Adding CTRL (squashed commit)

adding conversion script

adding first draft of modeling & tokenization

adding placeholder for test files

bunch of changes

registering the tokenizer/model/etc

tests

change link; something is very VERY wrong here

weird end-of-word thingy going on

i think the tokenization works now ; wrote the unit tests

overall structure works;load w next

the monster is alive!

works after some cleanup as well

adding emacs autosave to gitignore

currently only supporting the 48 layer one; seems to infer fine on my macbook

cleanup

fixing some documentation

fixing some documentation

tests passing?

now works on CUDA also

adding greedy?

adding greedy sampling

works well

examples/run_generation.py

@@ -14,7 +14,7 @@
 # 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.
-""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/Transformer-XL/XLNet)
+""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
 """
 from __future__ import absolute_import, division, print_function, unicode_literals
 
@@ -26,13 +26,13 @@ import torch
 import torch.nn.functional as F
 import numpy as np
 
-from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
+from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, CTRLConfig
 
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
 from transformers import XLNetLMHeadModel, XLNetTokenizer
 from transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
-
+from transformers import CTRLLMHeadModel, CTRLTokenizer
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                     datefmt = '%m/%d/%Y %H:%M:%S',
@@ -41,10 +41,11 @@ logger = logging.getLogger(__name__)
 
 MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop
 
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ())
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, CTRLConfig)), ())
 
 MODEL_CLASSES = {
     'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
+    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
     'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
     'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
     'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
@@ -103,7 +104,7 @@ def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')
     return logits
 
 
-def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, is_xlnet=False, device='cpu'):
+def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, repetition_penalty=1.0, is_xlnet=False, device='cpu'):
     context = torch.tensor(context, dtype=torch.long, device=device)
     context = context.unsqueeze(0).repeat(num_samples, 1)
     generated = context
@@ -122,9 +123,17 @@ 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][0, -1, :] / (temperature if temperature > 0 else 1.)
+
+            # reptition penalty from CTRL (https://arxiv.org/abs/1909.05858)
+            for _ in set(generated):
+                next_token_logits[_] /= repetition_penalty
+                
             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)
+            if temperature == 0: #greedy sampling:
+                next_token = torch.argmax(filtered_logits).unsqueeze(0)
+            else:
+                next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
             generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1)
     return generated
 
@@ -138,7 +147,10 @@ def main():
     parser.add_argument("--prompt", type=str, default="")
     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("--temperature", type=float, default=1.0,
+                        help="temperature of 0 implies greedy sampling")
+    parser.add_argument("--repetition_penalty", type=float, default=1.0,
+                        help="primarily useful for CTRL model; in that case, use 1.2")
     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',
@@ -146,7 +158,10 @@ def main():
     parser.add_argument('--seed', type=int, default=42,
                         help="random seed for initialization")
     args = parser.parse_args()
-
+    if args.model_type in ["ctrl"]:
+        if args.temperature > 0.7 : 
+            print('CTRL typically works better with lower temperatures (and lower top_k).')
+        
     args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
     args.n_gpu = torch.cuda.device_count()
 
@@ -180,6 +195,7 @@ def main():
             temperature=args.temperature,
             top_k=args.top_k,
             top_p=args.top_p,
+            repetition_penalty=args.repetition_penalty,
             device=args.device,
             is_xlnet=bool(args.model_type == "xlnet"),
         )