sample_1 / run_pplm.py
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#! /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.
# perplex
"""
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 argparse
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 import GPT2Tokenizer
from transformers.file_utils import cached_path
from transformers.modeling_gpt2 import GPT2LMHeadModel
from pplm_classification_head import ClassificationHead
import nltk
nltk.download('words')
nltk.download('stopwords')
nltk.download('names')
import nltk.corpus as corpus
from nltk.corpus import words as words_corpus
PPLM_BOW = 1
PPLM_DISCRIM = 2
PPLM_BOW_DISCRIM = 3
SMALL_CONST = 1e-15
BIG_CONST = 1e10
QUIET = 0
REGULAR = 1
VERBOSE = 2
VERY_VERBOSE = 3
VERBOSITY_LEVELS = {
'quiet': QUIET,
'regular': REGULAR,
'verbose': VERBOSE,
'very_verbose': VERY_VERBOSE,
}
BAG_OF_WORDS_ARCHIVE_MAP = {
'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_classifier_head.pt",
"class_size": 2,
"embed_size": 1024,
"class_vocab": {"non_clickbait": 0, "clickbait": 1},
"default_class": 1,
"pretrained_model": "gpt2-medium",
},
"sentiment": {
"url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/SST_classifier_head.pt",
"class_size": 5,
"embed_size": 1024,
"class_vocab": {"very_positive": 2, "very_negative": 3},
"default_class": 3,
"pretrained_model": "gpt2-medium",
},
"3_PerSoothe": {
"path": "/content/drive/Shareddrives/COS_IW04_ZL/COSIW04/Discriminators/3_class_opt_lowlr_medgpt/3_PerSoothe_classifier_head_epoch_10.pt",
"class_size": 3,
"embed_size": 1024,
"class_vocab": {"soothes": 0, "neutral": 1, "worsens": 2},
"default_class": 2,
"pretrained_model": "microsoft/DialoGPT-medium",
},
"3_PerSoothe_eot": {
"path": "/content/drive/Shareddrives/COS_IW04_ZL/COSIW04/Discriminators/3_class_opt_eot_lowlr_medgpt/3_PerSoothe_classifier_head_epoch_10.pt",
"class_size": 3,
"embed_size": 1024,
"class_vocab": {"soothes": 0, "neutral": 1, "worsens": 2},
"default_class": 2,
"pretrained_model": "microsoft/DialoGPT-medium",
},
"3_PerSoothe_lrg": {
"class_size": 3,
"embed_size": 1280,
"class_vocab": {"soothes": 0, "neutral": 1, "worsens": 2},
"default_class": 2,
"pretrained_model": "microsoft/DialoGPT-large",
},
"3_PerSoothe_med": {
"class_size": 3,
"embed_size": 1024,
"class_vocab": {"soothes": 0, "neutral": 1, "worsens": 2},
"default_class": 2,
"pretrained_model": "microsoft/DialoGPT-medium",
},
"2_PerSoothe_lrg": {
"class_size": 2,
"embed_size": 1280,
"class_vocab": {"soothes": 0, "neutral": 1},
"default_class": 2,
"pretrained_model": "microsoft/DialoGPT-large",
},
"2_PerSoothe_med": {
"class_size": 2,
"embed_size": 1024,
"class_vocab": {"soothes": 0, "neutral": 1},
"default_class": 2,
"pretrained_model": "microsoft/DialoGPT-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',
verbosity_level=REGULAR
):
# 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
_, _, _, 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 = []
new_accumulated_hidden = None
for i in range(num_iterations):
if verbosity_level >= VERBOSE:
print("Iteration ", i + 1)
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_key_values=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
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_loss = -torch.log(torch.sum(bow_logits))
loss += bow_loss
loss_list.append(bow_loss)
if verbosity_level >= VERY_VERBOSE:
print(" pplm_bow_loss:", loss.data.cpu().numpy())
if loss_type == PPLM_DISCRIM or loss_type == PPLM_BOW_DISCRIM:
ce_loss = torch.nn.CrossEntropyLoss()
# 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_key_values=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(prediction.shape[0] * [class_label],
device=device,
dtype=torch.long)
discrim_loss = ce_loss(prediction, label)
if verbosity_level >= VERY_VERBOSE:
print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy())
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_loss = kl_scale * (
(corrected_probs * (corrected_probs / unpert_probs).log()).sum()
)
if verbosity_level >= VERY_VERBOSE:
print(' kl_loss', kl_loss.data.cpu().numpy())
loss += kl_loss
loss_per_iter.append(loss.data.cpu().numpy())
if verbosity_level >= VERBOSE:
print(' pplm_loss', (loss - kl_loss).data.cpu().numpy())
# compute gradients
loss.backward(retain_graph=True)
# calculate gradient norms
if grad_norms is not None and loss_type == PPLM_BOW:
grad_norms = [
torch.max(grad_norms[index], torch.norm(p_.grad * window_mask))
for index, p_ in enumerate(curr_perturbation)
]
else:
grad_norms = [
(torch.norm(p_.grad * window_mask) + 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, loss_per_iter
def get_classifier(
name: Optional[str],
class_label: Union[str, int],
device: str,
verbosity_level: int = REGULAR,
fp: str = None,
is_deep: bool = False,
is_deeper: bool =False
) -> 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'],
is_deep=is_deep,
is_deeper=is_deeper
).to(device)
if "url" in params:
resolved_archive_file = cached_path(params["url"])
elif "path" in params:
resolved_archive_file = params["path"]
elif fp != None:
resolved_archive_file = fp
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"]
if verbosity_level >= REGULAR:
print("class_label {} not in class_vocab".format(class_label))
print("available values are: {}".format(params["class_vocab"]))
print("using default class {}".format(label_id))
elif isinstance(class_label, int):
if class_label in set(params["class_vocab"].values()):
label_id = class_label
else:
label_id = params["default_class"]
if verbosity_level >= REGULAR:
print("class_label {} not in class_vocab".format(class_label))
print("available values are: {}".format(params["class_vocab"]))
print("using default class {}".format(label_id))
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",
bag_of_words=None,
discrim=None,
class_label=None,
length=100,
stepsize=0.02,
temperature=1.0,
top_k=10,
sample=True,
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,
verbosity_level=REGULAR,
fp=None,
is_deep=False,
is_deeper=False,
stop_eot=False,
**kwargs
):
classifier, class_id = get_classifier(
discrim,
class_label,
device,
REGULAR,
fp,
is_deep,
is_deeper
)
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
if verbosity_level >= REGULAR:
print("Both PPLM-BoW and PPLM-Discrim are on. "
"This is not optimized.")
elif bag_of_words:
loss_type = PPLM_BOW
if verbosity_level >= REGULAR:
print("Using PPLM-BoW")
elif classifier is not None:
loss_type = PPLM_DISCRIM
if verbosity_level >= REGULAR:
print("Using 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,
sample=sample,
perturb=False,
verbosity_level=verbosity_level,
stop_eot=stop_eot
)
if device == 'cuda':
torch.cuda.empty_cache()
pert_gen_tok_texts = []
discrim_losses = []
losses_in_time = []
perplexities = []
for i in range(num_samples):
pert_gen_tok_text, discrim_loss, loss_in_time, perplexity = generate_text_pplm(
model=model,
tokenizer=tokenizer,
context=context,
device=device,
perturb=True,
bow_indices=bow_indices,
classifier=classifier,
class_label=class_id,
loss_type=loss_type,
length=length,
stepsize=stepsize,
temperature=temperature,
top_k=top_k,
sample=sample,
num_iterations=num_iterations,
grad_length=grad_length,
horizon_length=horizon_length,
window_length=window_length,
decay=decay,
gamma=gamma,
gm_scale=gm_scale,
kl_scale=kl_scale,
verbosity_level=verbosity_level,
stop_eot=stop_eot
)
pert_gen_tok_texts.append(pert_gen_tok_text)
if classifier is not None:
discrim_losses.append(discrim_loss.data.cpu().numpy())
losses_in_time.append(loss_in_time)
perplexities.append(perplexity)
if device == 'cuda':
torch.cuda.empty_cache()
return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time, perplexities
def generate_text_pplm(
model,
tokenizer,
context=None,
past=None,
device="cuda",
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=True,
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,
verbosity_level=REGULAR,
stop_eot=False
):
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)
grad_norms = None
last = None
unpert_discrim_loss = 0
loss_in_time = []
if verbosity_level >= VERBOSE:
range_func = trange(length, ascii=True)
else:
range_func = range(length)
pert_total_prob = 1
pert_times = 0
for i in range_func:
# 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, loss_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,
verbosity_level=verbosity_level
)
loss_in_time.append(loss_this_iter)
else:
pert_past = past
pert_logits, past, pert_all_hidden = model(last, past_key_values=pert_past)
pert_logits = pert_logits[:, -1, :] / temperature # + SMALL_CONST
pert_probs = F.softmax(pert_logits, dim=-1)
if classifier is not None:
ce_loss = torch.nn.CrossEntropyLoss()
prediction = classifier(torch.mean(unpert_last_hidden, dim=1))
label = torch.tensor([class_label], device=device,
dtype=torch.long)
unpert_discrim_loss = ce_loss(prediction, label)
if verbosity_level >= VERBOSE:
print(
"unperturbed discrim loss",
unpert_discrim_loss.data.cpu().numpy()
)
else:
unpert_discrim_loss = 0
# 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)
pert_total_prob = pert_total_prob * pert_probs[0][last[0][0]]
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 verbosity_level >= REGULAR:
print(tokenizer.decode(output_so_far.tolist()[0]))
pert_times += 1
if last[0][0] == 50256 and stop_eot:
break
perplexity = (1/pert_total_prob)**(1/pert_times)
return output_so_far, unpert_discrim_loss, loss_in_time, perplexity
def get_perplexity(
model,
tokenizer,
past=None,
device="cuda",
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=True,
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,
verbosity_level=REGULAR,
stop_eot=False,
test_text=None
):
if test_text == None:
print("No text to test")
return
test_text = torch.tensor(test_text, device=device, dtype=torch.long)
while len(test_text.shape) < 2:
test_text = test_text.unsqueeze(0)
eos_pos = (test_text == 50256).nonzero(as_tuple=True)[1]
start = int(eos_pos[eos_pos.size(dim=0)-2]+1)
end = int(eos_pos[eos_pos.size(dim=0)-1])
pert_total_prob = 1
pert_times = 0
error_occured = False
# collect one hot vectors for bags of words
one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, tokenizer,
device)
grad_norms = None
last = None
unpert_discrim_loss = 0
loss_in_time = []
for i in range(start, end):
output_so_far = test_text[:][:i]
cur_word = str(tokenizer.decode([test_text[0][i]])).lower().strip()
last_word = str(tokenizer.decode([test_text[0][i-1]])).lower().strip()
# 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:]
_, 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, loss_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,
verbosity_level=verbosity_level
)
loss_in_time.append(loss_this_iter)
else:
pert_past = past
pert_logits, past, pert_all_hidden = model(last, past_key_values=pert_past)
pert_logits = pert_logits[:, -1, :] / temperature # + SMALL_CONST
pert_probs = F.softmax(pert_logits, dim=-1)
if classifier is not None:
ce_loss = torch.nn.CrossEntropyLoss()
prediction = classifier(torch.mean(unpert_last_hidden, dim=1))
label = torch.tensor([class_label], device=device,
dtype=torch.long)
unpert_discrim_loss = ce_loss(prediction, label)
if verbosity_level >= VERBOSE:
print(
"unperturbed discrim loss",
unpert_discrim_loss.data.cpu().numpy()
)
else:
unpert_discrim_loss = 0
# 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)
if (not cur_word in words_corpus.words()) or cur_word in corpus.names.words() or cur_word in corpus.stopwords.words():
pass
else:
if pert_probs[0][test_text[0][i]] != 0:
pert_total_prob = pert_total_prob * pert_probs[0][test_text[0][i]]
pert_times += 1
else:
error_occured = True
else:
_, last = torch.topk(pert_probs, k=1, dim=-1)
# update context/output_so_far appending the new token
# backward
output_so_far = (
last if output_so_far is None
else torch.cat((output_so_far, last), dim=1)
)
if last[0][0] == 50256 and stop_eot:
break
if pert_times != 0:
perplexity = (1/pert_total_prob)**(1/pert_times)
else:
perplexity = -2 if error_occured else -1
return perplexity
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_pplm_example(
pretrained_model="gpt2-medium",
cond_text="",
uncond=False,
num_samples=1,
bag_of_words=None,
discrim=None,
discrim_weights=None,
discrim_meta=None,
class_label=-1,
length=100,
stepsize=0.02,
temperature=1.0,
top_k=10,
sample=True,
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,
seed=0,
no_cuda=False,
colorama=False,
verbosity='regular',
fp=None,
model_fp=None,
calc_perplexity=False,
is_deep=False,
is_deeper=False,
stop_eot=False
):
# set Random seed
torch.manual_seed(seed)
np.random.seed(seed)
# set verbosiry
verbosity_level = VERBOSITY_LEVELS.get(verbosity.lower(), REGULAR)
# set the device
device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
if discrim == 'generic':
set_generic_model_params(discrim_weights, discrim_meta)
if discrim is not None:
discriminator_pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][
"pretrained_model"
]
if pretrained_model != discriminator_pretrained_model:
pretrained_model = discriminator_pretrained_model
if verbosity_level >= REGULAR:
print("discrim = {}, pretrained_model set "
"to discriminator's = {}".format(discrim, pretrained_model))
# load pretrained model
model = GPT2LMHeadModel.from_pretrained(
pretrained_model,
output_hidden_states=True
)
if model_fp != None:
try:
model.load_state_dict(torch.load(model_fp))
except:
print("Can't load local model")
model.to(device)
model.eval()
# load tokenizer
tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
# Freeze GPT-2 weights
for param in model.parameters():
param.requires_grad = False
# figure out conditioning text
if uncond:
tokenized_cond_text = tokenizer.encode(
[tokenizer.bos_token],
add_special_tokens=False
)
else:
raw_text = cond_text
while not raw_text:
print("Did you forget to add `--cond_text`? ")
raw_text = input("Model prompt >>> ")
tokenized_cond_text = tokenizer.encode(
tokenizer.bos_token + raw_text,
add_special_tokens=False
)
print("= Prefix of sentence =")
print(tokenizer.decode(tokenized_cond_text))
print()
# generate unperturbed and perturbed texts
# full_text_generation returns:
# unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
unpert_gen_tok_text, pert_gen_tok_texts, _, _, perplexities = full_text_generation(
model=model,
tokenizer=tokenizer,
context=tokenized_cond_text,
device=device,
num_samples=num_samples,
bag_of_words=bag_of_words,
discrim=discrim,
class_label=class_label,
length=length,
stepsize=stepsize,
temperature=temperature,
top_k=top_k,
sample=sample,
num_iterations=num_iterations,
grad_length=grad_length,
horizon_length=horizon_length,
window_length=window_length,
decay=decay,
gamma=gamma,
gm_scale=gm_scale,
kl_scale=kl_scale,
verbosity_level=verbosity_level,
fp=fp,
is_deep=is_deep,
is_deeper=is_deeper,
stop_eot=stop_eot
)
# untokenize unperturbed text
unpert_gen_text = tokenizer.decode(unpert_gen_tok_text.tolist()[0])
if verbosity_level >= REGULAR:
print("=" * 80)
print("= Unperturbed generated text =")
print(unpert_gen_text)
print()
generated_texts = []
bow_word_ids = set()
if bag_of_words and colorama:
bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
tokenizer)
for single_bow_list in bow_indices:
# filtering all words in the list composed of more than 1 token
filtered = list(filter(lambda x: len(x) <= 1, single_bow_list))
# w[0] because we are sure w has only 1 item because previous fitler
bow_word_ids.update(w[0] for w in filtered)
# iterate through the perturbed texts
for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts):
try:
# untokenize unperturbed text
if colorama:
import colorama
pert_gen_text = ''
for word_id in pert_gen_tok_text.tolist()[0]:
if word_id in bow_word_ids:
pert_gen_text += '{}{}{}'.format(
colorama.Fore.RED,
tokenizer.decode([word_id]),
colorama.Style.RESET_ALL
)
else:
pert_gen_text += tokenizer.decode([word_id])
else:
pert_gen_text = tokenizer.decode(pert_gen_tok_text.tolist()[0])
print("= Perturbed generated text {} =".format(i + 1))
print(pert_gen_text)
if calc_perplexity:
print("Perplexity:", perplexities[i])
print()
except:
pass
# keep the prefix, perturbed seq, original seq for each index
generated_texts.append(
(tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text)
)
return
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"--pretrained_model",
"-M",
type=str,
default="gpt2-medium",
help="pretrained model name or path to local checkpoint",
)
parser.add_argument(
"--cond_text", type=str, default="The lake",
help="Prefix texts to condition on"
)
parser.add_argument(
"--uncond", action="store_true",
help="Generate from end-of-text as prefix"
)
parser.add_argument(
"--num_samples",
type=int,
default=1,
help="Number of samples to generate from the modified latents",
)
parser.add_argument(
"--bag_of_words",
"-B",
type=str,
default=None,
help="Bags of words used for PPLM-BoW. "
"Either a BOW id (see list in code) or a filepath. "
"Multiple BoWs separated by ;",
)
parser.add_argument(
"--discrim",
"-D",
type=str,
default=None,
choices=("clickbait", "sentiment", "toxicity", "generic", "3_PerSoothe",
"3_PerSoothe_eot", "3_PerSoothe_lrg", "3_PerSoothe_med", "2_PerSoothe_lrg", "2_PerSoothe_med"),
help="Discriminator to use",
)
parser.add_argument('--discrim_weights', type=str, default=None,
help='Weights for the generic discriminator')
parser.add_argument('--discrim_meta', type=str, default=None,
help='Meta information for the generic discriminator')
parser.add_argument(
"--class_label",
type=int,
default=-1,
help="Class label used for the discriminator",
)
parser.add_argument("--length", type=int, default=100)
parser.add_argument("--stepsize", type=float, default=0.02)
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--top_k", type=int, default=10)
parser.add_argument(
"--sample", action="store_true",
help="Generate from end-of-text as prefix"
)
parser.add_argument("--num_iterations", type=int, default=3)
parser.add_argument("--grad_length", type=int, default=10000)
parser.add_argument(
"--window_length",
type=int,
default=0,
help="Length of past which is being optimized; "
"0 corresponds to infinite window length",
)
parser.add_argument(
"--horizon_length",
type=int,
default=1,
help="Length of future to optimize over",
)
parser.add_argument("--decay", action="store_true",
help="whether to decay or not")
parser.add_argument("--gamma", type=float, default=1.5)
parser.add_argument("--gm_scale", type=float, default=0.9)
parser.add_argument("--kl_scale", type=float, default=0.01)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--no_cuda", action="store_true", help="no cuda")
parser.add_argument("--colorama", action="store_true",
help="colors keywords")
parser.add_argument("--verbosity", type=str, default="very_verbose",
choices=(
"quiet", "regular", "verbose", "very_verbose"),
help="verbosiry level")
parser.add_argument("--fp", type=str, default="")
parser.add_argument("--model_fp", type=str, default="")
parser.add_argument("--calc_perplexity", action="store_true", help="calculate perplexity")
parser.add_argument("--is_deep", action="store_true",
help="whether to use deep classifier")
parser.add_argument("--is_deeper", action="store_true",
help="whether to use deep classifier")
parser.add_argument("--stop_eot", action="store_true",
help="whether to stop at eot token")
args = parser.parse_args()
run_pplm_example(**vars(args))