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| # coding=utf-8 | |
| # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. | |
| # | |
| # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX | |
| # and OPT implementations in this library. It has been modified from its | |
| # original forms to accommodate minor architectural differences compared | |
| # to GPT-NeoX and OPT used by the Meta AI team that trained the model. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from typing import List, Optional, Tuple, Union | |
| import torch | |
| from torch import nn | |
| from transformers.cache_utils import Cache | |
| from transformers.modeling_outputs import CausalLMOutputWithPast | |
| from transformers.models.llama import LlamaForCausalLM | |
| class LlamaskForCausalLM(LlamaForCausalLM): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.special_tokens = nn.Embedding(2, config.hidden_size) # 0 -> mask encoding, 1 -> buffer token | |
| self.post_init() | |
| def generate( | |
| self, | |
| input_ids: torch.LongTensor = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| max_tokens: int=32, | |
| temperature: float=1.0, | |
| ): | |
| eos_token_tensor = torch.tensor(self.config.eos_token_id, device=input_ids.device) | |
| for _ in range(max_tokens): | |
| outputs = self.forward(input_ids=input_ids, attention_mask=attention_mask) | |
| logits = outputs['logits'][:,-1,:]/temperature | |
| probs = torch.nn.functional.softmax(logits, dim=-1) | |
| next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) | |
| batch_size, seq_len, _ = attention_mask.shape | |
| expanded_mask = torch.zeros(batch_size, seq_len + 1, seq_len + 1, dtype=attention_mask.dtype, device=attention_mask.device) | |
| # Step 1: Copy the existing attention mask (top-left block of the expanded mask) | |
| expanded_mask[:, :seq_len, :seq_len] = attention_mask | |
| # Step 2: Copy the last row of the original attention mask into the new row (excluding the last position) | |
| expanded_mask[:, seq_len, :seq_len] = attention_mask[:, -1, :] | |
| # Step 3: Set the diagonal of the new token to attend to all previous tokens by setting the new last element to 1 | |
| expanded_mask[:, seq_len, seq_len] = 1 | |
| next_tokens = next_tokens[:, None] | |
| input_ids = torch.cat([input_ids, next_tokens], dim=-1) | |
| attention_mask = expanded_mask | |
| if torch.all(torch.any(next_tokens==eos_token_tensor, dim=1)): | |
| break | |
| return input_ids | |
| def forward( | |
| self, | |
| input_ids: torch.LongTensor = None, | |
| num_buffer_token: Optional[int] = 0, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.LongTensor] = None, | |
| past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, | |
| inputs_embeds: Optional[torch.FloatTensor] = None, | |
| labels: Optional[torch.LongTensor] = None, | |
| use_cache: Optional[bool] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| cache_position: Optional[torch.LongTensor] = None | |
| ) -> Union[Tuple, CausalLMOutputWithPast]: | |
| batch_size = input_ids.shape[0] | |
| # print("BEWARE PRIVACY TAG DISABLE") | |
| # privacy_tag = self.special_tokens(torch.tensor([0], device=input_ids.device)) | |
| # buffer_token = self.special_tokens(torch.tensor([0], device=input_ids.device)).unsqueeze(0) | |
| inputs_embeds = self.model.embed_tokens(input_ids) | |
| # buffer_tokens = buffer_token.repeat(batch_size, num_buffer_token, 1) | |
| # inputs_embeds = torch.cat([inputs_embeds, buffer_tokens], dim=1) | |
| # inputs_embeds[attention_mask[:,-1,:]==0] = inputs_embeds[attention_mask[:,-1,:]==0] + privacy_tag | |
| attention_mask = attention_mask.unsqueeze(1) | |
| attention_mask = attention_mask.to(inputs_embeds.dtype) | |
| attention_mask = attention_mask.masked_fill(attention_mask == 0, -1e9) | |
| attention_mask = attention_mask.masked_fill(attention_mask == 1, float(0.0)) | |
| outputs = super().forward( | |
| input_ids=None, | |
| attention_mask=attention_mask, | |
| position_ids=position_ids, | |
| past_key_values=past_key_values, | |
| inputs_embeds=inputs_embeds, | |
| labels=labels, | |
| use_cache=use_cache, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| cache_position=cache_position, | |
| ) | |
| return outputs | |