mirror of
https://github.com/jiaxiaojunQAQ/OmniSafeBench-MM.git
synced 2026-06-06 23:53:54 +02:00
leo0481
333 lines
10 KiB
Python
333 lines
10 KiB
Python
import torch
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from tqdm import tqdm
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import random
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from ..minigpt_utils import prompt_wrapper, generator
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from torchvision.utils import save_image
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import matplotlib.pyplot as plt
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import seaborn as sns
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def normalize(images):
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mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).cuda()
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std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).cuda()
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images = images - mean[None, :, None, None]
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images = images / std[None, :, None, None]
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return images
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def denormalize(images):
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mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).cuda()
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std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).cuda()
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images = images * std[None, :, None, None]
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images = images + mean[None, :, None, None]
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return images
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class Attacker:
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def __init__(self, args, model, targets, device="cuda:0", is_rtp=False):
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self.args = args
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self.model = model
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self.device = device
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self.is_rtp = is_rtp
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self.targets = targets
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self.num_targets = len(targets)
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self.loss_buffer = []
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# freeze and set to eval model:
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self.model.eval()
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self.model.requires_grad_(False)
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def attack_unconstrained(
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self, text_prompt, img, batch_size=8, num_iter=2000, alpha=1 / 255
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):
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print(">>> batch_size:", batch_size)
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my_generator = generator.Generator(model=self.model)
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adv_noise = torch.rand_like(img).to(self.device) # [0,1]
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adv_noise.requires_grad_(
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True
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) # its gradient will be tracked during backpropagation
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adv_noise.retain_grad() # gradient will not be deleted after backpropagation
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for t in tqdm(range(num_iter + 1)):
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batch_targets = random.sample(self.targets, batch_size)
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text_prompts = [text_prompt] * batch_size
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x_adv = normalize(adv_noise)
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# Lines 64~68: Calculate loss for x_adv
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prompt = prompt_wrapper.Prompt(
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model=self.model, text_prompts=text_prompts, img_prompts=[[x_adv]]
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)
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prompt.img_embs = prompt.img_embs * batch_size
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prompt.update_context_embs()
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target_loss = self.attack_loss(prompt, batch_targets)
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target_loss.backward()
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adv_noise.data = (
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adv_noise.data - alpha * adv_noise.grad.detach().sign()
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).clamp(0, 1)
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adv_noise.grad.zero_()
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self.model.zero_grad()
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self.loss_buffer.append(target_loss.item())
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print("target_loss: %f" % (target_loss.item()))
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if t % 20 == 0:
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self.plot_loss()
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if t % 100 == 0:
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print("######### Output - Iter = %d ##########" % t)
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x_adv = normalize(adv_noise)
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prompt.update_img_prompts([[x_adv]])
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prompt.img_embs = prompt.img_embs * batch_size
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prompt.update_context_embs()
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with torch.no_grad():
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response, _ = my_generator.generate(prompt)
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print(">>>", response)
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adv_img_prompt = denormalize(x_adv).detach().cpu()
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adv_img_prompt = adv_img_prompt.squeeze(0)
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save_image(
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adv_img_prompt,
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"%s/bad_prompt_temp_%d.bmp" % (self.args.save_dir, t),
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)
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return adv_img_prompt
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def attack_constrained(
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self,
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text_prompt,
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img,
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batch_size=8,
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num_iter=2000,
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alpha=1 / 255,
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epsilon=128 / 255,
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):
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print(">>> batch_size:", batch_size)
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my_generator = generator.Generator(model=self.model)
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adv_noise = torch.rand_like(img).to(self.device) * 2 * epsilon - epsilon
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x = denormalize(img).clone().to(self.device)
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adv_noise.data = (adv_noise.data + x.data).clamp(0, 1) - x.data
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adv_noise.requires_grad_(True)
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adv_noise.retain_grad()
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for t in tqdm(range(num_iter + 1)):
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batch_targets = random.sample(self.targets, batch_size)
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text_prompts = [text_prompt] * batch_size
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x_adv = x + adv_noise
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x_adv = normalize(x_adv)
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prompt = prompt_wrapper.Prompt(
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model=self.model, text_prompts=text_prompts, img_prompts=[[x_adv]]
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)
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prompt.img_embs = prompt.img_embs * batch_size
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prompt.update_context_embs()
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target_loss = self.attack_loss(prompt, batch_targets)
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target_loss.backward()
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adv_noise.data = (
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adv_noise.data - alpha * adv_noise.grad.detach().sign()
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).clamp(-epsilon, epsilon)
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adv_noise.data = (adv_noise.data + x.data).clamp(0, 1) - x.data
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adv_noise.grad.zero_()
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self.model.zero_grad()
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self.loss_buffer.append(target_loss.item())
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print("target_loss: %f" % (target_loss.item()))
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if t % 20 == 0:
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self.plot_loss()
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if t % 100 == 0:
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print("######### Output - Iter = %d ##########" % t)
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x_adv = x + adv_noise
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x_adv = normalize(x_adv)
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prompt.update_img_prompts([[x_adv]])
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prompt.img_embs = prompt.img_embs * batch_size
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prompt.update_context_embs()
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with torch.no_grad():
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response, _ = my_generator.generate(prompt)
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print(">>>", response)
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adv_img_prompt = denormalize(x_adv).detach().cpu()
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adv_img_prompt = adv_img_prompt.squeeze(0)
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save_image(
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adv_img_prompt,
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"%s/bad_prompt_temp_%d.bmp" % (self.args.save_dir, t),
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)
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return adv_img_prompt
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def plot_loss(self):
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sns.set_theme()
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num_iters = len(self.loss_buffer)
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x_ticks = list(range(0, num_iters))
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# Plot and label the training and validation loss values
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plt.plot(x_ticks, self.loss_buffer, label="Target Loss")
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# Add in a title and axes labels
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plt.title("Loss Plot")
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plt.xlabel("Iters")
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plt.ylabel("Loss")
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# Display the plot
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plt.legend(loc="best")
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plt.savefig("%s/loss_curve.png" % (self.args.save_dir))
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plt.clf()
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torch.save(self.loss_buffer, "%s/loss" % (self.args.save_dir))
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def attack_loss(self, prompts, targets):
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context_embs = prompts.context_embs
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if len(context_embs) == 1:
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context_embs = context_embs * len(targets) # expand to fit the batch_size
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assert len(context_embs) == len(
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targets
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), f"Unmathced batch size of prompts and targets {len(context_embs)} != {len(targets)}"
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batch_size = len(targets)
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self.model.llama_tokenizer.padding_side = "right"
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to_regress_tokens = self.model.llama_tokenizer(
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targets,
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return_tensors="pt",
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padding="longest",
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truncation=True,
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max_length=self.model.max_txt_len,
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add_special_tokens=False,
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).to(self.device)
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to_regress_embs = self.model.llama_model.model.embed_tokens(
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to_regress_tokens.input_ids
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)
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bos = (
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torch.ones(
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[1, 1],
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dtype=to_regress_tokens.input_ids.dtype,
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device=to_regress_tokens.input_ids.device,
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)
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* self.model.llama_tokenizer.bos_token_id
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)
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bos_embs = self.model.llama_model.model.embed_tokens(bos)
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pad = (
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torch.ones(
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[1, 1],
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dtype=to_regress_tokens.input_ids.dtype,
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device=to_regress_tokens.input_ids.device,
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)
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* self.model.llama_tokenizer.pad_token_id
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)
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pad_embs = self.model.llama_model.model.embed_tokens(pad)
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T = to_regress_tokens.input_ids.masked_fill(
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to_regress_tokens.input_ids == self.model.llama_tokenizer.pad_token_id, -100
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)
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pos_padding = torch.argmin(
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T, dim=1
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) # a simple trick to find the start position of padding
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input_embs = []
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targets_mask = []
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target_tokens_length = []
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context_tokens_length = []
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seq_tokens_length = []
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for i in range(batch_size):
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pos = int(pos_padding[i])
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if T[i][pos] == -100:
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target_length = pos
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else:
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target_length = T.shape[1]
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targets_mask.append(T[i : i + 1, :target_length])
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input_embs.append(
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to_regress_embs[i : i + 1, :target_length]
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) # omit the padding tokens
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context_length = context_embs[i].shape[1]
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seq_length = target_length + context_length
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target_tokens_length.append(target_length)
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context_tokens_length.append(context_length)
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seq_tokens_length.append(seq_length)
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max_length = max(seq_tokens_length)
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attention_mask = []
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for i in range(batch_size):
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# masked out the context from loss computation
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context_mask = (
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torch.ones([1, context_tokens_length[i] + 1], dtype=torch.long)
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.to(self.device)
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.fill_(-100) # plus one for bos
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)
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# padding to align the length
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num_to_pad = max_length - seq_tokens_length[i]
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padding_mask = (
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torch.ones([1, num_to_pad], dtype=torch.long)
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.to(self.device)
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.fill_(-100)
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)
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targets_mask[i] = torch.cat(
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[context_mask, targets_mask[i], padding_mask], dim=1
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)
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input_embs[i] = torch.cat(
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[
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bos_embs,
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context_embs[i],
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input_embs[i],
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pad_embs.repeat(1, num_to_pad, 1),
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],
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dim=1,
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)
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attention_mask.append(
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torch.LongTensor([[1] * (1 + seq_tokens_length[i]) + [0] * num_to_pad])
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)
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targets = torch.cat(targets_mask, dim=0).to(self.device)
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inputs_embs = torch.cat(input_embs, dim=0).to(self.device)
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attention_mask = torch.cat(attention_mask, dim=0).to(self.device)
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outputs = self.model.llama_model(
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inputs_embeds=inputs_embs,
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attention_mask=attention_mask,
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return_dict=True,
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labels=targets,
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)
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loss = outputs.loss
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return loss
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