Upload TextGenerationPipeline

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "ZZJRabbitModelForCausalLM"
   ],
+  "auto_map": {
+    "AutoConfig": "zzjrabbit.ZZJRabbitConfig",
+    "AutoModelForCausalLM": "zzjrabbit.ZZJRabbitModelForCausalLM"
+  },
   "hidden_size": 256,
   "model_type": "zzjrabbit",
   "num_layers": 6,

tokenizer_config.json

@@ -9,6 +9,12 @@
       "special": true
     }
   },
+  "auto_map": {
+    "AutoTokenizer": [
+      "zzjrabbit.ZZJRabbitTokenizer",
+      null
+    ]
+  },
   "clean_up_tokenization_spaces": false,
   "extra_special_tokens": {},
   "mask_token": "0",

zzjrabbit.py

@@ -0,0 +1,121 @@
+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,)