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371 lines
11 KiB
371 lines
11 KiB
# Copyright (c) 2021 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 typing import Any
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from typing import List
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from typing import Tuple
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from typing import Union
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import paddle
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from paddle import nn
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from paddle.fluid import core
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from paddle.nn import functional as F
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from paddlespeech.s2t.utils.log import Log
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#TODO(Hui Zhang): remove fluid import
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logger = Log(__name__).getlog()
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########### hack logging #############
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logger.warn = logger.warning
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########### hack paddle #############
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paddle.half = 'float16'
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paddle.float = 'float32'
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paddle.double = 'float64'
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paddle.short = 'int16'
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paddle.int = 'int32'
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paddle.long = 'int64'
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paddle.uint16 = 'uint16'
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paddle.cdouble = 'complex128'
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def convert_dtype_to_string(tensor_dtype):
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"""
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Convert the data type in numpy to the data type in Paddle
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Args:
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tensor_dtype(core.VarDesc.VarType): the data type in numpy.
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Returns:
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core.VarDesc.VarType: the data type in Paddle.
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"""
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dtype = tensor_dtype
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if dtype == core.VarDesc.VarType.FP32:
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return paddle.float32
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elif dtype == core.VarDesc.VarType.FP64:
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return paddle.float64
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elif dtype == core.VarDesc.VarType.FP16:
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return paddle.float16
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elif dtype == core.VarDesc.VarType.INT32:
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return paddle.int32
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elif dtype == core.VarDesc.VarType.INT16:
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return paddle.int16
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elif dtype == core.VarDesc.VarType.INT64:
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return paddle.int64
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elif dtype == core.VarDesc.VarType.BOOL:
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return paddle.bool
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elif dtype == core.VarDesc.VarType.BF16:
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# since there is still no support for bfloat16 in NumPy,
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# uint16 is used for casting bfloat16
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return paddle.uint16
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elif dtype == core.VarDesc.VarType.UINT8:
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return paddle.uint8
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elif dtype == core.VarDesc.VarType.INT8:
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return paddle.int8
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elif dtype == core.VarDesc.VarType.COMPLEX64:
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return paddle.complex64
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elif dtype == core.VarDesc.VarType.COMPLEX128:
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return paddle.complex128
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else:
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raise ValueError("Not supported tensor dtype %s" % dtype)
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if not hasattr(paddle, 'softmax'):
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logger.warn("register user softmax to paddle, remove this when fixed!")
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setattr(paddle, 'softmax', paddle.nn.functional.softmax)
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if not hasattr(paddle, 'log_softmax'):
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logger.warn("register user log_softmax to paddle, remove this when fixed!")
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setattr(paddle, 'log_softmax', paddle.nn.functional.log_softmax)
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if not hasattr(paddle, 'sigmoid'):
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logger.warn("register user sigmoid to paddle, remove this when fixed!")
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setattr(paddle, 'sigmoid', paddle.nn.functional.sigmoid)
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if not hasattr(paddle, 'log_sigmoid'):
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logger.warn("register user log_sigmoid to paddle, remove this when fixed!")
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setattr(paddle, 'log_sigmoid', paddle.nn.functional.log_sigmoid)
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if not hasattr(paddle, 'relu'):
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logger.warn("register user relu to paddle, remove this when fixed!")
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setattr(paddle, 'relu', paddle.nn.functional.relu)
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def cat(xs, dim=0):
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return paddle.concat(xs, axis=dim)
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if not hasattr(paddle, 'cat'):
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logger.warn(
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"override cat of paddle if exists or register, remove this when fixed!")
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paddle.cat = cat
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########### hack paddle.Tensor #############
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def item(x: paddle.Tensor):
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return x.numpy().item()
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if not hasattr(paddle.Tensor, 'item'):
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logger.warn(
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"override item of paddle.Tensor if exists or register, remove this when fixed!"
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)
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paddle.Tensor.item = item
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def func_long(x: paddle.Tensor):
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return paddle.cast(x, paddle.long)
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if not hasattr(paddle.Tensor, 'long'):
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logger.warn(
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"override long of paddle.Tensor if exists or register, remove this when fixed!"
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)
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paddle.Tensor.long = func_long
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if not hasattr(paddle.Tensor, 'numel'):
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logger.warn(
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"override numel of paddle.Tensor if exists or register, remove this when fixed!"
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)
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paddle.Tensor.numel = paddle.numel
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def new_full(x: paddle.Tensor,
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size: Union[List[int], Tuple[int], paddle.Tensor],
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fill_value: Union[float, int, bool, paddle.Tensor],
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dtype=None):
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return paddle.full(size, fill_value, dtype=x.dtype)
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if not hasattr(paddle.Tensor, 'new_full'):
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logger.warn(
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"override new_full of paddle.Tensor if exists or register, remove this when fixed!"
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)
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paddle.Tensor.new_full = new_full
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def eq(xs: paddle.Tensor, ys: Union[paddle.Tensor, float]) -> paddle.Tensor:
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if convert_dtype_to_string(xs.dtype) == paddle.bool:
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xs = xs.astype(paddle.int)
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return xs.equal(
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paddle.to_tensor(
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ys, dtype=convert_dtype_to_string(xs.dtype), place=xs.place))
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if not hasattr(paddle.Tensor, 'eq'):
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logger.warn(
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"override eq of paddle.Tensor if exists or register, remove this when fixed!"
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)
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paddle.Tensor.eq = eq
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if not hasattr(paddle, 'eq'):
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logger.warn(
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"override eq of paddle if exists or register, remove this when fixed!")
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paddle.eq = eq
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def contiguous(xs: paddle.Tensor) -> paddle.Tensor:
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return xs
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if not hasattr(paddle.Tensor, 'contiguous'):
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logger.warn(
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"override contiguous of paddle.Tensor if exists or register, remove this when fixed!"
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)
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paddle.Tensor.contiguous = contiguous
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def size(xs: paddle.Tensor, *args: int) -> paddle.Tensor:
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nargs = len(args)
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assert (nargs <= 1)
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s = paddle.shape(xs)
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if nargs == 1:
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return s[args[0]]
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else:
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return s
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#`to_static` do not process `size` property, maybe some `paddle` api dependent on it.
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logger.warn(
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"override size of paddle.Tensor "
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"(`to_static` do not process `size` property, maybe some `paddle` api dependent on it), remove this when fixed!"
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)
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paddle.Tensor.size = size
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def view(xs: paddle.Tensor, *args: int) -> paddle.Tensor:
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return xs.reshape(args)
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if not hasattr(paddle.Tensor, 'view'):
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logger.warn("register user view to paddle.Tensor, remove this when fixed!")
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paddle.Tensor.view = view
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def view_as(xs: paddle.Tensor, ys: paddle.Tensor) -> paddle.Tensor:
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return xs.reshape(ys.size())
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if not hasattr(paddle.Tensor, 'view_as'):
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logger.warn(
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"register user view_as to paddle.Tensor, remove this when fixed!")
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paddle.Tensor.view_as = view_as
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def is_broadcastable(shp1, shp2):
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for a, b in zip(shp1[::-1], shp2[::-1]):
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if a == 1 or b == 1 or a == b:
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pass
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else:
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return False
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return True
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def masked_fill(xs: paddle.Tensor,
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mask: paddle.Tensor,
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value: Union[float, int]):
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assert is_broadcastable(xs.shape, mask.shape) is True
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bshape = paddle.broadcast_shape(xs.shape, mask.shape)
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mask = mask.broadcast_to(bshape)
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trues = paddle.ones_like(xs) * value
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xs = paddle.where(mask, trues, xs)
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return xs
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if not hasattr(paddle.Tensor, 'masked_fill'):
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logger.warn(
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"register user masked_fill to paddle.Tensor, remove this when fixed!")
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paddle.Tensor.masked_fill = masked_fill
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def masked_fill_(xs: paddle.Tensor,
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mask: paddle.Tensor,
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value: Union[float, int]) -> paddle.Tensor:
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assert is_broadcastable(xs.shape, mask.shape) is True
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bshape = paddle.broadcast_shape(xs.shape, mask.shape)
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mask = mask.broadcast_to(bshape)
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trues = paddle.ones_like(xs) * value
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ret = paddle.where(mask, trues, xs)
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paddle.assign(ret.detach(), output=xs)
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return xs
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if not hasattr(paddle.Tensor, 'masked_fill_'):
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logger.warn(
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"register user masked_fill_ to paddle.Tensor, remove this when fixed!")
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paddle.Tensor.masked_fill_ = masked_fill_
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def fill_(xs: paddle.Tensor, value: Union[float, int]) -> paddle.Tensor:
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val = paddle.full_like(xs, value)
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paddle.assign(val.detach(), output=xs)
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return xs
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if not hasattr(paddle.Tensor, 'fill_'):
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logger.warn("register user fill_ to paddle.Tensor, remove this when fixed!")
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paddle.Tensor.fill_ = fill_
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def repeat(xs: paddle.Tensor, *size: Any) -> paddle.Tensor:
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return paddle.tile(xs, size)
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if not hasattr(paddle.Tensor, 'repeat'):
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logger.warn(
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"register user repeat to paddle.Tensor, remove this when fixed!")
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paddle.Tensor.repeat = repeat
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if not hasattr(paddle.Tensor, 'softmax'):
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logger.warn(
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"register user softmax to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'softmax', paddle.nn.functional.softmax)
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if not hasattr(paddle.Tensor, 'sigmoid'):
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logger.warn(
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"register user sigmoid to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'sigmoid', paddle.nn.functional.sigmoid)
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if not hasattr(paddle.Tensor, 'relu'):
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logger.warn("register user relu to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'relu', paddle.nn.functional.relu)
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def type_as(x: paddle.Tensor, other: paddle.Tensor) -> paddle.Tensor:
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return x.astype(other.dtype)
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if not hasattr(paddle.Tensor, 'type_as'):
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logger.warn(
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"register user type_as to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'type_as', type_as)
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def to(x: paddle.Tensor, *args, **kwargs) -> paddle.Tensor:
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assert len(args) == 1
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if isinstance(args[0], str): # dtype
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return x.astype(args[0])
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elif isinstance(args[0], paddle.Tensor): #Tensor
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return x.astype(args[0].dtype)
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else: # Device
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return x
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if not hasattr(paddle.Tensor, 'to'):
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logger.warn("register user to to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'to', to)
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def func_float(x: paddle.Tensor) -> paddle.Tensor:
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return x.astype(paddle.float)
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if not hasattr(paddle.Tensor, 'float'):
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logger.warn("register user float to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'float', func_float)
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def func_int(x: paddle.Tensor) -> paddle.Tensor:
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return x.astype(paddle.int)
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if not hasattr(paddle.Tensor, 'int'):
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logger.warn("register user int to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'int', func_int)
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def tolist(x: paddle.Tensor) -> List[Any]:
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return x.numpy().tolist()
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if not hasattr(paddle.Tensor, 'tolist'):
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logger.warn(
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"register user tolist to paddle.Tensor, remove this when fixed!")
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setattr(paddle.Tensor, 'tolist', tolist)
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########### hack paddle.nn #############
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class GLU(nn.Layer):
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"""Gated Linear Units (GLU) Layer"""
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def __init__(self, dim: int=-1):
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super().__init__()
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self.dim = dim
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def forward(self, xs):
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return F.glu(xs, axis=self.dim)
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if not hasattr(paddle.nn, 'GLU'):
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logger.warn("register user GLU to paddle.nn, remove this when fixed!")
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setattr(paddle.nn, 'GLU', GLU)
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