Create evo_core_gpt.py

evo_core_gpt.py

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+# evo_core_gpt.py — minimal decoder-only LM matching your checkpoint keys
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class SelfAttention(nn.Module):
+    def __init__(self, d_model: int, num_heads: int):
+        super().__init__()
+        assert d_model % num_heads == 0
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.head_dim = d_model // num_heads
+        # names match your state_dict:
+        self.qkv_proj = nn.Linear(d_model, 3 * d_model, bias=True)
+        self.out_proj = nn.Linear(d_model, d_model, bias=True)
+
+    def forward(self, x, attn_mask=None):
+        B, T, C = x.shape
+        qkv = self.qkv_proj(x)                           # (B,T,3C)
+        q, k, v = qkv.chunk(3, dim=-1)                   # each (B,T,C)
+        # reshape to heads
+        q = q.view(B, T, self.num_heads, self.head_dim).transpose(1, 2)  # (B,H,T,Hd)
+        k = k.view(B, T, self.num_heads, self.head_dim).transpose(1, 2)  # (B,H,T,Hd)
+        v = v.view(B, T, self.num_heads, self.head_dim).transpose(1, 2)  # (B,H,T,Hd)
+        # scaled dot-product attention with causal mask
+        att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)       # (B,H,T,T)
+        # causal mask
+        causal = torch.ones((T, T), device=x.device, dtype=torch.bool).tril()
+        att = att.masked_fill(~causal, float("-inf"))
+        if attn_mask is not None:
+            att = att + attn_mask
+        att = F.softmax(att, dim=-1)
+        y = att @ v                                                     # (B,H,T,Hd)
+        y = y.transpose(1, 2).contiguous().view(B, T, C)                # (B,T,C)
+        return self.out_proj(y)
+
+class FFN(nn.Module):
+    def __init__(self, d_model: int, mult: int = 4):
+        super().__init__()
+        # match your names: ffn.net.0 (Linear), .3 (Linear), with GELU in between
+        self.net = nn.Sequential(
+            nn.Linear(d_model, mult * d_model, bias=True),
+            nn.GELU(),
+            nn.Dropout(p=0.0),
+            nn.Linear(mult * d_model, d_model, bias=True),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+class Block(nn.Module):
+    def __init__(self, d_model: int, num_heads: int):
+        super().__init__()
+        # names must match: blocks.N.ln1/ln2, .attn.*, .ffn.*
+        self.ln1 = nn.LayerNorm(d_model)
+        self.attn = SelfAttention(d_model, num_heads)
+        self.ln2 = nn.LayerNorm(d_model)
+        self.ffn = FFN(d_model)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.ffn(self.ln2(x))
+        return x
+
+class EvoGPT(nn.Module):
+    def __init__(self, vocab_size: int, d_model: int, n_layers: int, n_positions: int, num_heads: int):
+        super().__init__()
+        # names must match your checkpoint:
+        self.token_emb = nn.Embedding(vocab_size, d_model)
+        self.pos_emb   = nn.Embedding(n_positions, d_model)
+        self.blocks = nn.ModuleList([Block(d_model, num_heads) for _ in range(n_layers)])
+        # (no final ln_f in your keys; tokens are projected by tying to token_emb)
+        self.register_buffer("pos_idx", torch.arange(0, n_positions).long(), persistent=False)
+
+    def forward(self, input_ids):
+        B, T = input_ids.shape
+        pos = self.pos_idx[:T]
+        x = self.token_emb(input_ids) + self.pos_emb(pos)[None, :, :]
+        for blk in self.blocks:
+            x = blk(x)
+        # weight tying: logits = x @ token_emb^T
+        logits = x @ self.token_emb.weight.t()
+        return logits
+
+    @torch.no_grad()
+    def generate(self, input_ids, max_new_tokens=128, temperature=0.7, eos_token_id=None):
+        self.eval()
+        for _ in range(int(max_new_tokens)):
+            logits = self.forward(input_ids)[:, -1, :]         # (B, V)
+            if temperature and temperature > 0.0:
+                probs = F.softmax(logits / max(0.01, temperature), dim=-1)
+                next_id = torch.multinomial(probs, num_samples=1)
+            else:
+                next_id = torch.argmax(logits, dim=-1, keepdim=True)
+            input_ids = torch.cat([input_ids, next_id], dim=1)
+            if eos_token_id is not None and (next_id == eos_token_id).all():
+                break
+        return input_ids