from typing import Optional, Sequence, Generator from llama_cpp import Llama, LogitsProcessorList, LlamaGrammar, llama_cpp, npt, np, StoppingCriteriaList from ctypes import POINTER from KMP_list import kmp_search, compute_lps_array class StreamingLLM(Llama): def __init__(self, model_path: str, **kwargs): super().__init__(model_path, **kwargs) self._venv_init() def str_detokenize(self, tokens) -> str: return self.detokenize(tokens).decode('utf-8', errors='ignore') def kv_cache_seq_trim(self): self._ctx.kv_cache_seq_rm(-1, self.n_tokens, -1) def _venv_init(self): self.venv = [0] self.venv_idx_map = [] def venv_create(self, name: str): self.venv.append(0) self.venv_idx_map.append(name) return name def venv_disband(self, name_set): if len(self.venv) <= 1: return False name_set = {x for x in name_set if x in self.venv_idx_map} if not name_set: return False while self.venv_idx_map: if self.venv_idx_map[0] in name_set: self.venv_idx_map.pop(0) # 删除 tmp = self.venv.pop(1) # 对应的 venv 移入上一层 self.venv[0] += tmp else: break return True def venv_revision(self, name: str): if len(self.venv) <= 1: return False if name not in self.venv_idx_map: return False _s = 0 while self.venv_idx_map: if self.venv_idx_map[-1] == name: break self.venv_idx_map.pop() # 删除 _s += self.venv.pop() if _s: self.n_tokens -= min(_s, self.n_tokens) self.kv_cache_seq_trim() return True def venv_remove(self, name: str): if len(self.venv) <= 1: return False if name not in self.venv_idx_map: return False venv_idx = self.venv_idx_map.index(name) + 1 while self.venv_idx_map: self.venv_idx_map.pop(venv_idx - 1) # 删除 if venv_idx == len(self.venv) - 1: # 最后一层 self.n_tokens -= min(self.venv.pop(), self.n_tokens) self.kv_cache_seq_trim() break else: # 非最后一层 n_keep = self.n_tokens - sum(self.venv[i] for i in range(venv_idx, len(self.venv))) n_discard = self.venv.pop(venv_idx) self.kv_cache_seq_ltrim(n_keep, n_discard) try: venv_idx = self.venv_idx_map.index(name, venv_idx - 1) + 1 except ValueError: # 没有了 break return True def venv_pop_token(self, n=1): self.n_tokens -= n self.venv[-1] -= n self.kv_cache_seq_trim() @property def venv_info(self): return str((self.n_tokens, self.venv, self.venv_idx_map)) def kv_cache_seq_ltrim(self, n_keep, n_discard=256, n_past=-1, im_start=None): if n_keep < 0: return if n_past < 0: n_past = self.n_tokens if im_start is not None: # [<|im_start|>, name, nl] lps = compute_lps_array(im_start) _idx = kmp_search(self.input_ids, im_start, n_keep + n_discard, n_past, lps) if _idx >= n_keep: # 其实是大于等于 n_keep + n_discard n_discard = _idx - n_keep # 截断到最近的 im_start 序列结构 else: _idx = kmp_search(self.input_ids, im_start, n_keep, n_past, lps) if _idx >= n_keep: n_keep = _idx + len(im_start) # 至少保留一个 im_start 序列结构 self._ctx.kv_cache_seq_rm(-1, n_keep, n_keep + n_discard) self._ctx.kv_cache_seq_shift(0, n_keep + n_discard, n_past, -n_discard) self.input_ids[n_keep:n_past - n_discard] = self.input_ids[n_keep + n_discard:n_past] self.n_tokens = n_past - n_discard def eval_t(self, tokens, n_keep=4, n_discard=256, im_start=None): if self._n_ctx < self.n_tokens + len(tokens): tmp_n_discard = max(n_discard, self.n_tokens + len(tokens) - self._n_ctx) self.kv_cache_seq_ltrim(n_keep, tmp_n_discard, im_start=im_start) for i in range(0, len(tokens), self.n_batch): batch = tokens[i: i + self.n_batch] n_past = self.n_tokens n_tokens = len(batch) self._batch.set_batch( batch=batch, n_past=n_past, logits_all=self.context_params.logits_all ) self._ctx.decode(self._batch) # Save tokens self.input_ids[n_past: n_past + n_tokens] = batch # Save logits rows = n_tokens cols = self._n_vocab offset = ( 0 if self.context_params.logits_all else n_tokens - 1 ) # NOTE: Only save the last token logits if logits_all is False self.scores[n_past + offset: n_past + n_tokens, :].reshape(-1)[ : ] = self._ctx.get_logits()[offset * cols: rows * cols] # Update n_tokens self.n_tokens += n_tokens self.venv[-1] += n_tokens return self.n_tokens def sample_t( self, top_k: int = 40, top_p: float = 0.95, min_p: float = 0.05, typical_p: float = 1.0, temp: float = 0.80, repeat_penalty: float = 1.1, repeat_last_n: int = 64, frequency_penalty: float = 0.0, presence_penalty: float = 0.0, tfs_z: float = 1.0, mirostat_mode: int = 0, mirostat_eta: float = 0.1, mirostat_tau: float = 5.0, penalize_nl: bool = True, logits_processor: Optional[LogitsProcessorList] = None, grammar: Optional[LlamaGrammar] = None, ): last_n_tokens_data = [llama_cpp.llama_token(0)] * max( 0, repeat_last_n - self.n_tokens ) + self._input_ids[-repeat_last_n:].tolist() last_n_tokens_size = len(last_n_tokens_data) n_vocab = self._n_vocab n_ctx = self._n_ctx top_k = n_vocab if top_k <= 0 else top_k last_n_tokens_size = n_ctx if last_n_tokens_size < 0 else last_n_tokens_size last_n_tokens_data_c = (llama_cpp.llama_token * last_n_tokens_size)( *last_n_tokens_data ) logits: npt.NDArray[np.single] = self.scores[self.n_tokens - 1: self.n_tokens, :].ravel() if logits_processor is not None: logits[:] = logits_processor(self._input_ids, logits) self._candidates.copy_logits(logits) self._ctx.sample_repetition_penalties( candidates=self._candidates, last_tokens_data=last_n_tokens_data_c, penalty_last_n=last_n_tokens_size, penalty_repeat=repeat_penalty, penalty_freq=frequency_penalty, penalty_present=presence_penalty, ) if not penalize_nl: nl_logit = logits[self._token_nl] self._candidates.candidates.data[self._token_nl].logit = llama_cpp.c_float( nl_logit ) if grammar is not None: self._ctx.sample_grammar( candidates=self._candidates, grammar=grammar, ) if temp < 0.0: self._ctx.sample_softmax(candidates=self._candidates) id_ = self._candidates.candidates.data[0].id elif temp == 0.0: id_ = self._ctx.sample_token_greedy(candidates=self._candidates) elif mirostat_mode == 1: self._ctx.sample_temp(candidates=self._candidates, temp=temp) id_ = self._ctx.sample_token_mirostat( candidates=self._candidates, tau=mirostat_tau, eta=mirostat_eta, mu=2.0 * mirostat_tau, m=100, ) elif mirostat_mode == 2: self._ctx.sample_temp(candidates=self._candidates, temp=temp) id_ = self._ctx.sample_token_mirostat_v2( candidates=self._candidates, tau=mirostat_tau, eta=mirostat_eta, mu=2.0 * mirostat_tau, ) else: self._ctx.sample_top_k(candidates=self._candidates, k=top_k, min_keep=1) self._ctx.sample_tail_free(candidates=self._candidates, z=tfs_z, min_keep=1) self._ctx.sample_typical( candidates=self._candidates, p=typical_p, min_keep=1 ) self._ctx.sample_top_p(candidates=self._candidates, p=top_p, min_keep=1) self._ctx.sample_min_p(candidates=self._candidates, p=min_p, min_keep=1) self._ctx.sample_temp(candidates=self._candidates, temp=temp) id_ = self._ctx.sample_token(candidates=self._candidates) if grammar is not None: self._ctx.grammar_accept_token(grammar=grammar, token=id_) return id_ def generate_t( self, tokens: Sequence[int], n_keep, n_discard: int = 256, im_start=None, top_k: int = 40, top_p: float = 0.95, min_p: float = 0.05, typical_p: float = 1.0, temp: float = 0.80, repeat_penalty: float = 1.1, repeat_last_n: int = 64, frequency_penalty: float = 0.0, presence_penalty: float = 0.0, tfs_z: float = 1.0, mirostat_mode: int = 0, mirostat_tau: float = 5.0, mirostat_eta: float = 0.1, logits_processor: Optional[LogitsProcessorList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None, grammar: Optional[LlamaGrammar] = None, ) -> Generator[int, Optional[Sequence[int]], None]: typical_p = float(typical_p) frequency_penalty = float(frequency_penalty) presence_penalty = float(presence_penalty) tfs_z = float(tfs_z) mirostat_tau = float(mirostat_tau) while True: self.eval_t(tokens, n_keep, n_discard, im_start=im_start) token = self.sample_t( top_k=top_k, top_p=top_p, min_p=min_p, typical_p=typical_p, temp=temp, repeat_penalty=repeat_penalty, repeat_last_n=repeat_last_n, frequency_penalty=frequency_penalty, presence_penalty=presence_penalty, tfs_z=tfs_z, mirostat_mode=mirostat_mode, mirostat_tau=mirostat_tau, mirostat_eta=mirostat_eta, logits_processor=logits_processor, grammar=grammar, ) if stopping_criteria is not None and stopping_criteria( self._input_ids, self._scores[-1, :] ): return tokens_or_none = yield token tokens = [token] if tokens_or_none is not None: tokens.extend(tokens_or_none) def load_session(self, filepath: str): n_tokens = POINTER(llama_cpp.c_size_t)(llama_cpp.c_size_t(0)) tokens = (llama_cpp.llama_token * self.n_ctx())() retn = llama_cpp.llama_load_session_file(self._ctx.ctx, filepath.encode('utf-8'), tokens, self.n_ctx(), n_tokens) self.n_tokens = n_tokens.contents.value self.input_ids[:self.n_tokens] = tokens[:self.n_tokens] self._venv_init() return retn def save_session(self, filepath: str): tokens = self._input_ids.tolist() tokens = (llama_cpp.llama_token * len(tokens))(*tokens) return llama_cpp.llama_save_session_file(self._ctx.ctx, filepath.encode('utf-8'), tokens, self.n_tokens)