modeling_sovythos.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_sovythos import SovythosConfig


class RMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-06):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        dtype = x.dtype
        x = x.float()
        rms = x.pow(2).mean(-1, keepdim=True)
        x = x * torch.rsqrt(rms + self.eps)
        return self.weight * x.to(dtype)


def precompute_rope(head_dim, max_seq_len, theta, device, dtype=torch.float32):
    inv_freq = 1.0 / theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim)
    t = torch.arange(max_seq_len, device=device).float()
    freqs = torch.outer(t, inv_freq)
    emb = torch.cat((freqs, freqs), dim=-1)
    return emb.cos().to(dtype), emb.sin().to(dtype)


def rotate_half(x):
    x1, x2 = x.chunk(2, dim=-1)
    return torch.cat((-x2, x1), dim=-1)


def apply_rope(x, cos, sin, offset=0):
    T = x.shape[-2]
    cos = cos[offset:offset + T][None, None, :, :]
    sin = sin[offset:offset + T][None, None, :, :]
    return x * cos + rotate_half(x) * sin


def repeat_kv(x, n_rep):
    if n_rep == 1:
        return x
    B, Hkv, T, D = x.shape
    return x[:, :, None, :, :].expand(B, Hkv, n_rep, T, D).reshape(B, Hkv * n_rep, T, D)


class Attention(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.n_heads = cfg.n_heads
        self.n_kv_heads = cfg.n_kv_heads
        self.n_rep = self.n_heads // self.n_kv_heads
        self.head_dim = cfg.dim // cfg.n_heads
        self.dropout = cfg.dropout
        self.q_proj = nn.Linear(cfg.dim, cfg.n_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(cfg.n_heads * self.head_dim, cfg.dim, bias=False)
        self.q_norm = RMSNorm(self.head_dim, cfg.norm_eps)
        self.k_norm = RMSNorm(self.head_dim, cfg.norm_eps)

    def forward(self, x, cos, sin):
        B, T, C = x.shape
        q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
        k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
        v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
        q, k = self.q_norm(q), self.k_norm(k)
        q = apply_rope(q, cos, sin)
        k = apply_rope(k, cos, sin)
        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)
        out = out.transpose(1, 2).contiguous().view(B, T, -1)
        return self.o_proj(out)


class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        hidden = int(2 * (4 * cfg.dim) / 3)
        if cfg.ffn_dim_multiplier is not None:
            hidden = int(cfg.ffn_dim_multiplier * hidden)
        hidden = cfg.ffn_multiple_of * ((hidden + cfg.ffn_multiple_of - 1) // cfg.ffn_multiple_of)
        self.gate_proj = nn.Linear(cfg.dim, hidden, bias=False)
        self.up_proj = nn.Linear(cfg.dim, hidden, bias=False)
        self.down_proj = nn.Linear(hidden, cfg.dim, bias=False)

    def forward(self, x):
        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))


class DecoderBlock(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.input_layernorm = RMSNorm(cfg.dim, cfg.norm_eps)
        self.self_attn = Attention(cfg)
        self.post_attention_layernorm = RMSNorm(cfg.dim, cfg.norm_eps)
        self.mlp = FeedForward(cfg)

    def forward(self, x, cos, sin):
        x = x + self.self_attn(self.input_layernorm(x), cos, sin)
        x = x + self.mlp(self.post_attention_layernorm(x))
        return x


class SovythosPreTrainedModel(PreTrainedModel):
    config_class = SovythosConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True

    def _init_weights(self, 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)


class SovythosForCausalLM(SovythosPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.embed_tokens = nn.Embedding(config.vocab_size, config.dim)
        self.layers = nn.ModuleList([DecoderBlock(config) for _ in range(config.n_layers)])
        self.norm = RMSNorm(config.dim, config.norm_eps)
        self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False)
        if config.tie_embeddings:
            self.lm_head.weight = self.embed_tokens.weight
        self.head_dim = config.dim // config.n_heads
        self._rope_cache = {}
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def _rope_for(self, device, dtype):
        # ملاحظة: بنحسب rope live بدل ما نعتمد على register_buffer، لأن
        # transformers بيحمل الموديل عادةً عبر meta-device (low_cpu_mem_usage)
        # وأي buffer معمول persistent=False ومش موجود في checkpoint بيفضل
        # ذاكرة غير مهيأة (garbage) بدل القيم الحقيقية بعد النقل من meta لـ real.
        key = str(device)
        if key not in self._rope_cache:
            cos, sin = precompute_rope(self.head_dim, self.config.max_seq_len, self.config.rope_theta, device=device)
            self._rope_cache[key] = (cos, sin)
        return self._rope_cache[key]

    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
        cos, sin = self._rope_for(input_ids.device, torch.float32)
        h = self.embed_tokens(input_ids)
        for layer in self.layers:
            h = layer(h, cos, sin)
        h = self.norm(h)
        logits = self.lm_head(h)
        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),
                ignore_index=-100,
            )
        return CausalLMOutputWithPast(loss=loss, logits=logits)

    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        return {"input_ids": input_ids}