File size: 12,135 Bytes
21a3280 763975d 21a3280 08d77f3 21a3280 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 |
import os
import json
import random
from typing import Optional, Tuple, List, Dict, Union
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import torch
import numpy as np
from ftfy import fix_text
from vllm import LLM, SamplingParams, RequestOutput
from .rankllm import Prompt, PromptMode, RankLLM
from .result import Result
ALPH_START_IDX = ord('A') - 1
class RankListwiseOSLLM(RankLLM):
def __init__(
self,
model: str,
context_size: int = 4096,
prompt_mode: PromptMode = PromptMode.RANK_GPT,
num_few_shot_examples: int = 0,
device: str = "cuda",
num_gpus: int = 1,
variable_passages: bool = False,
window_size: int = 20,
system_message: Optional[str] = None,
batched: bool = False,
rerank_type: str = "text",
code_prompt_type: str = "docstring",
) -> None:
super().__init__(model, context_size, prompt_mode, num_few_shot_examples)
self._device = device
if self._device == "cuda":
assert torch.cuda.is_available(), "CUDA is not available on this device"
self.world_size = torch.cuda.device_count()
print(f"WORLD SIZE: {self.world_size}")
if self.world_size > 1:
os.environ['NCCL_P2P_DISABLE']='1'
os.environ['VLLM_WORKER_MULTIPROC_METHOD']='spawn'
if prompt_mode != PromptMode.RANK_GPT:
raise ValueError(
f"Unsupported prompt mode: {prompt_mode}. Only RANK_GPT is supported."
)
self._llm = LLM(
model=model, max_logprobs=30,
enforce_eager=True,
gpu_memory_utilization=0.9,
max_model_len=2048,
trust_remote_code=True,
enable_chunked_prefill=True,
tensor_parallel_size=1
)
self._tokenizer = self._llm.get_tokenizer()
self.system_message_supported = "system" in self._tokenizer.chat_template
self._batched = batched
self._variable_passages = variable_passages
self._window_size = window_size
self._system_message = system_message
self._output_token_estimate = None
self._rerank_type = rerank_type
self._code_prompt_type = code_prompt_type
if num_few_shot_examples > 0:
with open("data/output_v2_aug_filtered.jsonl", "r") as json_file:
self._examples = list(json_file)[1:-1]
def run_llm(
self, prompt: Prompt, current_window_size: Optional[int] = None
) -> Tuple[str, int]:
"""Run the language model with appropriate restrictions for code vs text reranking"""
temp = 0.
if current_window_size is None:
current_window_size = self._window_size
params = SamplingParams(
temperature=temp,
max_tokens=self.get_total_output_tokens(current_window_size),
)
output = self._llm.generate([prompt], sampling_params=params, use_tqdm=True)[0]
output_text = output.outputs[0].text.replace(self._tokenizer.eos_token, "")
self._history.append({
"prompt": prompt,
"response": output_text,
"second_run": {}
})
return output_text, len(output_text)
def run_llm_batched(
self,
prompts: List[Union[str, List[Dict[str, str]]]],
current_window_size: Optional[int] = None,
) -> List[Tuple[str, int]]:
"""Run batched inference with appropriate restrictions for code vs text reranking"""
temp = 0.
if current_window_size is None:
current_window_size = self._window_size
max_new_tokens = self.get_total_output_tokens(current_window_size)
min_new_tokens = self.get_total_output_tokens(current_window_size)
params = SamplingParams(
temperature=temp,
max_tokens=max_new_tokens,
min_tokens=min_new_tokens,
)
outputs = self._llm.generate(prompts, sampling_params=params, use_tqdm=True)
return [
(output.outputs[0].text, len(output.outputs[0].token_ids))
for output in outputs
]
def num_output_tokens(self, current_window_size: Optional[int] = None) -> int:
if current_window_size is None:
current_window_size = self._window_size
if self._output_token_estimate and self._window_size == current_window_size:
return self._output_token_estimate
token_str = " > ".join([f"[{chr(ALPH_START_IDX+i+1)}]" for i in range(current_window_size)])
_output_token_estimate = len(self._tokenizer.encode(token_str)) + 2
if self._window_size == current_window_size:
self._output_token_estimate = _output_token_estimate
return _output_token_estimate
def get_total_output_tokens(self, current_window_size: Optional[int] = None) -> int:
"""Get total number of output tokens"""
base_tokens = self.num_output_tokens(current_window_size)
return base_tokens
def _add_prefix_prompt(self, query: str, num: int) -> str:
if self._code_prompt_type == "docstring":
return self._add_prefix_prompt_doc_string(query, num)
else:
raise ValueError(f"Invalid code_prompt_type: {self._code_prompt_type}")
def _add_post_prompt(self, query: str, num: int) -> str:
if self._code_prompt_type == "docstring":
return self._add_post_prompt_doc_string(query, num)
else:
raise ValueError(f"Invalid code_prompt_type: {self._code_prompt_type}")
def _add_prefix_prompt_doc_string(self, query: str, num: int) -> str:
return f"I will provide you with {num} code snippets, each indicated by a numerical identifier []. Rank the code snippets based on their relevance to the functionality described by the following doc string: {query}.\n"
def _add_post_prompt_doc_string(self, query: str, num: int) -> str:
example_ordering = "[2] > [1]" if self._variable_passages else "[4] > [2]"
return f"Doc String: {query}.\nRank the {num} code snippets above based on their relevance to the functionality described by the doc string. All the code snippets should be included and listed using identifiers, in descending order of relevance. The output format should be [] > [], e.g., {example_ordering}. Only respond with the ranking results, do not say any word or explain."
def _add_prefix_prompt_github_issue(self, query: str, num: int) -> str:
prefix_prompt = f"I will provide you with {num} code functions, each indicated by a numerical identifier []."
prefix_prompt += f" Rank the code functions based on their relevance to contain the faults causing the GitHub issue: {query}.\n"
return prefix_prompt
def _add_few_shot_examples(self, conv):
for _ in range(self._num_few_shot_examples):
ex = random.choice(self._examples)
obj = json.loads(ex)
prompt = obj["conversations"][0]["value"]
response = obj["conversations"][1]["value"]
conv.append_message(conv.roles[0], prompt)
conv.append_message(conv.roles[1], response)
return conv
def _add_few_shot_examples_messages(self, messages):
for _ in range(self._num_few_shot_examples):
ex = random.choice(self._examples)
obj = json.loads(ex)
prompt = obj["conversations"][0]["value"]
response = obj["conversations"][1]["value"]
messages.append({"role": "user", "content": prompt})
messages.append({"role": "assistant", "content": response})
return messages
def create_prompt(self, result: Result, rank_start: int, rank_end: int) -> Tuple[str, int]:
query = result.query
max_query_len = self.get_num_tokens(query)
num = len(result.hits[rank_start:rank_end])
max_doc_length = 1024 if (self._rerank_type == "code") else 300
min_doc_length = 300
while True:
messages = list()
if self._system_message and self.system_message_supported:
messages.append({"role": "system", "content": self._system_message})
messages = self._add_few_shot_examples_messages(messages)
query_tokens = self._tokenizer.tokenize(query)[:int(max_query_len)]
truncated_query = self._tokenizer.convert_tokens_to_string(query_tokens)
prefix = self._add_prefix_prompt(truncated_query, num)
rank = 0
input_context = f"{prefix}\n"
for hit in result.hits[rank_start:rank_end]:
rank += 1
if self._rerank_type == "code":
content = hit["content"]
content = content.replace("Title: Content: ", "")
tokenized_content = self._tokenizer.tokenize(content)
content_tokens = tokenized_content[:int(max_doc_length)]
truncated_content = self._tokenizer.convert_tokens_to_string(content_tokens)
identifier = str(rank)
input_context += f"[{identifier}] {self._replace_number(truncated_content)}\n"
else:
content = hit["content"].replace("Title: Content: ", "").strip()
content = " ".join(content.split()[:max_doc_length])
identifier = str(rank)
input_context += f"[{identifier}] {self._replace_number(content)}\n"
input_context += self._add_post_prompt(truncated_query, num)
messages.append({"role": "user", "content": input_context})
if self._system_message and not self.system_message_supported:
messages[0]["content"] = self._system_message + "\n " + messages[0]["content"]
prompt = self._tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
prompt = fix_text(prompt)
num_tokens = self.get_num_tokens(prompt)
if num_tokens <= self.max_tokens() - self.get_total_output_tokens(rank_end - rank_start):
break
else:
prefix_len = len(self._tokenizer.encode(prefix))
if (len(query_tokens) + prefix_len) > (self.max_tokens() - min_doc_length *(rank_end - rank_start) - self.get_total_output_tokens(rank_end - rank_start)):
# Query truncation to ensure min doc length for each candidate document/code
offset = num_tokens - (self.max_tokens() - self.get_total_output_tokens(rank_end - rank_start))
max_query_len -= (offset//2 + 1)
else:
# Document truncation
max_doc_length -= max(
1,
(
num_tokens - self.max_tokens() + self.get_total_output_tokens(rank_end - rank_start)
) // ((rank_end - rank_start) * 4),
)
return prompt, num_tokens
def create_prompt_batched(
self,
results: List[Result],
rank_start: int,
rank_end: int,
batch_size: int = 32,
) -> List[Tuple[Prompt, int]]:
def chunks(lst, n):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), n):
yield lst[i : i + n]
all_completed_prompts = []
with ThreadPoolExecutor() as executor:
for batch in chunks(results, batch_size):
completed_prompts = list(
executor.map(
lambda result: self.create_prompt(result, rank_start, rank_end),
batch,
)
)
all_completed_prompts.extend(completed_prompts)
return all_completed_prompts
def get_num_tokens(self, prompt: str) -> int:
return len(self._tokenizer.encode(prompt))
def cost_per_1k_token(self, input_token: bool) -> float:
return 0
|