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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.modeling_utils import PreTrainedModel
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import CausalLMOutput
try: # carregado como remote code (pacote)
from .configuration_lowonmind import LowOnMindConfig
except ImportError: # carregado como arquivo solto no sys.path
from configuration_lowonmind import LowOnMindConfig
class LowOnMindRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.eps = eps
def forward(self, x):
dtype = x.dtype
x = x.float()
var = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(var + self.eps)
return (self.weight * x).to(dtype)
class LowOnMindRotary(nn.Module):
"""RoPE com cos/sin pre-computados e cacheados.
O DynamicMind-Mini recalculava inv_freq/cos/sin a cada forward. Aqui o cache
e construido uma vez e reaproveitado; se aparecer uma sequencia mais longa
(ou outro device) ele e reconstruido em vez de estourar num broadcast error.
O cache NAO e um buffer registrado de proposito: buffers nao-persistentes
criados no __init__ sao materializados com lixo/NaN pelo carregamento em
meta-device do from_pretrained. Como atributo simples ele e sempre
reconstruido no primeiro forward.
"""
def __init__(self, head_dim, max_position_embeddings, base):
super().__init__()
self.head_dim = head_dim
self.base = base
self.max_position_embeddings = max_position_embeddings
self._cos = None
self._sin = None
self._cached_len = 0
def _build(self, seq_len, device):
inv_freq = 1.0 / (
self.base
** (torch.arange(0, self.head_dim, 2, device=device, dtype=torch.float32) / self.head_dim)
)
t = torch.arange(seq_len, device=device, dtype=torch.float32)
freqs = torch.outer(t, inv_freq)
self._cos = freqs.cos()[None, None]
self._sin = freqs.sin()[None, None]
self._cached_len = seq_len
def forward(self, seq_len, dtype, device):
if self._cos is None or seq_len > self._cached_len or self._cos.device != device:
self._build(max(seq_len, self.max_position_embeddings, self._cached_len), device)
return self._cos[:, :, :seq_len].to(dtype), self._sin[:, :, :seq_len].to(dtype)
def apply_rope(x, cos, sin):
# x: [B, H, T, head_dim]; cos/sin: [1, 1, T, head_dim // 2]
x_even, x_odd = x[..., 0::2], x[..., 1::2]
out_even = x_even * cos - x_odd * sin
out_odd = x_even * sin + x_odd * cos
return torch.stack((out_even, out_odd), dim=-1).flatten(-2)
class LowOnMindAttention(nn.Module):
def __init__(self, config, rotary):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.num_kv_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // config.num_attention_heads
self.attention_dropout = config.attention_dropout
self.rotary = rotary
assert self.hidden_size % self.num_heads == 0
assert self.num_heads % self.num_kv_heads == 0
assert self.head_dim % 2 == 0
self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=False)
self.o_proj._is_residual_proj = True
if config.use_qk_norm:
self.q_norm = LowOnMindRMSNorm(self.head_dim, config.rms_norm_eps)
self.k_norm = LowOnMindRMSNorm(self.head_dim, config.rms_norm_eps)
else:
self.q_norm = None
self.k_norm = None
def forward(self, x):
bsz, seq_len, _ = x.shape
q = self.q_proj(x).view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(bsz, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(bsz, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
if self.q_norm is not None:
q = self.q_norm(q)
k = self.k_norm(k)
cos, sin = self.rotary(seq_len, q.dtype, q.device)
q = apply_rope(q, cos, sin)
k = apply_rope(k, cos, sin)
if self.num_kv_heads != self.num_heads:
repeats = self.num_heads // self.num_kv_heads
k = k.repeat_interleave(repeats, dim=1)
v = v.repeat_interleave(repeats, dim=1)
y = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=None,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=True,
)
y = y.transpose(1, 2).contiguous().view(bsz, seq_len, -1)
return self.o_proj(y)
class LowOnMindMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.down_proj._is_residual_proj = True
def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class LowOnMindBlock(nn.Module):
def __init__(self, config, rotary):
super().__init__()
self.input_layernorm = LowOnMindRMSNorm(config.hidden_size, config.rms_norm_eps)
self.self_attn = LowOnMindAttention(config, rotary)
self.post_attention_layernorm = LowOnMindRMSNorm(config.hidden_size, config.rms_norm_eps)
self.mlp = LowOnMindMLP(config)
def forward(self, x):
x = x + self.self_attn(self.input_layernorm(x))
x = x + self.mlp(self.post_attention_layernorm(x))
return x
class LowOnMindPreTrainedModel(PreTrainedModel):
config_class = LowOnMindConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = ["LowOnMindBlock"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
# projecoes que escrevem no residual: init escalado por 1/sqrt(2L)
if getattr(module, "_is_residual_proj", False):
std = std / math.sqrt(2 * self.config.num_hidden_layers)
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)
elif isinstance(module, LowOnMindRMSNorm):
nn.init.ones_(module.weight)
class LowOnMindForCausalLM(LowOnMindPreTrainedModel, GenerationMixin):
_tied_weights_keys = {"lm_head.weight": "embed_tokens.weight"}
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config):
super().__init__(config)
head_dim = config.hidden_size // config.num_attention_heads
self.rotary = LowOnMindRotary(head_dim, config.max_position_embeddings, config.rope_theta)
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList(
[LowOnMindBlock(config, self.rotary) for _ in range(config.num_hidden_layers)]
)
self.norm = LowOnMindRMSNorm(config.hidden_size, config.rms_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.embed_tokens.weight
self.post_init()
def tie_weights(self, *args, **kwargs):
if getattr(self.config, "tie_word_embeddings", True):
self.lm_head.weight = self.embed_tokens.weight
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 set_output_embeddings(self, value):
self.lm_head = value
def forward(self, input_ids=None, labels=None, **kwargs):
x = self.embed_tokens(input_ids)
for layer in self.layers:
x = layer(x)
x = self.norm(x)
logits = self.lm_head(x)
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 CausalLMOutput(loss=loss, logits=logits)
def state_dict(self, *args, **kwargs):
sd = super().state_dict(*args, **kwargs)
# lm_head.weight e tied com embed_tokens.weight; safetensors nao guarda
# tensores compartilhados duplicados.
if getattr(self.config, "tie_word_embeddings", True):
for k in list(sd.keys()):
if k == "lm_head.weight" or k.endswith(".lm_head.weight"):
del sd[k]
return sd
def prepare_inputs_for_generation(self, input_ids, **kwargs):
# sem KV cache: a janela e truncada em max_position_embeddings
input_ids = input_ids[:, -self.config.max_position_embeddings:]
return {"input_ids": input_ids}
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