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from typing import List, Dict, Tuple, Union, Any, Optional
import os
import json
import torch
from torch import nn
from transformers import AutoConfig, AutoModel, AutoTokenizer
from transformers.utils import is_flash_attn_2_available
INSTRUCTION_CONFIG = {
"nl2code": {
"query": "Find the most relevant code snippet given the following query:\n",
"passage": "Candidate code snippet:\n"
},
"qa": {
"query": "Find the most relevant answer given the following question:\n",
"passage": "Candidate answer:\n"
},
"code2code": {
"query": "Find an equivalent code snippet given the following code snippet:\n",
"passage": "Candidate code snippet:\n"
},
"code2nl": {
"query": "Find the most relevant comment given the following code snippet:\n",
"passage": "Candidate comment:\n"
},
"code2completion": {
"query": "Find the most relevant completion given the following start of code snippet:\n",
"passage": "Candidate completion:\n"
}
}
def batch(iterable, n=1):
items = len(iterable)
for ndx in range(0, items, n):
yield iterable[ndx : min(ndx + n, items)]
def last_token_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return token_embeddings[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = token_embeddings.shape[0]
return token_embeddings[torch.arange(batch_size, device=token_embeddings.device), sequence_lengths].float()
class Transformer(nn.Module):
def __init__(
self,
model_name_or_path: str,
max_seq_length: int = None,
model_args: Dict[str, Any] = None,
tokenizer_args: Dict[str, Any] = None,
config_args: Dict[str, Any] = None,
cache_dir: str = None,
do_lower_case: bool = False,
tokenizer_name_or_path: str = None,
**kwargs,
) -> None:
super().__init__()
self.config_keys = ["max_seq_length", "do_lower_case"]
self.do_lower_case = do_lower_case
if model_args is None:
model_args = {}
if tokenizer_args is None:
tokenizer_args = {}
if config_args is None:
config_args = {}
self.config = AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir)
self.task_names = self.config.task_names
self.default_task = model_args.pop('default_task', None)
model_args["attn_implementation"] = "flash_attention_2" if is_flash_attn_2_available() else "sdpa"
self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, cache_dir=cache_dir, **model_args)
if max_seq_length is not None and "model_max_length" not in tokenizer_args:
tokenizer_args["model_max_length"] = max_seq_length
self.tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,
cache_dir=cache_dir,
**tokenizer_args,
)
# No max_seq_length set. Try to infer from model
if max_seq_length is None:
if (
hasattr(self.auto_model, "config")
and hasattr(self.auto_model.config, "max_position_embeddings")
and hasattr(self.tokenizer, "model_max_length")
):
max_seq_length = min(self.auto_model.config.max_position_embeddings, self.tokenizer.model_max_length)
self.max_seq_length = max_seq_length
if tokenizer_name_or_path is not None:
self.auto_model.config.tokenizer_class = self.tokenizer.__class__.__name__
@property
def default_task(self):
return self._default_task
@default_task.setter
def default_task(self, task: Union[None, str]):
self._validate_task(task)
self._default_task = task
def _validate_task(self, task: str):
if task and task not in self.task_names:
raise ValueError(
f"Unsupported task '{task}'. "
f"Supported tasks are: {', '.join(self.config.task_names)}."
)
def forward(
self,
features: Dict[str, torch.Tensor],
task: Optional[str] = None
) -> Dict[str, torch.Tensor]:
"""
Forward pass through the model.
"""
features.pop('prompt_length', None)
output_states = self.auto_model.forward(
**features,
output_attentions=False,
return_dict=True
)
output_tokens = output_states[0]
features.update({"token_embeddings": output_tokens, "attention_mask": features["attention_mask"]})
return features
def get_word_embedding_dimension(self) -> int:
return self.auto_model.config.hidden_size
def tokenize(
self,
texts: Union[List[str], List[dict], List[Tuple[str, str]]],
padding: Union[str, bool] = True
) -> Dict[str, torch.Tensor]:
"""Tokenizes a text and maps tokens to token-ids"""
output = {}
if isinstance(texts[0], str):
to_tokenize = [texts]
elif isinstance(texts[0], dict):
to_tokenize = []
output["text_keys"] = []
for lookup in texts:
text_key, text = next(iter(lookup.items()))
to_tokenize.append(text)
output["text_keys"].append(text_key)
to_tokenize = [to_tokenize]
else:
batch1, batch2 = [], []
for text_tuple in texts:
batch1.append(text_tuple[0])
batch2.append(text_tuple[1])
to_tokenize = [batch1, batch2]
# strip
to_tokenize = [[str(s).strip() for s in col] for col in to_tokenize]
# Lowercase
if self.do_lower_case:
to_tokenize = [[s.lower() for s in col] for col in to_tokenize]
output.update(
self.tokenizer(
*to_tokenize,
padding=padding,
truncation=True,
return_tensors="pt",
max_length=self.max_seq_length,
)
)
return output
def get_config_dict(self) -> Dict[str, Any]:
return {key: self.__dict__[key] for key in self.config_keys}
def save(self, output_path: str, safe_serialization: bool = True) -> None:
self.auto_model.save_pretrained(output_path, safe_serialization=safe_serialization)
self.tokenizer.save_pretrained(output_path)
with open(os.path.join(output_path, "sentence_transformer_config.json"), "w") as fOut:
json.dump(self.get_config_dict(), fOut, indent=2)
@classmethod
def load(cls, input_path: str) -> "Transformer":
config_name = "sentence_transformer_config.json"
stransformer_config_path = os.path.join(input_path, config_name)
with open(stransformer_config_path) as fIn:
config = json.load(fIn)
# Don't allow configs to set trust_remote_code
if "model_args" in config and "trust_remote_code" in config["model_args"]:
config["model_args"].pop("trust_remote_code")
if "tokenizer_args" in config and "trust_remote_code" in config["tokenizer_args"]:
config["tokenizer_args"].pop("trust_remote_code")
if "config_args" in config and "trust_remote_code" in config["config_args"]:
config["config_args"].pop("trust_remote_code")
return cls(model_name_or_path=input_path, **config)
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