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c7f3ffb 4a3004b c7f3ffb 4a3004b c7f3ffb 4a3004b c7f3ffb 4a3004b c7f3ffb 4a3004b c7f3ffb 4a3004b c7f3ffb 4a3004b c7f3ffb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 | from transformers import LlamaConfig, LlamaModel
import torch
import torch.nn as nn
from typing import List, Optional, Tuple, Union
import math
from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaRotaryEmbedding
from transformers.models.llama.modeling_llama import BaseModelOutputWithPast
# sinusoidal positional encoding
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :] * 1.0
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class LlamaAdaptiveRMSNorm(nn.Module):
def __init__(self, hidden_size=1024, eps=1e-6, dim_cond=1024):
super().__init__()
self.to_weight = nn.Linear(dim_cond, hidden_size)
nn.init.zeros_(self.to_weight.weight)
nn.init.ones_(self.to_weight.bias)
self.variance_epsilon = eps
self._is_hf_initialized = True # disable automatic init
def forward(self, hidden_states, cond_embedding):
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
weight = self.to_weight(cond_embedding)
if len(weight.shape) == 2:
weight = weight.unsqueeze(1)
return (weight * hidden_states).to(input_dtype)
class LlamaNARDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: LlamaConfig, layer_idx: int):
"""Override to adaptive layer norm"""
super().__init__(config, layer_idx) # init attention, mlp, etc.
self.input_layernorm = LlamaAdaptiveRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, dim_cond=config.hidden_size
)
self.post_attention_layernorm = LlamaAdaptiveRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, dim_cond=config.hidden_size
)
# add `cond` in forward function
def forward(
self,
hidden_states: torch.Tensor,
cond_embedding: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
past_key_value=None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
residual = hidden_states
hidden_states = self.input_layernorm(
hidden_states, cond_embedding=cond_embedding
)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
past_key_value=past_key_value,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(
hidden_states, cond_embedding=cond_embedding
)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class DiffLlama(LlamaModel):
def __init__(
self,
mel_dim=100,
hidden_size=1024,
num_heads=16,
num_layers=16,
dropout=0.1,
ffn_dropout=0.1,
attention_dropout=0.0,
config=LlamaConfig(0, 256, 1024, 1, 1),
):
super().__init__(config)
self.layers = nn.ModuleList(
[
LlamaNARDecoderLayer(
LlamaConfig(
hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=4096,
intermediate_size=hidden_size * 4,
),
layer_idx=i,
)
for i in range(num_layers)
]
)
self.norm = LlamaAdaptiveRMSNorm(hidden_size, dim_cond=hidden_size)
self.diff_step_embedding = SinusoidalPosEmb(hidden_size)
self.diff_step_mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
self.cond_mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
self.mel_mlp = nn.Sequential(
nn.Linear(mel_dim, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
self.mel_out_mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, mel_dim),
)
for layer in self.layers:
layer.input_layernorm = LlamaAdaptiveRMSNorm(
hidden_size, dim_cond=hidden_size
)
layer.post_attention_layernorm = LlamaAdaptiveRMSNorm(
hidden_size, dim_cond=hidden_size
)
self.embed_tokens = None
# Re-create rotary_emb with the actual layer config dimensions
layer_config = LlamaConfig(
hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=4096,
intermediate_size=hidden_size * 4,
)
self.rotary_emb = LlamaRotaryEmbedding(config=layer_config)
self.post_init()
# self.reset_parameters()
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create noncausal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
def _expand_mask(
mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = (
mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
x,
diffusion_step,
cond,
x_mask,
input_ids: torch.LongTensor = None, # [num_quant, B, T]
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = False,
) -> Union[Tuple, BaseModelOutputWithPast]:
# retrieve some shape info
batch_size, seq_length, _ = x.shape
# condtion mlp
cond_embedding = self.cond_mlp(cond) # (B, T, C)
# condition mel
x = self.mel_mlp(x)
# diffusion step embedding
diffusion_step = self.diff_step_embedding(diffusion_step).to(x.device)
diffusion_step = self.diff_step_mlp(diffusion_step) # (B, C)
x = x + cond_embedding
inputs_embeds = x
attention_mask = x_mask
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past),
dtype=torch.bool,
device=inputs_embeds.device,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
hidden_states = inputs_embeds
# Compute rotary position embeddings
position_embeddings = self.rotary_emb(hidden_states, position_ids)
if self.gradient_checkpointing and self.training:
if use_cache:
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
all_layer_hidden_states = []
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = (
past_key_values[idx] if past_key_values is not None else None
)
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_embeddings=position_embeddings,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cond_embedding=diffusion_step,
)
hidden_states = layer_outputs[0]
all_layer_hidden_states.append(hidden_states.clone())
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states, cond_embedding=diffusion_step)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
hidden_states = self.mel_out_mlp(hidden_states)
# if not return_dict:
# return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
# return BaseModelOutputWithPast(
# last_hidden_state=hidden_states,
# past_key_values=next_cache,
# hidden_states=all_hidden_states,
# attentions=all_self_attns,
# )
if return_dict:
return {
"output": hidden_states,
"hidden_states": all_layer_hidden_states,
}
return hidden_states
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