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from typing import Optional, Tuple, List
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.generation.utils import GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_veronica import VeronicaConfig
from .modeling_components import PolymorphicMLP, router_aux_loss, Fp32LayerNorm, apply_rotary_pos_emb
class MultiHeadSelfAttention(nn.Module):
def __init__(self, hidden_size: int, num_heads: int, dropout: float = 0.0, max_position_embeddings: int = 4096, rope_theta: float = 10000.0):
super().__init__()
assert hidden_size % num_heads == 0, "hidden_size must be divisible by n_head"
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.scale = 1.0 / math.sqrt(self.head_dim)
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.qkv = nn.Linear(hidden_size, 3 * hidden_size)
self.out_proj = nn.Linear(hidden_size, hidden_size)
self.attn_drop = nn.Dropout(dropout)
self.resid_drop = nn.Dropout(dropout)
# Precomputa RoPE frequencies
self._rope_cached_seq_len = 0
self._rope_cos_cached = None
self._rope_sin_cached = None
def _split_heads(self, x: torch.Tensor) -> torch.Tensor:
B, T, C = x.shape
x = x.view(B, T, self.num_heads, self.head_dim).transpose(1, 2) # (B, nh, T, hd)
return x
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
B, nh, T, hd = x.shape
return x.transpose(1, 2).contiguous().view(B, T, nh * hd)
def _get_rope_cos_sin(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]:
"""Genera o recupera dalla cache cos/sin per RoPE."""
if seq_len <= self._rope_cached_seq_len and self._rope_cos_cached is not None:
return self._rope_cos_cached[:, :, :seq_len, :].to(device=device, dtype=dtype), \
self._rope_sin_cached[:, :, :seq_len, :].to(device=device, dtype=dtype)
# Genera nuove frequenze
self._rope_cached_seq_len = max(seq_len, self.max_position_embeddings)
# inv_freq: (hd/2,)
dim = self.head_dim
inv_freq = 1.0 / (self.rope_theta ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
# t: (seq_len,)
t = torch.arange(self._rope_cached_seq_len, dtype=torch.float32, device=device)
# freqs: (seq_len, hd/2)
freqs = torch.outer(t, inv_freq)
# Duplica per avere shape (seq_len, hd)
emb = torch.cat([freqs, freqs], dim=-1) # (seq_len, hd)
# cos, sin: (1, 1, seq_len, hd)
cos = emb.cos().unsqueeze(0).unsqueeze(0)
sin = emb.sin().unsqueeze(0).unsqueeze(0)
self._rope_cos_cached = cos
self._rope_sin_cached = sin
return cos[:, :, :seq_len, :].to(dtype=dtype), sin[:, :, :seq_len, :].to(dtype=dtype)
def forward(
self,
x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None, # additive mask [B,1,T,S] in float32
past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: bool = False,
position_offset: int = 0, # offset per posizione (per KV cache)
) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
B, T, C = x.shape
qkv = self.qkv(x)
q, k, v = qkv.split(C, dim=-1)
q = self._split_heads(q) # (B, nh, T, hd)
k = self._split_heads(k)
v = self._split_heads(v)
# Applica RoPE a q e k
cos, sin = self._get_rope_cos_sin(position_offset + T, q.device, q.dtype)
# Prendi solo le posizioni rilevanti [position_offset : position_offset+T]
cos = cos[:, :, position_offset:position_offset+T, :]
sin = sin[:, :, position_offset:position_offset+T, :]
q, k = apply_rotary_pos_emb(q, k, cos, sin)
present = None
if past_key_value is not None:
pk, pv = past_key_value # (B, nh, Tp, hd)
k = torch.cat([pk, k], dim=-2)
v = torch.cat([pv, v], dim=-2)
if use_cache:
present = (k, v)
att = (q @ k.transpose(-2, -1)) * self.scale # (B, nh, T, S)
att = att.float()
if attn_mask is not None:
att = att + attn_mask # additive bias: -inf on masked pos
att = F.softmax(att, dim=-1)
att = self.attn_drop(att)
att = att.to(v.dtype) # Cast back to match v's dtype (BF16/FP16/FP32)
y = att @ v # (B, nh, T, hd)
y = self._merge_heads(y)
y = self.out_proj(y)
y = self.resid_drop(y)
return y, present
class VeronicaBlock(nn.Module):
def __init__(self, config: VeronicaConfig):
super().__init__()
self.ln_1 = Fp32LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = MultiHeadSelfAttention(
config.n_embd,
config.n_head,
dropout=config.dropout,
max_position_embeddings=config.max_position_embeddings,
rope_theta=getattr(config, 'rope_theta', 10000.0)
)
self.ln_2 = Fp32LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.mlp = PolymorphicMLP(
hidden_size=config.n_embd,
mlp_mult=config.mlp_mult,
num_funcs=config.num_funcs,
router_dim=config.router_dim,
dropout=config.dropout,
use_channel_attention=config.use_channel_attention,
router_tau=config.router_tau,
)
def forward(
self,
x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: bool = False,
position_offset: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
h = self.ln_1(x)
attn_out, present = self.attn(h, attn_mask, past_key_value=past_key_value, use_cache=use_cache, position_offset=position_offset)
x = x + attn_out
h = self.ln_2(x)
y, alpha = self.mlp(h)
x = x + y
return x, alpha, present
class VeronicaModel(PreTrainedModel):
config_class = VeronicaConfig
def __init__(self, config: VeronicaConfig):
super().__init__(config)
self.embed_dim = config.n_embd
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
# RoPE sostituisce positional embeddings assoluti (wpe rimosso)
self.drop = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList([VeronicaBlock(config) for _ in range(config.n_layer)])
self.ln_f = Fp32LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.register_buffer(
"causal_mask",
torch.tril(
torch.ones(
config.max_position_embeddings,
config.max_position_embeddings,
dtype=torch.uint8,
)
).view(1, 1, config.max_position_embeddings, config.max_position_embeddings),
persistent=False,
)
# Monitoring
self.router_alpha_entropy: Optional[torch.Tensor] = None
self.router_alpha_mean: Optional[torch.Tensor] = None
self._use_gradient_checkpointing: bool = getattr(config, "gradient_checkpointing", False)
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, value):
self.wte = value
def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
self._use_gradient_checkpointing = True
def gradient_checkpointing_disable(self):
self._use_gradient_checkpointing = False
def _build_attn_mask(
self,
attention_mask: Optional[torch.Tensor],
seq_len: int,
past_kv_len: int,
device: torch.device,
dtype: torch.dtype,
) -> torch.Tensor:
# Causal mask additiva in float32
T, P = seq_len, past_kv_len
causal = torch.full((T, T + P), float("-inf"), device=device, dtype=dtype)
causal = torch.triu(causal, diagonal=1 + P) # -inf per future, 0 altrove
if attention_mask is None:
return causal.unsqueeze(0).unsqueeze(1) # [1,1,T,T+P]
# attention_mask shape: [B, T+P] (0 pad, 1 valid)
attn_full = attention_mask.to(dtype)
pad_add = (1.0 - attn_full) * torch.finfo(dtype).min # [B, T+P]
pad_add = pad_add.unsqueeze(1).unsqueeze(2) # [B,1,1,T+P]
causal = causal.unsqueeze(0).unsqueeze(1) # [1,1,T,T+P]
return causal + pad_add
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
output_router_stats: bool = True,
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
use_cache: Optional[bool] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[Tuple[torch.Tensor, torch.Tensor]]]]:
device = input_ids.device
B, T = input_ids.shape
if use_cache is None:
use_cache = False if self.training else True
pkv_list: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = [] if use_cache else None
P = 0
if (
past_key_values is not None
and len(past_key_values) > 0
and past_key_values[0] is not None
and isinstance(past_key_values[0], (tuple, list))
and past_key_values[0][0] is not None
):
P = past_key_values[0][0].size(-2)
# Solo token embeddings (RoPE gestisce le posizioni)
x = self.wte(input_ids)
x = self.drop(x)
# attention_mask full [B, T+P]
attn_full = None
if attention_mask is not None:
if attention_mask.size(-1) == T + P:
attn_full = attention_mask
elif attention_mask.size(-1) == T:
if P > 0:
ones = torch.ones((B, P), dtype=attention_mask.dtype, device=attention_mask.device)
attn_full = torch.cat([ones, attention_mask], dim=-1)
else:
attn_full = attention_mask
else:
attn_full = None
attn_bias = self._build_attn_mask(attn_full, T, P, device, torch.float32)
alpha_list: List[torch.Tensor] = []
if self.training:
self._acc_aux_sum = 0.0
self._acc_aux_count = 0
if getattr(self, "_use_gradient_checkpointing", False) and self.training:
def create_custom_forward(module, pkv):
def custom_forward(x):
out_x, out_alpha, _ = module(x, attn_bias, past_key_value=pkv, use_cache=False, position_offset=P)
return out_x, out_alpha
return custom_forward
if past_key_values is not None:
curr_past = [
pkv
if (pkv is not None and isinstance(pkv, (tuple, list)) and pkv[0] is not None and pkv[1] is not None)
else None
for pkv in past_key_values
]
else:
curr_past = [None] * len(self.blocks)
for layer_idx, block in enumerate(self.blocks):
x, alpha = torch.utils.checkpoint.checkpoint(
create_custom_forward(block, curr_past[layer_idx]), x, use_reentrant=False
)
alpha_list.append(alpha)
if self.training and getattr(block.mlp, "last_aux", None) is not None:
self._acc_aux_sum = self._acc_aux_sum + block.mlp.last_aux
self._acc_aux_count += 1
else:
if past_key_values is not None:
curr_past = [
pkv
if (pkv is not None and isinstance(pkv, (tuple, list)) and pkv[0] is not None and pkv[1] is not None)
else None
for pkv in past_key_values
]
else:
curr_past = [None] * len(self.blocks)
for layer_idx, block in enumerate(self.blocks):
x, alpha, present = block(x, attn_bias, past_key_value=curr_past[layer_idx], use_cache=use_cache, position_offset=P)
alpha_list.append(alpha)
if self.training and getattr(block.mlp, "last_aux", None) is not None:
self._acc_aux_sum = self._acc_aux_sum + block.mlp.last_aux
self._acc_aux_count += 1
if use_cache and pkv_list is not None:
pkv_list.append(present)
x = self.ln_f(x)
# Router stats
if output_router_stats and len(alpha_list) > 0:
alpha_stack = torch.stack(alpha_list, dim=0) # (L, B, T, K)
alpha_mean = alpha_stack.mean(dim=(0, 1, 2)) # (K,)
self.router_alpha_mean = alpha_mean.detach()
self.router_alpha_entropy = router_aux_loss(alpha_stack.mean(dim=0))
# Aux-loss medio su profondità
if hasattr(self, "_acc_aux_sum"):
if self._acc_aux_count > 0:
self._last_router_aux = self._acc_aux_sum / self._acc_aux_count
else:
self._last_router_aux = None
delattr(self, "_acc_aux_sum")
delattr(self, "_acc_aux_count")
return x, pkv_list
class VeronicaForCausalLM(VeronicaModel, GenerationMixin):
def __init__(self, config: VeronicaConfig):
super().__init__(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def tie_weights(self):
self._tie_or_clone_weights(self.lm_head, self.get_input_embeddings())
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
):
if past_key_values is not None and len(past_key_values) > 0:
input_ids = input_ids[:, -1:]
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"attention_mask": attention_mask,
"use_cache": True,
}
def _reorder_cache(self, past_key_values, beam_idx: torch.LongTensor):
if past_key_values is None:
return past_key_values
reordered = []
for (k, v) in past_key_values:
reordered.append((k.index_select(0, beam_idx), v.index_select(0, beam_idx)))
return reordered
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
**kwargs,
) -> CausalLMOutputWithPast:
hidden_states, present = super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
labels=None,
use_cache=use_cache,
past_key_values=past_key_values,
**kwargs,
) # (B, T, H)
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),
ignore_index=-100,
)
aux = getattr(self, "_last_router_aux", None)
if aux is not None and getattr(self.config, "router_aux_weight", 0.0) > 0:
if not torch.is_tensor(aux):
aux = torch.as_tensor(aux, device=logits.device, dtype=logits.dtype)
else:
aux = aux.to(device=logits.device, dtype=logits.dtype)
aux = aux.clamp_min(0.0)
loss = loss + float(self.config.router_aux_weight) * aux
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=present if use_cache else None,
hidden_states=None,
attentions=None,
)
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