Files
agentic-pd-hybrid/src/agentic_pd_hybrid/trace_profiles.py

128 lines
4.6 KiB
Python

from __future__ import annotations
import json
from collections import defaultdict
from dataclasses import asdict, dataclass
from math import ceil
from pathlib import Path
from agentic_pd_hybrid.trace import TraceRequest, load_trace
BLOCK_TOKEN_BUDGET = 24
@dataclass(frozen=True)
class NormalizeTraceLengthsConfig:
trace_path: Path
output_path: Path
initial_input_length: int = 10_000
append_input_length: int = 1_000
output_length: int = 1_000
max_requests: int | None = None
@dataclass(frozen=True)
class NormalizeTraceLengthsSummary:
input_trace_path: str
output_trace_path: str
request_count: int
session_count: int
multi_turn_session_count: int
initial_input_length: int
append_input_length: int
output_length: int
max_turns_per_session: int
max_input_length: int
def normalize_trace_lengths(
config: NormalizeTraceLengthsConfig,
) -> NormalizeTraceLengthsSummary:
if config.initial_input_length < 0:
raise ValueError("initial_input_length must be >= 0")
if config.append_input_length < 0:
raise ValueError("append_input_length must be >= 0")
if config.output_length < 0:
raise ValueError("output_length must be >= 0")
requests = load_trace(config.trace_path, request_limit=config.max_requests)
sessions: dict[str, list[TraceRequest]] = defaultdict(list)
for request in requests:
sessions[request.session_id].append(request)
normalized_records: list[dict[str, object]] = []
max_turns_per_session = 0
max_input_length = 0
for session_idx, session_id in enumerate(sorted(sessions, key=_session_sort_key)):
session_requests = sorted(
sessions[session_id],
key=lambda request: (request.timestamp_s, request.turn_id, request.chat_id),
)
max_turns_per_session = max(max_turns_per_session, len(session_requests))
base_block_count = ceil(config.initial_input_length / BLOCK_TOKEN_BUDGET)
base_hash_ids = [
_hash_id_for(session_idx=session_idx, block_idx=block_idx)
for block_idx in range(base_block_count)
]
for turn_idx, request in enumerate(session_requests):
input_length = config.initial_input_length + turn_idx * (
config.append_input_length + config.output_length
)
total_block_count = ceil(input_length / BLOCK_TOKEN_BUDGET)
hash_ids = base_hash_ids + [
_hash_id_for(
session_idx=session_idx,
block_idx=base_block_count + append_block_idx,
)
for append_block_idx in range(max(0, total_block_count - base_block_count))
]
max_input_length = max(max_input_length, input_length)
normalized_records.append(
{
"chat_id": request.chat_id,
"parent_chat_id": request.parent_chat_id,
"timestamp": request.timestamp_s,
"input_length": input_length,
"output_length": config.output_length,
"type": request.request_type,
"turn": request.turn_id,
"hash_ids": hash_ids,
}
)
normalized_records.sort(key=lambda item: float(item["timestamp"]))
config.output_path.parent.mkdir(parents=True, exist_ok=True)
with config.output_path.open("w", encoding="utf-8") as handle:
for record in normalized_records:
handle.write(json.dumps(record, sort_keys=True) + "\n")
summary = NormalizeTraceLengthsSummary(
input_trace_path=str(config.trace_path),
output_trace_path=str(config.output_path),
request_count=len(normalized_records),
session_count=len(sessions),
multi_turn_session_count=sum(
1 for session_requests in sessions.values() if len(session_requests) > 1
),
initial_input_length=config.initial_input_length,
append_input_length=config.append_input_length,
output_length=config.output_length,
max_turns_per_session=max_turns_per_session,
max_input_length=max_input_length,
)
summary_path = config.output_path.with_suffix(config.output_path.suffix + ".summary.json")
with summary_path.open("w", encoding="utf-8") as handle:
json.dump(asdict(summary), handle, indent=2, sort_keys=True)
return summary
def _hash_id_for(*, session_idx: int, block_idx: int) -> int:
return session_idx * 1_000_000 + block_idx
def _session_sort_key(session_id: str) -> tuple[int, str]:
return (0, session_id) if session_id.isdigit() else (1, session_id)