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from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from config import ModelConfig
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1.0e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x: Tensor) -> Tensor:
dtype = x.dtype
x32 = x.float()
rms = x32.pow(2).mean(dim=-1, keepdim=True).add_(self.eps).rsqrt_()
out = (x32 * rms).to(dtype)
return out * self.weight.to(dtype)
class RotaryEmbedding(nn.Module):
def __init__(self, head_dim: int, max_seq_len: int, theta: float = 10_000.0):
super().__init__()
self.head_dim = head_dim
self.max_seq_len = max_seq_len
self.theta = theta
self._cached_len: int = 0
self._cos_cache: Optional[Tensor] = None
self._sin_cache: Optional[Tensor] = None
def _build_cache(self, seq_len: int, device, dtype):
inv_freq = 1.0 / (
self.theta ** (torch.arange(0, self.head_dim, 2, dtype=torch.float32, device=device) / self.head_dim)
)
t = torch.arange(seq_len, dtype=torch.float32, device=device)
freqs = torch.outer(t, inv_freq)
emb = torch.cat([freqs, freqs], dim=-1)
self._cos_cache = emb.cos().to(dtype)
self._sin_cache = emb.sin().to(dtype)
self._cached_len = seq_len
def forward(self, seq_len: int, device, dtype) -> tuple[Tensor, Tensor]:
if (
self._cos_cache is None
or seq_len > self._cached_len
or self._cos_cache.device != device
or self._cos_cache.dtype != dtype
):
self._build_cache(max(seq_len, self.max_seq_len), device, dtype)
return self._cos_cache[:seq_len], self._sin_cache[:seq_len]
def _rotate_half(x: Tensor) -> Tensor:
x1, x2 = x.chunk(2, dim=-1)
return torch.cat([-x2, x1], dim=-1)
def apply_rope(q: Tensor, k: Tensor, cos: Tensor, sin: Tensor) -> tuple[Tensor, Tensor]:
cos = cos.unsqueeze(0).unsqueeze(0)
sin = sin.unsqueeze(0).unsqueeze(0)
q_rot = (q * cos) + (_rotate_half(q) * sin)
k_rot = (k * cos) + (_rotate_half(k) * sin)
return q_rot, k_rot
class Attention(nn.Module):
def __init__(self, cfg: ModelConfig, layer_idx: int):
super().__init__()
self.cfg = cfg
self.layer_idx = layer_idx
self.num_heads = cfg.num_heads
self.num_kv_heads = cfg.num_kv_heads
self.head_dim = cfg.head_dim
self.kv_groups = cfg.kv_groups
self.scale = self.head_dim ** -0.5
h, hd = cfg.hidden_size, self.head_dim
self.q_proj = nn.Linear(h, self.num_heads * hd, bias=False)
self.k_proj = nn.Linear(h, self.num_kv_heads * hd, bias=False)
self.v_proj = nn.Linear(h, self.num_kv_heads * hd, bias=False)
self.o_proj = nn.Linear(self.num_heads * hd, h, bias=False)
if cfg.qk_norm:
self.q_norm = RMSNorm(hd, eps=cfg.rms_norm_eps)
self.k_norm = RMSNorm(hd, eps=cfg.rms_norm_eps)
else:
self.q_norm = nn.Identity()
self.k_norm = nn.Identity()
self.attn_dropout = cfg.attn_dropout
def forward(self, x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
B, S, _ = x.shape
q = self.q_proj(x).view(B, S, self.num_heads, self.head_dim)
k = self.k_proj(x).view(B, S, self.num_kv_heads, self.head_dim)
v = self.v_proj(x).view(B, S, self.num_kv_heads, self.head_dim)
q = self.q_norm(q)
k = self.k_norm(k)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
q, k = apply_rope(q, k, cos, sin)
if self.kv_groups > 1:
k = k.repeat_interleave(self.kv_groups, dim=1)
v = v.repeat_interleave(self.kv_groups, dim=1)
out = F.scaled_dot_product_attention(
q, k, v,
attn_mask=None,
dropout_p=self.attn_dropout if self.training else 0.0,
is_causal=True,
)
out = out.transpose(1, 2).contiguous().view(B, S, self.num_heads * self.head_dim)
return self.o_proj(out)
class SwiGLU(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
h, i = cfg.hidden_size, cfg.intermediate_size
self.gate_proj = nn.Linear(h, i, bias=False)
self.up_proj = nn.Linear(h, i, bias=False)
self.down_proj = nn.Linear(i, h, bias=False)
def forward(self, x: Tensor) -> Tensor:
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class Block(nn.Module):
def __init__(self, cfg: ModelConfig, layer_idx: int):
super().__init__()
self.input_norm = RMSNorm(cfg.hidden_size, cfg.rms_norm_eps)
self.attn = Attention(cfg, layer_idx)
self.post_attn_norm = RMSNorm(cfg.hidden_size, cfg.rms_norm_eps)
self.mlp = SwiGLU(cfg)
self.resid_drop = nn.Dropout(cfg.resid_dropout) if cfg.resid_dropout > 0 else nn.Identity()
def forward(self, x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
x = x + self.resid_drop(self.attn(self.input_norm(x), cos, sin))
x = x + self.resid_drop(self.mlp(self.post_attn_norm(x)))
return x
class MarulLLM(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.cfg = cfg
self.embed_tokens = nn.Embedding(cfg.vocab_size, cfg.hidden_size)
self.rotary = RotaryEmbedding(cfg.head_dim, cfg.max_seq_len, cfg.rope_theta)
self.layers = nn.ModuleList(Block(cfg, i) for i in range(cfg.num_layers))
self.final_norm = RMSNorm(cfg.hidden_size, cfg.rms_norm_eps)
if cfg.tie_word_embeddings:
self.lm_head = None
else:
self.lm_head = nn.Linear(cfg.hidden_size, cfg.vocab_size, bias=False)
self.apply(self._init_weights)
self._scale_residual_inits()
self.num_params = sum(p.numel() for p in self.parameters())
self.num_params_trainable = sum(p.numel() for p in self.parameters() if p.requires_grad)
embed_params = cfg.vocab_size * cfg.hidden_size
self.num_params_non_embed = self.num_params - embed_params
def _init_weights(self, module: nn.Module) -> None:
std = self.cfg.initializer_range
if isinstance(module, nn.Linear):
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)
def _scale_residual_inits(self) -> None:
scale = (2 * self.cfg.num_layers) ** -0.5
with torch.no_grad():
for block in self.layers:
block.attn.o_proj.weight.mul_(scale)
block.mlp.down_proj.weight.mul_(scale)
def forward(
self,
input_ids: Tensor,
targets: Optional[Tensor] = None,
return_logits: bool = True,
) -> tuple[Optional[Tensor], Optional[Tensor]]:
B, S = input_ids.shape
assert S <= self.cfg.max_seq_len, (
f"dizi uzunluğu {S}, modelin bağlam sınırı {self.cfg.max_seq_len}")
x = self.embed_tokens(input_ids)
cos, sin = self.rotary(S, x.device, x.dtype)
for block in self.layers:
x = block(x, cos, sin)
x = self.final_norm(x)
if self.cfg.tie_word_embeddings:
logits = F.linear(x, self.embed_tokens.weight)
else:
logits = self.lm_head(x)
loss: Optional[Tensor] = None
if targets is not None:
flat_logits = logits.view(-1, logits.size(-1))
flat_targets = targets.view(-1)
ce = F.cross_entropy(
flat_logits, flat_targets, ignore_index=-100, reduction="mean"
)
loss = ce
if self.cfg.z_loss_coef > 0:
mask = flat_targets != -100
lse = torch.logsumexp(flat_logits, dim=-1)
if mask.any():
z = (lse[mask].float().pow(2)).mean()
loss = loss + self.cfg.z_loss_coef * z
if not return_logits and targets is not None:
logits = None
return logits, loss
@torch.no_grad()
def generate(
self,
input_ids: Tensor,
max_new_tokens: int = 128,
temperature: float = 0.6,
top_k: int = 40,
top_p: float = 0.88,
repetition_penalty: float = 1.20,
no_repeat_ngram_size: int = 4,
min_p: float = 0.05,
eos_token_id: Optional[int] = None,
) -> Tensor:
self.eval()
eos = eos_token_id if eos_token_id is not None else self.cfg.eos_token_id
out = input_ids.clone()
device = out.device
for _ in range(max_new_tokens):
ctx = out[:, -self.cfg.max_seq_len:]
logits, _ = self.forward(ctx)
logits = logits[:, -1, :].float()
if repetition_penalty is not None and repetition_penalty != 1.0:
for b in range(out.size(0)):
seen = torch.unique(out[b])
vals = logits[b, seen]
logits[b, seen] = torch.where(
vals > 0, vals / repetition_penalty, vals * repetition_penalty
)
if no_repeat_ngram_size and no_repeat_ngram_size > 0:
n = no_repeat_ngram_size
if out.size(1) >= n - 1:
for b in range(out.size(0)):
seq = out[b].tolist()
ngrams: dict = {}
for i in range(len(seq) - n + 1):
prefix = tuple(seq[i : i + n - 1])
ngrams.setdefault(prefix, set()).add(seq[i + n - 1])
curr = tuple(seq[-(n - 1):])
if curr in ngrams:
banned = torch.tensor(list(ngrams[curr]), device=device, dtype=torch.long)
logits[b, banned] = float("-inf")
if temperature is not None and temperature != 1.0:
logits = logits / max(temperature, 1.0e-6)
if top_k is not None and top_k > 0:
v, _ = torch.topk(logits, k=min(top_k, logits.size(-1)))
logits[logits < v[:, -1:]] = float("-inf")
if min_p is not None and min_p > 0.0:
probs_tmp = F.softmax(logits, dim=-1)
max_probs, _ = probs_tmp.max(dim=-1, keepdim=True)
logits = logits.masked_fill(probs_tmp < (max_probs * min_p), float("-inf"))
if top_p is not None and 0.0 < top_p < 1.0:
sorted_logits, sorted_idx = torch.sort(logits, descending=True, dim=-1)
probs = F.softmax(sorted_logits, dim=-1)
cumprobs = probs.cumsum(dim=-1)
mask = cumprobs > top_p
mask[..., 1:] = mask[..., :-1].clone()
mask[..., 0] = False
sorted_logits = sorted_logits.masked_fill(mask, float("-inf"))
logits = torch.full_like(logits, float("-inf")).scatter(-1, sorted_idx, sorted_logits)
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
out = torch.cat([out, next_token], dim=1)
if eos is not None and (next_token == eos).all():
break
return out
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