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685e018 | 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 | """GPT-sized bidirectional Transformer used as a masked-token denoiser."""
from __future__ import annotations
import argparse
import math
from pathlib import Path
import torch
from torch import Tensor, nn
from torch.utils.checkpoint import checkpoint
from diffusion_lm.config import ModelConfig, load_config
class DiffusionTransformer(nn.Module):
"""A GPT-like Transformer with the causal mask deliberately removed.
The network predicts clean tokens from an input containing absorbing mask
tokens. Passing ``output_positions`` avoids materializing vocabulary logits
for already-visible tokens during training.
"""
def __init__(self, config: ModelConfig) -> None:
super().__init__()
self.config = config
self.tokenizer_sha256: str | None = None
self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
self.position_embedding = nn.Embedding(config.max_seq_len, config.d_model)
self.embedding_dropout = nn.Dropout(config.dropout)
if config.use_flex_attention:
from diffusion_lm.flexattn import FlexEncoder
self.transformer = FlexEncoder(
d_model=config.d_model,
n_heads=config.n_heads,
d_ff=config.d_ff,
dropout=config.dropout,
n_layers=config.n_layers,
activation_checkpointing=config.activation_checkpointing,
)
else:
layer = nn.TransformerEncoderLayer(
d_model=config.d_model,
nhead=config.n_heads,
dim_feedforward=config.d_ff,
dropout=config.dropout,
activation="gelu",
batch_first=True,
norm_first=True,
)
self.transformer = nn.TransformerEncoder(
layer,
num_layers=config.n_layers,
norm=nn.LayerNorm(config.d_model),
enable_nested_tensor=False,
)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.apply(self._init_weights)
self._init_residual_outputs()
if config.tie_embeddings:
self.lm_head.weight = self.token_embedding.weight
self.register_buffer(
"_forbidden_output_token_ids",
torch.tensor(config.forbidden_output_token_ids, dtype=torch.long),
persistent=False,
)
@staticmethod
def _init_weights(module: nn.Module) -> None:
if isinstance(module, (nn.Linear, nn.Embedding)):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
def _init_residual_outputs(self) -> None:
"""Scale residual branch outputs as in GPT-2 for stable deep training."""
if self.config.use_flex_attention:
self.transformer.init_residual_outputs(self.config.n_layers)
return
residual_std = 0.02 / math.sqrt(2 * self.config.n_layers)
for layer in self.transformer.layers:
nn.init.normal_(layer.self_attn.out_proj.weight, mean=0.0, std=residual_std)
nn.init.normal_(layer.linear2.weight, mean=0.0, std=residual_std)
def _checkpointed_transformer(
self,
hidden: Tensor,
padding_mask: Tensor | None,
attn_mask: Tensor | None = None,
) -> Tensor:
for layer in self.transformer.layers:
def run_layer(layer_input: Tensor, *, current_layer: nn.Module = layer) -> Tensor:
return current_layer(
layer_input, src_mask=attn_mask, src_key_padding_mask=padding_mask
)
hidden = checkpoint(run_layer, hidden, use_reentrant=False)
if self.transformer.norm is not None:
hidden = self.transformer.norm(hidden)
return hidden
def _expand_attn_mask(self, attn_mask: Tensor | None, input_ids: Tensor) -> Tensor | None:
"""Broadcast a per-sample boolean blocking mask across attention heads.
Accepts ``[L, L]`` shared masks or ``[B, L, L]`` per-sample masks with
``True`` marking blocked key positions, matching the src_mask convention.
"""
if attn_mask is None:
return None
batch_size, sequence_length = input_ids.shape
if attn_mask.dtype != torch.bool:
raise ValueError("attn_mask must be boolean with True marking blocked positions")
if attn_mask.shape == (sequence_length, sequence_length):
return attn_mask
if attn_mask.shape != (batch_size, sequence_length, sequence_length):
raise ValueError("attn_mask must have shape [L, L] or [batch, L, L]")
return attn_mask.repeat_interleave(self.config.n_heads, dim=0)
def encode(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
attn_mask: Tensor | None = None,
) -> Tensor:
"""Return contextual token states; ``attn_mask`` restricts attention topology."""
if input_ids.ndim != 2:
raise ValueError("input_ids must have shape [batch, sequence]")
batch_size, sequence_length = input_ids.shape
if sequence_length > self.config.max_seq_len:
raise ValueError(
f"sequence length {sequence_length} exceeds max_seq_len "
f"{self.config.max_seq_len}"
)
if attention_mask is not None and attention_mask.shape != input_ids.shape:
raise ValueError("attention_mask must match input_ids")
positions = torch.arange(sequence_length, device=input_ids.device)
hidden = self.token_embedding(input_ids) + self.position_embedding(positions)[None, :, :]
hidden = self.embedding_dropout(hidden)
# TransformerEncoder expects True for padding, the inverse of the common
# attention-mask convention. src_mask is only supplied by region-aware callers.
padding_mask = None if attention_mask is None else ~attention_mask.bool()
if self.config.use_flex_attention:
from diffusion_lm.flexattn import build_block_mask
if attn_mask is not None and attn_mask.dtype != torch.bool:
raise ValueError("attn_mask must be boolean with True marking blocked positions")
block_mask = build_block_mask(
attn_mask, padding_mask, batch_size, sequence_length, hidden.device
)
return self.transformer(hidden, block_mask)
expanded_attn_mask = self._expand_attn_mask(attn_mask, input_ids)
if (
self.config.activation_checkpointing
and self.training
and torch.is_grad_enabled()
):
return self._checkpointed_transformer(hidden, padding_mask, expanded_attn_mask)
return self.transformer(
hidden, mask=expanded_attn_mask, src_key_padding_mask=padding_mask
)
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
output_positions: Tensor | None = None,
attn_mask: Tensor | None = None,
) -> Tensor:
"""Predict vocabulary logits for all tokens or selected positions only."""
hidden = self.encode(input_ids, attention_mask=attention_mask, attn_mask=attn_mask)
if output_positions is not None:
if output_positions.shape != input_ids.shape:
raise ValueError("output_positions must match input_ids")
hidden = hidden[output_positions.bool()]
logits = self.lm_head(hidden)
# Corruption/control tokens are never valid clean-token predictions. EOS
# deliberately remains available so generation can terminate naturally.
if self._forbidden_output_token_ids.numel():
logits.index_fill_(
-1,
self._forbidden_output_token_ids,
torch.finfo(logits.dtype).min,
)
return logits
@property
def num_parameters(self) -> int:
"""Count unique trainable parameters (shared embeddings count once)."""
return sum(parameter.numel() for parameter in self.parameters() if parameter.requires_grad)
def build_denoiser(
config: ModelConfig,
*,
load_pretrained: bool = True,
dtype: torch.dtype | None = None,
) -> nn.Module:
"""Construct the denoiser a config describes: project transformer or pretrained backbone.
``load_pretrained=False`` builds the architecture only, for callers that immediately
restore weights from a project checkpoint.
"""
if config.backbone == "hf-qwen3":
from diffusion_lm.hf_bridge import Qwen3Denoiser
return Qwen3Denoiser(config, load_pretrained=load_pretrained, dtype=dtype)
return DiffusionTransformer(config)
def format_parameter_count(count: int) -> str:
if count >= 1_000_000:
return f"{count / 1_000_000:.2f}M"
if count >= 1_000:
return f"{count / 1_000:.2f}K"
return str(count)
def main() -> None:
parser = argparse.ArgumentParser(description="Report the exact model parameter count")
parser.add_argument("--config", type=Path, required=True, help="experiment YAML")
args = parser.parse_args()
config = load_config(args.config)
# Parameter inspection should not allocate four gigabytes for the 1B preset.
with torch.device("meta"):
model = DiffusionTransformer(config.model)
print(f"parameters: {model.num_parameters:,} ({format_parameter_count(model.num_parameters)})")
if __name__ == "__main__":
main()
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