File size: 4,634 Bytes
463fc7e | 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 | import torch
import torch.nn as nn
from torch.optim import AdamW
from torch.utils.data import DataLoader
from tqdm import tqdm
import os
import logging
from .model import CodeEmbedder
# Setup Logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class CodeTrainer:
def __init__(
self,
model: CodeEmbedder,
train_loader: DataLoader,
val_loader: DataLoader = None,
epochs: int = 3,
learning_rate: float = 2e-5,
accumulation_steps: int = 1,
mixed_precision: bool = True,
output_dir: str = "./output",
device: str = "cuda" if torch.cuda.is_available() else "cpu"
):
self.model = model.to(device)
self.train_loader = train_loader
self.val_loader = val_loader
self.epochs = epochs
self.lr = learning_rate
self.accumulation_steps = accumulation_steps
self.mixed_precision = mixed_precision
self.output_dir = output_dir
self.device = device
# Optimizer
self.optimizer = AdamW(self.model.parameters(), lr=self.lr)
# Scheduler (Optional: constant for now, can transform to Linear later)
# self.scheduler = ...
# Mixed Precision Scaler
self.scaler = torch.cuda.amp.GradScaler(enabled=self.mixed_precision)
# Loss Function: Triplet Margin Loss (Standard for Sentence Embeddings)
# Tries to maximize distance between Anchor-Negative and minimize Anchor-Positive
self.criterion = nn.TripletMarginLoss(margin=1.0, p=2)
def train_step(self, batch):
"""
Runs one training step. Returns loss.
"""
# Unpack the Triplet Batch
# We assume the Dataset returns keys: 'anchor_input_ids', 'anchor_attention_mask', etc.
# Helper to move dict to device
to_device = lambda x: x.to(self.device)
# Autocast for Mixed Precision
with torch.cuda.amp.autocast(enabled=self.mixed_precision):
# 1. Forward Pass for all 3 components
anchor_emb = self.model(to_device(batch['anchor_input_ids']), to_device(batch['anchor_attention_mask']))
positive_emb = self.model(to_device(batch['positive_input_ids']), to_device(batch['positive_attention_mask']))
negative_emb = self.model(to_device(batch['negative_input_ids']), to_device(batch['negative_attention_mask']))
# 2. Compute Triplet Loss
loss = self.criterion(anchor_emb, positive_emb, negative_emb)
return loss
def train(self):
logger.info(f"Starting training on {self.device}...")
logger.info(f"Batch Size: {self.train_loader.batch_size}, Accumulation Steps: {self.accumulation_steps}")
logger.info(f"Effective Batch Size: {self.train_loader.batch_size * self.accumulation_steps}")
self.model.train()
for epoch in range(self.epochs):
total_loss = 0
self.optimizer.zero_grad()
progress_bar = tqdm(self.train_loader, desc=f"Epoch {epoch+1}/{self.epochs}")
for step, batch in enumerate(progress_bar):
# Forward + Loss Calculation
loss = self.train_step(batch)
# Gradient Accumulation: Normalize loss
loss = loss / self.accumulation_steps
# Backward Pass (Scaled)
self.scaler.scale(loss).backward()
if (step + 1) % self.accumulation_steps == 0:
# Update Weights
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad()
total_loss += loss.item() * self.accumulation_steps
progress_bar.set_postfix({'loss': total_loss / (step + 1)})
# Save Checkpoint
self.save_model(epoch+1)
def save_model(self, epoch):
save_path = os.path.join(self.output_dir, f"checkpoint-{epoch}")
os.makedirs(save_path, exist_ok=True)
logger.info(f"Saving model to {save_path}...")
# Save explicitly as safetensors via transformers API
self.model.encoder.save_pretrained(save_path, safe_serialization=True)
self.model.config.save_pretrained(save_path)
# Note: We save the 'encoder' which is the AutoModel,
# so it can be loaded easily by others.
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