Flatten 1_raw_dataset submodules into plain tracked files
FocalLoRA, Should-It-Be-Executed-Or-Processed, and topicattack were nested git repos (with an inner FocalLoRA/data/FocalLoRA/.git as well). Drop their .git history and track the contents directly in this repo instead of as submodules/gitlinks. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
HenryChou020514
@ -0,0 +1,18 @@
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__version__ = '0.0.1'
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from .suffix_manager import (
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AttackPrompt,
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PromptManager,
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MultiPromptAttack,
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ProgressiveMultiPromptAttack,
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get_embedding_layer,
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get_embedding_matrix,
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get_embeddings,
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get_nonascii_toks,
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get_goals_and_targets,
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get_workers
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)
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from .gcg import GCGAttackPrompt as AttackPrompt
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from .gcg import GCGPromptManager as PromptManager
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from .gcg import GCGMultiPromptAttack as MultiPromptAttack
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@ -0,0 +1,213 @@
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import gc
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import numpy as np
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import torch
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import torch.nn as nn
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from tqdm.auto import tqdm
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from rpo import AttackPrompt, MultiPromptAttack, PromptManager
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from rpo import get_embedding_matrix, get_embeddings
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def print_gpu_memory(label):
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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if device.type == 'cuda':
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torch.cuda.synchronize() # Wait for all operations to complete
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total_memory = torch.cuda.get_device_properties(device).total_memory
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allocated_memory = torch.cuda.memory_allocated(device)
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cached_memory = torch.cuda.memory_reserved(device)
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free_memory = total_memory - allocated_memory
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print(f"Memory log for cuda:{torch.cuda.current_device()}. Label::: {label}:" )
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print(f"Total GPU Memory: {total_memory / 1e9:.2f} GB")
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print(f"Allocated Memory: {allocated_memory / 1e9:.2f} GB")
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print(f"Cached Memory: {cached_memory / 1e9:.2f} GB")
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print(f"Free Memory: {free_memory / 1e9:.2f} GB")
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else:
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print("No CUDA device available")
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def token_gradients(model, input_ids, input_slice, target_slice, loss_slice):
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"""
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Computes gradients of the loss with respect to the coordinates.
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Parameters
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----------
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model : Transformer Model
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The transformer model to be used.
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input_ids : torch.Tensor
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The input sequence in the form of token ids.
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input_slice : slice
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The slice of the input sequence for which gradients need to be computed.
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target_slice : slice
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The slice of the input sequence to be used as targets.
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loss_slice : slice
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The slice of the logits to be used for computing the loss.
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Returns
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-------
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torch.Tensor
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The gradients of each token in the input_slice with respect to the loss.
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"""
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embed_weights = get_embedding_matrix(model)
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one_hot = torch.zeros(
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input_ids[input_slice].shape[0],
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embed_weights.shape[0],
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device=model.device,
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dtype=embed_weights.dtype
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)
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one_hot.scatter_(
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1,
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input_ids[input_slice].unsqueeze(1),
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torch.ones(one_hot.shape[0], 1, device=model.device, dtype=embed_weights.dtype)
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)
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one_hot.requires_grad_()
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input_embeds = (one_hot @ embed_weights).unsqueeze(0)
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# now stitch it together with the rest of the embeddings
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embeds = get_embeddings(model, input_ids.unsqueeze(0)).detach()
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full_embeds = torch.cat(
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[
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embeds[:,:input_slice.start,:],
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input_embeds,
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embeds[:,input_slice.stop:,:]
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],
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dim=1)
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logits = model(inputs_embeds=full_embeds).logits
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targets = input_ids[target_slice]
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loss = nn.CrossEntropyLoss()(logits[0,loss_slice,:], targets)
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loss.backward()
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return one_hot.grad.clone()
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class GCGAttackPrompt(AttackPrompt):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def grad(self, model):
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return token_gradients(
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model,
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self.input_ids.to(model.device),
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self._control_slice,
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self._target_slice,
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self._loss_slice
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)
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class GCGPromptManager(PromptManager):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def sample_control(self, grad, batch_size, topk=256, temp=1, allow_non_ascii=True):
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if not allow_non_ascii:
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grad[:, self._nonascii_toks.to(grad.device)] = np.infty
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top_indices = (-grad).topk(topk, dim=1).indices
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control_toks = self.control_toks.to(grad.device)
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original_control_toks = control_toks.repeat(batch_size, 1)
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new_token_pos = torch.arange(
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0,
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len(control_toks),
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len(control_toks) / batch_size,
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device=grad.device
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).type(torch.int64)
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new_token_val = torch.gather(
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top_indices[new_token_pos], 1,
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torch.randint(0, topk, (batch_size, 1),
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device=grad.device)
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)
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new_control_toks = original_control_toks.scatter_(1, new_token_pos.unsqueeze(-1), new_token_val)
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return new_control_toks
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class GCGMultiPromptAttack(MultiPromptAttack):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def step(self,
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batch_size=1024,
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topk=256,
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temp=1,
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allow_non_ascii=True,
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target_weight=1,
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control_weight=0.1,
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verbose=False,
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opt_only=False,
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filter_cand=True):
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# GCG currently does not support optimization_only mode,
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# so opt_only does not change the inner loop.
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opt_only = False
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main_device = self.models[0].device
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control_cands = []
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for j, worker in enumerate(self.workers):
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worker(self.prompts[j], "grad", worker.model)
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# Aggregate gradients
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grad = None
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for j, worker in enumerate(self.workers):
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new_grad = worker.results.get().to(main_device)
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new_grad = new_grad / new_grad.norm(dim=-1, keepdim=True)
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if grad is None:
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grad = torch.zeros_like(new_grad)
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if grad.shape != new_grad.shape:
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with torch.no_grad():
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control_cand = self.prompts[j-1].sample_control(grad, batch_size, topk, temp, allow_non_ascii)
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control_cands.append(self.get_filtered_cands(j-1, control_cand, filter_cand=filter_cand, curr_control=self.control_str))
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grad = new_grad
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else:
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grad += new_grad
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with torch.no_grad():
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control_cand = self.prompts[j].sample_control(grad, batch_size, topk, temp, allow_non_ascii)
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control_cands.append(self.get_filtered_cands(j, control_cand, filter_cand=filter_cand, curr_control=self.control_str))
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del grad, control_cand ; gc.collect()
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# Search
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loss = torch.zeros(len(control_cands) * batch_size).to(main_device)
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with torch.no_grad():
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for j, cand in enumerate(control_cands):
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# Looping through the prompts at this level is less elegant, but
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# we can manage VRAM better this way
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progress = tqdm(range(len(self.prompts[0])), total=len(self.prompts[0])) if verbose else enumerate(self.prompts[0])
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for i in progress:
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for k, worker in enumerate(self.workers):
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worker(self.prompts[k][i], "logits", worker.model, cand, return_ids=True)
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logits, ids = zip(*[worker.results.get() for worker in self.workers])
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loss[j*batch_size:(j+1)*batch_size] += sum([
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target_weight*self.prompts[k][i].target_loss(logit, id).mean(dim=-1).to(main_device)
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for k, (logit, id) in enumerate(zip(logits, ids))
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])
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if control_weight != 0:
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loss[j*batch_size:(j+1)*batch_size] += sum([
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control_weight*self.prompts[k][i].control_loss(logit, id).mean(dim=-1).to(main_device)
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for k, (logit, id) in enumerate(zip(logits, ids))
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])
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del logits, ids ; gc.collect()
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if verbose:
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progress.set_description(f"gcg step loss={loss[j*batch_size:(j+1)*batch_size].min().item()/(i+1):.4f}")
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min_idx = loss.argmin()
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model_idx = min_idx // batch_size
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batch_idx = min_idx % batch_size
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next_control, cand_loss = control_cands[model_idx][batch_idx], loss[min_idx]
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del control_cands, loss ; gc.collect()
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print('Current length:', len(self.workers[0].tokenizer(next_control).input_ids[1:]))
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print(next_control)
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return next_control, cand_loss.item() / len(self.prompts[0]) / len(self.workers)
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@ -0,0 +1,272 @@
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import gc
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import numpy as np
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import torch
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import torch.nn as nn
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from rpo import get_embedding_matrix, get_embeddings
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def token_gradients(model, input_ids, input_slice, target_slice, loss_slice):
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"""
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Computes gradients of the loss with respect to the coordinates.
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Parameters
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----------
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model : Transformer Model
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The transformer model to be used.
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input_ids : torch.Tensor
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The input sequence in the form of token ids.
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input_slice : slice
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The slice of the input sequence for which gradients need to be computed.
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target_slice : slice
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The slice of the input sequence to be used as targets.
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loss_slice : slice
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The slice of the logits to be used for computing the loss.
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Returns
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-------
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torch.Tensor
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The gradients of each token in the input_slice with respect to the loss.
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"""
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embed_weights = get_embedding_matrix(model)
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one_hot = torch.zeros(
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input_ids[input_slice].shape[0],
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embed_weights.shape[0],
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device=model.device,
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dtype=embed_weights.dtype
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)
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one_hot.scatter_(
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1,
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input_ids[input_slice].unsqueeze(1),
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torch.ones(one_hot.shape[0], 1, device=model.device, dtype=embed_weights.dtype)
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)
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one_hot.requires_grad_()
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input_embeds = (one_hot @ embed_weights).unsqueeze(0)
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# now stitch it together with the rest of the embeddings
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embeds = get_embeddings(model, input_ids.unsqueeze(0)).detach()
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full_embeds = torch.cat(
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[
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embeds[:,:input_slice.start,:],
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input_embeds,
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embeds[:,input_slice.stop:,:]
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],
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dim=1)
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logits = model(inputs_embeds=full_embeds).logits
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targets = input_ids[target_slice]
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loss = nn.CrossEntropyLoss()(logits[0,loss_slice,:], targets)
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loss.backward()
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grad = one_hot.grad.clone()
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grad = grad / grad.norm(dim=-1, keepdim=True)
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return grad
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def sample_control(control_toks, grad, batch_size, topk=256, temp=1, not_allowed_tokens=None):
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if not_allowed_tokens is not None:
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grad[:, not_allowed_tokens.to(grad.device)] = np.infty
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top_indices = (-grad).topk(topk, dim=1).indices
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control_toks = control_toks.to(grad.device)
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original_control_toks = control_toks.repeat(batch_size, 1)
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new_token_pos = torch.arange(
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0,
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len(control_toks),
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len(control_toks) / batch_size,
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device=grad.device
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).type(torch.int64)
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new_token_val = torch.gather(
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top_indices[new_token_pos], 1,
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torch.randint(0, topk, (batch_size, 1),
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device=grad.device)
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)
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new_control_toks = original_control_toks.scatter_(1, new_token_pos.unsqueeze(-1), new_token_val)
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return new_control_toks
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def get_filtered_cands(tokenizer, control_cand, filter_cand=True, curr_control=None):
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cands, count = [], 0
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for i in range(control_cand.shape[0]):
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decoded_str = tokenizer.decode(control_cand[i], skip_special_tokens=True)
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if filter_cand:
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if decoded_str != curr_control and len(tokenizer(decoded_str, add_special_tokens=False).input_ids) == len(control_cand[i]):
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cands.append(decoded_str)
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else:
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count += 1
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else:
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cands.append(decoded_str)
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if filter_cand:
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cands = cands + [cands[-1]] * (len(control_cand) - len(cands))
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return cands
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def combined_gradients(model, input_ids_adv, input_ids_safe, input_slice,
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target_slice_adv, target_slice_safe,
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loss_slice_adv, loss_slice_safe):
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model.zero_grad()
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embed_weights = get_embedding_matrix(model)
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# Generate one-hot encodings for the slices of interest in both adv and safe sequences
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one_hot_adv = get_one_hot(input_ids_adv[input_slice], embed_weights, model.device)
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one_hot_safe = get_one_hot(input_ids_safe[input_slice], embed_weights, model.device)
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# Get the embeddings from the one-hot encodings
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input_embeds_adv = (one_hot_adv @ embed_weights).unsqueeze(0)
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input_embeds_safe = (one_hot_safe @ embed_weights).unsqueeze(0)
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# Get the embeddings for the full sequences and detach them
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embeds_adv = get_embeddings(model, input_ids_adv.unsqueeze(0)).detach()
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embeds_safe = get_embeddings(model, input_ids_safe.unsqueeze(0)).detach()
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# Combine the embeddings with the slice of interest
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full_embeds_adv = torch.cat([embeds_adv[:,:input_slice.start,:],
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input_embeds_adv,
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embeds_adv[:,input_slice.stop:,:]], dim=1)
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full_embeds_safe = torch.cat([embeds_safe[:,:input_slice.start,:],
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input_embeds_safe,
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embeds_safe[:,input_slice.stop:,:]], dim=1)
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# Compute logits and loss for adversarial case
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logits_adv = model(inputs_embeds=full_embeds_adv).logits
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targets_adv = input_ids_adv[target_slice_adv]
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loss_adv = nn.CrossEntropyLoss()(logits_adv[0, loss_slice_adv, :], targets_adv)
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loss_adv.backward(retain_graph=True)
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grad_adv = one_hot_adv.grad.clone()
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grad_adv = grad_adv / grad_adv.norm(dim=-1, keepdim=True)
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model.zero_grad() # Reset gradients so they don't accumulate
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# Compute logits and loss for safe case
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logits_safe = model(inputs_embeds=full_embeds_safe).logits
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targets_safe = input_ids_safe[target_slice_safe]
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loss_safe = nn.CrossEntropyLoss()(logits_safe[0, loss_slice_safe, :], targets_safe)
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loss_safe.backward()
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grad_safe = one_hot_safe.grad.clone()
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grad_safe = grad_safe / grad_safe.norm(dim=-1, keepdim=True)
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return grad_adv, grad_safe
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def get_one_hot(slice_ids, embed_weights, device):
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one_hot = torch.zeros(slice_ids.shape[0], embed_weights.shape[0], device=device, dtype=embed_weights.dtype)
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one_hot.scatter_(1, slice_ids.unsqueeze(1), torch.ones(one_hot.shape[0], 1, device=device, dtype=embed_weights.dtype))
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one_hot.requires_grad_()
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return one_hot
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def get_logits(*, model, tokenizer, input_ids, control_slice, test_controls=None, return_ids=False, batch_size=512):
|
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|
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if isinstance(test_controls[0], str):
|
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max_len = control_slice.stop - control_slice.start
|
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test_ids = [
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torch.tensor(tokenizer(control, add_special_tokens=False).input_ids[:max_len], device=model.device)
|
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for control in test_controls
|
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]
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pad_tok = 0
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while pad_tok in input_ids or any([pad_tok in ids for ids in test_ids]):
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pad_tok += 1
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nested_ids = torch.nested.nested_tensor(test_ids)
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test_ids = torch.nested.to_padded_tensor(nested_ids, pad_tok, (len(test_ids), max_len))
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else:
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raise ValueError(f"test_controls must be a list of strings, got {type(test_controls)}")
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|
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if not(test_ids[0].shape[0] == control_slice.stop - control_slice.start):
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raise ValueError((
|
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f"test_controls must have shape "
|
||||
f"(n, {control_slice.stop - control_slice.start}), "
|
||||
f"got {test_ids.shape}"
|
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))
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locs = torch.arange(control_slice.start, control_slice.stop).repeat(test_ids.shape[0], 1).to(model.device)
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ids = torch.scatter(
|
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input_ids.unsqueeze(0).repeat(test_ids.shape[0], 1).to(model.device),
|
||||
1,
|
||||
locs,
|
||||
test_ids
|
||||
)
|
||||
if pad_tok >= 0:
|
||||
attn_mask = (ids != pad_tok).type(ids.dtype)
|
||||
else:
|
||||
attn_mask = None
|
||||
|
||||
if return_ids:
|
||||
del locs, test_ids ; gc.collect()
|
||||
return forward(model=model, input_ids=ids, attention_mask=attn_mask, batch_size=batch_size), ids
|
||||
else:
|
||||
del locs, test_ids
|
||||
logits = forward(model=model, input_ids=ids, attention_mask=attn_mask, batch_size=batch_size)
|
||||
del ids ; gc.collect()
|
||||
return logits
|
||||
|
||||
|
||||
def forward(*, model, input_ids, attention_mask, batch_size=512):
|
||||
|
||||
logits = []
|
||||
for i in range(0, input_ids.shape[0], batch_size):
|
||||
|
||||
batch_input_ids = input_ids[i:i+batch_size]
|
||||
if attention_mask is not None:
|
||||
batch_attention_mask = attention_mask[i:i+batch_size]
|
||||
else:
|
||||
batch_attention_mask = None
|
||||
|
||||
logits.append(model(input_ids=batch_input_ids, attention_mask=batch_attention_mask).logits)
|
||||
|
||||
gc.collect()
|
||||
|
||||
del batch_input_ids, batch_attention_mask
|
||||
|
||||
return torch.cat(logits, dim=0)
|
||||
|
||||
def target_loss(logits, ids, target_slice):
|
||||
crit = nn.CrossEntropyLoss(reduction='none')
|
||||
loss_slice = slice(target_slice.start-1, target_slice.stop-1)
|
||||
loss = crit(logits[:,loss_slice,:].transpose(1,2), ids[:,target_slice])
|
||||
return loss.mean(dim=-1)
|
||||
|
||||
|
||||
def load_model_and_tokenizer(model_path, tokenizer_path=None, device='cuda:0', **kwargs):
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_path,
|
||||
torch_dtype=torch.float16,
|
||||
trust_remote_code=True,
|
||||
device_map="auto",
|
||||
**kwargs
|
||||
).eval()
|
||||
|
||||
tokenizer_path = model_path if tokenizer_path is None else tokenizer_path
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
tokenizer_path,
|
||||
trust_remote_code=True,
|
||||
use_fast=False
|
||||
)
|
||||
|
||||
if 'oasst-sft-6-llama-30b' in tokenizer_path:
|
||||
tokenizer.bos_token_id = 1
|
||||
tokenizer.unk_token_id = 0
|
||||
if 'guanaco' in tokenizer_path:
|
||||
tokenizer.eos_token_id = 2
|
||||
tokenizer.unk_token_id = 0
|
||||
if 'llama-2' in tokenizer_path:
|
||||
tokenizer.pad_token = tokenizer.unk_token
|
||||
tokenizer.padding_side = 'left'
|
||||
if 'falcon' in tokenizer_path:
|
||||
tokenizer.padding_side = 'left'
|
||||
if not tokenizer.pad_token:
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
return model, tokenizer
|
||||
@ -0,0 +1,152 @@
|
||||
import torch
|
||||
import fastchat
|
||||
|
||||
def load_conversation_template(template_name):
|
||||
conv_template = fastchat.model.get_conversation_template(template_name)
|
||||
if conv_template.name == 'zero_shot':
|
||||
conv_template.roles = tuple(['### ' + r for r in conv_template.roles])
|
||||
conv_template.sep = '\n'
|
||||
elif conv_template.name == 'llama-2':
|
||||
conv_template.sep2 = conv_template.sep2.strip()
|
||||
|
||||
return conv_template
|
||||
|
||||
def get_nonascii_toks(tokenizer, device='cpu'):
|
||||
|
||||
def is_ascii(s):
|
||||
return s.isascii() and s.isprintable()
|
||||
|
||||
ascii_toks = []
|
||||
for i in range(3, tokenizer.vocab_size):
|
||||
if not is_ascii(tokenizer.decode([i])):
|
||||
ascii_toks.append(i)
|
||||
|
||||
if tokenizer.bos_token_id is not None:
|
||||
ascii_toks.append(tokenizer.bos_token_id)
|
||||
if tokenizer.eos_token_id is not None:
|
||||
ascii_toks.append(tokenizer.eos_token_id)
|
||||
if tokenizer.pad_token_id is not None:
|
||||
ascii_toks.append(tokenizer.pad_token_id)
|
||||
if tokenizer.unk_token_id is not None:
|
||||
ascii_toks.append(tokenizer.unk_token_id)
|
||||
|
||||
return torch.tensor(ascii_toks, device=device)
|
||||
|
||||
class SuffixManager:
|
||||
def __init__(self, *, tokenizer, conv_template, instruction, system_message, target, rpo_string):
|
||||
|
||||
self.tokenizer = tokenizer
|
||||
self.conv_template = conv_template
|
||||
self.instruction = instruction
|
||||
self.system = system_message
|
||||
self.target = target
|
||||
self.rpo_string = rpo_string
|
||||
|
||||
def get_prompt(self, rpo_string=None):
|
||||
|
||||
if rpo_string is not None:
|
||||
self.rpo_string = rpo_string
|
||||
self.conv_template.system = self.system
|
||||
self.conv_template.append_message(self.conv_template.roles[0], f"{self.instruction} {self.rpo_string}")
|
||||
self.conv_template.append_message(self.conv_template.roles[1], f"{self.target}")
|
||||
prompt = self.conv_template.get_prompt()
|
||||
|
||||
encoding = self.tokenizer(prompt)
|
||||
toks = encoding.input_ids
|
||||
|
||||
if self.conv_template.name == 'llama-2':
|
||||
self.conv_template.messages = []
|
||||
|
||||
self.conv_template.append_message(self.conv_template.roles[0], None)
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._user_role_slice = slice(None, len(toks))
|
||||
|
||||
self.conv_template.update_last_message(f"{self.instruction}")
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._goal_slice = slice(self._user_role_slice.stop, max(self._user_role_slice.stop, len(toks)))
|
||||
|
||||
separator = ' ' if self.instruction else ''
|
||||
self.conv_template.update_last_message(f"{self.instruction}{separator}{self.rpo_string}")
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._control_slice = slice(self._goal_slice.stop, len(toks))
|
||||
|
||||
self.conv_template.append_message(self.conv_template.roles[1], None)
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._assistant_role_slice = slice(self._control_slice.stop, len(toks))
|
||||
|
||||
self.conv_template.update_last_message(f"{self.target}")
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._target_slice = slice(self._assistant_role_slice.stop, len(toks)-2)
|
||||
self._loss_slice = slice(self._assistant_role_slice.stop-1, len(toks)-3)
|
||||
|
||||
else:
|
||||
python_tokenizer = False or self.conv_template.name == 'oasst_pythia'
|
||||
try:
|
||||
encoding.char_to_token(len(prompt)-1)
|
||||
except:
|
||||
python_tokenizer = True
|
||||
|
||||
if python_tokenizer:
|
||||
self.conv_template.messages = []
|
||||
|
||||
self.conv_template.append_message(self.conv_template.roles[0], None)
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._user_role_slice = slice(None, len(toks))
|
||||
|
||||
self.conv_template.update_last_message(f"{self.instruction}")
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._goal_slice = slice(self._user_role_slice.stop, max(self._user_role_slice.stop, len(toks)-1))
|
||||
|
||||
separator = ' ' if self.instruction else ''
|
||||
self.conv_template.update_last_message(f"{self.instruction}{separator}{self.rpo_string}")
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._control_slice = slice(self._goal_slice.stop, len(toks)-1)
|
||||
|
||||
self.conv_template.append_message(self.conv_template.roles[1], None)
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._assistant_role_slice = slice(self._control_slice.stop, len(toks))
|
||||
|
||||
self.conv_template.update_last_message(f"{self.target}")
|
||||
toks = self.tokenizer(self.conv_template.get_prompt()).input_ids
|
||||
self._target_slice = slice(self._assistant_role_slice.stop, len(toks)-1)
|
||||
self._loss_slice = slice(self._assistant_role_slice.stop-1, len(toks)-2)
|
||||
else:
|
||||
self._system_slice = slice(
|
||||
None,
|
||||
encoding.char_to_token(len(self.conv_template.system))
|
||||
)
|
||||
self._user_role_slice = slice(
|
||||
encoding.char_to_token(prompt.find(self.conv_template.roles[0])),
|
||||
encoding.char_to_token(prompt.find(self.conv_template.roles[0]) + len(self.conv_template.roles[0]) + 1)
|
||||
)
|
||||
self._goal_slice = slice(
|
||||
encoding.char_to_token(prompt.find(self.instruction)),
|
||||
encoding.char_to_token(prompt.find(self.instruction) + len(self.instruction))
|
||||
)
|
||||
self._control_slice = slice(
|
||||
encoding.char_to_token(prompt.find(self.rpo_string)),
|
||||
encoding.char_to_token(prompt.find(self.rpo_string) + len(self.rpo_string))
|
||||
)
|
||||
self._assistant_role_slice = slice(
|
||||
encoding.char_to_token(prompt.find(self.conv_template.roles[1])),
|
||||
encoding.char_to_token(prompt.find(self.conv_template.roles[1]) + len(self.conv_template.roles[1]) + 1)
|
||||
)
|
||||
self._target_slice = slice(
|
||||
encoding.char_to_token(prompt.find(self.target)),
|
||||
encoding.char_to_token(prompt.find(self.target) + len(self.target))
|
||||
)
|
||||
self._loss_slice = slice(
|
||||
encoding.char_to_token(prompt.find(self.target)) - 1,
|
||||
encoding.char_to_token(prompt.find(self.target) + len(self.target)) - 1
|
||||
)
|
||||
|
||||
self.conv_template.messages = []
|
||||
|
||||
return prompt
|
||||
|
||||
def get_input_ids(self, rpo_string=None):
|
||||
prompt = self.get_prompt(rpo_string=rpo_string)
|
||||
toks = self.tokenizer(prompt).input_ids
|
||||
input_ids = torch.tensor(toks[:self._target_slice.stop])
|
||||
|
||||
return input_ids
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user