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dc59b01 | 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 | from __future__ import annotations
import os
import torch
from datasets import load_dataset
from peft import LoraConfig, get_peft_model
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer,
DataCollatorForSeq2Seq,
Seq2SeqTrainer,
Seq2SeqTrainingArguments,
)
from prompting import clean_gold_sql, get_schema_text, build_prompt
# =====================================================
# SETTINGS
# =====================================================
BASE_MODEL = "Salesforce/codet5-base"
PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
OUT_DIR = os.path.join(PROJECT_ROOT, "checkpoints", "sft_adapter_codet5")
TRAIN_SPLIT = "train[:7000]"
EPOCHS = 10
LR = 2e-4
PER_DEVICE_BATCH = 2 # codet5 bigger -> reduce
GRAD_ACCUM = 4
MAX_INPUT = 512
MAX_OUTPUT = 160
# =====================================================
# DEVICE
# =====================================================
os.environ["TOKENIZERS_PARALLELISM"] = "false"
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
print("Using device:", device)
# =====================================================
# TOKENIZER
# =====================================================
print("Loading tokenizer/model:", BASE_MODEL)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# =====================================================
# PREPROCESS FUNCTION
# =====================================================
def preprocess_function(example):
question = example["question"]
db_id = example["db_id"]
gold_sql = clean_gold_sql(example["query"])
schema_text = get_schema_text(db_id)
prompt = build_prompt(question, db_id, schema_text=schema_text, training_sql=None)
model_inputs = tokenizer(
prompt,
max_length=MAX_INPUT,
truncation=True,
padding="max_length",
)
labels = tokenizer(
gold_sql,
max_length=MAX_OUTPUT,
truncation=True,
padding="max_length",
)["input_ids"]
labels = [(tok if tok != tokenizer.pad_token_id else -100) for tok in labels]
model_inputs["labels"] = labels
return model_inputs
# =====================================================
# DATASET
# =====================================================
print("Loading Spider subset:", TRAIN_SPLIT)
dataset = load_dataset("spider", split=TRAIN_SPLIT)
dataset = dataset.train_test_split(test_size=0.1, seed=42)
train_ds = dataset["train"]
eval_ds = dataset["test"]
print("Tokenizing dataset...")
train_tok = train_ds.map(
preprocess_function,
batched=False,
num_proc=1,
remove_columns=train_ds.column_names,
load_from_cache_file=False,
)
eval_tok = eval_ds.map(
preprocess_function,
batched=False,
num_proc=1,
remove_columns=eval_ds.column_names,
load_from_cache_file=False,
)
print("Train dataset size:", len(train_tok))
print("Eval dataset size:", len(eval_tok))
# =====================================================
# MODEL + LoRA (CODET5 FIXED)
# =====================================================
base_model = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL)
base_model.config.use_cache = False
base_model.gradient_checkpointing_enable()
# 🔥 DIFFERENT FROM T5
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="SEQ_2_SEQ_LM",
target_modules=["q", "v"], # IMPORTANT FOR CODET5
)
model = get_peft_model(base_model, lora_config)
model.to(device)
model.print_trainable_parameters()
# =====================================================
# TRAINER
# =====================================================
data_collator = DataCollatorForSeq2Seq(
tokenizer=tokenizer,
model=model,
padding=True,
)
args = Seq2SeqTrainingArguments(
output_dir=os.path.join(PROJECT_ROOT, "checkpoints", "sft_runs_codet5"),
num_train_epochs=EPOCHS,
learning_rate=LR,
per_device_train_batch_size=PER_DEVICE_BATCH,
per_device_eval_batch_size=PER_DEVICE_BATCH,
gradient_accumulation_steps=GRAD_ACCUM,
dataloader_num_workers=0,
dataloader_pin_memory=False,
evaluation_strategy="epoch",
save_strategy="epoch",
save_total_limit=1,
logging_steps=50,
report_to=[],
fp16=False,
bf16=False,
predict_with_generate=True,
)
trainer = Seq2SeqTrainer(
model=model,
args=args,
train_dataset=train_tok,
eval_dataset=eval_tok,
tokenizer=tokenizer,
data_collator=data_collator,
)
# =====================================================
# TRAIN
# =====================================================
trainer.train()
# =====================================================
# SAVE
# =====================================================
# =====================================================
# SAVE (SAFE PEFT SAVE)
# =====================================================
print("Saving LoRA adapter to:", OUT_DIR)
os.makedirs(OUT_DIR, exist_ok=True)
# unwrap trainer model (important!)
peft_model = trainer.model
# ensure on cpu before saving (mac mps bug fix)
peft_model = peft_model.to("cpu")
# save adapter only
peft_model.save_pretrained(OUT_DIR)
tokenizer.save_pretrained(OUT_DIR)
print("DONE ✔ CodeT5 SFT finished") |