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config.json

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+{
+  "architectures": ["LLaDA_ModelForCausalLM"],
+  "model_type": "llada",
+  "vocab_size": 5000,
+  "max_seq_len": 512,
+  "d_model": 128,
+  "n_layers": 8,
+  "n_heads": 8,
+  "dropout": 0.1
+}

modeling_llada.py

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+# modeling_llada.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.modeling_utils import PreTrainedModel
+from transformers import PretrainedConfig
+
+# --- 1) Config Sınıfı --------------------------------------------------------
+class LLaDAConfig(PretrainedConfig):
+    model_type = "llada"
+
+    def __init__(
+        self,
+        vocab_size=50000,
+        max_seq_len=512,
+        d_model=128,
+        n_layers=16,
+        n_heads=8,
+        dropout=0.1,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size   = vocab_size
+        self.max_seq_len  = max_seq_len
+        self.d_model      = d_model
+        self.n_layers     = n_layers
+        self.n_heads      = n_heads
+        self.d_head       = d_model // n_heads
+        self.d_ffn        = 4 * d_model
+        self.dropout      = dropout
+
+
+# --- 2) PreTrainedModel Tabanı ------------------------------------------------
+class LLaDAPreTrainedModel(PreTrainedModel):
+    config_class = LLaDAConfig
+    base_model_prefix = "llada"
+
+
+# --- 3) Alt Modüller ---------------------------------------------------------
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps    = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self._norm(x.float()).type_as(x) * self.weight
+
+
+class RotaryPositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len=512):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        t = torch.arange(max_seq_len, device=inv_freq.device).type_as(inv_freq)
+        freqs = torch.einsum("i,j->ij", t, inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos", emb.cos())
+        self.register_buffer("sin", emb.sin())
+
+    def forward(self, x):
+        return self.cos[: x.shape[2], :], self.sin[: x.shape[2], :]
+
+
+def apply_rotary(q, k, cos, sin):
+    q2 = (q * cos) + (torch.cat([-q[..., 1::2], q[..., ::2]], -1) * sin)
+    k2 = (k * cos) + (torch.cat([-k[..., 1::2], k[..., ::2]], -1) * sin)
+    return q2, k2
+
+
+class Attention(nn.Module):
+    def __init__(self, config: LLaDAConfig):
+        super().__init__()
+        self.n_heads = config.n_heads
+        self.d_head  = config.d_head
+
+        self.wq = nn.Linear(config.d_model, config.n_heads * config.d_head, bias=False)
+        self.wk = nn.Linear(config.d_model, config.n_heads * config.d_head, bias=False)
+        self.wv = nn.Linear(config.d_model, config.n_heads * config.d_head, bias=False)
+        self.wo = nn.Linear(config.n_heads * config.d_head, config.d_model, bias=False)
+
+        self.rotary = RotaryPositionalEmbedding(config.d_head, config.max_seq_len)
+
+    def forward(self, x):
+        b, seq, _ = x.size()
+        q = self.wq(x).view(b, seq, self.n_heads, self.d_head).transpose(1, 2)
+        k = self.wk(x).view(b, seq, self.n_heads, self.d_head).transpose(1, 2)
+        v = self.wv(x).view(b, seq, self.n_heads, self.d_head).transpose(1, 2)
+        cos, sin = self.rotary(q)
+        q, k     = apply_rotary(q, k, cos, sin)
+        out      = F.scaled_dot_product_attention(q, k, v, is_causal=False)
+        out      = out.transpose(1, 2).reshape(b, seq, -1)
+        return self.wo(out)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, config: LLaDAConfig):
+        super().__init__()
+        self.w1 = nn.Linear(config.d_model, config.d_ffn, bias=False)
+        self.w2 = nn.Linear(config.d_ffn, config.d_model, bias=False)
+        self.w3 = nn.Linear(config.d_model, config.d_ffn, bias=False)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: LLaDAConfig):
+        super().__init__()
+        self.attn      = Attention(config)
+        self.ff        = FeedForward(config)
+        self.norm1     = RMSNorm(config.d_model)
+        self.norm2     = RMSNorm(config.d_model)
+
+    def forward(self, x):
+        h = x + self.attn(self.norm1(x))
+        return h + self.ff(self.norm2(h))
+
+
+# --- 4) Ana Model Sınıfı ------------------------------------------------------
+class LLaDA_Model(nn.Module):
+    def __init__(self, config: LLaDAConfig):
+        super().__init__()
+        self.embed  = nn.Embedding(config.vocab_size, config.d_model)
+        self.layers = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layers)])
+        self.norm   = RMSNorm(config.d_model)
+
+    def forward(self, input_ids):
+        x = self.embed(input_ids)
+        for layer in self.layers:
+            x = layer(x)
+        return self.norm(x)
+
+
+# --- 5) LM Head ile CausalLM --------------------------------------------------
+class LLaDA_ModelForCausalLM(LLaDAPreTrainedModel):
+    def __init__(self, config: LLaDAConfig):
+        super().__init__(config)
+        self.llada   = LLaDA_Model(config)
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+    def forward(self, input_ids, **kwargs):
+        hidden = self.llada(input_ids)
+        logits = self.lm_head(hidden)
+        return logits