# -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ transformer/nn_transformer.py ]
# Synopsis [ wrapper class for downstream feature extraction or finetune ]
# Author [ Andy T. Liu (Andi611) ]
# Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ]
"""*********************************************************************************************"""
###############
# IMPORTATION #
###############
import math
import yaml
import torch
import random
import numpy as np
import torch.nn as nn
from functools import lru_cache
from distutils.util import strtobool
from transformer.model import TransformerConfig, TransformerModel
###############
# TRANSFORMER #
###############
"""
Use this class to extract features from the Transformer model,
or to finetune the pre-trained Transformer with any downstream tasks.
Also, this class is `pytorch-kaldi` ready,
hence we need to use `str` instead of `bool` in the options dict,
as pytorch-kaldi scripts will pass in str.
Params:
`options`: a python dictionary containing the following keys:
ckpt_file: str, a path specifying the pre-trained ckpt file
load_pretrain: str, ['True', 'False'], whether to load pre-trained weights
no_grad: str, ['True', 'False'], whether to have gradient flow over this class
dropout: float/str, use float to modify dropout value during downstream finetune, or use the str `default` for pre-train default values
spec_aug: str, ['True', 'False'], whether to apply SpecAugment on inputs (used for ASR training)
spec_aug_prev: str, ['True', 'False'], apply spec augment on input acoustic features if True, else apply on output representations (used for ASR training)
weighted_sum: str, ['True', 'False'], whether to use a learnable weighted sum to integrate hidden representations from all layers, if False then use the last
select_layer: int, select from all hidden representations, set to -1 to select the last (will only be used when weighted_sum is False)
`intput_dim`: int, input dimension of model
An example `options` dictionary:
options = {
'ckpt_file' : './result/result_transformer/libri_sd1337_fmllrBase960-F-N-K-RA/states-1000000.ckpt',
'load_pretrain' : 'True',
'no_grad' : 'True',
'dropout' : 'default',
'spec_aug' : 'False',
'spec_aug_prev' : 'True',
'weighted_sum' : 'False',
'select_layer' : -1,
}
"""
class TRANSFORMER(nn.Module):
def __init__(self, options, inp_dim, config=None):
super(TRANSFORMER, self).__init__()
if config is not None:
self.config = yaml.load(open(config, 'r'), Loader=yaml.FullLoader)
else:
all_states = torch.load(options["ckpt_file"], map_location='cpu')
self.config = all_states['Settings']['Config']
self.no_grad = bool(strtobool(options['no_grad']))
self.spec_aug = bool(strtobool(options['spec_aug']))
self.spec_aug_prev = bool(strtobool(options['spec_aug_prev']))
self.weighted_sum = bool(strtobool(options['weighted_sum']))
self.select_layer = int(options['select_layer'])
if (not self.no_grad) and (not self.spec_aug_prev): raise RuntimeError('Only one of them can be set False!')
# increase dropout
if str(options['dropout']) != 'default':
self.config['transformer']['hidden_dropout_prob'] = float(options['dropout'])
self.config['transformer']['attention_probs_dropout_prob'] = float(options['dropout'])
# Model Config
self.model_config = TransformerConfig(self.config)
self.dr = self.model_config.downsample_rate
self.hidden_size = self.model_config.hidden_size
self.num_layers = self.model_config.num_hidden_layers
self.max_input_length = self.config['transformer']['max_input_length'] if 'max_input_length' in self.config['transformer'] else 0
if self.max_input_length > 0: print('[Transformer] - Maximum input length: ', self.max_input_length)
if not (self.select_layer in list(range(-1, self.num_layers))): raise RuntimeError('Out of range int for \'select_layer\'!')
# use weighted sum from all layers
if self.weighted_sum:
self.weight = nn.Parameter(torch.ones(self.num_layers) / self.num_layers)
# Build model
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self.model = TransformerModel(self.model_config, inp_dim).to(self.device)
self.model.eval() if self.no_grad else self.model.train()
# Load from a PyTorch state_dict
load = bool(strtobool(options["load_pretrain"]))
if load:
self.load_model(all_states['Transformer'])
print('[Transformer] - Number of parameters: ' + str(sum(p.numel() for p in self.model.parameters() if p.requires_grad)))
self.out_dim = self.hidden_size # 768, This attribute is for pytorch-kaldi and downstream runner
self.permute_input = True # This attribute is for the forward method. If Ture then input ouput is in the shape of (T, B, D), if False then in (B, T, D)
def load_model(self, state_dict):
try:
old_keys = []
new_keys = []
for key in state_dict.keys():
new_key = None
if 'gamma' in key:
new_key = key.replace('gamma', 'weight')
if 'beta' in key:
new_key = key.replace('beta', 'bias')
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(self.model)
if len(missing_keys) > 0:
print('Weights of {} not initialized from pretrained model: {}'.format(
self.model.__class__.__name__, missing_keys))
if len(unexpected_keys) > 0:
print('Weights from pretrained model not used in {}: {}'.format(
self.model.__class__.__name__, unexpected_keys))
if len(error_msgs) > 0:
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
self.model.__class__.__name__, '\n\t'.join(error_msgs)))
print('[Transformer] - Pre-trained weights loaded!')
except:
raise RuntimeError('[Transformer] - Pre-trained weights NOT loaded!')
def down_sample_frames(self, spec):
spec = spec.contiguous()
left_over = spec.shape[1] % self.dr
if left_over != 0: spec = spec[:, :-left_over, :]
spec_stacked = spec.view(spec.shape[0], spec.shape[1]//self.dr, spec.shape[2]*self.dr)
return spec_stacked
def process_input_data(self, spec):
"""Process input data for the model"""
# add arbitary batch axis B if input `spec` has shape of TxD
if len(spec.shape) == 2:
spec = spec.unsqueeze(0)
# input `spec` should have shape BxTxD
elif len(spec.shape) != 3:
raise ValueError('Input argument `spec` has invalid shape: {}'.format(spec.shape))
# Down sample
if self.dr > 1:
spec_stacked = self.down_sample_frames(spec) # (batch_size, seq_len, feature_dim * dr)
else:
spec_stacked = spec
# Record length for each uttr
spec_len = np.sum(np.sum(spec_stacked.cpu().data.numpy(), axis=-1) != 0, axis=-1)
spec_len = [int(sl) for sl in spec_len]
batch_size = spec_stacked.shape[0]
seq_len = spec_stacked.shape[1]
pos_enc = position_encoding(seq_len, self.hidden_size) # (seq_len, hidden_size)
attn_mask = np.ones((batch_size, seq_len)) # (batch_size, seq_len)
# zero vectors for padding dimension
for idx in range(len(spec_stacked)):
attn_mask[idx][spec_len[idx]:] = 0
if self.spec_aug and self.spec_aug_prev and self.model.training:
spec_stacked = spec_augment(spec_stacked, mask_T=70, mask_F=4, num_T=2, num_F=2, p=1.0) # (batch_size, seq_len, feature_dim * dr)
spec_stacked = spec_stacked.to(device=self.device, dtype=torch.float32) # (batch_size, seq_len, feature_dim * dr)
pos_enc = torch.FloatTensor(pos_enc).to(device=self.device, dtype=torch.float32).expand(spec_stacked.size(0), *pos_enc.size()) # (batch_size, seq_len, hidden_size)
attn_mask = torch.FloatTensor(attn_mask).to(device=self.device, dtype=torch.float32) # (batch_size, seq_len)
return spec_stacked, pos_enc, attn_mask # (x, pos_enc, attention_mask)
def tile_representations(self, reps):
"""
Tile up the speech representations to match the amount of input frames.
Input - encoded_layers shape: (batch_size, sequence_length, hidden_size)
Output - tiled_encoded_layers shape: (batch_size, sequence_length * downsample_rate, hidden_size)
"""
if len(reps.shape) != 3:
raise ValueError('Input argument `reps` has invalid shape: {}'.format(reps.shape))
tiled_reps = reps.repeat(1, 1, self.dr)
tiled_reps = tiled_reps.reshape(reps.size(0), reps.size(1)*self.dr, reps.size(2))
return tiled_reps # (batch_size, sequence_length * downsample_rate, hidden_size)
def upsample(self, x, input_len):
# Compute padding to compromise the downsample loss
left_over = input_len % self.dr
if left_over % 2 == 0:
left_pad = left_over // 2
right_pad = left_pad
else:
left_pad = left_over // 2
right_pad = left_over // 2 + 1
x = self.tile_representations(x)
# padding
x = x.permute(0, 2, 1).contiguous() # (B, T, D) -> (B, D, T)
padding = nn.ReplicationPad1d((left_pad, right_pad))
x = padding(x)
x = x.permute(0, 2, 1).contiguous() # (B, D, T) -> (B, T, D)
return x
def _forward(self, x):
if self.permute_input:
x = x.permute(1, 0, 2).contiguous() # (T, B, D) -> (B, T, D)
input_len = x.shape[1]
# forward the whole sequence at once
if self.max_input_length == 0 or input_len <= self.max_input_length:
spec_stacked, pos_enc, attn_mask = self.process_input_data(x) # x shape: (B, T, D)
x = self.model(spec_stacked, pos_enc, attn_mask, output_all_encoded_layers=self.weighted_sum or self.select_layer != -1) # (B, T, D) or # (N, B, T, D)
# forward the sequence in chunks then concat
else:
chunks = torch.chunk(x, chunks=math.ceil(input_len / self.max_input_length), dim=1)
x_ = []
for chunk in chunks:
spec_stacked, pos_enc, attn_mask = self.process_input_data(chunk) # x shape: (B, T, D)
chunk = self.model(spec_stacked, pos_enc, attn_mask, output_all_encoded_layers=self.weighted_sum or self.select_layer != -1) # (B, T, D) or # (N, B, T, D)
x_.append(torch.stack(chunk) if type(chunk) is list else chunk)
x = torch.cat(x_, dim=2 if (self.weighted_sum or self.select_layer != -1) else 1)
# Apply weighted sum
if self.weighted_sum:
if type(x) is list: x = torch.stack(x)
softmax_weight = nn.functional.softmax(self.weight, dim=-1)
B, T, D = x.shape[1], x.shape[2], x.shape[3]
x = x.reshape(self.num_layers, -1)
x = torch.matmul(softmax_weight, x).reshape(B, T, D)
# Select a specific layer
elif self.select_layer != -1:
x = x[self.select_layer]
if self.spec_aug and not self.spec_aug_prev and self.model.training:
x = spec_augment(x, mask_T=70, mask_F=86, num_T=2, num_F=2, p=1.0) # (B, T, D)
# If using a downsampling model, apply tile and padding
if self.dr > 1:
x = self.upsample(x, input_len) # (B, T, D)
# permute to output
if self.permute_input:
x = x.permute(1, 0, 2).contiguous() # (B, T, D) -> (T, B, D)
return x # (B, T, D) or (T, B, D)
def forward(self, x):
if self.no_grad:
with torch.no_grad():
self.model.eval()
x = self._forward(x)
else:
x = self._forward(x)
return x
#######################
# POSITIONAL ENCODING #
#######################
MAX_SEQLEN = 5000
@lru_cache(maxsize=1)
def get_sinusoid_table(hidden_size):
def _cal_angle(position, hid_idx):
return position / np.power(10000, 2 * (hid_idx // 2) / hidden_size)
def _get_posi_angle_vec(position):
return [_cal_angle(position, hid_j) for hid_j in range(hidden_size)]
sinusoid_table = np.array([_get_posi_angle_vec(pos_i) for pos_i in range(MAX_SEQLEN)])
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
return torch.FloatTensor(sinusoid_table)
def position_encoding(seq_len, hidden_size):
""" position encoding table """
table = get_sinusoid_table(hidden_size)[:seq_len]
# no extra CPU and GPU memory allocation
# after getting the (seq_len, hidden_size) tensor, one should first put
# this tensor into GPU then expand it
return table # (seq_len, hidden_size)
################
# SPEC AUGMENT #
################
"""
Process training data for the supervised ASR model by
masking to time-steps and channels during training
which delays overfitting and significantly improves the final accuracy numbers.
Input:
`spec`: input real frames, with shape: (batch_size, seq_len, feature_dim)
`mask_T`: the time mask parameter T described in the SpecAugment paper,
we use default values based on the LD Policy
(In paper: T=100, we use 70 since we are training on the 100 hr subset only)
`mask_F`: the frequency mask parameter F described in the SpecAugment paper,
we use default values based on the LD Policy
(In paper: F=27:D=80*3 -> F=4.5:D=40, where D is acoustic dimension)
`num_T` : the number of time masks applied (In paper: mT=2)
`num_F` : the number of frequency masks applied (In paper: mF=2)
`p` : upper bound ratio (In paper: p=1.0)
Output:
`spec`: augmented frames, with shape: (batch_size, seq_len, feature_dim)
"""
def spec_augment(spec, mask_T=70, mask_F=4, num_T=2, num_F=2, p=1.0):
def _start_to_intervals(starts, consecutive):
tiled = starts.expand(consecutive, starts.size(0)).permute(1, 0)
offset = torch.arange(consecutive).expand_as(tiled)
intervals = tiled + offset
return intervals.view(-1)
with torch.no_grad():
upper_bound = spec.shape[1] * p # upper bound on the time mask so that a time mask cannot be wider than p times the number of time steps
for idx in range(spec.shape[0]):
# time masking
if mask_T > 0 and mask_T < upper_bound:
for _ in range(num_T):
rand_consecutive = random.randint(0, mask_T)
chosen_start = torch.randperm(spec.shape[1] - rand_consecutive)[:1]
chosen_intervals = _start_to_intervals(chosen_start, rand_consecutive)
spec[idx, chosen_intervals, :] = 0
# frequency masking
if mask_F > 0:
for _ in range(num_F):
rand_bandwidth = random.randint(0, mask_F)
chosen_start = torch.randperm(spec.shape[2] - rand_bandwidth)[:1]
chosen_intervals = _start_to_intervals(chosen_start, rand_bandwidth)
spec[idx, :, chosen_intervals] = 0
return spec
#######
# LIN #
#######
"""
Linear Input Networks (LIN) for domain adaptation
Params:
`options`: a python dictionary containing arguments for pytorch kaldi, give None if not using with pytorch-kaldi:
`intput_dim`: int, input dimension of model
"""
class LIN(nn.Module):
def __init__(self, options, inp_dim):
super(LIN, self).__init__()
self.out_dim = inp_dim # This attribute is for pytorch-kaldi
self.linear = nn.Linear(inp_dim, inp_dim)
self.linear.weight.data.copy_(torch.eye(inp_dim))
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self.linear = self.linear.to(self.device)
self.linear.train()
def forward(self, x):
x = self.linear(x)
return x
