#! /usr/bin/env python3
# coding=utf-8
# Copyright 2018 The Uber AI Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Example command with bag of words:
python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
Example command with discriminator:
python examples/run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95
"""
import json
from operator import add
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from tqdm import trange
from transformers.file_utils import cached_path
import time
from run_pplm_discrim_train import ClassificationHead
PPLM_BOW = 1
PPLM_DISCRIM = 2
PPLM_BOW_DISCRIM = 3
SMALL_CONST = 1e-15
BIG_CONST = 1e10
BAG_OF_WORDS_ARCHIVE_MAP = {
'kitchen': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/kitchen.txt",
'legal': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/legal.txt",
'military': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/military.txt",
'monsters': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/monsters.txt",
'politics': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/politics.txt",
'positive_words': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/positive_words.txt",
'religion': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/religion.txt",
'science': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/science.txt",
'space': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/space.txt",
'technology': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/technology.txt",
}
DISCRIMINATOR_MODELS_PARAMS = {
"clickbait": {
"url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/clickbait_classifierhead.pt",
"class_size": 2,
"embed_size": 1024,
"class_vocab": {"non_clickbait": 0, "clickbait": 1},
"class_id": {0: "non_clickbait", 1: "clickbait"},
"default_class": 1,
"pretrained_model": "gpt2-medium",
},
"sentiment": {
"url": "http://s.yosinski.com/SST_classifier_head.pt",
"class_size": 5,
"embed_size": 1024,
"class_vocab": {"very_positive": 2, "very_negative": 3},
"class_id": {2: "very_positive", 3: "very_negative"},
"default_class": 3,
"pretrained_model": "gpt2-medium",
},
"toxicity": {
"url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/toxicity_classifierhead.pt",
"class_size": 2,
"embed_size": 1024,
"class_vocab": {"non_toxic": 0, "toxic": 1},
"class_id": {0: "non_toxic", 1: "toxic"},
"default_class": 0,
"pretrained_model": "gpt2-medium",
},
}
def to_var(x, requires_grad=False, volatile=False, device='cuda'):
if torch.cuda.is_available() and device == 'cuda':
x = x.cuda()
elif device != 'cuda':
x = x.to(device)
return Variable(x, requires_grad=requires_grad, volatile=volatile)
def top_k_filter(logits, k, probs=False):
"""
Masks everything but the k top entries as -infinity (1e10).
Used to mask logits such that e^-infinity -> 0 won't contribute to the
sum of the denominator.
"""
if k == 0:
return logits
else:
values = torch.topk(logits, k)[0]
batch_mins = values[:, -1].view(-1, 1).expand_as(logits)
if probs:
return torch.where(logits < batch_mins,
torch.ones_like(logits) * 0.0, logits)
return torch.where(logits < batch_mins,
torch.ones_like(logits) * -BIG_CONST,
logits)
def perturb_past(
past,
model,
last,
unpert_past=None,
unpert_logits=None,
accumulated_hidden=None,
grad_norms=None,
stepsize=0.01,
one_hot_bows_vectors=None,
classifier=None,
class_label=None,
loss_type=0,
num_iterations=3,
horizon_length=1,
window_length=0,
decay=False,
gamma=1.5,
kl_scale=0.01,
device='cuda',
):
# Generate inital perturbed past
grad_accumulator = [
(np.zeros(p.shape).astype("float32"))
for p in past
]
if accumulated_hidden is None:
accumulated_hidden = 0
if decay:
decay_mask = torch.arange(
0.,
1.0 + SMALL_CONST,
1.0 / (window_length)
)[1:]
else:
decay_mask = 1.0
# TODO fix this comment (SUMANTH)
# Generate a mask is gradient perturbated is based on a past window
_, batch_size, _, curr_length, _ = past[0].shape
if curr_length > window_length and window_length > 0:
ones_key_val_shape = (
tuple(past[0].shape[:-2])
+ tuple([window_length])
+ tuple(past[0].shape[-1:])
)
zeros_key_val_shape = (
tuple(past[0].shape[:-2])
+ tuple([curr_length - window_length])
+ tuple(past[0].shape[-1:])
)
ones_mask = torch.ones(ones_key_val_shape)
ones_mask = decay_mask * ones_mask.permute(0, 1, 2, 4, 3)
ones_mask = ones_mask.permute(0, 1, 2, 4, 3)
window_mask = torch.cat(
(ones_mask, torch.zeros(zeros_key_val_shape)),
dim=-2
).to(device)
else:
window_mask = torch.ones_like(past[0]).to(device)
# accumulate perturbations for num_iterations
loss_per_iter = []
losses_per_iter = []
new_accumulated_hidden = None
for i in range(num_iterations):
curr_perturbation = [
to_var(torch.from_numpy(p_), requires_grad=True, device=device)
for p_ in grad_accumulator
]
# Compute hidden using perturbed past
perturbed_past = list(map(add, past, curr_perturbation))
_, _, _, curr_length, _ = curr_perturbation[0].shape
all_logits, _, all_hidden = model(last, past=perturbed_past)
hidden = all_hidden[-1]
new_accumulated_hidden = accumulated_hidden + torch.sum(
hidden,
dim=1
).detach()
# TODO: Check the layer-norm consistency of this with trained discriminator (Sumanth)
logits = all_logits[:, -1, :]
probs = F.softmax(logits, dim=-1)
loss = 0.0
losses = torch.zeros(batch_size, device=device)
loss_list = []
if loss_type == PPLM_BOW or loss_type == PPLM_BOW_DISCRIM:
for one_hot_bow in one_hot_bows_vectors:
bow_logits = torch.mm(probs, torch.t(one_hot_bow))
bow_losses = -torch.log(torch.sum(bow_logits, dim=-1))
losses += bow_losses
bow_loss = torch.sum(bow_losses) # sum over batches
loss += bow_loss
loss_list.append(bow_loss)
if loss_type == 2 or loss_type == 3:
ce_loss = torch.nn.CrossEntropyLoss(reduction='none')
# TODO why we need to do this assignment and not just using unpert_past? (Sumanth)
curr_unpert_past = unpert_past
curr_probs = torch.unsqueeze(probs, dim=1)
wte = model.resize_token_embeddings()
for _ in range(horizon_length):
inputs_embeds = torch.matmul(curr_probs, wte.weight.data)
_, curr_unpert_past, curr_all_hidden = model(
past=curr_unpert_past,
inputs_embeds=inputs_embeds
)
curr_hidden = curr_all_hidden[-1]
new_accumulated_hidden = new_accumulated_hidden + torch.sum(
curr_hidden, dim=1)
prediction = classifier(new_accumulated_hidden /
(curr_length + 1 + horizon_length))
label = torch.tensor(batch_size * [class_label],
device=device,
dtype=torch.long)
discrim_losses = ce_loss(prediction, label)
losses += discrim_losses
discrim_loss = discrim_losses.sum(-1)
loss += discrim_loss
loss_list.append(discrim_loss)
kl_loss = 0.0
if kl_scale > 0.0:
unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
unpert_probs = (
unpert_probs + SMALL_CONST *
(unpert_probs <= SMALL_CONST).float().to(device).detach()
)
correction = SMALL_CONST * (probs <= SMALL_CONST).float().to(
device).detach()
corrected_probs = probs + correction.detach()
kl_losses = kl_scale * (
(corrected_probs * (corrected_probs / unpert_probs).log()).sum(-1)
)
losses += kl_losses
kl_loss = kl_losses.sum()
loss += kl_loss
loss_per_iter.append(loss.data.cpu().numpy())
losses_per_iter.append(losses.data.cpu().numpy())
# compute gradients
loss.backward()
# calculate gradient norms
if grad_norms is not None and loss_type == PPLM_BOW:
grad_norms = [
torch.max(grad_norms[index],
torch.norm_except_dim(p_.grad * window_mask, dim=1))
#torch.norm(p_.grad * window_mask))
for index, p_ in enumerate(curr_perturbation)
]
else:
grad_norms = [
(torch.norm_except_dim(p_.grad * window_mask, dim=1) + SMALL_CONST)
for index, p_ in enumerate(curr_perturbation)
]
# normalize gradients
grad = [
-stepsize *
(p_.grad * window_mask / grad_norms[
index] ** gamma).data.cpu().numpy()
for index, p_ in enumerate(curr_perturbation)
]
# accumulate gradient
grad_accumulator = list(map(add, grad, grad_accumulator))
# reset gradients, just to make sure
for p_ in curr_perturbation:
p_.grad.data.zero_()
# removing past from the graph
new_past = []
for p_ in past:
new_past.append(p_.detach())
past = new_past
# apply the accumulated perturbations to the past
grad_accumulator = [
to_var(torch.from_numpy(p_), requires_grad=True, device=device)
for p_ in grad_accumulator
]
pert_past = list(map(add, past, grad_accumulator))
return pert_past, new_accumulated_hidden, grad_norms, losses_per_iter
def get_classifier(
name: Optional[str], class_label: Union[str, int],
device: str
) -> Tuple[Optional[ClassificationHead], Optional[int]]:
if name is None:
return None, None
params = DISCRIMINATOR_MODELS_PARAMS[name]
classifier = ClassificationHead(
class_size=params['class_size'],
embed_size=params['embed_size']
).to(device)
if "url" in params:
resolved_archive_file = cached_path(params["url"])
elif "path" in params:
resolved_archive_file = params["path"]
else:
raise ValueError("Either url or path have to be specified "
"in the discriminator model parameters")
classifier.load_state_dict(
torch.load(resolved_archive_file, map_location=device))
classifier.eval()
if isinstance(class_label, str):
if class_label in params["class_vocab"]:
label_id = params["class_vocab"][class_label]
else:
label_id = params["default_class"]
elif isinstance(class_label, int):
if class_label in set(params["class_vocab"].values()):
label_id = class_label
else:
label_id = params["default_class"]
else:
label_id = params["default_class"]
return classifier, label_id
def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \
List[List[List[int]]]:
bow_indices = []
for id_or_path in bag_of_words_ids_or_paths:
if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP:
filepath = cached_path(BAG_OF_WORDS_ARCHIVE_MAP[id_or_path])
else:
filepath = id_or_path
with open(filepath, "r") as f:
words = f.read().strip().split("\n")
bow_indices.append(
[tokenizer.encode(word.strip(), add_prefix_space=True,
add_special_tokens=False) for word in
words])
return bow_indices
def build_bows_one_hot_vectors(bow_indices, tokenizer, device='cuda'):
if bow_indices is None:
return None
one_hot_bows_vectors = []
for single_bow in bow_indices:
single_bow = list(filter(lambda x: len(x) <= 1, single_bow))
single_bow = torch.tensor(single_bow).to(device)
num_words = single_bow.shape[0]
one_hot_bow = torch.zeros(num_words, tokenizer.vocab_size).to(device)
one_hot_bow.scatter_(1, single_bow, 1)
one_hot_bows_vectors.append(one_hot_bow)
return one_hot_bows_vectors
def full_text_generation(
model,
tokenizer,
context=None,
num_samples=1,
device="cuda",
max_time=5,
sample=False,
discrim=None,
class_label=None,
bag_of_words=None,
length=100,
grad_length=10000,
stepsize=0.02,
num_iterations=3,
temperature=1.0,
gm_scale=0.9,
kl_scale=0.01,
top_k=10,
window_length=0,
horizon_length=1,
decay=False,
gamma=1.5,
):
classifier, class_id = get_classifier(
discrim,
class_label,
device
)
bow_indices = []
if bag_of_words:
bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
tokenizer)
if bag_of_words and classifier:
loss_type = PPLM_BOW_DISCRIM
elif bag_of_words:
loss_type = PPLM_BOW
elif classifier is not None:
loss_type = PPLM_DISCRIM
else:
raise Exception("Specify either a bag of words or a discriminator")
# unpert_gen_tok_text = generate_text_pplm(
# model=model,
# tokenizer=tokenizer,
# context=context,
# device=device,
# length=length,
# perturb=False
# )
# if device == 'cuda':
# torch.cuda.empty_cache()
print(context, bow_indices, top_k, gm_scale, kl_scale)
pert_gen_tok_text, last_losses = generate_text_pplm(
model=model,
context=context,
tokenizer=tokenizer,
device=device,
max_time=max_time,
sample=sample,
perturb=True,
bow_indices=bow_indices,
classifier=classifier,
class_label=class_id,
loss_type=loss_type,
length=length,
grad_length=grad_length,
stepsize=stepsize,
num_iterations=num_iterations,
temperature=temperature,
gm_scale=gm_scale,
kl_scale=kl_scale,
top_k=top_k,
window_length=window_length,
horizon_length=horizon_length,
decay=decay,
gamma=gamma,
)
if device == 'cuda':
torch.cuda.empty_cache()
return pert_gen_tok_text, last_losses
def generate_text_pplm(
model,
tokenizer,
context=None,
past=None,
device="cuda",
max_time=5,
perturb=True,
bow_indices=None,
classifier=None,
class_label=None,
loss_type=0,
length=100,
stepsize=0.02,
temperature=1.0,
top_k=10,
sample=False,
num_iterations=3,
grad_length=10000,
horizon_length=1,
window_length=0,
decay=False,
gamma=1.5,
gm_scale=0.9,
kl_scale=0.01,
):
output_so_far = None
if context:
context_t = torch.tensor(context, device=device, dtype=torch.long)
while len(context_t.shape) < 2:
context_t = context_t.unsqueeze(0)
output_so_far = context_t
# collect one hot vectors for bags of words
one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, tokenizer,
device)
start = time.time()
grad_norms = None
last = None
losses_this_iter = None
losses_in_time = []
for i in trange(length, ascii=True):
# Get past/probs for current output, except for last word
# Note that GPT takes 2 inputs: past + current_token
# run model forward to obtain unperturbed
if past is None and output_so_far is not None:
last = output_so_far[:, -1:]
if output_so_far.shape[1] > 1:
_, past, _ = model(output_so_far[:, :-1])
unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far)
unpert_last_hidden = unpert_all_hidden[-1]
# check if we are abowe grad max length
if i >= grad_length:
current_stepsize = stepsize * 0
else:
current_stepsize = stepsize
# modify the past if necessary
if not perturb or num_iterations == 0:
pert_past = past
else:
accumulated_hidden = unpert_last_hidden[:, :-1, :]
accumulated_hidden = torch.sum(accumulated_hidden, dim=1)
if past is not None:
pert_past, _, grad_norms, losses_this_iter = perturb_past(
past,
model,
last,
unpert_past=unpert_past,
unpert_logits=unpert_logits,
accumulated_hidden=accumulated_hidden,
grad_norms=grad_norms,
stepsize=current_stepsize,
one_hot_bows_vectors=one_hot_bows_vectors,
classifier=classifier,
class_label=class_label,
loss_type=loss_type,
num_iterations=num_iterations,
horizon_length=horizon_length,
window_length=window_length,
decay=decay,
gamma=gamma,
kl_scale=kl_scale,
device=device,
)
losses_in_time.append(losses_this_iter)
else:
pert_past = past
pert_logits, past, pert_all_hidden = model(last, past=pert_past)
pert_logits = pert_logits[:, -1, :] / temperature # + SMALL_CONST
pert_probs = F.softmax(pert_logits, dim=-1)
# Fuse the modified model and original model
if perturb:
unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
pert_probs = ((pert_probs ** gm_scale) * (
unpert_probs ** (1 - gm_scale))) # + SMALL_CONST
pert_probs = top_k_filter(pert_probs, k=top_k,
probs=True) # + SMALL_CONST
# rescale
if torch.sum(pert_probs) <= 1:
pert_probs = pert_probs / torch.sum(pert_probs)
else:
pert_logits = top_k_filter(pert_logits, k=top_k) # + SMALL_CONST
pert_probs = F.softmax(pert_logits, dim=-1)
# sample or greedy
if sample:
last = torch.multinomial(pert_probs, num_samples=1)
else:
_, last = torch.topk(pert_probs, k=1, dim=-1)
# update context/output_so_far appending the new token
output_so_far = (
last if output_so_far is None
else torch.cat((output_so_far, last), dim=1)
)
if time.time() - start > max_time and max_time != -1:
break
final_losses = losses_this_iter[-1] if losses_this_iter else None
return output_so_far, final_losses
def set_generic_model_params(discrim_weights, discrim_meta):
if discrim_weights is None:
raise ValueError('When using a generic discriminator, '
'discrim_weights need to be specified')
if discrim_meta is None:
raise ValueError('When using a generic discriminator, '
'discrim_meta need to be specified')
with open(discrim_meta, 'r') as discrim_meta_file:
meta = json.load(discrim_meta_file)
meta['path'] = discrim_weights
DISCRIMINATOR_MODELS_PARAMS['generic'] = meta
def run_model(
model,
tokenizer,
device,
raw_text,
max_time,
bag_of_words=None,
discrim=None,
discrim_weights=None,
discrim_meta=None,
discrim_label=-1,
stepsize=0.02,
length=10,
seed=None,
temperature=1.0,
top_k=10,
gm_scale=0.9,
kl_scale=0.01,
uncond=False,
num_iterations=3,
grad_length=10000,
num_samples=1,
horizon_length=1,
window_length=0,
decay=False,
gamma=1.5,
use_sampling=False
):
print(seed)
if seed is not None:
# set Random seed
torch.manual_seed(seed)
np.random.seed(seed)
if discrim == 'generic':
set_generic_model_params(discrim_weights, discrim_meta)
tokenized_cond_text = [tokenizer.encode(
tokenizer.bos_token + raw_text, max_length=512 - length - 1)] * num_samples
# Freeze GPT-2 weights
for param in model.parameters():
param.requires_grad = False
# generate unperturbed and perturbed texts
# full_text_generation returns:
# unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
pert_gen_tok_text, last_losses = full_text_generation(
model=model,
tokenizer=tokenizer,
context=tokenized_cond_text,
device=device,
max_time=max_time,
num_samples=num_samples,
discrim=discrim,
class_label=discrim_label,
bag_of_words=bag_of_words,
length=length,
grad_length=grad_length,
stepsize=stepsize,
num_iterations=num_iterations,
temperature=temperature,
gm_scale=gm_scale,
kl_scale=kl_scale,
top_k=top_k,
window_length=window_length,
horizon_length=horizon_length,
decay=decay,
gamma=gamma,
sample=use_sampling
)
generated_texts = []
# iterate through the perturbed texts
for sample, loss in zip(pert_gen_tok_text.tolist(), last_losses.tolist()):
generated_part = sample[len(tokenized_cond_text[0]):]
pert_gen_text = tokenizer.decode(generated_part)
# keep the prefix, perturbed seq, original seq for each index
generated_texts.append(
{
"value": pert_gen_text,
"tokens": len(generated_part),
"loss": loss
}
)
return generated_texts