generate_text_pplm now works with batch_size > 1

examples/run_pplm.py

@@ -231,7 +231,8 @@ def perturb_past(
             prediction = classifier(new_accumulated_hidden /
                                     (curr_length + 1 + horizon_length))
 
-            label = torch.tensor([class_label], device=device,
+            label = torch.tensor(prediction.shape[0] * [class_label],
+                                 device=device,
                                  dtype=torch.long)
             discrim_loss = ce_loss(prediction, label)
             print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy())
@@ -508,11 +509,12 @@ def generate_text_pplm(
         gm_scale=0.9,
         kl_scale=0.01,
 ):
-    output_so_far = (
-        torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0)
-        if context
-        else None
-    )
+    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,