Add DomainTransformerForCausalLM — GPT-style NoPE model with SDPA attention, weight tying, HF Trainer compatible

src/domain_tokenizer/models/modeling.py

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+"""
+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")