modeling.py

from __future__ import annotations

from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GenerationMixin, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast

from .configuration import CustomTransformerConfig


def precompute_rope(head_dim: int, max_seq_len: int, theta: float):
    inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
    t = torch.arange(max_seq_len).float()
    freqs = torch.outer(t, inv_freq)
    return torch.cos(freqs), torch.sin(freqs)


def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
    x1, x2 = x[..., ::2], x[..., 1::2]
    cos = cos[None, None, :, :]
    sin = sin[None, None, :, :]
    rx1 = x1 * cos - x2 * sin
    rx2 = x1 * sin + x2 * cos
    return torch.stack([rx1, rx2], dim=-1).flatten(-2)


def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
    if n_rep == 1:
        return x
    b, n_kv, t, d = x.shape
    x = x[:, :, None, :, :].expand(b, n_kv, n_rep, t, d)
    return x.reshape(b, n_kv * n_rep, t, d)


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        norm_x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
        return norm_x * self.weight


class GQAAttention(nn.Module):
    """Grouped-query attention with RoPE, Q/K bias, and per-head gating."""

    def __init__(self, cfg: CustomTransformerConfig):
        super().__init__()
        self.n_heads = cfg.num_attention_heads
        self.n_kv_heads = cfg.num_key_value_heads
        self.n_rep = cfg.num_attention_heads // cfg.num_key_value_heads
        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
        self.dropout = cfg.dropout

        self.wq = nn.Linear(cfg.hidden_size, cfg.num_attention_heads * self.head_dim, bias=cfg.qk_bias)
        self.wk = nn.Linear(cfg.hidden_size, cfg.num_key_value_heads * self.head_dim, bias=cfg.qk_bias)
        self.wv = nn.Linear(cfg.hidden_size, cfg.num_key_value_heads * self.head_dim, bias=False)
        self.wo = nn.Linear(cfg.num_attention_heads * self.head_dim, cfg.hidden_size, bias=False)

        self.use_head_gating = cfg.use_head_gating
        if self.use_head_gating:
            self.head_gate = nn.Linear(cfg.hidden_size, cfg.num_attention_heads, bias=True)

    def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
        b, t, _ = x.shape

        q = self.wq(x).view(b, t, self.n_heads, self.head_dim).transpose(1, 2)
        k = self.wk(x).view(b, t, self.n_kv_heads, self.head_dim).transpose(1, 2)
        v = self.wv(x).view(b, t, self.n_kv_heads, self.head_dim).transpose(1, 2)

        q = apply_rope(q, cos[:t], sin[:t])
        k = apply_rope(k, cos[:t], sin[:t])

        k = repeat_kv(k, self.n_rep)
        v = repeat_kv(v, self.n_rep)

        out = F.scaled_dot_product_attention(
            q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0.0
        )

        if self.use_head_gating:
            gate = torch.sigmoid(self.head_gate(x))
            gate = gate.transpose(1, 2).unsqueeze(-1)
            out = out * gate

        out = out.transpose(1, 2).contiguous().view(b, t, self.n_heads * self.head_dim)
        return self.wo(out)


class SwiGLU(nn.Module):
    """SwiGLU feed-forward with explicit hidden size."""

    def __init__(self, dim: int, hidden_size: int):
        super().__init__()
        self.w1 = nn.Linear(dim, hidden_size, bias=False)
        self.w3 = nn.Linear(dim, hidden_size, bias=False)
        self.w2 = nn.Linear(hidden_size, dim, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))


def attn_res_aggregate(sources: list, proj: nn.Linear, norm: RMSNorm) -> torch.Tensor:
    v = torch.stack(sources, dim=0)
    k = norm(v)
    logits = torch.einsum("d,sbtd->sbt", proj.weight.squeeze(0), k)
    weights = logits.softmax(dim=0)
    return torch.einsum("sbt,sbtd->btd", weights, v)


class AttnResUnit(nn.Module):
    def __init__(self, dim: int, eps: float):
        super().__init__()
        self.norm = RMSNorm(dim, eps)
        self.proj = nn.Linear(dim, 1, bias=False)
        nn.init.zeros_(self.proj.weight)

    def forward(self, sources: list) -> torch.Tensor:
        return attn_res_aggregate(sources, self.proj, self.norm)


class Block(nn.Module):
    def __init__(self, cfg: CustomTransformerConfig):
        super().__init__()
        self.mode = cfg.attn_res_mode
        if self.mode != "none":
            self.attn_res_unit = AttnResUnit(cfg.hidden_size, cfg.norm_eps)
            self.mlp_res_unit = AttnResUnit(cfg.hidden_size, cfg.norm_eps)

        self.attn_norm = RMSNorm(cfg.hidden_size, cfg.norm_eps)
        self.attn = GQAAttention(cfg)
        self.ffn_norm = RMSNorm(cfg.hidden_size, cfg.norm_eps)
        self.ffn = SwiGLU(cfg.hidden_size, cfg.ffn_hidden_size)


class CustomTransformerForCausalLM(PreTrainedModel, GenerationMixin):
    config_class = CustomTransformerConfig
    base_model_prefix = "custom_transformer"
    main_input_name = "input_ids"

    _tied_weights_keys = ["lm_head.weight"]
    all_tied_weights_keys = {"lm_head.weight": "tok_emb.weight"}
    _keys_to_ignore_on_load_missing = [r"lm_head\.weight"]

    def __init__(self, config: CustomTransformerConfig):
        super().__init__(config)
        self.cfg = config
        self.tok_emb = nn.Embedding(config.vocab_size, config.hidden_size)
        self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_hidden_layers)])
        self.norm_f = RMSNorm(config.hidden_size, config.norm_eps)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        if config.tie_word_embeddings:
            self.lm_head.weight = self.tok_emb.weight

        if config.attn_res_mode != "none":
            self.output_res = AttnResUnit(config.hidden_size, config.norm_eps)

        head_dim = config.hidden_size // config.num_attention_heads
        cos, sin = precompute_rope(head_dim, config.max_position_embeddings, config.rope_theta)
        self.register_buffer("rope_cos", cos, persistent=False)
        self.register_buffer("rope_sin", sin, persistent=False)

        self.apply(self._init_weights)
        self._zero_init_attn_res_queries()
        self.tie_weights()

    @staticmethod
    def _init_weights(module: nn.Module):
        if isinstance(module, nn.Linear):
            nn.init.normal_(module.weight, std=0.02)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, std=0.02)

    def _zero_init_attn_res_queries(self):
        for m in self.modules():
            if isinstance(m, AttnResUnit):
                nn.init.zeros_(m.proj.weight)

    def get_input_embeddings(self):
        return self.tok_emb

    def set_input_embeddings(self, value):
        self.tok_emb = value
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.tok_emb.weight

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.tok_emb.weight

    def tie_weights(self, *args, **kwargs):
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.tok_emb.weight

    def prepare_inputs_for_generation(

        self,

        input_ids,

        past_key_values=None,

        attention_mask=None,

        **kwargs,

    ):
        # No KV cache in this architecture, so generation reuses the full prompt each step.
        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "use_cache": False,
        }

    def _forward_none(self, x: torch.Tensor, cos, sin) -> torch.Tensor:
        h = x
        for block in self.blocks:
            h = h + block.attn(block.attn_norm(h), cos, sin)
            h = h + block.ffn(block.ffn_norm(h))
        return h

    def _forward_full(self, x: torch.Tensor, cos, sin) -> torch.Tensor:
        history = [x]
        for block in self.blocks:
            h_attn = block.attn_res_unit(history)
            v_attn = block.attn(block.attn_norm(h_attn), cos, sin)
            history.append(v_attn)

            h_mlp = block.mlp_res_unit(history)
            v_mlp = block.ffn(block.ffn_norm(h_mlp))
            history.append(v_mlp)
        return self.output_res(history)

    def _forward_block(self, x: torch.Tensor, cos, sin) -> torch.Tensor:
        S = self.config.attn_res_block_size
        blocks_list = [x]
        partial = None
        intra_idx = 0

        def sources():
            return blocks_list if partial is None else (blocks_list + [partial])

        for block in self.blocks:
            intra_idx += 1
            h_attn = attn_res_aggregate(sources(), block.attn_res_unit.proj, block.attn_res_unit.norm)
            v_attn = block.attn(block.attn_norm(h_attn), cos, sin)
            partial = v_attn if partial is None else (partial + v_attn)
            if intra_idx >= S:
                blocks_list = blocks_list + [partial]
                partial = None
                intra_idx = 0

            intra_idx += 1
            h_mlp = attn_res_aggregate(sources(), block.mlp_res_unit.proj, block.mlp_res_unit.norm)
            v_mlp = block.ffn(block.ffn_norm(h_mlp))
            partial = v_mlp if partial is None else (partial + v_mlp)
            if intra_idx >= S:
                blocks_list = blocks_list + [partial]
                partial = None
                intra_idx = 0

        return self.output_res(sources())

    def forward(

        self,

        input_ids: torch.Tensor,

        attention_mask: Optional[torch.Tensor] = None,

        labels: Optional[torch.Tensor] = None,

        use_cache: Optional[bool] = None,

        past_key_values=None,

        output_attentions: Optional[bool] = None,

        output_hidden_states: Optional[bool] = None,

        return_dict: bool = True,

        **kwargs,

    ):
        if use_cache:
            raise ValueError("This architecture does not implement KV caching yet; set use_cache=False.")

        x = self.tok_emb(input_ids)
        cos, sin = self.rope_cos, self.rope_sin

        if self.config.attn_res_mode == "none":
            final = self._forward_none(x, cos, sin)
        elif self.config.attn_res_mode == "full":
            final = self._forward_full(x, cos, sin)
        else:
            final = self._forward_block(x, cos, sin)

        hidden_states = self.norm_f(final)
        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, shift_logits.size(-1)),
                shift_labels.view(-1),
            )

        if not return_dict:
            return (loss, logits)

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=None,
            hidden_states=None,
            attentions=None,
        )