huggingface
diff --git a/‎.gitignore
+2 b/‎.gitignore
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diff --git a/‎docs/source/index.rst
+1 b/‎docs/source/index.rst
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diff --git a/‎docs/source/model_doc/rag.rst
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diff --git a/‎docs/source/model_summary.rst
+22-1 b/‎docs/source/model_summary.rst
+22-1
diff --git a/‎examples/lightning_base.py
+2 b/‎examples/lightning_base.py
+2
diff --git a/‎examples/longform-qa/eli5_app.py
+1-1 b/‎examples/longform-qa/eli5_app.py
+1-1
diff --git a/‎examples/longform-qa/eli5_utils.py
+1-1 b/‎examples/longform-qa/eli5_utils.py
+1-1
diff --git a/‎examples/rag/README.md
+88 b/‎examples/rag/README.md
+88
diff --git a/‎examples/rag/__init__.py b/‎examples/rag/__init__.py
diff --git a/‎examples/rag/callbacks.py
+30 b/‎examples/rag/callbacks.py
+30
@@ -11,6 +11,7 @@ __pycache__/
 # tests and logs
 tests/fixtures
 logs/
+lightning_logs/
 
 # Distribution / packaging
 .Python
@@ -139,6 +140,7 @@ runs
 /wandb
 /examples/runs
 /examples/**/*.args
+/examples/rag/sweep
 
 # data
 /data
 
@@ -231,6 +231,7 @@ conversion utilities for the following models:
     model_doc/lxmert
     model_doc/bertgeneration
     model_doc/layoutlm
+    model_doc/rag
     internal/modeling_utils
     internal/tokenization_utils
     internal/pipelines_utils
@@ -0,0 +1,88 @@
+RAG
+----------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq models.
+RAG models retrieve docs, pass them to a seq2seq model, then marginalize to generate outputs.
+The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt to downstream tasks.
+
+It is based on the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks <https://arxiv.org/abs/2005.11401>`__ by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
+
+The abstract from the paper is the following:
+
+*Large pre-trained language models have been shown to store factual knowledge
+in their parameters, and achieve state-of-the-art results when fine-tuned on
+downstream NLP tasks. However, their ability to access and precisely manipulate
+knowledge is still limited, and hence on knowledge-intensive tasks, their
+performance lags behind task-specific architectures. Additionally, providing
+provenance for their decisions and updating their world knowledge remain open
+research problems. Pre-trained models with a differentiable access mechanism to
+explicit nonparametric memory can overcome this issue, but have so far been only
+investigated for extractive downstream tasks. We explore a general-purpose
+fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine
+pre-trained parametric and non-parametric memory for language generation. We
+introduce RAG models where the parametric memory is a pre-trained seq2seq model and
+the non-parametric memory is a dense vector index of Wikipedia, accessed with
+a pre-trained neural retriever. We compare two RAG formulations, one which
+conditions on the same retrieved passages across the whole generated sequence, the
+other can use different passages per token. We fine-tune and evaluate our models
+on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art
+on three open domain QA tasks, outperforming parametric seq2seq models and
+task-specific retrieve-and-extract architectures. For language generation tasks, we
+find that RAG models generate more specific, diverse and factual language than a
+state-of-the-art parametric-only seq2seq baseline.*
+
+
+
+RagConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RagConfig
+    :members:
+
+
+RagTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RagTokenizer
+    :members:
+
+
+Rag specific outputs
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_rag.RetrievAugLMMarginOutput
+    :members:
+
+.. autoclass:: transformers.modeling_rag.RetrievAugLMOutput
+    :members:
+
+
+RAGRetriever
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RagRetriever
+    :members:
+
+
+RagModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RagModel
+    :members: forward
+
+
+RagSequenceForGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RagSequenceForGeneration
+    :members: forward, generate
+
+
+RagTokenForGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RagTokenForGeneration
+    :members: forward, generate
@@ -654,7 +654,7 @@ DPR
    <a href="https://huggingface.co/models?filter=dpr">
        <img alt="Models" src="https://img.shields.io/badge/All_model_pages-dpr-blueviolet">
    </a>
-   <a href="model_doc/ctrl.dpr">
+   <a href="model_doc/dpr.html">
        <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-dpr-blueviolet">
    </a>
 
@@ -672,6 +672,27 @@ DPR consists in three models:
 
 DPR's pipeline (not implemented yet) uses a retrieval step to find the top k contexts given a certain question, and then it calls the reader with the question and the retrieved documents to get the answer.
 
+RAG
+----------------------------------------------
+
+.. raw:: html
+
+   <a href="https://huggingface.co/models?filter=rag">
+       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-rag-blueviolet">
+   </a>
+   <a href="model_doc/rag.html">
+       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-rag-blueviolet">
+   </a>
+
+`Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks <https://arxiv.org/abs/2005.11401>`_,
+Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela
+
+Retrieval-augmented generation ("RAG") models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq models.
+RAG models retrieve docs, pass them to a seq2seq model, then marginalize to generate outputs.
+The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt to downstream tasks.
+
+The two models RAG-Token and RAG-Sequence are available for generation.
+
 More technical aspects
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
@@ -366,6 +366,8 @@ def generic_train(
     if args.gpus > 1:
         train_params["distributed_backend"] = "ddp"
 
+    train_params["accumulate_grad_batches"] = args.accumulate_grad_batches
+
     trainer = pl.Trainer.from_argparse_args(
         args,
         weights_summary=None,
 
@@ -1,10 +1,10 @@
 import datasets
-import faiss
 import numpy as np
 import streamlit as st
 import torch
 from elasticsearch import Elasticsearch
 
+import faiss
 import transformers
 from eli5_utils import (
     embed_questions_for_retrieval,
 
@@ -5,7 +5,6 @@
 from time import time
 
 import datasets  # noqa: F401
-import faiss  # noqa: F401
 import numpy as np
 import pandas as pd
 import torch
@@ -15,6 +14,7 @@
 from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
 from tqdm import tqdm
 
+import faiss  # noqa: F401
 from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
 
 
 
@@ -0,0 +1,88 @@
+# Intro
+RAG is a seq2seq model which encapsulates two core components: a question encoder and a generator.
+During a forward pass, we encode the input with the question encoder and pass it
+to the retriever to extract relevant context documents. The documents are then prepended to the input.
+Such contextualized inputs is passed to the generator.
+
+The question encoder can be any `autoencoding` model, preferably :obj:`~transformers.DPRQuestionEncoder`, and the generator can be any `seq2seq` model, preferably :obj:`~transformers.BartForConditionalGeneration`.
+
+The model can be initialized with a :obj:`~transformers.RagRetriever` for end-to-end generation or used in combination with the outputs of a retriever in multiple steps - see examples for more details.
+The model is compatible any `autoencoding` model as the ``question_encoder`` and any `seq2seq` model with language model head as the ``generator``.
+The model has been tested with :class:`~transformers.DPRQuestionEncoder` as the ``question_encoder`` and :class:`~transformers.BartForConditionalGeneration` or :class:`~transformers.T5ForConditionalGeneration` as the ``generator``.
+
+RAG models were released with the paper `Retrieval-Augmented Generation for
+Knowledge-Intensive NLP Tasks <https://arxiv.org/abs/2005.11401>`_ by Patrick Lewis, Ethan Perez, Aleksandra Piktus et al.
+
+
+# Finetuning
+Our finetuning logic is based on scripts from [`examples/seq2seq`](https://github.com/huggingface/transformers/tree/master/examples/seq2seq).
+Follow instructions there regarding data preprocessing. A sample finetuning command:
+
+```
+python examples/rag/finetune.py \
+    --data_dir $DATA_DIR \
+    --output_dir $OUTPUT_DIR \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --model_type rag_sequence \
+    --fp16 \
+    --gpus 8
+```
+
+
+# Evaluation
+Apart from the parameters specifying the model to evaluate and some extra parameters, the evaluation script expects paths to two files:
+- `evaluation_set` - a path to a file specifying the evaluation dataset, a single datapoint per line, e.g.
+```who is the owner of reading football club```
+- `gold_data_path` - a path to a file contaning ground truth answers for datapoints from the `evaluation_set`.
+
+We expect the following formats of the gold data file:
+
+- for e2e evaluation, we support two formats of the gold file:
+    - `qa` - where a single line in the following format: input [tab] output_list, e.g.:
+    ```
+    who is the owner of reading football club	['Xiu Li Dai', 'Dai Yongge', 'Dai Xiuli', 'Yongge Dai']
+    ```
+    - `ans` - where a single line of the gold file contains the expected output string, e.g.:
+    ```
+    Xiu Li Dai
+    ```
+
+- for retrieval evaluation, we expect a tab-separated list of Wikipedia page titles constituting positive contexts for a given query, e.g. given a question `who sings does he love me with reba`, a line with ground truth retrieval data could look as follows:
+```
+Does He Love You	Does He Love You	Red Sandy Spika dress of Reba McEntire	Greatest Hits Volume Two (Reba McEntire album)	Shoot for the Moon (album)
+```
+
+## Retrieval evaluation
+
+We demonstrate how to evaluate retrieval against DPR evaluation data. You can download respective files from links listed [here](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py#L39-L45).
+
+1. Download and unzip the gold data file. We use the `biencoder-nq-dev` from https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz.
+2. Parse the unziped file using the `parse_dpr_relevance_data.py`
+```
+python examples/rag/parse_dpr_relevance_data.py --src_path path/to/unziped/biencoder-nq-dev.json --evaluation_set path/to/output/biencoder-nq-dev.questions --gold_data_path path/to/output/biencoder-nq-dev.pages
+```
+3. Run evaluation:
+```
+python examples/rag/eval_rag.py \
+    --model_name_or_path $MODEL_NAME_OR_PATH \ # model name or path of the model we're evaluating
+    --model_type rag_sequence \ # RAG model type (rag_token or rag_sequence)
+    --evaluation_set path/to/output/biencoder-nq-dev.questions \ # an input dataset for evaluation
+    --gold_data_path path/to/output/biencoder-nq-dev.pages \ # a dataset containing ground truth answers for samples from the evaluation_set
+    --predictions_path path/to/retrieval_preds.tsv  \ # name of file in which predictions will be stored
+    --eval_mode retrieval \ # indicates whether we're performing retrieval evaluation or e2e evaluation
+    --recalculate # if predictions_filename already exists, and this option is set - we regenerate the answers, otherwise we reuse the predicsion file to calculate metrics.
+```
+
+
+## End-to-end evaluation
+```
+python examples/rag/eval_rag.py \
+	--model_name_or_path $MODEL_NAME_OR_PATH \
+    --model_type rag_sequence \
+    --evaluation_set path/to/test.source \
+    --gold_data_path path/to/gold_data \
+    --predictions_path path/to/e2e_preds.txt \
+    --eval_mode e2e \ # indicates whether we're performing retrieval evaluation or e2e evaluation (default)
+    --n_docs 5 \ # You can experiment with retrieving different number of documents at evaluation time
+    --print_predictions
+```
@@ -0,0 +1,30 @@
+import logging
+import os
+
+from pytorch_lightning.callbacks import ModelCheckpoint
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_checkpoint_callback(output_dir, metric):
+    """Saves the best model by validation EM score."""
+    if metric == "rouge2":
+        exp = "{val_avg_rouge2:.4f}-{step_count}"
+    elif metric == "bleu":
+        exp = "{val_avg_bleu:.4f}-{step_count}"
+    elif metric == "em":
+        exp = "{val_avg_em:.4f}-{step_count}"
+    else:
+        raise NotImplementedError(
+            f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this function."
+        )
+
+    checkpoint_callback = ModelCheckpoint(
+        filepath=os.path.join(output_dir, exp),
+        monitor=f"val_{metric}",
+        mode="max",
+        save_top_k=3,
+        period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
+    )
+    return checkpoint_callback