removed deprecared use of Variable api from pplm example

examples/text-generation/pplm/run_pplm.py

@@ -31,7 +31,6 @@ 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 pplm_classification_head import ClassificationHead
@@ -76,14 +75,6 @@ DISCRIMINATOR_MODELS_PARAMS = {
 }
 
 
-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).
@@ -156,9 +147,7 @@ def perturb_past(
     new_accumulated_hidden = None
     for i in range(num_iterations):
         print("Iteration ", i + 1)
-        curr_perturbation = [
-            to_var(torch.from_numpy(p_), requires_grad=True, device=device) for p_ in grad_accumulator
-        ]
+        curr_perturbation = [torch.from_numpy(p_).requires_grad_(True).to(device=device) for p_ in grad_accumulator]
 
         # Compute hidden using perturbed past
         perturbed_past = list(map(add, past, curr_perturbation))
@@ -247,7 +236,7 @@ def perturb_past(
         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]
+    grad_accumulator = [torch.from_numpy(p_).requires_grad_(True).to(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
@@ -266,7 +255,7 @@ def get_classifier(
     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")
+        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()
 
@@ -569,9 +558,9 @@ def generate_text_pplm(
 
 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")
+        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")
+        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)
@@ -619,7 +608,7 @@ def run_pplm_example(
 
     if discrim is not None:
         pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim]["pretrained_model"]
-        print("discrim = {}, pretrained_model set " "to discriminator's = {}".format(discrim, pretrained_model))
+        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)
@@ -706,7 +695,7 @@ def run_pplm_example(
                 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
+                            colorama.Fore.RED, tokenizer.decode([word_id]), colorama.Style.RESET_ALL,
                         )
                     else:
                         pert_gen_text += tokenizer.decode([word_id])
@@ -744,9 +733,11 @@ if __name__ == "__main__":
         "-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 ;",
+        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",
@@ -756,9 +747,11 @@ if __name__ == "__main__":
         choices=("clickbait", "sentiment", "toxicity", "generic"),
         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"
+        "--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",
@@ -774,7 +767,7 @@ if __name__ == "__main__":
         "--window_length",
         type=int,
         default=0,
-        help="Length of past which is being optimized; " "0 corresponds to infinite window length",
+        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",