File size: 2,572 Bytes
80aea5b | 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 | """Configuration for DiffusionLM: masked (absorbing-state) discrete diffusion
language model in the LLaDA / Diffusion-LM family (~51M params at default
scale: hidden 512, 10 layers, heads 8, SwiGLU 1536, vocab 32768).
Training: sample t ~ U(0, 1), mask each token independently with prob t,
predict masked tokens with 1/t weighting, normalized by source token count.
"""
import math
from transformers import PretrainedConfig
class DiffusionLMConfig(PretrainedConfig):
model_type = "diffusion_lm"
def __init__(
self,
vocab_size: int = 32768,
hidden_size: int = 512,
intermediate_size: int = 1536,
num_hidden_layers: int = 10,
num_attention_heads: int = 8,
head_dim: int = 64,
max_position_embeddings: int = 2048,
rope_theta: float = 10000.0,
rms_norm_eps: float = 1e-5,
attention_dropout: float = 0.0,
tie_word_embeddings: bool = True,
bos_token_id: int = 1,
eos_token_id: int = 2,
pad_token_id: int = 0,
mask_token_id: int | None = None,
num_diffusion_steps: int = 64,
time_conditioning: str = "additive",
time_conditioning_scale: float = 0.02,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
self.attention_dropout = attention_dropout
# [MASK] has a separate learned input vector, outside the output vocab.
self.mask_token_id = mask_token_id if mask_token_id is not None else vocab_size
self.num_diffusion_steps = num_diffusion_steps
if time_conditioning not in ('additive', 'normalized', 'none'):
raise ValueError('Unknown diffusion time conditioning')
if not math.isfinite(time_conditioning_scale) or time_conditioning_scale < 0:
raise ValueError('Time conditioning scale must be finite and nonnegative')
self.time_conditioning = time_conditioning
self.time_conditioning_scale = time_conditioning_scale
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