chore: 更新项目名称
This commit is contained in:
@@ -0,0 +1,28 @@
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from astrai.inference.core import (
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disable_random_init,
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GeneratorCore,
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EmbeddingEncoderCore,
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KVCacheManager,
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)
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from astrai.inference.generator import (
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GenerationRequest,
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LoopGenerator,
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StreamGenerator,
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BatchGenerator,
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EmbeddingEncoder,
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GeneratorFactory,
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)
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__all__ = [
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"disable_random_init",
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"GeneratorCore",
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"EmbeddingEncoderCore",
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"KVCacheManager",
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"GenerationRequest",
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"LoopGenerator",
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"StreamGenerator",
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"BatchGenerator",
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"EmbeddingEncoder",
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"GeneratorFactory",
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]
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@@ -0,0 +1,298 @@
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import torch
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import torch.nn as nn
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from torch import Tensor
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from contextlib import contextmanager
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from typing import Any, Callable, List, Tuple, Union, Optional, Self
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from astrai.config import ModelParameter, ModelConfig
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def apply_sampling_strategies(
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logits: Tensor,
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temperature: float,
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top_k: int,
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top_p: float,
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filter_value: float = -float("inf"),
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) -> Tensor:
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"""
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Apply sampling strategies to the logits tensor.
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Args:
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logits (Tensor): The logits tensor.
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temperature (float): The temperature parameter.
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top_k (int): The top-k parameter.
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top_p (float): The top-p parameter.
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filter_value (float, optional): The filter value. Defaults to -float("inf").
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Returns:
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Tensor: The sampled logits tensor.
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"""
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if temperature != 1.0:
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logits = logits / temperature
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if top_k > 0:
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top_k = min(top_k, logits.size(-1))
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indices_to_remove = logits < torch.topk(logits, top_k, dim=-1)[0][..., -1, None]
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logits[indices_to_remove] = filter_value
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if top_p < 1.0:
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sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
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cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
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sorted_indices_to_remove = cumulative_probs > top_p
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sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
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sorted_indices_to_remove[..., 0] = 0
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indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
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indices_to_remove.scatter_(
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dim=1, index=sorted_indices, src=sorted_indices_to_remove
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)
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logits[indices_to_remove] = filter_value
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return logits
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@contextmanager
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def disable_random_init():
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init_functions = [
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"xavier_normal_",
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"xavier_uniform_",
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"kaiming_normal_",
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"kaiming_uniform_",
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"zeros_",
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"ones_",
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"constant_",
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"normal_",
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"uniform_",
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]
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original_funcs = {}
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for name in init_functions:
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if hasattr(nn.init, name):
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original_funcs[name] = getattr(nn.init, name)
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setattr(nn.init, name, lambda *args, **kwargs: None)
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try:
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yield
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finally:
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for name, orig_func in original_funcs.items():
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setattr(nn.init, name, orig_func)
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class GeneratorCore:
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def __init__(self, parameter: ModelParameter):
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self.model = parameter.model
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self.tokenizer = parameter.tokenizer
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self.config = parameter.config
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def generate_iterator(
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self,
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input_ids: Tensor,
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temperature: float,
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top_k: int,
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top_p: float,
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attn_mask: Optional[Tensor] = None,
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kv_caches: Optional[List[Tuple[Tensor, Tensor]]] = None,
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start_pos: int = 0,
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) -> Tuple[Tensor, int]:
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with torch.inference_mode():
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outputs = self.model(input_ids, attn_mask, kv_caches, start_pos)
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logits = outputs["logits"][:, -1, :]
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cache_increase = input_ids.size(-1)
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logits = apply_sampling_strategies(logits, temperature, top_k, top_p)
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probs = torch.softmax(logits, dim=-1)
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next_token_id = torch.multinomial(probs, num_samples=1)
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return next_token_id, cache_increase
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def generate_loop(
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self,
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input_ids: Tensor,
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ids: List[int],
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temperature: float,
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top_k: int,
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top_p: float,
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attn_mask: Optional[Tensor] = None,
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kv_caches: Optional[List[Tuple[Tensor, Tensor]]] = None,
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start_pos: int = 0,
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callback: Optional[Callable[..., Any]] = None,
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) -> List[int]:
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cur_cache_pos = start_pos
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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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temperature,
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top_k,
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top_p,
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attn_mask,
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kv_caches,
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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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if callback:
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callback(next_token_id.item(), ids.copy())
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if next_token_id.item() in self.tokenizer.stop_ids:
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break
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return ids
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def to(self, *args, **kargs) -> Self:
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self.model.to(*args, **kargs)
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return self
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class EmbeddingEncoderCore:
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def __init__(self, parameter: ModelParameter):
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self.model = parameter.model
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self.tokenizer = parameter.tokenizer
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self.config = parameter.config
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def encode(self, sentence: Union[str, List[str]]) -> Union[Tensor, List[Tensor]]:
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with_batch = isinstance(sentence, list)
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ids = self.tokenizer.encode(sentence)
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batch_ids = ids if with_batch else [ids]
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max_model_len = self.config.max_len
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all_fragments = []
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fragment_origin_idx = []
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for i, seq in enumerate(batch_ids):
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if len(seq) > max_model_len:
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fragments = [
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seq[j : j + max_model_len]
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for j in range(0, len(seq), max_model_len)
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]
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all_fragments.extend(fragments)
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fragment_origin_idx.extend([i] * len(fragments))
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else:
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all_fragments.append(seq)
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fragment_origin_idx.append(i)
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# if empty fragments
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if not all_fragments or not ids:
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return [] if with_batch else torch.tensor([])
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device = next(self.model.parameters()).device
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max_len = min(max(len(seq) for seq in all_fragments), max_model_len)
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padded_ids = []
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masks = []
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for seq in all_fragments:
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pad_len = max_len - len(seq)
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padded_seq = seq + [self.tokenizer.pad_id] * pad_len
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mask = [token_id != self.tokenizer.pad_id for token_id in padded_seq]
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padded_ids.append(padded_seq)
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masks.append(mask)
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input_tensor = torch.tensor(padded_ids, device=device, dtype=torch.long)
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seq_mask = torch.tensor(masks, device=device, dtype=torch.bool)
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with torch.inference_mode():
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outputs = self.model(input_tensor, seq_mask)["hidden_states"]
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# [num_fragments, seq_len, hidden_size]
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fragment_embs = torch.mul(outputs, seq_mask.unsqueeze(-1))
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sentence_embs: List[Tensor] = []
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for i in range(len(batch_ids)):
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indices = [
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idx for idx, orig_idx in enumerate(fragment_origin_idx) if orig_idx == i
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]
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if indices:
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sum_frags = torch.sum(
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fragment_embs[indices, :, :], dim=1
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) # [frags, hidden_size]
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length = torch.sum(seq_mask[indices, :], dim=1).unsqueeze(
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1
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) # [frags, 1]
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emb = torch.sum(sum_frags / length, dim=0) # [frags, hidden_size]
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sentence_embs.append(emb.flatten())
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if with_batch:
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return [emb.flatten() for emb in sentence_embs]
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else:
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return sentence_embs[0].flatten()
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def to(self, *args, **kargs) -> Self:
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self.model.to(*args, **kargs)
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return self
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class KVCacheManager:
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def __init__(
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self,
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config: ModelConfig,
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batch_size: int,
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device: torch.device = "cuda",
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dtype: torch.dtype = torch.bfloat16,
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):
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self.batch_size = batch_size
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self.device = device
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self.dtype = dtype
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self.num_layers = config.n_layers
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self.max_len = config.max_len
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self.num_heads = config.n_kv_heads
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self.head_dim = config.dim // config.n_heads
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self._kv_cache: Tuple[Tensor, Tensor] = None
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self._seq_mask: Tensor = None
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self._initialize()
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def _initialize(self):
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k_cache = torch.empty(
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(
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self.batch_size,
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self.max_len,
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self.num_layers,
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self.num_heads,
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self.head_dim,
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),
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device=self.device,
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dtype=self.dtype,
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)
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v_cache = torch.empty(
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(
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self.batch_size,
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self.max_len,
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self.num_layers,
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self.num_heads,
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self.head_dim,
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),
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device=self.device,
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dtype=self.dtype,
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)
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self._kv_cache = (k_cache, v_cache)
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self._seq_mask = torch.ones(
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(self.batch_size, self.max_len), device=self.device, dtype=torch.bool
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)
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def update(self, active_mask: Tensor):
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k_cache, v_cache = self._kv_cache
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self._kv_cache = (k_cache[active_mask], v_cache[active_mask])
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self._seq_mask = self._seq_mask[active_mask]
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def reset(self, full_reset=False):
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if full_reset:
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self._kv_cache = None
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self._seq_mask = None
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else:
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self._initialize()
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def set_seq_mask(self, input_ids: Tensor, pad_id: int):
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batch_size, seq_len = input_ids.shape
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bool_mask = input_ids != pad_id
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self._seq_mask[:batch_size, :seq_len] = bool_mask
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def get_kvcache(self) -> Tuple[Tensor, Tensor]:
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return self._kv_cache
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def get_seq_mask(self) -> Tensor:
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return self._seq_mask
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@@ -0,0 +1,310 @@
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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 astrai.inference.core import GeneratorCore, EmbeddingEncoderCore, KVCacheManager
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from astrai.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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|
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|
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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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|
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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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|
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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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|
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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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|
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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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|
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class BatchGenerator(GeneratorCore):
|
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def __init__(self, parameter: ModelParameter):
|
||||
super().__init__(parameter)
|
||||
|
||||
def generate(self, request: GenerationRequest) -> List[str]:
|
||||
batch_size = len(request.query)
|
||||
if request.history is None:
|
||||
request.history = [[] for _ in range(batch_size)]
|
||||
|
||||
prompts = [
|
||||
build_prompt(query, history)
|
||||
for query, history in zip(request.query, request.history)
|
||||
]
|
||||
|
||||
ids_list = [self.tokenizer.encode(prompt) for prompt in prompts]
|
||||
ids_list, max_ids_len = pad_sequence(ids_list, self.tokenizer.pad_id)
|
||||
|
||||
device = next(self.model.parameters()).device
|
||||
cache_manager = KVCacheManager(self.config, batch_size, device=device)
|
||||
|
||||
input_tensor = torch.tensor(ids_list, device=device, dtype=torch.long)
|
||||
cache_manager.set_seq_mask(input_tensor, self.tokenizer.pad_id)
|
||||
activate_task_mask = [True] * batch_size
|
||||
|
||||
start_cache_pos = max_ids_len
|
||||
cur_cache_pos = 0
|
||||
|
||||
while max_ids_len < self.config.max_len and sum(activate_task_mask) != 0:
|
||||
kv_caches = cache_manager.get_kvcache()
|
||||
attn_mask = cache_manager.get_seq_mask()
|
||||
|
||||
next_token_id, cache_increase = self.generate_iterator(
|
||||
input_tensor,
|
||||
request.temperature,
|
||||
request.top_k,
|
||||
request.top_p,
|
||||
attn_mask=attn_mask,
|
||||
kv_caches=kv_caches,
|
||||
start_pos=cur_cache_pos,
|
||||
)
|
||||
|
||||
cur_cache_pos += cache_increase
|
||||
active_mask = []
|
||||
c_ids = 0
|
||||
|
||||
for i in range(batch_size):
|
||||
if activate_task_mask[i]:
|
||||
token = next_token_id[c_ids, :].item()
|
||||
ids_list[i].append(token)
|
||||
c_ids += 1
|
||||
|
||||
is_active = token not in self.tokenizer.stop_ids
|
||||
activate_task_mask[i] = is_active
|
||||
active_mask.append(is_active)
|
||||
|
||||
active_mask = torch.tensor(active_mask, device=device, dtype=torch.bool)
|
||||
cache_manager.update(active_mask)
|
||||
input_tensor = next_token_id[active_mask, :]
|
||||
|
||||
max_ids_len += 1
|
||||
|
||||
responses = [str()] * batch_size
|
||||
for i in range(batch_size):
|
||||
responses[i] = self.tokenizer.decode(ids_list[i][start_cache_pos:])
|
||||
request.history[i].append((request.query[i], responses[i]))
|
||||
|
||||
return responses
|
||||
|
||||
|
||||
class EmbeddingEncoder(EmbeddingEncoderCore):
|
||||
def __init__(self, parameter: ModelParameter):
|
||||
super().__init__(parameter)
|
||||
|
||||
def encode(self, sentence: Union[str, List[str]]) -> Union[Tensor, List[Tensor]]:
|
||||
return super().encode(sentence)
|
||||
|
||||
|
||||
class GeneratorFactory:
|
||||
"""Factory class for creating generator instances.
|
||||
|
||||
Provides smart generator selection based on request characteristics:
|
||||
- Streaming: Use StreamGenerator for streaming output
|
||||
- Batch: Use BatchGenerator when query is a list
|
||||
- Single: Use LoopGenerator for single query non-streaming
|
||||
|
||||
Example usage:
|
||||
generator = GeneratorFactory.create_generator(parameter, request)
|
||||
result = generator.generate(request)
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def create_generator(
|
||||
parameter: ModelParameter, request: GenerationRequest
|
||||
) -> GeneratorCore:
|
||||
"""Create a generator based on request characteristics.
|
||||
|
||||
Args:
|
||||
parameter: Model parameters containing model, tokenizer, config
|
||||
request: Generation request with query, options, etc.
|
||||
|
||||
Returns:
|
||||
Appropriate GeneratorCore subclass instance
|
||||
"""
|
||||
# Streaming generation: check stream field first
|
||||
if request.stream:
|
||||
return StreamGenerator(parameter)
|
||||
|
||||
# Batch generation: query is a list of strings
|
||||
if isinstance(request.query, list):
|
||||
return BatchGenerator(parameter)
|
||||
|
||||
# Default: single query non-streaming
|
||||
return LoopGenerator(parameter)
|
||||
|
||||
@staticmethod
|
||||
def create_encoder(parameter: ModelParameter) -> EmbeddingEncoderCore:
|
||||
"""Create an embedding encoder instance.
|
||||
|
||||
Args:
|
||||
parameter: Model parameters
|
||||
|
||||
Returns:
|
||||
EmbeddingEncoderCore instance
|
||||
"""
|
||||
return EmbeddingEncoder(parameter)
|
||||
|
||||
@classmethod
|
||||
def create(
|
||||
cls, parameter: ModelParameter, request: GenerationRequest
|
||||
) -> GeneratorCore:
|
||||
"""Convenience method that delegates to create_generator.
|
||||
|
||||
Args:
|
||||
parameter: Model parameters
|
||||
request: Generation request
|
||||
|
||||
Returns:
|
||||
Generator instance
|
||||
"""
|
||||
return cls.create_generator(parameter, request)
|
||||
Reference in New Issue
Block a user