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import random
from typing import Dict, Iterator, List, Tuple, Union
from fairseq import utils
import numpy as np
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from fairseq.data import Dictionary
from src.slam_llm.models.vallex.transformers import (
LayerNorm,
TransformerEncoder,
TransformerEncoderLayer,
)
from src.slam_llm.models.vallex.vallex_config import VallexConfig
from transformers.modeling_utils import PreTrainedModel
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
from dataclasses import dataclass
@dataclass
class ModelOutput:
logits: torch.Tensor
loss: torch.Tensor
acc: torch.Tensor
def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True, scale=1, prob_mask=None):
if target.dim() == lprobs.dim() - 1:
target = target.unsqueeze(-1)
if prob_mask is not None:
lprobs = lprobs.masked_fill(prob_mask, 0.0)
n_class = (1-prob_mask.float()).sum()
else:
n_class = lprobs.size(-1)
nll_loss = -lprobs.gather(dim=-1, index=target)
# nll_loss = nll_loss * scale
smooth_loss = -lprobs.sum(dim=-1, keepdim=True) * scale
if ignore_index is not None:
pad_mask = target.eq(ignore_index)
nll_loss.masked_fill_(pad_mask, 0.0)
smooth_loss.masked_fill_(pad_mask, 0.0)
pad_mask_float = (1 - pad_mask.to(torch.float)).sum()
else:
nll_loss = nll_loss.squeeze(-1)
smooth_loss = smooth_loss.squeeze(-1)
if reduce:
nll_loss = nll_loss.sum()
smooth_loss = smooth_loss.sum()
eps_i = epsilon / (n_class - 1)
loss = (1.0 - epsilon - eps_i) * nll_loss + \
eps_i * smooth_loss
return loss / pad_mask_float, nll_loss / pad_mask_float
class SinusoidalPositionalEmbedding(nn.Module):
def __init__(self, embedding_dim, padding_idx, init_size=1024):
super().__init__()
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx if padding_idx is not None else 0
self.weights = SinusoidalPositionalEmbedding.get_embedding(
init_size, embedding_dim, padding_idx
)
self.onnx_trace = False
self.register_buffer("_float_tensor", torch.FloatTensor(1))
self.max_positions = int(1e5)
def prepare_for_onnx_export_(self):
self.onnx_trace = True
@staticmethod
def get_embedding(
num_embeddings: int, embedding_dim: int, padding_idx = None
):
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(
1
) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(
num_embeddings, -1
)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
def forward(
self,
input,
incremental_state = None,
timestep = None,
positions = None,
):
bspair = torch.onnx.operators.shape_as_tensor(input)
bsz, seq_len = bspair[0], bspair[1]
max_pos = self.padding_idx + 1 + seq_len
if self.weights is None or max_pos > self.weights.size(0):
# recompute/expand embeddings if needed
self.weights = SinusoidalPositionalEmbedding.get_embedding(
max_pos, self.embedding_dim, self.padding_idx
)
self.weights = self.weights.to(self._float_tensor)
if incremental_state is not None:
# positions is the same for every token when decoding a single step
pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
if self.onnx_trace:
return (
self.weights.index_select(index=self.padding_idx + pos, dim=0)
.unsqueeze(1)
.repeat(bsz, 1, 1)
)
return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
positions = utils.make_positions(
input, self.padding_idx, onnx_trace=self.onnx_trace
)
if self.onnx_trace:
flat_embeddings = self.weights.detach().index_select(0, positions.view(-1))
embedding_shape = torch.cat(
(bsz.view(1), seq_len.view(1), torch.tensor([-1], dtype=torch.long))
)
embeddings = torch.onnx.operators.reshape_from_tensor_shape(
flat_embeddings, embedding_shape
)
return embeddings
return (
self.weights.index_select(0, positions.view(-1))
.view(bsz, seq_len, -1)
.detach()
)
class Transpose(nn.Identity):
def forward(self, input: torch.Tensor) -> torch.Tensor:
return input.transpose(1, 2)
class VALLF(PreTrainedModel):
config_class = VallexConfig
def __init__(
self,
config: VallexConfig
):
super().__init__(config)
self.ar_at_dict = Dictionary.load(self.config.ar_at_dict)
self.ar_st_dict = Dictionary.load(self.config.ar_st_dict)
self.nar_at_dict = Dictionary.load(self.config.nar_at_dict)
self.nar_st_dict = Dictionary.load(self.config.nar_st_dict)
self.ar_at_dict.tts_flag = self.ar_at_dict.add_symbol("<TTS>")
self.ar_st_dict.asr_flag = self.ar_st_dict.add_symbol("<ASR>")
self.ar_st_dict.mt_flag = self.ar_st_dict.add_symbol("<MT>")
self.padding_idx = self.ar_at_dict.pad()
self.config = config
d_model = self.config.n_dim
nar_scale_factor = self.config.nar_scale_factor
prepend_bos = self.config.prepend_bos
norm_first = self.config.norm_first
num_layers = self.config.n_layer
self.NUM_AUDIO_TOKENS = self.ar_at_dict.eos()
nar_d_model = int(d_model * nar_scale_factor)
self.ar_text_embedding = nn.Embedding(len(self.ar_st_dict), d_model, self.ar_st_dict.pad()) # W_x
if config.only_ar:
pass
else:
self.nar_text_embedding = nn.Embedding(len(self.nar_st_dict), d_model, self.nar_st_dict.pad())
# ID self.NUM_AUDIO_TOKENS -> PAD
# ID self.NUM_AUDIO_TOKENS + 1 -> BOS
self.ar_audio_prepend_bos = prepend_bos
self.ar_audio_embedding = EncodecDecoderLstm(
dictionary=self.ar_at_dict, emb_dim=d_model
)
self.ar_text_prenet = nn.Identity()
self.ar_audio_prenet = nn.Identity()
self.ar_text_position = SinusoidalPositionalEmbedding(
d_model,
padding_idx=self.ar_at_dict.pad(),
init_size=1024+self.ar_at_dict.pad()+1
)
self.ar_audio_position = SinusoidalPositionalEmbedding(
d_model,
padding_idx=self.ar_at_dict.pad(),
init_size=1024+self.ar_at_dict.pad()+1
)
self.ar_decoder = TransformerEncoder(
TransformerEncoderLayer(
d_model,
self.config.n_head,
dim_feedforward=d_model * 4,
dropout=0.1,
batch_first=True,
norm_first=norm_first,
),
num_layers=num_layers,
norm=LayerNorm(d_model) if norm_first else None,
)
self.ar_predict_layer = nn.Linear(
d_model, len(self.ar_at_dict), bias=False
)
self.rng = random.Random(0)
self.num_heads = self.config.n_head
self.prefix_mode = self.config.prefix_mode
self.num_quantizers = self.config.num_quantizers
assert self.num_quantizers >= 1
if config.only_ar:
pass
else:
if self.num_quantizers > 1:
self.nar_audio_embeddings = NATEncodecDecoderLstm(
codecs=[0, 1, 2, 3, 4, 5, 6, 7], dictionary=self.nar_at_dict, emb_dim=d_model
) # W_a
self.nar_text_prenet = nn.Identity()
self.nar_audio_prenet = nn.Identity()
self.nar_text_position = SinusoidalPositionalEmbedding(
d_model,
padding_idx=self.nar_at_dict.pad(),
init_size=1024+self.nar_at_dict.pad()+1
)
self.nar_audio_position = SinusoidalPositionalEmbedding(
d_model,
padding_idx=self.nar_at_dict.pad(),
init_size=1024+self.nar_at_dict.pad()+1
)
self.nar_decoder = TransformerEncoder(
TransformerEncoderLayer(
nar_d_model,
int(self.num_heads * nar_scale_factor),
dim_feedforward=nar_d_model * 4,
dropout=0.1,
batch_first=True,
norm_first=norm_first,
adaptive_layer_norm=True,
),
num_layers=int(num_layers * nar_scale_factor),
norm=nn.LayerNorm(nar_d_model)
if norm_first
else None,
)
self.nar_predict_layers = nn.ModuleList(
[
nn.Linear(nar_d_model, len(self.nar_at_dict), bias=False)
for i in range(self.num_quantizers)
]
)
self.nar_stage_embeddings = None
def stage_parameters(self, stage: int = 1) -> Iterator[nn.Parameter]:
assert stage > 0
if stage == 1:
for name, param in self.named_parameters():
if name.startswith("ar_"):
print(f" AR parameter: {name}")
yield param
if stage == 2:
for name, param in self.named_parameters():
if name.startswith("nar_"):
print(f"NAR parameter: {name}")
yield param
def stage_named_parameters(
self, stage: int = 1
) -> Iterator[Tuple[str, nn.Parameter]]:
assert stage > 0
if stage == 1:
for pair in self.named_parameters():
if pair[0].startswith("ar_"):
yield pair
if stage == 2:
for pair in self.named_parameters():
if pair[0].startswith("nar_"):
yield pair
def pad_y_eos(self, y, y_mask_int, eos_id):
targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(
y_mask_int, (0, 1), value=1
)
# inputs, targets
if self.ar_audio_prepend_bos:
return (
F.pad(targets[:, :-1], (1, 0), value=self.NUM_AUDIO_TOKENS + 1),
targets,
)
return targets[:, :-1], targets[:, 1:]
class VALLE(VALLF):
config_class = VallexConfig
def __init__(
self,
config: VallexConfig,
**kwargs,
):
super(VALLE, self).__init__(
config,
**kwargs,
)
print(config)
self.config = config
d_model = self.config.n_dim
self.eps = config.eps
self.language_ID = {
'en': 0,
'zh': 1,
}
self.ar_language_embedding = nn.Embedding(3, d_model, padding_idx=2)
self.nar_language_embedding = nn.Embedding(3, d_model, padding_idx=2)
self.embed_scale = 32.0
def forward(
self,
zh,
en
):
"""
"zh": {
"st_tokens": zh_st,
"at_tokens_wbos": zh_prev_at,
"at_tokens_tgt": zh_tgt_at,
"self_atten_mask": zh_self_atten_mask,
"padding_mask": zh_padding_mask,
"langid": zh_id.long()
},
"en": {
"st_tokens": en_st,
"at_tokens_wbos": en_prev_at,
"at_tokens_tgt": en_tgt_at,
"self_atten_mask": en_self_atten_mask,
"padding_mask": en_padding_mask,
"langid": en_id.long()
}
"""
flag = (np.random.randint(low=0, high=1000) % 2 == 0) # zh or en
if flag:
data = zh
else:
data = en
st_tokens = data["st_tokens"]
at_tokens_wbos = data["at_tokens_wbos"]
at_tokens_tgt = data["at_tokens_tgt"]
self_atten_mask = data["self_atten_mask"]
padding_mask = data["padding_mask"]
langid = data["langid"]
st_len = st_tokens.size(1)
st_emb = self.embed_scale * self.ar_text_embedding(st_tokens)
src_lang_emb = self.embed_scale * self.ar_language_embedding(langid)
st_emb += src_lang_emb
st_pos = self.ar_text_position(st_tokens)
st_emb += st_pos
at_emb, _ = self.ar_audio_embedding(at_tokens_wbos, None)
at_emb = self.embed_scale * at_emb
tgt_lang_emb = self.embed_scale * self.ar_language_embedding(langid)
at_emb += tgt_lang_emb
at_pos = self.ar_audio_position(at_tokens_wbos)
at_emb += at_pos
x = torch.concat([st_emb, at_emb], dim=1)
x = self.ar_decoder(
x,
mask=self_atten_mask,
src_key_padding_mask=padding_mask
)
x = self.ar_predict_layer(x)
x = x[:, st_len:, :]
loss, nll_loss, lprob, right_rate = self.calculate_loss(
x, at_tokens_tgt
)
return ModelOutput(logits=lprob, loss=loss, acc=right_rate), right_rate
def calculate_loss(self, encoder_out, target, reduce=True, scale=1.0, prob_mask=None, acc=True):
lprob = self.get_normalized_probs(encoder_out, log_probs=True)
with torch.no_grad():
mask = target.ne(self.padding_idx)
n_correct = torch.sum(
lprob.argmax(-1).masked_select(mask).eq(target.masked_select(mask))
)
total = torch.sum(mask)
right_rate = n_correct * 100.0 / total
lprob, target = lprob.view(-1, lprob.size(-1)), target.view(-1)
loss, nll_loss = label_smoothed_nll_loss(
lprob,
target,
self.eps,
ignore_index=self.padding_idx,
reduce=reduce,
scale=scale,
prob_mask=prob_mask
)
return loss, nll_loss, lprob, right_rate
def get_normalized_probs(self, encoder_out, log_probs, sample=None):
if torch.is_tensor(encoder_out):
logits = encoder_out.float()
if log_probs:
return F.log_softmax(logits, dim=-1)
else:
return F.softmax(logits, dim=-1)
def inference_24L(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
y: torch.Tensor,
enroll_x_lens: torch.Tensor,
top_k: int = -100,
temperature: float = 1.0,
prompt_language: str = None,
text_language: str = None,
best_of: int = 1,
length_penalty: float = 1.0,
return_worst: bool = False,
at_eos: int = -1
) -> torch.Tensor:
assert x.ndim == 2, x.shape
assert x_lens.ndim == 1, x_lens.shape
assert y.ndim == 3, y.shape
assert y.shape[0] == 1, y.shape
assert torch.all(x_lens > 0)
self.NUM_AUDIO_TOKENS = at_eos
text = x
x = self.embed_scale * self.ar_text_embedding(text)
prompt_language_id = prompt_language.to(x.device)
text_language_id = text_language.to(x.device)
src_lang_emb = self.embed_scale * self.ar_language_embedding(prompt_language_id)
tgt_lang_emb = self.embed_scale * self.ar_language_embedding(text_language_id)
x[:, :enroll_x_lens, :] += src_lang_emb
x[:, enroll_x_lens:, :] += tgt_lang_emb
x = self.ar_text_prenet(x)
x_pos = self.ar_text_position(text)
text_len = x_lens.max()
prompts = y
prefix_len = y.shape[1]
# AR Decoder
# TODO: Managing decoder steps avoid repetitive computation
y = prompts[..., 0]
if self.ar_audio_prepend_bos:
y = F.pad(y, (1, 0), value=self.ar_at_dict.tts_flag)
x_len = x_lens.max()
x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
kv_cache = None
use_kv_caching = True
sum_logprobs = torch.zeros(best_of, device=y.device) # implement batch decoding here
x = x.repeat(best_of, 1, 1)
y = y.repeat(best_of, 1)
lstm_h = None
first_ar = True
while True:
if first_ar:
y_emb, lstm_h = self.ar_audio_embedding(y, lstm_h)
y_emb = y_emb * self.embed_scale
y_emb = self.ar_audio_prenet(y_emb)
y_pos = self.ar_audio_position(y)
y_emb[:, :prefix_len] = y_emb[:, :prefix_len] + src_lang_emb
y_emb[:, prefix_len:] = y_emb[:, prefix_len:] + tgt_lang_emb
xy_pos_token = torch.concat([x_pos+x, y_pos+y_emb], dim=1)
first_ar = False
else:
y_emb_cur, lstm_h = self.ar_audio_embedding(y[:, -1:], lstm_h)
y_emb_cur = y_emb_cur * self.embed_scale
y_emb_cur = self.ar_audio_prenet(y_emb_cur)
y_pos_cur = self.ar_audio_position(y)[:, -1:]
y_emb_cur = y_emb_cur + src_lang_emb
y_emb_cur = y_emb_cur + tgt_lang_emb
xy_pos_token = torch.concat([xy_pos_token, y_pos_cur+y_emb_cur], dim=1)
# print(xy_pos_token.size())
y_len = y.shape[1]
x_attn_mask_pad = F.pad(
x_attn_mask,
(0, y_len),
value=True,
)
y_attn_mask = F.pad(
torch.triu(
torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1
),
(x_len, 0),
value=False,
)
xy_attn_mask = torch.concat(
[x_attn_mask_pad, y_attn_mask], dim=0
).to(y.device)
if use_kv_caching and kv_cache is not None:
xy_pos = xy_pos_token[:, [-1]]
xy_attn_mask = xy_attn_mask[:, [-1]]
else:
xy_pos = xy_pos_token
xy_dec, kv_cache = self.ar_decoder.infer(
xy_pos,
mask=xy_attn_mask,
past_kv=kv_cache,
use_cache=use_kv_caching,
)
logits = self.ar_predict_layer(xy_dec[:, -1])
samples, current_logprobs = topk_sampling(
logits, top_k=top_k, top_p=1, temperature=temperature
)
sum_logprobs += current_logprobs * (y[:, -1] != self.NUM_AUDIO_TOKENS)
samples[y[:, -1] == self.NUM_AUDIO_TOKENS] = self.NUM_AUDIO_TOKENS
completed = (samples[:, -1] == self.NUM_AUDIO_TOKENS).all()
if (
completed
or (y.shape[1] - prompts.shape[1]) > x_lens.max() * 32
):
if prompts.shape[1] == y.shape[1]:
raise SyntaxError(
"well trained model shouldn't reach here."
)
lengths = torch.sum(y != self.NUM_AUDIO_TOKENS, dim=1)
avg_logprobs = sum_logprobs / lengths ** length_penalty
# choose the best beam according to sum_logprobs
best_beam = y[torch.argmax(avg_logprobs), :]
worst_beam = y[torch.argmin(avg_logprobs), :]
# strip all eos tokens
best_beam = best_beam[best_beam != self.NUM_AUDIO_TOKENS]
worst_beam = worst_beam[worst_beam != self.NUM_AUDIO_TOKENS]
if return_worst:
y = worst_beam.unsqueeze(0)
else:
y = best_beam.unsqueeze(0)
print(f"VALL-E EOS [{prompts.shape[1]} -> {y.shape[1]}]")
break
y = torch.concat([y, samples], dim=1)
codes = [y[:, prefix_len + int(self.ar_audio_prepend_bos) :]]
if self.num_quantizers == 1:
return torch.stack(codes, dim=-1)
if self.prefix_mode in [2, 4]: # Exclude enrolled_phonemes
enrolled_len = enroll_x_lens.max().item()
# SOS + Synthesis Text + EOS
text = torch.concat(
[
text[:, :1],
text[:, enrolled_len - 1 :],
],
dim=1,
)
text_len = text_len - (enrolled_len - 2)
assert text.shape[0] == 1
x = self.embed_scale * self.nar_text_embedding(text)
# Add language embedding
prompt_language_id = prompt_language.to(x.device)
text_language_id = text_language.to(x.device)
src_lang_emb = self.embed_scale * self.nar_language_embedding(prompt_language_id)
tgt_lang_emb = self.embed_scale * self.nar_language_embedding(text_language_id)
x[:, :enroll_x_lens, :] += src_lang_emb
x[:, enroll_x_lens:, :] += tgt_lang_emb
x = self.nar_text_prenet(x)
x_pos = self.nar_text_position(text)
if self.prefix_mode == 0:
for i, predict_layer in enumerate(
self.nar_predict_layers
):
y_pos = self.nar_audio_prenet(y_emb)
y_pos = self.nar_audio_position(y_pos)
xy_pos = torch.concat([x, y_pos], dim=1)
xy_dec, _ = self.nar_decoder(
(xy_pos, self.nar_stage_embeddings[i].weight)
)
logits = predict_layer(xy_dec[:, text_len + prefix_len :])
samples = torch.argmax(logits, dim=-1)
codes.append(samples)
if i < self.num_quantizers - 2:
y_emb[:, :prefix_len] += self.embed_scale * self.nar_audio_embeddings(
prompts[..., i + 1]
)[0]
y_emb[:, prefix_len:] += self.embed_scale * self.nar_audio_embeddings(samples)[0]
else:
y_pos = self.nar_audio_position(y[:, int(self.ar_audio_prepend_bos):])
ref_at_emb = self.embed_scale * self.nar_audio_embeddings(prompts)[0] + src_lang_emb
est_at = y[:, prefix_len+int(self.ar_audio_prepend_bos):].unsqueeze(-1)
#
for i in range(1, 8):
y_emb, _ = self.nar_audio_embeddings(est_at)
y_emb = self.embed_scale * y_emb + tgt_lang_emb
y_emb = torch.concat([ref_at_emb, y_emb], dim=1)
xy_pos = torch.concat([x+x_pos, y_emb+y_pos], dim=1)
xy_dec = self.nar_decoder(
xy_pos
)
logits = self.nar_predict_layers[i-1](xy_dec[:, text_len + prefix_len :])
# print(logits.size(), xy_pos.size(), xy_dec.size())
samples = torch.argmax(logits, dim=-1)
est_at = torch.concat([est_at, samples.unsqueeze(-1)], dim=-1)
codes.append(samples)
assert len(codes) == self.num_quantizers
return torch.stack(codes, dim=-1)
def top_k_top_p_filtering(
logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
):
if top_k > 0:
top_k = min(
max(top_k, min_tokens_to_keep), logits.size(-1)
) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(
F.softmax(sorted_logits, dim=-1), dim=-1
)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[
..., :-1
].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(
1, sorted_indices, sorted_indices_to_remove
)
logits[indices_to_remove] = filter_value
return logits
def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
if temperature != 1.0:
logits = logits / temperature
# Top-p/top-k filtering
logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
# Sample
token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
logprobs = F.log_softmax(logits.float(), dim=-1)
current_logprobs = logprobs[torch.arange(logprobs.shape[0]), token.squeeze(1)]
return token, current_logprobs
class SLSTM(nn.Module):
def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True, bidirectional=False):
super().__init__()
self.skip = skip
self.lstm = nn.LSTM(dimension, dimension, num_layers, bidirectional=bidirectional)
if bidirectional:
self.out_fc = nn.Linear(dimension*2, dimension)
else:
self.out_fc = None
def forward(self, x, hidden=None):
x = x.permute(2, 0, 1)
y, hidden = self.lstm(x, hidden)
if self.out_fc is not None:
y = self.out_fc(y)
if self.skip:
y = y + x
y = y.permute(1, 2, 0)
return y, hidden
class EncodecDecoderLstm(nn.Module):
def __init__(self, dictionary, emb_dim,
out_dim=None,
num_layers=3, lstm_skip=True, lstm_bidire=False,
activation_param={'alpha': 1.0}, **kwargs):
super().__init__()
# Identity()
if out_dim is None:
out_dim = emb_dim
self.slstm = SLSTM(dimension=out_dim, num_layers=num_layers, skip=lstm_skip, bidirectional=lstm_bidire)
self.elu = nn.ELU(**activation_param)
self.embedding_dim = emb_dim
self.padding_idx = dictionary.pad()
self.emb = nn.Embedding(len(dictionary), emb_dim, dictionary.pad_index)
def forward(self, x, hidden=None):
"""
Args:
x (_type_): B,T,D
"""
# print(x.size())
quantized_out = self.emb(x)
out, hidden = self.slstm(quantized_out.permute(0,2,1), hidden)
out = self.elu(out)
return out.permute(0,2,1), hidden
class NATEncodecDecoderLstm(nn.Module):
def __init__(self, codecs, dictionary, emb_dim,
out_dim=None,
num_layers=3, lstm_skip=True, lstm_bidire=False,
activation_param={'alpha': 1.0}, **kwargs):
super().__init__()
# Identity()
if out_dim is None:
out_dim = emb_dim
self.slstm = SLSTM(dimension=out_dim, num_layers=num_layers, skip=lstm_skip, bidirectional=lstm_bidire)
self.elu = nn.ELU(**activation_param)
self.codecs = codecs
self.embedding_dim = emb_dim
self.padding_idx = dictionary.pad()
self.emb_list = nn.ModuleList(
[nn.Embedding(len(dictionary), emb_dim, dictionary.pad_index) for i in range(len(self.codecs))]
)
def forward(self, x, hidden=None):
"""
Args:
x (_type_): B,T,D
"""
if len(x.size()) == 2:
x = x.unsqueeze(-1)
if x.size(2) != len(self.codecs) and x.size(1) == len(self.codecs):
x = x.permute(0, 2, 1)
quantized_out = 0
for i in range(x.size(2)):
quantized = self.emb_list[i](x[: , :, i])
quantized_out = quantized_out + quantized
# quantized_out = quantized_out / len(self.codecs)
out, hidden = self.slstm(quantized_out.permute(0,2,1), hidden)
out = self.elu(out)
return out.permute(0,2,1), hidden
AutoModel.register(VallexConfig, VALLE) |