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Copy pathgenerator.py
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368 lines (300 loc) · 17 KB
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import re
import os
import time
import json
import torch
import requests
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
from openai import OpenAI
import multiprocessing as mp
from functools import partial
from multiprocessing import Pool
from vllm import LLM, SamplingParams
from tree_sitter import Language, Parser
from utils.eval_utils import is_identifier
from concurrent.futures import ThreadPoolExecutor
from transformers import AutoTokenizer, AutoModelForCausalLM
from torch.utils.data import DataLoader, Dataset, SequentialSampler
from utils.eval_utils import postprocess_code_lines, remove_comments
def process_single_item(example, output, language_name=None, ts_lib=None):
language = Language(ts_lib, language_name)
parser = Parser()
parser.set_language(language)
return remove_comments(postprocess_code_lines(example.left_context, output, parser, example.language))
class CustomDataset(Dataset):
"""
A dataset class for code generation.
Args:
args: Configuration parameters.
tokenizer: Tokenizer.
examples: A collection of examples.
retrieved_codeblocks: Retrieved code blocks.
"""
def __init__(self, args, tokenizer, examples, retrieved_codeblocks, generation=False):
self.args = args
self.tokenizer = tokenizer
self.examples = examples
self.retrieved_codeblocks = retrieved_codeblocks
self.generation = generation
def __len__(self):
return len(self.examples)
def construct_prompts(self, example, retrieved_codeblocks):
filter_codeblocks = []
for x in retrieved_codeblocks:
if x.file_path != "":
filter_codeblocks.append(x)
else:
break
crossfile_context = "\n\n".join([str(retrieved_codeblock) for retrieved_codeblock in filter_codeblocks])
crossfile_context = self.tokenizer.encode(crossfile_context[:self.args.generator_max_crossfile_length * 10], add_special_tokens=False)[:self.args.generator_max_crossfile_length]
path_context = f"\n\n# file path: {example.file_path}\n\n"
path_context = self.tokenizer.encode(path_context, add_special_tokens=False)
allowed_prompt_length = self.args.generator_max_context_length - (len(crossfile_context) + len(path_context) + 10)
infile_context = self.tokenizer.encode(example.left_context, add_special_tokens=False)[-allowed_prompt_length:]
prompt = self.tokenizer.decode(crossfile_context + path_context + infile_context)
return prompt
def __getitem__(self, idx):
example = self.examples[idx]
retrieved_codeblocks = self.retrieved_codeblocks[idx]
prompt = self.construct_prompts(example, retrieved_codeblocks)
prompt_ids = self.tokenizer.encode(prompt)[-self.args.generator_max_context_length:]
if self.generation:
padding_length = self.args.generator_max_context_length - len(prompt_ids)
input_ids = [self.tokenizer.pad_token_id] * padding_length + prompt_ids
return torch.tensor(input_ids)
target_ids = self.tokenizer.encode(example.target_code, add_special_tokens=False)[:self.args.generator_max_generation_length]
input_ids = prompt_ids + target_ids
labels = [-100 for _ in prompt_ids] + target_ids
padding_length = self.args.generator_max_context_length + self.args.generator_max_generation_length - len(input_ids)
input_ids = [self.tokenizer.pad_token_id] * padding_length + input_ids
labels = [-100] * padding_length + labels
return torch.tensor(input_ids), torch.tensor(labels)
class Model(nn.Module):
def __init__(self, generator_model_path, tokenizer, max_generation_length=64):
super(Model, self).__init__()
self.base_model = AutoModelForCausalLM.from_pretrained(generator_model_path, torch_dtype=torch.float16)
self.tokenizer = tokenizer
self.max_generation_length = max_generation_length
self.generator_model_path = generator_model_path
def forward(self, inputs=None, labels=None, lang='python', weighted_keywords=False, sample_number=0):
"""
Forward propagation method for calculating loss.
:param inputs: Input data.
:param labels: Label data.
:return: The average loss per sample.
"""
if inputs is None:
return None
if labels is not None:
logits = self.base_model(inputs, attention_mask=inputs.ne(self.tokenizer.pad_token_id))[0]
logits = logits[:, :-1]
labels = labels[:, 1:]
label_tokens = [self.tokenizer.convert_ids_to_tokens(id.item()) if id != -100 else '<pad>' for id in labels.reshape(-1)]
loss = torch.nn.functional.cross_entropy(logits.reshape(-1, logits.size(-1)), labels.reshape(-1), ignore_index=-100, reduction='none')
if weighted_keywords:
id_weight, first_token_weight = 3, 5
weights = torch.tensor([first_token_weight if i < 1 else id_weight if is_identifier(token, lang) and any(c.isalpha() or c.isdigit() or c == '_' for c in token) else 1 for i, token in enumerate(label_tokens)], dtype=torch.float).cuda()
loss = loss * weights
loss_per_label = loss.reshape(labels.size(0), -1).sum(dim=1) / labels.ne(-100).sum(dim=1)
return loss_per_label
else:
if sample_number:
generated_ids = self.base_model.generate(inputs, attention_mask=inputs.ne(self.tokenizer.pad_token_id), max_length=inputs.size(1) + self.max_generation_length, pad_token_id=self.tokenizer.pad_token_id, do_sample=True, temperature=0.8, top_p=0.95)
else:
generated_ids = self.base_model.generate(inputs, attention_mask=inputs.ne(self.tokenizer.pad_token_id), max_length=inputs.size(1) + self.max_generation_length, pad_token_id=self.tokenizer.pad_token_id)
return generated_ids[:, inputs.size(1):]
class Generator:
"""
Code generator class.
Args:
args: Configuration parameters.
"""
def __init__(self, args):
self.tokenizer = AutoTokenizer.from_pretrained(args.generator_model_path)
self.tokenizer.model_max_length = 1e10
if self.tokenizer.pad_token_id == None:
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
if not args.disable_generator:
self.model = Model(args.generator_model_path, self.tokenizer)
self.model = torch.nn.DataParallel(self.model).cuda()
self.model.eval()
self.args = args
def evaluate(self, examples, retrieved_codeblocks):
"""
Evaluates the generated code.
Args:
examples: A collection of examples.
retrieved_codeblocks: Retrieved code blocks.
Returns:
A list of loss values.
"""
losses = []
dataset = CustomDataset(self.args, self.tokenizer, examples, retrieved_codeblocks)
sampler = SequentialSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=self.args.generator_batch_size, num_workers=self.args.num_workers)
pbar = tqdm(dataloader, disable=not self.args.enable_tqdm)
with torch.no_grad():
for batch in pbar:
inputs, labels = [x.cuda() for x in batch]
loss_per_label = self.model(inputs, labels, lang=examples[0].language, weighted_keywords=self.args.weighted_keywords)
losses.extend(loss_per_label.tolist())
current_ppl = np.exp(np.mean(losses))
pbar.set_description(f"Loss/PPL: {np.mean(losses):.3f}/{current_ppl:.3f}")
return losses
def generate(self, examples, retrieved_codeblocks, max_generation_length, sample_number=0, deduplicated=False):
"""
Generates code.
Args:
examples: A collection of examples.
retrieved_codeblocks: Retrieved code blocks.
max_generation_length: Maximum length of generation.
Returns:
A list of generated codes.
"""
generated_codes = []
if sample_number:
examples = [example for example in examples for _ in range(sample_number)]
retrieved_codeblocks = [codeblocks for codeblocks in retrieved_codeblocks for _ in range(sample_number)]
dataset = CustomDataset(self.args, self.tokenizer, examples, retrieved_codeblocks, generation=True)
sampler = SequentialSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=self.args.generator_batch_size, num_workers=self.args.num_workers)
if hasattr(self.model, "module"):
self.model.module.max_generation_length = max_generation_length
else:
self.model.max_generation_length = max_generation_length
pbar = tqdm(dataloader, disable=not self.args.enable_tqdm, desc="Generating")
with torch.no_grad():
for batch in pbar:
if batch is not None:
output_temp = self.model(batch.cuda(), sample_number=sample_number)
if output_temp is not None:
generated_codes.append(output_temp)
generated_codes = torch.cat(generated_codes, 0)
outputs = [self.tokenizer.decode(generated_id, skip_special_tokens=True) for generated_id in generated_codes]
if not sample_number:
return outputs
else:
if examples[0].language == 'python':
ts_lib = "utils/build/python-lang-parser.so"
else:
ts_lib = "utils/build/java-lang-parser.so"
language_name = examples[0].language
with mp.Pool(processes=64) as pool:
func = partial(process_single_item, language_name=language_name, ts_lib=ts_lib)
outputs = pool.starmap(func, zip(examples, outputs))
if not deduplicated:
return outputs
else:
deduplicated_outputs, counts_per_batch = [], []
for i in range(0, len(outputs), sample_number):
batch = outputs[i:i+sample_number]
set_temp, unique_batch = set(), list()
for item in batch:
if item not in set_temp and item.strip() != '':
set_temp.add(item)
unique_batch.append(item)
deduplicated_outputs.extend(unique_batch)
counts_per_batch.append(len(unique_batch))
return deduplicated_outputs, counts_per_batch
class vLLM_online_Generator:
def __init__(self, args):
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
self.client = OpenAI(api_key=openai_api_key, base_url=openai_api_base)
self.tokenizer = AutoTokenizer.from_pretrained(args.generator_model_path)
self.tokenizer.model_max_length = 1e10
if self.tokenizer.pad_token_id == None:
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
self.args = args
def generate(self, examples, retrieved_codeblocks, temperature, top_p, sample_number=0, deduplicated=False):
dataset = CustomDataset(self.args, self.tokenizer, examples, retrieved_codeblocks, generation=True)
sampler = SequentialSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=100000000, num_workers=self.args.num_workers)
sampling_params = {"temperature": temperature, "max_tokens": self.args.generator_max_generation_length, "top_p": top_p}
pbar = tqdm(dataloader, disable=not self.args.enable_tqdm, desc="Generating")
with torch.no_grad():
for batch in pbar:
prompts = [self.tokenizer.decode(x, skip_special_tokens=True) for x in batch]
if sample_number != 0:
outputs = self.client.completions.create(model=self.args.generator_model_path, prompt=prompts, n=sample_number, timeout=7200, **sampling_params, seed=123)
else:
outputs = self.client.completions.create(model=self.args.generator_model_path, prompt=prompts, timeout=7200, **sampling_params, seed=123)
if sample_number != 0:
outputs = [output.text for output in outputs.choices]
examples = [example for example in examples for _ in range(sample_number)]
language_name = examples[0].language
if language_name == 'python':
ts_lib = "utils/build/python-lang-parser.so"
else:
ts_lib = "utils/build/java-lang-parser.so"
with mp.Pool(processes=64) as pool:
func = partial(process_single_item, language_name=language_name, ts_lib=ts_lib)
outputs = pool.starmap(func, zip(examples, outputs))
if not deduplicated:
return outputs
else:
deduplicated_outputs, counts_per_batch = [], []
for i in range(0, len(outputs), sample_number):
batch = outputs[i:i + sample_number]
set_temp, unique_batch = set(), []
for item in batch:
if item not in set_temp and item.strip() != '':
set_temp.add(item)
unique_batch.append(item)
deduplicated_outputs.extend(unique_batch)
counts_per_batch.append(len(unique_batch))
return deduplicated_outputs, counts_per_batch
else:
return [output.text for output in outputs.choices]
class vLLM_offline_Generator:
def __init__(self, args):
self.llm = LLM(model=args.generator_model_path, task='generate')
self.tokenizer = self.llm.get_tokenizer()
self.tokenizer.model_max_length = 1e10
if self.tokenizer.pad_token_id == None:
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
self.args = args
def generate(self, examples, retrieved_codeblocks, temperature, top_p, sample_number=0, deduplicated=True):
dataset = CustomDataset(self.args, self.tokenizer, examples, retrieved_codeblocks, generation=True)
sampler = SequentialSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=100000000, num_workers=self.args.num_workers)
if sample_number != 0:
sampling_params = SamplingParams(temperature=temperature, top_p=top_p, max_tokens=self.args.generator_max_generation_length, n=sample_number, seed=123)
else:
sampling_params = SamplingParams(temperature=temperature, top_p=top_p, max_tokens=self.args.generator_max_generation_length, seed=123)
pbar = tqdm(dataloader, disable=not self.args.enable_tqdm, desc="Generating")
with torch.no_grad():
for batch in pbar:
prompts = [self.tokenizer.decode(x, skip_special_tokens=True) for x in batch]
outputs = self.llm.generate(prompts, sampling_params)
if sample_number != 0:
outputs = [completion.text for output in outputs for completion in output.outputs]
examples = [example for example in examples for _ in range(sample_number)]
if not deduplicated:
return outputs
else:
if examples[0].language == 'python':
ts_lib = "utils/build/python-lang-parser.so"
else:
ts_lib = "utils/build/java-lang-parser.so"
language_name = examples[0].language
with mp.Pool(processes=64) as pool:
func = partial(process_single_item, language_name=language_name, ts_lib=ts_lib)
outputs = pool.starmap(func, zip(examples, outputs))
deduplicated_outputs, counts_per_batch = [], []
for i in range(0, len(outputs), sample_number):
batch = outputs[i:i+sample_number]
set_temp = set()
unique_batch = []
for item in batch:
if item not in set_temp and item.strip() != '':
set_temp.add(item)
unique_batch.append(item)
deduplicated_outputs.extend(unique_batch)
counts_per_batch.append(len(unique_batch))
return deduplicated_outputs, counts_per_batch
else:
return [output.outputs[0].text for output in outputs]
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