Add files using upload-large-folder tool

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import torch
import random
import numpy as np
from torch import nn
import torch.nn.functional as F


def seed_everything(seed=42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


def compute_kl_divergence(model, target_model, inputs):
    with torch.no_grad():
        ref_outputs = target_model(**inputs)

    ref_probs = F.log_softmax(ref_outputs.logits, dim=-1)
    ref_probs = ref_probs.view(-1, ref_outputs.logits.shape[-1])

    outputs = model(**inputs)
    current_probs = F.log_softmax(outputs.logits, dim=-1)
    current_probs = current_probs.view(-1, outputs.logits.shape[-1])

    # minimum KL divergence
    return nn.functional.kl_div(
        current_probs, ref_probs, reduction="batchmean", log_target=True
    ), outputs


def compute_batch_nll(model, inputs):
    # get the sum loss for each sequence in a batch
    # NOTE: not same as model(**inputs).loss but has sum loss for each seq in a batch
    outputs = model(**inputs)
    logits = outputs.logits
    labels = inputs["labels"]
    shifted_labels = labels[..., 1:].contiguous()
    logits = logits[..., :-1, :].contiguous()
    loss_function = nn.CrossEntropyLoss(ignore_index=-100, reduction="none")
    loss = loss_function(logits.transpose(-1, -2), shifted_labels).sum(dim=-1)
    return loss, outputs


def compute_dpo_loss(model, ref_model, win_inputs=None, lose_inputs=None, beta=1.0):
    if win_inputs is None and lose_inputs is None:
        raise ValueError("Both win_inputs and lose_inputs can't be None")

    win_log_ratio, lose_log_ratio = 0.0, 0.0
    win_outputs, lose_outputs = None, None

    if win_inputs is not None:
        win_loss, win_outputs = compute_batch_nll(model, win_inputs)
        with torch.no_grad():
            win_ref_loss, _ = compute_batch_nll(ref_model, win_inputs)
        win_log_ratio = -(win_loss - win_ref_loss)

    if lose_inputs is not None:
        lose_loss, lose_outputs = compute_batch_nll(model, lose_inputs)
        with torch.no_grad():
            lose_ref_loss, _ = compute_batch_nll(ref_model, lose_inputs)
        lose_log_ratio = -(lose_loss - lose_ref_loss)

    loss = -2 / beta * F.logsigmoid(beta * (win_log_ratio - lose_log_ratio)).mean()
    return loss, (win_outputs, lose_outputs)


def compute_undial_loss(model, ref_model, inputs, beta):
    # Forward pass on the student (trainable) model
    outputs = model(**inputs)
    logits = outputs.logits
    labels = inputs["labels"]

    shift_labels = labels[..., 1:].contiguous()
    shift_logits = logits[..., :-1, :].contiguous()

    # Forward pass on the teacher model (no grad)
    with torch.no_grad():
        teacher_logits = ref_model(**inputs).logits
    shift_teacher_logits = teacher_logits[..., :-1, :].contiguous()

    # Build the mask that identifies the tokens need to be unlearned
    mask = torch.zeros_like(shift_teacher_logits)
    batch_idx = torch.arange(mask.shape[0]).view(-1, 1, 1)
    seq_idx = torch.arange(mask.shape[1]).view(1, -1, 1)
    mask[batch_idx, seq_idx, shift_labels.unsqueeze(-1)] = 1.0

    # Adjust teacher logits: subtract di_strength on the correct token
    pre_softmax = shift_teacher_logits - mask * beta
    soft_label = F.softmax(pre_softmax, dim=-1)

    loss_fct = nn.CrossEntropyLoss(reduction="none")
    loss = loss_fct(
        shift_logits.view(-1, shift_logits.size(-1)),
        soft_label.view(-1, soft_label.size(-1)),
    )
    return loss.mean(), outputs


def compute_wga_loss(model, inputs, beta):
    outputs = model(**inputs)
    labels = inputs["labels"]
    labels = labels.to(outputs.logits.device)

    shift_logits = outputs.logits[..., :-1, :].contiguous()
    shift_labels = labels[..., 1:].contiguous()

    lm_loss = nn.CrossEntropyLoss(ignore_index=-100, reduction="none")(
        shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
    )
    weight_ce = ((-lm_loss).exp().detach()) ** beta
    forget_loss = -(weight_ce * lm_loss)[shift_labels.view(-1) != -100].mean()
    return forget_loss, outputs


def compute_satimp_loss(model, inputs, beta1, beta2):
    outputs = model(**inputs)
    labels = inputs["labels"]
    labels = labels.to(outputs.logits.device)

    shift_logits = outputs.logits[..., :-1, :].contiguous()
    shift_labels = labels[..., 1:].contiguous()

    lm_loss = nn.CrossEntropyLoss(ignore_index=-100, reduction="none")(
        shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
    )
    weight_sat = ((-lm_loss).exp().detach()) ** beta1
    weight_imp = (1 - (-lm_loss).exp().detach()) ** beta2
    forget_loss = -((weight_sat * weight_imp) * lm_loss)[
        shift_labels.view(-1) != -100
    ].mean()
    return forget_loss, outputs

	import torch
	import random
	import numpy as np
	from torch import nn
	import torch.nn.functional as F


	def seed_everything(seed=42):
	random.seed(seed)
	np.random.seed(seed)
	torch.manual_seed(seed)
	torch.cuda.manual_seed_all(seed)
	torch.backends.cudnn.deterministic = True
	torch.backends.cudnn.benchmark = False


	def compute_kl_divergence(model, target_model, inputs):
	with torch.no_grad():
	ref_outputs = target_model(**inputs)

	ref_probs = F.log_softmax(ref_outputs.logits, dim=-1)
	ref_probs = ref_probs.view(-1, ref_outputs.logits.shape[-1])

	outputs = model(**inputs)
	current_probs = F.log_softmax(outputs.logits, dim=-1)
	current_probs = current_probs.view(-1, outputs.logits.shape[-1])

	# minimum KL divergence
	return nn.functional.kl_div(
	current_probs, ref_probs, reduction="batchmean", log_target=True
	), outputs


	def compute_batch_nll(model, inputs):
	# get the sum loss for each sequence in a batch
	# NOTE: not same as model(**inputs).loss but has sum loss for each seq in a batch
	outputs = model(**inputs)
	logits = outputs.logits
	labels = inputs["labels"]
	shifted_labels = labels[..., 1:].contiguous()
	logits = logits[..., :-1, :].contiguous()
	loss_function = nn.CrossEntropyLoss(ignore_index=-100, reduction="none")
	loss = loss_function(logits.transpose(-1, -2), shifted_labels).sum(dim=-1)
	return loss, outputs


	def compute_dpo_loss(model, ref_model, win_inputs=None, lose_inputs=None, beta=1.0):
	if win_inputs is None and lose_inputs is None:
	raise ValueError("Both win_inputs and lose_inputs can't be None")

	win_log_ratio, lose_log_ratio = 0.0, 0.0
	win_outputs, lose_outputs = None, None

	if win_inputs is not None:
	win_loss, win_outputs = compute_batch_nll(model, win_inputs)
	with torch.no_grad():
	win_ref_loss, _ = compute_batch_nll(ref_model, win_inputs)
	win_log_ratio = -(win_loss - win_ref_loss)

	if lose_inputs is not None:
	lose_loss, lose_outputs = compute_batch_nll(model, lose_inputs)
	with torch.no_grad():
	lose_ref_loss, _ = compute_batch_nll(ref_model, lose_inputs)
	lose_log_ratio = -(lose_loss - lose_ref_loss)

	loss = -2 / beta * F.logsigmoid(beta * (win_log_ratio - lose_log_ratio)).mean()
	return loss, (win_outputs, lose_outputs)


	def compute_undial_loss(model, ref_model, inputs, beta):
	# Forward pass on the student (trainable) model
	outputs = model(**inputs)
	logits = outputs.logits
	labels = inputs["labels"]

	shift_labels = labels[..., 1:].contiguous()
	shift_logits = logits[..., :-1, :].contiguous()

	# Forward pass on the teacher model (no grad)
	with torch.no_grad():
	teacher_logits = ref_model(**inputs).logits
	shift_teacher_logits = teacher_logits[..., :-1, :].contiguous()

	# Build the mask that identifies the tokens need to be unlearned
	mask = torch.zeros_like(shift_teacher_logits)
	batch_idx = torch.arange(mask.shape[0]).view(-1, 1, 1)
	seq_idx = torch.arange(mask.shape[1]).view(1, -1, 1)
	mask[batch_idx, seq_idx, shift_labels.unsqueeze(-1)] = 1.0

	# Adjust teacher logits: subtract di_strength on the correct token
	pre_softmax = shift_teacher_logits - mask * beta
	soft_label = F.softmax(pre_softmax, dim=-1)

	loss_fct = nn.CrossEntropyLoss(reduction="none")
	loss = loss_fct(
	shift_logits.view(-1, shift_logits.size(-1)),
	soft_label.view(-1, soft_label.size(-1)),
	)
	return loss.mean(), outputs


	def compute_wga_loss(model, inputs, beta):
	outputs = model(**inputs)
	labels = inputs["labels"]
	labels = labels.to(outputs.logits.device)

	shift_logits = outputs.logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()

	lm_loss = nn.CrossEntropyLoss(ignore_index=-100, reduction="none")(
	shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
	)
	weight_ce = ((-lm_loss).exp().detach()) ** beta
	forget_loss = -(weight_ce * lm_loss)[shift_labels.view(-1) != -100].mean()
	return forget_loss, outputs


	def compute_satimp_loss(model, inputs, beta1, beta2):
	outputs = model(**inputs)
	labels = inputs["labels"]
	labels = labels.to(outputs.logits.device)

	shift_logits = outputs.logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()

	lm_loss = nn.CrossEntropyLoss(ignore_index=-100, reduction="none")(
	shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
	)
	weight_sat = ((-lm_loss).exp().detach()) ** beta1
	weight_imp = (1 - (-lm_loss).exp().detach()) ** beta2
	forget_loss = -((weight_sat * weight_imp) * lm_loss)[
	shift_labels.view(-1) != -100
	].mean()
	return forget_loss, outputs