File size: 10,214 Bytes
08af434 | 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 | """ELF transformer model."""
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from modules.layers import (
Attention, BottleneckTextProj, FinalLayer, RMSNorm, SwiGLUFFN,
TextRotaryEmbeddingFast, TimestepEmbedder,
DEFAULT_KERNEL_INIT, DEFAULT_BIAS_INIT, NORMAL_INIT_002,
_make_linear,
)
class ELFBlock(nn.Module):
"""ELF Transformer block."""
def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0,
attn_drop: float = 0.0, proj_drop: float = 0.0):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.attn_drop = attn_drop
self.proj_drop = proj_drop
mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.norm1 = RMSNorm(hidden_size, eps=1e-6)
self.attn = Attention(
hidden_size, num_heads, qkv_bias=True, qk_norm=True,
attn_drop=attn_drop, proj_drop=proj_drop,
)
self.norm2 = RMSNorm(hidden_size, eps=1e-6)
self.mlp = SwiGLUFFN(hidden_size, mlp_hidden_dim, drop=proj_drop)
def forward(self, x: torch.Tensor, rope_fn: Optional[nn.Module] = None,
attention_mask: Optional[torch.Tensor] = None,
deterministic: bool = True) -> torch.Tensor:
x_normed = self.norm1(x)
attn_out = self.attn(x_normed, rope_fn, attention_mask=attention_mask,
deterministic=deterministic)
x = x + attn_out
x_normed = self.norm2(x)
mlp_out = self.mlp(x_normed, deterministic=deterministic)
x = x + mlp_out
return x
class ELF(nn.Module):
"""Text ELF Transformer."""
def __init__(
self,
text_encoder_dim: int,
max_length: int,
hidden_size: int = 1024,
depth: int = 24,
num_heads: int = 16,
mlp_ratio: float = 4.0,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
bottleneck_dim: int = 128,
num_time_tokens: int = 4,
num_self_cond_cfg_tokens: int = 4,
num_model_mode_tokens: int = 0,
vocab_size: int = 0,
gradient_checkpointing: bool = False,
):
super().__init__()
self.text_encoder_dim = text_encoder_dim
self.max_length = max_length
self.hidden_size = hidden_size
self.depth = depth
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.attn_drop = attn_drop
self.proj_drop = proj_drop
self.bottleneck_dim = bottleneck_dim
self.num_time_tokens = num_time_tokens
self.num_self_cond_cfg_tokens = num_self_cond_cfg_tokens
self.num_model_mode_tokens = num_model_mode_tokens
self.vocab_size = vocab_size
self.gradient_checkpointing = gradient_checkpointing
# Self-conditioning input projection (only used when input is [z, x_pred]).
self.self_cond_proj = _make_linear(2 * text_encoder_dim, text_encoder_dim, bias=True)
# Text bottleneck projection.
self.text_proj = BottleneckTextProj(text_encoder_dim, hidden_size, bottleneck_dim)
# Time / SC-CFG embedders + learned prefix tokens.
if num_time_tokens <= 0:
raise ValueError("num_time_tokens must be positive for prefix time conditioning")
self.t_embedder = TimestepEmbedder(hidden_size)
self.t_emb_tokens = nn.Parameter(torch.empty(1, num_time_tokens, hidden_size))
NORMAL_INIT_002(self.t_emb_tokens)
if num_self_cond_cfg_tokens > 0:
self.self_cond_cfg_embedder = TimestepEmbedder(hidden_size)
self.self_cond_cfg_tokens = nn.Parameter(torch.empty(1, num_self_cond_cfg_tokens, hidden_size))
NORMAL_INIT_002(self.self_cond_cfg_tokens)
if num_model_mode_tokens > 0:
self.mode_tokens = nn.Parameter(torch.empty(1, num_model_mode_tokens, hidden_size))
NORMAL_INIT_002(self.mode_tokens)
head_dim = hidden_size // num_heads
prefix_total = num_model_mode_tokens + num_time_tokens
if num_self_cond_cfg_tokens > 0:
prefix_total += num_self_cond_cfg_tokens
self.feat_rope = TextRotaryEmbeddingFast(
dim=head_dim, pt_seq_len=max_length, num_empty_token=prefix_total,
)
self.blocks = nn.ModuleList()
q1, q3 = depth // 4, depth // 4 * 3
for i in range(depth):
in_drop_range = q3 > i >= q1
self.blocks.append(ELFBlock(
hidden_size, num_heads, mlp_ratio=mlp_ratio,
attn_drop=attn_drop if in_drop_range else 0.0,
proj_drop=proj_drop if in_drop_range else 0.0,
))
# Final flow-matching output head.
self.final_layer = FinalLayer(hidden_size, patch_size=1, out_channels=text_encoder_dim)
# Factored decoder unembedding: hidden -> text_encoder_dim -> vocab.
bn = text_encoder_dim
self.proj_kernel = nn.Parameter(torch.empty(hidden_size, bn))
self.proj_bias = nn.Parameter(torch.empty(bn))
self.unembed_kernel = nn.Parameter(torch.empty(bn, vocab_size))
self.unembed_bias = nn.Parameter(torch.empty(vocab_size))
DEFAULT_KERNEL_INIT(self.proj_kernel)
DEFAULT_BIAS_INIT(self.proj_bias)
DEFAULT_KERNEL_INIT(self.unembed_kernel)
DEFAULT_BIAS_INIT(self.unembed_bias)
def build_context(self, t: torch.Tensor,
self_cond_cfg_scale: Optional[torch.Tensor] = None) -> list:
B = t.shape[0]
prefix_tokens = []
time_emb = self.t_embedder(t) # (B, hidden)
prefix_tokens.append(
self.t_emb_tokens.expand(B, -1, -1) + time_emb.unsqueeze(1)
)
if self_cond_cfg_scale is not None and self.num_self_cond_cfg_tokens > 0:
sc_emb = self.self_cond_cfg_embedder(self_cond_cfg_scale)
prefix_tokens.append(
self.self_cond_cfg_tokens.expand(B, -1, -1) + sc_emb.unsqueeze(1)
)
return prefix_tokens
def forward(
self,
x: torch.Tensor,
t: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
deterministic: bool = True,
self_cond_cfg_scale: Optional[torch.Tensor] = None,
decoder_step_active: Optional[bool] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""x: (N, S, C) or (N, S, 2C) with self-cond. t: (N,). attention_mask: (N, S), 1=valid."""
B = x.shape[0]
# Self-conditioning: input is [z, x_pred] when 2x encoder dim
with torch.amp.autocast('cuda', enabled=False):
if x.shape[-1] == 2 * self.text_encoder_dim:
x = self.self_cond_proj(x.float())
x = self.text_proj(x.float())
context_prefix_tokens = self.build_context(t, self_cond_cfg_scale)
# Prepend learnable model-mode tokens (gated by decoder_step_active).
# decoder_step_active may be None / Python bool / (B,) tensor — the last
# form supports per-example branching at training time.
model_mode_offset = 0
if self.num_model_mode_tokens > 0:
mode_tokens = self.mode_tokens.expand(B, -1, -1)
if decoder_step_active is None:
active_gate = 0.0
elif isinstance(decoder_step_active, torch.Tensor) and decoder_step_active.dim() > 0:
active_gate = decoder_step_active.to(mode_tokens.dtype).view(-1, 1, 1)
else:
active_gate = float(decoder_step_active)
mode_tokens = mode_tokens * active_gate
x = torch.cat([mode_tokens, x], dim=1)
model_mode_offset = self.num_model_mode_tokens
if attention_mask is not None:
mode_mask = torch.ones((B, self.num_model_mode_tokens),
dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat([mode_mask, attention_mask], dim=1)
prefix_len = 0
if context_prefix_tokens:
prefix_tokens = torch.cat(context_prefix_tokens, dim=1)
prefix_len = prefix_tokens.shape[1]
x = torch.cat([prefix_tokens, x], dim=1)
if attention_mask is not None:
prefix_mask = torch.ones((B, prefix_len),
dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat([prefix_mask, attention_mask], dim=1)
use_checkpoint = self.gradient_checkpointing and self.training and torch.is_grad_enabled()
for block in self.blocks:
if use_checkpoint:
def _block_forward(hidden: torch.Tensor, block: ELFBlock = block) -> torch.Tensor:
return block(hidden, rope_fn=self.feat_rope, attention_mask=attention_mask,
deterministic=deterministic)
x = checkpoint(_block_forward, x, use_reentrant=False)
else:
x = block(x, rope_fn=self.feat_rope, attention_mask=attention_mask,
deterministic=deterministic)
x = x[:, prefix_len + model_mode_offset:]
# Factored decoder unembedding: hidden -> text_encoder_dim -> vocab
with torch.amp.autocast('cuda', enabled=False):
decoder_logits = None
if decoder_step_active is not None:
x_f32 = x.float()
hidden = F.gelu(x_f32 @ self.proj_kernel + self.proj_bias, approximate="tanh")
decoder_logits = hidden @ self.unembed_kernel + self.unembed_bias
output = self.final_layer(x.float())
return output, decoder_logits
# Model factory functions
def ELF_B(**kwargs): return ELF(depth=12, hidden_size=768, num_heads=12, **kwargs)
def ELF_M(**kwargs): return ELF(depth=24, hidden_size=1056, num_heads=16, **kwargs)
def ELF_L(**kwargs): return ELF(depth=32, hidden_size=1280, num_heads=16, **kwargs)
ELF_models = {
'ELF-B': ELF_B, 'ELF-M': ELF_M, 'ELF-L': ELF_L,
}
|