""" model.py -- standalone architecture definition for GTM-3-base. This is a plain PyTorch nanoGPT-style GPT model with RoPE (rotary position embeddings), NOT a HuggingFace `transformers` AutoModel. To load the released weights: pip install torch safetensors tiktoken import json, torch from safetensors.torch import load_file from model import GPT, GPTConfig with open("config.json") as f: config = GPTConfig(**json.load(f)) model = GPT(config) state_dict = load_file("model.safetensors") model.load_state_dict(state_dict) model.eval() import tiktoken enc = tiktoken.get_encoding("gpt2") ids = enc.encode_ordinary("Once upon a time,") x = torch.tensor([ids], dtype=torch.long) out = model.generate(x, max_new_tokens=100, temperature=0.8, top_k=50, eot_token=enc.eot_token, repetition_penalty=1.3) print(enc.decode(out[0].tolist())) """ import math from dataclasses import dataclass import torch import torch.nn as nn import torch.nn.functional as F @dataclass class GPTConfig: vocab_size: int = 50257 block_size: int = 1024 n_layer: int = 10 n_head: int = 8 n_embd: int = 608 dropout: float = 0.0 bias: bool = True rope_theta: float = 10000.0 # standard RoPE base frequency def precompute_rope_freqs(head_dim, max_seq_len, theta=10000.0, device="cpu"): """Precompute the complex rotation frequencies used by RoPE, one pair per (position, frequency-band). Standard formula: theta_i = theta^(-2i/dim).""" assert head_dim % 2 == 0, "RoPE requires an even head_dim" freqs = 1.0 / (theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim)) t = torch.arange(max_seq_len, device=device).float() freqs = torch.outer(t, freqs) # (max_seq_len, head_dim/2) return torch.polar(torch.ones_like(freqs), freqs) # complex64, (max_seq_len, head_dim/2) def apply_rope(x, freqs_cis): """Apply rotary position embeddings to a (B, n_head, T, head_dim) tensor.""" B, n_head, T, head_dim = x.shape x_complex = torch.view_as_complex(x.float().reshape(B, n_head, T, head_dim // 2, 2)) freqs_cis = freqs_cis[:T].view(1, 1, T, head_dim // 2) x_rotated = x_complex * freqs_cis x_out = torch.view_as_real(x_rotated).reshape(B, n_head, T, head_dim) return x_out.type_as(x) class CausalSelfAttention(nn.Module): def __init__(self, config): super().__init__() assert config.n_embd % config.n_head == 0 self.n_head = config.n_head self.n_embd = config.n_embd self.head_dim = config.n_embd // config.n_head self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias) self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) self.attn_dropout = nn.Dropout(config.dropout) self.resid_dropout = nn.Dropout(config.dropout) self.dropout = config.dropout def forward(self, x, freqs_cis): B, T, C = x.shape q, k, v = self.c_attn(x).split(self.n_embd, dim=2) q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # RoPE is applied to queries and keys only, not values -- this is what # makes attention scores depend on *relative* position between tokens q = apply_rope(q, freqs_cis) k = apply_rope(k, freqs_cis) y = F.scaled_dot_product_attention( q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0.0, ) y = y.transpose(1, 2).contiguous().view(B, T, C) return self.resid_dropout(self.c_proj(y)) class MLP(nn.Module): def __init__(self, config): super().__init__() self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) self.gelu = nn.GELU() self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) self.dropout = nn.Dropout(config.dropout) def forward(self, x): return self.dropout(self.c_proj(self.gelu(self.c_fc(x)))) class Block(nn.Module): def __init__(self, config): super().__init__() self.ln_1 = nn.LayerNorm(config.n_embd) self.attn = CausalSelfAttention(config) self.ln_2 = nn.LayerNorm(config.n_embd) self.mlp = MLP(config) def forward(self, x, freqs_cis): x = x + self.attn(self.ln_1(x), freqs_cis) x = x + self.mlp(self.ln_2(x)) return x class GPT(nn.Module): def __init__(self, config): super().__init__() self.config = config self.transformer = nn.ModuleDict(dict( wte=nn.Embedding(config.vocab_size, config.n_embd), # NOTE: no wpe (learned position embedding) -- RoPE replaces it entirely, # applied inside attention rather than added to the input embeddings drop=nn.Dropout(config.dropout), h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ln_f=nn.LayerNorm(config.n_embd), )) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.transformer.wte.weight = self.lm_head.weight head_dim = config.n_embd // config.n_head freqs_cis = precompute_rope_freqs(head_dim, config.block_size, theta=config.rope_theta) self.register_buffer("freqs_cis", freqs_cis, persistent=False) self.apply(self._init_weights) for pn, p in self.named_parameters(): if pn.endswith("c_proj.weight"): nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer)) def _init_weights(self, module): if isinstance(module, nn.Linear): nn.init.normal_(module.weight, mean=0.0, std=0.02) 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=0.02) def forward(self, idx, targets=None): B, T = idx.shape assert T <= self.config.block_size, "sequence longer than block_size" x = self.transformer.drop(self.transformer.wte(idx)) freqs_cis = self.freqs_cis.to(x.device) for block in self.transformer.h: x = block(x, freqs_cis) x = self.transformer.ln_f(x) logits = self.lm_head(x) loss = None if targets is not None: loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) return logits, loss @torch.no_grad() def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None, eot_token=None, repetition_penalty=1.0): for _ in range(max_new_tokens): idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:] logits, _ = self(idx_cond) logits = logits[:, -1, :] / temperature if repetition_penalty != 1.0: for seen_id in set(idx[0].tolist()): if logits[0, seen_id] > 0: logits[0, seen_id] /= repetition_penalty else: logits[0, seen_id] *= repetition_penalty if top_k is not None: v, _ = torch.topk(logits, min(top_k, logits.size(-1))) logits[logits < v[:, [-1]]] = float("-inf") probs = F.softmax(logits, dim=-1) idx_next = torch.multinomial(probs, num_samples=1) idx = torch.cat((idx, idx_next), dim=1) if eot_token is not None and idx_next.item() == eot_token: break return idx