""" DomainTransformer Model — GPT-style causal decoder for domain token sequences. Architecture follows: - NoPE (no positional encoding) — Kazemnejad et al. 2023 (arXiv:2305.19466) - Pre-norm (LayerNorm before attention and FFN) — GPT-2 style - F.scaled_dot_product_attention with is_causal=True — auto FlashAttention - Weight tying between token embedding and LM head - Scaled residual initialization: 1/sqrt(2*N_layers) Reference sizes (Nubank nuFormer, arXiv:2507.23267): - 24M: 6 layers, d=512, 8 heads - 330M: 24 layers, d=1024, 16 heads """ import math from typing import Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from transformers import PreTrainedModel from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from .configuration import DomainTransformerConfig class DomainTransformerAttention(nn.Module): """Multi-head self-attention with NoPE. Uses F.scaled_dot_product_attention for automatic FlashAttention/SDPA dispatch. No positional encoding — causal masking via is_causal=True. """ def __init__(self, config: DomainTransformerConfig): super().__init__() self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.scaling = self.head_dim ** -0.5 self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.k_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.attn_dropout = config.attention_probs_dropout_prob def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: B, T, C = hidden_states.shape q = self.q_proj(hidden_states).view(B, T, self.num_heads, self.head_dim).transpose(1, 2) k = self.k_proj(hidden_states).view(B, T, self.num_heads, self.head_dim).transpose(1, 2) v = self.v_proj(hidden_states).view(B, T, self.num_heads, self.head_dim).transpose(1, 2) # Convert HF-style attention_mask (1=attend, 0=ignore, long) to SDPA format sdpa_mask = None use_causal = True if attention_mask is not None: sdpa_mask = attention_mask[:, None, None, :].to(dtype=q.dtype) sdpa_mask = (1.0 - sdpa_mask) * torch.finfo(q.dtype).min use_causal = False attn_out = F.scaled_dot_product_attention( q, k, v, attn_mask=sdpa_mask, dropout_p=self.attn_dropout if self.training else 0.0, is_causal=use_causal, scale=self.scaling, ) attn_out = attn_out.transpose(1, 2).contiguous().reshape(B, T, C) return self.out_proj(attn_out) class DomainTransformerMLP(nn.Module): """Two-layer FFN with GELU activation (GPT-2 style).""" def __init__(self, config: DomainTransformerConfig): super().__init__() self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=True) self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=True) self.act = nn.GELU(approximate="tanh") self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: return self.dropout(self.down_proj(self.act(self.up_proj(hidden_states)))) class DomainTransformerBlock(nn.Module): """Single transformer block with pre-norm architecture.""" def __init__(self, config: DomainTransformerConfig): super().__init__() self.ln_1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.attn = DomainTransformerAttention(config) self.ln_2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.mlp = DomainTransformerMLP(config) def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: residual = hidden_states hidden_states = self.attn(self.ln_1(hidden_states), attention_mask) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.mlp(self.ln_2(hidden_states)) hidden_states = residual + hidden_states return hidden_states class DomainTransformerPreTrainedModel(PreTrainedModel): """Base class with weight initialization.""" config_class = DomainTransformerConfig base_model_prefix = "model" supports_gradient_checkpointing = True def _init_weights(self, module: nn.Module): std = self.config.initializer_range if isinstance(module, nn.Linear): nn.init.normal_(module.weight, mean=0.0, std=std) 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=std) if module.padding_idx is not None: nn.init.zeros_(module.weight[module.padding_idx]) elif isinstance(module, nn.LayerNorm): nn.init.zeros_(module.bias) nn.init.ones_(module.weight) class DomainTransformerModel(DomainTransformerPreTrainedModel): """The bare DomainTransformer: embeddings + blocks + final layernorm.""" def __init__(self, config: DomainTransformerConfig): super().__init__(config) self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) self.embed_dropout = nn.Dropout(config.hidden_dropout_prob) self.blocks = nn.ModuleList([DomainTransformerBlock(config) for _ in range(config.num_hidden_layers)]) self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.gradient_checkpointing = False self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward(self, input_ids=None, attention_mask=None, inputs_embeds=None, **kwargs): if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) hidden_states = self.embed_dropout(inputs_embeds) for block in self.blocks: if self.gradient_checkpointing and self.training: hidden_states = torch.utils.checkpoint.checkpoint(block, hidden_states, attention_mask, use_reentrant=False) else: hidden_states = block(hidden_states, attention_mask) hidden_states = self.ln_f(hidden_states) return BaseModelOutputWithPast(last_hidden_state=hidden_states) class DomainTransformerForCausalLM(DomainTransformerPreTrainedModel): """DomainTransformer with a causal language modeling head. The LM head is weight-tied with the token embedding layer. Loss is computed via standard shifted cross-entropy. """ _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} def __init__(self, config: DomainTransformerConfig): super().__init__(config) self.model = DomainTransformerModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def forward(self, input_ids=None, attention_mask=None, labels=None, inputs_embeds=None, **kwargs): outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds) hidden_states = outputs.last_hidden_state logits = self.lm_head(hidden_states) loss = None if labels is not None: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() loss = F.cross_entropy(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1), ignore_index=-100) return CausalLMOutputWithPast(loss=loss, logits=logits) def get_user_embedding(self, input_ids, attention_mask=None): """Extract user-level embedding from the last non-padding token.""" outputs = self.model(input_ids=input_ids, attention_mask=attention_mask) hidden_states = outputs.last_hidden_state if attention_mask is not None: seq_lengths = attention_mask.sum(dim=1) - 1 batch_idx = torch.arange(hidden_states.size(0), device=hidden_states.device) return hidden_states[batch_idx, seq_lengths] else: return hidden_states[:, -1, :] DomainTransformerConfig.register_for_auto_class() DomainTransformerForCausalLM.register_for_auto_class("AutoModelForCausalLM")