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# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/utils.py
# reference: https://github.com/lifeiteng/vall-e
import torch
import torch.nn.functional as F
from typing import Tuple
def sequence_mask(length, max_length=None):
if max_length is None:
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return x.unsqueeze(0) < length.unsqueeze(1)
def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
"""
Args:
lengths:
A 1-D tensor containing sentence lengths.
max_len:
The length of masks.
Returns:
Return a 2-D bool tensor, where masked positions
are filled with `True` and non-masked positions are
filled with `False`.
#>>> lengths = torch.tensor([1, 3, 2, 5])
#>>> make_pad_mask(lengths)
tensor([[False, True, True, True, True],
[False, False, False, True, True],
[False, False, True, True, True],
[False, False, False, False, False]])
"""
assert lengths.ndim == 1, lengths.ndim
max_len = max(max_len, lengths.max())
n = lengths.size(0)
seq_range = torch.arange(0, max_len, device=lengths.device)
expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
return expaned_lengths >= lengths.unsqueeze(-1)
# https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
def top_k_top_p_filtering(
logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
):
"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
Make sure we keep at least min_tokens_to_keep per batch example in the output
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(
1, sorted_indices, sorted_indices_to_remove
)
logits[indices_to_remove] = filter_value
return logits
def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
# temperature: (`optional`) float
# The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
# top_k: (`optional`) int
# The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
# top_p: (`optional`) float
# The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
logits = logits / temperature
# Top-p/top-k filtering
logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
# Sample
token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
return token
from typing import Optional, Tuple
def multinomial_sample_one_no_sync(
probs_sort,
): # Does multinomial sampling without a cuda synchronization
q = torch.empty_like(probs_sort).exponential_(1)
return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
def logits_to_probs(
logits,
previous_tokens: Optional[torch.Tensor] = None,
temperature: float = 1.0,
top_k: Optional[int] = None,
top_p: Optional[int] = None,
repetition_penalty: float = 1.0,
):
if previous_tokens is not None:
previous_tokens = previous_tokens.squeeze()
# print(logits.shape,previous_tokens.shape)
# pdb.set_trace()
if previous_tokens is not None and repetition_penalty != 1.0:
previous_tokens = previous_tokens.long()
score = torch.gather(logits, dim=0, index=previous_tokens)
score = torch.where(
score < 0, score * repetition_penalty, score / repetition_penalty
)
logits.scatter_(dim=0, index=previous_tokens, src=score)
if top_p is not None and top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cum_probs = torch.cumsum(
torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
)
sorted_indices_to_remove = cum_probs > top_p
sorted_indices_to_remove[0] = False # keep at least one option
indices_to_remove = sorted_indices_to_remove.scatter(
dim=0, index=sorted_indices, src=sorted_indices_to_remove
)
logits = logits.masked_fill(indices_to_remove, -float("Inf"))
logits = logits / max(temperature, 1e-5)
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
pivot = v.select(-1, -1).unsqueeze(-1)
logits = torch.where(logits < pivot, -float("Inf"), logits)
probs = torch.nn.functional.softmax(logits, dim=-1)
return probs
def sample(
logits,
previous_tokens: Optional[torch.Tensor] = None,
**sampling_kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
probs = logits_to_probs(
logits=logits, previous_tokens=previous_tokens, **sampling_kwargs
)
idx_next = multinomial_sample_one_no_sync(probs)
return idx_next, probs
def dpo_loss(policy_chosen_logps: torch.FloatTensor,
policy_rejected_logps: torch.FloatTensor,
reference_chosen_logps: torch.FloatTensor,
reference_rejected_logps: torch.FloatTensor,
beta: float,
reference_free: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
pi_logratios = policy_chosen_logps - policy_rejected_logps
ref_logratios = reference_chosen_logps - reference_rejected_logps
if reference_free:
ref_logratios = 0
logits = pi_logratios - ref_logratios
losses = -F.logsigmoid(beta * logits)
chosen_rewards = beta * (policy_chosen_logps - reference_chosen_logps).detach()
rejected_rewards = beta * (policy_rejected_logps - reference_rejected_logps).detach()
return losses.mean(), chosen_rewards, rejected_rewards
def get_batch_logps(logits_target: torch.FloatTensor, logits_reject: torch.FloatTensor, labels_target: torch.LongTensor, labels_reject: torch.LongTensor, average_log_prob: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
# dummy token; we'll ignore the losses on these tokens later
per_token_logps_target = torch.gather(logits_target.log_softmax(-1), dim=2, index=labels_target.unsqueeze(2)).squeeze(2)
per_token_logps_reject = torch.gather(logits_reject.log_softmax(-1), dim=2, index=labels_reject.unsqueeze(2)).squeeze(2)
return per_token_logps_target.sum(-1), per_token_logps_reject.sum(-1)
def make_reject_y(y_o, y_lens):
def repeat_P(y):
range_idx, _ = torch.randint(0, len(y), size=(2,)).sort()
pre = y[:range_idx[0]]
shf = y[range_idx[1]:]
range_text = y[range_idx[0]:range_idx[1]]
new_y = torch.cat([pre, range_text, range_text, shf])
return new_y
def lost_P(y):
range_idx, _ = torch.randint(0, len(y), size=(2,)).sort()
pre = y[:range_idx[0]]
shf = y[range_idx[1]:]
range_text = y[range_idx[0]:range_idx[1]]
new_y = torch.cat([pre, shf])
return new_y
bs = len(y_lens)
reject_y = []
reject_y_lens = []
for b in range(bs):
process_item_idx = torch.randint(0, 1, size=(1, ))[0]
if process_item_idx == 0:
new_y = repeat_P(y_o[b])
reject_y.append(new_y)
reject_y_lens.append(len(new_y))
elif process_item_idx==1:
new_y = lost_P(y_o[b])
reject_y.append(new_y)
reject_y_lens.append(len(new_y))
max_length = max(reject_y_lens)
for b in range(bs):
pad_length = max_length - reject_y_lens[b]
reject_y[b] = torch.cat([reject_y[b], torch.zeros(pad_length, dtype=y_o.dtype, device=y_o.device)], dim=0)
reject_y = torch.stack(reject_y, dim = 0)
reject_y_lens = torch.tensor(reject_y_lens, device=y_lens.device)
return reject_y, reject_y_lens
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