msb-public
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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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pull/2155/head
huangyuxin 3 years ago
parent 30ba16e10a
commit 75997d8277
5 changed files with 10 additions and 210 deletions
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63
paddlespeech/s2t/__init__.py
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@ -18,7 +18,6 @@ from typing import Union
import paddle import paddle
from paddle import nn from paddle import nn
from paddle.fluid import core
from paddle.nn import functional as F from paddle.nn import functional as F
from paddlespeech.s2t.utils.log import Log from paddlespeech.s2t.utils.log import Log
@ -39,46 +38,6 @@ paddle.long = 'int64'
paddle.uint16 = 'uint16' paddle.uint16 = 'uint16'
paddle.cdouble = 'complex128' 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'): if not hasattr(paddle, 'softmax'):
logger.debug("register user softmax to paddle, remove this when fixed!") logger.debug("register user softmax to paddle, remove this when fixed!")
setattr(paddle, 'softmax', paddle.nn.functional.softmax) setattr(paddle, 'softmax', paddle.nn.functional.softmax)
@ -155,28 +114,6 @@ if not hasattr(paddle.Tensor, 'new_full'):
paddle.Tensor.new_full = new_full paddle.Tensor.new_full = new_full
paddle.static.Variable.new_full = new_full paddle.static.Variable.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
paddle.static.Variable.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: def contiguous(xs: paddle.Tensor) -> paddle.Tensor:
return xs return xs

2
paddlespeech/s2t/models/u2/u2.py
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@ -318,7 +318,7 @@ class U2BaseModel(ASRInterface, nn.Layer):
dim=1) # (B*N, i+1) dim=1) # (B*N, i+1)
# 2.6 Update end flag # 2.6 Update end flag
end_flag = paddle.eq(hyps[:, -1], self.eos).view(-1, 1) end_flag = paddle.equal(hyps[:, -1], self.eos).view(-1, 1)
# 3. Select best of best # 3. Select best of best
scores = scores.view(batch_size, beam_size) scores = scores.view(batch_size, beam_size)

15
paddlespeech/s2t/modules/align.py
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@ -13,8 +13,7 @@
# limitations under the License. # limitations under the License.
import paddle import paddle
from paddle import nn from paddle import nn
import math
from paddlespeech.s2t.modules.initializer import KaimingUniform
""" """
To align the initializer between paddle and torch, To align the initializer between paddle and torch,
the API below are set defalut initializer with priority higger than global initializer. the API below are set defalut initializer with priority higger than global initializer.
@ -82,10 +81,10 @@ class Linear(nn.Linear):
name=None): name=None):
if weight_attr is None: if weight_attr is None:
if global_init_type == "kaiming_uniform": if global_init_type == "kaiming_uniform":
weight_attr = paddle.ParamAttr(initializer=KaimingUniform()) weight_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
if bias_attr is None: if bias_attr is None:
if global_init_type == "kaiming_uniform": if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(initializer=KaimingUniform()) bias_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
super(Linear, self).__init__(in_features, out_features, weight_attr, super(Linear, self).__init__(in_features, out_features, weight_attr,
bias_attr, name) bias_attr, name)
@ -105,10 +104,10 @@ class Conv1D(nn.Conv1D):
data_format='NCL'): data_format='NCL'):
if weight_attr is None: if weight_attr is None:
if global_init_type == "kaiming_uniform": if global_init_type == "kaiming_uniform":
weight_attr = paddle.ParamAttr(initializer=KaimingUniform()) weight_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
if bias_attr is None: if bias_attr is None:
if global_init_type == "kaiming_uniform": if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(initializer=KaimingUniform()) bias_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
super(Conv1D, self).__init__( super(Conv1D, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation, in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, padding_mode, weight_attr, bias_attr, data_format) groups, padding_mode, weight_attr, bias_attr, data_format)
@ -129,10 +128,10 @@ class Conv2D(nn.Conv2D):
data_format='NCHW'): data_format='NCHW'):
if weight_attr is None: if weight_attr is None:
if global_init_type == "kaiming_uniform": if global_init_type == "kaiming_uniform":
weight_attr = paddle.ParamAttr(initializer=KaimingUniform()) weight_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
if bias_attr is None: if bias_attr is None:
if global_init_type == "kaiming_uniform": if global_init_type == "kaiming_uniform":
bias_attr = paddle.ParamAttr(initializer=KaimingUniform()) bias_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
super(Conv2D, self).__init__( super(Conv2D, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation, in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, padding_mode, weight_attr, bias_attr, data_format) groups, padding_mode, weight_attr, bias_attr, data_format)

2
paddlespeech/s2t/modules/attention.py
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@ -109,7 +109,7 @@ class MultiHeadedAttention(nn.Layer):
# 1. onnx(16/-1, -1/-1, 16/0) # 1. onnx(16/-1, -1/-1, 16/0)
# 2. jit (16/-1, -1/-1, 16/0, 16/4) # 2. jit (16/-1, -1/-1, 16/0, 16/4)
if paddle.shape(mask)[2] > 0: # time2 > 0 if paddle.shape(mask)[2] > 0: # time2 > 0
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) mask = mask.unsqueeze(1).equal(0) # (batch, 1, *, time2)
# for last chunk, time2 might be larger than scores.size(-1) # for last chunk, time2 might be larger than scores.size(-1)
mask = mask[:, :, :, :paddle.shape(scores)[-1]] mask = mask[:, :, :, :paddle.shape(scores)[-1]]
scores = scores.masked_fill(mask, -float('inf')) scores = scores.masked_fill(mask, -float('inf'))

136
paddlespeech/s2t/modules/initializer.py
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@ -12,142 +12,6 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import numpy as np import numpy as np
from paddle.fluid import framework
from paddle.fluid import unique_name
from paddle.fluid.core import VarDesc
from paddle.fluid.initializer import MSRAInitializer
__all__ = ['KaimingUniform']
class KaimingUniform(MSRAInitializer):
r"""Implements the Kaiming Uniform initializer
This class implements the weight initialization from the paper
`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification <https://arxiv.org/abs/1502.01852>`_
by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
robust initialization method that particularly considers the rectifier
nonlinearities.
In case of Uniform distribution, the range is [-x, x], where
.. math::
x = \sqrt{\frac{1.0}{fan\_in}}
In case of Normal distribution, the mean is 0 and the standard deviation
is
.. math::
\sqrt{\\frac{2.0}{fan\_in}}
Args:
fan_in (float32|None): fan_in for Kaiming uniform Initializer. If None, it is\
inferred from the variable. default is None.
Note:
It is recommended to set fan_in to None for most cases.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
linear = nn.Linear(2,
4,
weight_attr=nn.initializer.KaimingUniform())
data = paddle.rand([30, 10, 2], dtype='float32')
res = linear(data)
"""
def __init__(self, fan_in=None):
super(KaimingUniform, self).__init__(
uniform=True, fan_in=fan_in, seed=0)
def __call__(self, var, block=None):
"""Initialize the input tensor with MSRA initialization.
Args:
var(Tensor): Tensor that needs to be initialized.
block(Block, optional): The block in which initialization ops
should be added. Used in static graph only, default None.
Returns:
The initialization op
"""
block = self._check_block(block)
assert isinstance(var, framework.Variable)
assert isinstance(block, framework.Block)
f_in, f_out = self._compute_fans(var)
# If fan_in is passed, use it
fan_in = f_in if self._fan_in is None else self._fan_in
if self._seed == 0:
self._seed = block.program.random_seed
# to be compatible of fp16 initalizers
if var.dtype == VarDesc.VarType.FP16 or (
var.dtype == VarDesc.VarType.BF16 and not self._uniform):
out_dtype = VarDesc.VarType.FP32
out_var = block.create_var(
name=unique_name.generate(
".".join(['masra_init', var.name, 'tmp'])),
shape=var.shape,
dtype=out_dtype,
type=VarDesc.VarType.LOD_TENSOR,
persistable=False)
else:
out_dtype = var.dtype
out_var = var
if self._uniform:
limit = np.sqrt(1.0 / float(fan_in))
op = block.append_op(
type="uniform_random",
inputs={},
outputs={"Out": out_var},
attrs={
"shape": out_var.shape,
"dtype": int(out_dtype),
"min": -limit,
"max": limit,
"seed": self._seed
},
stop_gradient=True)
else:
std = np.sqrt(2.0 / float(fan_in))
op = block.append_op(
type="gaussian_random",
outputs={"Out": out_var},
attrs={
"shape": out_var.shape,
"dtype": int(out_dtype),
"mean": 0.0,
"std": std,
"seed": self._seed
},
stop_gradient=True)
if var.dtype == VarDesc.VarType.FP16 or (
var.dtype == VarDesc.VarType.BF16 and not self._uniform):
block.append_op(
type="cast",
inputs={"X": out_var},
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
if not framework.in_dygraph_mode():
var.op = op
return op
class DefaultInitializerContext(object): class DefaultInitializerContext(object):
""" """

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