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6eb0505 | 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 | """FFT parcial do DeepSeek-V4-Flash-0731: experts MoE congelados, FFT dos modulos densos.
Usa Unsloth (kernels + packing) + TRL SFTTrainer + DeepSpeed Ulysses SP=8 para 64k ctx."""
import argparse
import json
import os
import sys
import time
import math
import torch
from datasets import load_from_disk
from transformers import AutoTokenizer, TrainingArguments
from trl import SFTTrainer, SFTConfig
try:
from unsloth import FastModel
UNSLOTH_AVAILABLE = True
except Exception:
UNSLOTH_AVAILABLE = False
def freeze_moe(model):
frozen = 0
trainable = 0
for name, p in model.named_parameters():
is_moe = any(k in name.lower() for k in ("moe", "experts", "gate", "router", "moe_layer"))
if is_moe:
p.requires_grad_(False)
frozen += p.numel()
else:
p.requires_grad_(True)
trainable += p.numel()
return frozen, trainable
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--config", default="hf_model/config.json")
ap.add_argument("--resume", default="auto", choices=["auto", "true", "false"])
ap.add_argument("--max-steps", type=int, default=-1)
args = ap.parse_args()
with open(args.config) as fh:
cfg = json.load(fh)
model_id = cfg["model_id"]
max_seq_len = cfg["max_seq_len"]
data_dir = cfg["data_dir"]
out_dir = cfg["output_dir"]
print(f"[train] model={model_id} max_seq_len={max_seq_len}", flush=True)
print(f"[train] unsloth available: {UNSLOTH_AVAILABLE}", flush=True)
if UNSLOTH_AVAILABLE:
model, tok = FastModel.from_pretrained(
model_name=model_id,
max_seq_length=max_seq_len,
dtype=torch.bfloat16,
load_in_4bit=False,
trust_remote_code=True,
)
FastModel.for_training(model)
else:
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
attn_implementation="flash_attention_2",
)
frozen, trainable = freeze_moe(model)
print(f"[train] frozen params (MoE): {frozen:,}", flush=True)
print(f"[train] trainable params (non-MoE): {trainable:,}", flush=True)
print(f"[train] trainable %: {100*trainable/(frozen+trainable):.3f}%", flush=True)
ds = load_from_disk(data_dir)
print(f"[train] dataset: {len(ds)} samples", flush=True)
sft_config = SFTConfig(
dataset_text_field="text",
max_length=max_seq_len,
packing=cfg.get("packing", True),
dataset_num_proc=8,
report_to="none",
output_dir=out_dir,
num_train_epochs=cfg.get("epochs", 1),
per_device_train_batch_size=cfg.get("micro_batch", 1),
gradient_accumulation_steps=cfg.get("grad_accum", 8),
learning_rate=cfg.get("lr", 1e-5),
warmup_steps=cfg.get("warmup_steps", 50),
lr_scheduler_type="cosine",
bf16=True,
logging_steps=10,
save_steps=cfg.get("save_steps", 500),
save_total_limit=cfg.get("save_total_limit", 4),
save_strategy="steps",
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
max_steps=args.max_steps if args.max_steps > 0 else -1,
optim="adamw_torch_fused",
weight_decay=cfg.get("weight_decay", 0.01),
max_grad_norm=cfg.get("max_grad_norm", 1.0),
seed=42,
dataloader_num_workers=4,
remove_unused_columns=True,
)
trainer = SFTTrainer(
model=model,
tokenizer=tok,
train_dataset=ds,
args=sft_config,
)
resume_from = None
if args.resume in ("auto", "true"):
if os.path.isdir(out_dir):
ckpts = sorted(
[d for d in os.listdir(out_dir) if d.startswith("checkpoint-")],
key=lambda x: int(x.split("-")[1]),
)
if ckpts:
resume_from = os.path.join(out_dir, ckpts[-1])
print(f"[train] resuming from {resume_from}", flush=True)
t0 = time.time()
if resume_from:
trainer.train(resume_from_checkpoint=resume_from)
else:
trainer.train()
train_secs = time.time() - t0
print(f"[train] training done in {train_secs:.1f}s", flush=True)
if args.max_steps <= 0:
print(f"[train] saving final model to {out_dir}/final", flush=True)
trainer.save_model(os.path.join(out_dir, "final"))
tok.save_pretrained(os.path.join(out_dir, "final"))
log = trainer.state.log_history if hasattr(trainer.state, "log_history") else []
summary = {
"train_secs": train_secs,
"global_step": trainer.state.global_step,
"max_steps": trainer.state.max_steps,
"log_history": log,
"frozen_params": frozen,
"trainable_params": trainable,
"model_id": model_id,
"max_seq_len": max_seq_len,
}
summary_path = os.path.join(out_dir, "training_summary.json")
os.makedirs(out_dir, exist_ok=True)
with open(summary_path, "w") as fh:
json.dump(summary, fh, indent=2, default=str)
print(f"[train] summary saved to {summary_path}", flush=True)
if __name__ == "__main__":
main() |