File size: 5,070 Bytes
e6291f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | from typing import Optional, Union
from transformers import PreTrainedTokenizer, PreTrainedModel, PretrainedConfig, GenerationMixin
from transformers.modeling_outputs import BaseModelOutput, CausalLMOutput
from tokenizers.models import BPE
from tokenizers import Tokenizer
import torch.nn as nn
import torch
import os.path
class ZZJRabbitConfig(PretrainedConfig):
model_type = "zzjrabbit"
def __init__(self, num_layers: int = 6, vocab_size: int = 10000, hidden_size: int = 256, **kwargs):
self.num_layers = num_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
super().__init__(**kwargs)
class ZZJRabbitLayer(nn.Module):
def __init__(self, config: ZZJRabbitConfig):
super().__init__()
self.attn = nn.MultiheadAttention(config.hidden_size, 8, 0.1, batch_first=True)
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, q: torch.Tensor, k: torch.Tensor, v: 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(q.size(-2), q.size(-2), device=q.device), 1), 0)
if attention_mask is not None:
key_padding_mask = torch.lt(attention_mask, 1)
attn = self.attn(
q, k, v,
key_padding_mask=key_padding_mask,
attn_mask=attn_mask,
is_causal=True
)[0]
q = self.norm(q + attn)
o = self.l1(q)
o = self.activate(o)
o = self.l2(o)
return self.norm(q + o)
class ZZJRabbitModel(PreTrainedModel):
config_class = ZZJRabbitConfig
def __init__(self, config: ZZJRabbitConfig, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.emb = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([ZZJRabbitLayer(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):
emb = self.emb(input_ids)
res = emb
for l in self.layers:
res = l(res, res, res, attention_mask)
if not return_dict:
return (res,)
else:
return BaseModelOutput(res)
class ZZJRabbitModelForCausalLM(PreTrainedModel, GenerationMixin):
config_class = ZZJRabbitConfig
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.model = ZZJRabbitModel(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 ZZJRabbitTokenizer(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,)
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