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f7fd6df | 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 | # training/train_programmer.py
import os
import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
from datasets import load_dataset
from core.device import DEVICE
from language.tokenizer import SimpleTokenizer
from language.embeddings import EmbeddingLayer
from language.encoder import SentenceEncoder
# ================================
# CONFIG
# ================================
ARTIFACTS_DIR = "artifacts"
BATCH_SIZE = 16
EPOCHS = 5
LEARNING_RATE = 3e-4
MAX_SEQ_LEN = 128
os.makedirs(ARTIFACTS_DIR, exist_ok=True)
# ================================
# LOAD HF CODE DATASET
# ================================
print("[INFO] Loading CodeXGLUE dataset...")
dataset = load_dataset("google/code_x_glue_tc_nl_code_search_adv")
texts = []
labels = []
for item in dataset["train"]:
texts.append(item["docstring"]) # Natural language
labels.append(1) # Programming label
# Add some non-programming noise examples
noise_examples = [
"Hello how are you",
"Tell me a story",
"What is the weather today",
"Who are you"
]
for text in noise_examples:
texts.append(text)
labels.append(0)
print(f"[INFO] Loaded {len(texts)} samples")
# ================================
# TOKENIZER
# ================================
tokenizer = SimpleTokenizer()
tokenizer.build_vocab(texts)
tokenizer.freeze_vocab()
# ================================
# DATASET CLASS
# ================================
class ProgrammingDataset(Dataset):
def __init__(self, texts, labels):
self.texts = texts
self.labels = labels
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
token_ids = tokenizer.encode(self.texts[idx])[:MAX_SEQ_LEN]
token_ids = torch.tensor(token_ids, dtype=torch.long)
label = torch.tensor(self.labels[idx], dtype=torch.long)
return token_ids, label
def collate_fn(batch):
token_ids, labels = zip(*batch)
max_len = max(len(t) for t in token_ids)
padded = []
for t in token_ids:
pad_len = max_len - len(t)
padded.append(
torch.cat([
t,
torch.full(
(pad_len,),
tokenizer.vocab[tokenizer.PAD_TOKEN],
dtype=torch.long
)
])
)
return torch.stack(padded), torch.tensor(labels)
dataset = ProgrammingDataset(texts, labels)
loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)
# ================================
# MODEL
# ================================
embedder = EmbeddingLayer(len(tokenizer.vocab),
pad_index=tokenizer.vocab[tokenizer.PAD_TOKEN])
encoder = SentenceEncoder()
classifier = nn.Linear(encoder.projection.out_features, 2)
embedder, encoder, classifier = embedder.to(DEVICE), encoder.to(DEVICE), classifier.to(DEVICE)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(
list(embedder.parameters()) +
list(encoder.parameters()) +
list(classifier.parameters()),
lr=LEARNING_RATE
)
# ================================
# TRAIN
# ================================
def train():
best_loss = float("inf")
for epoch in range(EPOCHS):
total_loss = 0
for token_ids, labels_batch in loader:
token_ids = token_ids.to(DEVICE)
labels_batch = labels_batch.to(DEVICE)
embeddings = embedder(token_ids)
attention_mask = (token_ids != tokenizer.vocab[tokenizer.PAD_TOKEN]).long()
sentence_vec = encoder(embeddings, attention_mask=attention_mask)
logits = classifier(sentence_vec)
loss = criterion(logits, labels_batch)
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
list(embedder.parameters()) +
list(encoder.parameters()) +
list(classifier.parameters()),
1.0
)
optimizer.step()
total_loss += loss.item()
avg_loss = total_loss / len(loader)
print(f"[Epoch {epoch+1}/{EPOCHS}] Loss: {avg_loss:.6f}")
if avg_loss < best_loss:
best_loss = avg_loss
save_models()
print("[SUCCESS] Programming model training complete!")
# ================================
# SAVE
# ================================
def save_models():
torch.save(encoder.state_dict(),
os.path.join(ARTIFACTS_DIR, "programming_encoder.pt"))
torch.save(classifier.state_dict(),
os.path.join(ARTIFACTS_DIR, "programming_classifier.pt"))
torch.save(embedder.state_dict(),
os.path.join(ARTIFACTS_DIR, "programming_embedding.pt"))
print("[INFO] Programming models saved")
# ================================
# ENTRY
# ================================
if __name__ == "__main__":
train()
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