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b2b9f33 | 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 | import os, logging, torch, transformers
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional
from transformers import TrainingArguments, Trainer, TrainerCallback, set_seed
import sys
sys.path.insert(0, str(Path(__file__).parent.parent))
from model.architecture import CodeLLM, CodeLLMConfig
from model.tokenizer import get_gpt2_tokenizer_for_code, load_tokenizer
from data.dataset import TheStackStreamDataset, CodeCollator
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class TrainConfig:
model_config: CodeLLMConfig = field(default_factory=CodeLLMConfig)
tokenizer_path: Optional[str] = None
languages: list = field(default_factory=lambda: ["python", "javascript", "typescript", "rust"])
max_length: int = 2048
fim_rate: float = 0.5
output_dir: str = "./checkpoints"
num_train_steps: int = 100_000
per_device_batch_size: int = 4
gradient_accumulation_steps: int = 8
learning_rate: float = 3e-4
weight_decay: float = 0.1
max_grad_norm: float = 1.0
warmup_steps: int = 2000
lr_scheduler_type: str = "cosine"
bf16: bool = True
fp16: bool = False
gradient_checkpointing: bool = True
dataloader_num_workers: int = 4
logging_steps: int = 50
save_steps: int = 1000
push_to_hub: bool = True
hub_model_id: str = "devoppro/codellm-125m" # ← your HF username
seed: int = 42
class CodeLLMForTrainer(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, input_ids=None, labels=None, attention_mask=None, **kwargs):
out = self.model(input_ids=input_ids, labels=labels, attention_mask=attention_mask)
return transformers.modeling_outputs.CausalLMOutputWithPast(
loss=out["loss"], logits=out["logits"],
)
def gradient_checkpointing_enable(self, **kwargs):
for block in self.model.transformer.h:
block.use_checkpoint = True
@property
def config(self):
class FakeConfig:
is_encoder_decoder = False
model_type = "codellm"
return FakeConfig()
class GenerateSampleCallback(TrainerCallback):
def __init__(self, model, tokenizer, prompts):
self.model = model
self.tokenizer = tokenizer
self.prompts = prompts
def on_evaluate(self, args, state, control, **kwargs):
self.model.eval()
device = next(self.model.parameters()).device
print("\n" + "="*60)
for prompt in self.prompts:
ids = self.tokenizer.encode(prompt, return_tensors="pt").to(device)
out = self.model.generate(ids, max_new_tokens=128, temperature=0.8)
text = self.tokenizer.decode(out[0], skip_special_tokens=True)
print(f"\n[PROMPT] {prompt}\n[OUTPUT] {text[len(prompt):]}")
print("="*60 + "\n")
def train(cfg: TrainConfig):
set_seed(cfg.seed)
if cfg.tokenizer_path and Path(cfg.tokenizer_path).exists():
tokenizer = load_tokenizer(cfg.tokenizer_path)
else:
tokenizer = get_gpt2_tokenizer_for_code()
cfg.model_config.vocab_size = len(tokenizer)
model_core = CodeLLM(cfg.model_config)
model = CodeLLMForTrainer(model_core)
if cfg.gradient_checkpointing:
model.gradient_checkpointing_enable()
train_dataset = TheStackStreamDataset(
tokenizer=tokenizer, max_length=cfg.max_length,
languages=cfg.languages, fim_rate=cfg.fim_rate,
)
collator = CodeCollator(pad_token_id=tokenizer.pad_token_id or 0, max_length=cfg.max_length)
training_args = TrainingArguments(
output_dir=cfg.output_dir,
max_steps=cfg.num_train_steps,
per_device_train_batch_size=cfg.per_device_batch_size,
gradient_accumulation_steps=cfg.gradient_accumulation_steps,
learning_rate=cfg.learning_rate,
weight_decay=cfg.weight_decay,
max_grad_norm=cfg.max_grad_norm,
warmup_steps=cfg.warmup_steps,
lr_scheduler_type=cfg.lr_scheduler_type,
bf16=cfg.bf16, fp16=cfg.fp16,
dataloader_num_workers=cfg.dataloader_num_workers,
logging_steps=cfg.logging_steps,
save_steps=cfg.save_steps,
save_total_limit=3,
push_to_hub=cfg.push_to_hub,
hub_model_id=cfg.hub_model_id if cfg.push_to_hub else None,
report_to=["tensorboard"],
remove_unused_columns=False,
prediction_loss_only=True,
optim="adamw_torch_fused",
)
trainer = Trainer(
model=model, args=training_args,
train_dataset=train_dataset, data_collator=collator,
callbacks=[GenerateSampleCallback(model_core, tokenizer, [
"<|python|>def fibonacci(n):",
"<|javascript|>async function fetchData(url) {",
])],
)
trainer.train()
output_path = Path(cfg.output_dir) / "final"
output_path.mkdir(parents=True, exist_ok=True)
torch.save(model_core.state_dict(), output_path / "pytorch_model.bin")
tokenizer.save_pretrained(output_path)
if cfg.push_to_hub:
trainer.push_to_hub()
if __name__ == "__main__":
train(TrainConfig()) |