zzjrabbit2.py

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,)