mirror of
https://github.com/NousResearch/atropos.git
synced 2026-04-19 12:57:58 +00:00
refactor base
This commit is contained in:
Jai Suphavadeeprasit
parent
1c90fc71b0
commit
3910a58f9b
2 changed files with 290 additions and 263 deletions
|
|
@ -49,6 +49,7 @@ from .server_handling.server_manager import (
|
|||
ServerManager,
|
||||
ServerManagerConfig,
|
||||
)
|
||||
from .server_handling.teacher_client import TeacherClient
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
|
@ -297,6 +298,9 @@ class BaseEnv(ABC):
|
|||
self.curr_step = 0
|
||||
self.max_token_len = -1
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
|
||||
self.teacher_client = TeacherClient(
|
||||
config=self.config, tokenizer=self.tokenizer, logger=logger
|
||||
)
|
||||
self.completion_lengths = []
|
||||
self.max_num_workers = config.max_num_workers
|
||||
if self.max_num_workers == -1:
|
||||
|
|
@ -358,174 +362,11 @@ class BaseEnv(ABC):
|
|||
messages_list: Optional[List[List[Dict]]] = None,
|
||||
top_k: Optional[int] = None,
|
||||
) -> Tuple[List[List[List[int]]], List[List[List[float]]]]:
|
||||
"""
|
||||
Fetch top-K logprobs from teacher model for given sequences.
|
||||
|
||||
Supports any OpenAI-compatible API (vLLM, OpenAI, Together, etc.).
|
||||
|
||||
Args:
|
||||
token_sequences: List of token ID sequences to get logprobs for
|
||||
messages_list: Optional list of message histories (for chat APIs).
|
||||
If provided, uses chat/completions with logprobs.
|
||||
top_k: Number of top logprobs to fetch (defaults to config.teacher_top_k)
|
||||
|
||||
Returns:
|
||||
Tuple of (distill_token_ids, distill_logprobs), both shaped as:
|
||||
[batch][position][top_k].
|
||||
Returns ([], []) if teacher_base_url is not configured.
|
||||
"""
|
||||
logger.info(f"[TEACHER] get_teacher_logprobs called with {len(token_sequences)} sequences")
|
||||
logger.info(f"[TEACHER] teacher_base_url={self.config.teacher_base_url}")
|
||||
|
||||
if not self.config.teacher_base_url:
|
||||
logger.warning("[TEACHER] No teacher_base_url configured, returning empty")
|
||||
return [], []
|
||||
|
||||
if top_k is None:
|
||||
top_k = self.config.teacher_top_k
|
||||
|
||||
# Get API key from config or environment
|
||||
api_key = self.config.teacher_api_key or os.environ.get("TEACHER_API_KEY", "")
|
||||
model_name = self.config.teacher_model_name or "default"
|
||||
|
||||
logger.info(f"[TEACHER] Using model={model_name}, top_k={top_k}")
|
||||
|
||||
headers = {"Content-Type": "application/json"}
|
||||
if api_key:
|
||||
headers["Authorization"] = f"Bearer {api_key}"
|
||||
|
||||
token_id_results: List[List[List[int]]] = []
|
||||
logprob_results: List[List[List[float]]] = []
|
||||
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
for i, tokens in enumerate(token_sequences):
|
||||
logger.info(f"[TEACHER] Processing sequence {i+1}/{len(token_sequences)}, {len(tokens)} tokens")
|
||||
# Decode original sequence and optionally prepend teacher steering text.
|
||||
base_text = self.tokenizer.decode(tokens, skip_special_tokens=False)
|
||||
steering_prefix = ""
|
||||
if self.config.teacher_system_prompt:
|
||||
steering_prefix += (
|
||||
"System instruction:\n"
|
||||
f"{self.config.teacher_system_prompt.strip()}\n\n"
|
||||
)
|
||||
if self.config.teacher_prefix_text:
|
||||
steering_prefix += self.config.teacher_prefix_text
|
||||
full_text = steering_prefix + base_text
|
||||
prefix_token_len = (
|
||||
len(self.tokenizer.encode(steering_prefix, add_special_tokens=False))
|
||||
if steering_prefix
|
||||
else 0
|
||||
)
|
||||
|
||||
# Try vLLM-style completions first (supports prompt_logprobs)
|
||||
# This is most efficient as it doesn't generate new tokens
|
||||
request_data = {
|
||||
"model": model_name,
|
||||
"prompt": full_text,
|
||||
"max_tokens": 1,
|
||||
"temperature": 1.0,
|
||||
"logprobs": top_k,
|
||||
"echo": True, # Include prompt in response with logprobs
|
||||
}
|
||||
|
||||
try:
|
||||
async with session.post(
|
||||
f"{self.config.teacher_base_url}/completions",
|
||||
json=request_data,
|
||||
headers=headers,
|
||||
timeout=aiohttp.ClientTimeout(total=120),
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
seq_token_ids, seq_logprobs = self._parse_completion_logprobs(
|
||||
data, top_k
|
||||
)
|
||||
if seq_token_ids and seq_logprobs:
|
||||
aligned_ids, aligned_lps = self._align_teacher_topk_to_tokens(
|
||||
seq_token_ids,
|
||||
seq_logprobs,
|
||||
target_token_len=len(tokens),
|
||||
prefix_token_len=prefix_token_len,
|
||||
)
|
||||
token_id_results.append(aligned_ids)
|
||||
logprob_results.append(aligned_lps)
|
||||
continue
|
||||
except Exception:
|
||||
pass # Fall through to chat completions
|
||||
|
||||
# Fallback: Use chat/completions with logprobs (OpenAI style)
|
||||
# This requires messages format
|
||||
if messages_list and i < len(messages_list):
|
||||
messages = list(messages_list[i])
|
||||
if self.config.teacher_system_prompt:
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": self.config.teacher_system_prompt,
|
||||
}
|
||||
] + messages
|
||||
else:
|
||||
# Convert text to simple message format
|
||||
messages = []
|
||||
if self.config.teacher_system_prompt:
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": self.config.teacher_system_prompt,
|
||||
}
|
||||
)
|
||||
messages.append({"role": "user", "content": full_text})
|
||||
|
||||
chat_request = {
|
||||
"model": model_name,
|
||||
"messages": messages,
|
||||
"max_tokens": 1,
|
||||
"temperature": 1.0,
|
||||
"logprobs": True,
|
||||
"top_logprobs": top_k,
|
||||
}
|
||||
|
||||
try:
|
||||
async with session.post(
|
||||
f"{self.config.teacher_base_url}/chat/completions",
|
||||
json=chat_request,
|
||||
headers=headers,
|
||||
timeout=aiohttp.ClientTimeout(total=120),
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
seq_token_ids, seq_logprobs = self._parse_chat_logprobs(
|
||||
data, top_k
|
||||
)
|
||||
# Chat fallback logprobs are for generated tokens, not prompt tokens.
|
||||
# To keep alignment correct for distillation, return empty per-position rows.
|
||||
if seq_token_ids and len(seq_token_ids) >= len(tokens):
|
||||
aligned_ids, aligned_lps = self._align_teacher_topk_to_tokens(
|
||||
seq_token_ids,
|
||||
seq_logprobs,
|
||||
target_token_len=len(tokens),
|
||||
prefix_token_len=0,
|
||||
)
|
||||
else:
|
||||
aligned_ids = [[] for _ in range(len(tokens))]
|
||||
aligned_lps = [[] for _ in range(len(tokens))]
|
||||
token_id_results.append(aligned_ids)
|
||||
logprob_results.append(aligned_lps)
|
||||
else:
|
||||
logger.warning(f"Teacher API returned {response.status}")
|
||||
token_id_results.append([[] for _ in range(len(tokens))])
|
||||
logprob_results.append([[] for _ in range(len(tokens))])
|
||||
except Exception as e:
|
||||
logger.warning(f"Teacher chat request failed: {e}")
|
||||
token_id_results.append([[] for _ in range(len(tokens))])
|
||||
logprob_results.append([[] for _ in range(len(tokens))])
|
||||
|
||||
return token_id_results, logprob_results
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching teacher logprobs: {e}")
|
||||
return [], []
|
||||
return await self.teacher_client.get_teacher_logprobs(
|
||||
token_sequences=token_sequences,
|
||||
messages_list=messages_list,
|
||||
top_k=top_k,
|
||||
)
|
||||
|
||||
def _align_teacher_topk_to_tokens(
|
||||
self,
|
||||
|
|
@ -534,109 +375,22 @@ class BaseEnv(ABC):
|
|||
target_token_len: int,
|
||||
prefix_token_len: int = 0,
|
||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||
"""
|
||||
Trim teacher prefix positions and enforce exact length alignment with source tokens.
|
||||
"""
|
||||
n = min(len(seq_token_ids), len(seq_logprobs))
|
||||
aligned_ids = list(seq_token_ids[:n])
|
||||
aligned_lps = list(seq_logprobs[:n])
|
||||
|
||||
if prefix_token_len > 0:
|
||||
aligned_ids = aligned_ids[prefix_token_len:]
|
||||
aligned_lps = aligned_lps[prefix_token_len:]
|
||||
|
||||
# Truncate any tail token (e.g., generated token when max_tokens>0 with echo=True)
|
||||
aligned_ids = aligned_ids[:target_token_len]
|
||||
aligned_lps = aligned_lps[:target_token_len]
|
||||
|
||||
# Pad missing positions to preserve strict [seq][position][top_k] shape.
|
||||
if len(aligned_ids) < target_token_len:
|
||||
pad_count = target_token_len - len(aligned_ids)
|
||||
aligned_ids.extend([[] for _ in range(pad_count)])
|
||||
aligned_lps.extend([[] for _ in range(pad_count)])
|
||||
|
||||
return aligned_ids, aligned_lps
|
||||
return self.teacher_client._align_teacher_topk_to_tokens(
|
||||
seq_token_ids=seq_token_ids,
|
||||
seq_logprobs=seq_logprobs,
|
||||
target_token_len=target_token_len,
|
||||
prefix_token_len=prefix_token_len,
|
||||
)
|
||||
|
||||
def _parse_completion_logprobs(
|
||||
self, data: Dict, top_k: int
|
||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||
"""Parse token ids + logprobs from vLLM-style completion response."""
|
||||
try:
|
||||
choice = data.get("choices", [{}])[0]
|
||||
logprobs_data = choice.get("logprobs", {})
|
||||
|
||||
# vLLM returns top_logprobs as list of dicts
|
||||
top_logprobs = logprobs_data.get("top_logprobs", [])
|
||||
|
||||
if not top_logprobs:
|
||||
return [], []
|
||||
|
||||
seq_token_ids: List[List[int]] = []
|
||||
seq_logprobs: List[List[float]] = []
|
||||
for pos_logprobs in top_logprobs:
|
||||
if pos_logprobs is None:
|
||||
seq_token_ids.append([])
|
||||
seq_logprobs.append([])
|
||||
elif isinstance(pos_logprobs, dict):
|
||||
# Format: {token_str: logprob, ...}
|
||||
sorted_items = sorted(
|
||||
pos_logprobs.items(),
|
||||
key=lambda x: x[1],
|
||||
reverse=True
|
||||
)[:top_k]
|
||||
pos_ids: List[int] = []
|
||||
pos_lps: List[float] = []
|
||||
for token_str, logprob in sorted_items:
|
||||
# Convert token string to ID
|
||||
token_ids = self.tokenizer.encode(token_str, add_special_tokens=False)
|
||||
if token_ids:
|
||||
pos_ids.append(int(token_ids[0]))
|
||||
pos_lps.append(float(logprob))
|
||||
seq_token_ids.append(pos_ids)
|
||||
seq_logprobs.append(pos_lps)
|
||||
else:
|
||||
seq_token_ids.append([])
|
||||
seq_logprobs.append([])
|
||||
|
||||
return seq_token_ids, seq_logprobs
|
||||
except Exception as e:
|
||||
logger.warning(f"Error parsing completion logprobs: {e}")
|
||||
return [], []
|
||||
return self.teacher_client._parse_completion_logprobs(data=data, top_k=top_k)
|
||||
|
||||
def _parse_chat_logprobs(
|
||||
self, data: Dict, top_k: int
|
||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||
"""Parse token ids + logprobs from OpenAI-style chat completion response."""
|
||||
try:
|
||||
choice = data.get("choices", [{}])[0]
|
||||
logprobs_data = choice.get("logprobs", {})
|
||||
|
||||
if not logprobs_data:
|
||||
return [], []
|
||||
|
||||
content = logprobs_data.get("content", [])
|
||||
seq_token_ids: List[List[int]] = []
|
||||
seq_logprobs: List[List[float]] = []
|
||||
|
||||
for token_data in content:
|
||||
top_logprobs = token_data.get("top_logprobs", [])
|
||||
pos_ids: List[int] = []
|
||||
pos_lps: List[float] = []
|
||||
for item in top_logprobs[:top_k]:
|
||||
token_str = item.get("token", "")
|
||||
logprob = item.get("logprob", 0.0)
|
||||
# Convert token string to ID
|
||||
token_ids = self.tokenizer.encode(token_str, add_special_tokens=False)
|
||||
if token_ids:
|
||||
pos_ids.append(int(token_ids[0]))
|
||||
pos_lps.append(float(logprob))
|
||||
seq_token_ids.append(pos_ids)
|
||||
seq_logprobs.append(pos_lps)
|
||||
|
||||
return seq_token_ids, seq_logprobs
|
||||
except Exception as e:
|
||||
logger.warning(f"Error parsing chat logprobs: {e}")
|
||||
return [], []
|
||||
return self.teacher_client._parse_chat_logprobs(data=data, top_k=top_k)
|
||||
|
||||
@classmethod
|
||||
def config_init(
|
||||
|
|
|
|||
273
atroposlib/envs/server_handling/teacher_client.py
Normal file
273
atroposlib/envs/server_handling/teacher_client.py
Normal file
|
|
@ -0,0 +1,273 @@
|
|||
import os
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import aiohttp
|
||||
|
||||
|
||||
class TeacherClient:
|
||||
"""
|
||||
Transport/parsing client for teacher top-k logprobs.
|
||||
|
||||
This keeps distillation HTTP and parsing logic out of BaseEnv.
|
||||
"""
|
||||
|
||||
def __init__(self, config, tokenizer, logger):
|
||||
self.config = config
|
||||
self.tokenizer = tokenizer
|
||||
self.logger = logger
|
||||
|
||||
async def get_teacher_logprobs(
|
||||
self,
|
||||
token_sequences: List[List[int]],
|
||||
messages_list: Optional[List[List[Dict]]] = None,
|
||||
top_k: Optional[int] = None,
|
||||
) -> Tuple[List[List[List[int]]], List[List[List[float]]]]:
|
||||
self.logger.info(
|
||||
"[TEACHER] get_teacher_logprobs called with %s sequences",
|
||||
len(token_sequences),
|
||||
)
|
||||
self.logger.info("[TEACHER] teacher_base_url=%s", self.config.teacher_base_url)
|
||||
|
||||
if not self.config.teacher_base_url:
|
||||
self.logger.warning("[TEACHER] No teacher_base_url configured, returning empty")
|
||||
return [], []
|
||||
|
||||
if top_k is None:
|
||||
top_k = self.config.teacher_top_k
|
||||
|
||||
api_key = self.config.teacher_api_key or os.environ.get("TEACHER_API_KEY", "")
|
||||
model_name = self.config.teacher_model_name or "default"
|
||||
self.logger.info("[TEACHER] Using model=%s, top_k=%s", model_name, top_k)
|
||||
|
||||
headers = {"Content-Type": "application/json"}
|
||||
if api_key:
|
||||
headers["Authorization"] = f"Bearer {api_key}"
|
||||
|
||||
token_id_results: List[List[List[int]]] = []
|
||||
logprob_results: List[List[List[float]]] = []
|
||||
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
for i, tokens in enumerate(token_sequences):
|
||||
self.logger.info(
|
||||
"[TEACHER] Processing sequence %s/%s, %s tokens",
|
||||
i + 1,
|
||||
len(token_sequences),
|
||||
len(tokens),
|
||||
)
|
||||
base_text = self.tokenizer.decode(tokens, skip_special_tokens=False)
|
||||
steering_prefix = ""
|
||||
if self.config.teacher_system_prompt:
|
||||
steering_prefix += (
|
||||
"System instruction:\n"
|
||||
f"{self.config.teacher_system_prompt.strip()}\n\n"
|
||||
)
|
||||
if self.config.teacher_prefix_text:
|
||||
steering_prefix += self.config.teacher_prefix_text
|
||||
full_text = steering_prefix + base_text
|
||||
prefix_token_len = (
|
||||
len(
|
||||
self.tokenizer.encode(
|
||||
steering_prefix, add_special_tokens=False
|
||||
)
|
||||
)
|
||||
if steering_prefix
|
||||
else 0
|
||||
)
|
||||
|
||||
request_data = {
|
||||
"model": model_name,
|
||||
"prompt": full_text,
|
||||
"max_tokens": 1,
|
||||
"temperature": 1.0,
|
||||
"logprobs": top_k,
|
||||
"echo": True,
|
||||
}
|
||||
try:
|
||||
async with session.post(
|
||||
f"{self.config.teacher_base_url}/completions",
|
||||
json=request_data,
|
||||
headers=headers,
|
||||
timeout=aiohttp.ClientTimeout(total=120),
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
seq_token_ids, seq_logprobs = self._parse_completion_logprobs(
|
||||
data, top_k
|
||||
)
|
||||
if seq_token_ids and seq_logprobs:
|
||||
aligned_ids, aligned_lps = self._align_teacher_topk_to_tokens(
|
||||
seq_token_ids,
|
||||
seq_logprobs,
|
||||
target_token_len=len(tokens),
|
||||
prefix_token_len=prefix_token_len,
|
||||
)
|
||||
token_id_results.append(aligned_ids)
|
||||
logprob_results.append(aligned_lps)
|
||||
continue
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if messages_list and i < len(messages_list):
|
||||
messages = list(messages_list[i])
|
||||
if self.config.teacher_system_prompt:
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": self.config.teacher_system_prompt,
|
||||
}
|
||||
] + messages
|
||||
else:
|
||||
messages = []
|
||||
if self.config.teacher_system_prompt:
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": self.config.teacher_system_prompt,
|
||||
}
|
||||
)
|
||||
messages.append({"role": "user", "content": full_text})
|
||||
|
||||
chat_request = {
|
||||
"model": model_name,
|
||||
"messages": messages,
|
||||
"max_tokens": 1,
|
||||
"temperature": 1.0,
|
||||
"logprobs": True,
|
||||
"top_logprobs": top_k,
|
||||
}
|
||||
try:
|
||||
async with session.post(
|
||||
f"{self.config.teacher_base_url}/chat/completions",
|
||||
json=chat_request,
|
||||
headers=headers,
|
||||
timeout=aiohttp.ClientTimeout(total=120),
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
seq_token_ids, seq_logprobs = self._parse_chat_logprobs(
|
||||
data, top_k
|
||||
)
|
||||
if seq_token_ids and len(seq_token_ids) >= len(tokens):
|
||||
aligned_ids, aligned_lps = self._align_teacher_topk_to_tokens(
|
||||
seq_token_ids,
|
||||
seq_logprobs,
|
||||
target_token_len=len(tokens),
|
||||
prefix_token_len=0,
|
||||
)
|
||||
else:
|
||||
aligned_ids = [[] for _ in range(len(tokens))]
|
||||
aligned_lps = [[] for _ in range(len(tokens))]
|
||||
token_id_results.append(aligned_ids)
|
||||
logprob_results.append(aligned_lps)
|
||||
else:
|
||||
self.logger.warning(
|
||||
"Teacher API returned %s", response.status
|
||||
)
|
||||
token_id_results.append([[] for _ in range(len(tokens))])
|
||||
logprob_results.append([[] for _ in range(len(tokens))])
|
||||
except Exception as e:
|
||||
self.logger.warning("Teacher chat request failed: %s", e)
|
||||
token_id_results.append([[] for _ in range(len(tokens))])
|
||||
logprob_results.append([[] for _ in range(len(tokens))])
|
||||
|
||||
return token_id_results, logprob_results
|
||||
except Exception as e:
|
||||
self.logger.error("Error fetching teacher logprobs: %s", e)
|
||||
return [], []
|
||||
|
||||
def _align_teacher_topk_to_tokens(
|
||||
self,
|
||||
seq_token_ids: List[List[int]],
|
||||
seq_logprobs: List[List[float]],
|
||||
target_token_len: int,
|
||||
prefix_token_len: int = 0,
|
||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||
n = min(len(seq_token_ids), len(seq_logprobs))
|
||||
aligned_ids = list(seq_token_ids[:n])
|
||||
aligned_lps = list(seq_logprobs[:n])
|
||||
|
||||
if prefix_token_len > 0:
|
||||
aligned_ids = aligned_ids[prefix_token_len:]
|
||||
aligned_lps = aligned_lps[prefix_token_len:]
|
||||
|
||||
aligned_ids = aligned_ids[:target_token_len]
|
||||
aligned_lps = aligned_lps[:target_token_len]
|
||||
|
||||
if len(aligned_ids) < target_token_len:
|
||||
pad_count = target_token_len - len(aligned_ids)
|
||||
aligned_ids.extend([[] for _ in range(pad_count)])
|
||||
aligned_lps.extend([[] for _ in range(pad_count)])
|
||||
|
||||
return aligned_ids, aligned_lps
|
||||
|
||||
def _parse_completion_logprobs(
|
||||
self, data: Dict, top_k: int
|
||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||
try:
|
||||
choice = data.get("choices", [{}])[0]
|
||||
logprobs_data = choice.get("logprobs", {})
|
||||
top_logprobs = logprobs_data.get("top_logprobs", [])
|
||||
if not top_logprobs:
|
||||
return [], []
|
||||
|
||||
seq_token_ids: List[List[int]] = []
|
||||
seq_logprobs: List[List[float]] = []
|
||||
for pos_logprobs in top_logprobs:
|
||||
if pos_logprobs is None:
|
||||
seq_token_ids.append([])
|
||||
seq_logprobs.append([])
|
||||
elif isinstance(pos_logprobs, dict):
|
||||
sorted_items = sorted(
|
||||
pos_logprobs.items(), key=lambda x: x[1], reverse=True
|
||||
)[:top_k]
|
||||
pos_ids: List[int] = []
|
||||
pos_lps: List[float] = []
|
||||
for token_str, logprob in sorted_items:
|
||||
token_ids = self.tokenizer.encode(
|
||||
token_str, add_special_tokens=False
|
||||
)
|
||||
if token_ids:
|
||||
pos_ids.append(int(token_ids[0]))
|
||||
pos_lps.append(float(logprob))
|
||||
seq_token_ids.append(pos_ids)
|
||||
seq_logprobs.append(pos_lps)
|
||||
else:
|
||||
seq_token_ids.append([])
|
||||
seq_logprobs.append([])
|
||||
return seq_token_ids, seq_logprobs
|
||||
except Exception as e:
|
||||
self.logger.warning("Error parsing completion logprobs: %s", e)
|
||||
return [], []
|
||||
|
||||
def _parse_chat_logprobs(
|
||||
self, data: Dict, top_k: int
|
||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||
try:
|
||||
choice = data.get("choices", [{}])[0]
|
||||
logprobs_data = choice.get("logprobs", {})
|
||||
if not logprobs_data:
|
||||
return [], []
|
||||
|
||||
content = logprobs_data.get("content", [])
|
||||
seq_token_ids: List[List[int]] = []
|
||||
seq_logprobs: List[List[float]] = []
|
||||
for token_data in content:
|
||||
top_logprobs = token_data.get("top_logprobs", [])
|
||||
pos_ids: List[int] = []
|
||||
pos_lps: List[float] = []
|
||||
for item in top_logprobs[:top_k]:
|
||||
token_str = item.get("token", "")
|
||||
logprob = item.get("logprob", 0.0)
|
||||
token_ids = self.tokenizer.encode(
|
||||
token_str, add_special_tokens=False
|
||||
)
|
||||
if token_ids:
|
||||
pos_ids.append(int(token_ids[0]))
|
||||
pos_lps.append(float(logprob))
|
||||
seq_token_ids.append(pos_ids)
|
||||
seq_logprobs.append(pos_lps)
|
||||
return seq_token_ids, seq_logprobs
|
||||
except Exception as e:
|
||||
self.logger.warning("Error parsing chat logprobs: %s", e)
|
||||
return [], []
|
||||
Loading…
Add table
Add a link
Reference in a new issue