220 lines
7.9 KiB
Python
220 lines
7.9 KiB
Python
import torch
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from dataclasses import dataclass
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from torch import Tensor
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from typing import List, Tuple, Union, Optional, Generator
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from khaosz.inference.core import GeneratorCore, EmbeddingEncoderCore, KVCacheManager
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from khaosz.config.param_config import ModelParameter
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HistoryType = List[Tuple[str, str]]
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def build_prompt(
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query: str,
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init_prompt: Optional[str] = None,
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history: Optional[List[Tuple[str, str]]] = None
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) -> str:
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"""
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Build prompt in ChatML format for query and history
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Args:
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query(str): query string
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history(Optional[List[Tuple[str, str]]]): history list of query and response
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Returns:
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str: prompt string in ChatML format
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"""
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prompt = f"<|im_start|>system\n{init_prompt}<|im_end|>\n" if init_prompt else ""
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# (convert tuple format to ChatML)
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if history:
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for user_msg, assistant_msg in history:
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prompt += f"<|im_start|>user\n{user_msg}<|im_end|>\n"
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prompt += f"<|im_start|>assistant\n{assistant_msg}<|im_end|>\n"
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prompt += f"<|im_start|>user\n{query}<|im_end|>\n"
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prompt += "<|im_start|>assistant\n"
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return prompt
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def pad_sequence(ids_list: List[List[int]], pad_id: int) -> Tuple[List[List[int]], int]:
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"""
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Pad a list of sequences to a fixed length.
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Args:
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ids_list (List[List[int]]): A list of sequences.
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max_ids_len (int): The maximum length of sequences.
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pad_id (int): The id to pad sequences.
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Returns:
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List[List[int]]: A list of padded sequences.
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"""
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max_ids_len = max(len(ids) for ids in ids_list)
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new_ids_list = []
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for ids in ids_list:
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pad_len = max_ids_len - len(ids)
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padded_seq = [pad_id] * pad_len + ids
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new_ids_list.append(padded_seq)
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return new_ids_list, max_ids_len
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@dataclass
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class GenerationRequest:
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top_k: int
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top_p: float
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temperature: float
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max_len: int
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query: Union[str, List[str]]
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history: Optional[Union[HistoryType, List[HistoryType]]] = None
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system_prompt: Optional[str] = None
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build_prompt: bool = True
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def __post_init__(self):
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if not isinstance(self.top_k, int) or self.top_k < 0:
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raise ValueError("top_k must be a non-negative integer")
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if not isinstance(self.top_p, float) or self.top_p < 0.0 or self.top_p > 1.0:
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raise ValueError("top_p must be a float between 0.0 and 1.0")
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if not isinstance(self.temperature, float) or self.temperature < 0.0:
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raise ValueError("temperature must be a non-negative float")
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class LoopGenerator(GeneratorCore):
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def __init__(self, parameter: ModelParameter):
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super().__init__(parameter)
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def generate(self, request: GenerationRequest) -> str:
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device = next(self.model.parameters()).device
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cache_manager = KVCacheManager(self.config, 1, device=device)
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input_args = build_prompt(request.query, request.history) if request.build_prompt else request.query
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ids = self.tokenizer.encode(input_args)
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input_ids = torch.tensor([ids], device=device, dtype=torch.long)
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start_cache_pos = len(ids)
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cur_cache_pos = 0
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self.model.eval()
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kv_caches = cache_manager.get_kvcache()
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ids = self.generate_loop(
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input_ids, ids, request.temperature, request.top_k, request.top_p,
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kv_caches=kv_caches,
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start_pos=cur_cache_pos
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)
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response = self.tokenizer.decode(ids[start_cache_pos:])
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return response
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class StreamGenerator(GeneratorCore):
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def __init__(self, parameter: ModelParameter):
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super().__init__(parameter)
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def generate(self, request: GenerationRequest) -> Generator[Tuple[str, List[Tuple[str, str]]], None, None]:
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if request.history is None:
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request.history = []
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device = next(self.model.parameters()).device
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cache_manager = KVCacheManager(self.config, 1, device=device)
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input_args = build_prompt(request.query, request.history) if request.build_prompt else request.query
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ids = self.tokenizer.encode(input_args)
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input_ids = torch.tensor([ids], device=device, dtype=torch.long)
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start_cache_pos = len(ids)
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cur_cache_pos = 0
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self.model.eval()
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kv_caches = cache_manager.get_kvcache()
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for _ in range(len(ids), self.config.max_len):
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next_token_id, cache_increase = self.generate_iterator(
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input_ids, request.temperature, request.top_k, request.top_p,
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kv_caches=kv_caches,
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start_pos=cur_cache_pos
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)
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input_ids = next_token_id
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ids.append(next_token_id.item())
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cur_cache_pos += cache_increase
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response = self.tokenizer.decode(ids[start_cache_pos:])
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yield response, request.history + [(request.query, response)]
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if next_token_id.item() in self.tokenizer.stop_ids:
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yield response + "\n", request.history + [(request.query, response)]
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break
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class BatchGenerator(GeneratorCore):
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def __init__(self, parameter: ModelParameter):
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super().__init__(parameter)
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def generate(self, request: GenerationRequest) -> List[str]:
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batch_size = len(request.query)
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if request.history is None:
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request.history = [[] for _ in range(batch_size)]
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prompts = [build_prompt(query, history) for query, history in zip(request.query, request.history)]
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ids_list = [self.tokenizer.encode(prompt) for prompt in prompts]
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ids_list, max_ids_len = pad_sequence(ids_list, self.tokenizer.pad_id)
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device = next(self.model.parameters()).device
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cache_manager = KVCacheManager(self.config, batch_size, device=device)
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input_tensor = torch.tensor(ids_list, device=device, dtype=torch.long)
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cache_manager.set_seq_mask(input_tensor, self.tokenizer.pad_id)
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activate_task_mask = [True] * batch_size
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start_cache_pos = max_ids_len
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cur_cache_pos = 0
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while max_ids_len < self.config.max_len and sum(activate_task_mask) != 0:
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kv_caches = cache_manager.get_kvcache()
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attn_mask =cache_manager.get_seq_mask()
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next_token_id, cache_increase = self.generate_iterator(
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input_tensor, request.temperature, request.top_k, request.top_p,
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attn_mask=attn_mask,
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kv_caches=kv_caches,
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start_pos=cur_cache_pos
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)
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cur_cache_pos += cache_increase
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active_mask = []
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c_ids = 0
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for i in range(batch_size):
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if activate_task_mask[i]:
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token = next_token_id[c_ids, :].item()
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ids_list[i].append(token)
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c_ids += 1
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is_active = not token in self.tokenizer.stop_ids
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activate_task_mask[i] = is_active
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active_mask.append(is_active)
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active_mask = torch.tensor(active_mask, device=device, dtype=torch.bool)
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cache_manager.update(active_mask)
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input_tensor = next_token_id[active_mask, :]
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max_ids_len += 1
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responses = [str()] * batch_size
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for i in range(batch_size):
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responses[i] = self.tokenizer.decode(ids_list[i][start_cache_pos:])
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request.history[i].append((request.query[i], responses[i]))
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return responses
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class EmbeddingEncoder(EmbeddingEncoderCore):
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def __init__(self, parameter: ModelParameter):
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super().__init__(parameter)
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def encode(self, sentence: Union[str, List[str]]) -> Union[Tensor, List[Tensor]]:
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return super().encode(sentence)
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