modeling_stldec.py

import torch
from torch import nn
import torch.nn.functional as F
from typing import List, Optional, Tuple, Union, Dict
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from transformers.generation import GenerationMixin
from torch.utils.checkpoint import checkpoint
from .configuration_stldec import STLDecoderConfig

class STLPreTrainedModel(PreTrainedModel):
    config_class = STLDecoderConfig
    base_model_prefix = "model"
    def _init_weights(self, module):
        """Migliorata con Xavier Uniform per evitare gradienti esplosivi nelle fasi iniziali."""
        if isinstance(module, nn.Linear):
            torch.nn.init.xavier_uniform_(module.weight)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=0.02)

class STLAttention(nn.Module):
    def __init__(self, config):
        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.dropout = nn.Dropout(config.attention_dropout)

    def forward(self, hidden_states, encoder_hidden_states=None, past_key_value=None, attention_mask=None):
        bsz, q_len, _ = hidden_states.size()
        key_value_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
        kv_len = key_value_states.size(1)

        q = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) * self.scaling
        k = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
        v = self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)

        if past_key_value is not None and encoder_hidden_states is None:
            k = torch.cat([past_key_value[0], k], dim=2)
            v = torch.cat([past_key_value[1], v], dim=2)

        present_kv = (k, v) if encoder_hidden_states is None else None
        attn_weights = torch.matmul(q, k.transpose(-1, -2))

        if attention_mask is not None:
            attn_weights = attn_weights + attention_mask

        attn_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
        attn_output = torch.matmul(self.dropout(attn_probs), v)
        attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, self.hidden_size)

        return self.out_proj(attn_output), present_kv

class STLDecoderBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln1 = nn.LayerNorm(config.hidden_size)
        self.self_attn = STLAttention(config)
        self.ln_cross = nn.LayerNorm(config.hidden_size)
        self.cross_attn = STLAttention(config)
        self.ln2 = nn.LayerNorm(config.hidden_size)
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
        self.dropout = nn.Dropout(config.dropout)
        self.gradient_checkpointing = False

    def forward(self, hidden_states, encoder_hidden_states=None, past_key_value=None, attention_mask=None):
        if self.training and self.gradient_checkpointing:
            return checkpoint(
                self.internal_forward,
                hidden_states,
                encoder_hidden_states,
                past_key_value,
                attention_mask,
                use_reentrant=False
            )
        return self.internal_forward(hidden_states, encoder_hidden_states, past_key_value, attention_mask)

    def internal_forward(self, hidden_states, encoder_hidden_states=None, past_key_value=None, attention_mask=None):
        """Modificata in Pre-Norm per garantire la stabilità del gradiente."""
        # 1. Self-Attention
        residual = hidden_states
        hidden_states = self.ln1(hidden_states) # LN PRIMA
        hidden_states, pkv = self.self_attn(hidden_states, past_key_value=past_key_value, attention_mask=attention_mask)
        hidden_states = residual + self.dropout(hidden_states)

        # 2. Cross-Attention
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.ln_cross(hidden_states) # LN PRIMA
            hidden_states, _ = self.cross_attn(hidden_states, encoder_hidden_states=encoder_hidden_states)
            hidden_states = residual + self.dropout(hidden_states)

        # 3. Feed-Forward
        residual = hidden_states
        hidden_states = self.ln2(hidden_states) # LN PRIMA
        hidden_states = self.fc2(F.gelu(self.fc1(hidden_states)))
        hidden_states = residual + self.dropout(hidden_states)
        return hidden_states, pkv

class STLDecoderModel(STLPreTrainedModel, GenerationMixin):
    def __init__(self, config):
        super().__init__(config)
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
        # Posizionali Sinusoidali rimossi dal __init__ perché calcolati dinamicamente nel forward
        self.layers = nn.ModuleList([STLDecoderBlock(config) for _ in range(config.num_hidden_layers)])
        self.norm = nn.LayerNorm(config.hidden_size)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()

    def get_sinusoidal_embeddings(self, seq_len, d_model, device):
        """Genera posizioni matematiche stabili, evitando errori di indice della tabella fixed."""
        inv_freq = 1.0 / (10000 ** (torch.arange(0, d_model, 2).float() / d_model)).to(device)
        pos = torch.arange(seq_len, device=device).type_as(inv_freq)
        sin_inp = torch.einsum("i,j->ij", pos, inv_freq)
        emb = torch.cat((sin_inp.sin(), sin_inp.cos()), dim=-1)
        return emb[None, :, :]

    @property
    def supports_gradient_checkpointing(self): return True

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, STLDecoderBlock):
            module.gradient_checkpointing = value

    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, v): self.embed_tokens = v
    def get_output_embeddings(self): return self.lm_head

    def forward(self, input_ids=None, encoder_hidden_states=None, past_key_values=None, labels=None, use_cache=None, return_dict=None, **kwargs):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        bsz, seq_len = input_ids.size()
        past_len = 0
        if past_key_values is not None:
            past_len = past_key_values[0][0].shape[2]

        hidden_states = self.embed_tokens(input_ids)
        
        # Sostituzione con sinusoidali (più robusti dello Script 2)
        pos_emb = self.get_sinusoidal_embeddings(seq_len, self.config.hidden_size, input_ids.device)
        hidden_states = hidden_states + pos_emb[:, :seq_len, :]

        # Maschera causale ottimizzata
        causal_mask = torch.full((seq_len, seq_len + past_len), float("-inf"), device=input_ids.device, dtype=hidden_states.dtype)
        causal_mask.triu_(diagonal=past_len + 1)
        causal_mask = causal_mask[None, None, :, :]

        next_cache = () if use_cache else None
        for i, layer in enumerate(self.layers):
            pk = past_key_values[i] if past_key_values is not None else None
            hidden_states, pkv = layer(hidden_states, encoder_hidden_states=encoder_hidden_states, past_key_value=pk, attention_mask=causal_mask)
            if use_cache: next_cache += (pkv,)

        hidden_states = self.norm(hidden_states)
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            
            # --- MODIFICA DI SICUREZZA ---
            # Prendiamo il vocab_size dai logits correnti, non dal config fisso,
            # per evitare l'Assertion t < n_classes se hai fatto un resize_token_embeddings.
            current_vocab_size = logits.size(-1)
            loss = F.cross_entropy(
                shift_logits.view(-1, current_vocab_size), 
                shift_labels.view(-1),
                ignore_index=-100 # Standard HF per ignorare padding trasformato
            )

        if not return_dict:
            return (loss, logits, next_cache) if loss is not None else (logits, next_cache)
        return CausalLMOutputWithCrossAttentions(loss=loss, logits=logits, past_key_values=next_cache)

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        return tuple(tuple(s.index_select(0, beam_idx.to(s.device)) for s in layer) for layer in past_key_values)