from typing import Optional, Union from transformers import PreTrainedTokenizer, PreTrainedModel, PretrainedConfig, GenerationMixin from transformers.modeling_outputs import BaseModelOutput, CausalLMOutput from tokenizers import Tokenizer import torch.nn as nn import torch import os.path import math class ZZJRabbit2Config(PretrainedConfig): model_type = "zzjrabbit2" def __init__(self, num_layers: int = 12, num_attention_heads: int = 8, vocab_size: int = 10000, hidden_size: int = 1024, **kwargs): self.num_layers = num_layers self.num_attention_heads = num_attention_heads self.vocab_size = vocab_size self.hidden_size = hidden_size assert hidden_size % num_attention_heads == 0 super().__init__(**kwargs) class ZZJRabbit2PE(nn.Module): def __init__(self, hidden_size: int, max_len: int = 32768): super().__init__() pe = torch.zeros(max_len, hidden_size) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, hidden_size, 2).float() * (-math.log(10000.0) / hidden_size)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0).transpose(0, 1) self.register_buffer("pe", pe) def forward(self, x: torch.Tensor): return x + self.pe[:x.size(0), :] class ZZJRabbit2Attention(nn.Module): def __init__(self, config: ZZJRabbit2Config): super().__init__() self.config = config self.head_dim = config.hidden_size // config.num_attention_heads self.q_proj = nn.Linear(config.hidden_size, config.hidden_size) self.k_proj = nn.Linear(config.hidden_size, config.hidden_size) self.v_proj = nn.Linear(config.hidden_size, config.hidden_size) self.out_proj = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(0.1) def forward(self, x: torch.Tensor, key_padding_mask: Optional[torch.BoolTensor] = None, attn_mask: Optional[torch.BoolTensor] = None): batch_size = x.size(0) Q = self.q_proj(x).view(batch_size, -1, self.config.num_attention_heads, self.head_dim).transpose(1, 2) K = self.k_proj(x).view(batch_size, -1, self.config.num_attention_heads, self.head_dim).transpose(1, 2) V = self.v_proj(x).view(batch_size, -1, self.config.num_attention_heads, self.head_dim).transpose(1, 2) scores = torch.matmul(Q, K.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)) if key_padding_mask is not None: scores = scores.masked_fill(key_padding_mask.view(batch_size, 1, 1, -1), float("-inf")) if attn_mask is not None: scores = scores.masked_fill(attn_mask, float("-inf")) attn_weights = nn.functional.softmax(scores, dim=-1) attn_weights = self.dropout(attn_weights) context = torch.matmul(attn_weights, V) context = context.transpose(1, 2).contiguous() context = context.view(batch_size, -1, self.config.hidden_size) return self.out_proj(context) class ZZJRabbit2Layer(nn.Module): def __init__(self, config: ZZJRabbit2Config): super().__init__() self.attn = ZZJRabbit2Attention(config) self.l1 = nn.Linear(config.hidden_size, config.hidden_size) self.l2 = nn.Linear(config.hidden_size, config.hidden_size) self.activate = nn.ReLU() self.norm = nn.RMSNorm(config.hidden_size) def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: key_padding_mask = None attn_mask = None if self.training: attn_mask = torch.gt(torch.triu(torch.ones(x.size(-2), x.size(-2), device=x.device), 1), 0) if attention_mask is not None: key_padding_mask = torch.lt(attention_mask, 1) attn = self.attn( x, key_padding_mask=key_padding_mask, attn_mask=attn_mask, )[0] x = self.norm(x + attn) o = self.l1(x) o = self.activate(o) o = self.l2(o) return self.norm(x + o) class ZZJRabbit2Model(PreTrainedModel): config_class = ZZJRabbit2Config def __init__(self, config: ZZJRabbit2Config, **kwargs): super().__init__(config, **kwargs) self.config = config self.emb = nn.Embedding(config.vocab_size, config.hidden_size) self.pe = ZZJRabbit2PE(config.hidden_size) self.layers = nn.ModuleList([ZZJRabbit2Layer(config) for _ in range(config.num_layers)]) def forward(self, input_ids: torch.Tensor, return_dict: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, **kwargs): res = self.emb(input_ids) res = self.pe(res) res = res for l in self.layers: res = l(res, attention_mask) if not return_dict: return (res,) else: return BaseModelOutput(res) class ZZJRabbit2ForCausalLM(PreTrainedModel, GenerationMixin): config_class = ZZJRabbit2Config def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.model = ZZJRabbit2Model(config, **kwargs) self.l = nn.Linear(config.hidden_size, config.vocab_size) def forward(self, input_ids: torch.Tensor, return_dict: Optional[bool] = None, labels: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs): # print(input_ids, return_dict, labels, attention_mask, logits_to_keep, kwargs) hidden = self.model(input_ids=input_ids, attention_mask=attention_mask)[0] logits = self.l(hidden[:, slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep, :]) if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) print(loss) if not return_dict: return (loss, logits) if labels is not None else (logits,) else: return CausalLMOutput(logits=logits, loss=loss) if labels is not None else CausalLMOutput(logits=logits) @classmethod def can_generate(cls): return True def prepare_inputs_for_generation(self, input_ids, **kwargs): return {"input_ids": input_ids} class ZZJRabbit2Tokenizer(PreTrainedTokenizer): vocab_files_names = {"tokenizers_file": "tokenizer.json"} def __init__(self, tokenizers_file, **kwargs): self.internal = Tokenizer.from_file(tokenizers_file) super().__init__(**kwargs) def get_vocab(self): return {self.internal.id_to_token(i): i for i in range(self.vocab_size)} def tokenize(self, text, **kwargs): return self.internal.encode(text).tokens def convert_tokens_to_ids(self, tokens): return self.internal.token_to_id(tokens) if isinstance(tokens, str) else [self.internal.token_to_id(t) for t in tokens] def decode(self, tokens, skip_special_tokens=True, **kwargs): if isinstance(tokens, torch.Tensor): tokens = tokens.tolist() return self.internal.decode(tokens, skip_special_tokens=skip_special_tokens) @property def vocab_size(self): return self.internal.get_vocab_size() def save_vocabulary(self, path, *args, **kwargs) -> tuple[str]: p = os.path.join(path, "tokenizer.json") self.internal.save(p) return (p,)