From a9f24a16bc2965d2990b90127ed4b5a1f47344b9 Mon Sep 17 00:00:00 2001
From: mataney <mataneyal1@gmail.com>
Date: Wed, 25 Sep 2019 15:53:29 +0300
Subject: [PATCH] [FIX] fix run_generation.py to work with batch_size > 1

---
 examples/run_generation.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/examples/run_generation.py b/examples/run_generation.py
index a2a8f291031..935e578441d 100644
--- a/examples/run_generation.py
+++ b/examples/run_generation.py
@@ -81,7 +81,6 @@ def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')
                 Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
         From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
     """
-    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
     top_k = min(top_k, logits.size(-1))  # Safety check
     if top_k > 0:
         # Remove all tokens with a probability less than the last token of the top-k
@@ -98,7 +97,8 @@ def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')
         sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
         sorted_indices_to_remove[..., 0] = 0
 
-        indices_to_remove = sorted_indices[sorted_indices_to_remove]
+        # scatter sorted tensors to original indexing
+        indices_to_remove = sorted_indices_to_remove.scatter(dim=1, index=sorted_indices, src=sorted_indices_to_remove)
         logits[indices_to_remove] = filter_value
     return logits
 
@@ -122,10 +122,10 @@ def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=
                 inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping}
 
             outputs = model(**inputs)  # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)
-            next_token_logits = outputs[0][0, -1, :] / temperature
+            next_token_logits = outputs[0][:, -1, :] / temperature
             filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
             next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
-            generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1)
+            generated = torch.cat((generated, next_token), dim=1)
     return generated
 
 
@@ -139,6 +139,7 @@ def main():
     parser.add_argument("--padding_text", type=str, default="")
     parser.add_argument("--length", type=int, default=20)
     parser.add_argument("--temperature", type=float, default=1.0)
+    parser.add_argument("--num_samples", type=int, default=1)
     parser.add_argument("--top_k", type=int, default=0)
     parser.add_argument("--top_p", type=float, default=0.9)
     parser.add_argument("--no_cuda", action='store_true',
@@ -176,6 +177,7 @@ def main():
         out = sample_sequence(
             model=model,
             context=context_tokens,
+            num_samples=args.num_samples,
             length=args.length,
             temperature=args.temperature,
             top_k=args.top_k,
@@ -183,9 +185,10 @@ def main():
             device=args.device,
             is_xlnet=bool(args.model_type == "xlnet"),
         )
-        out = out[0, len(context_tokens):].tolist()
-        text = tokenizer.decode(out, clean_up_tokenization_spaces=True)
-        print(text)
+        out = out[:, len(context_tokens):].tolist()
+        for o in out:
+            text = tokenizer.decode(o, clean_up_tokenization_spaces=True)
+            print(text)
         if args.prompt:
             break
     return text