[FLAX] Question Answering Example (#13649)

* flax qa example

* Updated README:  Added Large model

* added utils_qa.py FULL_COPIES

* Updates:
1. Copyright Year updated
2. added dtype arg
3. passing seed and dtype to load model
4. Check eval flag before running eval

* updated README

* updated code comment

examples/flax/question-answering/README.md

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+<!---
+Copyright 2021 The Google Flax Team Authors and HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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.
+-->
+
+# Question Answering examples
+
+Based on the script [`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/flax/question-answering/run_qa.py).
+
+**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
+uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
+[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version
+of the script.
+
+
+The following example fine-tunes BERT on SQuAD:
+
+To begin with it is recommended to create a model repository to save the trained model and logs.
+Here we call the model `"bert-qa-squad-test"`, but you can change the model name as you like.
+
+You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
+you are logged in) or via the command line:
+
+```
+huggingface-cli repo create bert-qa-squad-test
+```
+
+Next we clone the model repository to add the tokenizer and model files.
+
+```
+git clone https://huggingface.co/<your-username>/bert-qa-squad-test
+```
+
+Great, we have set up our model repository. During training, we will automatically
+push the training logs and model weights to the repo.
+
+Next, let's add a symbolic link to the `run_qa.py`.
+
+```bash
+export MODEL_DIR="./bert-qa-squad-test"
+ln -s ~/transformers/examples/flax/question-answering/run_qa.py run_qa.py
+```
+
+```bash
+python run_qa.py \
+  --model_name_or_path bert-base-uncased \
+  --dataset_name squad \
+  --do_train   \
+  --do_eval   \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 2 \
+  --per_device_train_batch_size 12 \
+  --output_dir ${MODEL_DIR} \
+  --eval_steps 1000 \
+  --push_to_hub
+```
+
+Using the command above, the script will train for 2 epochs and run eval after each epoch. 
+Metrics and hyperparameters are stored in Tensorflow event files in `--output_dir`.
+You can see the results by running `tensorboard` in that directory:
+
+```bash
+$ tensorboard --logdir .
+```
+
+or directly on the hub under *Training metrics*.
+
+Training with the previously defined hyper-parameters yields the following results:
+
+```bash
+f1 = 88.62
+exact_match = 81.34
+```
+
+sample Metrics - [tfhub.dev](https://tensorboard.dev/experiment/6gU75Hx8TGCnc6tr4ZgI9Q)
+
+Here is an example training on 4 TITAN RTX GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python run_qa.py   \
+--model_name_or_path bert-large-uncased-whole-word-masking   \
+--dataset_name squad   \
+--do_train   \
+--do_eval   \
+--per_device_train_batch_size 6   \
+--learning_rate 3e-5   \
+--num_train_epochs 2   \
+--max_seq_length 384   \
+--doc_stride 128   \
+--output_dir /tmp/wwm_uncased_finetuned_squad/ \
+--eval_steps 1000
+```
+
+Training with the previously defined hyper-parameters yields the following results:
+
+```bash
+f1 = 93.31
+exact_match = 87.04
+```
+
+
+### Usage notes
+
+Note that when contexts are long they may be split into multiple training cases, not all of which may contain
+the answer span. 
+
+As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user
+inputs as well.
+
+### Memory usage and data loading
+
+One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small
+enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
+data streaming.

examples/flax/question-answering/requirements.txt

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+datasets >= 1.8.0
+jax>=0.2.17
+jaxlib>=0.1.68
+flax>=0.3.4
+optax>=0.0.8

examples/flax/question-answering/run_qa.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""
+Fine-tuning the library models for question answering.
+"""
+# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
+
+import logging
+import os
+import random
+import sys
+import time
+from dataclasses import dataclass, field
+from itertools import chain
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import datasets
+import numpy as np
+from datasets import load_dataset, load_metric
+from tqdm import tqdm
+
+import jax
+import jax.numpy as jnp
+import optax
+import transformers
+from flax import struct, traverse_util
+from flax.jax_utils import replicate, unreplicate
+from flax.metrics import tensorboard
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, onehot, shard
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    EvalPrediction,
+    FlaxAutoModelForQuestionAnswering,
+    HfArgumentParser,
+    PreTrainedTokenizerFast,
+    TrainingArguments,
+)
+from transformers.utils import check_min_version
+from utils_qa import postprocess_qa_predictions
+
+
+logger = logging.getLogger(__name__)
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.11.0.dev0")
+
+Array = Any
+Dataset = datasets.arrow_dataset.Dataset
+PRNGKey = Any
+
+
+# region Arguments
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    test_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input test data file to evaluate the perplexity on (a text file)."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_seq_length: int = field(
+        default=384,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded."
+        },
+    )
+    pad_to_max_length: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to pad all samples to `max_seq_length`. "
+            "If False, will pad the samples dynamically when batching to the maximum length in the batch (which can "
+            "be faster on GPU but will be slower on TPU)."
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+            "value if set."
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+            "value if set."
+        },
+    )
+    version_2_with_negative: bool = field(
+        default=False, metadata={"help": "If true, some of the examples do not have an answer."}
+    )
+    null_score_diff_threshold: float = field(
+        default=0.0,
+        metadata={
+            "help": "The threshold used to select the null answer: if the best answer has a score that is less than "
+            "the score of the null answer minus this threshold, the null answer is selected for this example. "
+            "Only useful when `version_2_with_negative=True`."
+        },
+    )
+    doc_stride: int = field(
+        default=128,
+        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
+    )
+    n_best_size: int = field(
+        default=20,
+        metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
+    )
+    max_answer_length: int = field(
+        default=30,
+        metadata={
+            "help": "The maximum length of an answer that can be generated. This is needed because the start "
+            "and end predictions are not conditioned on one another."
+        },
+    )
+
+    def __post_init__(self):
+        if (
+            self.dataset_name is None
+            and self.train_file is None
+            and self.validation_file is None
+            and self.test_file is None
+        ):
+            raise ValueError("Need either a dataset name or a training/validation file/test_file.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
+            if self.test_file is not None:
+                extension = self.test_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
+
+
+# endregion
+
+# region Create a train state
+def create_train_state(
+    model: FlaxAutoModelForQuestionAnswering,
+    learning_rate_fn: Callable[[int], float],
+    num_labels: int,
+    training_args: TrainingArguments,
+) -> train_state.TrainState:
+    """Create initial training state."""
+
+    class TrainState(train_state.TrainState):
+        """Train state with an Optax optimizer.
+
+        The two functions below differ depending on whether the task is classification
+        or regression.
+
+        Args:
+          logits_fn: Applied to last layer to obtain the logits.
+          loss_fn: Function to compute the loss.
+        """
+
+        logits_fn: Callable = struct.field(pytree_node=False)
+        loss_fn: Callable = struct.field(pytree_node=False)
+
+    # We use Optax's "masking" functionality to not apply weight decay
+    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
+    # mask boolean with the same structure as the parameters.
+    # The mask is True for parameters that should be decayed.
+    # Note that this mask is specifically adapted for FlaxBERT-like models.
+    # For other models, one should correct the layer norm parameter naming
+    # accordingly.
+    def decay_mask_fn(params):
+        flat_params = traverse_util.flatten_dict(params)
+        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
+        return traverse_util.unflatten_dict(flat_mask)
+
+    tx = optax.adamw(
+        learning_rate=learning_rate_fn,
+        b1=training_args.adam_beta1,
+        b2=training_args.adam_beta2,
+        eps=training_args.adam_epsilon,
+        weight_decay=training_args.weight_decay,
+        mask=decay_mask_fn,
+    )
+
+    def cross_entropy_loss(logits, labels):
+        start_loss = optax.softmax_cross_entropy(logits[0], onehot(labels[0], num_classes=num_labels))
+        end_loss = optax.softmax_cross_entropy(logits[1], onehot(labels[1], num_classes=num_labels))
+        xentropy = (start_loss + end_loss) / 2.0
+        return jnp.mean(xentropy)
+
+    return TrainState.create(
+        apply_fn=model.__call__,
+        params=model.params,
+        tx=tx,
+        logits_fn=lambda logits: logits,
+        loss_fn=cross_entropy_loss,
+    )
+
+
+# endregion
+
+
+# region Create learning rate function
+def create_learning_rate_fn(
+    train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
+) -> Callable[[int], jnp.array]:
+    """Returns a linear warmup, linear_decay learning rate function."""
+    steps_per_epoch = train_ds_size // train_batch_size
+    num_train_steps = steps_per_epoch * num_train_epochs
+    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
+    decay_fn = optax.linear_schedule(
+        init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
+    )
+    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
+    return schedule_fn
+
+
+# endregion
+
+# region train data iterator
+def train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
+    """Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
+    steps_per_epoch = len(dataset) // batch_size
+    perms = jax.random.permutation(rng, len(dataset))
+    perms = perms[: steps_per_epoch * batch_size]  # Skip incomplete batch.
+    perms = perms.reshape((steps_per_epoch, batch_size))
+
+    for perm in perms:
+        batch = dataset[perm]
+        batch = {k: np.array(v) for k, v in batch.items()}
+        batch = shard(batch)
+
+        yield batch
+
+
+# endregion
+
+# region eval data iterator
+def eval_data_collator(dataset: Dataset, batch_size: int):
+    """Returns batches of size `batch_size` from `eval dataset`, sharded over all local devices."""
+    for i in range(len(dataset) // batch_size):
+        batch = dataset[i * batch_size : (i + 1) * batch_size]
+        batch = {k: np.array(v) for k, v in batch.items()}
+        batch = shard(batch)
+
+        yield batch
+
+
+# endregion
+
+
+def main():
+    # region Argument parsing
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+    # endregion
+
+    # region Logging
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Setup logging, we only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+    # endregion
+
+    # region Load Data
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
+        )
+    else:
+        # Loading the dataset from local csv or json file.
+        data_files = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
+
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+            extension = data_args.validation_file.split(".")[-1]
+        if data_args.test_file is not None:
+            data_files["test"] = data_args.test_file
+            extension = data_args.test_file.split(".")[-1]
+        raw_datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+    # endregion
+
+    # region Load pretrained model and tokenizer
+    #
+    # Load pretrained model and tokenizer
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=True,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    # endregion
+
+    # region Tokenizer check: this script requires a fast tokenizer.
+    if not isinstance(tokenizer, PreTrainedTokenizerFast):
+        raise ValueError(
+            "This example script only works for models that have a fast tokenizer. Checkout the big table of models "
+            "at https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet this "
+            "requirement"
+        )
+    # endregion
+
+    # region Preprocessing the datasets
+    # Preprocessing is slightly different for training and evaluation.
+    if training_args.do_train:
+        column_names = raw_datasets["train"].column_names
+    elif training_args.do_eval:
+        column_names = raw_datasets["validation"].column_names
+    else:
+        column_names = raw_datasets["test"].column_names
+    question_column_name = "question" if "question" in column_names else column_names[0]
+    context_column_name = "context" if "context" in column_names else column_names[1]
+    answer_column_name = "answers" if "answers" in column_names else column_names[2]
+
+    # Padding side determines if we do (question|context) or (context|question).
+    pad_on_right = tokenizer.padding_side == "right"
+
+    if data_args.max_seq_length > tokenizer.model_max_length:
+        logger.warning(
+            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
+            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+        )
+    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
+    # Training preprocessing
+    def prepare_train_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
+        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
+        # in one example possible giving several features when a context is long, each of those features having a
+        # context that overlaps a bit the context of the previous feature.
+        tokenized_examples = tokenizer(
+            examples[question_column_name if pad_on_right else context_column_name],
+            examples[context_column_name if pad_on_right else question_column_name],
+            truncation="only_second" if pad_on_right else "only_first",
+            max_length=max_seq_length,
+            stride=data_args.doc_stride,
+            return_overflowing_tokens=True,
+            return_offsets_mapping=True,
+            padding="max_length",
+        )
+
+        # Since one example might give us several features if it has a long context, we need a map from a feature to
+        # its corresponding example. This key gives us just that.
+        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
+        # The offset mappings will give us a map from token to character position in the original context. This will
+        # help us compute the start_positions and end_positions.
+        offset_mapping = tokenized_examples.pop("offset_mapping")
+
+        # Let's label those examples!
+        tokenized_examples["start_positions"] = []
+        tokenized_examples["end_positions"] = []
+
+        for i, offsets in enumerate(offset_mapping):
+            # We will label impossible answers with the index of the CLS token.
+            input_ids = tokenized_examples["input_ids"][i]
+            cls_index = input_ids.index(tokenizer.cls_token_id)
+
+            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
+            sequence_ids = tokenized_examples.sequence_ids(i)
+
+            # One example can give several spans, this is the index of the example containing this span of text.
+            sample_index = sample_mapping[i]
+            answers = examples[answer_column_name][sample_index]
+            # If no answers are given, set the cls_index as answer.
+            if len(answers["answer_start"]) == 0:
+                tokenized_examples["start_positions"].append(cls_index)
+                tokenized_examples["end_positions"].append(cls_index)
+            else:
+                # Start/end character index of the answer in the text.
+                start_char = answers["answer_start"][0]
+                end_char = start_char + len(answers["text"][0])
+
+                # Start token index of the current span in the text.
+                token_start_index = 0
+                while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
+                    token_start_index += 1
+
+                # End token index of the current span in the text.
+                token_end_index = len(input_ids) - 1
+                while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
+                    token_end_index -= 1
+
+                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
+                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
+                    tokenized_examples["start_positions"].append(cls_index)
+                    tokenized_examples["end_positions"].append(cls_index)
+                else:
+                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
+                    # Note: we could go after the last offset if the answer is the last word (edge case).
+                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
+                        token_start_index += 1
+                    tokenized_examples["start_positions"].append(token_start_index - 1)
+                    while offsets[token_end_index][1] >= end_char:
+                        token_end_index -= 1
+                    tokenized_examples["end_positions"].append(token_end_index + 1)
+
+        return tokenized_examples
+
+    processed_raw_datasets = dict()
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            # We will select sample from whole data if agument is specified
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+        # Create train feature from dataset
+        train_dataset = train_dataset.map(
+            prepare_train_features,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
+        if data_args.max_train_samples is not None:
+            # Number of samples might increase during Feature Creation, We select only specified max samples
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+        processed_raw_datasets["train"] = train_dataset
+
+    # Validation preprocessing
+    def prepare_validation_features(examples):
+        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
+        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
+        # left whitespace
+        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
+
+        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
+        # in one example possible giving several features when a context is long, each of those features having a
+        # context that overlaps a bit the context of the previous feature.
+        tokenized_examples = tokenizer(
+            examples[question_column_name if pad_on_right else context_column_name],
+            examples[context_column_name if pad_on_right else question_column_name],
+            truncation="only_second" if pad_on_right else "only_first",
+            max_length=max_seq_length,
+            stride=data_args.doc_stride,
+            return_overflowing_tokens=True,
+            return_offsets_mapping=True,
+            padding="max_length",
+        )
+
+        # Since one example might give us several features if it has a long context, we need a map from a feature to
+        # its corresponding example. This key gives us just that.
+        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
+
+        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
+        # corresponding example_id and we will store the offset mappings.
+        tokenized_examples["example_id"] = []
+
+        for i in range(len(tokenized_examples["input_ids"])):
+            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
+            sequence_ids = tokenized_examples.sequence_ids(i)
+            context_index = 1 if pad_on_right else 0
+
+            # One example can give several spans, this is the index of the example containing this span of text.
+            sample_index = sample_mapping[i]
+            tokenized_examples["example_id"].append(examples["id"][sample_index])
+
+            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
+            # position is part of the context or not.
+            tokenized_examples["offset_mapping"][i] = [
+                (o if sequence_ids[k] == context_index else None)
+                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
+            ]
+
+        return tokenized_examples
+
+    if training_args.do_eval:
+        if "validation" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_examples = raw_datasets["validation"]
+        if data_args.max_eval_samples is not None:
+            # We will select sample from whole data
+            eval_examples = eval_examples.select(range(data_args.max_eval_samples))
+        # Validation Feature Creation
+        eval_dataset = eval_examples.map(
+            prepare_validation_features,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
+        if data_args.max_eval_samples is not None:
+            # During Feature creation dataset samples might increase, we will select required samples again
+            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
+        processed_raw_datasets["validation"] = eval_dataset
+
+    if training_args.do_predict:
+        if "test" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_examples = raw_datasets["test"]
+        if data_args.max_predict_samples is not None:
+            # We will select sample from whole data
+            predict_examples = predict_examples.select(range(data_args.max_predict_samples))
+        # Predict Feature Creation
+        predict_dataset = predict_examples.map(
+            prepare_validation_features,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
+        if data_args.max_predict_samples is not None:
+            # During Feature creation dataset samples might increase, we will select required samples again
+            predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
+        processed_raw_datasets["test"] = predict_dataset
+    # endregion
+
+    # region Metrics and Post-processing:
+    def post_processing_function(examples, features, predictions, stage="eval"):
+        # Post-processing: we match the start logits and end logits to answers in the original context.
+        predictions = postprocess_qa_predictions(
+            examples=examples,
+            features=features,
+            predictions=predictions,
+            version_2_with_negative=data_args.version_2_with_negative,
+            n_best_size=data_args.n_best_size,
+            max_answer_length=data_args.max_answer_length,
+            null_score_diff_threshold=data_args.null_score_diff_threshold,
+            output_dir=training_args.output_dir,
+            prefix=stage,
+        )
+        # Format the result to the format the metric expects.
+        if data_args.version_2_with_negative:
+            formatted_predictions = [
+                {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
+            ]
+        else:
+            formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
+
+        references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
+        return EvalPrediction(predictions=formatted_predictions, label_ids=references)
+
+    metric = load_metric("squad_v2" if data_args.version_2_with_negative else "squad")
+
+    def compute_metrics(p: EvalPrediction):
+        return metric.compute(predictions=p.predictions, references=p.label_ids)
+
+    # Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor
+    def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
+        """
+        Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor
+
+        Args:
+            start_or_end_logits(:obj:`tensor`):
+                This is the output predictions of the model. We can only enter either start or end logits.
+            eval_dataset: Evaluation dataset
+            max_len(:obj:`int`):
+                The maximum length of the output tensor. ( See the model.eval() part for more details )
+        """
+
+        step = 0
+        # create a numpy array and fill it with -100.
+        logits_concat = np.full((len(dataset), max_len), -100, dtype=np.float64)
+        # Now since we have create an array now we will populate it with the outputs of the model.
+        for i, output_logit in enumerate(start_or_end_logits):  # populate columns
+            # We have to fill it such that we have to take the whole tensor and replace it on the newly created array
+            # And after every iteration we have to change the step
+
+            batch_size = output_logit.shape[0]
+            cols = output_logit.shape[1]
+
+            if step + batch_size < len(dataset):
+                logits_concat[step : step + batch_size, :cols] = output_logit
+            else:
+                logits_concat[step:, :cols] = output_logit[: len(dataset) - step]
+
+            step += batch_size
+
+        return logits_concat
+
+    # endregion
+
+    # region Training steps and logging init
+    train_dataset = processed_raw_datasets["train"]
+    eval_dataset = processed_raw_datasets["validation"]
+
+    # Log a few random samples from the training set:
+    for index in random.sample(range(len(train_dataset)), 3):
+        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # Define a summary writer
+    summary_writer = tensorboard.SummaryWriter(training_args.output_dir)
+    summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
+
+    def write_train_metric(summary_writer, train_metrics, train_time, step):
+        summary_writer.scalar("train_time", train_time, step)
+
+        train_metrics = get_metrics(train_metrics)
+        for key, vals in train_metrics.items():
+            tag = f"train_{key}"
+            for i, val in enumerate(vals):
+                summary_writer.scalar(tag, val, step - len(vals) + i + 1)
+
+    def write_eval_metric(summary_writer, eval_metrics, step):
+        for metric_name, value in eval_metrics.items():
+            summary_writer.scalar(f"eval_{metric_name}", value, step)
+
+    num_epochs = int(training_args.num_train_epochs)
+    rng = jax.random.PRNGKey(training_args.seed)
+    dropout_rngs = jax.random.split(rng, jax.local_device_count())
+
+    train_batch_size = training_args.per_device_train_batch_size * jax.local_device_count()
+    eval_batch_size = training_args.per_device_eval_batch_size * jax.local_device_count()
+    # endregion
+
+    # region Load model
+    model = FlaxAutoModelForQuestionAnswering.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+        seed=training_args.seed,
+        dtype=getattr(jnp, model_args.dtype),
+    )
+
+    learning_rate_fn = create_learning_rate_fn(
+        len(train_dataset),
+        train_batch_size,
+        training_args.num_train_epochs,
+        training_args.warmup_steps,
+        training_args.learning_rate,
+    )
+
+    state = create_train_state(model, learning_rate_fn, num_labels=max_seq_length, training_args=training_args)
+    # endregion
+
+    # region Define train step functions
+    def train_step(
+        state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
+    ) -> Tuple[train_state.TrainState, float]:
+        """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
+        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
+        start_positions = batch.pop("start_positions")
+        end_positions = batch.pop("end_positions")
+        targets = (start_positions, end_positions)
+
+        def loss_fn(params):
+            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)
+            loss = state.loss_fn(logits, targets)
+            return loss
+
+        grad_fn = jax.value_and_grad(loss_fn)
+        loss, grad = grad_fn(state.params)
+        grad = jax.lax.pmean(grad, "batch")
+        new_state = state.apply_gradients(grads=grad)
+        metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
+        return new_state, metrics, new_dropout_rng
+
+    p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))
+    # endregion
+
+    # region Define eval step functions
+    def eval_step(state, batch):
+        logits = state.apply_fn(**batch, params=state.params, train=False)
+        return state.logits_fn(logits)
+
+    p_eval_step = jax.pmap(eval_step, axis_name="batch")
+    # endregion
+
+    # region Define train and eval loop
+    logger.info(f"===== Starting training ({num_epochs} epochs) =====")
+    train_time = 0
+
+    # make sure weights are replicated on each device
+    state = replicate(state)
+
+    train_time = 0
+    step_per_epoch = len(train_dataset) // train_batch_size
+    total_steps = step_per_epoch * num_epochs
+    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
+    for epoch in epochs:
+
+        train_start = time.time()
+        train_metrics = []
+
+        # Create sampling rng
+        rng, input_rng = jax.random.split(rng)
+
+        # train
+        for step, batch in enumerate(
+            tqdm(
+                train_data_collator(input_rng, train_dataset, train_batch_size),
+                total=step_per_epoch,
+                desc="Training...",
+                position=1,
+            ),
+            1,
+        ):
+            state, train_metric, dropout_rngs = p_train_step(state, batch, dropout_rngs)
+            train_metrics.append(train_metric)
+
+            cur_step = epoch * step_per_epoch + step
+
+            if cur_step % training_args.logging_steps == 0 and cur_step > 0:
+                # Save metrics
+                train_metric = unreplicate(train_metric)
+                train_time += time.time() - train_start
+                if jax.process_index() == 0:
+                    write_train_metric(summary_writer, train_metrics, train_time, cur_step)
+
+                epochs.write(
+                    f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
+                )
+
+                train_metrics = []
+
+            if (
+                training_args.do_eval
+                and (cur_step % training_args.eval_steps == 0 or cur_step % step_per_epoch == 0)
+                and cur_step > 0
+            ):
+
+                eval_metrics = {}
+                all_start_logits = []
+                all_end_logits = []
+                # evaluate
+                for batch in tqdm(
+                    eval_data_collator(eval_dataset, eval_batch_size),
+                    total=len(eval_dataset) // eval_batch_size,
+                    desc="Evaluating ...",
+                    position=2,
+                ):
+                    _ = batch.pop("example_id")
+                    _ = batch.pop("offset_mapping")
+                    predictions = p_eval_step(state, batch)
+                    start_logits = np.array([pred for pred in chain(*predictions[0])])
+                    end_logits = np.array([pred for pred in chain(*predictions[1])])
+                    all_start_logits.append(start_logits)
+                    all_end_logits.append(end_logits)
+
+                # evaluate also on leftover examples (not divisible by batch_size)
+                num_leftover_samples = len(eval_dataset) % eval_batch_size
+
+                # make sure leftover batch is evaluated on one device
+                if num_leftover_samples > 0 and jax.process_index() == 0:
+                    # take leftover samples
+                    batch = eval_dataset[-num_leftover_samples:]
+                    batch = {k: np.array(v) for k, v in batch.items()}
+                    _ = batch.pop("example_id")
+                    _ = batch.pop("offset_mapping")
+
+                    predictions = eval_step(unreplicate(state), batch)
+                    start_logits = np.array([pred for pred in predictions[0]])
+                    end_logits = np.array([pred for pred in predictions[1]])
+                    all_start_logits.append(start_logits)
+                    all_end_logits.append(end_logits)
+
+                max_len = max([x.shape[1] for x in all_start_logits])  # Get the max_length of the tensor
+
+                # concatenate the numpy array
+                start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len)
+                end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len)
+
+                # delete the list of numpy arrays
+                del all_start_logits
+                del all_end_logits
+                outputs_numpy = (start_logits_concat, end_logits_concat)
+                prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)
+                eval_metrics = compute_metrics(prediction)
+
+                logger.info(f"Step... ({cur_step}/{total_steps} | Evaluation metrics: {eval_metrics})")
+
+                if jax.process_index() == 0:
+                    write_eval_metric(summary_writer, eval_metrics, cur_step)
+
+            if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps):
+                # save checkpoint after each epoch and push checkpoint to the hub
+                if jax.process_index() == 0:
+                    params = jax.device_get(unreplicate(state.params))
+                    model.save_pretrained(
+                        training_args.output_dir,
+                        params=params,
+                        push_to_hub=training_args.push_to_hub,
+                        commit_message=f"Saving weights and logs of step {cur_step}",
+                    )
+        epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
+    # endregion
+
+
+if __name__ == "__main__":
+    main()

examples/flax/question-answering/utils_qa.py

@@ -0,0 +1,427 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""
+Post-processing utilities for question answering.
+"""
+import collections
+import json
+import logging
+import os
+from typing import Optional, Tuple
+
+import numpy as np
+from tqdm.auto import tqdm
+
+
+logger = logging.getLogger(__name__)
+
+
+def postprocess_qa_predictions(
+    examples,
+    features,
+    predictions: Tuple[np.ndarray, np.ndarray],
+    version_2_with_negative: bool = False,
+    n_best_size: int = 20,
+    max_answer_length: int = 30,
+    null_score_diff_threshold: float = 0.0,
+    output_dir: Optional[str] = None,
+    prefix: Optional[str] = None,
+    log_level: Optional[int] = logging.WARNING,
+):
+    """
+    Post-processes the predictions of a question-answering model to convert them to answers that are substrings of the
+    original contexts. This is the base postprocessing functions for models that only return start and end logits.
+
+    Args:
+        examples: The non-preprocessed dataset (see the main script for more information).
+        features: The processed dataset (see the main script for more information).
+        predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
+            The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
+            first dimension must match the number of elements of :obj:`features`.
+        version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not the underlying dataset contains examples with no answers.
+        n_best_size (:obj:`int`, `optional`, defaults to 20):
+            The total number of n-best predictions to generate when looking for an answer.
+        max_answer_length (:obj:`int`, `optional`, defaults to 30):
+            The maximum length of an answer that can be generated. This is needed because the start and end predictions
+            are not conditioned on one another.
+        null_score_diff_threshold (:obj:`float`, `optional`, defaults to 0):
+            The threshold used to select the null answer: if the best answer has a score that is less than the score of
+            the null answer minus this threshold, the null answer is selected for this example (note that the score of
+            the null answer for an example giving several features is the minimum of the scores for the null answer on
+            each feature: all features must be aligned on the fact they `want` to predict a null answer).
+
+            Only useful when :obj:`version_2_with_negative` is :obj:`True`.
+        output_dir (:obj:`str`, `optional`):
+            If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
+            :obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
+            answers, are saved in `output_dir`.
+        prefix (:obj:`str`, `optional`):
+            If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
+        log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
+            ``logging`` log level (e.g., ``logging.WARNING``)
+    """
+    assert len(predictions) == 2, "`predictions` should be a tuple with two elements (start_logits, end_logits)."
+    all_start_logits, all_end_logits = predictions
+
+    assert len(predictions[0]) == len(features), f"Got {len(predictions[0])} predictions and {len(features)} features."
+
+    # Build a map example to its corresponding features.
+    example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
+    features_per_example = collections.defaultdict(list)
+    for i, feature in enumerate(features):
+        features_per_example[example_id_to_index[feature["example_id"]]].append(i)
+
+    # The dictionaries we have to fill.
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    if version_2_with_negative:
+        scores_diff_json = collections.OrderedDict()
+
+    # Logging.
+    logger.setLevel(log_level)
+    logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
+
+    # Let's loop over all the examples!
+    for example_index, example in enumerate(tqdm(examples)):
+        # Those are the indices of the features associated to the current example.
+        feature_indices = features_per_example[example_index]
+
+        min_null_prediction = None
+        prelim_predictions = []
+
+        # Looping through all the features associated to the current example.
+        for feature_index in feature_indices:
+            # We grab the predictions of the model for this feature.
+            start_logits = all_start_logits[feature_index]
+            end_logits = all_end_logits[feature_index]
+            # This is what will allow us to map some the positions in our logits to span of texts in the original
+            # context.
+            offset_mapping = features[feature_index]["offset_mapping"]
+            # Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
+            # available in the current feature.
+            token_is_max_context = features[feature_index].get("token_is_max_context", None)
+
+            # Update minimum null prediction.
+            feature_null_score = start_logits[0] + end_logits[0]
+            if min_null_prediction is None or min_null_prediction["score"] > feature_null_score:
+                min_null_prediction = {
+                    "offsets": (0, 0),
+                    "score": feature_null_score,
+                    "start_logit": start_logits[0],
+                    "end_logit": end_logits[0],
+                }
+
+            # Go through all possibilities for the `n_best_size` greater start and end logits.
+            start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
+            end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
+                    # to part of the input_ids that are not in the context.
+                    if (
+                        start_index >= len(offset_mapping)
+                        or end_index >= len(offset_mapping)
+                        or offset_mapping[start_index] is None
+                        or offset_mapping[end_index] is None
+                    ):
+                        continue
+                    # Don't consider answers with a length that is either < 0 or > max_answer_length.
+                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:
+                        continue
+                    # Don't consider answer that don't have the maximum context available (if such information is
+                    # provided).
+                    if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
+                        continue
+                    prelim_predictions.append(
+                        {
+                            "offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
+                            "score": start_logits[start_index] + end_logits[end_index],
+                            "start_logit": start_logits[start_index],
+                            "end_logit": end_logits[end_index],
+                        }
+                    )
+        if version_2_with_negative:
+            # Add the minimum null prediction
+            prelim_predictions.append(min_null_prediction)
+            null_score = min_null_prediction["score"]
+
+        # Only keep the best `n_best_size` predictions.
+        predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
+
+        # Add back the minimum null prediction if it was removed because of its low score.
+        if version_2_with_negative and not any(p["offsets"] == (0, 0) for p in predictions):
+            predictions.append(min_null_prediction)
+
+        # Use the offsets to gather the answer text in the original context.
+        context = example["context"]
+        for pred in predictions:
+            offsets = pred.pop("offsets")
+            pred["text"] = context[offsets[0] : offsets[1]]
+
+        # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
+        # failure.
+        if len(predictions) == 0 or (len(predictions) == 1 and predictions[0]["text"] == ""):
+            predictions.insert(0, {"text": "empty", "start_logit": 0.0, "end_logit": 0.0, "score": 0.0})
+
+        # Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
+        # the LogSumExp trick).
+        scores = np.array([pred.pop("score") for pred in predictions])
+        exp_scores = np.exp(scores - np.max(scores))
+        probs = exp_scores / exp_scores.sum()
+
+        # Include the probabilities in our predictions.
+        for prob, pred in zip(probs, predictions):
+            pred["probability"] = prob
+
+        # Pick the best prediction. If the null answer is not possible, this is easy.
+        if not version_2_with_negative:
+            all_predictions[example["id"]] = predictions[0]["text"]
+        else:
+            # Otherwise we first need to find the best non-empty prediction.
+            i = 0
+            while predictions[i]["text"] == "":
+                i += 1
+            best_non_null_pred = predictions[i]
+
+            # Then we compare to the null prediction using the threshold.
+            score_diff = null_score - best_non_null_pred["start_logit"] - best_non_null_pred["end_logit"]
+            scores_diff_json[example["id"]] = float(score_diff)  # To be JSON-serializable.
+            if score_diff > null_score_diff_threshold:
+                all_predictions[example["id"]] = ""
+            else:
+                all_predictions[example["id"]] = best_non_null_pred["text"]
+
+        # Make `predictions` JSON-serializable by casting np.float back to float.
+        all_nbest_json[example["id"]] = [
+            {k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
+            for pred in predictions
+        ]
+
+    # If we have an output_dir, let's save all those dicts.
+    if output_dir is not None:
+        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+
+        prediction_file = os.path.join(
+            output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
+        )
+        nbest_file = os.path.join(
+            output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
+        )
+        if version_2_with_negative:
+            null_odds_file = os.path.join(
+                output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
+            )
+
+        logger.info(f"Saving predictions to {prediction_file}.")
+        with open(prediction_file, "w") as writer:
+            writer.write(json.dumps(all_predictions, indent=4) + "\n")
+        logger.info(f"Saving nbest_preds to {nbest_file}.")
+        with open(nbest_file, "w") as writer:
+            writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+        if version_2_with_negative:
+            logger.info(f"Saving null_odds to {null_odds_file}.")
+            with open(null_odds_file, "w") as writer:
+                writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+    return all_predictions
+
+
+def postprocess_qa_predictions_with_beam_search(
+    examples,
+    features,
+    predictions: Tuple[np.ndarray, np.ndarray],
+    version_2_with_negative: bool = False,
+    n_best_size: int = 20,
+    max_answer_length: int = 30,
+    start_n_top: int = 5,
+    end_n_top: int = 5,
+    output_dir: Optional[str] = None,
+    prefix: Optional[str] = None,
+    log_level: Optional[int] = logging.WARNING,
+):
+    """
+    Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the
+    original contexts. This is the postprocessing functions for models that return start and end logits, indices, as well as
+    cls token predictions.
+
+    Args:
+        examples: The non-preprocessed dataset (see the main script for more information).
+        features: The processed dataset (see the main script for more information).
+        predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
+            The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
+            first dimension must match the number of elements of :obj:`features`.
+        version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not the underlying dataset contains examples with no answers.
+        n_best_size (:obj:`int`, `optional`, defaults to 20):
+            The total number of n-best predictions to generate when looking for an answer.
+        max_answer_length (:obj:`int`, `optional`, defaults to 30):
+            The maximum length of an answer that can be generated. This is needed because the start and end predictions
+            are not conditioned on one another.
+        start_n_top (:obj:`int`, `optional`, defaults to 5):
+            The number of top start logits too keep when searching for the :obj:`n_best_size` predictions.
+        end_n_top (:obj:`int`, `optional`, defaults to 5):
+            The number of top end logits too keep when searching for the :obj:`n_best_size` predictions.
+        output_dir (:obj:`str`, `optional`):
+            If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
+            :obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
+            answers, are saved in `output_dir`.
+        prefix (:obj:`str`, `optional`):
+            If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
+        log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
+            ``logging`` log level (e.g., ``logging.WARNING``)
+    """
+    assert len(predictions) == 5, "`predictions` should be a tuple with five elements."
+    start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = predictions
+
+    assert len(predictions[0]) == len(
+        features
+    ), f"Got {len(predictions[0])} predicitions and {len(features)} features."
+
+    # Build a map example to its corresponding features.
+    example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
+    features_per_example = collections.defaultdict(list)
+    for i, feature in enumerate(features):
+        features_per_example[example_id_to_index[feature["example_id"]]].append(i)
+
+    # The dictionaries we have to fill.
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    scores_diff_json = collections.OrderedDict() if version_2_with_negative else None
+
+    # Logging.
+    logger.setLevel(log_level)
+    logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
+
+    # Let's loop over all the examples!
+    for example_index, example in enumerate(tqdm(examples)):
+        # Those are the indices of the features associated to the current example.
+        feature_indices = features_per_example[example_index]
+
+        min_null_score = None
+        prelim_predictions = []
+
+        # Looping through all the features associated to the current example.
+        for feature_index in feature_indices:
+            # We grab the predictions of the model for this feature.
+            start_log_prob = start_top_log_probs[feature_index]
+            start_indexes = start_top_index[feature_index]
+            end_log_prob = end_top_log_probs[feature_index]
+            end_indexes = end_top_index[feature_index]
+            feature_null_score = cls_logits[feature_index]
+            # This is what will allow us to map some the positions in our logits to span of texts in the original
+            # context.
+            offset_mapping = features[feature_index]["offset_mapping"]
+            # Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
+            # available in the current feature.
+            token_is_max_context = features[feature_index].get("token_is_max_context", None)
+
+            # Update minimum null prediction
+            if min_null_score is None or feature_null_score < min_null_score:
+                min_null_score = feature_null_score
+
+            # Go through all possibilities for the `n_start_top`/`n_end_top` greater start and end logits.
+            for i in range(start_n_top):
+                for j in range(end_n_top):
+                    start_index = int(start_indexes[i])
+                    j_index = i * end_n_top + j
+                    end_index = int(end_indexes[j_index])
+                    # Don't consider out-of-scope answers (last part of the test should be unnecessary because of the
+                    # p_mask but let's not take any risk)
+                    if (
+                        start_index >= len(offset_mapping)
+                        or end_index >= len(offset_mapping)
+                        or offset_mapping[start_index] is None
+                        or offset_mapping[end_index] is None
+                    ):
+                        continue
+                    # Don't consider answers with a length negative or > max_answer_length.
+                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:
+                        continue
+                    # Don't consider answer that don't have the maximum context available (if such information is
+                    # provided).
+                    if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
+                        continue
+                    prelim_predictions.append(
+                        {
+                            "offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
+                            "score": start_log_prob[i] + end_log_prob[j_index],
+                            "start_log_prob": start_log_prob[i],
+                            "end_log_prob": end_log_prob[j_index],
+                        }
+                    )
+
+        # Only keep the best `n_best_size` predictions.
+        predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
+
+        # Use the offsets to gather the answer text in the original context.
+        context = example["context"]
+        for pred in predictions:
+            offsets = pred.pop("offsets")
+            pred["text"] = context[offsets[0] : offsets[1]]
+
+        # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
+        # failure.
+        if len(predictions) == 0:
+            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": -2e-6})
+
+        # Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
+        # the LogSumExp trick).
+        scores = np.array([pred.pop("score") for pred in predictions])
+        exp_scores = np.exp(scores - np.max(scores))
+        probs = exp_scores / exp_scores.sum()
+
+        # Include the probabilities in our predictions.
+        for prob, pred in zip(probs, predictions):
+            pred["probability"] = prob
+
+        # Pick the best prediction and set the probability for the null answer.
+        all_predictions[example["id"]] = predictions[0]["text"]
+        if version_2_with_negative:
+            scores_diff_json[example["id"]] = float(min_null_score)
+
+        # Make `predictions` JSON-serializable by casting np.float back to float.
+        all_nbest_json[example["id"]] = [
+            {k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
+            for pred in predictions
+        ]
+
+    # If we have an output_dir, let's save all those dicts.
+    if output_dir is not None:
+        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+
+        prediction_file = os.path.join(
+            output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
+        )
+        nbest_file = os.path.join(
+            output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
+        )
+        if version_2_with_negative:
+            null_odds_file = os.path.join(
+                output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
+            )
+
+        logger.info(f"Saving predictions to {prediction_file}.")
+        with open(prediction_file, "w") as writer:
+            writer.write(json.dumps(all_predictions, indent=4) + "\n")
+        logger.info(f"Saving nbest_preds to {nbest_file}.")
+        with open(nbest_file, "w") as writer:
+            writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+        if version_2_with_negative:
+            logger.info(f"Saving null_odds to {null_odds_file}.")
+            with open(null_odds_file, "w") as writer:
+                writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+    return all_predictions, scores_diff_json

utils/check_copies.py

@@ -28,7 +28,10 @@ PATH_TO_DOCS = "docs/source"
 REPO_PATH = "."
 
 # Mapping for files that are full copies of others (keys are copies, values the file to keep them up to data with)
-FULL_COPIES = {"examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py"}
+FULL_COPIES = {
+    "examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
+    "examples/flax/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
+}
 
 
 LOCALIZED_READMES = {