: paddle.Tensor) -> paddle.Tensor:
|
|
return xs
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'contiguous'):
|
|
logger.debug(
|
|
"override contiguous of paddle.Tensor if exists or register, remove this when fixed!"
|
|
)
|
|
paddle.Tensor.contiguous = contiguous
|
|
|
|
|
|
def size(xs: paddle.Tensor, *args: int) -> paddle.Tensor:
|
|
nargs = len(args)
|
|
assert (nargs <= 1)
|
|
s = paddle.shape(xs)
|
|
if nargs == 1:
|
|
return s[args[0]]
|
|
else:
|
|
return s
|
|
|
|
|
|
#`to_static` do not process `size` property, maybe some `paddle` api dependent on it.
|
|
logger.debug(
|
|
"override size of paddle.Tensor "
|
|
"(`to_static` do not process `size` property, maybe some `paddle` api dependent on it), remove this when fixed!"
|
|
)
|
|
paddle.Tensor.size = size
|
|
|
|
|
|
def view(xs: paddle.Tensor, *args: int) -> paddle.Tensor:
|
|
return xs.reshape(args)
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'view'):
|
|
logger.debug("register user view to paddle.Tensor, remove this when fixed!")
|
|
paddle.Tensor.view = view
|
|
|
|
|
|
def view_as(xs: paddle.Tensor, ys: paddle.Tensor) -> paddle.Tensor:
|
|
return xs.reshape(ys.size())
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'view_as'):
|
|
logger.debug(
|
|
"register user view_as to paddle.Tensor, remove this when fixed!")
|
|
paddle.Tensor.view_as = view_as
|
|
|
|
|
|
def is_broadcastable(shp1, shp2):
|
|
for a, b in zip(shp1[::-1], shp2[::-1]):
|
|
if a == 1 or b == 1 or a == b:
|
|
pass
|
|
else:
|
|
return False
|
|
return True
|
|
|
|
|
|
def masked_fill(xs: paddle.Tensor,
|
|
mask: paddle.Tensor,
|
|
value: Union[float, int]):
|
|
assert is_broadcastable(xs.shape, mask.shape) is True
|
|
bshape = paddle.broadcast_shape(xs.shape, mask.shape)
|
|
mask = mask.broadcast_to(bshape)
|
|
trues = paddle.ones_like(xs) * value
|
|
xs = paddle.where(mask, trues, xs)
|
|
return xs
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'masked_fill'):
|
|
logger.debug(
|
|
"register user masked_fill to paddle.Tensor, remove this when fixed!")
|
|
paddle.Tensor.masked_fill = masked_fill
|
|
|
|
|
|
def masked_fill_(xs: paddle.Tensor,
|
|
mask: paddle.Tensor,
|
|
value: Union[float, int]) -> paddle.Tensor:
|
|
assert is_broadcastable(xs.shape, mask.shape) is True
|
|
bshape = paddle.broadcast_shape(xs.shape, mask.shape)
|
|
mask = mask.broadcast_to(bshape)
|
|
trues = paddle.ones_like(xs) * value
|
|
ret = paddle.where(mask, trues, xs)
|
|
paddle.assign(ret.detach(), output=xs)
|
|
return xs
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'masked_fill_'):
|
|
logger.debug(
|
|
"register user masked_fill_ to paddle.Tensor, remove this when fixed!")
|
|
paddle.Tensor.masked_fill_ = masked_fill_
|
|
|
|
|
|
def fill_(xs: paddle.Tensor, value: Union[float, int]) -> paddle.Tensor:
|
|
val = paddle.full_like(xs, value)
|
|
paddle.assign(val.detach(), output=xs)
|
|
return xs
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'fill_'):
|
|
logger.debug(
|
|
"register user fill_ to paddle.Tensor, remove this when fixed!")
|
|
paddle.Tensor.fill_ = fill_
|
|
|
|
|
|
def repeat(xs: paddle.Tensor, *size: Any) -> paddle.Tensor:
|
|
return paddle.tile(xs, size)
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'repeat'):
|
|
logger.debug(
|
|
"register user repeat to paddle.Tensor, remove this when fixed!")
|
|
paddle.Tensor.repeat = repeat
|
|
|
|
if not hasattr(paddle.Tensor, 'softmax'):
|
|
logger.debug(
|
|
"register user softmax to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'softmax', paddle.nn.functional.softmax)
|
|
|
|
if not hasattr(paddle.Tensor, 'sigmoid'):
|
|
logger.debug(
|
|
"register user sigmoid to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'sigmoid', paddle.nn.functional.sigmoid)
|
|
|
|
if not hasattr(paddle.Tensor, 'relu'):
|
|
logger.debug("register user relu to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'relu', paddle.nn.functional.relu)
|
|
|
|
|
|
def type_as(x: paddle.Tensor, other: paddle.Tensor) -> paddle.Tensor:
|
|
return x.astype(other.dtype)
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'type_as'):
|
|
logger.debug(
|
|
"register user type_as to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'type_as', type_as)
|
|
|
|
|
|
def to(x: paddle.Tensor, *args, **kwargs) -> paddle.Tensor:
|
|
assert len(args) == 1
|
|
if isinstance(args[0], str): # dtype
|
|
return x.astype(args[0])
|
|
elif isinstance(args[0], paddle.Tensor): #Tensor
|
|
return x.astype(args[0].dtype)
|
|
else: # Device
|
|
return x
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'to'):
|
|
logger.debug("register user to to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'to', to)
|
|
|
|
|
|
def func_float(x: paddle.Tensor) -> paddle.Tensor:
|
|
return x.astype(paddle.float)
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'float'):
|
|
logger.debug(
|
|
"register user float to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'float', func_float)
|
|
|
|
|
|
def func_int(x: paddle.Tensor) -> paddle.Tensor:
|
|
return x.astype(paddle.int)
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'int'):
|
|
logger.debug("register user int to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'int', func_int)
|
|
|
|
|
|
def tolist(x: paddle.Tensor) -> List[Any]:
|
|
return x.numpy().tolist()
|
|
|
|
|
|
if not hasattr(paddle.Tensor, 'tolist'):
|
|
logger.debug(
|
|
"register user tolist to paddle.Tensor, remove this when fixed!")
|
|
setattr(paddle.Tensor, 'tolist', tolist)
|