311 lines
9.9 KiB
Python
311 lines
9.9 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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system_prompt: Optional[str] = None,
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history: Optional[HistoryType] = 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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system_prompt (Optional[str]): system prompt string.
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history (Optional[HistoryType]): 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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result = ""
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if system_prompt:
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result += f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
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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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result += f"<|im_start|>user\n{user_msg}<|im_end|>\n"
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result += f"<|im_start|>assistant\n{assistant_msg}<|im_end|>\n"
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result += f"<|im_start|>user\n{query}<|im_end|>\n"
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result += "<|im_start|>assistant\n"
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return result
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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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"""
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Request parameters for text generation.
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Attributes:
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top_k: Top-k sampling parameter.
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top_p: Top-p (nucleus) sampling parameter.
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temperature: Sampling temperature.
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max_len: Maximum generation length.
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query: Input query (string or list of strings for batch).
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history: Conversation history.
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system_prompt: System prompt for the conversation.
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stream: Whether to use streaming generation.
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"""
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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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stream: bool = False
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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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prompt = build_prompt(request.query, request.history)
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ids = self.tokenizer.encode(prompt)
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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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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,
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ids,
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request.temperature,
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request.top_k,
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request.top_p,
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kv_caches=kv_caches,
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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[str, None, None]:
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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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prompt = build_prompt(request.query, request.history)
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ids = self.tokenizer.encode(prompt)
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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,
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request.temperature,
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request.top_k,
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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
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if next_token_id.item() in self.tokenizer.stop_ids:
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yield response + "\n"
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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 = [
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build_prompt(query, history)
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for query, history in zip(request.query, request.history)
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]
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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,
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request.temperature,
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request.top_k,
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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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class GeneratorFactory:
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"""Factory class for creating generator instances.
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Provides smart generator selection based on request characteristics:
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- Streaming: Use StreamGenerator for streaming output
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- Batch: Use BatchGenerator when query is a list
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- Single: Use LoopGenerator for single query non-streaming
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Example usage:
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generator = GeneratorFactory.create_generator(parameter, request)
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result = generator.generate(request)
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"""
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@staticmethod
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def create_generator(
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parameter: ModelParameter, request: GenerationRequest
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) -> GeneratorCore:
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"""Create a generator based on request characteristics.
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Args:
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parameter: Model parameters containing model, tokenizer, config
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request: Generation request with query, options, etc.
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Returns:
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Appropriate GeneratorCore subclass instance
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"""
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# Streaming generation: check stream field first
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if request.stream:
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return StreamGenerator(parameter)
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# Batch generation: query is a list of strings
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if isinstance(request.query, list):
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return BatchGenerator(parameter)
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# Default: single query non-streaming
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return LoopGenerator(parameter)
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@staticmethod
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def create_encoder(parameter: ModelParameter) -> EmbeddingEncoderCore:
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"""Create an embedding encoder instance.
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Args:
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parameter: Model parameters
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Returns:
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EmbeddingEncoderCore instance
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"""
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return EmbeddingEncoder(parameter)
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@classmethod
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def create(
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cls, parameter: ModelParameter, request: GenerationRequest
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) -> GeneratorCore:
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"""Convenience method that delegates to create_generator.
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Args:
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parameter: Model parameters
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request: Generation request
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Returns:
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Generator instance
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"""
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return cls.create_generator(parameter, request)
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