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from typing import Tuple, Optional
import torch
from transformers.generation.logits_process import TopKLogitsWarper, TopPLogitsWarper
def compute_rsa_probas(
logits: torch.Tensor, prior: torch.Tensor, rationality: float = 1.0
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
:param logits: (world_size, num_beam, vocab_size)
:param prior: (world_size, num_beam) for each beam the prior over the objects
:param rationality: rationality parameter, the higher the more rational ie the more the speaker will try to adapt
to the listener
:return: S1, L1: (world_size, num_beam, vocab_size).
S1[o, b, w] is the (log)probability of the word w given the object o and the current partial summary for the beam b
L1[o, b, w] is the (log)probability of the object o given the word w and the current partial summary for the beam b
"""
prod = logits + prior[..., None]
L0 = torch.nan_to_num(torch.log_softmax(prod, dim=0), nan=-float("inf"))
prod_s = logits + L0 * rationality
S1 = torch.log_softmax(prod_s, dim=-1)
S1 = torch.nan_to_num(S1, nan=-float("inf"))
prod_l = logits + L0
L1 = torch.log_softmax(prod_l, dim=0)
L1 = torch.nan_to_num(L1, nan=-float("inf"))
return S1, L1
def sample_from_probs(
logits: torch.Tensor, num_beams: torch.Tensor, do_sample: bool, K: int = 10
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
:param logits: (num_beams, vocab_size) log proba for the next token only for the wanted object
:param num_beams: number of beam to sample. (Can be different from the shape of logits since some beams might have
finished earlier)
:param do_sample: sample or use argmax
:param K: number of samples to draw per beam to create the new population
:return: idx_beam, idx_token, tokens_scores, the indices of the sampled tokens and their scores
"""
vocab_size = logits.shape[-1]
if do_sample:
# sample from the probability distribution
logits = logits.view(num_beams * logits.shape[-1])
probs = torch.softmax(logits, dim=-1)
samples = torch.multinomial(probs, num_samples=K * num_beams)
# get the indices of the sampled tokens
idx_beam, idx_token = samples // vocab_size, samples % vocab_size
logits = logits.view(num_beams * vocab_size)
tokens_scores = logits.gather(dim=-1, index=samples).squeeze(-1)
return idx_beam, idx_token, tokens_scores
else:
# get the indices of the most probable tokens
num_beams = logits.shape[0]
vocab_size = logits.shape[-1]
logits = logits.view(num_beams * vocab_size)
scores, samples = logits.topk(2 * num_beams, dim=-1)
idx_beam, idx_token = samples // vocab_size, samples % vocab_size
tokens_scores = scores.squeeze(-1)
return idx_beam, idx_token, tokens_scores
# Beam search RSA decoding
class RSAContextualDecoding:
def __init__(self, model, tokenizer, device):
"""
:param model:
:param tokenizer:
:param device:
"""
self.model = model.to(device)
self.tokenizer = tokenizer
self.device = device
def fwd_pass(
self,
input_ids: torch.Tensor,
decoder_input_ids: torch.Tensor,
attention_mask: torch.Tensor,
decoder_attention_mask: torch.Tensor,
) -> torch.Tensor:
"""
Make a forward pass through the model to get the logits for the next tokens
:param input_ids: (world_size, num_beams, input_length)
:param decoder_input_ids: (world_size, num_beams, partial_target_length)
:param attention_mask: (world_size, num_beams, input_length)
:param decoder_attention_mask: (world_size, num_beams, partial_target_length)
:return: logits: (world_size, num_beams, vocab_size)
"""
with torch.no_grad():
world_size, num_beams = input_ids.shape[0], decoder_input_ids.shape[1]
input_ids = input_ids.view(world_size * num_beams, input_ids.shape[2]).to(self.device)
attention_mask = attention_mask.view(
world_size * num_beams, attention_mask.shape[2]
).to(self.device)
decoder_input_ids = decoder_input_ids.view(
world_size * num_beams, decoder_input_ids.shape[2]
).to(self.device)
decoder_attention_mask = decoder_attention_mask.view(
world_size * num_beams, decoder_attention_mask.shape[2]
).to(self.device)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
)
logits = outputs.logits[..., -1, :]
logits = logits.view(self.world_size, num_beams, logits.shape[-1])
# return the probability of the next token when conditioned on the source text (world_size)
# and the partial target text (num_beam)
return logits
def duplicate_and_align_input_ids(
self,
input_ids: torch.Tensor,
input_ids_mask: torch.Tensor,
decoder_input_ids: torch.Tensor,
decoder_input_ids_mask: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Duplicate the input_ids and decoder_input_ids to have all pairs of input_ids[i] and decoder_input_ids[j]
It uses torch.repeat and torch.repeat_interleave to do get something like:
a 1
a 2
a 3
b 1
b 2
b 3
...
:param input_ids: (world_size, input_length)
:param decoder_input_ids: (num_beam, partial_target_length)
:return: input_ids: (world_size, num_beam, input_length)
decoder_input_ids: (world_size, num_beam, partial_target_length)
aligned such that all pairs of input_ids[i] and decoder_input_ids[j] are present
"""
num_beams = decoder_input_ids.shape[0]
input_ids = input_ids.unsqueeze(1).repeat(1, num_beams, 1)
input_ids_mask = input_ids_mask.unsqueeze(1).repeat(1, num_beams, 1)
# repeat interleave
decoder_input_ids = decoder_input_ids.repeat_interleave(self.world_size, dim=0)
decoder_input_ids_mask = decoder_input_ids_mask.repeat_interleave(
self.world_size, dim=0
)
decoder_input_ids = decoder_input_ids.view(self.world_size, num_beams, -1)
decoder_input_ids_mask = decoder_input_ids_mask.view(
self.world_size, num_beams, -1
)
# print(self.tokenizer.batch_decode(input_ids[0]))
# print(self.tokenizer.batch_decode(decoder_input_ids[0]))
return input_ids, input_ids_mask, decoder_input_ids, decoder_input_ids_mask
def compute_rsa_probas(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
decoder_input_ids: torch.Tensor,
decoder_attention_mask: torch.Tensor,
do_sample: bool = True,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
temperature: float = 1.0,
rationality: float = 8.0, # seems to be a good value
process_logits_before_rsa: bool = True,
beam_scores: torch.Tensor = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
:param input_ids: input_ids to the encoder/decoder model = source texts
:param attention_mask: attention_mask to the encoder/decoder model
:param decoder_input_ids: decoder ids / partial summaries
:param decoder_attention_mask: attention mask for the decoder
:param do_sample: are we planning on sampling the tokens or using argmax (to apply or not the logits processor)
:param top_p: parameters for the logits processor top p
:param top_k: parameters for the logits processor top k
:param temperature: sampling temperature
:param rationality: how rational is the speaker (higher means more rational)
:param process_logits_before_rsa: should we apply the logits processor before or after the RSA computation
:param beam_scores: (world_size, num_beams) the scores of the beams to be added to the logits
:return: S1, L1: (world_size, num_beam, vocab_size).
"""
# some sanity checks
assert (top_p is None) or (
top_k is None
), "top_p and top_k cannot be used together"
assert ((top_p is not None) and (do_sample)) or (
top_p is None
), "top_p can only be used with sampling"
assert ((top_k is not None) and (do_sample)) or (
top_k is None
), "top_k can only be used with sampling"
# duplicate the input_ids and decoder_input_ids to have all pairs of input_ids[i] and decoder_input_ids[j]
(
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
) = self.duplicate_and_align_input_ids(
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
)
logits = (
self.fwd_pass(
input_ids, decoder_input_ids, attention_mask, decoder_attention_mask
)
/ temperature # apply the temperature
)
logits = torch.nn.functional.log_softmax(logits, dim=-1)
world_size = input_ids.shape[0]
num_beams = decoder_input_ids.shape[1]
logits = logits.view(world_size * num_beams, -1)
if do_sample and process_logits_before_rsa:
if top_p is not None:
logits = TopPLogitsWarper(top_p=top_p)(input_ids=None, scores=logits)
if top_k is not None:
logits = TopKLogitsWarper(top_k=top_k)(input_ids=None, scores=logits)
logits = logits.view(world_size, num_beams, -1)
if beam_scores is not None:
logits = logits + beam_scores[None, ..., None]
# compute the RSA probabilities
S1, L1 = compute_rsa_probas(logits, self.prior, rationality=rationality)
logits = S1
if do_sample and not process_logits_before_rsa:
logits = logits.view(world_size * num_beams, -1)
if top_p is not None:
logits = TopPLogitsWarper(top_p=top_p)(input_ids=None, scores=logits)
if top_k is not None:
logits = TopKLogitsWarper(top_k=top_k)(input_ids=None, scores=logits)
logits = logits.view(world_size, num_beams, -1)
return logits, L1
def generate(
self,
target_id: int,
source_texts_ids: torch.Tensor,
source_text_attention_mask: torch.Tensor,
max_length: int = 100,
num_beams: int = 8,
do_sample=True,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
temperature: float = 1.0,
rationality: float = 1.0,
process_logits_before_rsa=True,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
:param target_id: the id of the target object
:param source_texts_ids: (world_size, input_length) the tokenized source texts
:param source_text_attention_mask: (world_size, input_length) the attention mask for the source texts
:param max_length: the maximum length to generate
:param do_sample: are we sampling or using argmax
:param top_p: parameters for the logits processor top p
:param top_k: parameters for the logits processor top k
:param temperature: sampling temperature
:param rationality: how rational is the speaker (higher means more rational)
:param process_logits_before_rsa: should we apply the logits processor before or after the RSA computation
:return: decoder_input_ids : (num_beams, max_length) decoded sequences, beam_scores: (num_beams) the scores
of the beams
"""
self.num_beam = num_beams
self.world_size = source_texts_ids.shape[0]
self.prior = torch.ones((self.world_size, self.num_beam)).to(self.device) / self.world_size
beam_scores = torch.zeros(self.num_beam).to(self.device)
# initialize the decoder input ids
decoder_input_ids = torch.full(
(self.num_beam, 2),
0,
dtype=torch.long,
device=self.device,
)
# initialize the decoder attention mask
decoder_attention_mask = torch.ones_like(decoder_input_ids).to(self.device)
new_beams = []
finished_beams = []
# run the beam search
for t in range(max_length):
# compute the RSA probabilities
num_beams = decoder_input_ids.shape[0]
S1, L1 = self.compute_rsa_probas(
source_texts_ids,
source_text_attention_mask,
decoder_input_ids,
decoder_attention_mask,
do_sample=do_sample,
top_p=top_p,
top_k=top_k,
temperature=temperature,
rationality=rationality,
beam_scores=beam_scores,
process_logits_before_rsa=process_logits_before_rsa,
)
# sample from the probabilities
idx_beam, idx_token, tokens_scores = sample_from_probs(
S1[target_id].squeeze(), num_beams, do_sample
)
# create all the new beams
new_beams = []
for idx_t, idx_b, token_score in zip(idx_token, idx_beam, tokens_scores):
new_beams.append(
(
decoder_input_ids[idx_b].tolist() + [idx_t.item()],
beam_scores[idx_b] + token_score.item(),
L1[:, idx_b, idx_t.item()],
)
)
# sort the beams
new_beams = sorted(new_beams, key=lambda x: x[1], reverse=True)
# keep only the best beams
new_beams = new_beams[: self.num_beam]
# check if the beams are finished
_new_beams = []
for beam in new_beams:
if beam[0][-1] == self.tokenizer.eos_token_id:
finished_beams.append(beam)
else:
_new_beams.append(beam)
new_beams = _new_beams
if len(new_beams) == 0:
break
# pad the beams
max_beam_len = max(len(x[0]) for x in new_beams)
new_beams = [
(
x[0] + [self.tokenizer.pad_token_id] * (max_beam_len - len(x[0])),
x[1],
x[2],
)
for x in new_beams
]
# update the beam scores
beam_scores = torch.tensor([x[1] for x in new_beams]).to(self.device)
# update the decoder input ids
decoder_input_ids: torch.Tensor = torch.tensor(
[x[0] for x in new_beams], device=self.device
)
# update the decoder attention mask based on pad tokens
decoder_attention_mask = (
decoder_input_ids != self.tokenizer.pad_token_id
).long()
self.prior = torch.stack([x[2] for x in new_beams], dim=1).to(self.device)
# self.prior = torch.ones((self.world_size, len(new_beams))) / self.world_size
results = []
# pad the beams
max_beam_len = max(len(x[0]) for x in finished_beams + new_beams)
for x in finished_beams + new_beams:
results.append(
(
x[0] + [self.tokenizer.pad_token_id] * (max_beam_len - len(x[0])),
x[1],
x[2],
)
)
decoder_input_ids = torch.tensor([x[0] for x in results], device=self.device)
beam_scores = torch.tensor([x[1] for x in results]).to(self.device)
return decoder_input_ids, beam_scores
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