from typing import Any, Dict, List from utils.log import quick_log from fastapi import APIRouter, HTTPException, Request, Response, status from pydantic import BaseModel import gc import copy import sys import torch router = APIRouter() trie = None dtrie: Dict = {} max_trie_len = 3000 loop_start_id = 1 # to prevent preloaded prompts from being deleted loop_del_trie_id = loop_start_id def init(): global trie try: import cyac # import mmap # import os # # if os.path.exists("state_cache.trie"): # with open("state_cache.trie", "r") as bf: # buff_object = mmap.mmap(bf.fileno(), 0, access=mmap.ACCESS_READ) # trie = cyac.Trie.from_buff(buff_object, copy=False) # else: trie = cyac.Trie() except ModuleNotFoundError: print("cyac not found") class AddStateBody(BaseModel): prompt: str tokens: List[str] state: Any logits: Any @router.post("/add-state") def add_state(body: AddStateBody): global trie, dtrie, loop_del_trie_id if trie is None: raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded") try: id: int = trie.insert(body.prompt) device: torch.device = body.state[0].device dtrie[id] = { "tokens": copy.deepcopy(body.tokens), "state": [tensor.cpu() for tensor in body.state] if device != torch.device("cpu") else copy.deepcopy(body.state), "logits": copy.deepcopy(body.logits), "device": device, } if len(trie) >= max_trie_len: del_prompt = trie[loop_del_trie_id] trie.remove(del_prompt) dtrie[loop_del_trie_id] = None loop_del_trie_id = loop_del_trie_id + 1 if loop_del_trie_id >= max_trie_len: loop_del_trie_id = loop_start_id quick_log( None, None, f"New Trie Id: {id}\nTrie Len: {len(trie)}\nTrie Buff Size: {trie.buff_size()}\nDtrie Buff Size Of Id: {_get_a_dtrie_buff_size(dtrie[id])}", ) return "success" except Exception as e: raise HTTPException( status.HTTP_400_BAD_REQUEST, f"insert failed, bad prompt.\n{e}" ) @router.post("/reset-state") def reset_state(): global trie, dtrie if trie is None: raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded") trie = cyac.Trie() dtrie = {} gc.collect() return "success" class LongestPrefixStateBody(BaseModel): prompt: str def _get_a_dtrie_buff_size(dtrie_v): # print(sys.getsizeof(dtrie_v["tokens"][0])) # str # print(sys.getsizeof(dtrie_v["tokens"][0]) * len(dtrie_v["tokens"])) # print(dtrie_v["state"][0][0].element_size()) # print(dtrie_v["state"][0].nelement()) # print(len(dtrie_v["state"])) # print( # len(dtrie_v["state"]) # * dtrie_v["state"][0].nelement() # * dtrie_v["state"][0][0].element_size() # ) # print(dtrie_v["logits"][0].element_size()) # print(dtrie_v["logits"].nelement()) # print(dtrie_v["logits"][0].element_size() * dtrie_v["logits"].nelement()) return 54 * len(dtrie_v["tokens"]) + 491520 + 262144 + 28 # TODO @router.post("/longest-prefix-state") def longest_prefix_state(body: LongestPrefixStateBody, request: Request): global trie if trie is None: raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded") id = -1 try: for id, len in trie.prefix(body.prompt): pass except: pass if id != -1: v = dtrie[id] device: torch.device = v["device"] prompt: str = trie[id] quick_log(request, body, "Hit:\n" + prompt) return { "prompt": prompt, "tokens": v["tokens"], "state": [tensor.to(device) for tensor in v["state"]] if device != torch.device("cpu") else v["state"], "logits": v["logits"], "device": device.type, } else: return { "prompt": "", "tokens": [], "state": None, "logits": None, "device": None, } @router.post("/save-state") def save_state(): global trie if trie is None: raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded") # trie.save("state_cache.trie") return "not implemented"