add greedy decoding and sampling

examples/run_generation.py

@@ -20,14 +20,10 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import argparse
 import logging
-from tqdm import trange
 
 import torch
-import torch.nn.functional as F
 import numpy as np
 
-from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig, XLMConfig, CTRLConfig
-
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
 from transformers import XLNetLMHeadModel, XLNetTokenizer
@@ -36,22 +32,22 @@ from transformers import CTRLLMHeadModel, CTRLTokenizer
 from transformers import XLMWithLMHeadModel, XLMTokenizer
 
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    level=logging.INFO,
+)
 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, XLMConfig, CTRLConfig)), ())
-
 MODEL_CLASSES = {
-    'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
-    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
-    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
-    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
-    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
-    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
+    "gpt2": (GPT2LMHeadModel, GPT2Tokenizer),
+    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
+    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
+    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
+    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
+    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
 }
 
 # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
@@ -75,81 +71,78 @@ def set_seed(args):
     if args.n_gpu > 0:
         torch.cuda.manual_seed_all(args.seed)
 
+#
+# Functions to prepare models' input
+#
+
+
+def prepare_ctrl_input(args, _, tokenizer, prompt_text):
+    if args.temperature > 0.7:
+        logger.info(
+            "CTRL typically works better with lower temperatures (and lower top_k)."
+        )
+
+    encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False)
+    if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
+        logger.info(
+            "WARNING! You are not starting your generation from a control code so you won't get good results"
+        )
+    return prompt_text, {}
+
 
-def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
-    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
-        Args:
-            logits: logits distribution shape (batch size x vocabulary size)
-            top_k > 0: keep only top k tokens with highest probability (top-k filtering).
-            top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
-                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
-        From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
-    """
-    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
-        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-        logits[indices_to_remove] = filter_value
-
-    if top_p > 0.0:
-        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
-
-        # Remove tokens with cumulative probability above the threshold
-        sorted_indices_to_remove = cumulative_probs > top_p
-        # Shift the indices to the right to keep also the first token above the threshold
-        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
-        sorted_indices_to_remove[..., 0] = 0
-
-        # 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
-
-
-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, is_xlm_mlm=False, xlm_mask_token=None, xlm_lang=None, device='cpu'):
-    context = torch.tensor(context, dtype=torch.long, device=device)
-    context = context.unsqueeze(0).repeat(num_samples, 1)
-    generated = context
-    with torch.no_grad():
-        for _ in trange(length):
-
-            inputs = {'input_ids': generated}
-            if is_xlnet: 
-                # XLNet is a direct (predict same token, not next token) and bi-directional model by default
-                # => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring)
-                input_ids = torch.cat((generated, torch.zeros((1, 1), dtype=torch.long, device=device)), dim=1)
-                perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device)
-                perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
-                target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float, device=device)
-                target_mapping[0, 0, -1] = 1.0  # predict last token
-                inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping}
-
-            if is_xlm_mlm and xlm_mask_token:
-                # XLM MLM models are direct models (predict same token, not next token)
-                # => need one additional dummy token in the input (will be masked and guessed)
-                input_ids = torch.cat((generated, torch.full((1, 1), xlm_mask_token, dtype=torch.long, device=device)), dim=1)
-                inputs = {'input_ids': input_ids}
-
-            if xlm_lang is not None:
-                inputs["langs"] = torch.tensor([xlm_lang] * inputs["input_ids"].shape[1], device=device).view(1, -1)
-
-            outputs = model(**inputs)  # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet/CTRL (cached hidden-states)
-            next_token_logits = outputs[0][:, -1, :] / (temperature if temperature > 0 else 1.)
-
-            # repetition penalty from CTRL (https://arxiv.org/abs/1909.05858)
-            for i in range(num_samples):
-                for _ in set(generated[i].tolist()):
-                    next_token_logits[i, _] /= repetition_penalty
-                
-            filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
-            if temperature == 0: # greedy sampling:
-                next_token = torch.argmax(filtered_logits, dim=-1).unsqueeze(-1)
+def prepare_xlm_input(args, model, tokenizer, prompt_text):
+    kwargs = {"language": None, "mask_token": None}
+
+    # Set the language
+    use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb
+    if hasattr(model.config, "lang2id") and use_lang_emb:
+        available_languages = model.config.lang2id.keys()
+        if args.xlm_language in available_languages:
+            language = args.xlm_language
         else:
-                next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
-            generated = torch.cat((generated, next_token), dim=1)
-    return generated
+            language = None
+            while language not in available_languages:
+                language = input(
+                    "Using XLM. Select language in "
+                    + str(list(available_languages))
+                    + " >>> "
+                )
+        kwargs["language"] = tokenizer.lang2id[language]
+
+    # XLM masked-language modeling (MLM) models need masked token
+    is_xlm_mlm = "mlm" in args.model_name_or_path
+    if is_xlm_mlm:
+        kwargs["mask_token"] = tokenizer.mask_token_id
+
+    return prompt_text, kwargs
+
+
+def prepare_xlnet_input(args, _, tokenizer, prompt_text):
+    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
+    return prompt_text, {}
+
+
+def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
+    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
+    return prompt_text, {}
+
+
+PREPROCESSING_FUNCTIONS = {
+    "ctrl": prepare_ctrl_input,
+    "xlm": prepare_xlm_input,
+    "xlnet": prepare_xlnet_input,
+    "transfo-xl": prepare_transfoxl_input,
+}
+
+
+def adjust_length_to_model(length, max_sequence_length):
+    if length < 0 and max_sequence_length > 0:
+        length = max_sequence_length
+    elif 0 < max_sequence_length < length:
+        length = max_sequence_length  # No generation bigger than model size
+    elif length < 0:
+        length = MAX_LENGTH  # avoid infinite loop
+    return length
 
 
 def main():
@@ -157,104 +150,81 @@ def main():
     parser.add_argument("--model_type", default=None, type=str, required=True,
                         help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
     parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
-                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
+                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
+
     parser.add_argument("--prompt", type=str, default="")
-    parser.add_argument("--padding_text", type=str, default="")
-    parser.add_argument("--xlm_lang", type=str, default="", help="Optional language when used with the XLM model.")
     parser.add_argument("--length", type=int, default=20)
-    parser.add_argument("--num_samples", type=int, default=1)
-    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',
-                        help="Avoid using CUDA when available")
-    parser.add_argument('--seed', type=int, default=42,
-                        help="random seed for initialization")
-    parser.add_argument('--stop_token', type=str, default=None,
-                        help="Token at which text generation is stopped")
+    parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped")
+
+    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("--k", type=int, default=0)
+    parser.add_argument("--p", type=float, default=0.9)
+
+    parser.add_argument("--padding_text", type=str, default="", help="Padding text for Transfo-XL and XLNet.")
+    parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")
+
+    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
+    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
     args = parser.parse_args()
 
-    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+    args.device = torch.device(
+        "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
+    )
     args.n_gpu = torch.cuda.device_count()
 
     set_seed(args)
 
+    # Initialize the model and tokenizer
+    try:
         args.model_type = args.model_type.lower()
         model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    except KeyError as ke:
+        raise ke(
+            "the model {} you specified is not supported. You are welcome to add it and open a PR :)"
+        )
+
     tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
     model = model_class.from_pretrained(args.model_name_or_path)
     model.to(args.device)
     model.eval()
 
-    if args.length < 0 and model.config.max_position_embeddings > 0:
-        args.length = model.config.max_position_embeddings
-    elif 0 < model.config.max_position_embeddings < args.length:
-        args.length = model.config.max_position_embeddings  # No generation bigger than model size 
-    elif args.length < 0:
-        args.length = MAX_LENGTH  # avoid infinite loop
-
+    args.length = adjust_length_to_model(
+        args.length, max_sequence_length=model.config.max_position_embeddings
+    )
     logger.info(args)
-    if args.model_type in ["ctrl"]:
-        if args.temperature > 0.7:
-            logger.info('CTRL typically works better with lower temperatures (and lower top_k).')
-
-    while True:
-        xlm_lang = None
-        # XLM Language usage detailed in the issues #1414
-        if args.model_type in ["xlm"] and hasattr(tokenizer, 'lang2id') and hasattr(model.config, 'use_lang_emb') \
-                and model.config.use_lang_emb:
-            if args.xlm_lang:
-                language = args.xlm_lang
-            else:
-                language = None
-                while language not in tokenizer.lang2id.keys():
-                    language = input("Using XLM. Select language in " + str(list(tokenizer.lang2id.keys())) + " >>> ")
-            xlm_lang = tokenizer.lang2id[language]
 
-        # XLM masked-language modeling (MLM) models need masked token (see details in sample_sequence)
-        is_xlm_mlm = args.model_type in ["xlm"] and 'mlm' in args.model_name_or_path
-        if is_xlm_mlm:
-            xlm_mask_token = tokenizer.mask_token_id
-        else:
-            xlm_mask_token = None
-
-        raw_text = args.prompt if args.prompt else input("Model prompt >>> ")
-        if args.model_type in ["transfo-xl", "xlnet"]:
-            # Models with memory likes to have a long prompt for short inputs.
-            raw_text = (args.padding_text if args.padding_text else PADDING_TEXT) + raw_text
-        context_tokens = tokenizer.encode(raw_text, add_special_tokens=False)
-        if args.model_type == "ctrl":
-            if not any(context_tokens[0] == x for x in tokenizer.control_codes.values()):
-                logger.info("WARNING! You are not starting your generation from a control code so you won't get good results")
-        out = sample_sequence(
-            model=model,
-            context=context_tokens,
-            num_samples=args.num_samples,
+    prompt_text = args.prompt if args.prompt else input("Model prompt >>> ")
+
+    # Different models need different input formatting and/or extra arguments
+    requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
+    model_kwargs = {}
+    if requires_preprocessing:
+        prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
+        prompt_text, model_kwargs = prepare_input(args, model, tokenizer, prompt_text)
+    encoded_prompt = torch.tensor(tokenizer.encode(prompt_text, add_special_tokens=False)).unsqueeze(0)
+
+    output_sequences = model.decode(
+        prompt_ids=encoded_prompt,
         length=args.length,
         temperature=args.temperature,
-            top_k=args.top_k,
-            top_p=args.top_p,
+        k=args.k,
+        p=args.p,
         repetition_penalty=args.repetition_penalty,
-            is_xlnet=bool(args.model_type == "xlnet"),
-            is_xlm_mlm=is_xlm_mlm,
-            xlm_mask_token=xlm_mask_token,
-            xlm_lang=xlm_lang,
         device=args.device,
+        **model_kwargs,
     )
-        out = out[:, len(context_tokens):].tolist()
-        for o in out:
-            text = tokenizer.decode(o, clean_up_tokenization_spaces=True)
+
+    generated_sequence = output_sequences.tolist()[
+        encoded_prompt.size(1) :
+    ]  # adapted to case where num_samples > 1
+    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
     text = text[: text.find(args.stop_token) if args.stop_token else None]
 
     print(text)
 
-        if args.prompt:
-            break
     return text
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

transformers/modeling_encoder_decoder.py

@@ -18,11 +18,14 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import logging
 import os
+import warnings
 
 import torch
 from torch import nn
+from tqdm import trange
 
 from .modeling_auto import AutoModel, AutoModelWithLMHead
+from .modeling_utils import Sampler
 
 logger = logging.getLogger(__name__)
 
@@ -117,8 +120,7 @@ class PreTrainedEncoderDecoder(nn.Module):
         kwargs_common = {
             argument: value
             for argument, value in kwargs.items()
-            if not argument.startswith("encoder_")
-            and not argument.startswith("decoder_")
+            if not argument.startswith("encoder_") and not argument.startswith("decoder_")
         }
         kwargs_decoder = kwargs_common.copy()
         kwargs_encoder = kwargs_common.copy()
@@ -186,51 +188,151 @@ class PreTrainedEncoderDecoder(nn.Module):
                 Indices of decoder input sequence tokens in the vocabulary.
             kwargs: (`optional`) Remaining dictionary of keyword arguments.
         """
-        # keyword arguments come in 3 flavors: encoder-specific (prefixed by
-        # `encoder_`), decoder-specific (prefixed by `decoder_`) and those
-        # that apply to the model as whole.
-        # We let the specific kwargs override the common ones in case of conflict.
+        kwargs_encoder, kwargs_decoder = self.prepare_model_kwargs(**kwargs)
+
+        # Encode if needed (training, first prediction pass)
+        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
+        if encoder_hidden_states is None:
+            encoder_outputs = self.encoder(encoder_input_ids, **kwargs_encoder)
+            encoder_hidden_states = encoder_outputs[0]
+        else:
+            encoder_outputs = ()
+
+        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
+        decoder_outputs = self.decoder(decoder_input_ids, encoder_hidden_states, **kwargs_decoder)
+
+        return decoder_outputs + encoder_outputs
+
+    def decode(
+        self,
+        encoder_input_ids,
+        decoder_prompt_ids=None,
+        device=torch.device("cpu"),
+        length=10,
+        do_sample=False,
+        temperature=1.0,
+        k=9,
+        p=0.,
+        repetition_penalty=1.,
+        **kwargs
+    ):
+        """ Generic sequence generator for encoder-decoder models.
+
+        For encoder-decoders the generation consists in:
+        - Performing a forward pass through the encoder once;
+        - Pass the encoder's hidden states to a decoding mechanism that
+          repeatedly calls the decoder to generate sequences.
+
+        The method currently supports greedy decoding and sampling. See the
+        documentation of the `Sampler` class for more information about the
+        parameters related to sampling.
+
+        Params:
+            **encoder_input_ids**: `torch.LongTensor` of shape (1, sequence_length)
+                The sequence to encode.
+            **decoder_prompt_ids**: (`optional`) `torch.LongTensor` of shape (1, sequence_length)
+                The sequence used as a prompt for the generation. If `None` the method initializes
+                it as an empty `torch.LongTensor` of shape (1,)
+            **device**: (`optional`) `torch.device`
+                The device on which the prompt_ids will be initialized if not provided.
+            **length**: (`optional`) int
+                The length of the sequence to be generated.
+            **do_sample**: (`optional`) bool
+                If set to `False` we use greedy decoding; otherwise sampling.
+            **temperature**: (`optional`) float
+                The value used to module the next token probabilities.
+            **k**: (`optional`) int
+                The parameter used for k-filtering.
+            **p**: (`optional`) float
+                The parameter for nucleus sampling. Must be between 0 and 1.
+            **repetition_penalty**: (`optional`) float
+                The parameter for repetition penalty.
+        """
+        if decoder_prompt_ids is None:
+            decoder_prompt_ids = torch.tensor([[]], dtype=torch.long, device=device)
+
+        # When the model does not have a LM head `get_output_embeddings`
+        # returns `None`. We use this mechanism to determine whether we
+        # should proceed with decoding or not.
+        if self.decoder.get_output_embeddings() is None:
+            raise AttributeError("You tried do generated sequences with a decoder that does not have a LM Head.")
+
+        # The followings checks that the decoder is on the same device as the one
+        # that is specified. It only works for models that fit on one GPU.
+        decoder_device = next(self.decoder.parameters()).device
+        if decoder_device != decoder_prompt_ids.device:
+            warnings.warn(
+                "The decoder is not on the same device as the prompt. Expected {}, got {}.".format(
+                    decoder_prompt_ids.device, decoder_device
+                )
+            )
+
+        kwargs_encoder, kwargs_decoder = self.prepare_model_kwargs(**kwargs)
+        with torch.no_grad():
+            encoder_outputs = self.encoder(encoder_input_ids, **kwargs)
+            encoder_hidden_states = encoder_outputs[0]
+            kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
+
+        sampler_config = {
+            "k": k,
+            "p": p,
+            "do_sample": do_sample,
+            "temperature": temperature,
+            "repetition_penalty": repetition_penalty,
+        }
+        return self._greedy_decode_or_sample(
+            decoder_prompt_ids, length, sampler_config, **kwargs_decoder
+        )
+
+    def _greedy_decode_or_sample(self, prompt_ids, length, sampler_config, **kwargs_decoder):
+        sampler = Sampler(**sampler_config)
+        with torch.no_grad():
+            generated_sequence = prompt_ids
+            for _ in trange(length):
+                arguments = self.decoder._prepare_inputs_for_decoding(generated_sequence, **kwargs_decoder)
+                outputs = self.decoder(**arguments)
+                next_tokens_logits = outputs[0][:, -1, :]
+                next_tokens = sampler.get_one_token(next_tokens_logits, generated_sequence)
+                generated_sequence = torch.cat((generated_sequence, next_tokens), dim=1)
+
+        return generated_sequence.squeeze(0)
+
+    @staticmethod
+    def prepare_model_kwargs(**kwargs):
+        """ Prepare the encoder and decoder's keyword arguments.
+
+        Keyword arguments come in 3 flavors:
+        - encoder-specific (prefixed by `encoder_`)
+        - decoder-specific (prefixed by `decoder_`)
+        - those that apply to the model as whole.
+
+        We let the specific kwargs override the common ones in case of
+        conflict.
+        """
         kwargs_common = {
             argument: value
             for argument, value in kwargs.items()
-            if not argument.startswith("encoder_")
-            and not argument.startswith("decoder_")
+            if not argument.startswith("encoder_") and not argument.startswith("decoder_")
         }
-        kwargs_decoder = kwargs_common.copy()
-        kwargs_encoder = kwargs_common.copy()
-        kwargs_encoder.update(
+        decoder_kwargs = kwargs_common.copy()
+        encoder_kwargs = kwargs_common.copy()
+        encoder_kwargs.update(
             {
                 argument[len("encoder_") :]: value
                 for argument, value in kwargs.items()
                 if argument.startswith("encoder_")
             }
         )
-        kwargs_decoder.update(
+        decoder_kwargs.update(
             {
                 argument[len("decoder_") :]: value
                 for argument, value in kwargs.items()
                 if argument.startswith("decoder_")
             }
         )
+        decoder_kwargs["encoder_attention_mask"] = encoder_kwargs.get("attention_mask", None)
 
-        # Encode if needed (training, first prediction pass)
-        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
-        if encoder_hidden_states is None:
-            encoder_outputs = self.encoder(encoder_input_ids, **kwargs_encoder)
-            encoder_hidden_states = encoder_outputs[
-                0
-            ]  # output the last layer hidden state
-        else:
-            encoder_outputs = ()
-
-        # Decode
-        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
-        kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get(
-            "attention_mask", None
-        )
-        decoder_outputs = self.decoder(decoder_input_ids, **kwargs_decoder)
-
-        return decoder_outputs + encoder_outputs
+        return encoder_kwargs, decoder_kwargs
 
 
 class Model2Model(PreTrainedEncoderDecoder):

transformers/modeling_transfo_xl.py

@@ -36,7 +36,7 @@ from torch.nn.parameter import Parameter
 
 from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
 from .configuration_transfo_xl import TransfoXLConfig
-from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax, sample_logits
+from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax, sample_logits, LogUniformSampler
 from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
@@ -908,3 +908,11 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
                 outputs = [softmax_output, None] + outputs
 
         return outputs  # (loss), logits or None if labels is not None (speed up adaptive softmax), new_mems, (all hidden states), (all attentions)
+
+    def get_output_embeddings(self):
+        """ Double-check if you are using adaptive softmax.
+        """
+        if self.sample_softmax > 0:
+            return self.out_layer
+        else:
+            return self.crit.out_layers[-1]

transformers/modeling_utils.py

@@ -23,12 +23,14 @@ import json
 import logging
 import os
 from io import open
+import warnings
 
 import six
 import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss
 from torch.nn import functional as F
+from tqdm import trange
 
 from .configuration_utils import PretrainedConfig
 from .file_utils import cached_path, WEIGHTS_NAME, TF_WEIGHTS_NAME, TF2_WEIGHTS_NAME
@@ -87,6 +89,93 @@ class PreTrainedModel(nn.Module):
     def base_model(self):
         return getattr(self, self.base_model_prefix, self)
 
+    def decode(self,
+               prompt_ids=None,
+               device=torch.device('cpu'),
+               length=10,
+               do_sample=False,
+               temperature=1.,
+               k=9,
+               p=0,
+               repetition_penalty=1,
+               **model_kwargs):
+        """ Generic sequence generator for single-stack models with a LM head.
+
+        The method currently supports greedy decoding and sampling. See the
+        documentation of the `Sampler` class for more information about the
+        parameters related to sampling.
+
+        Params:
+            **encoder_input_ids**: `torch.LongTensor` of shape (1, sequence_length)
+                The sequence to encode.
+            **decoder_prompt_ids**: (`optional`) `torch.LongTensor` of shape (1, sequence_length)
+                The sequence used as a prompt for the generation. If `None` the method initializes
+                it as an empty `torch.LongTensor` of shape (1,)
+            **device**: (`optional`) `torch.device`
+                The device on which the prompt_ids will be initialized if not provided.
+            **length**: (`optional`) int
+                The length of the sequence to be generated.
+            **do_sample**: (`optional`) bool
+                If set to `False` we use greedy decoding; otherwise sampling.
+            **temperature**: (`optional`) float
+                The value used to module the next token probabilities.
+            **k**: (`optional`) int
+                The parameter used for k-filtering.
+            **p**: (`optional`) float
+                The parameter for nucleus sampling. Must be between 0 and 1.
+            **repetition_penalty**: (`optional`) float
+                The parameter for repetition penalty.
+        """
+
+        if prompt_ids is None:
+            prompt_ids = torch.tensor([[]], dtype=torch.long, device=device)
+
+        # When the model does not have a LM head `get_output_embeddings`
+        # returns `None`. We use this mechanism to determine whether we
+        # should proceed with decoding or not.
+        if self.get_output_embeddings() is None:
+            raise AttributeError("You tried do generated sequences with a model that does not have a LM Head.")
+
+        # The followings checks that the model is on the same device as the one
+        # that is specified. It only works for models that fit on one GPU.
+        model_device = next(self.parameters()).device
+        if model_device != prompt_ids.device:
+            warnings.warn(
+                "The model is not on the same device as the prompts. Expected {}, got {}.".format(
+                    prompt_ids.device, model_device
+                )
+            )
+
+        sampler_config = {
+            "k": k,
+            "p": p,
+            "do_sample": do_sample,
+            "temperature": temperature,
+            "repetition_penalty": repetition_penalty,
+        }
+        return self._greedy_decode_or_sample(prompt_ids, length, sampler_config, **model_kwargs)
+
+    def _greedy_decode_or_sample(self, prompt_ids, length, sampler_config, **model_kwargs):
+        """ Generate text using greedy decoding or by sampling tokens."""
+        sampler = Sampler(**sampler_config)
+        generated_sequence = prompt_ids
+        with torch.no_grad():
+            for _ in trange(length):
+                arguments = self._prepare_inputs_for_decoding(generated_sequence, **model_kwargs)
+                outputs = self(**arguments)
+                next_tokens_logits = outputs[0][:, -1, :]
+                next_tokens = sampler.get_one_token(
+                    next_tokens_logits, generated_sequence
+                )
+                generated_sequence = torch.cat((generated_sequence, next_tokens), dim=1)
+
+        return generated_sequence.squeeze(0)
+
+    def _prepare_inputs_for_decoding(self, input_ids, **kwargs):
+        arguments = {"input_ids": input_ids}
+        arguments.update(kwargs)
+        return arguments
+
     def get_input_embeddings(self):
         """ Get model's input embeddings
         """
@@ -859,3 +948,143 @@ def prune_layer(layer, index, dim=None):
         return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
     else:
         raise ValueError("Can't prune layer of class {}".format(layer.__class__))
+
+
+class Sampler(object):
+    r""" Sampler is used to generate sequences of ids from logit inputs.
+
+    Greedy decoding, which consists in chosing the most probable token at each
+    step, is the default behaviour. Sampling with varying temperature, top_k
+    and nucleus filtering is also implemented.
+
+    Attributes:
+        **device**: ``torch.device``
+            Device on which the computations will be run.
+        **do_sample**: bool
+            Whether to sample or do greedy decoding.
+        **k**: int between 0 and vocab_size
+            Parameter for the top-k filtering
+        **p**: float between 0 and 1
+            Parameter for the nucleus filtering
+        **temperature**: strictly positive float
+            Parameter used to modulate the distribution over ids. Low temperatures
+            put more emphasis on highly probably token while high temperatures tend
+            to smooth the probability distribution.
+        **repetition_penalty**: strictly postitive float
+            The penalty applied to repeating ids
+    """
+
+    def __init__(
+        self, do_sample=False, k=9, p=0.0, temperature=1.0, repetition_penalty=1.0
+    ):
+        self.k = k
+        self.p = p
+        self.do_sample = do_sample
+        self.temperature = temperature
+        self.repetition_penalty = repetition_penalty
+
+        self.do_apply_repetition_penalty = True if repetition_penalty > 1 else False
+
+        if self.p > 1:
+            warnings.warn(
+                """You are trying to apply nucleus filtering with a value of p greater than 1 ({}).
+                However p is a probability and its value must lie between 0 and 1. In effect, no filtering
+                will be applied. If this is not the behavior you expect, change the value of p.""".format(
+                    self.p
+                )
+            )
+
+    def get_one_token(self, next_token_logits, past_sequence):
+        logits = self.apply_repetition_penalty(next_token_logits, past_sequence)
+        if self.do_sample:
+            logits = self.apply_temperature(logits)
+            logits = self.apply_top_k_filter(logits)
+            logits = self.apply_nucleus_filter(logits)
+            return torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+        return torch.argmax(logits, dim=-1).unsqueeze(-1)
+
+    def apply_repetition_penalty(self, logits, past_sequence):
+        """ Apply a penalty to tokens that appear more than once in the
+        generated sequence.
+
+        .. Keskar, Nitish Shirish, et al. "Ctrl: A conditional transformer
+           language model for controllable generation." arXiv preprint
+           arXiv:1909.05858 (2019).
+        """
+        if self.do_apply_repetition_penalty:
+            generated_token_idx = set(past_sequence[0].tolist())
+            for token_idx in generated_token_idx:
+                logits[0, token_idx] /= self.repetition_penalty
+        return logits
+
+    def apply_temperature(self, logits):
+        """ Shape the tokens' distribution through temperature. The higher the value
+        of the temperature, the more skewed towards high probability events the
+        distribution is.
+
+        .. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning.
+        MIT press, 2016.
+        """
+        # when dividing a float by 0, torch returns inf which in turns breaks the
+        # multinomial with an error message that is not very helpful. It is better
+        # for the user to break the execution and explain why.
+        if self.temperature == 0:
+            raise ZeroDivisionError(
+                """You are trying to sample with a temperature equal to 0.
+                If you wanted to do greedy sampling, set instead `do_sample` to False.
+                Otherwise set the temperature to a value different from 0."""
+            )
+        return logits / self.temperature
+
+    def apply_top_k_filter(self, logits):
+        """ Use the probability distribution of the tokens to determine the set
+        to be sampled from. Specifically we select the set of size k such that
+        the sum of its items' probabilities is maximum.
+
+        .. Fan, Angela, Mike Lewis, and Yann Dauphin. "Hierarchical neural
+        story generation." arXiv preprint arXiv:1805.04833 (2018).
+        """
+        if self.k > 0:
+            vocabulary_size = logits.size(-1)
+            if self.k > vocabulary_size:
+                warnings.warn(
+                    """You provided a value for k ({}) that is larger than the vocabulary size ({}).
+                    We adjusted k's value to the vocabulary size; if that was what you intended to do
+                    we recommend setting k to 0 instead. It this is not the behavior you expected,
+                    choose a value of k that is smaller than the vocabulary size.""".format(
+                        self.k, vocabulary_size
+                    )
+                )
+                self.k = vocabulary_size
+
+            indices_to_remove = logits < torch.topk(logits, self.k)[0][..., -1, None]
+            logits[indices_to_remove] = -float("Inf")
+
+        return logits
+
+    def apply_nucleus_filter(self, logits):
+        """ Use the probability distribution of the tokens to determine the set
+        to be sampled from. Specifically, choose the smallest set such that the
+        sum of its items' probabilities is greater than a number p in [0,1].
+
+        .. Holtzman, Ari, et al. "The curious case of neural text
+           degeneration." arXiv preprint arXiv:1904.09751 (2019).
+        """
+        if self.p > 0:
+            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+            sorted_probabilities = F.softmax(sorted_logits, dim=-1)
+            cumulative_probabilities = torch.cumsum(sorted_probabilities, dim=-1)
+
+            # Remove tokens with cumulative probability above the threshold,
+            # but keep the first token above the threshold.
+            sorted_indices_to_remove = cumulative_probabilities > self.p
+            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+            sorted_indices_to_remove[..., 0] = 0
+
+            # 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] = -float("Inf")
+
+        return logits

transformers/modeling_xlm.py

@@ -657,6 +657,33 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
 
         return outputs
 
+    def _prepare_inputs_for_decoding(self, input_ids, **model_kwargs):
+        mask_token = model_kwargs.pop("mask_token", None)
+        language = model_kwargs.pop("language", None)
+        input_ids = self._append_mask_token(input_ids, mask_token)
+        langs = self._create_language_embeddings(input_ids, language)
+        arguments = {"input_ids": input_ids, "langs": langs}
+        arguments.update(model_kwargs)
+
+        return arguments
+
+    @staticmethod
+    def _append_mask_token(sequence, mask_token_id):
+        """ Append a [MASK] token at the end of the sequence that the MLM model
+        is going to try to predict.
+        """
+        if mask_token_id is not None:
+            tokens_to_append = torch.full((1, 1), mask_token_id, dtype=torch.long)
+            return torch.cat((sequence, tokens_to_append), dim=1)
+
+        return sequence
+
+    @staticmethod
+    def _create_language_embeddings(sequence, language):
+        if language is not None:
+            return torch.tensor([language] * sequence.shape[1]).view(1, -1)
+        return None
+
 
 @add_start_docstrings("""XLM Model with a sequence classification/regression head on top (a linear layer on top of
     the pooled output) e.g. for GLUE tasks. """,

transformers/modeling_xlnet.py

@@ -972,6 +972,40 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
 
         return outputs  # return (loss), logits, (mems), (hidden states), (attentions)
 
+    def _prepare_inputs_for_decoding(self, input_ids, **model_kwargs):
+        input_ids = self._add_dummy_token(input_ids)
+        perm_mask = self._create_perm_mask(input_ids)
+        target_mapping = self._create_target_mapping(input_ids)
+        arguments = {
+            "input_ids": input_ids,
+            "perm_mask": perm_mask,
+            "target_mapping": target_mapping,
+        }
+        return arguments
+
+    @staticmethod
+    def _add_dummy_token(sequence):
+        dummy = torch.zeros((sequence.size(0), 1), dtype=torch.long)
+        return torch.cat((sequence, dummy), dim=1)
+
+    @staticmethod
+    def _create_perm_mask(sequence):
+        mask = torch.zeros(
+            (sequence.shape[0], sequence.shape[1], sequence.shape[1]),
+            dtype=torch.float,
+        )
+        mask[:, :, -1] = 1.0  # Previous tokens don't see last token
+        return mask
+
+    @staticmethod
+    def _create_target_mapping(sequence):
+        target_mapping = torch.zeros(
+            (sequence.shape[0], 1, sequence.shape[1]),
+            dtype=torch.float,
+        )
+        target_mapping[0, 0, -1] = 1.0  # predict last token
+        return target_mapping
+
 
 @add_start_docstrings("""XLNet Model with a sequence classification/regression head on top (a linear layer on top of
     the pooled output) e.g. for GLUE tasks. """,

transformers/tests/sampling_test.py

+# coding=utf-8
+import sys
+import unittest
+
+import numpy as np
+import pytest
+
+from transformers import is_torch_available
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        BertConfig,
+        BertModel,
+        GPT2Config,
+        GPT2LMHeadModel,
+        OpenAIGPTConfig,
+        OpenAIGPTLMHeadModel,
+        TransfoXLConfig,
+        TransfoXLLMHeadModel,
+        XLMConfig,
+        XLMWithLMHeadModel,
+        XLNetConfig,
+        XLNetLMHeadModel,
+        Model2Model,
+    )
+    from transformers.modeling_utils import Sampler
+else:
+    pytestmark = pytest.mark.skip("Require Torch")
+
+
+class SamplerTest(unittest.TestCase):
+    def test_nucleus_sampling(self):
+        inf = -float("Inf")
+        test_cases = (
+            {
+                "p": 0,
+                "logits": torch.tensor([0.3, 0.1, 0.2]),
+                "expected": torch.tensor([0.3, 0.1, 0.2]),
+            },
+            {
+                "p": 0.01,
+                "logits": torch.tensor([0.3, 0.1, 0.2]),
+                "expected": torch.tensor([0.3, inf, inf]),
+            },
+            {
+                "p": 1,
+                "logits": torch.tensor([0.3, 0.1, 0.2]),
+                "expected": torch.tensor([0.3, 0.1, 0.2]),
+            },
+            {
+                "p": 0.2,
+                "logits": torch.tensor([0.7, 0.1, 0.2]),
+                "expected": torch.tensor([0.7, inf, inf]),
+            },
+            {
+                "p": 0.71,
+                "logits": torch.tensor([0.7, 0.1, 0.2]),
+                "expected": torch.tensor([0.7, inf, 0.2]),
+            },
+            {
+                "p": 0.71,
+                "logits": torch.tensor([0.1, 0.7, 0.2]),
+                "expected": torch.tensor([inf, 0.7, 0.2]),
+            },
+            {
+                "p": 0.71,
+                "logits": torch.tensor([0.7, 0.2, 0.1]),
+                "expected": torch.tensor([0.7, 0.2, inf]),
+            },
+            {
+                "p": 0.91,
+                "logits": torch.tensor([0.7, 0.1, 0.2]),
+                "expected": torch.tensor([0.7, 0.1, 0.2]),
+            },
+        )
+        for case in test_cases:
+            config = {
+                "do_sample": True,
+                "temperature": 1.0,
+                "k": 0,
+                "p": case["p"],
+                "repetition_penalty": 1.0,
+            }
+            sampler = Sampler(**config)
+            filtered_logits = sampler.apply_nucleus_filter(case["logits"])
+            np.testing.assert_array_equal(case["expected"].numpy(), filtered_logits.numpy())
+
+    def test_top_k_filter(self):
+        inf = -float("Inf")
+        test_cases = (
+            {
+                "k": 0,
+                "logits": torch.tensor([0.7, 0.1, 0.2]),
+                "expected": torch.tensor([0.7, 0.1, 0.2]),
+            },
+            {
+                "k": 1,
+                "logits": torch.tensor([0.7, 0.1, 0.2]),
+                "expected": torch.tensor([0.7, inf, inf]),
+            },
+            {
+                "k": 2,
+                "logits": torch.tensor([0.7, 0.1, 0.2]),
+                "expected": torch.tensor([0.7, inf, 0.2]),
+            },
+            {
+                "k": 3,
+                "logits": torch.tensor([0.7, 0.1, 0.2]),
+                "expected": torch.tensor([0.7, 0.1, 0.2]),
+            },
+        )
+        for case in test_cases:
+            config = {
+                "do_sample": True,
+                "temperature": 1.0,
+                "k": case["k"],
+                "p": 0,
+                "repetition_penalty": 1.0,
+            }
+            sampler = Sampler(**config)
+            filtered_logits = sampler.apply_top_k_filter(case["logits"])
+            np.testing.assert_array_equal(case["expected"].numpy(), filtered_logits.numpy())
+
+    @pytest.mark.skipif(sys.version_info < (3, 2), reason="assertWarns() requires Python >= 3.2")
+    def test_wrong_k_value(self):
+        case = {"k": 10, "vocab_size": 5}
+        config = {
+            "do_sample": True,
+            "temperature": 1.0,
+            "k": case["k"],
+            "p": 0,
+            "repetition_penalty": 1.0,
+        }
+        sampler = Sampler(**config)
+        next_token_logits = torch.rand(case["vocab_size"]).unsqueeze(0)
+        past_sequence = torch.tensor([])
+        with self.assertWarns(UserWarning):
+            _ = sampler.get_one_token(next_token_logits, past_sequence)
+
+    def test_zero_temperature(self):
+        temperature = 0
+        config = {
+            "do_sample": True,
+            "temperature": temperature,
+            "k": 0,
+            "p": 0,
+            "repetition_penalty": 1.0,
+        }
+        sampler = Sampler(**config)
+        next_token_logits = torch.rand(10).unsqueeze(0)
+        past_sequence = torch.tensor([])
+        with self.assertRaises(ZeroDivisionError):
+            _ = sampler.get_one_token(next_token_logits, past_sequence)
+
+
+class SamplerSingleStackTest(unittest.TestCase):
+    def test_raises_exception_when_no_LM_head(self):
+        models = [BertModel(BertConfig())]
+        for model in models:
+            with self.assertRaises(AttributeError):
+                model.decode()
+
+    @pytest.mark.slow
+    def test_forward_pass_and_output_length(self):
+        models = {
+            "XLNet": XLNetLMHeadModel(XLNetConfig()),
+            "XLM": XLMWithLMHeadModel(XLMConfig()),
+            "TransfoXL": TransfoXLLMHeadModel(TransfoXLConfig()),
+            "GPT2": GPT2LMHeadModel(GPT2Config()),
+            "GPT": OpenAIGPTLMHeadModel(OpenAIGPTConfig()),
+        }
+        kwargs = {
+            "XLNet": {},
+            "XLM": {"mask_token": 0},
+            "TransfoXL": {},
+            "GPT2": {},
+            "GPT": {},
+        }
+        prompt = torch.tensor([[1, 2, 3]], dtype=torch.long)
+        generated_length = 5
+        expected_length = 8
+
+        for name, model in models.items():
+            kwargs_model = kwargs[name]
+            output = model.decode(prompt_ids=prompt, length=generated_length, **kwargs_model)
+            self.assertEqual(len(output), expected_length)
+
+
+class SamplerEncoderDecoderTest(unittest.TestCase):
+    @pytest.mark.slow
+    def test_forward_pass_and_output_length(self):
+        model = Model2Model.from_pretrained("bert-base-uncased")
+
+        encoder_input_ids = torch.tensor([[1, 2, 3]], dtype=torch.long)
+        prompt = torch.tensor([[1, 2, 3]], dtype=torch.long)
+        generated_length = 5
+        expected_length = 8
+
+        output = model.decode(
+            encoder_input_ids,
+            decoder_prompt_ids=prompt,
+            k=2,
+            p=0.5,
+            repetition_penalty=2,
+            length=generated_length,
+        )
+        self.assertEqual(len(output), expected_length)
+
+
+if __name__ == "__main__":
+    unittest.main()