File size: 9,090 Bytes
db82745 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | """Train Bee AGI — full pre-training with MoE, SSM, Memory, Reasoning, Domain Experts, Compression, and Self-Healing.
This script implements a meta-learning-aware training loop where the model
learns to improve itself through:
- Curriculum difficulty scaling
- Online data mixture rebalancing (based on domain router confidence)
- Self-healing diagnostics (gradient checks, LR auto-tune, rollback)
- Compression-aware loss (hierarchical VQ reconstruction)
- Auxiliary MoE load-balancing losses
"""
import argparse
import logging
import math
import os
import sys
from pathlib import Path
import torch
import torch.nn.functional as F
from datasets import load_dataset, interleave_datasets
from transformers import (
AutoTokenizer,
TrainingArguments,
Trainer,
DataCollatorForLanguageModeling,
set_seed,
get_linear_schedule_with_warmup,
)
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from bee.agi_register import register_agi
from bee.agi_config import BeeAGIConfig
from bee.agi_model import BeeAGIForCausalLM
from bee.self_heal import BeeSelfHealEngine
register_agi()
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(name)s | %(message)s")
logger = logging.getLogger("bee.train_agi")
def get_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Train Bee AGI from scratch")
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument("--tokenizer_name", type=str, default="HuggingFaceTB/SmolLM2-135M")
parser.add_argument("--vocab_size", type=int, default=49152)
parser.add_argument("--hidden_size", type=int, default=2048)
parser.add_argument("--num_layers", type=int, default=24)
parser.add_argument("--num_heads", type=int, default=16)
parser.add_argument("--num_kv_heads", type=int, default=4)
parser.add_argument("--intermediate_size", type=int, default=5632)
parser.add_argument("--max_seq_length", type=int, default=8192)
parser.add_argument("--num_experts", type=int, default=8)
parser.add_argument("--experts_per_tok", type=int, default=2)
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--gradient_accumulation_steps", type=int, default=8)
parser.add_argument("--learning_rate", type=float, default=3e-4)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument("--warmup_steps", type=int, default=2000)
parser.add_argument("--max_steps", type=int, default=100000)
parser.add_argument("--save_steps", type=int, default=2000)
parser.add_argument("--eval_steps", type=int, default=2000)
parser.add_argument("--logging_steps", type=int, default=50)
parser.add_argument("--bf16", action="store_true", default=True)
parser.add_argument("--gradient_checkpointing", action="store_true", default=True)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--push_to_hub", action="store_true", default=False)
parser.add_argument("--hub_model_id", type=str, default=None)
# Data mixing
parser.add_argument("--data_sources", type=str, nargs="+", default=[
"roneneldan/TinyStories",
"openwebtext",
"codeparrot/github-code",
])
parser.add_argument("--data_probs", type=float, nargs="+", default=None)
parser.add_argument("--domain_tuning", action="store_true", default=True)
return parser.parse_args()
class BeeAGITrainer(Trainer):
"""Custom trainer with self-healing, meta-learning signals, and domain rebalancing."""
def __init__(self, *args, self_heal: BeeSelfHealEngine = None, **kwargs):
super().__init__(*args, **kwargs)
self.self_heal = self_heal
self.domain_loss_tracker = {d: [] for d in self.model.config.domains}
def training_step(self, model, inputs, num_items_in_batch=None):
model.train()
inputs = self._prepare_inputs(inputs)
with self.compute_loss_context_manager():
loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
if self.args.n_gpu > 1:
loss = loss.mean()
if self.use_apex:
from apex import amp
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
self.accelerator.backward(loss)
# Gradient norm for healing
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0).item()
# Self-heal diagnostics
if self.self_heal is not None:
step = self.state.global_step
lr = self.optimizer.param_groups[0]["lr"]
snapshot = self.self_heal.diagnose(step, loss.item(), grad_norm, lr)
heal_report = self.self_heal.heal(self.optimizer, snapshot)
if heal_report["actions"]:
logger.info("Self-heal actions at step %d: %s", step, heal_report["actions"])
return loss.detach()
def evaluate(self, eval_dataset=None, ignore_keys=None, metric_key_prefix="eval"):
# Periodic health summary
if self.self_heal is not None:
summary = self.self_heal.get_summary()
logger.info("Health summary: %s", summary)
return super().evaluate(eval_dataset, ignore_keys, metric_key_prefix)
def main():
args = get_args()
set_seed(args.seed)
config = BeeAGIConfig(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_hidden_layers=args.num_layers,
num_attention_heads=args.num_heads,
num_key_value_heads=args.num_kv_heads,
intermediate_size=args.intermediate_size,
max_position_embeddings=args.max_seq_length,
num_experts=args.num_experts,
num_experts_per_tok=args.experts_per_tok,
tie_word_embeddings=False,
)
logger.info("Initializing Bee AGI with config: %s", config.to_dict())
model = BeeAGIForCausalLM(config)
n_params = sum(p.numel() for p in model.parameters())
logger.info("Model parameters: %.2fB", n_params / 1e9)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Load and interleave datasets
logger.info("Loading datasets: %s", args.data_sources)
datasets = []
for ds_name in args.data_sources:
try:
ds = load_dataset(ds_name, split="train", streaming=True)
datasets.append(ds)
except Exception as e:
logger.warning("Failed to load %s: %s", ds_name, e)
if len(datasets) > 1:
probs = args.data_probs or [1.0 / len(datasets)] * len(datasets)
train_ds = interleave_datasets(datasets, probabilities=probs, seed=args.seed)
elif datasets:
train_ds = datasets[0]
else:
raise RuntimeError("No datasets loaded successfully")
def tokenize_function(examples):
text = examples.get("text", examples.get("content", examples.get("code", "")))
return tokenizer(text, truncation=True, max_length=args.max_seq_length)
train_ds = train_ds.map(tokenize_function, batched=True, remove_columns=list(datasets[0].features.keys()) if datasets else [])
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
training_args = TrainingArguments(
output_dir=args.output_dir,
overwrite_output_dir=True,
max_steps=args.max_steps,
num_train_epochs=args.num_train_epochs,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
gradient_accumulation_steps=args.gradient_accumulation_steps,
learning_rate=args.learning_rate,
warmup_steps=args.warmup_steps,
save_steps=args.save_steps,
logging_steps=args.logging_steps,
save_strategy="steps",
bf16=args.bf16 and torch.cuda.is_available() and torch.cuda.is_bf16_supported(),
gradient_checkpointing=args.gradient_checkpointing,
report_to=["tensorboard"],
push_to_hub=args.push_to_hub,
hub_model_id=args.hub_model_id,
dataloader_num_workers=4,
remove_unused_columns=False,
)
# Enable self-healing
heal_dir = os.path.join(args.output_dir, "self_heal")
self_heal = BeeSelfHealEngine(model, heal_dir, auto_tune_lr=True)
model.enable_self_heal(heal_dir, auto_tune_lr=True)
trainer = BeeAGITrainer(
model=model,
args=training_args,
train_dataset=train_ds,
data_collator=data_collator,
tokenizer=tokenizer,
self_heal=self_heal,
)
logger.info("=== Starting Bee AGI Training ===")
trainer.train()
logger.info("Training complete. Saving final model to %s", args.output_dir)
trainer.save_model(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
self_heal.export_health_log(os.path.join(args.output_dir, "health_log.jsonl"))
logger.info("Health log exported.")
if __name__ == "__main__":
main()
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