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from transformers import PreTrainedTokenizerFast
from tokenizers import Tokenizer, models, trainers, pre_tokenizers, decoders, processors
import json
import os
def train_tokenizer(corpus_files, vocab_size=32768, output_path="fsi_edge_tokenizer"):
"""
Train a BPE tokenizer optimized for code + NLP.
Uses byte-level BPE with special tokens for code structure.
"""
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)
trainer = trainers.BpeTrainer(
vocab_size=vocab_size,
special_tokens=[
"<unk>", "<s>", "</s>", "<pad>", "<|begin_of_text|>",
"<|end_of_text|>", "<|code|>", "<|nl|>", "<|py|>", "<|js|>",
"<|java|>", "<|cpp|>", "<|go|>", "<|rust|>", "<|sql|>",
"<|explain|>", "<|debug|>", "<|solve|>", "<|test|>",
"<|exec|>", "<|trace|>", "<|ast|>", "<|scope_start|>", "<|scope_end|>",
"<|thought|>", "<|answer|>"
],
min_frequency=2,
initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
)
tokenizer.train(corpus_files, trainer)
tokenizer.save(f"{output_path}/tokenizer.json")
# Wrap as HuggingFace tokenizer
hf_tokenizer = PreTrainedTokenizerFast(
tokenizer_object=tokenizer,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="<pad>",
)
hf_tokenizer.save_pretrained(output_path)
return hf_tokenizer
class CodeDataset(torch.utils.data.Dataset):
"""
Dataset for code + NLP training with AST annotations.
Generates on-the-fly structural features for the model.
"""
STRUCTURAL_TOKENS = {
'import': 1, 'function_def': 2, 'class_def': 3, 'if': 4, 'elif': 5,
'else': 6, 'for': 7, 'while': 8, 'try': 9, 'except': 10,
'return': 11, 'assignment': 12, 'call': 13, 'comment': 14,
'string': 15, 'number': 16, 'operator': 17, 'delimiter': 18,
'indent': 19, 'dedent': 20, 'decorator': 21, 'lambda': 22,
'with': 23, 'async': 24, 'await': 25, 'break': 26, 'continue': 27,
'raise': 28, 'yield': 29, 'assert': 30, 'global': 31, 'nonlocal': 32,
}
def __init__(self, data_path, tokenizer_path, max_length=8192, split='train'):
self.max_length = max_length
self.samples = []
if isinstance(tokenizer_path, str):
from transformers import PreTrainedTokenizerFast
self.tokenizer = PreTrainedTokenizerFast.from_pretrained(tokenizer_path)
else:
self.tokenizer = tokenizer_path
self._load_data(data_path)
def _load_data(self, data_path):
"""Load code samples from directory or single file."""
if os.path.isfile(data_path):
with open(data_path, 'r') as f:
for line in f:
if line.strip():
self.samples.append(json.loads(line))
elif os.path.isdir(data_path):
for root, _, files in os.walk(data_path):
for fname in files:
if fname.endswith('.jsonl'):
fpath = os.path.join(root, fname)
with open(fpath, 'r') as f:
for line in f:
if line.strip():
self.samples.append(json.loads(line))
def _compute_ast_depth(self, line):
"""Estimate AST depth from indentation."""
depth = 0
depths = []
for ch in line:
if ch in '({[':
depth += 1
elif ch in ')}]':
depth = max(0, depth - 1)
depths.append(min(depth, 31))
if not depths:
depths = [0]
return depths
def _detect_structural_tokens(self, tokens):
"""Detect code structure boundaries."""
ast_types = []
for tok in tokens:
tok_lower = tok.lower().strip()
found = False
for keyword, idx in self.STRUCTURAL_TOKENS.items():
if tok_lower == keyword:
ast_types.append(idx)
found = True
break
if not found:
ast_types.append(0)
return ast_types
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
sample = self.samples[idx]
code = sample.get('code', sample.get('text', ''))
encoded = self.tokenizer(
code,
truncation=True,
max_length=self.max_length,
padding=False,
return_tensors=None,
)
input_ids = encoded['input_ids']
if len(input_ids) > self.max_length:
input_ids = input_ids[:self.max_length]
# Decode tokens for structural analysis
tokens = [self.tokenizer.decode([tid]) for tid in input_ids]
ast_types = self._detect_structural_tokens(tokens)
ast_depths = self._compute_ast_depth(code[:len(input_ids)])
# Pad to max_length
pad_len = self.max_length - len(input_ids)
if pad_len > 0:
input_ids = input_ids + [self.tokenizer.pad_token_id] * pad_len
ast_types = ast_types + [0] * pad_len
ast_depths = ast_depths + [0] * pad_len
else:
input_ids = input_ids[:self.max_length]
ast_types = ast_types[:self.max_length]
ast_depths = ast_depths[:self.max_length]
return {
'input_ids': torch.tensor(input_ids, dtype=torch.long),
'labels': torch.tensor(input_ids, dtype=torch.long),
'ast_types': torch.tensor(ast_types[:self.max_length], dtype=torch.long),
'ast_depths': torch.tensor(ast_depths[:self.max_length], dtype=torch.long),
'attention_mask': torch.tensor(
[1 if tid != self.tokenizer.pad_token_id else 0 for tid in input_ids],
dtype=torch.long),
}
def collate_fn(batch):
"""Collate batch, stacking tensors."""
keys = batch[0].keys()
result = {}
for k in keys:
result[k] = torch.stack([b[k] for b in batch], dim=0)
return result
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