backend/PPLM.py

#! /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