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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 |