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from __future__ import annotations
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import argparse, json, math, pathlib, random, time, os
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from contextlib import nullcontext
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from typing import Dict, Any, List, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from datasets import load_dataset
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from transformers import AutoTokenizer, logging as hf_log
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from tqdm.auto import tqdm
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hf_log.set_verbosity_error()
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DEV = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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torch.backends.cuda.matmul.allow_tf32 = True
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try:
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torch.set_float32_matmul_precision("high")
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except Exception:
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pass
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TOKENIZER_ID = os.environ.get(
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"TOKENIZER_ID",
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"deepseek-ai/DeepSeek-V3.2-Exp"
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)
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tok = AutoTokenizer.from_pretrained(TOKENIZER_ID, use_fast=True, trust_remote_code=True)
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if tok.pad_token is None:
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tok.add_special_tokens({"pad_token": "<|pad|>"})
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VOCAB, EOS = (
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max(tok.get_vocab().values()) + 1,
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tok.eos_token_id if tok.eos_token_id is not None else tok.sep_token_id
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)
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PRESETS: Dict[str, Dict[str, int]] = {
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"small": dict(d=512, layers=8, heads=16, rank=64),
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"smallx2": dict(d=512, layers=16, heads=16, rank=64),
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"base": dict(d=768, layers=12, heads=24, rank=96),
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"large": dict(d=1024, layers=24, heads=16, rank=128),
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}
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DEFAULT_BLOCK = 576
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SAT_BLOCK = 2
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LR_CORE, LR_HEAD = 5e-5, 2e-4
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EMIT_LAMBDA = 0.1
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DEFAULT_SAVE_SEC = 24 * 3600
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CKDIR = pathlib.Path("ckpts_joint")
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def rng_state():
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if DEV.type == "cuda":
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try:
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return torch.cuda.get_rng_state(DEV)
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except TypeError:
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return torch.cuda.get_rng_state()
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return torch.get_rng_state()
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def _is_probably_ckpt(path: pathlib.Path) -> bool:
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try:
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return path.is_file() and path.suffix == ".pt" and not path.name.endswith(".pt.tmp") and path.stat().st_size > (1<<20)
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except Exception:
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return False
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def _resolve_ckpt(path: pathlib.Path) -> pathlib.Path | None:
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try:
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if path.is_dir():
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cands = sorted([p for p in path.glob(".pt") if _is_probably_ckpt(p)],
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key=lambda p: p.stat().st_mtime, reverse=True)
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return cands[0] if cands else None
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if path.suffix == ".tmp":
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solid = path.with_suffix("")
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return solid if _is_probably_ckpt(solid) else _resolve_ckpt(path.parent)
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return path if _is_probably_ckpt(path) else _resolve_ckpt(path.parent)
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except Exception:
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return None
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def _try_load(path: pathlib.Path, map_location="cpu"):
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try:
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return torch.load(path, map_location="cpu")
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except Exception as e:
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print(f"[ckpt-skip] {path} not usable: {e}")
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return None
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try:
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from torch.amp import autocast as _ac, GradScaler
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except ImportError:
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from torch.cuda.amp import autocast as _ac, GradScaler
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def _auto_amp_dtype():
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if DEV.type == "cuda":
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try:
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if torch.cuda.is_bf16_supported():
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return torch.bfloat16
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return torch.float16
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except Exception:
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return torch.float16
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return torch.float32
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def amp(enabled: bool):
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return nullcontext() if not (enabled and DEV.type == "cuda") else _ac(device_type="cuda", dtype=_auto_amp_dtype())
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def token_stream(ds_name: str, target: int, seed: int = 42):
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ds = load_dataset(ds_name, split="train", streaming=True)
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ds = ds.shuffle(buffer_size=10_000, seed=seed)
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emitted = 0
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for ex in ds:
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enc = tok.encode(ex["text"])
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if EOS is not None and (len(enc) == 0 or enc[-1] != EOS):
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enc = enc + [EOS]
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for t in enc:
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yield t
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emitted += 1
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if emitted >= target:
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return
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def _alibi_slopes(n_heads: int):
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import math
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def pow2slopes(n):
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start = 2 ** (-2 ** -(math.log2(n) - 3))
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ratio = start
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return [start * (ratio ** i) for i in range(n)]
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if math.log2(n_heads).is_integer():
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vals = pow2slopes(n_heads)
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else:
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closest = 2 ** math.floor(math.log2(n_heads))
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vals = pow2slopes(closest)
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extra = pow2slopes(2 * closest)
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vals += extra[0::2][: n_heads - closest]
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return torch.tensor(vals, device=DEV).view(1, n_heads, 1, 1)
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def alibi_bias(n_heads: int, n_tokens: int):
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i = torch.arange(n_tokens, device=DEV).view(1, 1, n_tokens, 1)
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j = torch.arange(n_tokens, device=DEV).view(1, 1, 1, n_tokens)
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dist = (j - i).clamp_min(0)
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slopes = _alibi_slopes(n_heads)
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return -slopes * dist
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class LowRankMHA(nn.Module):
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def __init__(self, d: int, h: int, r: int, use_relpos: bool = True):
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super().__init__()
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assert d % h == 0, "d must be divisible by number of heads"
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self.h, self.dk = h, d // h
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self.use_relpos = use_relpos
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self.q = nn.Linear(d, d, bias=False)
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self.k = nn.Linear(d, d, bias=False)
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self.v = nn.Linear(d, d, bias=False)
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self.U = nn.Parameter(torch.randn(self.dk, r))
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nn.init.orthogonal_(self.U)
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self.proj = nn.Linear(h * r, d, bias=False)
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self.drop = nn.Dropout(0.1)
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def _proj(self, x):
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B, N, _ = x.shape
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return (x.view(B, N, self.h, self.dk).transpose(1, 2) @ self.U)
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def forward(
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self,
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x: torch.Tensor,
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mask: Optional[torch.Tensor] = None,
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rel_bias_tokens: Optional[int] = None,
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kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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use_cache: bool = False,
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):
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q = self._proj(self.q(x))
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k_new = self._proj(self.k(x))
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v_new = self._proj(self.v(x))
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if kv_cache is None:
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k, v = k_new, v_new
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else:
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k, v = kv_cache
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if use_cache:
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k = torch.cat([k, k_new], dim=2)
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v = torch.cat([v, v_new], dim=2)
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att = (q @ k.transpose(-1, -2)) / math.sqrt(self.dk)
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if q.size(2) == k.size(2):
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if self.use_relpos and rel_bias_tokens is not None:
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att = att + alibi_bias(self.h, rel_bias_tokens)
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if mask is not None:
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att = att + mask
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z = (att.softmax(-1) @ v).transpose(1, 2)
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z = z.reshape(x.size(0), x.size(1), -1)
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out = self.drop(self.proj(z))
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return (out, (k, v)) if use_cache else out
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class Block(nn.Module):
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def __init__(self, d: int, h: int, r: int):
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super().__init__()
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self.ln1, self.ln2 = nn.LayerNorm(d), nn.LayerNorm(d)
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self.mha = LowRankMHA(d, h, r, use_relpos=True)
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self.ff = nn.Sequential(nn.Linear(d, 4 * d), nn.ReLU(), nn.Linear(4 * d, d))
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def forward(
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self,
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x: torch.Tensor,
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mask: Optional[torch.Tensor],
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kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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use_cache: bool = False
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|
):
|
|
|
n = x.size(1)
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|
|
if use_cache:
|
|
|
y, new_kv = self.mha(self.ln1(x), mask, rel_bias_tokens=n if mask is not None else None, kv_cache=kv, use_cache=True)
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x = x + y
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x = x + self.ff(self.ln2(x))
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return x, new_kv
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|
else:
|
|
|
x = x + self.mha(self.ln1(x), mask, rel_bias_tokens=n)
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return x + self.ff(self.ln2(x))
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|
|
|
class Encoder(nn.Module):
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def __init__(self, cfg: Dict[str, int]):
|
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|
super().__init__()
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|
d, l, h, r = cfg["d"], cfg["layers"], cfg["heads"], cfg["rank"]
|
|
|
self.emb = nn.Embedding(VOCAB, d)
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|
self.blocks = nn.ModuleList([Block(d, h, r) for _ in range(l)])
|
|
|
self.ln = nn.LayerNorm(d)
|
|
|
|
|
|
def forward(
|
|
|
self,
|
|
|
ids: torch.Tensor,
|
|
|
mask: Optional[torch.Tensor],
|
|
|
kv_caches: Optional[List[Optional[Tuple[torch.Tensor, torch.Tensor]]]] = None,
|
|
|
use_cache: bool = False
|
|
|
):
|
|
|
x = self.emb(ids)
|
|
|
if not use_cache:
|
|
|
for blk in self.blocks:
|
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|
x = blk(x, mask)
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|
return self.ln(x)
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|
|
|
|
|
new_kvs: List[Tuple[torch.Tensor, torch.Tensor]] = []
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|
|
for i, blk in enumerate(self.blocks):
|
|
|
kv = kv_caches[i] if (kv_caches is not None) else None
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|
|
x, kv_out = blk(x, mask, kv, use_cache=True)
|
|
|
new_kvs.append(kv_out)
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|
return self.ln(x), new_kvs
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|
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|
|
|
class ARHead(nn.Module):
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|
def __init__(self, d):
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|
super().__init__()
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|
self.proj = nn.Linear(d, VOCAB)
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|
def forward(self, h): return self.proj(h)
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|
|
|
|
|
class SATHead(nn.Module):
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|
def __init__(self, d, mode="var"):
|
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|
super().__init__()
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|
self.proj = nn.Linear(d, VOCAB)
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|
self.mode = mode
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|
self.gate = nn.Linear(d, 2) if mode == "var" else None
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|
def forward(self, h_last):
|
|
|
logits = self.proj(h_last)
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|
gate = self.gate(h_last[:, 0]) if self.gate is not None else None
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|
return logits, gate
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|
|
|
|
|
|
def causal_mask(n):
|
|
|
m = torch.full((1, 1, n, n), float("-inf"), device=DEV)
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|
return torch.triu(m, 1)
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|
|
|
|
|
def sat_mask(n, block=SAT_BLOCK):
|
|
|
idx = torch.arange(n, device=DEV)
|
|
|
grp = idx.unsqueeze(0) // block
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|
allow = (grp.T == grp) | (grp.T > grp)
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|
return torch.where(allow, 0.0, float("-inf")).unsqueeze(0).unsqueeze(0)
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|
|
|
|
|
|
|
def _prune_old_ckpts(dir_path: pathlib.Path, max_keep: int):
|
|
|
"""
|
|
|
Keeps only the 'max_keep' most recent step-based checkpoints.
|
|
|
Assumes checkpoints are named 'stepXXXXXXXX.pt'.
|
|
|
"""
|
|
|
if max_keep <= 0:
|
|
|
return
|
|
|
|
|
|
|
|
|
ckpts = sorted([p for p in dir_path.glob("step*.pt") if _is_probably_ckpt(p)])
|
|
|
|
|
|
if len(ckpts) > max_keep:
|
|
|
|
|
|
num_to_delete = len(ckpts) - max_keep
|
|
|
for i in range(num_to_delete):
|
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|
victim = ckpts[i]
|
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|
try:
|
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|
victim.unlink()
|
|
|
|
|
|
tmp_v = victim.with_suffix(".pt.tmp")
|
|
|
if tmp_v.exists(): tmp_v.unlink()
|
|
|
print(f" [prune] deleted old checkpoint {victim.name}")
|
|
|
except Exception as e:
|
|
|
print(f" [prune] failed to delete {victim.name}: {e}")
|
|
|
|
|
|
def save_ckpt(
|
|
|
path: pathlib.Path,
|
|
|
core: nn.Module,
|
|
|
ar_h: nn.Module,
|
|
|
sat_h: nn.Module,
|
|
|
opt: torch.optim.Optimizer,
|
|
|
scaler: GradScaler,
|
|
|
meta: Dict[str, Any],
|
|
|
max_ckpts: int | None = None,
|
|
|
):
|
|
|
path.parent.mkdir(exist_ok=True, parents=True)
|
|
|
tmp = path.with_suffix(path.suffix + ".tmp")
|
|
|
state = {
|
|
|
"core": core.state_dict(),
|
|
|
"ar": ar_h.state_dict(),
|
|
|
"sat": sat_h.state_dict(),
|
|
|
"opt": opt.state_dict(),
|
|
|
"scaler": scaler.state_dict(),
|
|
|
"cfg": meta.get("cfg"),
|
|
|
"tokenizer_id": TOKENIZER_ID,
|
|
|
**{k: v for k, v in meta.items() if k not in {"cfg"}},
|
|
|
}
|
|
|
torch.save(state, tmp, _use_new_zipfile_serialization=False)
|
|
|
tmp.replace(path)
|
|
|
(path.parent / "latest.json").write_text(json.dumps({"path": str(path), "step": meta["step"]}))
|
|
|
print(f"\n✓ saved checkpoint {path.name}")
|
|
|
|
|
|
if max_ckpts is not None:
|
|
|
_prune_old_ckpts(path.parent, max_ckpts)
|
|
|
|
|
|
def load_ckpt(
|
|
|
path: pathlib.Path,
|
|
|
core: nn.Module,
|
|
|
ar_h: nn.Module,
|
|
|
sat_h: nn.Module,
|
|
|
opt: torch.optim.Optimizer,
|
|
|
scaler: GradScaler,
|
|
|
):
|
|
|
p = _resolve_ckpt(path) or path
|
|
|
ck = _try_load(p, map_location="cpu")
|
|
|
if ck is None:
|
|
|
raise FileNotFoundError(f"No valid checkpoint at {p}")
|
|
|
core.load_state_dict(ck["core"])
|
|
|
ar_h.load_state_dict(ck["ar"])
|
|
|
sat_h.load_state_dict(ck["sat"])
|
|
|
opt.load_state_dict(ck["opt"])
|
|
|
scaler.load_state_dict(ck["scaler"])
|
|
|
return ck.get("step", 0), ck.get("seen_tok", 0), ck.get("wall_time", time.time())
|
|
|
|
|
|
def _safe_load_any(path: pathlib.Path, tgt: nn.Module, key: str | None = None, rename: str | None = None):
|
|
|
p = _resolve_ckpt(path) or path
|
|
|
if not p.exists(): return 0
|
|
|
ck = _try_load(p, map_location="cpu")
|
|
|
if ck is None: return 0
|
|
|
sd = ck.get(key, ck) if key else ck
|
|
|
if isinstance(sd, dict) and "state_dict" in sd:
|
|
|
sd = sd["state_dict"]
|
|
|
|
|
|
if rename:
|
|
|
sd = {k.replace(rename, "proj."): v for k, v in sd.items() if rename in k}
|
|
|
|
|
|
tgt_sd = tgt.state_dict()
|
|
|
filt = {k: v for k, v in sd.items() if k in tgt_sd and v.shape == tgt_sd[k].shape}
|
|
|
if filt:
|
|
|
tgt.load_state_dict(filt, strict=False)
|
|
|
return len(filt)
|
|
|
|
|
|
def infer_cfg_from_ckpt(path: pathlib.Path):
|
|
|
p = _resolve_ckpt(path) or path
|
|
|
if not p.exists(): return None
|
|
|
sd = _try_load(p, map_location="cpu")
|
|
|
if sd is None: return None
|
|
|
|
|
|
if isinstance(sd, dict) and "cfg" in sd and isinstance(sd["cfg"], dict):
|
|
|
return dict(sd["cfg"])
|
|
|
|
|
|
core = sd.get("core")
|
|
|
if core is None: return None
|
|
|
|
|
|
emb_w = core.get("emb.weight")
|
|
|
if emb_w is None: return None
|
|
|
d = emb_w.shape[1]
|
|
|
|
|
|
layer_ids = []
|
|
|
for k in core.keys():
|
|
|
if k.startswith("blocks."):
|
|
|
parts = k.split(".")
|
|
|
if len(parts) > 2 and parts[1].isdigit():
|
|
|
layer_ids.append(int(parts[1]))
|
|
|
layers = (max(layer_ids) + 1) if layer_ids else None
|
|
|
|
|
|
U = core.get("blocks.0.mha.U")
|
|
|
heads = rank = None
|
|
|
if U is not None:
|
|
|
dk, r = U.shape
|
|
|
rank = r
|
|
|
heads = d // dk if dk > 0 else None
|
|
|
|
|
|
out = {"d": d}
|
|
|
if layers is not None: out["layers"] = layers
|
|
|
if heads is not None: out["heads"] = heads
|
|
|
if rank is not None: out["rank"] = rank
|
|
|
return out
|
|
|
|
|
|
|
|
|
def _parse_grow_plan(s: str) -> List[int]:
|
|
|
steps = []
|
|
|
for part in s.split(","):
|
|
|
part = part.strip()
|
|
|
if part:
|
|
|
v = int(part)
|
|
|
if v >= 128:
|
|
|
steps.append(v)
|
|
|
return sorted(set(steps))
|
|
|
|
|
|
def _init_save_timers(resume_wall_time: float | None, interval_sec: int) -> Tuple[float, float]:
|
|
|
now_wall = time.time()
|
|
|
now_mono = time.monotonic()
|
|
|
if resume_wall_time is None:
|
|
|
return now_wall, now_mono
|
|
|
|
|
|
elapsed_wall = max(0.0, now_wall - resume_wall_time)
|
|
|
elapsed_clamped = min(float(interval_sec), elapsed_wall)
|
|
|
return now_wall, now_mono - elapsed_clamped
|
|
|
|
|
|
def train(args):
|
|
|
cfg = PRESETS[args.preset].copy()
|
|
|
|
|
|
if not args.fresh:
|
|
|
src_probe = pathlib.Path(args.warmstart_from) if args.warmstart_from else pathlib.Path(args.save_dir) / "final.pt"
|
|
|
prev_cfg = infer_cfg_from_ckpt(src_probe)
|
|
|
else:
|
|
|
prev_cfg = None
|
|
|
|
|
|
if prev_cfg:
|
|
|
cfg["d"] = prev_cfg.get("d", cfg["d"])
|
|
|
if prev_cfg.get("heads"):
|
|
|
cfg["heads"] = prev_cfg["heads"]
|
|
|
if args.rank is None and prev_cfg.get("rank"):
|
|
|
cfg["rank"] = prev_cfg["rank"]
|
|
|
if prev_cfg.get("layers"):
|
|
|
cfg["layers"] = prev_cfg["layers"]
|
|
|
if args.x2 and prev_cfg.get("layers"):
|
|
|
cfg["layers"] = max(cfg["layers"], prev_cfg["layers"] * 2)
|
|
|
|
|
|
if args.rank:
|
|
|
cfg["rank"] = args.rank
|
|
|
if args.x2 and not prev_cfg:
|
|
|
cfg["layers"] *= 2
|
|
|
|
|
|
BLOCK = args.block or DEFAULT_BLOCK
|
|
|
|
|
|
core = Encoder(cfg).to(DEV)
|
|
|
ar_h = ARHead(cfg["d"]).to(DEV)
|
|
|
sat_h = SATHead(cfg["d"], mode="var").to(DEV)
|
|
|
|
|
|
loaded = 0
|
|
|
if not args.fresh:
|
|
|
src = pathlib.Path(args.warmstart_from) if args.warmstart_from else pathlib.Path(args.save_dir) / "final.pt"
|
|
|
src = _resolve_ckpt(src)
|
|
|
if src:
|
|
|
loaded += _safe_load_any(src, core, key="core")
|
|
|
loaded += _safe_load_any(src, ar_h, key="ar")
|
|
|
loaded += _safe_load_any(src, sat_h, key="sat")
|
|
|
if loaded:
|
|
|
print(f"Warm-start: loaded {loaded} matching tensors from {src}")
|
|
|
|
|
|
opt = torch.optim.AdamW(
|
|
|
[
|
|
|
{"params": core.parameters(), "lr": LR_CORE},
|
|
|
{"params": ar_h.parameters(), "lr": LR_HEAD},
|
|
|
{"params": sat_h.parameters(), "lr": LR_HEAD},
|
|
|
]
|
|
|
)
|
|
|
scaler = GradScaler(enabled=(args.amp and DEV.type == "cuda"))
|
|
|
|
|
|
ce_tok = nn.CrossEntropyLoss(label_smoothing=0.1)
|
|
|
ce_gate = nn.CrossEntropyLoss()
|
|
|
|
|
|
start_step, seen_tok = 0, 0
|
|
|
last_save_wall = None
|
|
|
|
|
|
if args.resume and not args.fresh:
|
|
|
start_step, seen_tok, last_save_wall = load_ckpt(
|
|
|
pathlib.Path(args.resume), core, ar_h, sat_h, opt, scaler
|
|
|
)
|
|
|
print(f"✓ resumed from step {start_step:,}, seen_tokens={seen_tok:,}")
|
|
|
|
|
|
last_save_wall, last_save_mono = _init_save_timers(last_save_wall, args.save_every_sec)
|
|
|
|
|
|
if args.target_tokens:
|
|
|
target_tokens = args.target_tokens
|
|
|
else:
|
|
|
param_count = sum(p.numel() for p in core.parameters())
|
|
|
|
|
|
ratio = 51.2 if args.chilla_max_double else 25
|
|
|
target_tokens = int(ratio * param_count)
|
|
|
print(f"[config] Chinchilla ratio: {ratio}x tokens/param")
|
|
|
|
|
|
new_tokens_needed = target_tokens - seen_tok
|
|
|
if new_tokens_needed <= 0:
|
|
|
print("Target already reached – nothing to train.")
|
|
|
return
|
|
|
|
|
|
new_steps = new_tokens_needed // BLOCK
|
|
|
if args.steps:
|
|
|
new_steps = min(new_steps, args.steps)
|
|
|
new_tokens_needed = new_steps * BLOCK
|
|
|
|
|
|
total_tokens_needed = seen_tok + new_tokens_needed
|
|
|
print(f"[auto-steps] {new_steps:,} training steps (@ {BLOCK} tokens/step)")
|
|
|
|
|
|
grow_plan = _parse_grow_plan(args.grow_plan) if args.auto_grow else []
|
|
|
if args.auto_grow:
|
|
|
if BLOCK not in grow_plan:
|
|
|
grow_plan = sorted(set(grow_plan + [BLOCK]))
|
|
|
print(f"[auto-grow] plan: {grow_plan} every {args.grow_every_steps} steps")
|
|
|
|
|
|
stream = token_stream(args.source, target_tokens, seed=42)
|
|
|
buf: list[int] = []
|
|
|
pbar = tqdm(total=total_tokens_needed, initial=seen_tok, unit="tok")
|
|
|
step = start_step
|
|
|
steps_since_last_grow = 0
|
|
|
|
|
|
while seen_tok < total_tokens_needed:
|
|
|
try:
|
|
|
while len(buf) < BLOCK:
|
|
|
buf.append(next(stream))
|
|
|
except StopIteration:
|
|
|
break
|
|
|
|
|
|
ids = torch.tensor(buf[:BLOCK], device=DEV).unsqueeze(0)
|
|
|
buf = buf[BLOCK:]
|
|
|
|
|
|
tgt_ar = ids.clone()
|
|
|
|
|
|
try:
|
|
|
with amp(args.amp):
|
|
|
h_ar = core(ids, causal_mask(ids.size(1)))
|
|
|
logits_ar = ar_h(h_ar)[:, :-1]
|
|
|
loss_ar = ce_tok(logits_ar.reshape(-1, VOCAB), tgt_ar[:, 1:].reshape(-1))
|
|
|
|
|
|
h_sat = core(ids, sat_mask(ids.size(1)))
|
|
|
logits_sat, gate = sat_h(h_sat[:, -SAT_BLOCK:])
|
|
|
tgt_sat = ids[:, 1:SAT_BLOCK+1]
|
|
|
loss_sat = ce_tok(logits_sat.reshape(-1, VOCAB), tgt_sat.reshape(-1))
|
|
|
if gate is not None:
|
|
|
loss_sat += EMIT_LAMBDA * ce_gate(gate, torch.ones(ids.size(0), device=DEV, dtype=torch.long))
|
|
|
|
|
|
loss = loss_ar + loss_sat
|
|
|
|
|
|
scaler.scale(loss).backward()
|
|
|
scaler.unscale_(opt)
|
|
|
nn.utils.clip_grad_norm_(core.parameters(), 1.0)
|
|
|
scaler.step(opt)
|
|
|
scaler.update()
|
|
|
opt.zero_grad(set_to_none=True)
|
|
|
|
|
|
except RuntimeError as e:
|
|
|
msg = str(e).lower()
|
|
|
if "out of memory" in msg or "cuda error" in msg:
|
|
|
new_block = max(128, BLOCK // 2)
|
|
|
if new_block < BLOCK:
|
|
|
print(f"\n[OOM] reducing block from {BLOCK} -> {new_block}")
|
|
|
BLOCK = new_block
|
|
|
if DEV.type == "cuda":
|
|
|
torch.cuda.empty_cache()
|
|
|
buf = ids[0].tolist() + buf
|
|
|
steps_since_last_grow = 0
|
|
|
continue
|
|
|
raise
|
|
|
|
|
|
step += 1
|
|
|
seen_tok += BLOCK
|
|
|
pbar.update(BLOCK)
|
|
|
pbar.set_postfix(loss=f"{loss.item():.3f}", block=BLOCK)
|
|
|
|
|
|
if args.save_every_sec > 0:
|
|
|
now_mono = time.monotonic()
|
|
|
if now_mono - last_save_mono >= args.save_every_sec:
|
|
|
ck_name = f"step{step:08d}.pt"
|
|
|
save_ckpt(
|
|
|
pathlib.Path(args.save_dir) / ck_name,
|
|
|
core, ar_h, sat_h, opt, scaler,
|
|
|
meta={
|
|
|
"cfg": cfg,
|
|
|
"step": step,
|
|
|
"seen_tok": seen_tok,
|
|
|
"wall_time": time.time(),
|
|
|
"py_state": random.getstate(),
|
|
|
"torch_state": rng_state(),
|
|
|
},
|
|
|
max_ckpts=args.max_ckpts
|
|
|
)
|
|
|
last_save_mono = now_mono
|
|
|
last_save_wall = time.time()
|
|
|
|
|
|
if args.auto_grow:
|
|
|
steps_since_last_grow += 1
|
|
|
if steps_since_last_grow >= args.grow_every_steps:
|
|
|
steps_since_last_grow = 0
|
|
|
try:
|
|
|
idx = grow_plan.index(BLOCK)
|
|
|
if idx + 1 < len(grow_plan):
|
|
|
candidate = grow_plan[idx + 1]
|
|
|
print(f"[auto-grow] attempting BLOCK {BLOCK} -> {candidate}")
|
|
|
BLOCK = candidate
|
|
|
if DEV.type == "cuda":
|
|
|
torch.cuda.empty_cache()
|
|
|
else:
|
|
|
print("[auto-grow] at max planned block; no further growth.")
|
|
|
except ValueError:
|
|
|
grow_plan = sorted(set(grow_plan + [BLOCK]))
|
|
|
idx = grow_plan.index(BLOCK)
|
|
|
if idx + 1 < len(grow_plan):
|
|
|
candidate = grow_plan[idx + 1]
|
|
|
print(f"[auto-grow] moving to planned BLOCK {candidate}")
|
|
|
BLOCK = candidate
|
|
|
if DEV.type == "cuda":
|
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
pbar.close()
|
|
|
|
|
|
save_ckpt(
|
|
|
pathlib.Path(args.save_dir) / "final.pt",
|
|
|
core, ar_h, sat_h, opt, scaler,
|
|
|
meta={
|
|
|
"cfg": cfg,
|
|
|
"step": step,
|
|
|
"seen_tok": seen_tok,
|
|
|
"wall_time": time.time(),
|
|
|
"py_state": random.getstate(),
|
|
|
"torch_state": rng_state(),
|
|
|
},
|
|
|
max_ckpts=None
|
|
|
)
|
|
|
print("🎉 training complete")
|
|
|
|
|
|
|
|
|
def _apply_no_repeat_ngram(logits: torch.Tensor, ids: torch.Tensor, n: int):
|
|
|
if n <= 0 or ids.size(1) < n - 1:
|
|
|
return logits
|
|
|
|
|
|
prefix = ids[0, - (n - 1):].tolist()
|
|
|
banned = []
|
|
|
tokens = ids[0].tolist()
|
|
|
for i in range(len(tokens) - n + 1):
|
|
|
if tokens[i:i + n - 1] == prefix:
|
|
|
banned.append(tokens[i + n - 1])
|
|
|
|
|
|
if banned:
|
|
|
banned_idx = torch.tensor(banned, device=logits.device, dtype=torch.long)
|
|
|
logits[..., banned_idx] = float("-inf")
|
|
|
return logits
|
|
|
|
|
|
def _apply_rep_presence_frequency(
|
|
|
logits: torch.Tensor, ids: torch.Tensor, last_n: int,
|
|
|
repetition_penalty: float, presence_penalty: float, frequency_penalty: float
|
|
|
):
|
|
|
if ids.numel() == 0:
|
|
|
return logits
|
|
|
|
|
|
if last_n > 0:
|
|
|
hist = ids[0, -last_n:].to(torch.long)
|
|
|
else:
|
|
|
hist = ids[0].to(torch.long)
|
|
|
|
|
|
if hist.numel() == 0:
|
|
|
return logits
|
|
|
|
|
|
uniq, counts = torch.unique(hist, return_counts=True)
|
|
|
|
|
|
if presence_penalty != 0.0 or frequency_penalty != 0.0:
|
|
|
adjust = presence_penalty + frequency_penalty * counts.to(logits.dtype)
|
|
|
logits[..., uniq] = logits[..., uniq] - adjust
|
|
|
|
|
|
if repetition_penalty and abs(repetition_penalty - 1.0) > 1e-6:
|
|
|
sel = logits[..., uniq]
|
|
|
sel = torch.where(sel > 0, sel / repetition_penalty, sel * repetition_penalty)
|
|
|
logits[..., uniq] = sel
|
|
|
|
|
|
return logits
|
|
|
|
|
|
def _filter_top_k_top_p_min_p(
|
|
|
logits: torch.Tensor, top_k: int, top_p: float, min_p: float, temperature: float
|
|
|
) -> torch.Tensor:
|
|
|
logits = logits / max(temperature, 1e-8)
|
|
|
|
|
|
if logits.dim() == 1:
|
|
|
logits = logits.unsqueeze(0)
|
|
|
|
|
|
B, V = logits.size(0), logits.size(-1)
|
|
|
probs = logits.softmax(-1)
|
|
|
|
|
|
if top_k and top_k < V:
|
|
|
vals, idx = torch.topk(probs, top_k, dim=-1)
|
|
|
mask = torch.full_like(probs, 0.0)
|
|
|
mask.scatter_(1, idx, 1.0)
|
|
|
probs = probs * mask
|
|
|
|
|
|
if top_p < 1.0:
|
|
|
sorted_probs, sorted_idx = torch.sort(probs, descending=True, dim=-1)
|
|
|
cumsum = torch.cumsum(sorted_probs, dim=-1)
|
|
|
keep = cumsum <= top_p
|
|
|
keep[..., 0] = True
|
|
|
mask = torch.zeros_like(probs)
|
|
|
mask.scatter_(1, sorted_idx, keep.to(mask.dtype))
|
|
|
probs = probs * mask
|
|
|
|
|
|
if min_p > 0.0:
|
|
|
probs = torch.where(probs >= min_p, probs, torch.zeros_like(probs))
|
|
|
|
|
|
sums = probs.sum(-1, keepdim=True)
|
|
|
empty = (sums == 0)
|
|
|
if empty.any():
|
|
|
fallback_idx = logits.argmax(-1, keepdim=True)
|
|
|
probs = torch.where(empty, torch.zeros_like(probs), probs)
|
|
|
probs.scatter_(-1, fallback_idx, torch.where(empty, torch.ones_like(sums), torch.zeros_like(sums)))
|
|
|
|
|
|
probs = probs / probs.sum(-1, keepdim=True)
|
|
|
return probs
|
|
|
|
|
|
|
|
|
def load_joint(ckpt: str, preset: str):
|
|
|
path = _resolve_ckpt(pathlib.Path(ckpt)) or pathlib.Path(ckpt)
|
|
|
sd = _try_load(path, map_location="cpu")
|
|
|
if sd is None:
|
|
|
raise FileNotFoundError(f"No valid checkpoint at {path}")
|
|
|
|
|
|
cfg = sd["cfg"] if "cfg" in sd and isinstance(sd["cfg"], dict) else (infer_cfg_from_ckpt(path) or PRESETS[preset])
|
|
|
|
|
|
core = Encoder(cfg).to(DEV)
|
|
|
ar_h = ARHead(cfg["d"]).to(DEV)
|
|
|
sat_h = SATHead(cfg["d"]).to(DEV)
|
|
|
|
|
|
core.load_state_dict(sd["core"])
|
|
|
ar_h.load_state_dict(sd["ar"])
|
|
|
sat_h.load_state_dict(sd["sat"])
|
|
|
|
|
|
return core, ar_h, sat_h
|
|
|
|
|
|
@torch.no_grad()
|
|
|
def ar_decode(core, ar_h, prompt: str, max_new: int, T: float,
|
|
|
greedy: bool, top_k: int, top_p: float, min_p: float,
|
|
|
repetition_penalty: float, presence_penalty: float,
|
|
|
frequency_penalty: float, penalty_last_n: int,
|
|
|
no_repeat_ngram_size: int):
|
|
|
ids = torch.tensor([tok.encode(prompt)], device=DEV)
|
|
|
if ids.size(1) == 0:
|
|
|
ids = torch.tensor([[EOS] if EOS is not None else [0]], device=DEV)
|
|
|
|
|
|
h_full, kvs = core(ids, causal_mask(ids.size(1)), use_cache=True)
|
|
|
|
|
|
start = time.time()
|
|
|
for _ in range(max_new):
|
|
|
logits = ar_h(h_full)[:, -1]
|
|
|
|
|
|
logits = _apply_no_repeat_ngram(logits, ids, no_repeat_ngram_size)
|
|
|
logits = _apply_rep_presence_frequency(
|
|
|
logits, ids, penalty_last_n, repetition_penalty, presence_penalty, frequency_penalty
|
|
|
)
|
|
|
|
|
|
if greedy:
|
|
|
nxt = logits.argmax(-1, keepdim=True)
|
|
|
else:
|
|
|
probs = _filter_top_k_top_p_min_p(logits.squeeze(0), top_k, top_p, min_p, T)
|
|
|
nxt = probs.multinomial(1)
|
|
|
|
|
|
ids = torch.cat([ids, nxt.unsqueeze(0) if nxt.dim()==1 else nxt], 1)
|
|
|
|
|
|
x = ids[:, -1:]
|
|
|
h_full, kvs = core(x, None, kv_caches=kvs, use_cache=True)
|
|
|
|
|
|
print(tok.decode(ids[0].tolist(), skip_special_tokens=True))
|
|
|
print(f"[{max_new} tok in {time.time() - start:.2f}s]")
|
|
|
|
|
|
@torch.no_grad()
|
|
|
def sat_decode(core, sat_h, prompt, max_new, T, var,
|
|
|
greedy: bool, top_k: int, top_p: float, min_p: float,
|
|
|
repetition_penalty: float, presence_penalty: float,
|
|
|
frequency_penalty: float, penalty_last_n: int,
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no_repeat_ngram_size: int):
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ids = torch.tensor([tok.encode(prompt)], device=DEV)
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added, t0 = 0, time.time()
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|
while added < max_new:
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|
h = core(ids, sat_mask(ids.size(1)))
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|
logits_all, gate = sat_h(h[:, -SAT_BLOCK:])
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|
|
|
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|
stride = 2 if (not var or gate is None) else (gate.softmax(-1).multinomial(1) + 1).item()
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|
stride = int(stride)
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|
|
|
|
|
for pos in range(stride):
|
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|
row_logits = logits_all[:, pos, :]
|
|
|
|
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|
row_logits = _apply_no_repeat_ngram(row_logits, ids, no_repeat_ngram_size)
|
|
|
row_logits = _apply_rep_presence_frequency(
|
|
|
row_logits, ids, penalty_last_n, repetition_penalty, presence_penalty, frequency_penalty
|
|
|
)
|
|
|
|
|
|
if greedy:
|
|
|
nxt = row_logits.argmax(-1, keepdim=True)
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|
else:
|
|
|
probs = _filter_top_k_top_p_min_p(row_logits.squeeze(0), top_k, top_p, min_p, T)
|
|
|
nxt = probs.multinomial(1)
|
|
|
|
|
|
ids = torch.cat([ids, nxt], 1)
|
|
|
added += 1
|
|
|
if added >= max_new:
|
|
|
break
|
|
|
|
|
|
print(tok.decode(ids[0].tolist(), skip_special_tokens=True))
|
|
|
print(f"[{added} tok in {time.time() - t0:.2f}s]")
|
|
|
|
|
|
|
|
|
def main():
|
|
|
ap = argparse.ArgumentParser()
|
|
|
sub = ap.add_subparsers(dest="cmd", required=True)
|
|
|
|
|
|
tr = sub.add_parser("train")
|
|
|
tr.add_argument("--preset", choices=PRESETS, default="small")
|
|
|
tr.add_argument("--rank", type=int)
|
|
|
tr.add_argument("--block", type=int, default=DEFAULT_BLOCK)
|
|
|
tr.add_argument("--source", default="cerebras/SlimPajama-627B")
|
|
|
tr.add_argument("--target_tokens", type=int)
|
|
|
tr.add_argument("--steps", type=int)
|
|
|
tr.add_argument("--amp", action="store_true")
|
|
|
tr.add_argument("--save_every_sec", type=int, default=DEFAULT_SAVE_SEC)
|
|
|
tr.add_argument("--save_dir", default=str(CKDIR))
|
|
|
tr.add_argument("--resume", type=str)
|
|
|
tr.add_argument("--x2", action="store_true", help="~2x params by doubling layers")
|
|
|
tr.add_argument("--warmstart_from", type=str, default=None, help="Path to previous final.pt for shape-safe warm start")
|
|
|
tr.add_argument("--fresh", action="store_true", help="Start from scratch: do not probe or load any checkpoints")
|
|
|
|
|
|
|
|
|
tr.add_argument("--max_ckpts", type=int, default=None, help="Max number of recent step checkpoints to keep (deletes oldest)")
|
|
|
|
|
|
|
|
|
tr.add_argument("--chilla_max_double", action="store_true", help="Use 51.2x tokens/param (25.6 * 2) instead of default 25x")
|
|
|
|
|
|
tr.add_argument("--auto_grow", action="store_true", help="Automatically grow block size over time")
|
|
|
tr.add_argument("--grow_plan", type=str, default="576,640,768,896,1024", help="Comma list of block sizes to try in order")
|
|
|
tr.add_argument("--grow_every_steps", type=int, default=50000, help="Steps between growth attempts")
|
|
|
|
|
|
inf = sub.add_parser("infer")
|
|
|
inf.add_argument("--mode", choices=["ar", "sat"], required=True)
|
|
|
inf.add_argument("--ckpt", required=True)
|
|
|
inf.add_argument("--preset", default="small")
|
|
|
inf.add_argument("--prompt", required=True)
|
|
|
inf.add_argument("--max_new", type=int, default=120)
|
|
|
inf.add_argument("--temperature", type=float, default=1.0)
|
|
|
|
|
|
inf.add_argument("--greedy", action="store_true", help="Greedy decode (overrides sampling)")
|
|
|
inf.add_argument("--top_k", type=int, default=0)
|
|
|
inf.add_argument("--top_p", type=float, default=1.0)
|
|
|
inf.add_argument("--min_p", type=float, default=0.0)
|
|
|
|
|
|
inf.add_argument("--repetition_penalty", type=float, default=1.0)
|
|
|
inf.add_argument("--presence_penalty", type=float, default=0.0)
|
|
|
inf.add_argument("--frequency_penalty", type=float, default=0.0)
|
|
|
inf.add_argument("--penalty_last_n", type=int, default=64)
|
|
|
inf.add_argument("--no_repeat_ngram_size", type=int, default=0)
|
|
|
|
|
|
inf.add_argument("--var", action="store_true")
|
|
|
|
|
|
args = ap.parse_args()
|
|
|
|
|
|
if args.cmd == "train":
|
|
|
train(args)
|
|
|
else:
|
|
|
core, ar_h, sat_h = load_joint(args.ckpt, args.preset)
|
|
|
if args.mode == "ar":
|
|
|
ar_decode(core, ar_h, args.prompt, args.max_new, args.temperature,
|
|
|
args.greedy, args.top_k, args.top_p, args.min_p,
|
|
|
args.repetition_penalty, args.presence_penalty,
|
|
|
args.frequency_penalty, args.penalty_last_n,
|
|
|
args.no_repeat_ngram_size)
|
|
|
elif args.mode == "sat":
|
|
|
sat_decode(core, sat_h, args.prompt, args.max_new, args.temperature, args.var,
|
|
|
args.greedy, args.top_k, args.top_p, args.min_p,
|
|
|
args.repetition_penalty, args.presence_penalty,
|
|
|
args.frequency_penalty, args.penalty_last_n,
|
|
|
args.no_repeat_ngram_size)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
main() |
