Changed order of some parameters to be more consistent. Identical results.

examples/run_pplm.py

@@ -121,17 +121,17 @@ def perturb_past(
         accumulated_hidden=None,
         grad_norms=None,
         stepsize=0.01,
+        one_hot_bows_vectors=None,
         classifier=None,
         class_label=None,
-        one_hot_bows_vectors=None,
         loss_type=0,
         num_iterations=3,
-        kl_scale=0.01,
-        window_length=0,
         horizon_length=1,
+        window_length=0,
         decay=False,
         gamma=1.5,
-        device='cuda'
+        kl_scale=0.01,
+        device='cuda',
 ):
     # Generate inital perturbed past
     grad_accumulator = [
@@ -351,8 +351,7 @@ def get_classifier(
 
 
 def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \
-        List[
-            List[List[int]]]:
+        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:
@@ -388,22 +387,22 @@ def full_text_generation(
         context=None,
         num_samples=1,
         device="cuda",
-        sample=False,
+        bag_of_words=None,
         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,
+        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,
         **kwargs
 ):
     classifier, class_id = get_classifier(
@@ -454,24 +453,24 @@ def full_text_generation(
             tokenizer=tokenizer,
             context=context,
             device=device,
-            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,
+            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,
         )
         pert_gen_tok_texts.append(pert_gen_tok_text)
         if classifier is not None:
@@ -490,24 +489,24 @@ def generate_text_pplm(
         context=None,
         past=None,
         device="cuda",
-        sample=False,
         perturb=True,
+        bow_indices=None,
         classifier=None,
         class_label=None,
-        bow_indices=None,
         loss_type=0,
         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,
+        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 = (
         torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0)
@@ -561,17 +560,17 @@ def generate_text_pplm(
                     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,
-                    one_hot_bows_vectors=one_hot_bows_vectors,
                     loss_type=loss_type,
                     num_iterations=num_iterations,
-                    kl_scale=kl_scale,
-                    window_length=window_length,
                     horizon_length=horizon_length,
+                    window_length=window_length,
                     decay=decay,
                     gamma=gamma,
-                    device=device
+                    kl_scale=kl_scale,
+                    device=device,
                 )
                 loss_in_time.append(loss_this_iter)
             else:
@@ -685,7 +684,7 @@ def run_pplm_example(
         pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][
             "pretrained_model"
         ]
-        print("discrim = {}, setting pretrained_model "
+        print("discrim = {}, pretrained_model set "
               "to discriminator's = {}".format(discrim, pretrained_model))
 
     # load pretrained model
@@ -810,6 +809,20 @@ if __name__ == '__main__':
         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",
@@ -837,33 +850,22 @@ if __name__ == '__main__':
         default=-1,
         help="Class label used for the discriminator",
     )
-    parser.add_argument("--stepsize", type=float, default=0.02)
     parser.add_argument("--length", type=int, default=100)
-    parser.add_argument("--seed", type=int, default=0)
+    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("--gm_scale", type=float, default=0.9)
-    parser.add_argument("--kl_scale", type=float, default=0.01)
-    parser.add_argument("--no_cuda", action="store_true", help="no cuda")
     parser.add_argument(
         "--sample", action="store_true",
         help="Generate from end-of-text as prefix"
     )
-    parser.add_argument(
-        "--uncond", action="store_true",
-        help="Generate from end-of-text as prefix"
-    )
-    parser.add_argument(
-        "--cond_text", type=str, default="The lake",
-        help="Prefix texts to condition on"
-    )
     parser.add_argument("--num_iterations", type=int, default=3)
     parser.add_argument("--grad_length", type=int, default=10000)
     parser.add_argument(
-        "--num_samples",
+        "--window_length",
         type=int,
-        default=1,
-        help="Number of samples to generate from the modified latents",
+        default=0,
+        help="Length of past which is being optimized; "
+             "0 corresponds to infinite window length",
     )
     parser.add_argument(
         "--horizon_length",
@@ -871,16 +873,13 @@ if __name__ == '__main__':
         default=1,
         help="Length of future to optimize over",
     )
-    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("--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")