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Merge pull request #46 from iclementine/reborn 

Adding access  control
This commit is contained in:
Feiyu Chan 2020-12-10 11:34:13 +08:00 committed by GitHub
parent a6806389f9 a1b827460c
commit 07ce84c680
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29 changed files with 526 additions and 722 deletions
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4
parakeet/__init__.py
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@ -12,6 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
__version__ = "0.0.0"
__version__ = "0.2.0"
from parakeet import data, frontend, models, modules
from parakeet import audio, data, datasets, frontend, models, modules, training, utils

4
parakeet/audio/audio.py
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@ -16,6 +16,8 @@ import librosa
import soundfile as sf
import numpy as np
__all__ = ["AudioProcessor"]
class AudioProcessor(object):
def __init__(self,
sample_rate:int,
@ -26,7 +28,7 @@ class AudioProcessor(object):
f_min:int=0,
f_max:int=None,
window="hann",
center="True",
center=True,
pad_mode="reflect"):
# read & write
self.sample_rate = sample_rate

3
parakeet/audio/spec_normalizer.py
View File

@ -13,6 +13,9 @@ https://github.com/mozilla/TTS/issues/377
"""
import numpy as np
__all__ = ["NormalizerBase", "LogMagnitude", "UnitMagnitude"]
class NormalizerBase(object):
def transform(self, spec):
raise NotImplementedError("transform must be implemented")

1
parakeet/data/__init__.py
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@ -13,5 +13,4 @@
# limitations under the License.
from .dataset import *
from .sampler import *
from .batch import *

4
parakeet/data/batch.py
View File

@ -17,6 +17,10 @@ Batch functions for text sequences, audio and spectrograms are provided.
"""
import numpy as np
__all__ = [
"batch_text_id", "batch_wav", "batch_spec",
"TextIDBatcher", "WavBatcher", "SpecBatcher",
]
class TextIDBatcher(object):
"""A wrapper class for `batch_text_id`."""

5
parakeet/data/dataset.py
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@ -16,6 +16,11 @@ import six
import paddle
from paddle.io import Dataset
__all__ = [
"split", "TransformDataset", "CacheDataset", "TupleDataset",
"DictDataset", "SliceDataset", "SubsetDataset", "FilterDataset",
"ChainDataset",
]
def split(dataset, first_size):
"""A utility function to split a dataset into two datasets."""

200
parakeet/data/sampler.py
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@ -1,200 +0,0 @@
# Copyright (c) 2020 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.
"""
At most cases, we have non-stream dataset, which means we can random access it with __getitem__, and we can get the length of the dataset with __len__.
This suffices for a sampler. We implemente sampler as iterable of valid indices. By valid, we mean 0 <= index < N, where N is the length of the dataset. We then collect several indices within a batch and use them to collect examples from the dataset with __getitem__. Then transform these examples into a batch.
So the sampler is only responsible for generating valid indices.
"""
import numpy as np
import random
import paddle
from paddle.io import Sampler
class PartialyRandomizedSimilarTimeLengthSampler(Sampler):
"""Partially randmoized sampler, implemented as a example sampler
1. Sort by lengths
2. Pick a small patch and randomize it
3. Permutate mini-batchs
"""
def __init__(self,
lengths,
batch_size=4,
batch_group_size=None,
permutate=True):
"""[summary]
Args:
lengths (List[int]): The length of the examples of the dataset. This is the key to be considered as 'time length'.
batch_size (int, optional): batch size. Defaults to 4.
batch_group_size (int, optional): the size of a small batch. Random shuffling is applied within such patches. If `batch_group_size` is not provided, it is set to min(batch_size * 32, len(self.lengths)). Batch_group_size should be perfectly divided by batch_size. Defaults to None.
permutate (bool, optional): permutate batches. Defaults to True.
"""
_lengths = np.array(
lengths,
dtype=np.int64) # maybe better implement length as a sort key
self.lengths = np.sort(_lengths)
self.sorted_indices = np.argsort(_lengths)
self.batch_size = batch_size
if batch_group_size is None:
batch_group_size = min(batch_size * 32, len(self.lengths))
if batch_group_size % batch_size != 0:
batch_group_size -= batch_group_size % batch_size
self.batch_group_size = batch_group_size
assert batch_group_size % batch_size == 0
self.permutate = permutate
def __iter__(self):
indices = np.copy(self.sorted_indices)
batch_group_size = self.batch_group_size
s, e = 0, 0
for i in range(len(indices) // batch_group_size):
s = i * batch_group_size
e = s + batch_group_size
random.shuffle(indices[s:e]) # inplace
# Permutate batches
if self.permutate:
perm = np.arange(len(indices[:e]) // self.batch_size)
random.shuffle(perm)
indices[:e] = indices[:e].reshape(
-1, self.batch_size)[perm, :].reshape(-1)
# Handle last elements
s += batch_group_size
#print(indices)
if s < len(indices):
random.shuffle(indices[s:])
return iter(indices)
def __len__(self):
return len(self.sorted_indices)
class BucketSampler(Sampler):
def __init__(self,
lengths,
batch_size=4,
batch_group_size=None,
permutate=True,
num_trainers=1,
rank=0):
# maybe better implement length as a sort key
_lengths = np.array(lengths, dtype=np.int64)
self.lengths = np.sort(_lengths)
self.sorted_indices = np.argsort(_lengths)
self.num_trainers = num_trainers
self.rank = rank
self.dataset_size = len(_lengths)
self.num_samples = int(np.ceil(self.dataset_size / num_trainers))
self.total_size = self.num_samples * num_trainers
assert self.total_size >= self.dataset_size
self.batch_size = batch_size
total_batch_size = num_trainers * batch_size
self.total_batch_size = total_batch_size
if batch_group_size is None:
batch_group_size = min(total_batch_size * 32, len(self.lengths))
if batch_group_size % total_batch_size != 0:
batch_group_size -= batch_group_size % total_batch_size
self.batch_group_size = batch_group_size
assert batch_group_size % total_batch_size == 0
self.permutate = permutate
def __iter__(self):
indices = self.sorted_indices
# Append extra samples to make it evenly distributed on all trainers.
num_extras = self.total_size - self.dataset_size
extra_indices = np.random.choice(
indices, size=(num_extras, ), replace=False)
indices = np.concatenate((indices, extra_indices))
assert len(indices) == self.total_size
batch_group_size = self.batch_group_size
s, e = 0, 0
for i in range(len(indices) // batch_group_size):
s = i * batch_group_size
e = s + batch_group_size
random.shuffle(indices[s:e]) # inplace
# Permutate batches
total_batch_size = self.total_batch_size
if self.permutate:
perm = np.arange(len(indices[:e]) // total_batch_size)
random.shuffle(perm)
indices[:e] = indices[:e].reshape(
-1, total_batch_size)[perm, :].reshape(-1)
# Handle last elements
s += batch_group_size
#print(indices)
if s < len(indices):
random.shuffle(indices[s:])
# Subset samples for each trainer.
indices = indices[self.rank:self.total_size:self.num_trainers]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return len(self.sorted_indices)
class WeightedRandomSampler(Sampler):
"""Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights).
Args:
weights (List[float]): a sequence of weights, not necessary summing up to 1.
num_samples (int): number of samples to draw.
replacement (bool): whether samples are drawn with replacement. When replacement is False, num_samples should not be larger than len(weights).
Example:
>>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True))
[0, 0, 0, 1, 0]
>>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False))
[0, 1, 4, 3, 2]
"""
def __init__(self, weights, num_samples, replacement):
if not isinstance(num_samples, int) or num_samples <= 0:
raise ValueError("num_samples should be a positive integer "
"value, but got num_samples={}".format(
num_samples))
self.weights = np.array(weights, dtype=np.float64)
self.num_samples = num_samples
self.replacement = replacement
if replacement is False and num_samples > len(weights):
raise ValueError(
"when replacement is False, num_samples should not be"
"larger that length of weight.")
def __iter__(self):
return iter(
np.random.choice(
len(self.weights),
size=(self.num_samples, ),
replace=self.replacement,
p=self.weights).tolist())
def __len__(self):
return self.num_samples

2
parakeet/datasets/common.py
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@ -2,6 +2,8 @@ from paddle.io import Dataset
import os
import librosa
__all__ = ["AudioFolderDataset"]
class AudioFolderDataset(Dataset):
def __init__(self, path, sample_rate, extension="wav"):
self.root = os.path.expanduser(path)

2
parakeet/datasets/ljspeech.py
View File

@ -1,6 +1,8 @@
from paddle.io import Dataset
from pathlib import Path
__all__ = ["LJSpeechMetaData"]
class LJSpeechMetaData(Dataset):
def __init__(self, root):
self.root = Path(root).expanduser()

3
parakeet/frontend/phonectic.py
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@ -6,6 +6,9 @@ from parakeet.frontend import Vocab
from opencc import OpenCC
from parakeet.frontend.punctuation import get_punctuations
__all__ = ["Phonetics", "English", "Chinese"]
class Phonetics(ABC):
@abstractmethod
def __call__(self, sentence):

4
parakeet/frontend/vocab.py
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@ -2,6 +2,10 @@ from typing import Dict, Iterable, List
from ruamel import yaml
from collections import OrderedDict
__all__ = ["Vocab"]
class Vocab(object):
def __init__(self, symbols: Iterable[str],
padding_symbol="<pad>",

6
parakeet/models/__init__.py
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@ -12,10 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from parakeet.models.clarinet import *
#from parakeet.models.clarinet import *
from parakeet.models.waveflow import *
from parakeet.models.wavenet import *
#from parakeet.models.wavenet import *
from parakeet.models.transformer_tts import *
from parakeet.models.deepvoice3 import *
#from parakeet.models.deepvoice3 import *
# from parakeet.models.fastspeech import *

37
parakeet/models/transformer_tts.py
View File

@ -273,12 +273,14 @@ class MLPPreNet(nn.Layer):
super(MLPPreNet, self).__init__()
self.lin1 = nn.Linear(d_input, d_hidden)
self.lin2 = nn.Linear(d_hidden, d_hidden)
self.lin3 = nn.Linear(d_hidden, d_hidden)
self.dropout = dropout
def forward(self, x, dropout):
l1 = F.dropout(F.relu(self.lin1(x)), self.dropout, training=self.training)
l2 = F.dropout(F.relu(self.lin2(l1)), self.dropout, training=self.training)
return l2
l3 = self.lin3(l2)
return l3
# NOTE: not used in
class CNNPreNet(nn.Layer):
@ -317,6 +319,7 @@ class CNNPostNet(nn.Layer):
Conv1dBatchNorm(c_in, c_out, kernel_size,
weight_attr=I.XavierUniform(),
padding=padding))
self.last_bn = nn.BatchNorm1D(d_output)
# for a layer that ends with a normalization layer that is targeted to
# output a non zero-central output, it may take a long time to
# train the scale and bias
@ -328,7 +331,7 @@ class CNNPostNet(nn.Layer):
x = layer(x)
if i != (len(self.convs) - 1):
x = F.tanh(x)
x = x_in + x
x = self.last_bn(x_in + x)
return x
@ -491,7 +494,7 @@ class TransformerTTS(nn.Layer):
decoder_output = paddle.concat([decoder_output, mel_output[:, -self.r:, :]], 1)
# stop condition: (if any ouput frame of the output multiframes hits the stop condition)
if paddle.any(paddle.argmax(stop_logits[0, :, :], axis=-1) == self.stop_prob_index):
if paddle.any(paddle.argmax(stop_logits[0, -self.r:, :], axis=-1) == self.stop_prob_index):
if verbose:
print("Hits stop condition.")
break
@ -526,6 +529,34 @@ class TransformerTTSLoss(nn.Layer):
stop_loss = L.masked_softmax_with_cross_entropy(
stop_logits, stop_probs.unsqueeze(-1), mask2.unsqueeze(-1))
loss = mel_loss1 + mel_loss2 + stop_loss
losses = dict(
loss=loss, # total loss
mel_loss1=mel_loss1, # ouput mel loss
mel_loss2=mel_loss2, # intermediate mel loss
stop_loss=stop_loss # stop prob loss
)
return losses
class AdaptiveTransformerTTSLoss(nn.Layer):
def __init__(self):
super(AdaptiveTransformerTTSLoss, self).__init__()
def forward(self, mel_output, mel_intermediate, mel_target, stop_logits, stop_probs):
mask = masking.feature_mask(mel_target, axis=-1, dtype=mel_target.dtype)
mask1 = paddle.unsqueeze(mask, -1)
mel_loss1 = L.masked_l1_loss(mel_output, mel_target, mask1)
mel_loss2 = L.masked_l1_loss(mel_intermediate, mel_target, mask1)
batch_size, mel_len = mask.shape
valid_lengths = mask.sum(-1).astype("int64")
last_position = F.one_hot(valid_lengths - 1, num_classes=mel_len)
stop_loss_scale = valid_lengths.sum() / batch_size - 1
mask2 = mask + last_position.scale(stop_loss_scale - 1).astype(mask.dtype)
stop_loss = L.masked_softmax_with_cross_entropy(
stop_logits, stop_probs.unsqueeze(-1), mask2.unsqueeze(-1))
loss = mel_loss1 + mel_loss2 + stop_loss
losses = dict(
loss=loss, # total loss

13
parakeet/models/waveflow.py
View File

@ -141,6 +141,15 @@ class ResidualBlock(nn.Layer):
raise ValueError("Only use start sequence at evaluation mode.")
self._conv_buffer = None
# NOTE: call self.conv's weight norm hook expliccitly since
# its weight will be visited directly in `add_input` without
# calling its `__call__` method. If we do not trigger the weight
# norm hook, the weight may be outdated. e.g. after loading from
# a saved checkpoint
# see also: https://github.com/pytorch/pytorch/issues/47588
for hook in self.conv._forward_pre_hooks.values():
hook(self.conv, None)
def add_input(self, x_row, condition_row):
"""Compute the output for a row and update the buffer.
@ -158,10 +167,6 @@ class ResidualBlock(nn.Layer):
self._update_buffer(x_row)
rw = self.rw
# call self.conv's weight norm hook expliccitly since its __call__
# method is not called here
for hook in self.conv._forward_pre_hooks.values():
hook(self.conv, self._conv_buffer)
x_row = F.conv2d(
self._conv_buffer,
self.conv.weight,

348
parakeet/models/wavenet.py
View File

@ -12,9 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import division
import math
import time
from typing import Union, Sequence
from tqdm import trange
import numpy as np
@ -25,48 +25,19 @@ import paddle.fluid.initializer as I
import paddle.fluid.layers.distributions as D
from parakeet.modules.conv import Conv1dCell
__all__ = ["ConditionalWavenet"]
def quantize(values, n_bands):
"""Linearlly quantize a float Tensor in [-1, 1) to an interger Tensor in [0, n_bands).
Args:
values (Variable): dtype: flaot32 or float64. the floating point value.
n_bands (int): the number of bands. The output integer Tensor's value is in the range [0, n_bans).
Returns:
Variable: the quantized tensor, dtype: int64.
"""
quantized = paddle.cast((values + 1.0) / 2.0 * n_bands, "int64")
return quantized
def dequantize(quantized, n_bands, dtype=None):
"""Linearlly dequantize an integer Tensor into a float Tensor in the range [-1, 1).
Args:
quantized (Variable): dtype: int64. The quantized value in the range [0, n_bands).
n_bands (int): number of bands. The input integer Tensor's value is in the range [0, n_bans).
Returns:
Variable: the dequantized tensor, dtype is specified by dtype.
"""
dtype = dtype or paddle.get_default_dtype()
value = (paddle.cast(quantized, dtype) + 0.5) * (2.0 / n_bands) - 1.0
return value
from parakeet.modules.audio import quantize, dequantize, STFT
def crop(x, audio_start, audio_length):
"""Crop the upsampled condition to match audio_length. The upsampled condition has the same time steps as the whole audio does. But since audios are sliced to 0.5 seconds randomly while conditions are not, upsampled conditions should also be sliced to extaclt match the time steps of the audio slice.
Args:
x (Variable): shape(B, C, T), dtype float32, the upsample condition.
audio_start (Variable): shape(B, ), dtype: int64, the index the starting point.
x (Tensor): shape(B, C, T), dtype float32, the upsample condition.
audio_start (Tensor): shape(B, ), dtype: int64, the index the starting point.
audio_length (int): the length of the audio (number of samples it contaions).
Returns:
Variable: shape(B, C, audio_length), cropped condition.
Tensor: shape(B, C, audio_length), cropped condition.
"""
# crop audio
slices = [] # for each example
@ -81,9 +52,52 @@ def crop(x, audio_start, audio_length):
return out
class UpsampleNet(nn.LayerList):
def __init__(self, upscale_factors=[16, 16]):
"""UpsamplingNet.
It consists of several layers of Conv2DTranspose. Each Conv2DTranspose layer upsamples the time dimension by its `stride` times. And each Conv2DTranspose's filter_size at frequency dimension is 3.
Args:
upscale_factors (list[int], optional): time upsampling factors for each Conv2DTranspose Layer. The `UpsampleNet` contains len(upscale_factor) Conv2DTranspose Layers. Each upscale_factor is used as the `stride` for the corresponding Conv2DTranspose. Defaults to [16, 16].
Note:
np.prod(upscale_factors) should equals the `hop_length` of the stft transformation used to extract spectrogram features from audios. For example, 16 * 16 = 256, then the spectram extracted using a stft transformation whose `hop_length` is 256. See `librosa.stft` for more details.
"""
super(UpsampleNet, self).__init__()
self.upscale_factors = list(upscale_factors)
self.upscale_factor = 1
for item in upscale_factors:
self.upscale_factor *= item
for factor in self.upscale_factors:
self.append(
nn.utils.weight_norm(
nn.Conv2DTranspose(1, 1,
kernel_size=(3, 2 * factor),
stride=(1, factor),
padding=(1, factor // 2))))
def forward(self, x):
"""Compute the upsampled condition.
Args:
x (Tensor): shape(B, F, T), dtype float32, the condition (mel spectrogram here.) (F means the frequency bands). In the internal Conv2DTransposes, the frequency dimension is treated as `height` dimension instead of `in_channels`.
Returns:
Tensor: shape(B, F, T * upscale_factor), dtype float32, the upsampled condition.
"""
x = paddle.unsqueeze(x, 1)
for sublayer in self:
x = F.leaky_relu(sublayer(x), 0.4)
x = paddle.squeeze(x, 1)
return x
class ResidualBlock(nn.Layer):
def __init__(self, residual_channels, condition_dim, filter_size,
dilation):
def __init__(self,
residual_channels: int,
condition_dim: int,
filter_size: Union[int, Sequence[int]],
dilation: int):
"""A Residual block in wavenet. It does not have parametric residual or skip connection. It consists of a Conv1DCell and an Conv1D(filter_size = 1) to integrate the condition.
Args:
@ -108,7 +122,7 @@ class ResidualBlock(nn.Layer):
std = math.sqrt(1 / condition_dim)
condition_proj = Conv1dCell(condition_dim, dilated_channels, (1,),
weight_attr=I.Normal(scale=std))
weight_attr=I.Normal(scale=std))
self.condition_proj = nn.utils.weight_norm(condition_proj)
self.filter_size = filter_size
@ -121,20 +135,13 @@ class ResidualBlock(nn.Layer):
"""Conv1D gated-tanh Block.
Args:
x (Tensor): shape(B, C_res, T), the input. (B stands for batch_size,
C_res stands for residual channels, T stands for time steps.)
dtype float32.
condition (Tensor, optional): shape(B, C_cond, T), the condition,
it has been upsampled in time steps, so it has the same time
steps as the input does.(C_cond stands for the condition's channels).
Defaults to None.
x (Tensor): shape(B, C_res, T), the input. (B stands for batch_size, C_res stands for residual channels, T stands for time steps.) dtype float32.
condition (Tensor, optional): shape(B, C_cond, T), the condition, it has been upsampled in time steps, so it has the same time steps as the input does.(C_cond stands for the condition's channels). Defaults to None.
Returns:
(residual, skip_connection)
residual (Tensor): shape(B, C_res, T), the residual, which is used
as the input to the next layer of ResidualBlock.
skip_connection (Tensor): shape(B, C_res, T), the skip connection.
This output is accumulated with that of other ResidualBlocks.
residual (Tensor): shape(B, C_res, T), the residual, which is used as the input to the next layer of ResidualBlock.
skip_connection (Tensor): shape(B, C_res, T), the skip connection. This output is accumulated with that of other ResidualBlocks.
"""
h = x
@ -155,30 +162,22 @@ class ResidualBlock(nn.Layer):
return residual, skip_connection
def start_sequence(self):
"""
Prepare the ResidualBlock to generate a new sequence. This method
should be called before starting calling `add_input` multiple times.
"""Prepare the ResidualBlock to generate a new sequence. This method should be called before starting calling `add_input` multiple times.
"""
self.conv.start_sequence()
self.condition_proj.start_sequence()
def add_input(self, x, condition=None):
"""
Add a step input. This method works similarily with `forward` but
in a `step-in-step-out` fashion.
"""Add a step input. This method works similarily with `forward` but in a `step-in-step-out` fashion.
Args:
x (Variable): shape(B, C_res), input for a step, dtype float32.
condition (Variable, optional): shape(B, C_cond). condition for a
step, dtype float32. Defaults to None.
x (Tensor): shape(B, C_res), input for a step, dtype float32.
condition (Tensor, optional): shape(B, C_cond). condition for a step, dtype float32. Defaults to None.
Returns:
(residual, skip_connection)
residual (Variable): shape(B, C_res), the residual for a step,
which is used as the input to the next layer of ResidualBlock.
skip_connection (Variable): shape(B, C_res), the skip connection
for a step. This output is accumulated with that of other
ResidualBlocks.
residual (Tensor): shape(B, C_res), the residual for a step, which is used as the input to the next layer of ResidualBlock.
skip_connection (Tensor): shape(B, C_res), the skip connection for a step. This output is accumulated with that of other ResidualBlocks.
"""
h = x
@ -200,22 +199,24 @@ class ResidualBlock(nn.Layer):
class ResidualNet(nn.LayerList):
def __init__(self, n_loop, n_layer, residual_channels, condition_dim,
filter_size):
"""The residual network in wavenet. It consists of `n_layer` stacks,
each of which consists of `n_loop` ResidualBlocks.
def __init__(self,
n_stack: int,
n_loop: int,
residual_channels: int,
condition_dim: int,
filter_size: int):
"""The residual network in wavenet. It consists of `n_layer` stacks, each of which consists of `n_loop` ResidualBlocks.
Args:
n_stack (int): number of stacks in the `ResidualNet`.
n_loop (int): number of ResidualBlocks in a stack.
n_layer (int): number of stacks in the `ResidualNet`.
residual_channels (int): channels of each `ResidualBlock`'s input.
condition_dim (int): channels of the condition.
filter_size (int): filter size of the internal Conv1DCell of each
`ResidualBlock`.
filter_size (int): filter size of the internal Conv1DCell of each `ResidualBlock`.
"""
super(ResidualNet, self).__init__()
# double the dilation at each layer in a loop(n_loop layers)
dilations = [2**i for i in range(n_loop)] * n_layer
# double the dilation at each layer in a stack
dilations = [2**i for i in range(n_loop)] * n_stack
self.context_size = 1 + sum(dilations)
for dilation in dilations:
self.append(ResidualBlock(residual_channels, condition_dim, filter_size, dilation))
@ -223,13 +224,8 @@ class ResidualNet(nn.LayerList):
def forward(self, x, condition=None):
"""
Args:
x (Tensor): shape(B, C_res, T), dtype float32, the input.
(B stands for batch_size, C_res stands for residual channels,
T stands for time steps.)
condition (Tensor, optional): shape(B, C_cond, T), dtype float32,
the condition, it has been upsampled in time steps, so it has
the same time steps as the input does.(C_cond stands for the
condition's channels) Defaults to None.
x (Tensor): shape(B, C_res, T), dtype float32, the input. (B stands for batch_size, C_res stands for residual channels, T stands for time steps.)
condition (Tensor, optional): shape(B, C_cond, T), dtype float32, the condition, it has been upsampled in time steps, so it has the same time steps as the input does.(C_cond stands for the condition's channels) Defaults to None.
Returns:
skip_connection (Tensor): shape(B, C_res, T), dtype float32, the output.
@ -244,24 +240,20 @@ class ResidualNet(nn.LayerList):
return skip_connections
def start_sequence(self):
"""Prepare the ResidualNet to generate a new sequence. This method
should be called before starting calling `add_input` multiple times.
"""Prepare the ResidualNet to generate a new sequence. This method should be called before starting calling `add_input` multiple times.
"""
for block in self:
block.start_sequence()
def add_input(self, x, condition=None):
"""Add a step input. This method works similarily with `forward` but
in a `step-in-step-out` fashion.
"""Add a step input. This method works similarily with `forward` but in a `step-in-step-out` fashion.
Args:
x (Tensor): shape(B, C_res), dtype float32, input for a step.
condition (Tensor, optional): shape(B, C_cond), dtype float32,
condition for a step. Defaults to None.
condition (Tensor, optional): shape(B, C_cond), dtype float32, condition for a step. Defaults to None.
Returns:
skip_connection (Tensor): shape(B, C_res), dtype float32, the
output for a step.
skip_connection (Tensor): shape(B, C_res), dtype float32, the output for a step.
"""
for i, func in enumerate(self):
@ -275,31 +267,19 @@ class ResidualNet(nn.LayerList):
class WaveNet(nn.Layer):
def __init__(self, n_loop, n_layer, residual_channels, output_dim,
def __init__(self, n_stack, n_loop, residual_channels, output_dim,
condition_dim, filter_size, loss_type, log_scale_min):
"""Wavenet that transform upsampled mel spectrogram into waveform.
Args:
n_stack (int): n_stack for the internal ResidualNet.
n_loop (int): n_loop for the internal ResidualNet.
n_layer (int): n_loop for the internal ResidualNet.
residual_channels (int): the channel of the input.
output_dim (int): the channel of the output distribution.
condition_dim (int): the channel of the condition.
filter_size (int): the filter size of the internal ResidualNet.
loss_type (str): loss type of the wavenet. Possible values are
'softmax' and 'mog'.
If `loss_type` is 'softmax', the output is the logits of the
catrgotical(multinomial) distribution, `output_dim` means the
number of classes of the categorical distribution.
If `loss_type` is mog(mixture of gaussians), the output is the
parameters of a mixture of gaussians, which consists of weight
(in the form of logit) of each gaussian distribution and its
mean and log standard deviaton. So when `loss_type` is 'mog',
`output_dim` should be perfectly divided by 3.
log_scale_min (int): the minimum value of log standard deviation
of the output gaussian distributions. Note that this value is
only used for computing loss if `loss_type` is 'mog', values
less than `log_scale_min` is clipped when computing loss.
loss_type (str): loss type of the wavenet. Possible values are 'softmax' and 'mog'. If `loss_type` is 'softmax', the output is the logits of the catrgotical(multinomial) distribution, `output_dim` means the number of classes of the categorical distribution. If `loss_type` is mog(mixture of gaussians), the output is the parameters of a mixture of gaussians, which consists of weight(in the form of logit) of each gaussian distribution and its mean and log standard deviaton. So when `loss_type` is 'mog', `output_dim` should be perfectly divided by 3.
log_scale_min (int): the minimum value of log standard deviation of the output gaussian distributions. Note that this value is only used for computing loss if `loss_type` is 'mog', values less than `log_scale_min` is clipped when computing loss.
"""
super(WaveNet, self).__init__()
if loss_type not in ["softmax", "mog"]:
@ -312,7 +292,7 @@ class WaveNet(nn.Layer):
"with Mixture of Gaussians(mog) output, the output dim must be divisible by 3, but get {}".format(output_dim))
self.embed = nn.utils.weight_norm(nn.Linear(1, residual_channels), dim=-1)
self.resnet = ResidualNet(n_loop, n_layer, residual_channels,
self.resnet = ResidualNet(n_stack, n_loop, residual_channels,
condition_dim, filter_size)
self.context_size = self.resnet.context_size
@ -334,12 +314,10 @@ class WaveNet(nn.Layer):
Args:
x (Tensor): shape(B, T), dtype float32, the input waveform.
condition (Tensor, optional): shape(B, C_cond, T), dtype float32,
the upsampled condition. Defaults to None.
condition (Tensor, optional): shape(B, C_cond, T), dtype float32, the upsampled condition. Defaults to None.
Returns:
Tensor: shape(B, T, C_output), dtype float32, the parameter of
the output distributions.
Tensor: shape(B, T, C_output), dtype float32, the parameter of the output distributions.
"""
# Causal Conv
@ -362,24 +340,19 @@ class WaveNet(nn.Layer):
return y
def start_sequence(self):
"""Prepare the WaveNet to generate a new sequence. This method should
be called before starting calling `add_input` multiple times.
"""Prepare the WaveNet to generate a new sequence. This method should be called before starting calling `add_input` multiple times.
"""
self.resnet.start_sequence()
def add_input(self, x, condition=None):
"""compute the output distribution (represented by its parameters) for
a step. It works similarily with the `forward` method but in a
`step-in-step-out` fashion.
"""compute the output distribution (represented by its parameters) for a step. It works similarily with the `forward` method but in a `step-in-step-out` fashion.
Args:
x (Tensor): shape(B,), dtype float32, a step of the input waveform.
condition (Tensor, optional): shape(B, C_cond, ), dtype float32, a
step of the upsampled condition. Defaults to None.
condition (Tensor, optional): shape(B, C_cond, ), dtype float32, a step of the upsampled condition. Defaults to None.
Returns:
Tensor: shape(B, C_output), dtype float32, the parameter of the
output distributions.
Tensor: shape(B, C_output), dtype float32, the parameter of the output distributions.
"""
# Causal Conv
if self.loss_type == "softmax":
@ -402,12 +375,8 @@ class WaveNet(nn.Layer):
"""compute the loss where output distribution is a categorial distribution.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the logits of the
output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that
the target's corresponding time index is one step ahead of the
output distribution. And output distribution whose input contains
padding is neglected in loss computation.
y (Tensor): shape(B, T, C_output), dtype float32, the logits of the output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that the target's corresponding time index is one step ahead of the output distribution. And output distribution whose input contains padding is neglected in loss computation.
Returns:
Tensor: shape(1, ), dtype float32, the loss.
@ -420,15 +389,14 @@ class WaveNet(nn.Layer):
label = paddle.unsqueeze(quantized, -1)
loss = F.softmax_with_cross_entropy(y, label)
reduced_loss = paddle.reduce_mean(loss)
reduced_loss = paddle.mean(loss)
return reduced_loss
def sample_from_softmax(self, y):
"""Sample from the output distribution where the output distribution is
a categorical distriobution.
"""Sample from the output distribution where the output distribution is a categorical distriobution.
Args:
y (Tensor): shape(B, T, C_output), the logits of the output distribution.
y (Tensor): shape(B, T, C_output), the logits of the output distribution
Returns:
Tensor: shape(B, T), waveform sampled from the output distribution.
@ -446,16 +414,8 @@ class WaveNet(nn.Layer):
"""compute the loss where output distribution is a mixture of Gaussians.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution. It is the concatenation of 3 parts,
the logits of every distribution, the mean of each distribution
and the log standard deviation of each distribution. Each part's
shape is (B, T, n_mixture), where `n_mixture` means the number
of Gaussians in the mixture.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that
the target's corresponding time index is one step ahead of the
output distribution. And output distribution whose input contains
padding is neglected in loss computation.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution. It is the concatenation of 3 parts, the logits of every distribution, the mean of each distribution and the log standard deviation of each distribution. Each part's shape is (B, T, n_mixture), where `n_mixture` means the number of Gaussians in the mixture.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that the target's corresponding time index is one step ahead of the output distribution. And output distribution whose input contains padding is neglected in loss computation.
Returns:
Tensor: shape(1, ), dtype float32, the loss.
@ -483,22 +443,16 @@ class WaveNet(nn.Layer):
pdf_x = p_mixture * pdf_x
# pdf_x: [bs, len]
pdf_x = paddle.reduce_sum(pdf_x, -1)
pdf_x = paddle.sum(pdf_x, -1)
per_sample_loss = -paddle.log(pdf_x + 1e-9)
loss = paddle.reduce_mean(per_sample_loss)
loss = paddle.mean(per_sample_loss)
return loss
def sample_from_mog(self, y):
"""Sample from the output distribution where the output distribution is
a mixture of Gaussians.
"""Sample from the output distribution where the output distribution is a mixture of Gaussians.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution. It is the concatenation of 3 parts, the
logits of every distribution, the mean of each distribution and the
log standard deviation of each distribution. Each part's shape is
(B, T, n_mixture), where `n_mixture` means the number of Gaussians
in the mixture.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution. It is the concatenation of 3 parts, the logits of every distribution, the mean of each distribution and the log standard deviation of each distribution. Each part's shape is (B, T, n_mixture), where `n_mixture` means the number of Gaussians in the mixture.
Returns:
Tensor: shape(B, T), waveform sampled from the output distribution.
@ -529,8 +483,7 @@ class WaveNet(nn.Layer):
def sample(self, y):
"""Sample from the output distribution.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution.
Returns:
Tensor: shape(B, T), waveform sampled from the output distribution.
@ -544,12 +497,8 @@ class WaveNet(nn.Layer):
"""compute the loss where output distribution is a mixture of Gaussians.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that
the target's corresponding time index is one step ahead of the
output distribution. And output distribution whose input contains
padding is neglected in loss computation.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that the target's corresponding time index is one step ahead of the output distribution. And output distribution whose input contains padding is neglected in loss computation.
Returns:
Tensor: shape(1, ), dtype float32, the loss.
@ -560,64 +509,9 @@ class WaveNet(nn.Layer):
return self.compute_mog_loss(y, t)
class UpsampleNet(nn.LayerList):
def __init__(self, upscale_factors=[16, 16]):
"""UpsamplingNet.
It consists of several layers of Conv2DTranspose. Each Conv2DTranspose
layer upsamples the time dimension by its `stride` times. And each
Conv2DTranspose's filter_size at frequency dimension is 3.
Args:
upscale_factors (list[int], optional): time upsampling factors for
each Conv2DTranspose Layer. The `UpsampleNet` contains
len(upscale_factor) Conv2DTranspose Layers. Each upscale_factor
is used as the `stride` for the corresponding Conv2DTranspose.
Defaults to [16, 16].
Note:
np.prod(upscale_factors) should equals the `hop_length` of the stft
transformation used to extract spectrogram features from audios.
For example, 16 * 16 = 256, then the spectram extracted using a
stft transformation whose `hop_length` is 256. See `librosa.stft`
for more details.
"""
super(UpsampleNet, self).__init__()
self.upscale_factors = list(upscale_factors)
self.upscale_factor = 1
for item in upscale_factors:
self.upscale_factor *= item
for factor in self.upscale_factors:
self.append(
nn.utils.weight_norm(
nn.ConvTranspose2d(1, 1,
kernel_size=(3, 2 * factor),
stride=(1, factor),
padding=(1, factor // 2))))
def forward(self, x):
"""Compute the upsampled condition.
Args:
x (Tensor): shape(B, F, T), dtype float32, the condition
(mel spectrogram here.) (F means the frequency bands). In the
internal Conv2DTransposes, the frequency dimension is treated
as `height` dimension instead of `in_channels`.
Returns:
Tensor: shape(B, F, T * upscale_factor), dtype float32, the
upsampled condition.
"""
x = paddle.unsqueeze(x, 1)
for sublayer in self:
x = F.leaky_relu(sublayer(x), 0.4)
x = paddle.squeeze(x, 1)
return x
class ConditionalWavenet(nn.Layer):
def __init__(self, encoder, decoder):
"""Conditional Wavenet, which contains an UpsampleNet as the encoder
and a WaveNet as the decoder. It is an autoregressive model.
"""Conditional Wavenet, which contains an UpsampleNet as the encoder and a WaveNet as the decoder. It is an autoregressive model.
Args:
encoder (UpsampleNet): the UpsampleNet as the encoder.
@ -628,20 +522,15 @@ class ConditionalWavenet(nn.Layer):
self.decoder = decoder
def forward(self, audio, mel, audio_start):
"""Compute the output distribution given the mel spectrogram and the
input(for teacher force training).
"""Compute the output distribution given the mel spectrogram and the input(for teacher force training).
Args:
audio (Tensor): shape(B, T_audio), dtype float32, ground truth
waveform, used for teacher force training.
mel (Tensor): shape(B, F, T_mel), dtype float32, mel spectrogram.
Note that it is the spectrogram for the whole utterance.
audio_start (Tensor): shape(B, ), dtype: int, audio slices' start
positions for each utterance.
audio (Tensor): shape(B, T_audio), dtype float32, ground truth waveform, used for teacher force training.
mel (Tensor): shape(B, F, T_mel), dtype float32, mel spectrogram. Note that it is the spectrogram for the whole utterance.
audio_start (Tensor): shape(B, ), dtype: int, audio slices' start positions for each utterance.
Returns:
Tensor: shape(B, T_audio - 1, C_putput), parameters for the output
distribution.(C_output is the `output_dim` of the decoder.)
Tensor: shape(B, T_audio - 1, C_putput), parameters for the output distribution.(C_output is the `output_dim` of the decoder.)
"""
audio_length = audio.shape[1] # audio clip's length
condition = self.encoder(mel)
@ -655,12 +544,10 @@ class ConditionalWavenet(nn.Layer):
return y
def loss(self, y, t):
"""compute loss with respect to the output distribution and the targer
audio.
"""compute loss with respect to the output distribution and the targer audio.
Args:
y (Tensor): shape(B, T - 1, C_output), dtype float32, parameters of
the output distribution.
y (Tensor): shape(B, T - 1, C_output), dtype float32, parameters of the output distribution.
t (Tensor): shape(B, T), dtype float32, target waveform.
Returns:
@ -674,12 +561,10 @@ class ConditionalWavenet(nn.Layer):
"""Sample from the output distribution.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, parameters of the
output distribution.
y (Tensor): shape(B, T, C_output), dtype float32, parameters of the output distribution.
Returns:
Tensor: shape(B, T), dtype float32, sampled waveform from the output
distribution.
Tensor: shape(B, T), dtype float32, sampled waveform from the output distribution.
"""
samples = self.decoder.sample(y)
return samples
@ -692,9 +577,7 @@ class ConditionalWavenet(nn.Layer):
mel (Tensor): shape(B, F, T), condition(mel spectrogram here).
Returns:
Tensor: shape(B, T * upsacle_factor), synthesized waveform.
(`upscale_factor` is the `upscale_factor` of the encoder
`UpsampleNet`)
Tensor: shape(B, T * upsacle_factor), synthesized waveform.(`upscale_factor` is the `upscale_factor` of the encoder `UpsampleNet`)
"""
condition = self.encoder(mel)
batch_size, _, time_steps = condition.shape
@ -712,6 +595,3 @@ class ConditionalWavenet(nn.Layer):
samples = paddle.concat(samples, -1)
return samples
# TODO WaveNetLoss

32
parakeet/modules/stft.py → parakeet/modules/audio.py
View File

@ -4,6 +4,38 @@ from paddle.nn import functional as F
from scipy import signal
import numpy as np
__all__ = ["quantize", "dequantize", "STFT"]
def quantize(values, n_bands):
"""Linearlly quantize a float Tensor in [-1, 1) to an interger Tensor in [0, n_bands).
Args:
values (Tensor): dtype: flaot32 or float64. the floating point value.
n_bands (int): the number of bands. The output integer Tensor's value is in the range [0, n_bans).
Returns:
Tensor: the quantized tensor, dtype: int64.
"""
quantized = paddle.cast((values + 1.0) / 2.0 * n_bands, "int64")
return quantized
def dequantize(quantized, n_bands, dtype=None):
"""Linearlly dequantize an integer Tensor into a float Tensor in the range [-1, 1).
Args:
quantized (Tensor): dtype: int64. The quantized value in the range [0, n_bands).
n_bands (int): number of bands. The input integer Tensor's value is in the range [0, n_bans).
dtype (str, optional): data type of the output.
Returns:
Tensor: the dequantized tensor, dtype is specified by dtype.
"""
dtype = dtype or paddle.get_default_dtype()
value = (paddle.cast(quantized, dtype) + 0.5) * (2.0 / n_bands) - 1.0
return value
class STFT(nn.Layer):
def __init__(self, n_fft, hop_length, win_length, window="hanning"):
"""A module for computing differentiable stft transform. See `librosa.stft` for more details.

8
parakeet/modules/conv.py
View File

@ -42,6 +42,14 @@ class Conv1dCell(nn.Conv1D):
if self.training:
raise Exception("only use start_sequence in evaluation")
self._buffer = None
# NOTE: call self's weight norm hook expliccitly since self.weight
# is visited directly in this method without calling self.__call__
# method. If we do not trigger the weight norm hook, the weight
# may be outdated. e.g. after loading from a saved checkpoint
# see also: https://github.com/pytorch/pytorch/issues/47588
for hook in self._forward_pre_hooks.values():
hook(self, None)
self._reshaped_weight = paddle.reshape(
self.weight, (self._out_channels, -1))

33
parakeet/modules/losses.py
View File

@ -1,3 +1,5 @@
import numba
import numpy as np
import paddle
from paddle import nn
from paddle.nn import functional as F
@ -12,7 +14,7 @@ def weighted_mean(input, weight):
Returns:
Tensor: shape(1,), weighted mean tensor with the same dtype as input.
"""
weight = paddle.cast(weight, input.dtype)
weight = paddle.cast(weight, input.dtype)
return paddle.mean(input * weight)
def masked_l1_loss(prediction, target, mask):
@ -22,3 +24,32 @@ def masked_l1_loss(prediction, target, mask):
def masked_softmax_with_cross_entropy(logits, label, mask, axis=-1):
ce = F.softmax_with_cross_entropy(logits, label, axis=axis)
return weighted_mean(ce, mask)
def diagonal_loss(attentions, input_lengths, target_lengths, g=0.2, multihead=False):
"""A metric to evaluate how diagonal a attention distribution is."""
W = guided_attentions(input_lengths, target_lengths, g)
W_tensor = paddle.to_tensor(W)
if not multihead:
return paddle.mean(attentions * W_tensor)
else:
return paddle.mean(attentions * paddle.unsqueeze(W_tensor, 1))
@numba.jit(nopython=True)
def guided_attention(N, max_N, T, max_T, g):
W = np.zeros((max_T, max_N), dtype=np.float32)
for t in range(T):
for n in range(N):
W[t, n] = 1 - np.exp(-(n / N - t / T)**2 / (2 * g * g))
# (T_dec, T_enc)
return W
def guided_attentions(input_lengths, target_lengths, g=0.2):
B = len(input_lengths)
max_input_len = input_lengths.max()
max_target_len = target_lengths.max()
W = np.zeros((B, max_target_len, max_input_len), dtype=np.float32)
for b in range(B):
W[b] = guided_attention(input_lengths[b], max_input_len,
target_lengths[b], max_target_len, g)
# (B, T_dec, T_enc)
return W

202
parakeet/modules/multihead_attention.py
View File

@ -1,202 +0,0 @@
# Copyright (c) 2020 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.
import math
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
import paddle.fluid.layers as layers
class Linear(dg.Layer):
def __init__(self,
in_features,
out_features,
is_bias=True,
dtype="float32"):
super(Linear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.dtype = dtype
self.weight = fluid.ParamAttr(
initializer=fluid.initializer.XavierInitializer())
self.bias = is_bias
if is_bias is not False:
k = math.sqrt(1.0 / in_features)
self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-k, high=k))
self.linear = dg.Linear(
in_features,
out_features,
param_attr=self.weight,
bias_attr=self.bias, )
def forward(self, x):
x = self.linear(x)
return x
class ScaledDotProductAttention(dg.Layer):
def __init__(self, d_key):
"""Scaled dot product attention module.
Args:
d_key (int): the dim of key in multihead attention.
"""
super(ScaledDotProductAttention, self).__init__()
self.d_key = d_key
# please attention this mask is diff from pytorch
def forward(self,
key,
value,
query,
mask=None,
query_mask=None,
dropout=0.1):
"""
Compute scaled dot product attention.
Args:
key (Variable): shape(B, T, C), dtype float32, the input key of scaled dot product attention.
value (Variable): shape(B, T, C), dtype float32, the input value of scaled dot product attention.
query (Variable): shape(B, T, C), dtype float32, the input query of scaled dot product attention.
mask (Variable, optional): shape(B, T_q, T_k), dtype float32, the mask of key. Defaults to None.
query_mask (Variable, optional): shape(B, T_q, T_q), dtype float32, the mask of query. Defaults to None.
dropout (float32, optional): the probability of dropout. Defaults to 0.1.
Returns:
result (Variable): shape(B, T, C), the result of mutihead attention.
attention (Variable): shape(n_head * B, T, C), the attention of key.
"""
# Compute attention score
attention = layers.matmul(
query, key, transpose_y=True, alpha=self.d_key
**-0.5) #transpose the last dim in y
# Mask key to ignore padding
if mask is not None:
attention = attention + mask
attention = layers.softmax(attention, use_cudnn=True)
attention = layers.dropout(
attention, dropout, dropout_implementation='upscale_in_train')
# Mask query to ignore padding
if query_mask is not None:
attention = attention * query_mask
result = layers.matmul(attention, value)
return result, attention
class MultiheadAttention(dg.Layer):
def __init__(self,
num_hidden,
d_k,
d_q,
num_head=4,
is_bias=False,
dropout=0.1,
is_concat=True):
"""Multihead Attention.
Args:
num_hidden (int): the number of hidden layer in network.
d_k (int): the dim of key in multihead attention.
d_q (int): the dim of query in multihead attention.
num_head (int, optional): the head number of multihead attention. Defaults to 4.
is_bias (bool, optional): whether have bias in linear layers. Default to False.
dropout (float, optional): dropout probability of FFTBlock. Defaults to 0.1.
is_concat (bool, optional): whether concat query and result. Default to True.
"""
super(MultiheadAttention, self).__init__()
self.num_hidden = num_hidden
self.num_head = num_head
self.d_k = d_k
self.d_q = d_q
self.dropout = dropout
self.is_concat = is_concat
self.key = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
self.value = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
self.query = Linear(num_hidden, num_head * d_q, is_bias=is_bias)
self.scal_attn = ScaledDotProductAttention(d_k)
if self.is_concat:
self.fc = Linear(num_head * d_q * 2, num_hidden)
else:
self.fc = Linear(num_head * d_q, num_hidden)
self.layer_norm = dg.LayerNorm(num_hidden)
def forward(self, key, value, query_input, mask=None, query_mask=None):
"""
Compute attention.
Args:
key (Variable): shape(B, T, C), dtype float32, the input key of attention.
value (Variable): shape(B, T, C), dtype float32, the input value of attention.
query_input (Variable): shape(B, T, C), dtype float32, the input query of attention.
mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of key. Defaults to None.
query_mask (Variable, optional): shape(B, T_query, T_key), dtype float32, the mask of query. Defaults to None.
Returns:
result (Variable): shape(B, T, C), the result of mutihead attention.
attention (Variable): shape(num_head * B, T, C), the attention of key and query.
"""
batch_size = key.shape[0]
seq_len_key = key.shape[1]
seq_len_query = query_input.shape[1]
# Make multihead attention
key = layers.reshape(
self.key(key), [batch_size, seq_len_key, self.num_head, self.d_k])
value = layers.reshape(
self.value(value),
[batch_size, seq_len_key, self.num_head, self.d_k])
query = layers.reshape(
self.query(query_input),
[batch_size, seq_len_query, self.num_head, self.d_q])
key = layers.reshape(
layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
value = layers.reshape(
layers.transpose(value, [2, 0, 1, 3]),
[-1, seq_len_key, self.d_k])
query = layers.reshape(
layers.transpose(query, [2, 0, 1, 3]),
[-1, seq_len_query, self.d_q])
result, attention = self.scal_attn(
key, value, query, mask=mask, query_mask=query_mask)
# concat all multihead result
result = layers.reshape(
result, [self.num_head, batch_size, seq_len_query, self.d_q])
result = layers.reshape(
layers.transpose(result, [1, 2, 0, 3]),
[batch_size, seq_len_query, -1])
if self.is_concat:
result = layers.concat([query_input, result], axis=-1)
result = layers.dropout(
self.fc(result),
self.dropout,
dropout_implementation='upscale_in_train')
result = result + query_input
result = self.layer_norm(result)
return result, attention

21
parakeet/training/cli.py Normal file
View File

@ -0,0 +1,21 @@
import argparse
def default_argument_parser():
parser = argparse.ArgumentParser()
# data and outpu
parser.add_argument("--config", metavar="FILE", help="path of the config file to overwrite to default config with.")
parser.add_argument("--data", metavar="DATA_DIR", help="path to the datatset.")
parser.add_argument("--output", metavar="OUTPUT_DIR", help="path to save checkpoint and log. If not provided, a directory is created in runs/ to save outputs.")
# load from saved checkpoint
parser.add_argument("--checkpoint_path", type=str, help="path of the checkpoint to load")
# running
parser.add_argument("--device", type=str, choices=["cpu", "gpu"], help="device type to use, cpu and gpu are supported.")
parser.add_argument("--nprocs", type=int, default=1, help="number of parallel processes to use.")
# overwrite extra config and default config
parser.add_argument("--opts", nargs=argparse.REMAINDER, help="options to overwrite --config file and the default config, passing in KEY VALUE pairs")
return parser

12
parakeet/training/default_config.py Normal file
View File

@ -0,0 +1,12 @@
from yacs.config import CfgNode
_C = CfgNode(
dict(
valid_interval=1000, # validation
save_interval=10000, # checkpoint
max_iteration=900000, # max iteration to train
)
)
def get_default_training_config():
return _C.clone()

180
parakeet/training/experiment.py Normal file
View File

@ -0,0 +1,180 @@
import time
import logging
from pathlib import Path
import numpy as np
import paddle
from paddle import distributed as dist
from paddle.io import DataLoader, DistributedBatchSampler
from tensorboardX import SummaryWriter
from collections import defaultdict
import parakeet
from parakeet.utils import checkpoint, mp_tools
class ExperimentBase(object):
"""
An experiment template in order to structure the training code and take care of saving, loading, logging, visualization stuffs. It's intended to be flexible and simple.
So it only handles output directory (create directory for the outut, create a checkpoint directory, dump the config in use and create visualizer and logger)in a standard way without restricting the input/output protocols of the model and dataloader. It leaves the main part for the user to implement their own(setup the model, criterion, optimizer, defaine a training step, define a validation function and customize all the text and visual logs).
It does not save too much boilerplate code. The users still have to write the forward/backward/update mannually, but they are free to add non-standard behaviors if needed.
We have some conventions to follow.
1. Experiment should have `.model`, `.optimizer`, `.train_loader` and `.valid_loader`, `.config`, `.args` attributes.
2. The config should have a `.training` field, which has `valid_interval`, `save_interval` and `max_iteration` keys. It is used as the trigger to invoke validation, checkpointing and stop of the experiment.
3. There are three method, namely `train_batch`, `valid`, `setup_model` and `setup_dataloader` that should be implemented.
Feel free to add/overwrite other methods and standalone functions if you need.
Examples:
--------
def main_sp(config, args):
exp = Experiment(config, args)
exp.setup()
exp.run()
def main(config, args):
if args.nprocs > 1 and args.device == "gpu":
dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
else:
main_sp(config, args)
if __name__ == "__main__":
config = get_cfg_defaults()
parser = default_argument_parser()
args = parser.parse_args()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
print(args)
main(config, args)
"""
def __init__(self, config, args):
self.config = config
self.args = args
def setup(self):
if self.parallel:
self.init_parallel()
self.setup_output_dir()
self.dump_config()
self.setup_visualizer()
self.setup_logger()
self.setup_checkpointer()
self.setup_dataloader()
self.setup_model()
self.iteration = 0
self.epoch = 0
@property
def parallel(self):
return self.args.device == "gpu" and self.args.nprocs > 1
def init_parallel(self):
dist.init_parallel_env()
def save(self):
checkpoint.save_parameters(
self.checkpoint_dir, self.iteration, self.model, self.optimizer)
def resume_or_load(self):
iteration = checkpoint.load_parameters(
self.model,
self.optimizer,
checkpoint_dir=self.checkpoint_dir,
checkpoint_path=self.args.checkpoint_path)
self.iteration = iteration
def read_batch(self):
try:
batch = next(self.iterator)
except StopIteration:
self.new_epoch()
batch = next(self.iterator)
return batch
def new_epoch(self):
self.epoch += 1
if self.parallel:
self.train_loader.batch_sampler.set_epoch(self.epoch)
self.iterator = iter(self.train_loader)
def train(self):
self.new_epoch()
while self.iteration <= self.config.training.max_iteration:
self.iteration += 1
self.train_batch()
if self.iteration % self.config.training.valid_interval == 0:
self.valid()
if self.iteration % self.config.training.save_interval == 0:
self.save()
def run(self):
self.resume_or_load()
try:
self.train()
except KeyboardInterrupt:
self.save()
exit(-1)
@mp_tools.rank_zero_only
def setup_output_dir(self):
# output dir
output_dir = Path(self.args.output).expanduser()
output_dir.mkdir(exist_ok=True)
self.output_dir = output_dir
@mp_tools.rank_zero_only
def setup_checkpointer(self):
# checkpoint dir
checkpoint_dir = self.output_dir / "checkpoints"
checkpoint_dir.mkdir(exist_ok=True)
self.checkpoint_dir = checkpoint_dir
@mp_tools.rank_zero_only
def setup_visualizer(self):
# visualizer
visualizer = SummaryWriter(logdir=str(self.output_dir))
self.visualizer = visualizer
def setup_logger(self):
logger = logging.getLogger(__name__)
logger.setLevel("INFO")
logger.addHandler(logging.StreamHandler())
log_file = self.output_dir / 'worker_{}.log'.format(dist.get_rank())
logger.addHandler(logging.FileHandler(str(log_file)))
self.logger = logger
@mp_tools.rank_zero_only
def dump_config(self):
with open(self.output_dir / "config.yaml", 'wt') as f:
print(self.config, file=f)
def train_batch(self):
raise NotImplementedError("train_batch should be implemented.")
@mp_tools.rank_zero_only
@paddle.no_grad()
def valid(self):
raise NotImplementedError("valid should be implemented.")
def setup_model(self):
raise NotImplementedError("setup_model should be implemented.")
def setup_dataloader(self):
raise NotImplementedError("setup_dataloader should be implemented.")

2
parakeet/utils/__init__.py
View File

@ -12,4 +12,4 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from . import io, layer_tools, scheduler, display
from . import checkpoint, layer_tools, scheduler, display, mp_tools

108
parakeet/utils/io.py → parakeet/utils/checkpoint.py
View File

@ -14,41 +14,19 @@
import os
import time
import ruamel.yaml
import numpy as np
import paddle.fluid.dygraph as dg
from paddle.fluid.framework import convert_np_dtype_to_dtype_ as convert_np_dtype
import paddle
from paddle import distributed as dist
from paddle.nn import Layer
from paddle.optimizer import Optimizer
from parakeet.utils import mp_tools
__all__ = ["load_parameters", "save_parameters"]
def is_main_process():
local_rank = dg.parallel.Env().local_rank
return local_rank == 0
def add_yaml_config_to_args(config):
""" Add args in yaml config to the args parsed by argparse. The argument in
yaml config will be overwritten by the same argument in argparse if they
are both valid.
Args:
config (args): the args returned by `argparse.ArgumentParser().parse_args()`
Returns:
config: the args added yaml config.
"""
with open(config.config, 'rt') as f:
yaml_cfg = ruamel.yaml.safe_load(f)
cfg_vars = vars(config)
for k, v in yaml_cfg.items():
if k in cfg_vars and cfg_vars[k] is not None:
continue
cfg_vars[k] = v
return config
def _load_latest_checkpoint(checkpoint_dir):
"""Get the iteration number corresponding to the latest saved checkpoint
def _load_latest_checkpoint(checkpoint_dir: str) -> int:
"""Get the iteration number corresponding to the latest saved checkpoint.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
@ -57,19 +35,17 @@ def _load_latest_checkpoint(checkpoint_dir):
int: the latest iteration number.
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
# Create checkpoint index file if not exist.
if (not os.path.isfile(checkpoint_record)):
return 0
# Fetch the latest checkpoint index.
with open(checkpoint_record, "r") as handle:
with open(checkpoint_record, "rt") as handle:
latest_checkpoint = handle.readline().split()[-1]
iteration = int(latest_checkpoint.split("-")[-1])
return iteration
def _save_checkpoint(checkpoint_dir, iteration):
def _save_checkpoint(checkpoint_dir: str, iteration: int):
"""Save the iteration number of the latest model to be checkpointed.
Args:
@ -81,24 +57,20 @@ def _save_checkpoint(checkpoint_dir, iteration):
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
# Update the latest checkpoint index.
with open(checkpoint_record, "w") as handle:
with open(checkpoint_record, "wt") as handle:
handle.write("model_checkpoint_path: step-{}".format(iteration))
def load_parameters(model,
optimizer=None,
checkpoint_dir=None,
iteration=None,
checkpoint_path=None):
"""Load a specific model checkpoint from disk.
Args:
model (obj): model to load parameters.
optimizer (obj, optional): optimizer to load states if needed.
model (Layer): model to load parameters.
optimizer (Optimizer, optional): optimizer to load states if needed.
Defaults to None.
checkpoint_dir (str, optional): the directory where checkpoint is saved.
iteration (int, optional): if specified, load the specific checkpoint,
if not specified, load the latest one. Defaults to None.
checkpoint_path (str, optional): if specified, load the checkpoint
stored in the checkpoint_path and the argument 'checkpoint_dir' will
be ignored. Defaults to None.
@ -110,8 +82,7 @@ def load_parameters(model,
if checkpoint_path is not None:
iteration = int(os.path.basename(checkpoint_path).split("-")[-1])
elif checkpoint_dir is not None:
if iteration is None:
iteration = _load_latest_checkpoint(checkpoint_dir)
iteration = _load_latest_checkpoint(checkpoint_dir)
if iteration == 0:
return iteration
checkpoint_path = os.path.join(checkpoint_dir,
@ -121,52 +92,49 @@ def load_parameters(model,
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
)
local_rank = dg.parallel.Env().local_rank
model_dict, optimizer_dict = dg.load_dygraph(checkpoint_path)
state_dict = model.state_dict()
# cast to desired data type, for mixed-precision training/inference.
for k, v in model_dict.items():
if k in state_dict and convert_np_dtype(v.dtype) != state_dict[
k].dtype:
model_dict[k] = v.astype(state_dict[k].numpy().dtype)
local_rank = dist.get_rank()
params_path = checkpoint_path + ".pdparams"
model_dict = paddle.load(params_path)
model.set_state_dict(model_dict)
print("[checkpoint] Rank {}: loaded model from {}.pdparams".format(
local_rank, checkpoint_path))
if optimizer and optimizer_dict:
print("[checkpoint] Rank {}: loaded model from {}".format(
local_rank, params_path))
optimizer_path = checkpoint_path + ".pdopt"
if optimizer and os.path.isfile(optimizer_path):
optimizer_dict = paddle.load(optimizer_path)
optimizer.set_state_dict(optimizer_dict)
print("[checkpoint] Rank {}: loaded optimizer state from {}.pdopt".
format(local_rank, checkpoint_path))
print("[checkpoint] Rank {}: loaded optimizer state from {}".
format(local_rank, optimizer_path))
return iteration
@mp_tools.rank_zero_only
def save_parameters(checkpoint_dir, iteration, model, optimizer=None):
"""Checkpoint the latest trained model parameters.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
iteration (int): the latest iteration number.
model (obj): model to be checkpointed.
optimizer (obj, optional): optimizer to be checkpointed.
model (Layer): model to be checkpointed.
optimizer (Optimizer, optional): optimizer to be checkpointed.
Defaults to None.
Returns:
None
"""
checkpoint_path = os.path.join(checkpoint_dir, "step-{}".format(iteration))
model_dict = model.state_dict()
dg.save_dygraph(model_dict, checkpoint_path)
print("[checkpoint] Saved model to {}.pdparams".format(checkpoint_path))
params_path = checkpoint_path + ".pdparams"
paddle.save(model_dict, params_path)
print("[checkpoint] Saved model to {}".format(params_path))
if optimizer:
opt_dict = optimizer.state_dict()
dg.save_dygraph(opt_dict, checkpoint_path)
print("[checkpoint] Saved optimzier state to {}.pdopt".format(
checkpoint_path))
optimizer_path = checkpoint_path + ".pdopt"
paddle.save(opt_dict, optimizer_path)
print("[checkpoint] Saved optimzier state to {}".format(
optimizer_path))
_save_checkpoint(checkpoint_dir, iteration)

3
parakeet/utils/display.py
View File

@ -2,6 +2,9 @@ import numpy as np
import matplotlib
from matplotlib import cm, pyplot
__all__ = ["pack_attention_images", "add_attention_plots", "min_max_normalize"]
def pack_attention_images(attention_weights, rotate=False):
# add a box
attention_weights = np.pad(attention_weights,

3
parakeet/utils/internals.py
View File

@ -1,6 +1,9 @@
import numpy as np
from paddle.framework import core
__all__ = ["convert_dtype_to_np_dtype_"]
def convert_dtype_to_np_dtype_(dtype):
"""
Convert paddle's data type to corrsponding numpy data type.

2
parakeet/utils/layer_tools.py
View File

@ -15,6 +15,8 @@
import numpy as np
from paddle import nn
__all__ = ["summary","gradient_norm", "freeze", "unfreeze"]
def summary(layer: nn.Layer):
num_params = num_elements = 0

3
parakeet/utils/mp_tools.py
View File

@ -2,6 +2,9 @@ import paddle
from paddle import distributed as dist
from functools import wraps
__all__ = ["rank_zero_only"]
def rank_zero_only(func):
local_rank = dist.get_rank()

3
parakeet/utils/scheduler.py
View File

@ -1,5 +1,8 @@
import math
__all__ = ["SchedulerBase", "Constant", "PieceWise", "StepWise"]
class SchedulerBase(object):
def __call__(self, step):
raise NotImplementedError("You should implement the __call__ method.")