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import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
from .configuration_latex_decoder import LaTeXDecoderConfig
class RMSNorm(nn.Module):
def __init__(self, d_model: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(d_model))
def forward(self, x: torch.Tensor) -> torch.Tensor:
rms = x.pow(2).mean(-1, keepdim=True).add(self.eps).sqrt()
return x / rms * self.weight
def _build_rope_cache(seq_len, head_dim, theta=10000.0, device=None, dtype=torch.float32):
half = head_dim // 2
inv_freq = 1.0 / (theta ** (torch.arange(0, half, device=device, dtype=torch.float32) / half))
pos = torch.arange(seq_len, device=device, dtype=torch.float32)
freqs = torch.outer(pos, inv_freq)
emb = torch.cat([freqs, freqs], dim=-1)
return emb.cos().to(dtype), emb.sin().to(dtype)
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
half = x.shape[-1] // 2
x1, x2 = x[..., :half], x[..., half:]
return torch.cat([-x2, x1], dim=-1)
def apply_rope(q, k, cos, sin):
cos = cos.unsqueeze(0).unsqueeze(0)
sin = sin.unsqueeze(0).unsqueeze(0)
return q * cos + _rotate_half(q) * sin, k * cos + _rotate_half(k) * sin
class CausalSelfAttention(nn.Module):
def __init__(self, cfg: LaTeXDecoderConfig):
super().__init__()
self.n_heads = cfg.n_heads
self.head_dim = cfg.head_dim
self.d_model = cfg.d_model
self.dropout_p = cfg.dropout
self.rope_theta = cfg.rope_theta
self.qkv_proj = nn.Linear(cfg.d_model, 3 * cfg.d_model, bias=False)
self.out_proj = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
self._rope_cache: dict = {}
def _get_rope(self, seq_len, device, dtype):
key = (seq_len, str(device), dtype)
if key not in self._rope_cache:
self._rope_cache[key] = _build_rope_cache(seq_len, self.head_dim, self.rope_theta, device, dtype)
return self._rope_cache[key]
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
B, T, C = x.shape
q, k, v = self.qkv_proj(x).chunk(3, dim=-1)
q = q.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
cos, sin = self._get_rope(T, x.device, q.dtype)
q, k = apply_rope(q, k, cos, sin)
dropout_p = self.dropout_p if self.training else 0.0
if attention_mask is not None:
causal = torch.triu(torch.full((T, T), float("-inf"), device=x.device, dtype=q.dtype), diagonal=1)
pad = (~attention_mask).unsqueeze(1).unsqueeze(2)
attn_bias = causal.unsqueeze(0).unsqueeze(0).expand(B, 1, T, T).clone()
attn_bias = attn_bias.masked_fill(pad, float("-inf"))
out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias, dropout_p=dropout_p, is_causal=False)
else:
out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p, is_causal=True)
return self.out_proj(out.transpose(1, 2).contiguous().view(B, T, C))
class SwiGLUFFN(nn.Module):
def __init__(self, cfg: LaTeXDecoderConfig):
super().__init__()
self.gate_proj = nn.Linear(cfg.d_model, cfg.d_ff, bias=False)
self.up_proj = nn.Linear(cfg.d_model, cfg.d_ff, bias=False)
self.down_proj = nn.Linear(cfg.d_ff, cfg.d_model, bias=False)
self.dropout = nn.Dropout(cfg.dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.dropout(self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)))
class TransformerBlock(nn.Module):
def __init__(self, cfg: LaTeXDecoderConfig):
super().__init__()
self.norm1 = RMSNorm(cfg.d_model)
self.attn = CausalSelfAttention(cfg)
self.norm2 = RMSNorm(cfg.d_model)
self.ffn = SwiGLUFFN(cfg)
self.drop = nn.Dropout(cfg.dropout)
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
x = x + self.drop(self.attn(self.norm1(x), attention_mask))
x = x + self.drop(self.ffn(self.norm2(x)))
return x
class LaTeXDecoderForCausalLM(PreTrainedModel):
config_class = LaTeXDecoderConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
def __init__(self, config: LaTeXDecoderConfig):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, padding_idx=config.pad_id)
self.embed_drop = nn.Dropout(config.dropout)
self.layers = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layers)])
self.norm_final = RMSNorm(config.d_model)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
if config.tie_weights:
self.lm_head.weight = self.embed_tokens.weight
self.post_init()
def _init_weights(self, module: nn.Module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
**kwargs,
) -> CausalLMOutput:
x = self.embed_drop(self.embed_tokens(input_ids))
for layer in self.layers:
x = layer(x, attention_mask)
logits = self.lm_head(self.norm_final(x))
loss = None
if labels is not None:
shift_logits = logits[:, :-1, :].contiguous()
shift_labels = labels[:, 1:].contiguous()
shift_labels = shift_labels.masked_fill(shift_labels == self.config.pad_id, -100)
loss = F.cross_entropy(
shift_logits.view(-1, self.config.vocab_size),
shift_labels.view(-1),
ignore_index=-100,
)
return CausalLMOutput(loss=loss, logits=logits)
@torch.inference_mode()
def generate(
self,
prompt_ids: torch.Tensor,
max_new_tokens: int = 200,
temperature: float = 1.0,
top_p: float = 0.9,
eos_id: Optional[int] = None,
) -> torch.Tensor:
eos = eos_id if eos_id is not None else self.config.eos_id
generated = prompt_ids.clone()
for _ in range(max_new_tokens):
ctx = generated[:, -self.config.max_seq_len:]
logits = self.forward(ctx).logits[:, -1, :]
if temperature == 0.0:
next_id = logits.argmax(dim=-1, keepdim=True)
else:
probs = F.softmax(logits / temperature, dim=-1)
sorted_probs, sorted_idx = probs.sort(dim=-1, descending=True)
cumsum = sorted_probs.cumsum(dim=-1)
sorted_probs[cumsum - sorted_probs > top_p] = 0.0
sorted_probs /= sorted_probs.sum(dim=-1, keepdim=True)
next_id = sorted_idx.gather(-1, torch.multinomial(sorted_probs, 1))
generated = torch.cat([generated, next_id], dim=-1)
if next_id.item() == eos:
break
return generated
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