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Deepspeed/BingBertSquad/nvidia_run_squad_baseline.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""Run BERT on SQuAD."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import collections
+import logging
+import json
+import math
+import os
+import random
+import pickle
+from tqdm import tqdm, trange
+from utils import get_argument_parser, \
+    get_summary_writer, write_summary_events, \
+    is_time_to_exit, check_early_exit_warning
+
+import numpy as np
+import torch
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer
+from pytorch_pretrained_bert.optimization import BertAdam
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
+
+from apex import amp
+from turing.nvidia_modeling import BertForQuestionAnswering, BertConfig
+
+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__)
+
+
+class SquadExample(object):
+    """A single training/test example for the Squad dataset."""
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 doc_tokens,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (self.qas_id)
+        s += ", question_text: %s" % (self.question_text)
+        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.start_position:
+            s += ", end_position: %d" % (self.end_position)
+        return s
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+    def __init__(self,
+                 unique_id,
+                 example_index,
+                 doc_span_index,
+                 tokens,
+                 token_to_orig_map,
+                 token_is_max_context,
+                 input_ids,
+                 input_mask,
+                 segment_ids,
+                 start_position=None,
+                 end_position=None):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.doc_span_index = doc_span_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.token_is_max_context = token_is_max_context
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.start_position = start_position
+        self.end_position = end_position
+
+
+def read_squad_examples(input_file, is_training):
+    """Read a SQuAD json file into a list of SquadExample."""
+    with open(input_file, "r", encoding='utf-8') as reader:
+        input_data = json.load(reader)["data"]
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    examples = []
+    for entry in input_data:
+        for paragraph in entry["paragraphs"]:
+            paragraph_text = paragraph["context"]
+            doc_tokens = []
+            char_to_word_offset = []
+            prev_is_whitespace = True
+            for c in paragraph_text:
+                if is_whitespace(c):
+                    prev_is_whitespace = True
+                else:
+                    if prev_is_whitespace:
+                        doc_tokens.append(c)
+                    else:
+                        doc_tokens[-1] += c
+                    prev_is_whitespace = False
+                char_to_word_offset.append(len(doc_tokens) - 1)
+
+            for qa in paragraph["qas"]:
+                qas_id = qa["id"]
+                question_text = qa["question"]
+                start_position = None
+                end_position = None
+                orig_answer_text = None
+                if is_training:
+                    if len(qa["answers"]) != 1:
+                        raise ValueError(
+                            "For training, each question should have exactly 1 answer."
+                        )
+                    answer = qa["answers"][0]
+                    orig_answer_text = answer["text"]
+                    answer_offset = answer["answer_start"]
+                    answer_length = len(orig_answer_text)
+                    start_position = char_to_word_offset[answer_offset]
+                    end_position = char_to_word_offset[answer_offset +
+                                                       answer_length - 1]
+                    # Only add answers where the text can be exactly recovered from the
+                    # document. If this CAN'T happen it's likely due to weird Unicode
+                    # stuff so we will just skip the example.
+                    #
+                    # Note that this means for training mode, every example is NOT
+                    # guaranteed to be preserved.
+                    actual_text = " ".join(
+                        doc_tokens[start_position:(end_position + 1)])
+                    cleaned_answer_text = " ".join(
+                        whitespace_tokenize(orig_answer_text))
+                    if actual_text.find(cleaned_answer_text) == -1:
+                        logger.warning("Could not find answer: '%s' vs. '%s'",
+                                       actual_text, cleaned_answer_text)
+                        continue
+
+                example = SquadExample(qas_id=qas_id,
+                                       question_text=question_text,
+                                       doc_tokens=doc_tokens,
+                                       orig_answer_text=orig_answer_text,
+                                       start_position=start_position,
+                                       end_position=end_position)
+                examples.append(example)
+    return examples
+
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length,
+                                 doc_stride, max_query_length, is_training):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    unique_id = 1000000000
+
+    features = []
+    for (example_index, example) in enumerate(examples):
+        query_tokens = tokenizer.tokenize(example.question_text)
+
+        if len(query_tokens) > max_query_length:
+            query_tokens = query_tokens[0:max_query_length]
+
+        tok_to_orig_index = []
+        orig_to_tok_index = []
+        all_doc_tokens = []
+        for (i, token) in enumerate(example.doc_tokens):
+            orig_to_tok_index.append(len(all_doc_tokens))
+            sub_tokens = tokenizer.tokenize(token)
+            for sub_token in sub_tokens:
+                tok_to_orig_index.append(i)
+                all_doc_tokens.append(sub_token)
+
+        tok_start_position = None
+        tok_end_position = None
+        if is_training:
+            tok_start_position = orig_to_tok_index[example.start_position]
+            if example.end_position < len(example.doc_tokens) - 1:
+                tok_end_position = orig_to_tok_index[example.end_position +
+                                                     1] - 1
+            else:
+                tok_end_position = len(all_doc_tokens) - 1
+            (tok_start_position, tok_end_position) = _improve_answer_span(
+                all_doc_tokens, tok_start_position, tok_end_position,
+                tokenizer, example.orig_answer_text)
+
+        # The -3 accounts for [CLS], [SEP] and [SEP]
+        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+        # We can have documents that are longer than the maximum sequence length.
+        # To deal with this we do a sliding window approach, where we take chunks
+        # of the up to our max length with a stride of `doc_stride`.
+        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
+            "DocSpan", ["start", "length"])
+        doc_spans = []
+        start_offset = 0
+        while start_offset < len(all_doc_tokens):
+            length = len(all_doc_tokens) - start_offset
+            if length > max_tokens_for_doc:
+                length = max_tokens_for_doc
+            doc_spans.append(_DocSpan(start=start_offset, length=length))
+            if start_offset + length == len(all_doc_tokens):
+                break
+            start_offset += min(length, doc_stride)
+
+        for (doc_span_index, doc_span) in enumerate(doc_spans):
+            tokens = []
+            token_to_orig_map = {}
+            token_is_max_context = {}
+            segment_ids = []
+            tokens.append("[CLS]")
+            segment_ids.append(0)
+            for token in query_tokens:
+                tokens.append(token)
+                segment_ids.append(0)
+            tokens.append("[SEP]")
+            segment_ids.append(0)
+
+            for i in range(doc_span.length):
+                split_token_index = doc_span.start + i
+                token_to_orig_map[len(
+                    tokens)] = tok_to_orig_index[split_token_index]
+
+                is_max_context = _check_is_max_context(doc_spans,
+                                                       doc_span_index,
+                                                       split_token_index)
+                token_is_max_context[len(tokens)] = is_max_context
+                tokens.append(all_doc_tokens[split_token_index])
+                segment_ids.append(1)
+            tokens.append("[SEP]")
+            segment_ids.append(1)
+
+            input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+            # The mask has 1 for real tokens and 0 for padding tokens. Only real
+            # tokens are attended to.
+            input_mask = [1] * len(input_ids)
+
+            # Zero-pad up to the sequence length.
+            while len(input_ids) < max_seq_length:
+                input_ids.append(0)
+                input_mask.append(0)
+                segment_ids.append(0)
+
+            assert len(input_ids) == max_seq_length
+            assert len(input_mask) == max_seq_length
+            assert len(segment_ids) == max_seq_length
+
+            start_position = None
+            end_position = None
+            if is_training:
+                # For training, if our document chunk does not contain an annotation
+                # we throw it out, since there is nothing to predict.
+                doc_start = doc_span.start
+                doc_end = doc_span.start + doc_span.length - 1
+                if (example.start_position < doc_start
+                        or example.end_position < doc_start
+                        or example.start_position > doc_end
+                        or example.end_position > doc_end):
+                    continue
+
+                doc_offset = len(query_tokens) + 2
+                start_position = tok_start_position - doc_start + doc_offset
+                end_position = tok_end_position - doc_start + doc_offset
+
+            if example_index < 20:
+                logger.info("*** Example ***")
+                logger.info("unique_id: %s" % (unique_id))
+                logger.info("example_index: %s" % (example_index))
+                logger.info("doc_span_index: %s" % (doc_span_index))
+                logger.info("tokens: %s" % " ".join(tokens))
+                logger.info("token_to_orig_map: %s" % " ".join(
+                    ["%d:%d" % (x, y)
+                     for (x, y) in token_to_orig_map.items()]))
+                logger.info("token_is_max_context: %s" % " ".join([
+                    "%d:%s" % (x, y)
+                    for (x, y) in token_is_max_context.items()
+                ]))
+                logger.info("input_ids: %s" %
+                            " ".join([str(x) for x in input_ids]))
+                logger.info("input_mask: %s" %
+                            " ".join([str(x) for x in input_mask]))
+                logger.info("segment_ids: %s" %
+                            " ".join([str(x) for x in segment_ids]))
+                if is_training:
+                    answer_text = " ".join(
+                        tokens[start_position:(end_position + 1)])
+                    logger.info("start_position: %d" % (start_position))
+                    logger.info("end_position: %d" % (end_position))
+                    logger.info("answer: %s" % (answer_text))
+
+            features.append(
+                InputFeatures(unique_id=unique_id,
+                              example_index=example_index,
+                              doc_span_index=doc_span_index,
+                              tokens=tokens,
+                              token_to_orig_map=token_to_orig_map,
+                              token_is_max_context=token_is_max_context,
+                              input_ids=input_ids,
+                              input_mask=input_mask,
+                              segment_ids=segment_ids,
+                              start_position=start_position,
+                              end_position=end_position))
+            unique_id += 1
+
+    return features
+
+
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+                         orig_answer_text):
+    """Returns tokenized answer spans that better match the annotated answer."""
+
+    # The SQuAD annotations are character based. We first project them to
+    # whitespace-tokenized words. But then after WordPiece tokenization, we can
+    # often find a "better match". For example:
+    #
+    #   Question: What year was John Smith born?
+    #   Context: The leader was John Smith (1895-1943).
+    #   Answer: 1895
+    #
+    # The original whitespace-tokenized answer will be "(1895-1943).". However
+    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+    # the exact answer, 1895.
+    #
+    # However, this is not always possible. Consider the following:
+    #
+    #   Question: What country is the top exporter of electornics?
+    #   Context: The Japanese electronics industry is the lagest in the world.
+    #   Answer: Japan
+    #
+    # In this case, the annotator chose "Japan" as a character sub-span of
+    # the word "Japanese". Since our WordPiece tokenizer does not split
+    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+    # in SQuAD, but does happen.
+    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+    for new_start in range(input_start, input_end + 1):
+        for new_end in range(input_end, new_start - 1, -1):
+            text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+            if text_span == tok_answer_text:
+                return (new_start, new_end)
+
+    return (input_start, input_end)
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+    """Check if this is the 'max context' doc span for the token."""
+
+    # Because of the sliding window approach taken to scoring documents, a single
+    # token can appear in multiple documents. E.g.
+    #  Doc: the man went to the store and bought a gallon of milk
+    #  Span A: the man went to the
+    #  Span B: to the store and bought
+    #  Span C: and bought a gallon of
+    #  ...
+    #
+    # Now the word 'bought' will have two scores from spans B and C. We only
+    # want to consider the score with "maximum context", which we define as
+    # the *minimum* of its left and right context (the *sum* of left and
+    # right context will always be the same, of course).
+    #
+    # In the example the maximum context for 'bought' would be span C since
+    # it has 1 left context and 3 right context, while span B has 4 left context
+    # and 0 right context.
+    best_score = None
+    best_span_index = None
+    for (span_index, doc_span) in enumerate(doc_spans):
+        end = doc_span.start + doc_span.length - 1
+        if position < doc_span.start:
+            continue
+        if position > end:
+            continue
+        num_left_context = position - doc_span.start
+        num_right_context = end - position
+        score = min(num_left_context,
+                    num_right_context) + 0.01 * doc_span.length
+        if best_score is None or score > best_score:
+            best_score = score
+            best_span_index = span_index
+
+    return cur_span_index == best_span_index
+
+
+RawResult = collections.namedtuple("RawResult",
+                                   ["unique_id", "start_logits", "end_logits"])
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+                      max_answer_length, do_lower_case, output_prediction_file,
+                      output_nbest_file, verbose_logging):
+    """Write final predictions to the json file."""
+    logger.info("Writing predictions to: %s" % (output_prediction_file))
+    logger.info("Writing nbest to: %s" % (output_nbest_file))
+
+    example_index_to_features = collections.defaultdict(list)
+    for feature in all_features:
+        example_index_to_features[feature.example_index].append(feature)
+
+    unique_id_to_result = {}
+    for result in all_results:
+        unique_id_to_result[result.unique_id] = result
+
+    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "PrelimPrediction", [
+            "feature_index", "start_index", "end_index", "start_logit",
+            "end_logit"
+        ])
+
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+
+        prelim_predictions = []
+        for (feature_index, feature) in enumerate(features):
+            result = unique_id_to_result[feature.unique_id]
+
+            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    # We could hypothetically create invalid predictions, e.g., predict
+                    # that the start of the span is in the question. We throw out all
+                    # invalid predictions.
+                    if start_index >= len(feature.tokens):
+                        continue
+                    if end_index >= len(feature.tokens):
+                        continue
+                    if start_index not in feature.token_to_orig_map:
+                        continue
+                    if end_index not in feature.token_to_orig_map:
+                        continue
+                    if not feature.token_is_max_context.get(
+                            start_index, False):
+                        continue
+                    if end_index < start_index:
+                        continue
+                    length = end_index - start_index + 1
+                    if length > max_answer_length:
+                        continue
+                    prelim_predictions.append(
+                        _PrelimPrediction(
+                            feature_index=feature_index,
+                            start_index=start_index,
+                            end_index=end_index,
+                            start_logit=result.start_logits[start_index],
+                            end_logit=result.end_logits[end_index]))
+
+        prelim_predictions = sorted(prelim_predictions,
+                                    key=lambda x:
+                                    (x.start_logit + x.end_logit),
+                                    reverse=True)
+
+        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+            "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+        seen_predictions = {}
+        nbest = []
+        for pred in prelim_predictions:
+            if len(nbest) >= n_best_size:
+                break
+            feature = features[pred.feature_index]
+
+            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+            orig_doc_start = feature.token_to_orig_map[pred.start_index]
+            orig_doc_end = feature.token_to_orig_map[pred.end_index]
+            orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+            tok_text = " ".join(tok_tokens)
+
+            # De-tokenize WordPieces that have been split off.
+            tok_text = tok_text.replace(" ##", "")
+            tok_text = tok_text.replace("##", "")
+
+            # Clean whitespace
+            tok_text = tok_text.strip()
+            tok_text = " ".join(tok_text.split())
+            orig_text = " ".join(orig_tokens)
+
+            final_text = get_final_text(tok_text, orig_text, do_lower_case,
+                                        verbose_logging)
+            if final_text in seen_predictions:
+                continue
+
+            seen_predictions[final_text] = True
+            nbest.append(
+                _NbestPrediction(text=final_text,
+                                 start_logit=pred.start_logit,
+                                 end_logit=pred.end_logit))
+
+        # In very rare edge cases we could have no valid predictions. So we
+        # just create a nonce prediction in this case to avoid failure.
+        if not nbest:
+            nbest.append(
+                _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+        assert len(nbest) >= 1
+
+        total_scores = []
+        for entry in nbest:
+            total_scores.append(entry.start_logit + entry.end_logit)
+
+        probs = _compute_softmax(total_scores)
+
+        nbest_json = []
+        for (i, entry) in enumerate(nbest):
+            output = collections.OrderedDict()
+            output["text"] = entry.text
+            output["probability"] = probs[i]
+            output["start_logit"] = entry.start_logit
+            output["end_logit"] = entry.end_logit
+            nbest_json.append(output)
+
+        assert len(nbest_json) >= 1
+
+        all_predictions[example.qas_id] = nbest_json[0]["text"]
+        all_nbest_json[example.qas_id] = nbest_json
+
+    with open(output_prediction_file, "w") as writer:
+        writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+    with open(output_nbest_file, "w") as writer:
+        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+
+
+def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
+    """Project the tokenized prediction back to the original text."""
+
+    # When we created the data, we kept track of the alignment between original
+    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+    # now `orig_text` contains the span of our original text corresponding to the
+    # span that we predicted.
+    #
+    # However, `orig_text` may contain extra characters that we don't want in
+    # our prediction.
+    #
+    # For example, let's say:
+    #   pred_text = steve smith
+    #   orig_text = Steve Smith's
+    #
+    # We don't want to return `orig_text` because it contains the extra "'s".
+    #
+    # We don't want to return `pred_text` because it's already been normalized
+    # (the SQuAD eval script also does punctuation stripping/lower casing but
+    # our tokenizer does additional normalization like stripping accent
+    # characters).
+    #
+    # What we really want to return is "Steve Smith".
+    #
+    # Therefore, we have to apply a semi-complicated alignment heruistic between
+    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+    # can fail in certain cases in which case we just return `orig_text`.
+
+    def _strip_spaces(text):
+        ns_chars = []
+        ns_to_s_map = collections.OrderedDict()
+        for (i, c) in enumerate(text):
+            if c == " ":
+                continue
+            ns_to_s_map[len(ns_chars)] = i
+            ns_chars.append(c)
+        ns_text = "".join(ns_chars)
+        return (ns_text, ns_to_s_map)
+
+    # We first tokenize `orig_text`, strip whitespace from the result
+    # and `pred_text`, and check if they are the same length. If they are
+    # NOT the same length, the heuristic has failed. If they are the same
+    # length, we assume the characters are one-to-one aligned.
+    tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+
+    tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+    start_position = tok_text.find(pred_text)
+    if start_position == -1:
+        if verbose_logging:
+            logger.info("Unable to find text: '%s' in '%s'" %
+                        (pred_text, orig_text))
+        return orig_text
+    end_position = start_position + len(pred_text) - 1
+
+    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+    if len(orig_ns_text) != len(tok_ns_text):
+        if verbose_logging:
+            logger.info(
+                "Length not equal after stripping spaces: '%s' vs '%s'",
+                orig_ns_text, tok_ns_text)
+        return orig_text
+
+    # We then project the characters in `pred_text` back to `orig_text` using
+    # the character-to-character alignment.
+    tok_s_to_ns_map = {}
+    for (i, tok_index) in tok_ns_to_s_map.items():
+        tok_s_to_ns_map[tok_index] = i
+
+    orig_start_position = None
+    if start_position in tok_s_to_ns_map:
+        ns_start_position = tok_s_to_ns_map[start_position]
+        if ns_start_position in orig_ns_to_s_map:
+            orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+    if orig_start_position is None:
+        if verbose_logging:
+            logger.info("Couldn't map start position")
+        return orig_text
+
+    orig_end_position = None
+    if end_position in tok_s_to_ns_map:
+        ns_end_position = tok_s_to_ns_map[end_position]
+        if ns_end_position in orig_ns_to_s_map:
+            orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+    if orig_end_position is None:
+        if verbose_logging:
+            logger.info("Couldn't map end position")
+        return orig_text
+
+    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+    return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+    index_and_score = sorted(enumerate(logits),
+                             key=lambda x: x[1],
+                             reverse=True)
+
+    best_indexes = []
+    for i in range(len(index_and_score)):
+        if i >= n_best_size:
+            break
+        best_indexes.append(index_and_score[i][0])
+    return best_indexes
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs
+
+
+def warmup_linear(x, warmup=0.002):
+    if x < warmup:
+        return x / warmup
+    return 1.0 - x
+
+
+def copy_optimizer_params_to_model(named_params_model, named_params_optimizer):
+    """ Utility function for optimize_on_cpu and 16-bits training.
+        Copy the parameters optimized on CPU/RAM back to the model on GPU
+    """
+    for (name_opti, param_opti), (name_model,
+                                  param_model) in zip(named_params_optimizer,
+                                                      named_params_model):
+        if name_opti != name_model:
+            logger.error("name_opti != name_model: {} {}".format(
+                name_opti, name_model))
+            raise ValueError
+        param_model.data.copy_(param_opti.data)
+
+
+def set_optimizer_params_grad(named_params_optimizer,
+                              named_params_model,
+                              test_nan=False):
+    """ Utility function for optimize_on_cpu and 16-bits training.
+        Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model
+    """
+    is_nan = False
+    for (name_opti, param_opti), (name_model,
+                                  param_model) in zip(named_params_optimizer,
+                                                      named_params_model):
+        if name_opti != name_model:
+            logger.error("name_opti != name_model: {} {}".format(
+                name_opti, name_model))
+            raise ValueError
+        if param_model.grad is not None:
+            max = param_model.grad.max
+            if test_nan and max != max:
+                is_nan = True
+            if param_opti.grad is None:
+                param_opti.grad = torch.nn.Parameter(
+                    param_opti.data.new().resize_(*param_opti.data.size()))
+            param_opti.grad.data.copy_(param_model.grad.data)
+        else:
+            param_opti.grad = None
+    return is_nan
+
+
+from apex.multi_tensor_apply import multi_tensor_applier
+
+
+class GradientClipper:
+    """
+    Clips gradient norm of an iterable of parameters.
+    """
+    def __init__(self, max_grad_norm):
+        self.max_norm = max_grad_norm
+        if multi_tensor_applier.available:
+            import amp_C
+            self._overflow_buf = torch.cuda.IntTensor([0])
+            self.multi_tensor_l2norm = amp_C.multi_tensor_l2norm
+            self.multi_tensor_scale = amp_C.multi_tensor_scale
+        else:
+            raise RuntimeError('Gradient clipping requires cuda extensions')
+
+    def step(self, parameters):
+        l = [p.grad for p in parameters if p.grad is not None]
+        total_norm, _ = multi_tensor_applier(self.multi_tensor_l2norm,
+                                             self._overflow_buf, [l], False)
+        total_norm = total_norm.item()
+        if (total_norm == float('inf')): return
+        clip_coef = self.max_norm / (total_norm + 1e-6)
+        if clip_coef < 1:
+            multi_tensor_applier(self.multi_tensor_scale, self._overflow_buf,
+                                 [l, l], clip_coef)
+
+
+def main():
+    parser = get_argument_parser()
+    args = parser.parse_args()
+
+    check_early_exit_warning(args)
+
+    if args.local_rank == -1 or args.no_cuda:
+        device = torch.device("cuda" if torch.cuda.is_available()
+                              and not args.no_cuda else "cpu")
+        n_gpu = torch.cuda.device_count()
+    else:
+        torch.cuda.set_device(args.local_rank)
+        device = torch.device("cuda", args.local_rank)
+        n_gpu = 1
+        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        torch.distributed.init_process_group(backend='nccl')
+    logger.info(
+        "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
+        format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
+
+    if args.gradient_accumulation_steps < 1:
+        raise ValueError(
+            "Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
+            .format(args.gradient_accumulation_steps))
+
+    args.train_batch_size = int(args.train_batch_size /
+                                args.gradient_accumulation_steps)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+    if not args.do_train and not args.do_predict:
+        raise ValueError(
+            "At least one of `do_train` or `do_predict` must be True.")
+
+    if args.do_train:
+        if not args.train_file:
+            raise ValueError(
+                "If `do_train` is True, then `train_file` must be specified.")
+    if args.do_predict:
+        if not args.predict_file:
+            raise ValueError(
+                "If `do_predict` is True, then `predict_file` must be specified."
+            )
+
+    if os.path.exists(args.output_dir) and os.listdir(
+            args.output_dir) and args.do_train:
+        raise ValueError(
+            "Output directory () already exists and is not empty.")
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    # Prepare Summary writer
+    if torch.distributed.get_rank() == 0 and args.job_name is not None:
+        args.summary_writer = get_summary_writer(name=args.job_name,
+                                                 base=args.output_dir)
+    else:
+        args.summary_writer = None
+
+    tokenizer = BertTokenizer.from_pretrained(args.bert_model,
+                                              do_lower_case=args.do_lower_case)
+
+    train_examples = None
+    num_train_steps = None
+    if args.do_train:
+        train_examples = read_squad_examples(input_file=args.train_file,
+                                             is_training=True)
+        num_train_steps = int(
+            len(train_examples) / args.train_batch_size /
+            args.gradient_accumulation_steps * args.num_train_epochs)
+
+    # Prepare model
+    #model = BertForQuestionAnswering.from_pretrained(args.bert_model,
+    #            cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
+
+    ## Support for word embedding padding checkpoints
+    # Prepare model
+
+    bert_model_config = {
+        "vocab_size_or_config_json_file": 119547,
+        "hidden_size": 1024,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+        "intermediate_size": 4096,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 2,
+        "initializer_range": 0.02
+    }
+
+    bert_config = BertConfig(**bert_model_config)
+    bert_config.vocab_size = len(tokenizer.vocab)
+    # Padding for divisibility by 8
+    if bert_config.vocab_size % 8 != 0:
+        bert_config.vocab_size += 8 - (bert_config.vocab_size % 8)
+    model = BertForQuestionAnswering(bert_config, args)
+    print("VOCAB SIZE:", bert_config.vocab_size)
+    if args.model_file is not "0":
+        logger.info(f"Loading Pretrained Bert Encoder from: {args.model_file}")
+
+        checkpoint_state_dict = torch.load(args.model_file,
+                                           map_location=torch.device("cpu"))
+        model.load_state_dict(checkpoint_state_dict['model_state_dict'],
+                              strict=False)
+
+        #bert_state_dict = torch.load(args.model_file)
+        #model.bert.load_state_dict(bert_state_dict, strict=False)
+        logger.info(f"Pretrained Bert Encoder Loaded from: {args.model_file}")
+
+    model.to(device)
+
+    # Prepare optimizer
+    param_optimizer = list(model.named_parameters())
+
+    # hack to remove pooler, which is not used
+    # thus it produce None grad that break apex
+    param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
+
+    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+    optimizer_grouped_parameters = [{
+        'params':
+        [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
+        'weight_decay':
+        0.01
+    }, {
+        'params':
+        [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
+        'weight_decay':
+        0.0
+    }]
+
+    t_total = num_train_steps
+    if args.local_rank != -1:
+        t_total = t_total // torch.distributed.get_world_size()
+    if args.fp16:
+        try:
+            from apex.optimizers import FusedAdam
+        except ImportError:
+            raise ImportError(
+                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+            )
+
+        optimizer = FusedAdam(optimizer_grouped_parameters,
+                              lr=args.learning_rate,
+                              bias_correction=False)
+        if args.loss_scale == 0:
+            model, optimizer = amp.initialize(model,
+                                              optimizer,
+                                              opt_level="O2",
+                                              keep_batchnorm_fp32=False,
+                                              loss_scale="dynamic")
+        else:
+            raise NotImplementedError(
+                "dynamic loss scale is only supported in baseline, please set loss_scale=0"
+            )
+    else:
+        optimizer = BertAdam(optimizer_grouped_parameters,
+                             lr=args.learning_rate,
+                             warmup=args.warmup_proportion,
+                             t_total=t_total)
+
+    if args.local_rank != -1:
+        try:
+            from apex.parallel import DistributedDataParallel as DDP
+        except ImportError:
+            raise ImportError(
+                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
+            )
+
+        model = DDP(model)
+    elif n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
+    global_step = 0
+    if args.do_train:
+        cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}'.format(
+            list(filter(None, args.bert_model.split('/'))).pop(),
+            str(args.max_seq_length), str(args.doc_stride),
+            str(args.max_query_length))
+        train_features = None
+        try:
+            with open(cached_train_features_file, "rb") as reader:
+                train_features = pickle.load(reader)
+        except:
+            train_features = convert_examples_to_features(
+                examples=train_examples,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                doc_stride=args.doc_stride,
+                max_query_length=args.max_query_length,
+                is_training=True)
+            if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+                logger.info("  Saving train features into cached file %s",
+                            cached_train_features_file)
+                with open(cached_train_features_file, "wb") as writer:
+                    pickle.dump(train_features, writer)
+        logger.info("***** Running training *****")
+        logger.info("  Num orig examples = %d", len(train_examples))
+        logger.info("  Num split examples = %d", len(train_features))
+        logger.info("  Batch size = %d", args.train_batch_size)
+        logger.info("  Num steps = %d", num_train_steps)
+        all_input_ids = torch.tensor([f.input_ids for f in train_features],
+                                     dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in train_features],
+                                      dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
+                                       dtype=torch.long)
+        all_start_positions = torch.tensor(
+            [f.start_position for f in train_features], dtype=torch.long)
+        all_end_positions = torch.tensor(
+            [f.end_position for f in train_features], dtype=torch.long)
+        train_data = TensorDataset(all_input_ids, all_input_mask,
+                                   all_segment_ids, all_start_positions,
+                                   all_end_positions)
+        if args.local_rank == -1:
+            train_sampler = RandomSampler(train_data)
+        else:
+            train_sampler = DistributedSampler(train_data)
+        train_dataloader = DataLoader(train_data,
+                                      sampler=train_sampler,
+                                      batch_size=args.train_batch_size)
+
+        gradClipper = GradientClipper(max_grad_norm=1.0)
+
+        model.train()
+        ema_loss = 0.
+        sample_count = 0
+        num_epoch = 0
+        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
+            num_epoch += 1
+            epoch_step = 0
+            for step, batch in enumerate(
+                    tqdm(train_dataloader, desc="Iteration", smoothing=0)):
+                if n_gpu == 1:
+                    batch = tuple(
+                        t.to(device)
+                        for t in batch)  # multi-gpu does scattering it-self
+                input_ids, input_mask, segment_ids, start_positions, end_positions = batch
+
+                loss = model(input_ids, segment_ids, input_mask,
+                             start_positions, end_positions)
+
+                if n_gpu > 1:
+                    loss = loss.mean()  # mean() to average on multi-gpu.
+                if args.gradient_accumulation_steps > 1:
+                    loss = loss / args.gradient_accumulation_steps
+
+                ema_loss = args.loss_plot_alpha * ema_loss + (
+                    1 - args.loss_plot_alpha) * loss.item()
+
+                if args.local_rank != -1:
+                    model.disable_allreduce()
+                    if (step + 1) % args.gradient_accumulation_steps == 0:
+                        model.enable_allreduce()
+
+                if args.fp16:
+                    with amp.scale_loss(loss, optimizer) as scaled_loss:
+                        scaled_loss.backward()
+                else:
+                    loss.backward()
+
+                # gradient clipping
+                gradClipper.step(amp.master_params(optimizer))
+
+                sample_count += (args.train_batch_size *
+                                 torch.distributed.get_world_size())
+
+                if (step + 1) % args.gradient_accumulation_steps == 0:
+                    # modify learning rate with special warm up BERT uses
+                    lr_this_step = args.learning_rate * warmup_linear(
+                        global_step / t_total, args.warmup_proportion)
+                    for param_group in optimizer.param_groups:
+                        param_group['lr'] = lr_this_step
+
+                    optimizer.step()
+                    optimizer.zero_grad()
+                    global_step += 1
+                    epoch_step += 1
+
+                    if torch.distributed.get_rank(
+                    ) == 0 and args.summary_writer:
+                        summary_events = [
+                            (f'Train/Steps/lr', lr_this_step, global_step),
+                            (f'Train/Samples/train_loss', loss, sample_count),
+                            (f'Train/Samples/lr', lr_this_step, sample_count),
+                            (f'Train/Samples/train_ema_loss', ema_loss,
+                             sample_count)
+                        ]
+                        if args.fp16 and hasattr(optimizer, 'cur_scale'):
+                            summary_events.append(
+                                (f'Train/Samples/scale', optimizer.cur_scale,
+                                 sample_count))
+                        write_summary_events(args.summary_writer,
+                                             summary_events)
+
+                    if torch.distributed.get_rank() == 0 and (
+                            step + 1) % args.print_steps == 0:
+                        logger.info(
+                            f"bert_squad_progress: step={global_step} lr={lr_this_step} loss={ema_loss}"
+                        )
+
+                if is_time_to_exit(args=args,
+                                   epoch_steps=epoch_step,
+                                   global_steps=global_step):
+                    logger.info(
+                        f'Warning: Early epoch termination due to max steps limit, epoch step ={epoch_step}, global step = {global_step}, epoch = {num_epoch}'
+                    )
+                    break
+
+    # Save a trained model
+    #model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+    #output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+    #if args.do_train:
+    #    torch.save(model_to_save.state_dict(), output_model_file)
+
+    # Load a trained model that you have fine-tuned
+
+    #model_state_dict = torch.load(output_model_file)
+    #model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict)
+    #model.to(device)
+
+    if args.do_predict and (args.local_rank == -1
+                            or torch.distributed.get_rank() == 0):
+        eval_examples = read_squad_examples(input_file=args.predict_file,
+                                            is_training=False)
+        eval_features = convert_examples_to_features(
+            examples=eval_examples,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            doc_stride=args.doc_stride,
+            max_query_length=args.max_query_length,
+            is_training=False)
+
+        logger.info("***** Running predictions *****")
+        logger.info("  Num orig examples = %d", len(eval_examples))
+        logger.info("  Num split examples = %d", len(eval_features))
+        logger.info("  Batch size = %d", args.predict_batch_size)
+
+        all_input_ids = torch.tensor([f.input_ids for f in eval_features],
+                                     dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in eval_features],
+                                      dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
+                                       dtype=torch.long)
+        all_example_index = torch.arange(all_input_ids.size(0),
+                                         dtype=torch.long)
+        eval_data = TensorDataset(all_input_ids, all_input_mask,
+                                  all_segment_ids, all_example_index)
+        # Run prediction for full data
+        eval_sampler = SequentialSampler(eval_data)
+        eval_dataloader = DataLoader(eval_data,
+                                     sampler=eval_sampler,
+                                     batch_size=args.predict_batch_size)
+
+        model.eval()
+        all_results = []
+        logger.info("Start evaluating")
+        for input_ids, input_mask, segment_ids, example_indices in tqdm(
+                eval_dataloader, desc="Evaluating"):
+            if len(all_results) % 1000 == 0:
+                logger.info("Processing example: %d" % (len(all_results)))
+            input_ids = input_ids.to(device)
+            input_mask = input_mask.to(device)
+            segment_ids = segment_ids.to(device)
+            with torch.no_grad():
+                batch_start_logits, batch_end_logits = model(
+                    input_ids, segment_ids, input_mask)
+            for i, example_index in enumerate(example_indices):
+                start_logits = batch_start_logits[i].detach().cpu().tolist()
+                end_logits = batch_end_logits[i].detach().cpu().tolist()
+                eval_feature = eval_features[example_index.item()]
+                unique_id = int(eval_feature.unique_id)
+                all_results.append(
+                    RawResult(unique_id=unique_id,
+                              start_logits=start_logits,
+                              end_logits=end_logits))
+        output_prediction_file = os.path.join(args.output_dir,
+                                              "predictions.json")
+        output_nbest_file = os.path.join(args.output_dir,
+                                         "nbest_predictions.json")
+        write_predictions(eval_examples, eval_features, all_results,
+                          args.n_best_size, args.max_answer_length,
+                          args.do_lower_case, output_prediction_file,
+                          output_nbest_file, args.verbose_logging)
+
+
+if __name__ == "__main__":
+    main()

Deepspeed/BingBertSquad/nvidia_run_squad_deepspeed.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""Run BERT on SQuAD."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import collections
+import logging
+import json
+import math
+import os
+import random
+import pickle
+from tqdm import tqdm, trange
+from utils import get_argument_parser, \
+    get_summary_writer, write_summary_events, \
+    is_time_to_exit, check_early_exit_warning
+import deepspeed
+
+import time
+import numpy as np
+import torch
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer
+from pytorch_pretrained_bert.optimization import BertAdam
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
+
+from turing.nvidia_modeling import BertForQuestionAnswering, BertConfig
+from turing.nvidia_modelingpreln import BertConfig as BertConfigPreLN
+from turing.nvidia_modelingpreln import BertForQuestionAnswering as BertForQuestionAnsweringPreLN
+
+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__)
+all_step_time = 0.0
+
+
+class SquadExample(object):
+    """A single training/test example for the Squad dataset."""
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 doc_tokens,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (self.qas_id)
+        s += ", question_text: %s" % (self.question_text)
+        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.start_position:
+            s += ", end_position: %d" % (self.end_position)
+        return s
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+    def __init__(self,
+                 unique_id,
+                 example_index,
+                 doc_span_index,
+                 tokens,
+                 token_to_orig_map,
+                 token_is_max_context,
+                 input_ids,
+                 input_mask,
+                 segment_ids,
+                 start_position=None,
+                 end_position=None):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.doc_span_index = doc_span_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.token_is_max_context = token_is_max_context
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.start_position = start_position
+        self.end_position = end_position
+
+
+def read_squad_examples(input_file, is_training):
+    """Read a SQuAD json file into a list of SquadExample."""
+    with open(input_file, "r", encoding='utf-8') as reader:
+        input_data = json.load(reader)["data"]
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    examples = []
+    for entry in input_data:
+        for paragraph in entry["paragraphs"]:
+            paragraph_text = paragraph["context"]
+            doc_tokens = []
+            char_to_word_offset = []
+            prev_is_whitespace = True
+            for c in paragraph_text:
+                if is_whitespace(c):
+                    prev_is_whitespace = True
+                else:
+                    if prev_is_whitespace:
+                        doc_tokens.append(c)
+                    else:
+                        doc_tokens[-1] += c
+                    prev_is_whitespace = False
+                char_to_word_offset.append(len(doc_tokens) - 1)
+
+            for qa in paragraph["qas"]:
+                qas_id = qa["id"]
+                question_text = qa["question"]
+                start_position = None
+                end_position = None
+                orig_answer_text = None
+                if is_training:
+                    if len(qa["answers"]) != 1:
+                        raise ValueError(
+                            "For training, each question should have exactly 1 answer."
+                        )
+                    answer = qa["answers"][0]
+                    orig_answer_text = answer["text"]
+                    answer_offset = answer["answer_start"]
+                    answer_length = len(orig_answer_text)
+                    start_position = char_to_word_offset[answer_offset]
+                    end_position = char_to_word_offset[answer_offset +
+                                                       answer_length - 1]
+                    # Only add answers where the text can be exactly recovered from the
+                    # document. If this CAN'T happen it's likely due to weird Unicode
+                    # stuff so we will just skip the example.
+                    #
+                    # Note that this means for training mode, every example is NOT
+                    # guaranteed to be preserved.
+                    actual_text = " ".join(
+                        doc_tokens[start_position:(end_position + 1)])
+                    cleaned_answer_text = " ".join(
+                        whitespace_tokenize(orig_answer_text))
+                    if actual_text.find(cleaned_answer_text) == -1:
+                        logger.warning("Could not find answer: '%s' vs. '%s'",
+                                       actual_text, cleaned_answer_text)
+                        continue
+
+                example = SquadExample(qas_id=qas_id,
+                                       question_text=question_text,
+                                       doc_tokens=doc_tokens,
+                                       orig_answer_text=orig_answer_text,
+                                       start_position=start_position,
+                                       end_position=end_position)
+                examples.append(example)
+    return examples
+
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length,
+                                 doc_stride, max_query_length, is_training):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    unique_id = 1000000000
+
+    features = []
+    for (example_index, example) in enumerate(examples):
+        query_tokens = tokenizer.tokenize(example.question_text)
+
+        if len(query_tokens) > max_query_length:
+            query_tokens = query_tokens[0:max_query_length]
+
+        tok_to_orig_index = []
+        orig_to_tok_index = []
+        all_doc_tokens = []
+        for (i, token) in enumerate(example.doc_tokens):
+            orig_to_tok_index.append(len(all_doc_tokens))
+            sub_tokens = tokenizer.tokenize(token)
+            for sub_token in sub_tokens:
+                tok_to_orig_index.append(i)
+                all_doc_tokens.append(sub_token)
+
+        tok_start_position = None
+        tok_end_position = None
+        if is_training:
+            tok_start_position = orig_to_tok_index[example.start_position]
+            if example.end_position < len(example.doc_tokens) - 1:
+                tok_end_position = orig_to_tok_index[example.end_position +
+                                                     1] - 1
+            else:
+                tok_end_position = len(all_doc_tokens) - 1
+            (tok_start_position, tok_end_position) = _improve_answer_span(
+                all_doc_tokens, tok_start_position, tok_end_position,
+                tokenizer, example.orig_answer_text)
+
+        # The -3 accounts for [CLS], [SEP] and [SEP]
+        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+        # We can have documents that are longer than the maximum sequence length.
+        # To deal with this we do a sliding window approach, where we take chunks
+        # of the up to our max length with a stride of `doc_stride`.
+        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
+            "DocSpan", ["start", "length"])
+        doc_spans = []
+        start_offset = 0
+        while start_offset < len(all_doc_tokens):
+            length = len(all_doc_tokens) - start_offset
+            if length > max_tokens_for_doc:
+                length = max_tokens_for_doc
+            doc_spans.append(_DocSpan(start=start_offset, length=length))
+            if start_offset + length == len(all_doc_tokens):
+                break
+            start_offset += min(length, doc_stride)
+
+        for (doc_span_index, doc_span) in enumerate(doc_spans):
+            tokens = []
+            token_to_orig_map = {}
+            token_is_max_context = {}
+            segment_ids = []
+            tokens.append("[CLS]")
+            segment_ids.append(0)
+            for token in query_tokens:
+                tokens.append(token)
+                segment_ids.append(0)
+            tokens.append("[SEP]")
+            segment_ids.append(0)
+
+            for i in range(doc_span.length):
+                split_token_index = doc_span.start + i
+                token_to_orig_map[len(
+                    tokens)] = tok_to_orig_index[split_token_index]
+
+                is_max_context = _check_is_max_context(doc_spans,
+                                                       doc_span_index,
+                                                       split_token_index)
+                token_is_max_context[len(tokens)] = is_max_context
+                tokens.append(all_doc_tokens[split_token_index])
+                segment_ids.append(1)
+            tokens.append("[SEP]")
+            segment_ids.append(1)
+
+            input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+            # The mask has 1 for real tokens and 0 for padding tokens. Only real
+            # tokens are attended to.
+            input_mask = [1] * len(input_ids)
+
+            # Zero-pad up to the sequence length.
+            while len(input_ids) < max_seq_length:
+                input_ids.append(0)
+                input_mask.append(0)
+                segment_ids.append(0)
+
+            assert len(input_ids) == max_seq_length
+            assert len(input_mask) == max_seq_length
+            assert len(segment_ids) == max_seq_length
+
+            start_position = None
+            end_position = None
+            if is_training:
+                # For training, if our document chunk does not contain an annotation
+                # we throw it out, since there is nothing to predict.
+                doc_start = doc_span.start
+                doc_end = doc_span.start + doc_span.length - 1
+                if (example.start_position < doc_start
+                        or example.end_position < doc_start
+                        or example.start_position > doc_end
+                        or example.end_position > doc_end):
+                    continue
+
+                doc_offset = len(query_tokens) + 2
+                start_position = tok_start_position - doc_start + doc_offset
+                end_position = tok_end_position - doc_start + doc_offset
+
+            if example_index < 20:
+                logger.info("*** Example ***")
+                logger.info("unique_id: %s" % (unique_id))
+                logger.info("example_index: %s" % (example_index))
+                logger.info("doc_span_index: %s" % (doc_span_index))
+                logger.info("tokens: %s" % " ".join(tokens))
+                logger.info("token_to_orig_map: %s" % " ".join(
+                    ["%d:%d" % (x, y)
+                     for (x, y) in token_to_orig_map.items()]))
+                logger.info("token_is_max_context: %s" % " ".join([
+                    "%d:%s" % (x, y)
+                    for (x, y) in token_is_max_context.items()
+                ]))
+                logger.info("input_ids: %s" %
+                            " ".join([str(x) for x in input_ids]))
+                logger.info("input_mask: %s" %
+                            " ".join([str(x) for x in input_mask]))
+                logger.info("segment_ids: %s" %
+                            " ".join([str(x) for x in segment_ids]))
+                if is_training:
+                    answer_text = " ".join(
+                        tokens[start_position:(end_position + 1)])
+                    logger.info("start_position: %d" % (start_position))
+                    logger.info("end_position: %d" % (end_position))
+                    logger.info("answer: %s" % (answer_text))
+
+            features.append(
+                InputFeatures(unique_id=unique_id,
+                              example_index=example_index,
+                              doc_span_index=doc_span_index,
+                              tokens=tokens,
+                              token_to_orig_map=token_to_orig_map,
+                              token_is_max_context=token_is_max_context,
+                              input_ids=input_ids,
+                              input_mask=input_mask,
+                              segment_ids=segment_ids,
+                              start_position=start_position,
+                              end_position=end_position))
+            unique_id += 1
+
+    return features
+
+
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+                         orig_answer_text):
+    """Returns tokenized answer spans that better match the annotated answer."""
+
+    # The SQuAD annotations are character based. We first project them to
+    # whitespace-tokenized words. But then after WordPiece tokenization, we can
+    # often find a "better match". For example:
+    #
+    #   Question: What year was John Smith born?
+    #   Context: The leader was John Smith (1895-1943).
+    #   Answer: 1895
+    #
+    # The original whitespace-tokenized answer will be "(1895-1943).". However
+    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+    # the exact answer, 1895.
+    #
+    # However, this is not always possible. Consider the following:
+    #
+    #   Question: What country is the top exporter of electornics?
+    #   Context: The Japanese electronics industry is the lagest in the world.
+    #   Answer: Japan
+    #
+    # In this case, the annotator chose "Japan" as a character sub-span of
+    # the word "Japanese". Since our WordPiece tokenizer does not split
+    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+    # in SQuAD, but does happen.
+    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+    for new_start in range(input_start, input_end + 1):
+        for new_end in range(input_end, new_start - 1, -1):
+            text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+            if text_span == tok_answer_text:
+                return (new_start, new_end)
+
+    return (input_start, input_end)
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+    """Check if this is the 'max context' doc span for the token."""
+
+    # Because of the sliding window approach taken to scoring documents, a single
+    # token can appear in multiple documents. E.g.
+    #  Doc: the man went to the store and bought a gallon of milk
+    #  Span A: the man went to the
+    #  Span B: to the store and bought
+    #  Span C: and bought a gallon of
+    #  ...
+    #
+    # Now the word 'bought' will have two scores from spans B and C. We only
+    # want to consider the score with "maximum context", which we define as
+    # the *minimum* of its left and right context (the *sum* of left and
+    # right context will always be the same, of course).
+    #
+    # In the example the maximum context for 'bought' would be span C since
+    # it has 1 left context and 3 right context, while span B has 4 left context
+    # and 0 right context.
+    best_score = None
+    best_span_index = None
+    for (span_index, doc_span) in enumerate(doc_spans):
+        end = doc_span.start + doc_span.length - 1
+        if position < doc_span.start:
+            continue
+        if position > end:
+            continue
+        num_left_context = position - doc_span.start
+        num_right_context = end - position
+        score = min(num_left_context,
+                    num_right_context) + 0.01 * doc_span.length
+        if best_score is None or score > best_score:
+            best_score = score
+            best_span_index = span_index
+
+    return cur_span_index == best_span_index
+
+
+RawResult = collections.namedtuple("RawResult",
+                                   ["unique_id", "start_logits", "end_logits"])
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+                      max_answer_length, do_lower_case, output_prediction_file,
+                      output_nbest_file, verbose_logging):
+    """Write final predictions to the json file."""
+    logger.info("Writing predictions to: %s" % (output_prediction_file))
+    logger.info("Writing nbest to: %s" % (output_nbest_file))
+
+    example_index_to_features = collections.defaultdict(list)
+    for feature in all_features:
+        example_index_to_features[feature.example_index].append(feature)
+
+    unique_id_to_result = {}
+    for result in all_results:
+        unique_id_to_result[result.unique_id] = result
+
+    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "PrelimPrediction", [
+            "feature_index", "start_index", "end_index", "start_logit",
+            "end_logit"
+        ])
+
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+
+        prelim_predictions = []
+        for (feature_index, feature) in enumerate(features):
+            result = unique_id_to_result[feature.unique_id]
+
+            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    # We could hypothetically create invalid predictions, e.g., predict
+                    # that the start of the span is in the question. We throw out all
+                    # invalid predictions.
+                    if start_index >= len(feature.tokens):
+                        continue
+                    if end_index >= len(feature.tokens):
+                        continue
+                    if start_index not in feature.token_to_orig_map:
+                        continue
+                    if end_index not in feature.token_to_orig_map:
+                        continue
+                    if not feature.token_is_max_context.get(
+                            start_index, False):
+                        continue
+                    if end_index < start_index:
+                        continue
+                    length = end_index - start_index + 1
+                    if length > max_answer_length:
+                        continue
+                    prelim_predictions.append(
+                        _PrelimPrediction(
+                            feature_index=feature_index,
+                            start_index=start_index,
+                            end_index=end_index,
+                            start_logit=result.start_logits[start_index],
+                            end_logit=result.end_logits[end_index]))
+
+        prelim_predictions = sorted(prelim_predictions,
+                                    key=lambda x:
+                                    (x.start_logit + x.end_logit),
+                                    reverse=True)
+
+        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+            "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+        seen_predictions = {}
+        nbest = []
+        for pred in prelim_predictions:
+            if len(nbest) >= n_best_size:
+                break
+            feature = features[pred.feature_index]
+
+            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+            orig_doc_start = feature.token_to_orig_map[pred.start_index]
+            orig_doc_end = feature.token_to_orig_map[pred.end_index]
+            orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+            tok_text = " ".join(tok_tokens)
+
+            # De-tokenize WordPieces that have been split off.
+            tok_text = tok_text.replace(" ##", "")
+            tok_text = tok_text.replace("##", "")
+
+            # Clean whitespace
+            tok_text = tok_text.strip()
+            tok_text = " ".join(tok_text.split())
+            orig_text = " ".join(orig_tokens)
+
+            final_text = get_final_text(tok_text, orig_text, do_lower_case,
+                                        verbose_logging)
+            if final_text in seen_predictions:
+                continue
+
+            seen_predictions[final_text] = True
+            nbest.append(
+                _NbestPrediction(text=final_text,
+                                 start_logit=pred.start_logit,
+                                 end_logit=pred.end_logit))
+
+        # In very rare edge cases we could have no valid predictions. So we
+        # just create a nonce prediction in this case to avoid failure.
+        if not nbest:
+            nbest.append(
+                _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+        assert len(nbest) >= 1
+
+        total_scores = []
+        for entry in nbest:
+            total_scores.append(entry.start_logit + entry.end_logit)
+
+        probs = _compute_softmax(total_scores)
+
+        nbest_json = []
+        for (i, entry) in enumerate(nbest):
+            output = collections.OrderedDict()
+            output["text"] = entry.text
+            output["probability"] = probs[i]
+            output["start_logit"] = entry.start_logit
+            output["end_logit"] = entry.end_logit
+            nbest_json.append(output)
+
+        assert len(nbest_json) >= 1
+
+        all_predictions[example.qas_id] = nbest_json[0]["text"]
+        all_nbest_json[example.qas_id] = nbest_json
+
+    with open(output_prediction_file, "w") as writer:
+        writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+    with open(output_nbest_file, "w") as writer:
+        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+
+
+def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
+    """Project the tokenized prediction back to the original text."""
+
+    # When we created the data, we kept track of the alignment between original
+    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+    # now `orig_text` contains the span of our original text corresponding to the
+    # span that we predicted.
+    #
+    # However, `orig_text` may contain extra characters that we don't want in
+    # our prediction.
+    #
+    # For example, let's say:
+    #   pred_text = steve smith
+    #   orig_text = Steve Smith's
+    #
+    # We don't want to return `orig_text` because it contains the extra "'s".
+    #
+    # We don't want to return `pred_text` because it's already been normalized
+    # (the SQuAD eval script also does punctuation stripping/lower casing but
+    # our tokenizer does additional normalization like stripping accent
+    # characters).
+    #
+    # What we really want to return is "Steve Smith".
+    #
+    # Therefore, we have to apply a semi-complicated alignment heruistic between
+    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+    # can fail in certain cases in which case we just return `orig_text`.
+
+    def _strip_spaces(text):
+        ns_chars = []
+        ns_to_s_map = collections.OrderedDict()
+        for (i, c) in enumerate(text):
+            if c == " ":
+                continue
+            ns_to_s_map[len(ns_chars)] = i
+            ns_chars.append(c)
+        ns_text = "".join(ns_chars)
+        return (ns_text, ns_to_s_map)
+
+    # We first tokenize `orig_text`, strip whitespace from the result
+    # and `pred_text`, and check if they are the same length. If they are
+    # NOT the same length, the heuristic has failed. If they are the same
+    # length, we assume the characters are one-to-one aligned.
+    tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+
+    tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+    start_position = tok_text.find(pred_text)
+    if start_position == -1:
+        if verbose_logging:
+            logger.info("Unable to find text: '%s' in '%s'" %
+                        (pred_text, orig_text))
+        return orig_text
+    end_position = start_position + len(pred_text) - 1
+
+    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+    if len(orig_ns_text) != len(tok_ns_text):
+        if verbose_logging:
+            logger.info(
+                "Length not equal after stripping spaces: '%s' vs '%s'",
+                orig_ns_text, tok_ns_text)
+        return orig_text
+
+    # We then project the characters in `pred_text` back to `orig_text` using
+    # the character-to-character alignment.
+    tok_s_to_ns_map = {}
+    for (i, tok_index) in tok_ns_to_s_map.items():
+        tok_s_to_ns_map[tok_index] = i
+
+    orig_start_position = None
+    if start_position in tok_s_to_ns_map:
+        ns_start_position = tok_s_to_ns_map[start_position]
+        if ns_start_position in orig_ns_to_s_map:
+            orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+    if orig_start_position is None:
+        if verbose_logging:
+            logger.info("Couldn't map start position")
+        return orig_text
+
+    orig_end_position = None
+    if end_position in tok_s_to_ns_map:
+        ns_end_position = tok_s_to_ns_map[end_position]
+        if ns_end_position in orig_ns_to_s_map:
+            orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+    if orig_end_position is None:
+        if verbose_logging:
+            logger.info("Couldn't map end position")
+        return orig_text
+
+    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+    return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+    index_and_score = sorted(enumerate(logits),
+                             key=lambda x: x[1],
+                             reverse=True)
+
+    best_indexes = []
+    for i in range(len(index_and_score)):
+        if i >= n_best_size:
+            break
+        best_indexes.append(index_and_score[i][0])
+    return best_indexes
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs
+
+
+def warmup_linear(x, warmup=0.002):
+    if x < warmup:
+        return x / warmup
+    return 1.0 - x
+
+
+def copy_optimizer_params_to_model(named_params_model, named_params_optimizer):
+    """ Utility function for optimize_on_cpu and 16-bits training.
+        Copy the parameters optimized on CPU/RAM back to the model on GPU
+    """
+    for (name_opti, param_opti), (name_model,
+                                  param_model) in zip(named_params_optimizer,
+                                                      named_params_model):
+        if name_opti != name_model:
+            logger.error("name_opti != name_model: {} {}".format(
+                name_opti, name_model))
+            raise ValueError
+        param_model.data.copy_(param_opti.data)
+
+
+def set_optimizer_params_grad(named_params_optimizer,
+                              named_params_model,
+                              test_nan=False):
+    """ Utility function for optimize_on_cpu and 16-bits training.
+        Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model
+    """
+    is_nan = False
+    for (name_opti, param_opti), (name_model,
+                                  param_model) in zip(named_params_optimizer,
+                                                      named_params_model):
+        if name_opti != name_model:
+            logger.error("name_opti != name_model: {} {}".format(
+                name_opti, name_model))
+            raise ValueError
+        if param_model.grad is not None:
+            max = param_model.grad.max
+            if test_nan and max != max:
+                is_nan = True
+            if param_opti.grad is None:
+                param_opti.grad = torch.nn.Parameter(
+                    param_opti.data.new().resize_(*param_opti.data.size()))
+            param_opti.grad.data.copy_(param_model.grad.data)
+        else:
+            param_opti.grad = None
+    return is_nan
+
+
+def main():
+    parser = get_argument_parser()
+
+    deepspeed.init_distributed(dist_backend='nccl')
+
+    # Include DeepSpeed configuration arguments
+    parser = deepspeed.add_config_arguments(parser)
+
+    args = parser.parse_args()
+    args.local_rank = int(os.environ['LOCAL_RANK'])
+    args.train_batch_size = int(args.train_batch_size /
+                                args.gradient_accumulation_steps)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    tokenizer = BertTokenizer.from_pretrained(args.bert_model,
+                                              do_lower_case=args.do_lower_case)
+
+    train_examples = None
+    num_train_steps = None
+    if args.do_train:
+        train_examples = read_squad_examples(input_file=args.train_file,
+                                             is_training=True)
+        num_train_steps = int(
+            len(train_examples) / args.train_batch_size /
+            args.gradient_accumulation_steps * args.num_train_epochs)
+
+    # Prepare model
+    # model = BertForQuestionAnswering.from_pretrained(args.bert_model,
+    #            cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
+
+    # Support for word embedding padding checkpoints
+    # Prepare model
+
+    bert_model_config = {
+        "vocab_size_or_config_json_file": 119547,
+        "hidden_size": 1024,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+        "intermediate_size": 4096,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": args.dropout,
+        "attention_probs_dropout_prob": args.dropout,
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 2,
+        "initializer_range": 0.02
+    }
+
+    if args.ckpt_type == "DS":
+        if args.preln:
+            bert_config = BertConfigPreLN(**bert_model_config)
+        else:
+            bert_config = BertConfig(**bert_model_config)
+    else:
+        # Models from Tensorflow and Huggingface are post-LN.
+        if args.preln:
+            raise ValueError(
+                "Should NOT use --preln if the loading checkpoint doesn't use pre-layer-norm."
+            )
+
+        # Use the original bert config if want to load from non-DeepSpeed checkpoint.
+        if args.origin_bert_config_file is None:
+            raise ValueError(
+                "--origin_bert_config_file is required for loading non-DeepSpeed checkpoint."
+            )
+
+        bert_config = BertConfig.from_json_file(args.origin_bert_config_file)
+
+        if bert_config.vocab_size != len(tokenizer.vocab):
+            raise ValueError("vocab size from original checkpoint mismatch.")
+
+    bert_config.vocab_size = len(tokenizer.vocab)
+    # Padding for divisibility by 8
+    if bert_config.vocab_size % 8 != 0:
+        vocab_diff = 8 - (bert_config.vocab_size % 8)
+        bert_config.vocab_size += vocab_diff
+
+    if args.preln:
+        model = BertForQuestionAnsweringPreLN(bert_config, args)
+    else:
+        model = BertForQuestionAnswering(bert_config, args)
+
+    print("VOCAB SIZE:", bert_config.vocab_size)
+    if args.model_file is not "0":
+        logger.info(f"Loading Pretrained Bert Encoder from: {args.model_file}")
+
+        if args.ckpt_type == "DS":
+            checkpoint_state_dict = torch.load(
+                args.model_file, map_location=torch.device("cpu"))
+            if 'module' in checkpoint_state_dict:
+                logger.info('Loading DeepSpeed v2.0 style checkpoint')
+                model.load_state_dict(checkpoint_state_dict['module'],
+                                      strict=False)
+            elif 'model_state_dict' in checkpoint_state_dict:
+                model.load_state_dict(
+                    checkpoint_state_dict['model_state_dict'], strict=False)
+            else:
+                raise ValueError("Unable to find model state in checkpoint")
+        else:
+            from convert_bert_ckpt_to_deepspeed import convert_ckpt_to_deepspeed
+            convert_ckpt_to_deepspeed(model, args.ckpt_type, args.model_file,
+                                      vocab_diff,
+                                      args.deepspeed_transformer_kernel)
+
+        logger.info(f"Pretrained Bert Encoder Loaded from: {args.model_file}")
+
+    # Prepare optimizer
+    param_optimizer = list(model.named_parameters())
+
+    # hack to remove pooler, which is not used
+    # thus it produce None grad that break apex
+    param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
+
+    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+    if args.deepspeed_transformer_kernel:
+        no_decay = no_decay + [
+            'attn_nw', 'attn_nb', 'norm_w', 'norm_b', 'attn_qkvb', 'attn_ob',
+            'inter_b', 'output_b'
+        ]
+    optimizer_grouped_parameters = [{
+        'params':
+        [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
+        'weight_decay':
+        0.01
+    }, {
+        'params':
+        [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
+        'weight_decay':
+        0.0
+    }]
+
+    model, optimizer, _, _ = deepspeed.initialize(
+        args=args,
+        model=model,
+        model_parameters=optimizer_grouped_parameters,
+        dist_init_required=True)
+
+    if args.local_rank == -1 or args.no_cuda:
+        device = torch.device("cuda" if torch.cuda.is_available()
+                              and not args.no_cuda else "cpu")
+        n_gpu = torch.cuda.device_count()
+    else:
+        torch.cuda.set_device(args.local_rank)
+        device = torch.device("cuda", args.local_rank)
+        n_gpu = 1
+        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        #torch.distributed.init_process_group(backend='nccl')
+    logger.info(
+        "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
+        format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
+
+    if args.gradient_accumulation_steps < 1:
+        raise ValueError(
+            "Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
+            .format(args.gradient_accumulation_steps))
+    if n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+    if not args.do_train and not args.do_predict:
+        raise ValueError(
+            "At least one of `do_train` or `do_predict` must be True.")
+
+    if args.do_train:
+        if not args.train_file:
+            raise ValueError(
+                "If `do_train` is True, then `train_file` must be specified.")
+    if args.do_predict:
+        if not args.predict_file:
+            raise ValueError(
+                "If `do_predict` is True, then `predict_file` must be specified."
+            )
+
+    if os.path.exists(args.output_dir) and os.listdir(
+            args.output_dir) and args.do_train:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+    # Prepare Summary writer
+    if torch.distributed.get_rank() == 0 and args.job_name is not None:
+        args.summary_writer = get_summary_writer(name=args.job_name,
+                                                 base=args.output_dir)
+    else:
+        args.summary_writer = None
+
+    logger.info("propagate deepspeed-config settings to client settings")
+    args.train_batch_size = model.train_micro_batch_size_per_gpu()
+    args.gradient_accumulation_steps = model.gradient_accumulation_steps()
+    args.fp16 = model.fp16_enabled()
+    args.print_steps = model.steps_per_print()
+    args.learning_rate = model.get_lr()[0]
+    args.wall_clock_breakdown = model.wall_clock_breakdown()
+
+    t_total = num_train_steps
+    if args.local_rank != -1:
+        t_total = t_total // torch.distributed.get_world_size()
+
+    global_step = 0
+    if args.do_train:
+        cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}'.format(
+            list(filter(None, args.bert_model.split('/'))).pop(),
+            str(args.max_seq_length), str(args.doc_stride),
+            str(args.max_query_length))
+        train_features = None
+        try:
+            with open(cached_train_features_file, "rb") as reader:
+                train_features = pickle.load(reader)
+        except:
+            train_features = convert_examples_to_features(
+                examples=train_examples,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                doc_stride=args.doc_stride,
+                max_query_length=args.max_query_length,
+                is_training=True)
+            if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+                logger.info("  Saving train features into cached file %s",
+                            cached_train_features_file)
+                with open(cached_train_features_file, "wb") as writer:
+                    pickle.dump(train_features, writer)
+        logger.info("***** Running training *****")
+        logger.info("  Num orig examples = %d", len(train_examples))
+        logger.info("  Num split examples = %d", len(train_features))
+        logger.info("  Batch size = %d", args.train_batch_size)
+        logger.info("  Num steps = %d", num_train_steps)
+        all_input_ids = torch.tensor([f.input_ids for f in train_features],
+                                     dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in train_features],
+                                      dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
+                                       dtype=torch.long)
+        all_start_positions = torch.tensor(
+            [f.start_position for f in train_features], dtype=torch.long)
+        all_end_positions = torch.tensor(
+            [f.end_position for f in train_features], dtype=torch.long)
+        train_data = TensorDataset(all_input_ids, all_input_mask,
+                                   all_segment_ids, all_start_positions,
+                                   all_end_positions)
+        if args.local_rank == -1:
+            train_sampler = RandomSampler(train_data)
+        else:
+            train_sampler = DistributedSampler(train_data)
+        train_dataloader = DataLoader(train_data,
+                                      sampler=train_sampler,
+                                      batch_size=args.train_batch_size)
+
+        model.train()
+        ema_loss = 0.
+        sample_count = 0
+        num_epoch = 0
+        global all_step_time
+        ave_rounds = 20
+        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
+            num_epoch += 1
+            epoch_step = 0
+            for step, batch in enumerate(
+                    tqdm(train_dataloader, desc="Iteration", smoothing=0)):
+                start_time = time.time()
+                bs_size = batch[0].size()[0]
+                if n_gpu == 1:
+                    batch = tuple(
+                        t.to(device)
+                        for t in batch)  # multi-gpu does scattering it-self
+                input_ids, input_mask, segment_ids, start_positions, end_positions = batch
+
+                loss = model(input_ids, segment_ids, input_mask,
+                             start_positions, end_positions)
+                if n_gpu > 1:
+                    loss = loss.mean()  # mean() to average on multi-gpu.
+
+                if args.gradient_accumulation_steps > 1:
+                    loss = loss / args.gradient_accumulation_steps
+
+                ema_loss = args.loss_plot_alpha * ema_loss + (
+                    1 - args.loss_plot_alpha) * loss.item()
+
+                model.backward(loss)
+                loss_item = loss.item() * args.gradient_accumulation_steps
+                loss = None
+
+                sample_count += (args.train_batch_size *
+                                 torch.distributed.get_world_size())
+
+                if (step + 1) % args.gradient_accumulation_steps == 0:
+                    # modify learning rate with special warm up BERT uses
+                    lr_this_step = args.learning_rate * warmup_linear(
+                        global_step / t_total, args.warmup_proportion)
+                    for param_group in optimizer.param_groups:
+                        param_group['lr'] = lr_this_step
+
+                    model.step()
+                    global_step += 1
+                    epoch_step += 1
+
+                    if torch.distributed.get_rank(
+                    ) == 0 and args.summary_writer:
+                        summary_events = [
+                            (f'Train/Steps/lr', lr_this_step, global_step),
+                            (f'Train/Samples/train_loss', loss_item,
+                             sample_count),
+                            (f'Train/Samples/lr', lr_this_step, sample_count),
+                            (f'Train/Samples/train_ema_loss', ema_loss,
+                             sample_count)
+                        ]
+
+                        if args.fp16 and hasattr(optimizer, 'cur_scale'):
+                            summary_events.append(
+                                (f'Train/Samples/scale', optimizer.cur_scale,
+                                 sample_count))
+                        write_summary_events(args.summary_writer,
+                                             summary_events)
+                        args.summary_writer.flush()
+
+                    if torch.distributed.get_rank() == 0 and (
+                            step + 1) % args.print_steps == 0:
+                        logger.info(
+                            f"bert_squad_progress: step={global_step} lr={lr_this_step} loss={ema_loss}"
+                        )
+                else:
+                    model.step()
+
+                if is_time_to_exit(args=args,
+                                   epoch_steps=epoch_step,
+                                   global_steps=global_step):
+                    logger.info(
+                        f'Warning: Early epoch termination due to max steps limit, epoch step ={epoch_step}, global step = {global_step}, epoch = {num_epoch}'
+                    )
+                    break
+                one_step_time = time.time() - start_time
+                all_step_time += one_step_time
+                if (step + 1) % (
+                        ave_rounds) == 0 and torch.distributed.get_rank() == 0:
+                    print(
+                        ' At step {}, averaged throughput for {} rounds is: {} Samples/s'
+                        .format(
+                            step, ave_rounds,
+                            bs_size * ave_rounds *
+                            torch.distributed.get_world_size() /
+                            all_step_time),
+                        flush=True)
+                    all_step_time = 0.0
+
+    # Save a trained model
+    # model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+    #output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+    # if args.do_train:
+    #    torch.save(model_to_save.state_dict(), output_model_file)
+
+    # Load a trained model that you have fine-tuned
+
+    #model_state_dict = torch.load(output_model_file)
+    #model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict)
+    # model.to(device)
+
+    if args.do_predict and (args.local_rank == -1
+                            or torch.distributed.get_rank() == 0):
+        eval_examples = read_squad_examples(input_file=args.predict_file,
+                                            is_training=False)
+        eval_features = convert_examples_to_features(
+            examples=eval_examples,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            doc_stride=args.doc_stride,
+            max_query_length=args.max_query_length,
+            is_training=False)
+
+        logger.info("***** Running predictions *****")
+        logger.info("  Num orig examples = %d", len(eval_examples))
+        logger.info("  Num split examples = %d", len(eval_features))
+        logger.info("  Batch size = %d", args.predict_batch_size)
+
+        all_input_ids = torch.tensor([f.input_ids for f in eval_features],
+                                     dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in eval_features],
+                                      dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
+                                       dtype=torch.long)
+        all_example_index = torch.arange(all_input_ids.size(0),
+                                         dtype=torch.long)
+        eval_data = TensorDataset(all_input_ids, all_input_mask,
+                                  all_segment_ids, all_example_index)
+        # Run prediction for full data
+        eval_sampler = SequentialSampler(eval_data)
+        eval_dataloader = DataLoader(eval_data,
+                                     sampler=eval_sampler,
+                                     batch_size=args.predict_batch_size)
+
+        model.eval()
+        all_results = []
+        logger.info("Start evaluating")
+        for input_ids, input_mask, segment_ids, example_indices in tqdm(
+                eval_dataloader, desc="Evaluating"):
+            if len(all_results) % 1000 == 0:
+                logger.info("Processing example: %d" % (len(all_results)))
+            input_ids = input_ids.to(device)
+            input_mask = input_mask.to(device)
+            segment_ids = segment_ids.to(device)
+            with torch.no_grad():
+                batch_start_logits, batch_end_logits = model(
+                    input_ids, segment_ids, input_mask)
+            for i, example_index in enumerate(example_indices):
+                start_logits = batch_start_logits[i].detach().cpu().tolist()
+                end_logits = batch_end_logits[i].detach().cpu().tolist()
+                eval_feature = eval_features[example_index.item()]
+                unique_id = int(eval_feature.unique_id)
+                all_results.append(
+                    RawResult(unique_id=unique_id,
+                              start_logits=start_logits,
+                              end_logits=end_logits))
+        output_prediction_file = os.path.join(args.output_dir,
+                                              "predictions.json")
+        output_nbest_file = os.path.join(args.output_dir,
+                                         "nbest_predictions.json")
+        write_predictions(eval_examples, eval_features, all_results,
+                          args.n_best_size, args.max_answer_length,
+                          args.do_lower_case, output_prediction_file,
+                          output_nbest_file, args.verbose_logging)
+
+
+if __name__ == "__main__":
+    main()

Deepspeed/BingBertSquad/pytorch_pretrained_bert/__init__.py

+__version__ = "0.4.0"
+from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer
+from .modeling import (BertConfig, BertModel, BertForPreTraining,
+                       BertForMaskedLM, BertForNextSentencePrediction,
+                       BertForSequenceClassification, BertForMultipleChoice,
+                       BertForTokenClassification, BertForQuestionAnswering)
+from .optimization import BertAdam
+from .file_utils import PYTORCH_PRETRAINED_BERT_CACHE

Deepspeed/BingBertSquad/pytorch_pretrained_bert/file_utils.py

+"""
+Utilities for working with the local dataset cache.
+This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
+Copyright by the AllenNLP authors.
+"""
+
+import os
+import logging
+import shutil
+import tempfile
+import json
+from urllib.parse import urlparse
+from pathlib import Path
+from typing import Optional, Tuple, Union, IO, Callable, Set
+from hashlib import sha256
+from functools import wraps
+
+from tqdm import tqdm
+
+import boto3
+from botocore.exceptions import ClientError
+import requests
+
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+PYTORCH_PRETRAINED_BERT_CACHE = Path(
+    os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
+              Path.home() / '.pytorch_pretrained_bert'))
+
+
+def url_to_filename(url: str, etag: str = None) -> str:
+    """
+    Convert `url` into a hashed filename in a repeatable way.
+    If `etag` is specified, append its hash to the url's, delimited
+    by a period.
+    """
+    url_bytes = url.encode('utf-8')
+    url_hash = sha256(url_bytes)
+    filename = url_hash.hexdigest()
+
+    if etag:
+        etag_bytes = etag.encode('utf-8')
+        etag_hash = sha256(etag_bytes)
+        filename += '.' + etag_hash.hexdigest()
+
+    return filename
+
+
+def filename_to_url(filename: str,
+                    cache_dir: Union[str, Path] = None) -> Tuple[str, str]:
+    """
+    Return the url and etag (which may be ``None``) stored for `filename`.
+    Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    cache_path = os.path.join(cache_dir, filename)
+    if not os.path.exists(cache_path):
+        raise FileNotFoundError("file {} not found".format(cache_path))
+
+    meta_path = cache_path + '.json'
+    if not os.path.exists(meta_path):
+        raise FileNotFoundError("file {} not found".format(meta_path))
+
+    with open(meta_path) as meta_file:
+        metadata = json.load(meta_file)
+    url = metadata['url']
+    etag = metadata['etag']
+
+    return url, etag
+
+
+def cached_path(url_or_filename: Union[str, Path],
+                cache_dir: Union[str, Path] = None) -> str:
+    """
+    Given something that might be a URL (or might be a local path),
+    determine which. If it's a URL, download the file and cache it, and
+    return the path to the cached file. If it's already a local path,
+    make sure the file exists and then return the path.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if isinstance(url_or_filename, Path):
+        url_or_filename = str(url_or_filename)
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    parsed = urlparse(url_or_filename)
+
+    if parsed.scheme in ('http', 'https', 's3'):
+        # URL, so get it from the cache (downloading if necessary)
+        return get_from_cache(url_or_filename, cache_dir)
+    elif os.path.exists(url_or_filename):
+        # File, and it exists.
+        return url_or_filename
+    elif parsed.scheme == '':
+        # File, but it doesn't exist.
+        raise FileNotFoundError("file {} not found".format(url_or_filename))
+    else:
+        # Something unknown
+        raise ValueError(
+            "unable to parse {} as a URL or as a local path".format(
+                url_or_filename))
+
+
+def split_s3_path(url: str) -> Tuple[str, str]:
+    """Split a full s3 path into the bucket name and path."""
+    parsed = urlparse(url)
+    if not parsed.netloc or not parsed.path:
+        raise ValueError("bad s3 path {}".format(url))
+    bucket_name = parsed.netloc
+    s3_path = parsed.path
+    # Remove '/' at beginning of path.
+    if s3_path.startswith("/"):
+        s3_path = s3_path[1:]
+    return bucket_name, s3_path
+
+
+def s3_request(func: Callable):
+    """
+    Wrapper function for s3 requests in order to create more helpful error
+    messages.
+    """
+    @wraps(func)
+    def wrapper(url: str, *args, **kwargs):
+        try:
+            return func(url, *args, **kwargs)
+        except ClientError as exc:
+            if int(exc.response["Error"]["Code"]) == 404:
+                raise FileNotFoundError("file {} not found".format(url))
+            else:
+                raise
+
+    return wrapper
+
+
+@s3_request
+def s3_etag(url: str) -> Optional[str]:
+    """Check ETag on S3 object."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_object = s3_resource.Object(bucket_name, s3_path)
+    return s3_object.e_tag
+
+
+@s3_request
+def s3_get(url: str, temp_file: IO) -> None:
+    """Pull a file directly from S3."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
+
+
+def http_get(url: str, temp_file: IO) -> None:
+    req = requests.get(url, stream=True)
+    content_length = req.headers.get('Content-Length')
+    total = int(content_length) if content_length is not None else None
+    progress = tqdm(unit="B", total=total)
+    for chunk in req.iter_content(chunk_size=1024):
+        if chunk:  # filter out keep-alive new chunks
+            progress.update(len(chunk))
+            temp_file.write(chunk)
+    progress.close()
+
+
+def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
+    """
+    Given a URL, look for the corresponding dataset in the local cache.
+    If it's not there, download it. Then return the path to the cached file.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    os.makedirs(cache_dir, exist_ok=True)
+
+    # Get eTag to add to filename, if it exists.
+    if url.startswith("s3://"):
+        etag = s3_etag(url)
+    else:
+        response = requests.head(url, allow_redirects=True)
+        if response.status_code != 200:
+            raise IOError(
+                "HEAD request failed for url {} with status code {}".format(
+                    url, response.status_code))
+        etag = response.headers.get("ETag")
+
+    filename = url_to_filename(url, etag)
+
+    # get cache path to put the file
+    cache_path = os.path.join(cache_dir, filename)
+
+    if not os.path.exists(cache_path):
+        # Download to temporary file, then copy to cache dir once finished.
+        # Otherwise you get corrupt cache entries if the download gets interrupted.
+        with tempfile.NamedTemporaryFile() as temp_file:
+            logger.info("%s not found in cache, downloading to %s", url,
+                        temp_file.name)
+
+            # GET file object
+            if url.startswith("s3://"):
+                s3_get(url, temp_file)
+            else:
+                http_get(url, temp_file)
+
+            # we are copying the file before closing it, so flush to avoid truncation
+            temp_file.flush()
+            # shutil.copyfileobj() starts at the current position, so go to the start
+            temp_file.seek(0)
+
+            logger.info("copying %s to cache at %s", temp_file.name,
+                        cache_path)
+            with open(cache_path, 'wb') as cache_file:
+                shutil.copyfileobj(temp_file, cache_file)
+
+            logger.info("creating metadata file for %s", cache_path)
+            meta = {'url': url, 'etag': etag}
+            meta_path = cache_path + '.json'
+            with open(meta_path, 'w') as meta_file:
+                json.dump(meta, meta_file)
+
+            logger.info("removing temp file %s", temp_file.name)
+
+    return cache_path
+
+
+def read_set_from_file(filename: str) -> Set[str]:
+    '''
+    Extract a de-duped collection (set) of text from a file.
+    Expected file format is one item per line.
+    '''
+    collection = set()
+    with open(filename, 'r', encoding='utf-8') as file_:
+        for line in file_:
+            collection.add(line.rstrip())
+    return collection
+
+
+def get_file_extension(path: str, dot=True, lower: bool = True):
+    ext = os.path.splitext(path)[1]
+    ext = ext if dot else ext[1:]
+    return ext.lower() if lower else ext

Deepspeed/BingBertSquad/pytorch_pretrained_bert/modeling.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""PyTorch BERT model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import copy
+import json
+import math
+import logging
+import tarfile
+import tempfile
+import shutil
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from turing.loss import FocalLoss
+
+from .file_utils import cached_path
+
+logger = logging.getLogger(__name__)
+
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'bert-base-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
+    'bert-large-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
+    'bert-base-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
+    'bert-large-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
+    'bert-base-multilingual-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
+    'bert-base-multilingual-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
+    'bert-base-chinese':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
+}
+CONFIG_NAME = 'bert_config.json'
+WEIGHTS_NAME = 'pytorch_model.bin'
+
+
+def gelu(x):
+    """Implementation of the gelu activation function.
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+    """
+    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+
+def swish(x):
+    return x * torch.sigmoid(x)
+
+
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
+
+
+class BertConfig(object):
+    """Configuration class to store the configuration of a `BertModel`.
+    """
+    def __init__(self,
+                 vocab_size_or_config_json_file,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=2,
+                 initializer_range=0.02):
+        """Constructs BertConfig.
+
+        Args:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
+            hidden_size: Size of the encoder layers and the pooler layer.
+            num_hidden_layers: Number of hidden layers in the Transformer encoder.
+            num_attention_heads: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+                layer in the Transformer encoder.
+            hidden_act: The non-linear activation function (function or string) in the
+                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+            hidden_dropout_prob: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob: The dropout ratio for the attention
+                probabilities.
+            max_position_embeddings: The maximum sequence length that this model might
+                ever be used with. Typically set this to something large just in case
+                (e.g., 512 or 1024 or 2048).
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+                `BertModel`.
+            initializer_range: The sttdev of the truncated_normal_initializer for
+                initializing all weight matrices.
+        """
+        if isinstance(vocab_size_or_config_json_file, str):
+            with open(vocab_size_or_config_json_file, "r",
+                      encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.hidden_act = hidden_act
+            self.intermediate_size = intermediate_size
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.initializer_range = initializer_range
+        else:
+            raise ValueError(
+                "First argument must be either a vocabulary size (int)"
+                "or the path to a pretrained model config file (str)")
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `BertConfig` from a Python dictionary of parameters."""
+        config = BertConfig(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+
+try:
+    from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
+except ImportError:
+    print(
+        "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex."
+    )
+
+    class BertLayerNorm(nn.Module):
+        def __init__(self, hidden_size, eps=1e-12):
+            """Construct a layernorm module in the TF style (epsilon inside the square root).
+            """
+            super(BertLayerNorm, self).__init__()
+            self.weight = nn.Parameter(torch.ones(hidden_size))
+            self.bias = nn.Parameter(torch.zeros(hidden_size))
+            self.variance_epsilon = eps
+
+        def forward(self, x):
+            u = x.mean(-1, keepdim=True)
+            s = (x - u).pow(2).mean(-1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+            return self.weight * x + self.bias
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings.
+    """
+    def __init__(self, config):
+        super(BertEmbeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size,
+                                            config.hidden_size)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings,
+                                                config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
+                                                  config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, input_ids, token_type_ids=None):
+        seq_length = input_ids.size(1)
+        position_ids = torch.arange(seq_length,
+                                    dtype=torch.long,
+                                    device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = words_embeddings + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config):
+        super(BertSelfAttention, self).__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" %
+                (config.hidden_size, config.num_attention_heads))
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size /
+                                       config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+                                       self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(self, hidden_states, attention_mask):
+        mixed_query_layer = self.query(hidden_states)
+        mixed_key_layer = self.key(hidden_states)
+        mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer,
+                                        key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(
+            self.attention_head_size)
+        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+        attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (
+            self.all_head_size, )
+        context_layer = context_layer.view(*new_context_layer_shape)
+        return context_layer
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super(BertSelfOutput, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config):
+        super(BertAttention, self).__init__()
+        self.self = BertSelfAttention(config)
+        self.output = BertSelfOutput(config)
+
+    def forward(self, input_tensor, attention_mask):
+        self_output = self.self(input_tensor, attention_mask)
+        attention_output = self.output(self_output, input_tensor)
+        return attention_output
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super(BertIntermediate, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.intermediate_act_fn = ACT2FN[config.hidden_act] \
+            if isinstance(config.hidden_act, str) else config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super(BertOutput, self).__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config):
+        super(BertLayer, self).__init__()
+        self.attention = BertAttention(config)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(self, hidden_states, attention_mask):
+        attention_output = self.attention(hidden_states, attention_mask)
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super(BertEncoder, self).__init__()
+        layer = BertLayer(config)
+        self.layer = nn.ModuleList(
+            [copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
+
+    def forward(self,
+                hidden_states,
+                attention_mask,
+                output_all_encoded_layers=True):
+        all_encoder_layers = []
+        for layer_module in self.layer:
+            hidden_states = layer_module(hidden_states, attention_mask)
+            if output_all_encoded_layers:
+                all_encoder_layers.append(hidden_states)
+        if not output_all_encoded_layers:
+            all_encoder_layers.append(hidden_states)
+        return all_encoder_layers
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super(BertPooler, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super(BertPredictionHeadTransform, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.transform_act_fn = ACT2FN[config.hidden_act] \
+            if isinstance(config.hidden_act, str) else config.hidden_act
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertLMPredictionHead, self).__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
+                                 bert_model_embedding_weights.size(0),
+                                 bias=False)
+        self.decoder.weight = bert_model_embedding_weights
+        self.bias = nn.Parameter(
+            torch.zeros(bert_model_embedding_weights.size(0)))
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states) + self.bias
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertOnlyMLMHead, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super(BertOnlyNSPHead, self).__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output):
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+class BertPreTrainingHeads(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertPreTrainingHeads, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class PreTrainedBertModel(nn.Module):
+    """ An abstract class to handle weights initialization and
+        a simple interface for dowloading and loading pretrained models.
+    """
+    def __init__(self, config, *inputs, **kwargs):
+        super(PreTrainedBertModel, self).__init__()
+        if not isinstance(config, BertConfig):
+            raise ValueError(
+                "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
+                "To create a model from a Google pretrained model use "
+                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+                    self.__class__.__name__, self.__class__.__name__))
+        self.config = config
+
+    def init_bert_weights(self, module):
+        """ Initialize the weights.
+        """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0,
+                                       std=self.config.initializer_range)
+        elif isinstance(module, BertLayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    @classmethod
+    def from_pretrained(cls,
+                        pretrained_model_name,
+                        state_dict=None,
+                        cache_dir=None,
+                        *inputs,
+                        **kwargs):
+        """
+        Instantiate a PreTrainedBertModel from a pre-trained model file or a pytorch state dict.
+        Download and cache the pre-trained model file if needed.
+
+        Params:
+            pretrained_model_name: either:
+                - a str with the name of a pre-trained model to load selected in the list of:
+                    . `bert-base-uncased`
+                    . `bert-large-uncased`
+                    . `bert-base-cased`
+                    . `bert-large-cased`
+                    . `bert-base-multilingual-uncased`
+                    . `bert-base-multilingual-cased`
+                    . `bert-base-chinese`
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+            cache_dir: an optional path to a folder in which the pre-trained models will be cached.
+            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+            *inputs, **kwargs: additional input for the specific Bert class
+                (ex: num_labels for BertForSequenceClassification)
+        """
+        if pretrained_model_name in PRETRAINED_MODEL_ARCHIVE_MAP:
+            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name]
+        else:
+            archive_file = pretrained_model_name
+        # redirect to the cache, if necessary
+        try:
+            resolved_archive_file = cached_path(archive_file,
+                                                cache_dir=cache_dir)
+        except FileNotFoundError:
+            logger.error(
+                "Model name '{}' was not found in model name list ({}). "
+                "We assumed '{}' was a path or url but couldn't find any file "
+                "associated to this path or url.".format(
+                    pretrained_model_name,
+                    ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
+                    archive_file))
+            return None
+        if resolved_archive_file == archive_file:
+            logger.info("loading archive file {}".format(archive_file))
+        else:
+            logger.info("loading archive file {} from cache at {}".format(
+                archive_file, resolved_archive_file))
+        tempdir = None
+        if os.path.isdir(resolved_archive_file):
+            serialization_dir = resolved_archive_file
+        else:
+            # Extract archive to temp dir
+            tempdir = tempfile.mkdtemp()
+            logger.info("extracting archive file {} to temp dir {}".format(
+                resolved_archive_file, tempdir))
+            with tarfile.open(resolved_archive_file, 'r:gz') as archive:
+                archive.extractall(tempdir)
+            serialization_dir = tempdir
+        # Load config
+        config_file = os.path.join(serialization_dir, CONFIG_NAME)
+        config = BertConfig.from_json_file(config_file)
+        logger.info("Model config {}".format(config))
+        # Instantiate model.
+        model = cls(config, *inputs, **kwargs)
+        if state_dict is None:
+            weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
+            state_dict = torch.load(weights_path)
+
+        old_keys = []
+        new_keys = []
+        for key in state_dict.keys():
+            new_key = None
+            if 'gamma' in key:
+                new_key = key.replace('gamma', 'weight')
+            if 'beta' in key:
+                new_key = key.replace('beta', 'bias')
+            if new_key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+        for old_key, new_key in zip(old_keys, new_keys):
+            state_dict[new_key] = state_dict.pop(old_key)
+
+        missing_keys = []
+        unexpected_keys = []
+        error_msgs = []
+        # copy state_dict so _load_from_state_dict can modify it
+        metadata = getattr(state_dict, '_metadata', None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+
+        def load(module, prefix=''):
+            local_metadata = {} if metadata is None else metadata.get(
+                prefix[:-1], {})
+            module._load_from_state_dict(state_dict, prefix, local_metadata,
+                                         True, missing_keys, unexpected_keys,
+                                         error_msgs)
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, prefix + name + '.')
+
+        load(model, prefix='' if hasattr(model, 'bert') else 'bert.')
+        if len(missing_keys) > 0:
+            logger.info(
+                "Weights of {} not initialized from pretrained model: {}".
+                format(model.__class__.__name__, missing_keys))
+        if len(unexpected_keys) > 0:
+            logger.info(
+                "Weights from pretrained model not used in {}: {}".format(
+                    model.__class__.__name__, unexpected_keys))
+        if tempdir:
+            # Clean up temp dir
+            shutil.rmtree(tempdir)
+        return model
+
+
+class BertModel(PreTrainedBertModel):
+    """BERT model ("Bidirectional Embedding Representations from a Transformer").
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
+
+    Outputs: Tuple of (encoded_layers, pooled_output)
+        `encoded_layers`: controled by `output_all_encoded_layers` argument:
+            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
+                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
+                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
+                to the last attention block of shape [batch_size, sequence_length, hidden_size],
+        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
+            classifier pretrained on top of the hidden state associated to the first character of the
+            input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = modeling.BertModel(config=config)
+    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertModel, self).__init__(config)
+        self.embeddings = BertEmbeddings(config)
+        self.encoder = BertEncoder(config)
+        self.pooler = BertPooler(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                output_all_encoded_layers=True):
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # this attention mask is more simple than the triangular masking of causal attention
+        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(
+            dtype=next(self.parameters()).dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        embedding_output = self.embeddings(input_ids, token_type_ids)
+        encoded_layers = self.encoder(
+            embedding_output,
+            extended_attention_mask,
+            output_all_encoded_layers=output_all_encoded_layers)
+        sequence_output = encoded_layers[-1]
+        pooled_output = self.pooler(sequence_output)
+        if not output_all_encoded_layers:
+            encoded_layers = encoded_layers[-1]
+        return encoded_layers, pooled_output
+
+
+class BertForPreTraining(PreTrainedBertModel):
+    """BERT model with pre-training heads.
+    This module comprises the BERT model followed by the two pre-training heads:
+        - the masked language modeling head, and
+        - the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+        `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `masked_lm_labels` and `next_sentence_label` are not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `masked_lm_labels` or `next_sentence_label` is `None`:
+            Outputs a tuple comprising
+            - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
+            - the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForPreTraining(config)
+    masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForPreTraining, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertPreTrainingHeads(
+            config, self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                masked_lm_labels=None,
+                next_sentence_label=None):
+        sequence_output, pooled_output = self.bert(
+            input_ids,
+            token_type_ids,
+            attention_mask,
+            output_all_encoded_layers=False)
+        prediction_scores, seq_relationship_score = self.cls(
+            sequence_output, pooled_output)
+
+        if masked_lm_labels is not None and next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size),
+                masked_lm_labels.view(-1))
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            total_loss = masked_lm_loss + next_sentence_loss
+            return total_loss
+        else:
+            return prediction_scores, seq_relationship_score
+
+
+class BertForMaskedLM(PreTrainedBertModel):
+    """BERT model with the masked language modeling head.
+    This module comprises the BERT model followed by the masked language modeling head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+
+    Outputs:
+        if `masked_lm_labels` is  not `None`:
+            Outputs the masked language modeling loss.
+        if `masked_lm_labels` is `None`:
+            Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForMaskedLM(config)
+    masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForMaskedLM, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyMLMHead(config,
+                                   self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                masked_lm_labels=None):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        prediction_scores = self.cls(sequence_output)
+
+        if masked_lm_labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size),
+                masked_lm_labels.view(-1))
+            return masked_lm_loss
+        else:
+            return prediction_scores
+
+
+class BertForNextSentencePrediction(PreTrainedBertModel):
+    """BERT model with next sentence prediction head.
+    This module comprises the BERT model followed by the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `next_sentence_label` is not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `next_sentence_label` is `None`:
+            Outputs the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForNextSentencePrediction(config)
+    seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForNextSentencePrediction, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyNSPHead(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                next_sentence_label=None):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        seq_relationship_score = self.cls(pooled_output)
+
+        if next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            return next_sentence_loss
+        else:
+            return seq_relationship_score
+
+
+class BertForSequenceClassification(PreTrainedBertModel):
+    """BERT model for classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForSequenceClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels=2):
+        super(BertForSequenceClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # loss_fct = FocalLoss(class_num=self.num_labels, gamma=0.5)
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForMultipleChoice(PreTrainedBertModel):
+    """BERT model for multiple choice tasks.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_choices`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
+            and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_choices].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
+    input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
+    token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_choices = 2
+
+    model = BertForMultipleChoice(config, num_choices)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_choices=2):
+        super(BertForMultipleChoice, self).__init__(config)
+        self.num_choices = num_choices
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None):
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
+        _, pooled_output = self.bert(flat_input_ids,
+                                     flat_token_type_ids,
+                                     flat_attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, self.num_choices)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            return loss
+        else:
+            return reshaped_logits
+
+
+class BertForTokenClassification(PreTrainedBertModel):
+    """BERT model for token-level classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the full hidden state of the last layer.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForTokenClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels=2):
+        super(BertForTokenClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForQuestionAnswering(PreTrainedBertModel):
+    """BERT model for Question Answering (span extraction).
+    This module is composed of the BERT model with a linear layer on top of
+    the sequence output that computes start_logits and end_logits
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+        `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+
+    Outputs:
+        if `start_positions` and `end_positions` are not `None`:
+            Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
+        if `start_positions` or `end_positions` is `None`:
+            Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
+            position tokens of shape [batch_size, sequence_length].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForQuestionAnswering(config)
+    start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForQuestionAnswering, self).__init__(config)
+        self.bert = BertModel(config)
+        # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
+        # self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                start_positions=None,
+                end_positions=None):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+            return total_loss
+        else:
+            return start_logits, end_logits

Deepspeed/BingBertSquad/pytorch_pretrained_bert/optimization.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+#
+# 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.
+"""PyTorch optimization for BERT model."""
+
+import math
+import torch
+from torch.optim import Optimizer
+from torch.optim.optimizer import required
+from torch.nn.utils import clip_grad_norm_
+
+
+def warmup_cosine(x, warmup=0.002):
+    if x < warmup:
+        return x / warmup
+    return 0.5 * (1.0 + torch.cos(math.pi * x))
+
+
+def warmup_constant(x, warmup=0.002):
+    if x < warmup:
+        return x / warmup
+    return 1.0
+
+
+def warmup_linear(x, warmup=0.002):
+    if warmup == 0.0:
+        return 1.0
+    elif x < warmup:
+        return x / warmup
+    return 1.0 - x
+
+
+def warmup_linear_decay_exp(global_step,
+                            decay_rate,
+                            decay_steps,
+                            total_steps,
+                            warmup=0.002):
+    x = global_step / total_steps
+    warmup_end = warmup * total_steps
+    if warmup == 0.0:
+        return 1.0
+    elif x < warmup:
+        return x / warmup
+    return decay_rate**((global_step - warmup_end) / decay_steps)
+
+
+SCHEDULES = {
+    'warmup_cosine': warmup_cosine,
+    'warmup_constant': warmup_constant,
+    'warmup_linear': warmup_linear,
+    'warmup_linear_decay_exp': warmup_linear_decay_exp,
+}
+
+
+class BertAdam(Optimizer):
+    """Implements BERT version of Adam algorithm with weight decay fix.
+    Params:
+        lr: learning rate
+        warmup: portion of t_total for the warmup, -1  means no warmup. Default: -1
+        t_total: total number of training steps for the learning
+            rate schedule, -1  means constant learning rate. Default: -1
+        schedule: schedule to use for the warmup (see above). Default: 'warmup_linear'
+        b1: Adams b1. Default: 0.9
+        b2: Adams b2. Default: 0.999
+        e: Adams epsilon. Default: 1e-6
+        weight_decay: Weight decay. Default: 0.01
+        max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
+    """
+    def __init__(self,
+                 params,
+                 lr=required,
+                 warmup=-1,
+                 t_total=-1,
+                 schedule='warmup_linear',
+                 b1=0.9,
+                 b2=0.999,
+                 e=1e-6,
+                 weight_decay=0.01,
+                 max_grad_norm=1.0):
+        if lr is not required and lr < 0.0:
+            raise ValueError(
+                "Invalid learning rate: {} - should be >= 0.0".format(lr))
+        if schedule not in SCHEDULES:
+            raise ValueError("Invalid schedule parameter: {}".format(schedule))
+        if not 0.0 <= warmup < 1.0 and not warmup == -1:
+            raise ValueError(
+                "Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(
+                    warmup))
+        if not 0.0 <= b1 < 1.0:
+            raise ValueError(
+                "Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(
+                    b1))
+        if not 0.0 <= b2 < 1.0:
+            raise ValueError(
+                "Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(
+                    b2))
+        if not e >= 0.0:
+            raise ValueError(
+                "Invalid epsilon value: {} - should be >= 0.0".format(e))
+        defaults = dict(lr=lr,
+                        schedule=schedule,
+                        warmup=warmup,
+                        t_total=t_total,
+                        b1=b1,
+                        b2=b2,
+                        e=e,
+                        weight_decay=weight_decay,
+                        max_grad_norm=max_grad_norm)
+        super(BertAdam, self).__init__(params, defaults)
+
+    def get_lr(self):
+        lr = []
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+                if len(state) == 0:
+                    return [0]
+                if group['t_total'] != -1:
+                    schedule_fct = SCHEDULES[group['schedule']]
+                    lr_scheduled = group['lr'] * schedule_fct(
+                        state['step'] / group['t_total'], group['warmup'])
+                else:
+                    lr_scheduled = group['lr']
+                lr.append(lr_scheduled)
+        return lr
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'Adam does not support sparse gradients, please consider SparseAdam instead'
+                    )
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['next_m'] = torch.zeros_like(p.data)
+                    # Exponential moving average of squared gradient values
+                    state['next_v'] = torch.zeros_like(p.data)
+
+                next_m, next_v = state['next_m'], state['next_v']
+                beta1, beta2 = group['b1'], group['b2']
+
+                # Add grad clipping
+                if group['max_grad_norm'] > 0:
+                    clip_grad_norm_(p, group['max_grad_norm'])
+
+                # Decay the first and second moment running average coefficient
+                # In-place operations to update the averages at the same time
+                next_m.mul_(beta1).add_(1 - beta1, grad)
+                next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                update = next_m / (next_v.sqrt() + group['e'])
+
+                # Just adding the square of the weights to the loss function is *not*
+                # the correct way of using L2 regularization/weight decay with Adam,
+                # since that will interact with the m and v parameters in strange ways.
+                #
+                # Instead we want to decay the weights in a manner that doesn't interact
+                # with the m/v parameters. This is equivalent to adding the square
+                # of the weights to the loss with plain (non-momentum) SGD.
+                if group['weight_decay'] > 0.0:
+                    update += group['weight_decay'] * p.data
+
+                if group['t_total'] != -1:
+                    schedule_fct = SCHEDULES[group['schedule']]
+                    lr_scheduled = group['lr'] * schedule_fct(
+                        state['step'] / group['t_total'], group['warmup'])
+                else:
+                    lr_scheduled = group['lr']
+
+                update_with_lr = lr_scheduled * update
+                p.data.add_(-update_with_lr)
+
+                state['step'] += 1
+
+                # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
+                # No bias correction
+                # bias_correction1 = 1 - beta1 ** state['step']
+                # bias_correction2 = 1 - beta2 ** state['step']
+
+        return loss

Deepspeed/BingBertSquad/pytorch_pretrained_bert/tokenization.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+#
+# 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.
+"""Tokenization classes."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import unicodedata
+import os
+import logging
+
+from .file_utils import cached_path
+
+logger = logging.getLogger(__name__)
+
+PRETRAINED_VOCAB_ARCHIVE_MAP = {
+    'bert-base-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
+    'bert-large-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
+    'bert-base-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
+    'bert-large-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
+    'bert-base-multilingual-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
+    'bert-base-multilingual-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
+    'bert-base-chinese':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
+}
+PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
+    'bert-base-uncased': 512,
+    'bert-large-uncased': 512,
+    'bert-base-cased': 512,
+    'bert-large-cased': 512,
+    'bert-base-multilingual-uncased': 512,
+    'bert-base-multilingual-cased': 512,
+    'bert-base-chinese': 512,
+}
+VOCAB_NAME = 'vocab.txt'
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    index = 0
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        while True:
+            token = reader.readline()
+            if not token:
+                break
+            token = token.strip()
+            vocab[token] = index
+            index += 1
+    return vocab
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a peice of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class BertTokenizer(object):
+    """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
+    def __init__(self,
+                 vocab_file,
+                 do_lower_case=True,
+                 max_len=None,
+                 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
+                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+                .format(vocab_file))
+        self.vocab = load_vocab(vocab_file)
+        self.ids_to_tokens = collections.OrderedDict([
+            (ids, tok) for tok, ids in self.vocab.items()
+        ])
+        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
+                                              never_split=never_split)
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+        self.max_len = max_len if max_len is not None else int(1e12)
+
+    def tokenize(self, text):
+        split_tokens = []
+        for token in self.basic_tokenizer.tokenize(text):
+            for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                split_tokens.append(sub_token)
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        """Converts a sequence of tokens into ids using the vocab."""
+        ids = []
+        for token in tokens:
+            ids.append(self.vocab[token])
+        if len(ids) > self.max_len:
+            raise ValueError(
+                "Token indices sequence length is longer than the specified maximum "
+                " sequence length for this BERT model ({} > {}). Running this"
+                " sequence through BERT will result in indexing errors".format(
+                    len(ids), self.max_len))
+        return ids
+
+    def convert_ids_to_tokens(self, ids):
+        """Converts a sequence of ids in wordpiece tokens using the vocab."""
+        tokens = []
+        for i in ids:
+            tokens.append(self.ids_to_tokens[i])
+        return tokens
+
+    @classmethod
+    def from_pretrained(cls,
+                        pretrained_model_name,
+                        cache_dir=None,
+                        *inputs,
+                        **kwargs):
+        """
+        Instantiate a PreTrainedBertModel from a pre-trained model file.
+        Download and cache the pre-trained model file if needed.
+        """
+        if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
+            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
+        else:
+            vocab_file = pretrained_model_name
+        if os.path.isdir(vocab_file):
+            vocab_file = os.path.join(vocab_file, VOCAB_NAME)
+        # redirect to the cache, if necessary
+        try:
+            resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
+        except FileNotFoundError:
+            logger.error(
+                "Model name '{}' was not found in model name list ({}). "
+                "We assumed '{}' was a path or url but couldn't find any file "
+                "associated to this path or url.".format(
+                    pretrained_model_name,
+                    ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
+                    vocab_file))
+            return None
+        if resolved_vocab_file == vocab_file:
+            logger.info("loading vocabulary file {}".format(vocab_file))
+        else:
+            logger.info("loading vocabulary file {} from cache at {}".format(
+                vocab_file, resolved_vocab_file))
+        if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
+            # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
+            # than the number of positional embeddings
+            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[
+                pretrained_model_name]
+            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
+        # Instantiate tokenizer.
+        tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
+        return tokenizer
+
+
+class BasicTokenizer(object):
+    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+    def __init__(self,
+                 do_lower_case=True,
+                 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
+        """Constructs a BasicTokenizer.
+
+        Args:
+          do_lower_case: Whether to lower case the input.
+        """
+        self.do_lower_case = do_lower_case
+        self.never_split = never_split
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text."""
+        text = self._clean_text(text)
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        text = self._tokenize_chinese_chars(text)
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if self.do_lower_case and token not in self.never_split:
+                token = token.lower()
+                token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text):
+        """Splits punctuation on a piece of text."""
+        if text in self.never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
+            (cp >= 0x3400 and cp <= 0x4DBF) or  #
+            (cp >= 0x20000 and cp <= 0x2A6DF) or  #
+            (cp >= 0x2A700 and cp <= 0x2B73F) or  #
+            (cp >= 0x2B740 and cp <= 0x2B81F) or  #
+            (cp >= 0x2B820 and cp <= 0x2CEAF) or
+            (cp >= 0xF900 and cp <= 0xFAFF) or  #
+            (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xfffd or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text into its word pieces.
+
+        This uses a greedy longest-match-first algorithm to perform tokenization
+        using the given vocabulary.
+
+        For example:
+          input = "unaffable"
+          output = ["un", "##aff", "##able"]
+
+        Args:
+          text: A single token or whitespace separated tokens. This should have
+            already been passed through `BasicTokenizer`.
+
+        Returns:
+          A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens
+
+
+def _is_whitespace(char):
+    """Checks whether `chars` is a whitespace character."""
+    # \t, \n, and \r are technically contorl characters but we treat them
+    # as whitespace since they are generally considered as such.
+    if char == " " or char == "\t" or char == "\n" or char == "\r":
+        return True
+    cat = unicodedata.category(char)
+    if cat == "Zs":
+        return True
+    return False
+
+
+def _is_control(char):
+    """Checks whether `chars` is a control character."""
+    # These are technically control characters but we count them as whitespace
+    # characters.
+    if char == "\t" or char == "\n" or char == "\r":
+        return False
+    cat = unicodedata.category(char)
+    if cat.startswith("C"):
+        return True
+    return False
+
+
+def _is_punctuation(char):
+    """Checks whether `chars` is a punctuation character."""
+    cp = ord(char)
+    # We treat all non-letter/number ASCII as punctuation.
+    # Characters such as "^", "$", and "`" are not in the Unicode
+    # Punctuation class but we treat them as punctuation anyways, for
+    # consistency.
+    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64)
+            or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+        return True
+    cat = unicodedata.category(char)
+    if cat.startswith("P"):
+        return True
+    return False

Deepspeed/BingBertSquad/run_hf.sh

+NGPU_PER_NODE=4
+MODEL_FILE="./ckpt/bert-large-uncased-whole-word-masking-pytorch_model.bin"
+ORIGIN_CONFIG_FILE="./bert-large-uncased-whole-word-masking-config.json"
+SQUAD_DIR="./data"
+OUTPUT_DIR=$1
+LR=4e-5
+SEED=$RANDOM
+MASTER_PORT=12345
+DROPOUT=0.1
+
+sudo rm -rf ${OUTPUT_DIR}
+
+# Force deepspeed to run with only local node
+NUM_NODES=2
+HOSTFILE=2hosts
+
+NGPU=$((NGPU_PER_NODE*NUM_NODES))
+EFFECTIVE_BATCH_SIZE=24
+MAX_GPU_BATCH_SIZE=3
+PER_GPU_BATCH_SIZE=$((EFFECTIVE_BATCH_SIZE/NGPU))
+if [[ $PER_GPU_BATCH_SIZE -lt $MAX_GPU_BATCH_SIZE ]]; then
+       GRAD_ACCUM_STEPS=1
+else
+       GRAD_ACCUM_STEPS=$((PER_GPU_BATCH_SIZE/MAX_GPU_BATCH_SIZE))
+fi
+JOB_NAME="deepspeed_${NGPU}GPUs_${EFFECTIVE_BATCH_SIZE}batch_size"
+config_json=deepspeed_bsz24_config.json
+#run_cmd="deepspeed --num_nodes ${NUM_NODES} --num_gpus ${NGPU_PER_NODE} \
+#       --master_port=${MASTER_PORT} \
+run_cmd="python3.6 -m torch.distributed.launch \
+       --nproc_per_node=${NGPU_PER_NODE} \
+       --master_port=${MASTER_PORT} \
+       nvidia_run_squad_mpi.py \
+       --bert_model bert-large-uncased \
+       --do_train \
+       --do_lower_case \
+       --predict_batch_size 3 \
+       --do_predict \
+       --train_file $SQUAD_DIR/train-v1.1.json \
+       --predict_file $SQUAD_DIR/dev-v1.1.json \
+       --train_batch_size $PER_GPU_BATCH_SIZE \
+       --learning_rate ${LR} \
+       --num_train_epochs 2.0 \
+       --max_seq_length 384 \
+       --doc_stride 128 \
+       --output_dir $OUTPUT_DIR \
+       --job_name ${JOB_NAME} \
+       --gradient_accumulation_steps ${GRAD_ACCUM_STEPS} \
+       --fp16 \
+       --deepspeed \
+       --deepspeed_config ${config_json} \
+       --dropout ${DROPOUT} \
+       --model_file $MODEL_FILE \
+       --seed ${SEED} \
+       --ckpt_type HF \
+       --origin_bert_config_file ${ORIGIN_CONFIG_FILE} \
+       "
+echo ${run_cmd}
+eval ${run_cmd}

Deepspeed/BingBertSquad/run_squad_baseline.sh

+#~/bin/bash
+
+#1: number of GPUs
+#2: Model File Address
+#3: BertSquad Data Directory Address
+#4: Output Directory Address
+
+NGPU_PER_NODE=$1
+MODEL_FILE=$2
+SQUAD_DIR=$3
+OUTPUT_DIR=$4
+NUM_NODES=1
+NGPU=$((NGPU_PER_NODE*NUM_NODES))
+EFFECTIVE_BATCH_SIZE=24
+MAX_GPU_BATCH_SIZE=6
+PER_GPU_BATCH_SIZE=$((EFFECTIVE_BATCH_SIZE/NGPU))
+if [[ $PER_GPU_BATCH_SIZE -lt $MAX_GPU_BATCH_SIZE ]]; then
+       GRAD_ACCUM_STEPS=1
+else
+       GRAD_ACCUM_STEPS=$((PER_GPU_BATCH_SIZE/MAX_GPU_BATCH_SIZE))
+fi
+LR=3e-5
+MASTER_PORT=$((NGPU+12345))
+JOB_NAME="baseline_${NGPU}GPUs_${EFFECTIVE_BATCH_SIZE}batch_size"
+run_cmd="deepspeed --num_nodes ${NUM_NODES} --num_gpus ${NGPU_PER_NODE} \
+       nvidia_run_squad_baseline.py \
+       --bert_model bert-large-uncased \
+       --do_train \
+       --do_lower_case \
+       --do_predict \
+       --train_file $SQUAD_DIR/train-v1.1.json \
+       --predict_file $SQUAD_DIR/dev-v1.1.json \
+       --train_batch_size $PER_GPU_BATCH_SIZE \
+       --learning_rate ${LR} \
+       --num_train_epochs 2.0 \
+       --max_seq_length 384 \
+       --doc_stride 128 \
+       --output_dir $OUTPUT_DIR \
+       --job_name ${JOB_NAME} \
+       --gradient_accumulation_steps ${GRAD_ACCUM_STEPS} \
+       --fp16 \
+       --model_file $MODEL_FILE
+       "
+echo ${run_cmd}
+eval ${run_cmd}

Deepspeed/BingBertSquad/run_squad_deepspeed.sh

+#~/bin/bash
+
+#1: number of GPUs
+#2: Model File Address
+#3: BertSquad Data Directory Address
+#4: Output Directory Address
+
+NGPU_PER_NODE=$1
+MODEL_FILE=$2
+SQUAD_DIR=$3
+OUTPUT_DIR=$4
+LR=${5:-0.00003}
+SEED=${6:-12345}
+MASTER_PORT=${7:-29500}
+DROPOUT=${8:-0.1}
+echo "lr is ${LR}"
+echo "seed is $SEED"
+echo "master port is $MASTER_PORT"
+echo "dropout is ${DROPOUT}"
+
+# Force deepspeed to run with only local node
+NUM_NODES=1
+HOSTFILE=/dev/null
+
+NGPU=$((NGPU_PER_NODE*NUM_NODES))
+EFFECTIVE_BATCH_SIZE=24
+MAX_GPU_BATCH_SIZE=3
+PER_GPU_BATCH_SIZE=$((EFFECTIVE_BATCH_SIZE/NGPU))
+if [[ $PER_GPU_BATCH_SIZE -lt $MAX_GPU_BATCH_SIZE ]]; then
+       GRAD_ACCUM_STEPS=1
+else
+       GRAD_ACCUM_STEPS=$((PER_GPU_BATCH_SIZE/MAX_GPU_BATCH_SIZE))
+fi
+JOB_NAME="deepspeed_${NGPU}GPUs_${EFFECTIVE_BATCH_SIZE}batch_size"
+config_json=deepspeed_bsz24_config.json
+run_cmd="deepspeed --num_nodes ${NUM_NODES} --num_gpus ${NGPU_PER_NODE} \
+       --master_port=${MASTER_PORT} \
+       --hostfile ${HOSTFILE} \
+       nvidia_run_squad_deepspeed.py \
+       --bert_model bert-large-uncased \
+       --do_train \
+       --do_lower_case \
+       --predict_batch_size 3 \
+       --do_predict \
+       --train_file $SQUAD_DIR/train-v1.1.json \
+       --predict_file $SQUAD_DIR/dev-v1.1.json \
+       --train_batch_size $PER_GPU_BATCH_SIZE \
+       --learning_rate ${LR} \
+       --num_train_epochs 2.0 \
+       --max_seq_length 384 \
+       --doc_stride 128 \
+       --output_dir $OUTPUT_DIR \
+       --job_name ${JOB_NAME} \
+       --gradient_accumulation_steps ${GRAD_ACCUM_STEPS} \
+       --fp16 \
+       --deepspeed \
+       --deepspeed_config ${config_json} \
+       --dropout ${DROPOUT} \
+       --model_file $MODEL_FILE \
+       --seed ${SEED} \
+       --preln \
+       "
+echo ${run_cmd}
+eval ${run_cmd}

Deepspeed/BingBertSquad/turing/file_utils.py

+"""
+Utilities for working with the local dataset cache.
+This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
+Copyright by the AllenNLP authors.
+"""
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import json
+import logging
+import os
+import shutil
+import tempfile
+from functools import wraps
+from hashlib import sha256
+import sys
+from io import open
+
+import boto3
+import requests
+from botocore.exceptions import ClientError
+from tqdm import tqdm
+
+try:
+    from urllib.parse import urlparse
+except ImportError:
+    from urlparse import urlparse
+
+try:
+    from pathlib import Path
+    PYTORCH_PRETRAINED_BERT_CACHE = Path(
+        os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
+                  Path.home() / '.pytorch_pretrained_bert'))
+except AttributeError:
+    PYTORCH_PRETRAINED_BERT_CACHE = os.getenv(
+        'PYTORCH_PRETRAINED_BERT_CACHE',
+        os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_bert'))
+
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+
+def url_to_filename(url, etag=None):
+    """
+    Convert `url` into a hashed filename in a repeatable way.
+    If `etag` is specified, append its hash to the url's, delimited
+    by a period.
+    """
+    url_bytes = url.encode('utf-8')
+    url_hash = sha256(url_bytes)
+    filename = url_hash.hexdigest()
+
+    if etag:
+        etag_bytes = etag.encode('utf-8')
+        etag_hash = sha256(etag_bytes)
+        filename += '.' + etag_hash.hexdigest()
+
+    return filename
+
+
+def filename_to_url(filename, cache_dir=None):
+    """
+    Return the url and etag (which may be ``None``) stored for `filename`.
+    Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    cache_path = os.path.join(cache_dir, filename)
+    if not os.path.exists(cache_path):
+        raise EnvironmentError("file {} not found".format(cache_path))
+
+    meta_path = cache_path + '.json'
+    if not os.path.exists(meta_path):
+        raise EnvironmentError("file {} not found".format(meta_path))
+
+    with open(meta_path, encoding="utf-8") as meta_file:
+        metadata = json.load(meta_file)
+    url = metadata['url']
+    etag = metadata['etag']
+
+    return url, etag
+
+
+def cached_path(url_or_filename, cache_dir=None):
+    """
+    Given something that might be a URL (or might be a local path),
+    determine which. If it's a URL, download the file and cache it, and
+    return the path to the cached file. If it's already a local path,
+    make sure the file exists and then return the path.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
+        url_or_filename = str(url_or_filename)
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    parsed = urlparse(url_or_filename)
+
+    if parsed.scheme in ('http', 'https', 's3'):
+        # URL, so get it from the cache (downloading if necessary)
+        return get_from_cache(url_or_filename, cache_dir)
+    elif os.path.exists(url_or_filename):
+        # File, and it exists.
+        return url_or_filename
+    elif parsed.scheme == '':
+        # File, but it doesn't exist.
+        raise EnvironmentError("file {} not found".format(url_or_filename))
+    else:
+        # Something unknown
+        raise ValueError(
+            "unable to parse {} as a URL or as a local path".format(
+                url_or_filename))
+
+
+def split_s3_path(url):
+    """Split a full s3 path into the bucket name and path."""
+    parsed = urlparse(url)
+    if not parsed.netloc or not parsed.path:
+        raise ValueError("bad s3 path {}".format(url))
+    bucket_name = parsed.netloc
+    s3_path = parsed.path
+    # Remove '/' at beginning of path.
+    if s3_path.startswith("/"):
+        s3_path = s3_path[1:]
+    return bucket_name, s3_path
+
+
+def s3_request(func):
+    """
+    Wrapper function for s3 requests in order to create more helpful error
+    messages.
+    """
+    @wraps(func)
+    def wrapper(url, *args, **kwargs):
+        try:
+            return func(url, *args, **kwargs)
+        except ClientError as exc:
+            if int(exc.response["Error"]["Code"]) == 404:
+                raise EnvironmentError("file {} not found".format(url))
+            else:
+                raise
+
+    return wrapper
+
+
+@s3_request
+def s3_etag(url):
+    """Check ETag on S3 object."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_object = s3_resource.Object(bucket_name, s3_path)
+    return s3_object.e_tag
+
+
+@s3_request
+def s3_get(url, temp_file):
+    """Pull a file directly from S3."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
+
+
+def http_get(url, temp_file):
+    req = requests.get(url, stream=True)
+    content_length = req.headers.get('Content-Length')
+    total = int(content_length) if content_length is not None else None
+    progress = tqdm(unit="B", total=total)
+    for chunk in req.iter_content(chunk_size=1024):
+        if chunk:  # filter out keep-alive new chunks
+            progress.update(len(chunk))
+            temp_file.write(chunk)
+    progress.close()
+
+
+def get_from_cache(url, cache_dir=None):
+    """
+    Given a URL, look for the corresponding dataset in the local cache.
+    If it's not there, download it. Then return the path to the cached file.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+    # Get eTag to add to filename, if it exists.
+    if url.startswith("s3://"):
+        etag = s3_etag(url)
+    else:
+        response = requests.head(url, allow_redirects=True)
+        if response.status_code != 200:
+            raise IOError(
+                "HEAD request failed for url {} with status code {}".format(
+                    url, response.status_code))
+        etag = response.headers.get("ETag")
+
+    filename = url_to_filename(url, etag)
+
+    # get cache path to put the file
+    cache_path = os.path.join(cache_dir, filename)
+
+    if not os.path.exists(cache_path):
+        # Download to temporary file, then copy to cache dir once finished.
+        # Otherwise you get corrupt cache entries if the download gets interrupted.
+        with tempfile.NamedTemporaryFile() as temp_file:
+            logger.info("%s not found in cache, downloading to %s", url,
+                        temp_file.name)
+
+            # GET file object
+            if url.startswith("s3://"):
+                s3_get(url, temp_file)
+            else:
+                http_get(url, temp_file)
+
+            # we are copying the file before closing it, so flush to avoid truncation
+            temp_file.flush()
+            # shutil.copyfileobj() starts at the current position, so go to the start
+            temp_file.seek(0)
+
+            logger.info("copying %s to cache at %s", temp_file.name,
+                        cache_path)
+            with open(cache_path, 'wb') as cache_file:
+                shutil.copyfileobj(temp_file, cache_file)
+
+            logger.info("creating metadata file for %s", cache_path)
+            meta = {'url': url, 'etag': etag}
+            meta_path = cache_path + '.json'
+            with open(meta_path, 'w', encoding="utf-8") as meta_file:
+                json.dump(meta, meta_file)
+
+            logger.info("removing temp file %s", temp_file.name)
+
+    return cache_path
+
+
+def read_set_from_file(filename):
+    '''
+    Extract a de-duped collection (set) of text from a file.
+    Expected file format is one item per line.
+    '''
+    collection = set()
+    with open(filename, 'r', encoding='utf-8') as file_:
+        for line in file_:
+            collection.add(line.rstrip())
+    return collection
+
+
+def get_file_extension(path, dot=True, lower=True):
+    ext = os.path.splitext(path)[1]
+    ext = ext if dot else ext[1:]
+    return ext.lower() if lower else ext

Deepspeed/BingBertSquad/turing/loss.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+
+class FocalLoss(nn.Module):
+    r"""
+        This criterion is a implemenation of Focal Loss, which is proposed in
+        Focal Loss for Dense Object Detection.
+
+            Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])
+
+        The losses are averaged across observations for each minibatch.
+        Args:
+            alpha(1D Tensor, Variable) : the scalar factor for this criterion
+            gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5),
+                                   putting more focus on hard, misclassified examples
+            size_average(bool): size_average(bool): By default, the losses are averaged over observations for each minibatch.
+                                However, if the field size_average is set to False, the losses are
+                                instead summed for each minibatch.
+    """
+    def __init__(self, class_num, alpha=None, gamma=2, size_average=True):
+        super(FocalLoss, self).__init__()
+        if alpha is None:
+            self.alpha = torch.ones(class_num, 1)
+        else:
+            if isinstance(alpha, Variable):
+                self.alpha = alpha
+            else:
+                self.alpha = Variable(alpha)
+        self.gamma = gamma
+        self.class_num = class_num
+        self.size_average = size_average
+
+    def forward(self, inputs, targets):
+        N = inputs.size(0)
+        C = inputs.size(1)
+        P = F.softmax(inputs)
+
+        class_mask = inputs.data.new(N, C).fill_(0)
+        # class_mask = Variable(class_mask)
+        ids = targets.view(-1, 1)
+        class_mask.scatter_(1, ids.data, 1.)
+
+        if inputs.is_cuda and not self.alpha.is_cuda:
+            self.alpha = self.alpha.cuda()
+        alpha = self.alpha[ids.data.view(-1)]
+
+        probs = (P * class_mask).sum(1).view(-1, 1)
+
+        log_p = probs.log()
+
+        batch_loss = -alpha * (torch.pow((1 - probs), self.gamma)) * log_p
+
+        if self.size_average:
+            loss = batch_loss.mean()
+        else:
+            loss = batch_loss.sum()
+        return loss

Deepspeed/BingBertSquad/turing/modelingpreln_layerdrop.py

+# DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f
+# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/modeling.py
+
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""PyTorch BERT model."""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import copy
+import json
+import logging
+import math
+import os
+import shutil
+import tarfile
+import tempfile
+import sys
+from io import open
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from torch.utils import checkpoint
+
+from turing.file_utils import cached_path
+
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+import torch.nn.functional as F
+import torch.nn.init as init
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'bert-base-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
+    'bert-large-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
+    'bert-base-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
+    'bert-large-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
+    'bert-base-multilingual-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
+    'bert-base-multilingual-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
+    'bert-base-chinese':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
+}
+CONFIG_NAME = 'bert_config.json'
+WEIGHTS_NAME = 'pytorch_model.bin'
+TF_WEIGHTS_NAME = 'model.ckpt'
+
+
+def get_deepspeed_config(args):
+    if hasattr(args, 'deepspeed_config') and args.deepspeed_config:
+        from deepspeed import DeepSpeedConfig
+        return DeepSpeedConfig(args.deepspeed_config)
+    else:
+        raise RuntimeError('deepspeed_config is not found in args.')
+
+
+def get_sparse_attention_config(args, num_heads):
+    if args.deepspeed_sparse_attention:
+        ds_config = get_deepspeed_config(args)
+        if hasattr(ds_config,
+                   'sparse_attention') and ds_config.sparse_attention:
+            sa_config = ds_config.sparse_attention
+            sa_mode = sa_config.get('mode')
+            if (sa_mode == 'dense'):
+                from deepspeed.ops.sparse_attention import DenseSparsityConfig as STConfig
+            elif (sa_mode == 'fixed'):
+                from deepspeed.ops.sparse_attention import FixedSparsityConfig as STConfig
+            elif (sa_mode == 'bigbird'):
+                from deepspeed.ops.sparse_attention import BigBirdSparsityConfig as STConfig
+            elif (sa_mode == 'bslongformer'):
+                from deepspeed.ops.sparse_attention import BSLongformerSparsityConfig as STConfig
+            elif (sa_mode == 'variable'):
+                from deepspeed.ops.sparse_attention import VariableSparsityConfig as STConfig
+            else:
+                raise NotImplementedError(
+                    f'Given sparsity mode, {sa_mode}, has not been implemented yet!'
+                )
+            del sa_config['mode']
+            return STConfig(num_heads=num_heads, **sa_config)
+        else:
+            from deepspeed.ops.sparse_attention import FixedSparsityConfig as STConfig
+            print(
+                'deepspeed sparse attention is not set; Fixed sparsity is used as default.'
+            )
+            return STConfig(num_heads=num_heads)
+    else:
+        return None
+
+def get_sparse_attention_utils(sparse_attention_config):
+    if sparse_attention_config is not None:
+        from deepspeed.ops.sparse_attention import SparseAttentionUtils
+        return SparseAttentionUtils
+    return None
+
+def load_tf_weights_in_bert(model, tf_checkpoint_path):
+    """ Load tf checkpoints in a pytorch model
+    """
+    try:
+        import re
+        import numpy as np
+        import tensorflow as tf
+    except ImportError:
+        print(
+            "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions."
+        )
+        raise
+    tf_path = os.path.abspath(tf_checkpoint_path)
+    print("Converting TensorFlow checkpoint from {}".format(tf_path))
+    # Load weights from TF model
+    init_vars = tf.train.list_variables(tf_path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        print("Loading TF weight {} with shape {}".format(name, shape))
+        array = tf.train.load_variable(tf_path, name)
+        names.append(name)
+        arrays.append(array)
+
+    for name, array in zip(names, arrays):
+        name = name.split('/')
+        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+        # which are not required for using pretrained model
+        if any(n in ["adam_v", "adam_m"] for n in name):
+            print("Skipping {}".format("/".join(name)))
+            continue
+        pointer = model
+        for m_name in name:
+            if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
+                l = re.split(r'_(\d+)', m_name)
+            else:
+                l = [m_name]
+            if l[0] == 'kernel' or l[0] == 'gamma':
+                pointer = getattr(pointer, 'weight')
+            elif l[0] == 'output_bias' or l[0] == 'beta':
+                pointer = getattr(pointer, 'bias')
+            elif l[0] == 'output_weights':
+                pointer = getattr(pointer, 'weight')
+            else:
+                pointer = getattr(pointer, l[0])
+            if len(l) >= 2:
+                num = int(l[1])
+                pointer = pointer[num]
+        if m_name[-11:] == '_embeddings':
+            pointer = getattr(pointer, 'weight')
+        elif m_name == 'kernel':
+            array = np.transpose(array)
+        try:
+            assert pointer.shape == array.shape
+        except AssertionError as e:
+            e.args += (pointer.shape, array.shape)
+            raise
+        print("Initialize PyTorch weight {}".format(name))
+        pointer.data = torch.from_numpy(array)
+    return model
+
+
+@torch.jit.script
+def f_gelu(x):
+    pdtype = x.dtype
+    x = x.float()
+    y = x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+    return y.to(pdtype)
+
+
+@torch.jit.script
+def bias_gelu(bias, y):
+    x = bias + y
+    return x * 0.5 * (1.0 + torch.erf(x / 1.41421))
+
+
+@torch.jit.script
+def bias_tanh(bias, y):
+    x = bias + y
+    return torch.tanh(x)
+
+
+def gelu(x):
+    """Implementation of the gelu activation function.
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+        Also see https://arxiv.org/abs/1606.08415
+    """
+    return f_gelu(x)
+
+
+def swish(x):
+    return x * torch.sigmoid(x)
+
+
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
+
+
+class LinearActivation(Module):
+    r"""Fused Linear and activation Module.
+    """
+    __constants__ = ['bias']
+
+    def __init__(self, in_features, out_features, act='gelu', bias=True):
+        super(LinearActivation, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.fused_gelu = False
+        self.fused_tanh = False
+        if isinstance(act, str) or (sys.version_info[0] == 2
+                                    and isinstance(act, unicode)):
+            if bias and act == 'gelu':
+                self.fused_gelu = True
+            elif bias and act == 'tanh':
+                self.fused_tanh = True
+            else:
+                self.act_fn = ACT2FN[act]
+        else:
+            self.act_fn = act
+        self.weight = Parameter(torch.Tensor(out_features, in_features))
+        if bias:
+            self.bias = Parameter(torch.Tensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, input):
+        if self.fused_gelu:
+            return bias_gelu(self.bias, F.linear(input, self.weight, None))
+        elif self.fused_tanh:
+            return bias_tanh(self.bias, F.linear(input, self.weight, None))
+        else:
+            return self.act_fn(F.linear(input, self.weight, self.bias))
+
+    def extra_repr(self):
+        return 'in_features={}, out_features={}, bias={}'.format(
+            self.in_features, self.out_features, self.bias is not None)
+
+
+class BertConfig(object):
+    """Configuration class to store the configuration of a `BertModel`.
+    """
+    def __init__(self,
+                 vocab_size_or_config_json_file,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=2,
+                 initializer_range=0.02):
+        """Constructs BertConfig.
+
+        Args:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
+            hidden_size: Size of the encoder layers and the pooler layer.
+            num_hidden_layers: Number of hidden layers in the Transformer encoder.
+            num_attention_heads: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+                layer in the Transformer encoder.
+            hidden_act: The non-linear activation function (function or string) in the
+                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+            hidden_dropout_prob: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob: The dropout ratio for the attention
+                probabilities.
+            max_position_embeddings: The maximum sequence length that this model might
+                ever be used with. Typically set this to something large just in case
+                (e.g., 512 or 1024 or 2048).
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+                `BertModel`.
+            initializer_range: The sttdev of the truncated_normal_initializer for
+                initializing all weight matrices.
+        """
+        if isinstance(vocab_size_or_config_json_file,
+                      str) or (sys.version_info[0] == 2 and isinstance(
+                          vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r",
+                      encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.hidden_act = hidden_act
+            self.intermediate_size = intermediate_size
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.initializer_range = initializer_range
+        else:
+            raise ValueError(
+                "First argument must be either a vocabulary size (int)"
+                "or the path to a pretrained model config file (str)")
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `BertConfig` from a Python dictionary of parameters."""
+        config = BertConfig(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+
+try:
+    import apex
+    #apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm')
+    import apex.normalization
+    #apex.amp.register_float_function(apex.normalization.FusedLayerNorm, 'forward')
+    BertLayerNorm = apex.normalization.FusedLayerNorm
+except ImportError:
+    print(
+        "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex."
+    )
+
+    class BertLayerNorm(nn.Module):
+        def __init__(self, hidden_size, eps=1e-12):
+            """Construct a layernorm module in the TF style (epsilon inside the square root).
+            """
+            super(BertLayerNorm, self).__init__()
+            self.weight = nn.Parameter(torch.ones(hidden_size))
+            self.bias = nn.Parameter(torch.zeros(hidden_size))
+            self.variance_epsilon = eps
+
+        def forward(self, x):
+            pdtype = x.dtype
+            x = x.float()
+            u = x.mean(-1, keepdim=True)
+            s = (x - u).pow(2).mean(-1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+            return self.weight * x.to(pdtype) + self.bias
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings.
+    """
+    def __init__(self, config):
+        super(BertEmbeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size,
+                                            config.hidden_size)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings,
+                                                config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
+                                                  config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, input_ids, token_type_ids=None):
+        seq_length = input_ids.size(1)
+        position_ids = torch.arange(seq_length,
+                                    dtype=torch.long,
+                                    device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = words_embeddings + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config):
+        super(BertSelfAttention, self).__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" %
+                (config.hidden_size, config.num_attention_heads))
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size /
+                                       config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+                                       self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def transpose_key_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+                                       self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 3, 1)
+
+    def forward(self, hidden_states, attention_mask):
+        mixed_query_layer = self.query(hidden_states)
+        mixed_key_layer = self.key(hidden_states)
+        mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_key_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer)
+        attention_scores = attention_scores / math.sqrt(
+            self.attention_head_size)
+        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+        attention_scores = attention_scores + attention_mask
+
+        pdtype = attention_scores.dtype
+        # Normalize the attention scores to probabilities.
+        attention_probs = self.softmax(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (
+            self.all_head_size, )
+        context_layer = context_layer.view(*new_context_layer_shape)
+        return context_layer
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super(BertSelfOutput, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dense.bert_output_layer = True
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config):
+        super(BertAttention, self).__init__()
+        self.self = BertSelfAttention(config)
+        self.output = BertSelfOutput(config)
+
+    def forward(self, input_tensor, attention_mask):
+        self_output = self.self(input_tensor, attention_mask)
+        attention_output = self.output(self_output, input_tensor)
+        return attention_output
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super(BertIntermediate, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.intermediate_size,
+                                          act=config.hidden_act)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_act(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super(BertOutput, self).__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dense.bert_output_layer = True
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config):
+        super(BertLayer, self).__init__()
+        self.attention = BertAttention(config)
+        self.PreAttentionLayerNorm = BertLayerNorm(config.hidden_size,
+                                                   eps=1e-12)
+        self.PostAttentionLayerNorm = BertLayerNorm(config.hidden_size,
+                                                    eps=1e-12)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(self, hidden_states, attention_mask, action=1, keep_prob=1.0):
+        if action == 0:
+            intermediate_input = hidden_states
+        else:
+            input_layer_norm = self.PreAttentionLayerNorm(hidden_states)
+            attention_output = self.attention(input_layer_norm, attention_mask)
+            attention_output = attention_output * 1 / keep_prob
+            intermediate_input = hidden_states + attention_output
+        
+        if action == 0:
+            layer_output = intermediate_input
+        else:
+            intermediate_layer_norm = self.PostAttentionLayerNorm(intermediate_input)
+            intermediate_output = self.intermediate(intermediate_layer_norm)
+            layer_output = self.output(intermediate_output)
+            layer_output = layer_output * 1 / keep_prob
+            layer_output = layer_output + intermediate_input
+        
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config, args, sparse_attention_config=None):
+        super(BertEncoder, self).__init__()
+
+        #Added later to make it similar to GPT-2
+        self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+        if args.deepspeed_transformer_kernel and args.deepspeed_sparse_attention:
+            raise NotImplementedError(
+                f'Currently DeepSpeed Transformer Kernels do not support Sparse Attention. To use Sparse Attention, you need to disable Transformer Kernels!'
+            )
+
+        if args.deepspeed_transformer_kernel:
+            from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
+
+            ds_config = get_deepspeed_config(args)
+            cuda_config = DeepSpeedTransformerConfig(
+                batch_size=ds_config.train_micro_batch_size_per_gpu,
+                max_seq_length=args.max_seq_length,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                heads=config.num_attention_heads,
+                attn_dropout_ratio=config.attention_probs_dropout_prob,
+                hidden_dropout_ratio=config.hidden_dropout_prob,
+                num_hidden_layers=config.num_hidden_layers,
+                initializer_range=config.initializer_range,
+                local_rank=args.local_rank
+                if hasattr(args, 'local_rank') else -1,
+                seed=args.seed,
+                fp16=ds_config.fp16_enabled,
+                pre_layer_norm=True,
+                attn_dropout_checkpoint=args.attention_dropout_checkpoint,
+                normalize_invertible=args.normalize_invertible,
+                gelu_checkpoint=args.gelu_checkpoint,
+                stochastic_mode=args.stochastic_mode)
+
+            self.layer = nn.ModuleList([
+                copy.deepcopy(DeepSpeedTransformerLayer(i, cuda_config))
+                for i in range(config.num_hidden_layers)
+            ])
+        else:
+            layer = BertLayer(config)
+            if sparse_attention_config is not None:
+                from deepspeed.ops.sparse_attention import BertSparseSelfAttention
+
+                layer.attention.self = BertSparseSelfAttention(
+                    config, sparsity_config=sparse_attention_config)
+
+            self.layer = nn.ModuleList([
+                copy.deepcopy(layer) for _ in range(config.num_hidden_layers)
+            ])
+
+    # def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+    #     all_encoder_layers = []
+    #     for layer_module in self.layer:
+    #         hidden_states = layer_module(hidden_states, attention_mask)
+    #         if output_all_encoded_layers:
+    #             all_encoder_layers.append(hidden_states)
+    #     if not output_all_encoded_layers:
+    #         all_encoder_layers.append(hidden_states)
+    #     return all_encoder_layers
+    def forward(self,
+                hidden_states,
+                attention_mask,
+                output_all_encoded_layers=True,
+                checkpoint_activations=False,
+                progressive_layer_drop=False, theta=0.5):
+        all_encoder_layers = []
+
+        def custom(start, end):
+            def custom_forward(*inputs):
+                layers = self.layer[start:end]
+                x_ = inputs[0]
+                for layer in layers:
+                    x_ = layer(x_, inputs[1])
+                return x_
+
+            return custom_forward
+
+        if checkpoint_activations:
+            l = 0
+            num_layers = len(self.layer)
+            chunk_length = math.ceil(math.sqrt(num_layers))
+            while l < num_layers:
+                hidden_states = checkpoint.checkpoint(
+                    custom(l, l + chunk_length), hidden_states,
+                    attention_mask * 1)
+                l += chunk_length
+            # decoder layers
+        else:
+            if not progressive_layer_drop:
+                for i, layer_module in enumerate(self.layer):
+                    hidden_states = layer_module(hidden_states, attention_mask)
+
+                    if output_all_encoded_layers:
+                        all_encoder_layers.append(hidden_states)
+            else:
+                drop_prob = 1 - theta
+                step = drop_prob / len(self.layer)
+                p = 1.0
+
+                for i,layer_module in enumerate(self.layer):
+
+                    action = np.random.choice([1, 0], p=[p, 1-p])
+                    p = p - step
+                    hidden_states = layer_module(hidden_states, attention_mask, action, p)
+                    if output_all_encoded_layers:
+                        all_encoder_layers.append(hidden_states)
+
+        if not output_all_encoded_layers or checkpoint_activations:
+            hidden_states = self.FinalLayerNorm(hidden_states)
+            all_encoder_layers.append(hidden_states)
+        return all_encoder_layers
+
+
+#class BertEncoder(nn.Module):
+#    def __init__(self, config):
+#        super(BertEncoder, self).__init__()
+#        layer = BertLayer(config)
+#        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
+#
+#    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+#        all_encoder_layers = []
+#        for layer_module in self.layer:
+#            hidden_states = layer_module(hidden_states, attention_mask)
+#            if output_all_encoded_layers:
+#                all_encoder_layers.append(hidden_states)
+#        if not output_all_encoded_layers:
+#            all_encoder_layers.append(hidden_states)
+#        return all_encoder_layers
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super(BertPooler, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.hidden_size,
+                                          act="tanh")
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense_act(first_token_tensor)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super(BertPredictionHeadTransform, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.hidden_size,
+                                          act=config.hidden_act)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_act(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertLMPredictionHead, self).__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
+                                 bert_model_embedding_weights.size(0),
+                                 bias=False)
+        self.decoder.weight = bert_model_embedding_weights
+        self.bias = nn.Parameter(
+            torch.zeros(bert_model_embedding_weights.size(0)))
+
+    def forward(self, hidden_states, masked_token_indexes):
+        hidden_states = self.transform(hidden_states)
+
+        if masked_token_indexes is not None:
+            hidden_states = torch.index_select(
+                hidden_states.view(-1, hidden_states.shape[-1]), 0,
+                masked_token_indexes)
+
+        torch.cuda.nvtx.range_push(
+            "decoder input.size() = {}, weight.size() = {}".format(
+                hidden_states.size(), self.decoder.weight.size()))
+        hidden_states = self.decoder(hidden_states) + self.bias
+        torch.cuda.nvtx.range_pop()
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertOnlyMLMHead, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super(BertOnlyNSPHead, self).__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output):
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+class BertPreTrainingHeads(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertPreTrainingHeads, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self,
+                sequence_output,
+                pooled_output,
+                masked_token_indexes=None):
+        prediction_scores = self.predictions(sequence_output,
+                                             masked_token_indexes)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class BertPreTrainedModel(nn.Module):
+    """ An abstract class to handle weights initialization and
+        a simple interface for dowloading and loading pretrained models.
+    """
+    def __init__(self, config, *inputs, **kwargs):
+        super(BertPreTrainedModel, self).__init__()
+        if not isinstance(config, BertConfig):
+            raise ValueError(
+                "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
+                "To create a model from a Google pretrained model use "
+                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+                    self.__class__.__name__, self.__class__.__name__))
+        self.config = config
+
+    def init_bert_weights(self, module):
+        """ Initialize the weights.
+        """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            num_layers = self.config.num_hidden_layers
+            std = self.config.initializer_range
+            if hasattr(module, 'bert_output_layer'):
+                # "Accounting for accumulation on the residual path"
+                #print("Accounting for accumulation on the residual path")
+                std = self.config.initializer_range / math.sqrt(
+                    2.0 * num_layers)
+            module.weight.data.normal_(mean=0.0, std=std)
+        elif isinstance(module, BertLayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    @classmethod
+    def from_pretrained(cls,
+                        pretrained_model_name_or_path,
+                        state_dict=None,
+                        cache_dir=None,
+                        from_tf=False,
+                        *inputs,
+                        **kwargs):
+        """
+        Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
+        Download and cache the pre-trained model file if needed.
+
+        Params:
+            pretrained_model_name_or_path: either:
+                - a str with the name of a pre-trained model to load selected in the list of:
+                    . `bert-base-uncased`
+                    . `bert-large-uncased`
+                    . `bert-base-cased`
+                    . `bert-large-cased`
+                    . `bert-base-multilingual-uncased`
+                    . `bert-base-multilingual-cased`
+                    . `bert-base-chinese`
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `model.chkpt` a TensorFlow checkpoint
+            from_tf: should we load the weights from a locally saved TensorFlow checkpoint
+            cache_dir: an optional path to a folder in which the pre-trained models will be cached.
+            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+            *inputs, **kwargs: additional input for the specific Bert class
+                (ex: num_labels for BertForSequenceClassification)
+        """
+        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
+            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[
+                pretrained_model_name_or_path]
+        else:
+            archive_file = pretrained_model_name_or_path
+        # redirect to the cache, if necessary
+        try:
+            resolved_archive_file = cached_path(archive_file,
+                                                cache_dir=cache_dir)
+        except EnvironmentError:
+            logger.error(
+                "Model name '{}' was not found in model name list ({}). "
+                "We assumed '{}' was a path or url but couldn't find any file "
+                "associated to this path or url.".format(
+                    pretrained_model_name_or_path,
+                    ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
+                    archive_file))
+            return None
+        if resolved_archive_file == archive_file:
+            logger.info("loading archive file {}".format(archive_file))
+        else:
+            logger.info("loading archive file {} from cache at {}".format(
+                archive_file, resolved_archive_file))
+        tempdir = None
+        if os.path.isdir(resolved_archive_file) or from_tf:
+            serialization_dir = resolved_archive_file
+        else:
+            # Extract archive to temp dir
+            tempdir = tempfile.mkdtemp()
+            logger.info("extracting archive file {} to temp dir {}".format(
+                resolved_archive_file, tempdir))
+            with tarfile.open(resolved_archive_file, 'r:gz') as archive:
+                archive.extractall(tempdir)
+            serialization_dir = tempdir
+        # Load config
+        config_file = os.path.join(serialization_dir, CONFIG_NAME)
+        config = BertConfig.from_json_file(config_file)
+        logger.info("Model config {}".format(config))
+        # Instantiate model.
+        model = cls(config, *inputs, **kwargs)
+        if state_dict is None and not from_tf:
+            weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
+            state_dict = torch.load(
+                weights_path,
+                map_location='cpu' if not torch.cuda.is_available() else None)
+        if tempdir:
+            # Clean up temp dir
+            shutil.rmtree(tempdir)
+        if from_tf:
+            # Directly load from a TensorFlow checkpoint
+            weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
+            return load_tf_weights_in_bert(model, weights_path)
+        # Load from a PyTorch state_dict
+        old_keys = []
+        new_keys = []
+        for key in state_dict.keys():
+            new_key = None
+            if 'gamma' in key:
+                new_key = key.replace('gamma', 'weight')
+            if 'beta' in key:
+                new_key = key.replace('beta', 'bias')
+            if new_key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+        for old_key, new_key in zip(old_keys, new_keys):
+            state_dict[new_key] = state_dict.pop(old_key)
+
+        missing_keys = []
+        unexpected_keys = []
+        error_msgs = []
+        # copy state_dict so _load_from_state_dict can modify it
+        metadata = getattr(state_dict, '_metadata', None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+
+        def load(module, prefix=''):
+            local_metadata = {} if metadata is None else metadata.get(
+                prefix[:-1], {})
+            module._load_from_state_dict(state_dict, prefix, local_metadata,
+                                         True, missing_keys, unexpected_keys,
+                                         error_msgs)
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, prefix + name + '.')
+
+        start_prefix = ''
+        if not hasattr(model, 'bert') and any(
+                s.startswith('bert.') for s in state_dict.keys()):
+            start_prefix = 'bert.'
+        load(model, prefix=start_prefix)
+        if len(missing_keys) > 0:
+            logger.info(
+                "Weights of {} not initialized from pretrained model: {}".
+                format(model.__class__.__name__, missing_keys))
+        if len(unexpected_keys) > 0:
+            logger.info(
+                "Weights from pretrained model not used in {}: {}".format(
+                    model.__class__.__name__, unexpected_keys))
+        if len(error_msgs) > 0:
+            raise RuntimeError(
+                'Error(s) in loading state_dict for {}:\n\t{}'.format(
+                    model.__class__.__name__, "\n\t".join(error_msgs)))
+        return model
+
+
+class BertModel(BertPreTrainedModel):
+    """BERT model ("Bidirectional Embedding Representations from a Transformer").
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
+
+    Outputs: Tuple of (encoded_layers, pooled_output)
+        `encoded_layers`: controled by `output_all_encoded_layers` argument:
+            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
+                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
+                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
+                to the last attention block of shape [batch_size, sequence_length, hidden_size],
+        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
+            classifier pretrained on top of the hidden state associated to the first character of the
+            input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = modeling.BertModel(config=config)
+    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args=None):
+        super(BertModel, self).__init__(config)
+        self.embeddings = BertEmbeddings(config)
+        # set pad_token_id that is used for sparse attention padding
+        self.pad_token_id = config.pad_token_id if hasattr(
+            config, 'pad_token_id') and config.pad_token_id is not None else 0
+        # set sparse_attention_config if it has been selected
+        self.sparse_attention_config = get_sparse_attention_config(
+            args, config.num_attention_heads)
+        self.sparse_attention_utils = get_sparse_attention_utils(self.sparse_attention_config)
+        self.encoder = BertEncoder(
+            config, args, sparse_attention_config=self.sparse_attention_config)
+        self.pooler = BertPooler(config)
+        self.apply(self.init_bert_weights)
+        logger.info("Init BERT pretrain model")
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                output_all_encoded_layers=True,
+                checkpoint_activations=False,
+                progressive_layer_drop=False, theta=0.5):
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # this attention mask is more simple than the triangular masking of causal attention
+        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(
+            dtype=next(self.parameters()).dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        # If BertEncoder uses sparse attention, it needs to be padded based on the sparse attention block size
+        if self.sparse_attention_config is not None:
+            pad_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds = self.sparse_attention_utils.pad_to_block_size(
+                block_size=self.sparse_attention_config.block,
+                input_ids=input_ids,
+                attention_mask=extended_attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=None,
+                inputs_embeds=None,
+                pad_token_id=self.pad_token_id,
+                model_mbeddings=self.embeddings)
+
+        embedding_output = self.embeddings(input_ids, token_type_ids)
+        encoded_layers = self.encoder(
+            embedding_output,
+            extended_attention_mask,
+            output_all_encoded_layers=output_all_encoded_layers,
+            checkpoint_activations=checkpoint_activations,
+            progressive_layer_drop=progressive_layer_drop, theta=theta)
+        sequence_output = encoded_layers[-1]
+        pooled_output = self.pooler(sequence_output)
+
+        # If BertEncoder uses sparse attention, and input_ids were padded, sequence output needs to be unpadded to original length
+        if self.sparse_attention_config is not None and pad_len > 0:
+            encoded_layers[-1] = self.sparse_attention_utils.unpad_sequence_output(
+                pad_len, encoded_layers[-1])
+
+        if not output_all_encoded_layers:
+            encoded_layers = encoded_layers[-1]
+        return encoded_layers, pooled_output
+
+
+class BertForPreTrainingPreLN(BertPreTrainedModel):
+    """BERT model with pre-training heads.
+    This module comprises the BERT model followed by the two pre-training heads:
+        - the masked language modeling head, and
+        - the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+        `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `masked_lm_labels` and `next_sentence_label` are not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `masked_lm_labels` or `next_sentence_label` is `None`:
+            Outputs a tuple comprising
+            - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
+            - the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForPreTraining(config)
+    masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args):
+        super(BertForPreTrainingPreLN, self).__init__(config)
+        self.bert = BertModel(config, args)
+        self.cls = BertPreTrainingHeads(
+            config, self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+        self.args = args
+
+    def forward(self, batch, **kwargs):
+        progressive_layer_drop = kwargs['progressive_layer_drop']
+        theta = kwargs['theta']
+        log = kwargs['log']
+
+        input_ids = batch[1]
+        token_type_ids = batch[3]
+        attention_mask = batch[2]
+        masked_lm_labels = batch[5]
+        next_sentence_label = batch[4]
+        checkpoint_activations = False
+
+        sequence_output, pooled_output = self.bert(
+            input_ids,
+            token_type_ids,
+            attention_mask,
+            output_all_encoded_layers=False,
+            checkpoint_activations=checkpoint_activations,
+            progressive_layer_drop=progressive_layer_drop, theta=theta)
+
+        if masked_lm_labels is not None and next_sentence_label is not None:
+            # filter out all masked labels.
+            masked_token_indexes = torch.nonzero(
+                (masked_lm_labels + 1).view(-1)).view(-1)
+            prediction_scores, seq_relationship_score = self.cls(
+                sequence_output, pooled_output, masked_token_indexes)
+            target = torch.index_select(masked_lm_labels.view(-1), 0,
+                                        masked_token_indexes)
+
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), target)
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            total_loss = masked_lm_loss + next_sentence_loss
+            return total_loss
+        else:
+            prediction_scores, seq_relationship_score = self.cls(
+                sequence_output, pooled_output)
+            return prediction_scores, seq_relationship_score
+
+
+class BertForMaskedLM(BertPreTrainedModel):
+    """BERT model with the masked language modeling head.
+    This module comprises the BERT model followed by the masked language modeling head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+
+    Outputs:
+        if `masked_lm_labels` is  not `None`:
+            Outputs the masked language modeling loss.
+        if `masked_lm_labels` is `None`:
+            Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForMaskedLM(config)
+    masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForMaskedLM, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyMLMHead(config,
+                                   self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                masked_lm_labels=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        prediction_scores = self.cls(sequence_output)
+
+        if masked_lm_labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size),
+                masked_lm_labels.view(-1))
+            return masked_lm_loss
+        else:
+            return prediction_scores
+
+
+class BertForNextSentencePrediction(BertPreTrainedModel):
+    """BERT model with next sentence prediction head.
+    This module comprises the BERT model followed by the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `next_sentence_label` is not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `next_sentence_label` is `None`:
+            Outputs the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForNextSentencePrediction(config)
+    seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForNextSentencePrediction, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyNSPHead(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                next_sentence_label=None,
+                checkpoint_activations=False):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        seq_relationship_score = self.cls(pooled_output)
+
+        if next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            return next_sentence_loss
+        else:
+            return seq_relationship_score
+
+
+class BertForSequenceClassification(BertPreTrainedModel):
+    """BERT model for classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForSequenceClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels):
+        super(BertForSequenceClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForMultipleChoice(BertPreTrainedModel):
+    """BERT model for multiple choice tasks.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_choices`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
+            and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_choices].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
+    input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
+    token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_choices = 2
+
+    model = BertForMultipleChoice(config, num_choices)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_choices):
+        super(BertForMultipleChoice, self).__init__(config)
+        self.num_choices = num_choices
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
+        _, pooled_output = self.bert(flat_input_ids,
+                                     flat_token_type_ids,
+                                     flat_attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, self.num_choices)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            return loss
+        else:
+            return reshaped_logits
+
+
+class BertForTokenClassification(BertPreTrainedModel):
+    """BERT model for token-level classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the full hidden state of the last layer.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForTokenClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels):
+        super(BertForTokenClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # Only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)[active_loss]
+                active_labels = labels.view(-1)[active_loss]
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels),
+                                labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForQuestionAnswering(BertPreTrainedModel):
+    """BERT model for Question Answering (span extraction).
+    This module is composed of the BERT model with a linear layer on top of
+    the sequence output that computes start_logits and end_logits
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+        `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+
+    Outputs:
+        if `start_positions` and `end_positions` are not `None`:
+            Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
+        if `start_positions` or `end_positions` is `None`:
+            Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
+            position tokens of shape [batch_size, sequence_length].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForQuestionAnswering(config)
+    start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForQuestionAnswering, self).__init__(config)
+        self.bert = BertModel(config)
+        # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
+        # self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                start_positions=None,
+                end_positions=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+            return total_loss
+        else:
+            return start_logits, end_logits

Deepspeed/BingBertSquad/turing/nvidia_modeling.py

+# DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f
+# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/modeling.py
+
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""PyTorch BERT model."""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import copy
+import json
+import logging
+import math
+import os
+import shutil
+import tarfile
+import tempfile
+import sys
+from io import open
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from torch.utils import checkpoint
+import torch.distributed as dist
+
+from turing.file_utils import cached_path
+
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+import torch.nn.functional as F
+import torch.nn.init as init
+
+logger = logging.getLogger(__name__)
+
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'bert-base-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
+    'bert-large-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
+    'bert-base-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
+    'bert-large-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
+    'bert-base-multilingual-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
+    'bert-base-multilingual-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
+    'bert-base-chinese':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
+}
+CONFIG_NAME = 'bert_config.json'
+WEIGHTS_NAME = 'pytorch_model.bin'
+TF_WEIGHTS_NAME = 'model.ckpt'
+
+
+def load_tf_weights_in_bert(model, tf_checkpoint_path):
+    """ Load tf checkpoints in a pytorch model
+    """
+    try:
+        import re
+        import numpy as np
+        import tensorflow as tf
+    except ImportError:
+        print(
+            "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions."
+        )
+        raise
+    tf_path = os.path.abspath(tf_checkpoint_path)
+    print("Converting TensorFlow checkpoint from {}".format(tf_path))
+    # Load weights from TF model
+    init_vars = tf.train.list_variables(tf_path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        print("Loading TF weight {} with shape {}".format(name, shape))
+        array = tf.train.load_variable(tf_path, name)
+        names.append(name)
+        arrays.append(array)
+
+    for name, array in zip(names, arrays):
+        name = name.split('/')
+        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+        # which are not required for using pretrained model
+        if any(n in ["adam_v", "adam_m"] for n in name):
+            print("Skipping {}".format("/".join(name)))
+            continue
+        pointer = model
+        for m_name in name:
+            if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
+                l = re.split(r'_(\d+)', m_name)
+            else:
+                l = [m_name]
+            if l[0] == 'kernel' or l[0] == 'gamma':
+                pointer = getattr(pointer, 'weight')
+            elif l[0] == 'output_bias' or l[0] == 'beta':
+                pointer = getattr(pointer, 'bias')
+            elif l[0] == 'output_weights':
+                pointer = getattr(pointer, 'weight')
+            else:
+                pointer = getattr(pointer, l[0])
+            if len(l) >= 2:
+                num = int(l[1])
+                pointer = pointer[num]
+        if m_name[-11:] == '_embeddings':
+            pointer = getattr(pointer, 'weight')
+        elif m_name == 'kernel':
+            array = np.transpose(array)
+        try:
+            assert pointer.shape == array.shape
+        except AssertionError as e:
+            e.args += (pointer.shape, array.shape)
+            raise
+        print("Initialize PyTorch weight {}".format(name))
+        pointer.data = torch.from_numpy(array)
+    return model
+
+
+@torch.jit.script
+def f_gelu(x):
+    return x * 0.5 * (1.0 + torch.erf(x / 1.41421))
+
+
+@torch.jit.script
+def bias_gelu(bias, y):
+    x = bias + y
+    return x * 0.5 * (1.0 + torch.erf(x / 1.41421))
+
+
+@torch.jit.script
+def bias_tanh(bias, y):
+    x = bias + y
+    return torch.tanh(x)
+
+
+def gelu(x):
+    """Implementation of the gelu activation function.
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+        Also see https://arxiv.org/abs/1606.08415
+    """
+    return f_gelu(x)
+
+
+def swish(x):
+    return x * torch.sigmoid(x)
+
+
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
+
+
+class LinearActivation(Module):
+    r"""Fused Linear and activation Module.
+    """
+    __constants__ = ['bias']
+
+    def __init__(self, in_features, out_features, act='gelu', bias=True):
+        super(LinearActivation, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.fused_gelu = False
+        self.fused_tanh = False
+        if isinstance(act, str) or (sys.version_info[0] == 2
+                                    and isinstance(act, unicode)):
+            if bias and act == 'gelu':
+                self.fused_gelu = True
+            elif bias and act == 'tanh':
+                self.fused_tanh = True
+            else:
+                self.act_fn = ACT2FN[act]
+        else:
+            self.act_fn = act
+        self.weight = Parameter(torch.Tensor(out_features, in_features))
+        if bias:
+            self.bias = Parameter(torch.Tensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, input):
+        if self.fused_gelu:
+            return bias_gelu(self.bias, F.linear(input, self.weight, None))
+        elif self.fused_tanh:
+            return bias_tanh(self.bias, F.linear(input, self.weight, None))
+        else:
+            return self.act_fn(F.linear(input, self.weight, self.bias))
+
+    def extra_repr(self):
+        return 'in_features={}, out_features={}, bias={}'.format(
+            self.in_features, self.out_features, self.bias is not None)
+
+
+class BertConfig(object):
+    """Configuration class to store the configuration of a `BertModel`.
+    """
+    def __init__(self,
+                 vocab_size_or_config_json_file,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=2,
+                 initializer_range=0.02):
+        """Constructs BertConfig.
+
+        Args:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
+            hidden_size: Size of the encoder layers and the pooler layer.
+            num_hidden_layers: Number of hidden layers in the Transformer encoder.
+            num_attention_heads: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+                layer in the Transformer encoder.
+            hidden_act: The non-linear activation function (function or string) in the
+                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+            hidden_dropout_prob: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob: The dropout ratio for the attention
+                probabilities.
+            max_position_embeddings: The maximum sequence length that this model might
+                ever be used with. Typically set this to something large just in case
+                (e.g., 512 or 1024 or 2048).
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+                `BertModel`.
+            initializer_range: The sttdev of the truncated_normal_initializer for
+                initializing all weight matrices.
+        """
+        if isinstance(vocab_size_or_config_json_file,
+                      str) or (sys.version_info[0] == 2 and isinstance(
+                          vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r",
+                      encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.hidden_act = hidden_act
+            self.intermediate_size = intermediate_size
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.initializer_range = initializer_range
+        else:
+            raise ValueError(
+                "First argument must be either a vocabulary size (int)"
+                "or the path to a pretrained model config file (str)")
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `BertConfig` from a Python dictionary of parameters."""
+        config = BertConfig(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+
+try:
+    import apex
+    #apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm')
+    import apex.normalization
+    #apex.amp.register_float_function(apex.normalization.FusedLayerNorm, 'forward')
+    BertLayerNorm = apex.normalization.FusedLayerNorm
+except ImportError:
+    print(
+        "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex."
+    )
+
+    class BertLayerNorm(nn.Module):
+        def __init__(self, hidden_size, eps=1e-12):
+            """Construct a layernorm module in the TF style (epsilon inside the square root).
+            """
+            super(BertLayerNorm, self).__init__()
+            self.weight = nn.Parameter(torch.ones(hidden_size))
+            self.bias = nn.Parameter(torch.zeros(hidden_size))
+            self.variance_epsilon = eps
+
+        def forward(self, x):
+            u = x.mean(-1, keepdim=True)
+            s = (x - u).pow(2).mean(-1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+            return self.weight * x + self.bias
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings.
+    """
+    def __init__(self, config):
+        super(BertEmbeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size,
+                                            config.hidden_size)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings,
+                                                config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
+                                                  config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, input_ids, token_type_ids=None):
+        seq_length = input_ids.size(1)
+        position_ids = torch.arange(seq_length,
+                                    dtype=torch.long,
+                                    device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = words_embeddings + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config):
+        super(BertSelfAttention, self).__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" %
+                (config.hidden_size, config.num_attention_heads))
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size /
+                                       config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+                                       self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def transpose_key_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+                                       self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 3, 1)
+
+    def forward(self, hidden_states, attention_mask):
+        mixed_query_layer = self.query(hidden_states)
+        mixed_key_layer = self.key(hidden_states)
+        mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_key_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer)
+        attention_scores = attention_scores / math.sqrt(
+            self.attention_head_size)
+        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+        attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = self.softmax(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (
+            self.all_head_size, )
+        context_layer = context_layer.view(*new_context_layer_shape)
+        return context_layer
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super(BertSelfOutput, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config):
+        super(BertAttention, self).__init__()
+        self.self = BertSelfAttention(config)
+        self.output = BertSelfOutput(config)
+
+    def forward(self, input_tensor, attention_mask):
+        self_output = self.self(input_tensor, attention_mask)
+        attention_output = self.output(self_output, input_tensor)
+        return attention_output
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super(BertIntermediate, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.intermediate_size,
+                                          act=config.hidden_act)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_act(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super(BertOutput, self).__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config):
+        super(BertLayer, self).__init__()
+        self.attention = BertAttention(config)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(self, hidden_states, attention_mask):
+        attention_output = self.attention(hidden_states, attention_mask)
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config, args):
+        super(BertEncoder, self).__init__()
+
+        if args.deepspeed_transformer_kernel:
+            from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig, DeepSpeedConfig
+
+            if hasattr(args, 'deepspeed_config') and args.deepspeed_config:
+                ds_config = DeepSpeedConfig(args.deepspeed_config)
+            else:
+                raise RuntimeError('deepspeed_config is not found in args.')
+
+            cuda_config = DeepSpeedTransformerConfig(
+                batch_size=ds_config.train_micro_batch_size_per_gpu,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                heads=config.num_attention_heads,
+                attn_dropout_ratio=config.attention_probs_dropout_prob,
+                hidden_dropout_ratio=config.hidden_dropout_prob,
+                num_hidden_layers=config.num_hidden_layers,
+                initializer_range=config.initializer_range,
+                local_rank=args.local_rank,
+                seed=args.seed,
+                fp16=ds_config.fp16_enabled,
+                pre_layer_norm=False)
+
+            self.layer = nn.ModuleList([
+                copy.deepcopy(DeepSpeedTransformerLayer(cuda_config))
+                for _ in range(config.num_hidden_layers)
+            ])
+        else:
+            layer = BertLayer(config)
+            self.layer = nn.ModuleList([
+                copy.deepcopy(layer) for _ in range(config.num_hidden_layers)
+            ])
+
+    # def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+    #     all_encoder_layers = []
+    #     for layer_module in self.layer:
+    #         hidden_states = layer_module(hidden_states, attention_mask)
+    #         if output_all_encoded_layers:
+    #             all_encoder_layers.append(hidden_states)
+    #     if not output_all_encoded_layers:
+    #         all_encoder_layers.append(hidden_states)
+    #     return all_encoder_layers
+    def forward(self,
+                hidden_states,
+                attention_mask,
+                output_all_encoded_layers=True,
+                checkpoint_activations=False):
+        all_encoder_layers = []
+
+        def custom(start, end):
+            def custom_forward(*inputs):
+                layers = self.layer[start:end]
+                x_ = inputs[0]
+                for layer in layers:
+                    x_ = layer(x_, inputs[1])
+                return x_
+
+            return custom_forward
+
+        if checkpoint_activations:
+            l = 0
+            num_layers = len(self.layer)
+            chunk_length = math.ceil(math.sqrt(num_layers))
+            while l < num_layers:
+                hidden_states = checkpoint.checkpoint(
+                    custom(l, l + chunk_length), hidden_states,
+                    attention_mask * 1)
+                l += chunk_length
+            # decoder layers
+        else:
+            for i, layer_module in enumerate(self.layer):
+                hidden_states = layer_module(hidden_states, attention_mask)
+
+                if output_all_encoded_layers:
+                    all_encoder_layers.append(hidden_states)
+
+        if not output_all_encoded_layers or checkpoint_activations:
+            all_encoder_layers.append(hidden_states)
+        return all_encoder_layers
+
+
+#class BertEncoder(nn.Module):
+#    def __init__(self, config):
+#        super(BertEncoder, self).__init__()
+#        layer = BertLayer(config)
+#        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
+#
+#    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+#        all_encoder_layers = []
+#        for layer_module in self.layer:
+#            hidden_states = layer_module(hidden_states, attention_mask)
+#            if output_all_encoded_layers:
+#                all_encoder_layers.append(hidden_states)
+#        if not output_all_encoded_layers:
+#            all_encoder_layers.append(hidden_states)
+#        return all_encoder_layers
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super(BertPooler, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.hidden_size,
+                                          act="tanh")
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense_act(first_token_tensor)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super(BertPredictionHeadTransform, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.hidden_size,
+                                          act=config.hidden_act)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_act(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertLMPredictionHead, self).__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
+                                 bert_model_embedding_weights.size(0),
+                                 bias=False)
+        self.decoder.weight = bert_model_embedding_weights
+        self.bias = nn.Parameter(
+            torch.zeros(bert_model_embedding_weights.size(0)))
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        torch.cuda.nvtx.range_push(
+            "decoder input.size() = {}, weight.size() = {}".format(
+                hidden_states.size(), self.decoder.weight.size()))
+        hidden_states = self.decoder(hidden_states) + self.bias
+        torch.cuda.nvtx.range_pop()
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertOnlyMLMHead, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super(BertOnlyNSPHead, self).__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output):
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+class BertPreTrainingHeads(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertPreTrainingHeads, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class BertPreTrainedModel(nn.Module):
+    """ An abstract class to handle weights initialization and
+        a simple interface for dowloading and loading pretrained models.
+    """
+    def __init__(self, config, *inputs, **kwargs):
+        super(BertPreTrainedModel, self).__init__()
+        if not isinstance(config, BertConfig):
+            raise ValueError(
+                "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
+                "To create a model from a Google pretrained model use "
+                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+                    self.__class__.__name__, self.__class__.__name__))
+        self.config = config
+
+    def init_bert_weights(self, module):
+        """ Initialize the weights.
+        """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0,
+                                       std=self.config.initializer_range)
+        elif isinstance(module, BertLayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    @classmethod
+    def from_pretrained(cls,
+                        pretrained_model_name_or_path,
+                        state_dict=None,
+                        cache_dir=None,
+                        from_tf=False,
+                        *inputs,
+                        **kwargs):
+        """
+        Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
+        Download and cache the pre-trained model file if needed.
+
+        Params:
+            pretrained_model_name_or_path: either:
+                - a str with the name of a pre-trained model to load selected in the list of:
+                    . `bert-base-uncased`
+                    . `bert-large-uncased`
+                    . `bert-base-cased`
+                    . `bert-large-cased`
+                    . `bert-base-multilingual-uncased`
+                    . `bert-base-multilingual-cased`
+                    . `bert-base-chinese`
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `model.chkpt` a TensorFlow checkpoint
+            from_tf: should we load the weights from a locally saved TensorFlow checkpoint
+            cache_dir: an optional path to a folder in which the pre-trained models will be cached.
+            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+            *inputs, **kwargs: additional input for the specific Bert class
+                (ex: num_labels for BertForSequenceClassification)
+        """
+        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
+            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[
+                pretrained_model_name_or_path]
+        else:
+            archive_file = pretrained_model_name_or_path
+        # redirect to the cache, if necessary
+        try:
+            resolved_archive_file = cached_path(archive_file,
+                                                cache_dir=cache_dir)
+        except EnvironmentError:
+            logger.error(
+                "Model name '{}' was not found in model name list ({}). "
+                "We assumed '{}' was a path or url but couldn't find any file "
+                "associated to this path or url.".format(
+                    pretrained_model_name_or_path,
+                    ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
+                    archive_file))
+            return None
+        if resolved_archive_file == archive_file:
+            logger.info("loading archive file {}".format(archive_file))
+        else:
+            logger.info("loading archive file {} from cache at {}".format(
+                archive_file, resolved_archive_file))
+        tempdir = None
+        if os.path.isdir(resolved_archive_file) or from_tf:
+            serialization_dir = resolved_archive_file
+        else:
+            # Extract archive to temp dir
+            tempdir = tempfile.mkdtemp()
+            logger.info("extracting archive file {} to temp dir {}".format(
+                resolved_archive_file, tempdir))
+            with tarfile.open(resolved_archive_file, 'r:gz') as archive:
+                archive.extractall(tempdir)
+            serialization_dir = tempdir
+        # Load config
+        config_file = os.path.join(serialization_dir, CONFIG_NAME)
+        config = BertConfig.from_json_file(config_file)
+        logger.info("Model config {}".format(config))
+        # Instantiate model.
+        model = cls(config, *inputs, **kwargs)
+        if state_dict is None and not from_tf:
+            weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
+            state_dict = torch.load(
+                weights_path,
+                map_location='cpu' if not torch.cuda.is_available() else None)
+        if tempdir:
+            # Clean up temp dir
+            shutil.rmtree(tempdir)
+        if from_tf:
+            # Directly load from a TensorFlow checkpoint
+            weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
+            return load_tf_weights_in_bert(model, weights_path)
+        # Load from a PyTorch state_dict
+        old_keys = []
+        new_keys = []
+        for key in state_dict.keys():
+            new_key = None
+            if 'gamma' in key:
+                new_key = key.replace('gamma', 'weight')
+            if 'beta' in key:
+                new_key = key.replace('beta', 'bias')
+            if new_key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+        for old_key, new_key in zip(old_keys, new_keys):
+            state_dict[new_key] = state_dict.pop(old_key)
+
+        missing_keys = []
+        unexpected_keys = []
+        error_msgs = []
+        # copy state_dict so _load_from_state_dict can modify it
+        metadata = getattr(state_dict, '_metadata', None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+
+        def load(module, prefix=''):
+            local_metadata = {} if metadata is None else metadata.get(
+                prefix[:-1], {})
+            module._load_from_state_dict(state_dict, prefix, local_metadata,
+                                         True, missing_keys, unexpected_keys,
+                                         error_msgs)
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, prefix + name + '.')
+
+        start_prefix = ''
+        if not hasattr(model, 'bert') and any(
+                s.startswith('bert.') for s in state_dict.keys()):
+            start_prefix = 'bert.'
+        load(model, prefix=start_prefix)
+        if len(missing_keys) > 0:
+            logger.info(
+                "Weights of {} not initialized from pretrained model: {}".
+                format(model.__class__.__name__, missing_keys))
+        if len(unexpected_keys) > 0:
+            logger.info(
+                "Weights from pretrained model not used in {}: {}".format(
+                    model.__class__.__name__, unexpected_keys))
+        if len(error_msgs) > 0:
+            raise RuntimeError(
+                'Error(s) in loading state_dict for {}:\n\t{}'.format(
+                    model.__class__.__name__, "\n\t".join(error_msgs)))
+        return model
+
+
+class BertModel(BertPreTrainedModel):
+    """BERT model ("Bidirectional Embedding Representations from a Transformer").
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
+
+    Outputs: Tuple of (encoded_layers, pooled_output)
+        `encoded_layers`: controled by `output_all_encoded_layers` argument:
+            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
+                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
+                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
+                to the last attention block of shape [batch_size, sequence_length, hidden_size],
+        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
+            classifier pretrained on top of the hidden state associated to the first character of the
+            input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = modeling.BertModel(config=config)
+    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args):
+        super(BertModel, self).__init__(config)
+        self.embeddings = BertEmbeddings(config)
+        self.encoder = BertEncoder(config, args)
+        self.pooler = BertPooler(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                output_all_encoded_layers=True,
+                checkpoint_activations=False):
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # this attention mask is more simple than the triangular masking of causal attention
+        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(
+            dtype=next(self.parameters()).dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        embedding_output = self.embeddings(input_ids, token_type_ids)
+        encoded_layers = self.encoder(
+            embedding_output,
+            extended_attention_mask,
+            output_all_encoded_layers=output_all_encoded_layers,
+            checkpoint_activations=checkpoint_activations)
+        sequence_output = encoded_layers[-1]
+        pooled_output = self.pooler(sequence_output)
+        if not output_all_encoded_layers:
+            encoded_layers = encoded_layers[-1]
+        return encoded_layers, pooled_output
+
+
+class BertForPreTraining(BertPreTrainedModel):
+    """BERT model with pre-training heads.
+    This module comprises the BERT model followed by the two pre-training heads:
+        - the masked language modeling head, and
+        - the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+        `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `masked_lm_labels` and `next_sentence_label` are not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `masked_lm_labels` or `next_sentence_label` is `None`:
+            Outputs a tuple comprising
+            - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
+            - the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForPreTraining(config)
+    masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args):
+        super(BertForPreTraining, self).__init__(config)
+        self.summary_writer = None
+        if dist.get_rank() == 0:
+            self.summary_writer = args.summary_writer
+        self.samples_per_step = dist.get_world_size() * args.train_batch_size
+        self.sample_count = self.samples_per_step
+        self.bert = BertModel(config)
+        self.cls = BertPreTrainingHeads(
+            config, self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+
+    def log_summary_writer(self, logs: dict, base='Train'):
+        if dist.get_rank() == 0:
+            module_name = "Samples"  #self._batch_module_name.get(batch_type, self._get_batch_type_error(batch_type))
+            for key, log in logs.items():
+                self.summary_writer.add_scalar(f'{base}/{module_name}/{key}',
+                                               log, self.sample_count)
+            self.sample_count += self.samples_per_step
+
+    def forward(self, batch, log=True):
+        #input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, checkpoint_activations=False):
+        input_ids = batch[1]
+        token_type_ids = batch[3]
+        attention_mask = batch[2]
+        masked_lm_labels = batch[5]
+        next_sentence_label = batch[4]
+        checkpoint_activations = False
+
+        sequence_output, pooled_output = self.bert(
+            input_ids,
+            token_type_ids,
+            attention_mask,
+            output_all_encoded_layers=False,
+            checkpoint_activations=checkpoint_activations)
+        prediction_scores, seq_relationship_score = self.cls(
+            sequence_output, pooled_output)
+
+        if masked_lm_labels is not None and next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size),
+                masked_lm_labels.view(-1))
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            #print("loss is {} {}".format(masked_lm_loss, next_sentence_loss))
+            total_loss = masked_lm_loss + next_sentence_loss
+            #            if log:
+            #                self.log_summary_writer(logs={'train_loss': total_loss.item()})
+            return total_loss
+        else:
+            return prediction_scores, seq_relationship_score
+
+
+class BertForMaskedLM(BertPreTrainedModel):
+    """BERT model with the masked language modeling head.
+    This module comprises the BERT model followed by the masked language modeling head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+
+    Outputs:
+        if `masked_lm_labels` is  not `None`:
+            Outputs the masked language modeling loss.
+        if `masked_lm_labels` is `None`:
+            Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForMaskedLM(config)
+    masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForMaskedLM, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyMLMHead(config,
+                                   self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                masked_lm_labels=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        prediction_scores = self.cls(sequence_output)
+
+        if masked_lm_labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size),
+                masked_lm_labels.view(-1))
+            return masked_lm_loss
+        else:
+            return prediction_scores
+
+
+class BertForNextSentencePrediction(BertPreTrainedModel):
+    """BERT model with next sentence prediction head.
+    This module comprises the BERT model followed by the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `next_sentence_label` is not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `next_sentence_label` is `None`:
+            Outputs the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForNextSentencePrediction(config)
+    seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForNextSentencePrediction, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyNSPHead(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                next_sentence_label=None,
+                checkpoint_activations=False):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        seq_relationship_score = self.cls(pooled_output)
+
+        if next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            return next_sentence_loss
+        else:
+            return seq_relationship_score
+
+
+class BertForSequenceClassification(BertPreTrainedModel):
+    """BERT model for classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForSequenceClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels):
+        super(BertForSequenceClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForMultipleChoice(BertPreTrainedModel):
+    """BERT model for multiple choice tasks.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_choices`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
+            and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_choices].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
+    input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
+    token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_choices = 2
+
+    model = BertForMultipleChoice(config, num_choices)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_choices):
+        super(BertForMultipleChoice, self).__init__(config)
+        self.num_choices = num_choices
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
+        _, pooled_output = self.bert(flat_input_ids,
+                                     flat_token_type_ids,
+                                     flat_attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, self.num_choices)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            return loss
+        else:
+            return reshaped_logits
+
+
+class BertForTokenClassification(BertPreTrainedModel):
+    """BERT model for token-level classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the full hidden state of the last layer.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForTokenClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels):
+        super(BertForTokenClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # Only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)[active_loss]
+                active_labels = labels.view(-1)[active_loss]
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels),
+                                labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForQuestionAnswering(BertPreTrainedModel):
+    """BERT model for Question Answering (span extraction).
+    This module is composed of the BERT model with a linear layer on top of
+    the sequence output that computes start_logits and end_logits
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+        `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+
+    Outputs:
+        if `start_positions` and `end_positions` are not `None`:
+            Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
+        if `start_positions` or `end_positions` is `None`:
+            Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
+            position tokens of shape [batch_size, sequence_length].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForQuestionAnswering(config)
+    start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args):
+        super(BertForQuestionAnswering, self).__init__(config)
+        self.bert = BertModel(config, args)
+        # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
+        # self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                start_positions=None,
+                end_positions=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+            return total_loss
+        else:
+            return start_logits, end_logits

Deepspeed/BingBertSquad/turing/nvidia_modelingpreln.py

+# DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f
+# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/modeling.py
+
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""PyTorch BERT model."""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import copy
+import json
+import logging
+import math
+import os
+import shutil
+import tarfile
+import tempfile
+import sys
+from io import open
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from torch.utils import checkpoint
+import torch.distributed as dist
+
+from turing.file_utils import cached_path
+
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+import torch.nn.functional as F
+import torch.nn.init as init
+
+logger = logging.getLogger(__name__)
+
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'bert-base-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
+    'bert-large-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
+    'bert-base-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
+    'bert-large-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
+    'bert-base-multilingual-uncased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
+    'bert-base-multilingual-cased':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
+    'bert-base-chinese':
+    "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
+}
+CONFIG_NAME = 'bert_config.json'
+WEIGHTS_NAME = 'pytorch_model.bin'
+TF_WEIGHTS_NAME = 'model.ckpt'
+
+
+def load_tf_weights_in_bert(model, tf_checkpoint_path):
+    """ Load tf checkpoints in a pytorch model
+    """
+    try:
+        import re
+        import numpy as np
+        import tensorflow as tf
+    except ImportError:
+        print(
+            "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions."
+        )
+        raise
+    tf_path = os.path.abspath(tf_checkpoint_path)
+    print("Converting TensorFlow checkpoint from {}".format(tf_path))
+    # Load weights from TF model
+    init_vars = tf.train.list_variables(tf_path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        print("Loading TF weight {} with shape {}".format(name, shape))
+        array = tf.train.load_variable(tf_path, name)
+        names.append(name)
+        arrays.append(array)
+
+    for name, array in zip(names, arrays):
+        name = name.split('/')
+        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+        # which are not required for using pretrained model
+        if any(n in ["adam_v", "adam_m"] for n in name):
+            print("Skipping {}".format("/".join(name)))
+            continue
+        pointer = model
+        for m_name in name:
+            if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
+                l = re.split(r'_(\d+)', m_name)
+            else:
+                l = [m_name]
+            if l[0] == 'kernel' or l[0] == 'gamma':
+                pointer = getattr(pointer, 'weight')
+            elif l[0] == 'output_bias' or l[0] == 'beta':
+                pointer = getattr(pointer, 'bias')
+            elif l[0] == 'output_weights':
+                pointer = getattr(pointer, 'weight')
+            else:
+                pointer = getattr(pointer, l[0])
+            if len(l) >= 2:
+                num = int(l[1])
+                pointer = pointer[num]
+        if m_name[-11:] == '_embeddings':
+            pointer = getattr(pointer, 'weight')
+        elif m_name == 'kernel':
+            array = np.transpose(array)
+        try:
+            assert pointer.shape == array.shape
+        except AssertionError as e:
+            e.args += (pointer.shape, array.shape)
+            raise
+        print("Initialize PyTorch weight {}".format(name))
+        pointer.data = torch.from_numpy(array)
+    return model
+
+
+@torch.jit.script
+def f_gelu(x):
+    pdtype = x.dtype
+    x = x.float()
+    y = x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+    return y.to(pdtype)
+
+
+@torch.jit.script
+def bias_gelu(bias, y):
+    x = bias + y
+    return x * 0.5 * (1.0 + torch.erf(x / 1.41421))
+
+
+@torch.jit.script
+def bias_tanh(bias, y):
+    x = bias + y
+    return torch.tanh(x)
+
+
+def gelu(x):
+    """Implementation of the gelu activation function.
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+        Also see https://arxiv.org/abs/1606.08415
+    """
+    return f_gelu(x)
+
+
+def swish(x):
+    return x * torch.sigmoid(x)
+
+
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
+
+
+class LinearActivation(Module):
+    r"""Fused Linear and activation Module.
+    """
+    __constants__ = ['bias']
+
+    def __init__(self, in_features, out_features, act='gelu', bias=True):
+        super(LinearActivation, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.fused_gelu = False
+        self.fused_tanh = False
+        if isinstance(act, str) or (sys.version_info[0] == 2
+                                    and isinstance(act, unicode)):
+            if bias and act == 'gelu':
+                self.fused_gelu = True
+            elif bias and act == 'tanh':
+                self.fused_tanh = True
+            else:
+                self.act_fn = ACT2FN[act]
+        else:
+            self.act_fn = act
+        self.weight = Parameter(torch.Tensor(out_features, in_features))
+        if bias:
+            self.bias = Parameter(torch.Tensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, input):
+        if self.fused_gelu:
+            return bias_gelu(self.bias, F.linear(input, self.weight, None))
+        elif self.fused_tanh:
+            return bias_tanh(self.bias, F.linear(input, self.weight, None))
+        else:
+            return self.act_fn(F.linear(input, self.weight, self.bias))
+
+    def extra_repr(self):
+        return 'in_features={}, out_features={}, bias={}'.format(
+            self.in_features, self.out_features, self.bias is not None)
+
+
+class BertConfig(object):
+    """Configuration class to store the configuration of a `BertModel`.
+    """
+    def __init__(self,
+                 vocab_size_or_config_json_file,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=2,
+                 initializer_range=0.02,
+                 cuda_ext_config=""):
+        """Constructs BertConfig.
+
+        Args:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
+            hidden_size: Size of the encoder layers and the pooler layer.
+            num_hidden_layers: Number of hidden layers in the Transformer encoder.
+            num_attention_heads: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+                layer in the Transformer encoder.
+            hidden_act: The non-linear activation function (function or string) in the
+                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+            hidden_dropout_prob: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob: The dropout ratio for the attention
+                probabilities.
+            max_position_embeddings: The maximum sequence length that this model might
+                ever be used with. Typically set this to something large just in case
+                (e.g., 512 or 1024 or 2048).
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+                `BertModel`.
+            initializer_range: The sttdev of the truncated_normal_initializer for
+                initializing all weight matrices.
+        """
+        if isinstance(vocab_size_or_config_json_file,
+                      str) or (sys.version_info[0] == 2 and isinstance(
+                          vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r",
+                      encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.hidden_act = hidden_act
+            self.intermediate_size = intermediate_size
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.initializer_range = initializer_range
+            self.cuda_ext_config = cuda_ext_config
+        else:
+            raise ValueError(
+                "First argument must be either a vocabulary size (int)"
+                "or the path to a pretrained model config file (str)")
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `BertConfig` from a Python dictionary of parameters."""
+        config = BertConfig(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+
+try:
+    import apex
+    #apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm')
+    import apex.normalization
+    #apex.amp.register_float_function(apex.normalization.FusedLayerNorm, 'forward')
+    BertLayerNorm = apex.normalization.FusedLayerNorm
+except ImportError:
+    print(
+        "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex."
+    )
+
+    class BertLayerNorm(nn.Module):
+        def __init__(self, hidden_size, eps=1e-12):
+            """Construct a layernorm module in the TF style (epsilon inside the square root).
+            """
+            super(BertLayerNorm, self).__init__()
+            self.weight = nn.Parameter(torch.ones(hidden_size))
+            self.bias = nn.Parameter(torch.zeros(hidden_size))
+            self.variance_epsilon = eps
+
+        def forward(self, x):
+            pdtype = x.dtype
+            x = x.float()
+            u = x.mean(-1, keepdim=True)
+            s = (x - u).pow(2).mean(-1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+            return self.weight * x.to(pdtype) + self.bias
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings.
+    """
+    def __init__(self, config):
+        super(BertEmbeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size,
+                                            config.hidden_size)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings,
+                                                config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
+                                                  config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, input_ids, token_type_ids=None):
+        seq_length = input_ids.size(1)
+        position_ids = torch.arange(seq_length,
+                                    dtype=torch.long,
+                                    device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = words_embeddings + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config):
+        super(BertSelfAttention, self).__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" %
+                (config.hidden_size, config.num_attention_heads))
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size /
+                                       config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+                                       self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def transpose_key_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads,
+                                       self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 3, 1)
+
+    def forward(self, hidden_states, attention_mask):
+        mixed_query_layer = self.query(hidden_states)
+        mixed_key_layer = self.key(hidden_states)
+        mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_key_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer)
+        attention_scores = attention_scores / math.sqrt(
+            self.attention_head_size)
+        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+        attention_scores = attention_scores + attention_mask
+
+        pdtype = attention_scores.dtype
+        # Normalize the attention scores to probabilities.
+        attention_probs = self.softmax(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (
+            self.all_head_size, )
+        context_layer = context_layer.view(*new_context_layer_shape)
+        return context_layer
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super(BertSelfOutput, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dense.bert_output_layer = True
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config):
+        super(BertAttention, self).__init__()
+        self.self = BertSelfAttention(config)
+        self.output = BertSelfOutput(config)
+
+    def forward(self, input_tensor, attention_mask):
+        self_output = self.self(input_tensor, attention_mask)
+        attention_output = self.output(self_output, input_tensor)
+        return attention_output
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super(BertIntermediate, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.intermediate_size,
+                                          act=config.hidden_act)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_act(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super(BertOutput, self).__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dense.bert_output_layer = True
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config):
+        super(BertLayer, self).__init__()
+        self.attention = BertAttention(config)
+        self.PreAttentionLayerNorm = BertLayerNorm(config.hidden_size,
+                                                   eps=1e-12)
+        self.PostAttentionLayerNorm = BertLayerNorm(config.hidden_size,
+                                                    eps=1e-12)
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+    def forward(self, hidden_states, attention_mask):
+        input_layer_norm = self.PreAttentionLayerNorm(hidden_states)
+        attention_output = self.attention(input_layer_norm, attention_mask)
+
+        intermediate_input = hidden_states + attention_output
+
+        intermediate_layer_norm = self.PostAttentionLayerNorm(
+            intermediate_input)
+        intermediate_output = self.intermediate(intermediate_layer_norm)
+        layer_output = self.output(intermediate_output)
+
+        return layer_output + intermediate_input
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config, args):
+        super(BertEncoder, self).__init__()
+
+        #Added later to make it similar to GPT-2
+        self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+        if args.deepspeed_transformer_kernel:
+            from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig, DeepSpeedConfig
+
+            if hasattr(args, 'deepspeed_config') and args.deepspeed_config:
+                ds_config = DeepSpeedConfig(args.deepspeed_config)
+            else:
+                raise RuntimeError('deepspeed_config is not found in args.')
+
+            cuda_config = DeepSpeedTransformerConfig(
+                batch_size=ds_config.train_micro_batch_size_per_gpu,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                heads=config.num_attention_heads,
+                attn_dropout_ratio=config.attention_probs_dropout_prob,
+                hidden_dropout_ratio=config.hidden_dropout_prob,
+                num_hidden_layers=config.num_hidden_layers,
+                initializer_range=config.initializer_range,
+                local_rank=args.local_rank,
+                seed=args.seed,
+                fp16=ds_config.fp16_enabled,
+                pre_layer_norm=True)
+
+            self.layer = nn.ModuleList([
+                copy.deepcopy(DeepSpeedTransformerLayer(cuda_config))
+                for _ in range(config.num_hidden_layers)
+            ])
+        else:
+            layer = BertLayer(config)
+            self.layer = nn.ModuleList([
+                copy.deepcopy(layer) for _ in range(config.num_hidden_layers)
+            ])
+
+    # def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+    #     all_encoder_layers = []
+    #     for layer_module in self.layer:
+    #         hidden_states = layer_module(hidden_states, attention_mask)
+    #         if output_all_encoded_layers:
+    #             all_encoder_layers.append(hidden_states)
+    #     if not output_all_encoded_layers:
+    #         all_encoder_layers.append(hidden_states)
+    #     return all_encoder_layers
+    def forward(self,
+                hidden_states,
+                attention_mask,
+                output_all_encoded_layers=True,
+                checkpoint_activations=False):
+        all_encoder_layers = []
+
+        def custom(start, end):
+            def custom_forward(*inputs):
+                layers = self.layer[start:end]
+                x_ = inputs[0]
+                for layer in layers:
+                    x_ = layer(x_, inputs[1])
+                return x_
+
+            return custom_forward
+
+        if checkpoint_activations:
+            l = 0
+            num_layers = len(self.layer)
+            chunk_length = math.ceil(math.sqrt(num_layers))
+            while l < num_layers:
+                hidden_states = checkpoint.checkpoint(
+                    custom(l, l + chunk_length), hidden_states,
+                    attention_mask * 1)
+                l += chunk_length
+            # decoder layers
+        else:
+            for i, layer_module in enumerate(self.layer):
+                hidden_states = layer_module(hidden_states, attention_mask)
+
+                if output_all_encoded_layers:
+                    all_encoder_layers.append(hidden_states)
+
+        if not output_all_encoded_layers or checkpoint_activations:
+            hidden_states = self.FinalLayerNorm(hidden_states)
+            all_encoder_layers.append(hidden_states)
+        return all_encoder_layers
+
+
+#class BertEncoder(nn.Module):
+#    def __init__(self, config):
+#        super(BertEncoder, self).__init__()
+#        layer = BertLayer(config)
+#        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
+#
+#    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+#        all_encoder_layers = []
+#        for layer_module in self.layer:
+#            hidden_states = layer_module(hidden_states, attention_mask)
+#            if output_all_encoded_layers:
+#                all_encoder_layers.append(hidden_states)
+#        if not output_all_encoded_layers:
+#            all_encoder_layers.append(hidden_states)
+#        return all_encoder_layers
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super(BertPooler, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.hidden_size,
+                                          act="tanh")
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense_act(first_token_tensor)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super(BertPredictionHeadTransform, self).__init__()
+        self.dense_act = LinearActivation(config.hidden_size,
+                                          config.hidden_size,
+                                          act=config.hidden_act)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_act(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertLMPredictionHead, self).__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
+                                 bert_model_embedding_weights.size(0),
+                                 bias=False)
+        self.decoder.weight = bert_model_embedding_weights
+        self.bias = nn.Parameter(
+            torch.zeros(bert_model_embedding_weights.size(0)))
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        torch.cuda.nvtx.range_push(
+            "decoder input.size() = {}, weight.size() = {}".format(
+                hidden_states.size(), self.decoder.weight.size()))
+        hidden_states = self.decoder(hidden_states) + self.bias
+        torch.cuda.nvtx.range_pop()
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertOnlyMLMHead, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super(BertOnlyNSPHead, self).__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output):
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+class BertPreTrainingHeads(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super(BertPreTrainingHeads, self).__init__()
+        self.predictions = BertLMPredictionHead(config,
+                                                bert_model_embedding_weights)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class BertPreTrainedModel(nn.Module):
+    """ An abstract class to handle weights initialization and
+        a simple interface for dowloading and loading pretrained models.
+    """
+    def __init__(self, config, *inputs, **kwargs):
+        super(BertPreTrainedModel, self).__init__()
+        if not isinstance(config, BertConfig):
+            raise ValueError(
+                "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
+                "To create a model from a Google pretrained model use "
+                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+                    self.__class__.__name__, self.__class__.__name__))
+        self.config = config
+
+    def init_bert_weights(self, module):
+        """ Initialize the weights.
+        """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            num_layers = self.config.num_hidden_layers
+            std = self.config.initializer_range
+            if hasattr(module, 'bert_output_layer'):
+                if torch.distributed.get_rank() == 0:
+                    print("Accounting for accumulation on the residual path")
+                    std = self.config.initializer_range / math.sqrt(
+                        2.0 * num_layers)
+            module.weight.data.normal_(mean=0.0, std=std)
+        elif isinstance(module, BertLayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    @classmethod
+    def from_pretrained(cls,
+                        pretrained_model_name_or_path,
+                        state_dict=None,
+                        cache_dir=None,
+                        from_tf=False,
+                        *inputs,
+                        **kwargs):
+        """
+        Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
+        Download and cache the pre-trained model file if needed.
+
+        Params:
+            pretrained_model_name_or_path: either:
+                - a str with the name of a pre-trained model to load selected in the list of:
+                    . `bert-base-uncased`
+                    . `bert-large-uncased`
+                    . `bert-base-cased`
+                    . `bert-large-cased`
+                    . `bert-base-multilingual-uncased`
+                    . `bert-base-multilingual-cased`
+                    . `bert-base-chinese`
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `model.chkpt` a TensorFlow checkpoint
+            from_tf: should we load the weights from a locally saved TensorFlow checkpoint
+            cache_dir: an optional path to a folder in which the pre-trained models will be cached.
+            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+            *inputs, **kwargs: additional input for the specific Bert class
+                (ex: num_labels for BertForSequenceClassification)
+        """
+        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
+            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[
+                pretrained_model_name_or_path]
+        else:
+            archive_file = pretrained_model_name_or_path
+        # redirect to the cache, if necessary
+        try:
+            resolved_archive_file = cached_path(archive_file,
+                                                cache_dir=cache_dir)
+        except EnvironmentError:
+            logger.error(
+                "Model name '{}' was not found in model name list ({}). "
+                "We assumed '{}' was a path or url but couldn't find any file "
+                "associated to this path or url.".format(
+                    pretrained_model_name_or_path,
+                    ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
+                    archive_file))
+            return None
+        if resolved_archive_file == archive_file:
+            logger.info("loading archive file {}".format(archive_file))
+        else:
+            logger.info("loading archive file {} from cache at {}".format(
+                archive_file, resolved_archive_file))
+        tempdir = None
+        if os.path.isdir(resolved_archive_file) or from_tf:
+            serialization_dir = resolved_archive_file
+        else:
+            # Extract archive to temp dir
+            tempdir = tempfile.mkdtemp()
+            logger.info("extracting archive file {} to temp dir {}".format(
+                resolved_archive_file, tempdir))
+            with tarfile.open(resolved_archive_file, 'r:gz') as archive:
+                archive.extractall(tempdir)
+            serialization_dir = tempdir
+        # Load config
+        config_file = os.path.join(serialization_dir, CONFIG_NAME)
+        config = BertConfig.from_json_file(config_file)
+        logger.info("Model config {}".format(config))
+        # Instantiate model.
+        model = cls(config, *inputs, **kwargs)
+        if state_dict is None and not from_tf:
+            weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
+            state_dict = torch.load(
+                weights_path,
+                map_location='cpu' if not torch.cuda.is_available() else None)
+        if tempdir:
+            # Clean up temp dir
+            shutil.rmtree(tempdir)
+        if from_tf:
+            # Directly load from a TensorFlow checkpoint
+            weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
+            return load_tf_weights_in_bert(model, weights_path)
+        # Load from a PyTorch state_dict
+        old_keys = []
+        new_keys = []
+        for key in state_dict.keys():
+            new_key = None
+            if 'gamma' in key:
+                new_key = key.replace('gamma', 'weight')
+            if 'beta' in key:
+                new_key = key.replace('beta', 'bias')
+            if new_key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+        for old_key, new_key in zip(old_keys, new_keys):
+            state_dict[new_key] = state_dict.pop(old_key)
+
+        missing_keys = []
+        unexpected_keys = []
+        error_msgs = []
+        # copy state_dict so _load_from_state_dict can modify it
+        metadata = getattr(state_dict, '_metadata', None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+
+        def load(module, prefix=''):
+            local_metadata = {} if metadata is None else metadata.get(
+                prefix[:-1], {})
+            module._load_from_state_dict(state_dict, prefix, local_metadata,
+                                         True, missing_keys, unexpected_keys,
+                                         error_msgs)
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, prefix + name + '.')
+
+        start_prefix = ''
+        if not hasattr(model, 'bert') and any(
+                s.startswith('bert.') for s in state_dict.keys()):
+            start_prefix = 'bert.'
+        load(model, prefix=start_prefix)
+        if len(missing_keys) > 0:
+            logger.info(
+                "Weights of {} not initialized from pretrained model: {}".
+                format(model.__class__.__name__, missing_keys))
+        if len(unexpected_keys) > 0:
+            logger.info(
+                "Weights from pretrained model not used in {}: {}".format(
+                    model.__class__.__name__, unexpected_keys))
+        if len(error_msgs) > 0:
+            raise RuntimeError(
+                'Error(s) in loading state_dict for {}:\n\t{}'.format(
+                    model.__class__.__name__, "\n\t".join(error_msgs)))
+        return model
+
+
+class BertModel(BertPreTrainedModel):
+    """BERT model ("Bidirectional Embedding Representations from a Transformer").
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
+
+    Outputs: Tuple of (encoded_layers, pooled_output)
+        `encoded_layers`: controled by `output_all_encoded_layers` argument:
+            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
+                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
+                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
+                to the last attention block of shape [batch_size, sequence_length, hidden_size],
+        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
+            classifier pretrained on top of the hidden state associated to the first character of the
+            input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = modeling.BertModel(config=config)
+    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args=None):
+        super(BertModel, self).__init__(config)
+        self.embeddings = BertEmbeddings(config)
+        self.encoder = BertEncoder(config, args)
+        self.pooler = BertPooler(config)
+        self.apply(self.init_bert_weights)
+        logger.info("Init BERT pretrain model")
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                output_all_encoded_layers=True,
+                checkpoint_activations=False):
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # this attention mask is more simple than the triangular masking of causal attention
+        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(
+            dtype=next(self.parameters()).dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        embedding_output = self.embeddings(input_ids, token_type_ids)
+        encoded_layers = self.encoder(
+            embedding_output,
+            extended_attention_mask,
+            output_all_encoded_layers=output_all_encoded_layers,
+            checkpoint_activations=checkpoint_activations)
+        sequence_output = encoded_layers[-1]
+        pooled_output = self.pooler(sequence_output)
+        if not output_all_encoded_layers:
+            encoded_layers = encoded_layers[-1]
+        return encoded_layers, pooled_output
+
+
+class BertForPreTrainingPreLN(BertPreTrainedModel):
+    """BERT model with pre-training heads.
+    This module comprises the BERT model followed by the two pre-training heads:
+        - the masked language modeling head, and
+        - the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+        `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `masked_lm_labels` and `next_sentence_label` are not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `masked_lm_labels` or `next_sentence_label` is `None`:
+            Outputs a tuple comprising
+            - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
+            - the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForPreTraining(config)
+    masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args):
+        super(BertForPreTrainingPreLN, self).__init__(config)
+        self.summary_writer = None
+        if dist.get_rank() == 0:
+            self.summary_writer = args.summary_writer
+        self.samples_per_step = dist.get_world_size() * args.train_batch_size
+        self.sample_count = self.samples_per_step
+        self.bert = BertModel(config, args)
+        self.cls = BertPreTrainingHeads(
+            config, self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+
+    def log_summary_writer(self, logs: dict, base='Train'):
+        if dist.get_rank() == 0:
+            module_name = "Samples"  #self._batch_module_name.get(batch_type, self._get_batch_type_error(batch_type))
+            for key, log in logs.items():
+                self.summary_writer.add_scalar(f'{base}/{module_name}/{key}',
+                                               log, self.sample_count)
+            self.sample_count += self.samples_per_step
+
+    def forward(self, batch, log=True):
+        #input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, checkpoint_activations=False):
+        input_ids = batch[1]
+        token_type_ids = batch[3]
+        attention_mask = batch[2]
+        masked_lm_labels = batch[5]
+        next_sentence_label = batch[4]
+        checkpoint_activations = False
+
+        sequence_output, pooled_output = self.bert(
+            input_ids,
+            token_type_ids,
+            attention_mask,
+            output_all_encoded_layers=False,
+            checkpoint_activations=checkpoint_activations)
+        prediction_scores, seq_relationship_score = self.cls(
+            sequence_output, pooled_output)
+
+        if masked_lm_labels is not None and next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size),
+                masked_lm_labels.view(-1))
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            #print("loss is {} {}".format(masked_lm_loss, next_sentence_loss))
+            total_loss = masked_lm_loss + next_sentence_loss
+            #            if log:
+            #                self.log_summary_writer(logs={'train_loss': total_loss.item()})
+            return total_loss
+        else:
+            return prediction_scores, seq_relationship_score
+
+
+class BertForMaskedLM(BertPreTrainedModel):
+    """BERT model with the masked language modeling head.
+    This module comprises the BERT model followed by the masked language modeling head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
+            is only computed for the labels set in [0, ..., vocab_size]
+
+    Outputs:
+        if `masked_lm_labels` is  not `None`:
+            Outputs the masked language modeling loss.
+        if `masked_lm_labels` is `None`:
+            Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForMaskedLM(config)
+    masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForMaskedLM, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyMLMHead(config,
+                                   self.bert.embeddings.word_embeddings.weight)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                masked_lm_labels=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        prediction_scores = self.cls(sequence_output)
+
+        if masked_lm_labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size),
+                masked_lm_labels.view(-1))
+            return masked_lm_loss
+        else:
+            return prediction_scores
+
+
+class BertForNextSentencePrediction(BertPreTrainedModel):
+    """BERT model with next sentence prediction head.
+    This module comprises the BERT model followed by the next sentence classification head.
+
+    Params:
+        config: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, 1].
+            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
+
+    Outputs:
+        if `next_sentence_label` is not `None`:
+            Outputs the total_loss which is the sum of the masked language modeling loss and the next
+            sentence classification loss.
+        if `next_sentence_label` is `None`:
+            Outputs the next sentence classification logits of shape [batch_size, 2].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForNextSentencePrediction(config)
+    seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config):
+        super(BertForNextSentencePrediction, self).__init__(config)
+        self.bert = BertModel(config)
+        self.cls = BertOnlyNSPHead(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                next_sentence_label=None,
+                checkpoint_activations=False):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        seq_relationship_score = self.cls(pooled_output)
+
+        if next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-1)
+            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
+                                          next_sentence_label.view(-1))
+            return next_sentence_loss
+        else:
+            return seq_relationship_score
+
+
+class BertForSequenceClassification(BertPreTrainedModel):
+    """BERT model for classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForSequenceClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels):
+        super(BertForSequenceClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        _, pooled_output = self.bert(input_ids,
+                                     token_type_ids,
+                                     attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForMultipleChoice(BertPreTrainedModel):
+    """BERT model for multiple choice tasks.
+    This module is composed of the BERT model with a linear layer on top of
+    the pooled output.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_choices`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
+            with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
+            and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
+            with indices selected in [0, ..., num_choices].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
+    input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
+    token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_choices = 2
+
+    model = BertForMultipleChoice(config, num_choices)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_choices):
+        super(BertForMultipleChoice, self).__init__(config)
+        self.num_choices = num_choices
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
+        _, pooled_output = self.bert(flat_input_ids,
+                                     flat_token_type_ids,
+                                     flat_attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, self.num_choices)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            return loss
+        else:
+            return reshaped_logits
+
+
+class BertForTokenClassification(BertPreTrainedModel):
+    """BERT model for token-level classification.
+    This module is composed of the BERT model with a linear layer on top of
+    the full hidden state of the last layer.
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+        `num_labels`: the number of classes for the classifier. Default = 2.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length]
+            with indices selected in [0, ..., num_labels].
+
+    Outputs:
+        if `labels` is not `None`:
+            Outputs the CrossEntropy classification loss of the output with the labels.
+        if `labels` is `None`:
+            Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    num_labels = 2
+
+    model = BertForTokenClassification(config, num_labels)
+    logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, num_labels):
+        super(BertForTokenClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                labels=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # Only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)[active_loss]
+                active_labels = labels.view(-1)[active_loss]
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels),
+                                labels.view(-1))
+            return loss
+        else:
+            return logits
+
+
+class BertForQuestionAnswering(BertPreTrainedModel):
+    """BERT model for Question Answering (span extraction).
+    This module is composed of the BERT model with a linear layer on top of
+    the sequence output that computes start_logits and end_logits
+
+    Params:
+        `config`: a BertConfig class instance with the configuration to build a new model.
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+        `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+
+    Outputs:
+        if `start_positions` and `end_positions` are not `None`:
+            Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
+        if `start_positions` or `end_positions` is `None`:
+            Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
+            position tokens of shape [batch_size, sequence_length].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = BertForQuestionAnswering(config)
+    start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+    def __init__(self, config, args=None):
+        super(BertForQuestionAnswering, self).__init__(config)
+        self.bert = BertModel(config, args)
+        # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
+        # self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+        self.apply(self.init_bert_weights)
+
+    def forward(self,
+                input_ids,
+                token_type_ids=None,
+                attention_mask=None,
+                start_positions=None,
+                end_positions=None,
+                checkpoint_activations=False):
+        sequence_output, _ = self.bert(input_ids,
+                                       token_type_ids,
+                                       attention_mask,
+                                       output_all_encoded_layers=False)
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+            return total_loss
+        else:
+            return start_logits, end_logits

Deepspeed/BingBertSquad/utils.py

+import os
+import sys
+import logging
+import argparse
+from tensorboardX import SummaryWriter
+
+SUMMARY_WRITER_DIR_NAME = 'runs'
+
+
+def get_argument_parser():
+    parser = argparse.ArgumentParser()
+
+    # Required parameters
+    parser.add_argument(
+        "--bert_model",
+        default=None,
+        type=str,
+        required=True,
+        help="Bert pre-trained model selected in the list: bert-base-uncased, "
+        "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
+        "bert-base-multilingual-cased, bert-base-chinese.")
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help=
+        "The output directory where the model checkpoints and predictions will be written."
+    )
+
+    # Other parameters
+    parser.add_argument("--train_file",
+                        default=None,
+                        type=str,
+                        help="SQuAD json for training. E.g., train-v1.1.json")
+    parser.add_argument(
+        "--predict_file",
+        default=None,
+        type=str,
+        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json"
+    )
+    parser.add_argument(
+        "--max_seq_length",
+        default=384,
+        type=int,
+        help=
+        "The maximum total input sequence length after WordPiece tokenization. Sequences "
+        "longer than this will be truncated, and sequences shorter than this will be padded."
+    )
+    parser.add_argument(
+        "--doc_stride",
+        default=128,
+        type=int,
+        help=
+        "When splitting up a long document into chunks, how much stride to take between chunks."
+    )
+    parser.add_argument(
+        "--max_query_length",
+        default=64,
+        type=int,
+        help=
+        "The maximum number of tokens for the question. Questions longer than this will "
+        "be truncated to this length.")
+    parser.add_argument("--do_train",
+                        action='store_true',
+                        help="Whether to run training.")
+    parser.add_argument("--do_predict",
+                        action='store_true',
+                        help="Whether to run eval on the dev set.")
+    parser.add_argument("--train_batch_size",
+                        default=32,
+                        type=int,
+                        help="Total batch size for training.")
+    parser.add_argument("--predict_batch_size",
+                        default=8,
+                        type=int,
+                        help="Total batch size for predictions.")
+    parser.add_argument("--learning_rate",
+                        default=5e-5,
+                        type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--num_train_epochs",
+                        default=3.0,
+                        type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument(
+        "--warmup_proportion",
+        default=0.1,
+        type=float,
+        help=
+        "Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% "
+        "of training.")
+    parser.add_argument(
+        "--n_best_size",
+        default=20,
+        type=int,
+        help=
+        "The total number of n-best predictions to generate in the nbest_predictions.json "
+        "output file.")
+    parser.add_argument(
+        "--max_answer_length",
+        default=30,
+        type=int,
+        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.")
+    parser.add_argument(
+        "--verbose_logging",
+        action='store_true',
+        help=
+        "If true, all of the warnings related to data processing will be printed. "
+        "A number of warnings are expected for a normal SQuAD evaluation.")
+    parser.add_argument("--no_cuda",
+                        action='store_true',
+                        help="Whether not to use CUDA when available")
+    parser.add_argument('--seed',
+                        type=int,
+                        default=42,
+                        help="random seed for initialization")
+    parser.add_argument(
+        '--gradient_accumulation_steps',
+        type=int,
+        default=1,
+        help=
+        "Number of updates steps to accumulate before performing a backward/update pass."
+    )
+    parser.add_argument(
+        "--do_lower_case",
+        action='store_true',
+        help=
+        "Whether to lower case the input text. True for uncased models, False for cased models."
+    )
+    parser.add_argument("--local_rank",
+                        type=int,
+                        default=-1,
+                        help="local_rank for distributed training on gpus")
+    parser.add_argument(
+        '--fp16',
+        action='store_true',
+        help="Whether to use 16-bit float precision instead of 32-bit")
+    parser.add_argument(
+        '--wall_clock_breakdown',
+        action='store_true',
+        default=False,
+        help=
+        "Whether to display the breakdown of the wall-clock time for foraward, backward and step"
+    )
+    parser.add_argument(
+        '--loss_scale',
+        type=float,
+        default=0,
+        help=
+        "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+        "0 (default value): dynamic loss scaling.\n"
+        "Positive power of 2: static loss scaling value.\n")
+    parser.add_argument("--model_file",
+                        type=str,
+                        default="0",
+                        help="Path to the Pretrained BERT Encoder File.")
+
+    parser.add_argument("--max_grad_norm",
+                        default=1.,
+                        type=float,
+                        help="Gradient clipping for FusedAdam.")
+    parser.add_argument('--job_name',
+                        type=str,
+                        default=None,
+                        help='Output path for Tensorboard event files.')
+
+    parser.add_argument(
+        '--preln',
+        action='store_true',
+        default=False,
+        help=
+        "Whether to display the breakdown of the wall-clock time for foraward, backward and step"
+    )
+
+    parser.add_argument(
+        '--loss_plot_alpha',
+        type=float,
+        default=0.2,
+        help='Alpha factor for plotting moving average of loss.')
+
+    parser.add_argument(
+        '--max_steps',
+        type=int,
+        default=sys.maxsize,
+        help=
+        'Maximum number of training steps of effective batch size to complete.'
+    )
+
+    parser.add_argument(
+        '--max_steps_per_epoch',
+        type=int,
+        default=sys.maxsize,
+        help=
+        'Maximum number of training steps of effective batch size within an epoch to complete.'
+    )
+
+    parser.add_argument('--print_steps',
+                        type=int,
+                        default=100,
+                        help='Interval to print training details.')
+
+    parser.add_argument('--deepspeed_transformer_kernel',
+                        default=False,
+                        action='store_true',
+                        help='Use DeepSpeed transformer kernel to accelerate.')
+
+    parser.add_argument('--dropout', type=float, default=0.1, help='dropout')
+
+    parser.add_argument(
+        '--ckpt_type',
+        type=str,
+        default="DS",
+        help="Checkpoint's type, DS - DeepSpeed, TF - Tensorflow, HF - Huggingface.")
+
+    parser.add_argument(
+        "--origin_bert_config_file",
+        type=str,
+        default=None,
+        help="The config json file corresponding to the non-DeepSpeed pre-trained BERT model."
+    )
+
+    return parser
+
+
+def get_summary_writer(name, base=".."):
+    """Returns a tensorboard summary writer
+    """
+    return SummaryWriter(
+        log_dir=os.path.join(base, SUMMARY_WRITER_DIR_NAME, name))
+
+
+def write_summary_events(summary_writer, summary_events):
+    for event in summary_events:
+        summary_writer.add_scalar(event[0], event[1], event[2])
+
+
+def is_time_to_exit(args, epoch_steps=0, global_steps=0):
+    return (epoch_steps >= args.max_steps_per_epoch) or \
+            (global_steps >= args.max_steps)
+
+
+def check_early_exit_warning(args):
+    # Issue warning if early exit from epoch is configured
+    if args.max_steps < sys.maxsize:
+        logging.warning(
+            'Early training exit is set after {} global steps'.format(
+                args.max_steps))
+
+    if args.max_steps_per_epoch < sys.maxsize:
+        logging.warning('Early epoch exit is set after {} global steps'.format(
+            args.max_steps_per_epoch))

Deepspeed/CODEOWNERS

+*       @jeffra @samyam @tjruwase @ShadenSmith @conglongli @awan-10 @arashashari @cli99 @eltonzheng @minjiaz @RezaYazdaniAminabadi @niumanar

Deepspeed/CODE_OF_CONDUCT.md

+# Microsoft Open Source Code of Conduct
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+
+Resources:
+
+- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
+- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
+- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

Deepspeed/HelloDeepSpeed/README.md

+# Training a Masked Language Model with PyTorch and DeepSpeed
+
+In this tutorial, we will create and train a Transformer encoder on the Masked Language Modeling (MLM) task. Then we will show the changes necessary to integrate DeepSpeed, and show some of the advantages of doing so.
+
+Table of contents
+=================
+
+<!--toc-start-->
+  * [(1) Training a Transformer Encoder (BERT / Roberta) model for MLM](#1-training-a-transformer-encoder-bert--roberta-model-for-mlm)
+    * [1.0 Some Good Practices](#10-some-good-practices)
+    * [1.1 The Masked Language Modeling Task](#11-the-masked-language-modeling-task)
+    * [1.2 Creating a Transformer model](#12-creating-a-transformer-model)
+    * [1.3 Training the Model](#13-training-the-model)
+  * [(2) Integrating DeepSpeed For More Efficient Training](#2-integrating-deepspeed-for-more-efficient-training)
+    * [2.0 Core DeepSpeed Code Changes](#20-core-deepspeed-code-changes)
+    * [2.1 Launching Training](#21-launching-training)
+    * [2.2 Mixed Precision Training (fp16)](#22-mixed-precision-training-fp16)
+    * [2.3 Zero Redundancy Optimizer (ZeRO)](#23-zero-redundancy-optimizer-zero)
+  * [References](#references)
+<!--toc-end-->
+
+## 1. Training a Transformer Encoder (BERT / Roberta) model for MLM
+
+### 1.0 Some Good Practices
+
+### Version Control and Reproducibility
+
+One of the most important parts of training ML models is for the experiments to be reproducible (either by someone else, or by you 3 months later). Some steps that help with this are:
+
+* Use some form of version control (eg: `git`). Additionally, make sure to save the `gitdiff` and `githash`, so that anyone trying to replicate the experiment can easily do so
+
+* Save all the hyperparameters associated with the experiment (be it taken from a config or parsed from the command line)
+
+* Seed your random generators. Some useful tips can be found [here](https://pytorch.org/docs/stable/notes/randomness.html?highlight=reproducibility).
+
+* Specify all the packages and their versions. This can be a `requirements.txt` file, a conda `env.yaml` file or a `pyproject.toml` file. If you want complete reproducibility, you can also include a `Dockerfile` to specify the environment to run the experiment in.
+
+In this example, the checkpoint directory has the following format:
+
+```bash
+{exp_type}.{YYYY}.{MM}.{DD}.{HH}.{MM}.{SS}.{uuid}
+    `-experiment-name
+        |- hparams.json
+        |- githash.log
+        |- gitdiff.log
+        `- tb_dir/
+```
+This ensures that if you revisit this experiment, it is easy to understand what the experiment was about, when was it run, with what hyperparameters and what was the status of the code when the run was executed.
+
+### Writing Unit Tests
+
+Unit tests can help catch bugs early, and also set up a fast feedback loop. Since some experiments can take days or even weeks to run, both of these things are quite invaluable.
+
+In this tutorial, two primary parts are data creating and model training. Hence, we test both these parts (see [here](./tests/test_train_bert.py)):
+
+1. Testing Data creation: Data creation for the MLM task involves randomly masking words to generate model inputs. To test for correctness, we test if the fraction of masked tokens matches what we expect from our `DataLoader`. For more details, please take a look at
+
+```python
+def test_masking_stats(tol: float = 1e-3):
+    """Test to check that the masking probabilities
+    match what we expect them to be.
+    """
+    ...
+```
+
+2. Model training and checkpointing: Since pretraining experiments are usually quite expensive (take days / weeks to complete), chances are that you might run into some hardware failure before the training completes. Thus, it is crucial that the checkpointing logic is correct to allow a model to resume training. One way to do this is to train a small model for a few iterations and see if the model can resume training and if the checkpoints are loaded correctly. See `test_model_checkpointing` for an example test.
+
+---
+
+💡 **_Tip:_** While saving checkpoints, make sure to also save the optimizer states along with the model parameters !
+
+---
+
+### 1.1 The Masked Language Modeling Task
+
+The main idea behind the MLM task is to get the model to fill in the blanks based on contextual clues present **both before and after** the blank. Consider, for example, the following sentence:
+
+> In the beautiful season of ____ the ____ shed their leaves.
+
+Given the left context `season` and the right context `shed their leaves`, one can guess that the blanks are `Autumn` and `trees` respectively. This is exactly what we want the model to do: utilize both the left and right context to fill in the blanks.
+
+In order to do that, we do the following:
+
+1. Tokenize a sentence into word(pieces)
+2. Randomly select some words to mask, and replace them with a special \<Mask\> token
+    * Of the masked tokens, it is common to replace a fraction of them with a random token, and leave a fraction of them unchanged.
+3. Collect the actual words that were masked, and use them as targets for the model to predict against:
+    * From the model's perspective, this is a simple `CrossEntropy` loss over the vocabulary of the model.
+
+In this tutorial, we use the [wikitext-2-v1](https://huggingface.co/datasets/wikitext) dataset from [HuggingFace datasets](https://github.com/huggingface/datasets). To see how this is done in code, take a look at `masking_function` in [train_bert.py](./train_bert.py).
+
+
+### 1.2 Creating a Transformer model
+
+A Transformer model repeatedly applies a (Multi-Headed) Self-Attention block and a FeedForward layer to generate contextual representations for each token. Thus, the key hyperparameters for a Transformer model usually are
+
+1. The number of Self-Attention + FeedForward blocks (depth)
+2. The size of the hidden representation
+3. The number of Self Attention Heads
+4. The size of the intermediate representation between the FeedForward block
+
+Check out the `create_model` function in [train_bert.py](./train_bert.py) to see how this is done in code. In this example, we create a Roberta model [[3](#3)]
+
+---
+📌 **Note:** You can check out [[1](#1), [2](#2)] as a starting point for better understanding Transformers. Additionally, there are a number of blogs that do a nice deep dive into the workings of these models (eg: [this](https://nlp.seas.harvard.edu/2018/04/03/attention.html), [this](https://jalammar.github.io/illustrated-bert/) and [this](https://jalammar.github.io/illustrated-transformer/)).
+
+---
+
+### 1.3 Training the Model
+
+In order to train the model, you can run the following command
+
+```bash
+python train_bert.py --checkpoint_dir ./experiments
+```
+This will create a model with the default parameters (as specified by the arguments to the `train` function), and train it on the wikitext dataset. Other parameters can be configured from the command line as:
+
+```bash
+python train_bert.py --checkpoint_dir ./experiments \
+    --mask_prob ${mask_prob} \
+    --random_replace_prob ${random_replace_prob} \
+    --unmask_replace_prob ${unmask_replace_prob} \
+    --max_seq_length ${max_seq_length} \
+    --tokenizer ${tokenizer} \
+    --num_layers ${num_layers} \
+    --num_heads ${num_heads} \
+    --ff_dim ${ff_dim} \
+    --h_dim ${h_dim} \
+    --dropout ${dropout} \
+    --batch_size ${batch_size} \
+    --num_iterations ${num_iterations} \
+    --checkpoint_every ${checkpoint_every} \
+    --log_every ${log_every} \
+    --local_rank ${local_rank}
+
+```
+
+The parameters are explained in more details in the docstring of `train`.
+
+---
+💡 **_Tip:_** If you have a GPU available, you can considerably speedup your training by running it on the GPU. Simply set the `local_rank` to the GPU you want to run it on. Eg: for a single GPU machine, this would look like
+```bash
+--local_rank 0
+```
+
+---
+
+## 2. Integrating DeepSpeed For More Efficient Training
+
+In this next section we'll add DeepSpeed to the model presented in Section 1 and turn on several features.
+
+## 2.0 Core DeepSpeed Code Changes
+
+Please see the [Writing DeepSpeed Models](https://www.deepspeed.ai/getting-started/#writing-deepspeed-models) instructions written on modifying an existing model to use DeepSpeed. Also we will heavily rely on the [DeepSpeed API documentation](https://deepspeed.readthedocs.io/en/latest/) and [config JSON documentation](https://www.deepspeed.ai/docs/config-json/) going forward.
+
+Please install DeepSpeed via `pip install deepspeed` if you haven't already done so, after installing you can check if your current version and other information via `ds_report`. For this tutorial we assume a DeepSpeed version of >= 0.5.4 and a torch version >= 1.6. Please upgrade via `pip install --upgrade deepspeed` if you are running an older version of DeepSpeed.
+
+### Add deepspeed.initialize + config
+
+Our first task is to identify where to add `deepspeed.initialize()` to the existing code in order to use the DeepSpeed training engine. Please see the [deepspeed.initialize API documentation](https://deepspeed.readthedocs.io/en/latest/initialize.html#training-initialization) for more details. This needs to be done after the model has been created and before the training loop has started. Most of our edits will be inside the `train` function inside [train_bert.py](./train_bert.py).
+
+After the model is created and before the optimizer is created we want to add the following lines:
+
+```python
+ds_config = {
+  "train_micro_batch_size_per_gpu": batch_size,
+  "optimizer": {
+      "type": "Adam",
+      "params": {
+          "lr": 1e-4
+      }
+  },
+}
+model, _, _, _ = deepspeed.initialize(model=model,
+                                      model_parameters=model.parameters(),
+                                      config=ds_config)
+```
+
+This will create the DeepSpeed training engine based on the previously instantiated model and the new `ds_config` dictionary. We can now also remove the previous lines of code that created an Adam optimizer, this will now be done via the DeepSpeed engine. It should be noted, you can optionally created your own optimizer and pass it into `deepspeed.initialize` however DeepSpeed is able to make further performance optimizations by instantiating its own optimizers.
+
+### Update the training-loop
+
+Next we want to update our training-loop to use the new model engine with the following changes:
+
+* `model.to(device)` can be removed
+  * DeepSpeed will be careful on when to move the model to GPU to reduce GPU memory usage (e.g., converts to half on CPU then moves to GPU)
+* `optimizer.zero_grad()` can be removed
+  * DeepSpeed will do this for you at the right time.
+* Replace `loss.backward()` with `model.backward(loss)`
+  * There are several cases where the engine will properly scale the loss when using certain features (e.g., fp16, gradient-accumulation).
+* Replace `optimizer.step()` with `model.step()`
+  * The optimizer step is handled by the engine now and is responsible for dispatching to the right optimizer depending on certain features.
+
+### Update checkpoint save and load
+
+Immediately after our new `deepspeed.initialize` you will see a checkpoint load and in the training-loop you will see a few checkpoint save calls. DeepSpeed handles the complexities of checkpoint saving for you so we can simplify these codepaths in the following way. Please refer to the [model checkpoint API documentation](https://deepspeed.readthedocs.io/en/latest/model-checkpointing.html) for more details.
+
+__Checkpoint saving__: DeepSpeed will construct and save the state_dict for you, we can replace the *two* checkpoint saving snippets (i.e., `state_dict` construction and `torch.save`)  and replace them with the snippet below. The `client_state` being passed in here is an example of state outside the view of DeepSpeed that will be saved with the checkpoint.
+
+```python
+model.save_checkpoint(save_dir=exp_dir, client_state={'checkpoint_step': step})
+```
+
+__Checkpoint loading__: The checkpoint loading is happening right before the training-loop starts. It invokes the `load_model_checkpoint` function which consists of around 30 lines of code. We can replace the `load_model_checkpoint(load_checkpoint_dir, model, optimizer)` call with the following snippet:
+
+```python
+_, client_state = model.load_checkpoint(load_dir=load_checkpoint_dir)
+checkpoint_step = client_state['checkpoint_step']
+```
+
+---
+📌 **Note:** You may also want/need to make additional changes to your code if you run on multiple GPUs as DeepSpeed will launch multiple processes. You will want to avoid potential race conditions with creating directories or writing to file and restrict logging to a single process. Take a look at `train_bert_ds.py` for an example of how to do this.
+
+---
+
+## 2.1 Launching Training
+
+We are now ready to launch our training! As a convenience, DeepSpeed provides its own launcher that is seamlessly compatible with clusters that provide a `/job/hostfile` containing all available machines in your job. You can now try running your model on your available GPU(s) with the command below. By default this will attempt to run distributed data-parallel (DDP) training across all available GPUs on the current machine + any external machines listed in your `/job/hostfile`. Please read [more details about the DeepSpeed launcher](https://www.deepspeed.ai/getting-started/#launching-deepspeed-training) and its assumptions on our website.
+
+```bash
+deepspeed train_bert.py --checkpoint_dir .
+```
+
+---
+📌 **Note:** If using the deepspeed launcher you should not pass the `--local_rank` explicitly. This will be done by the launcher in the same way as if you launched with `torch.distributed.launch` from PyTorch.
+
+---
+
+## 2.2 Mixed Precision Training (fp16)
+
+Now that we are setup to use the DeepSpeed engine with our model we can start trying out a few different features of DeepSpeed. One feature is mixed precision training that utilizes half precision (floating-point 16 or fp16) data types. If you want to learn more about how mixed precision training works please refer to the Mixed Precision Training paper [[3]](https://arxiv.org/pdf/1710.03740v3.pdf) from Baidu and NVIDIA on the topic.
+
+To enable this mode in DeepSpeed we need to update our `ds_config` before the engine is created. Please see [fp16 training options](https://www.deepspeed.ai/docs/config-json/#fp16-training-options) in the config documentation for more information. In our case let's simple enable it by adding the following to our `ds_config` dictionary:
+
+```python
+  "fp16": {
+    "enabled": True
+  }
+```
+
+The memory reduction by switching from fp32 to fp16 results in the *model parameters* using half the amount of GPU memory, however the overall GPU memory reduction is not as simple. Since fp16 has half the available bits as fp32 it is not able to represent the same expressiveness as fp32, which can result in numeric instabilities during training. We are able to get around these instabilities in most cases by keeping some states in fp16 and others remain in fp32 (see Section 3 in [[3]](https://arxiv.org/pdf/1710.03740v3.pdf) if you'd like to learn more).
+
+The primary reason to utilize fp16 training is due to *Tensor Cores*. If you are training with NVIDIA V100 or A100 GPUs they include Tensor Cores which in some cases can accelerate computation by as much as 8x if certain conditions are met. One of the most important conditions is that your model parameters are stored as fp16. For more details on other conditions and tips to better utilize these cores please see this guide from NVIDIA on [Tips for Optimizing GPU Performance Using Tensor Cores](https://developer.nvidia.com/blog/optimizing-gpu-performance-tensor-cores/).
+
+---
+📌 **Note:** At the start of training you will probably see several log messages about loss scaling and overflows, this is normal. In order for fp16 training to be numerically stable we utilize a common technique called "loss scaling" (similar to Section 3.2 in [[3]](https://arxiv.org/pdf/1710.03740v3.pdf)). This attempts to find a scaling value to mitigate gradient over/under-flows during training.
+
+---
+
+## 2.3 Zero Redundancy Optimizer (ZeRO)
+
+ZeRO leverages the aggregate computation and memory resources of data parallelism to reduce the memory and compute requirements of each device (GPU) used for model training. ZeRO reduces the memory consumption of each GPU by partitioning the various model training states (weights, gradients, and optimizer states) across the available devices (GPUs and CPUs) in the distributed training hardware. Concretely, ZeRO is  implemented as incremental stages of optimizations, where optimizations in earlier stages are available in the later stages. There are 3 different stages of ZeRO, Stage 1: optimizer state partitioning, Stage 2: optimizer state + gradient partitioning, and Stage 3: optimizer state + gradient + weight partitioning. We will focus on two features of ZeRO here, ZeRO Stage 1 and ZeRO-Offload. For further information, please refer to our [tutorial deep diving ZeRO](https://www.deepspeed.ai/tutorials/zero/) and our [tutorial deep diving ZeRO Offload](https://www.deepspeed.ai/tutorials/zero-offload/) on our website. To deep dive into ZeRO, please see our three papers [[4](https://arxiv.org/pdf/1910.02054.pdf), [5](https://www.usenix.org/system/files/atc21-ren-jie.pdf), [6](https://arxiv.org/abs/2104.07857)] that explore different optimizations in this space.
+
+* ZeRO Stage 1: The optimizer states (e.g., for the Adam optimizer, 32-bit weights, and the first, and second moment estimates) are partitioned across the processes, so that each process updates only its partition.
+* ZeRO-Offload: Supports efficiently offloading optimizer memory and computation from the GPU to the host CPU. ZeRO-Offload enables large models with up to 13 billion parameters to be trained on a single GPU.
+
+To enable ZeRO Stage 1 in DeepSpeed we need to again update our `ds_config` before the engine is created. Please see [ZeRO optimizations](https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training) in the DeepSpeed config documentation for more information. In our case let's simply enable stage 1 it by adding the following to our `ds_config` dictionary:
+
+```python
+  "zero_optimization": {
+    "stage": 1
+  }
+```
+
+We can re-run our training now with ZeRO stage 1 enabled and will see a per-GPU memory reduction as we scale up the total number of GPUs. Typically you can now use this extra GPU memory to either scale up your model size or scale up your per-GPU training batch size. However, if we only have 1 GPU available we probably want to enable ZeRO-Offload to allow us to train larger model sizes. Please update your `ds_config` to include the following:
+
+```python
+  "zero_optimization": {
+    "stage": 1,
+    "offload_optimizer": {
+      "device": "cpu"
+    }
+  }
+```
+
+This config will now allow us to train a much larger model than we were previously able to do. For example on a single P40 GPU with 24GB of memory we are unable to train a 2 billion parameter model (i.e., `--num_layers 24 --h_dim 4096`), however with ZeRO-Offload we now can!
+
+```bash
+deepspeed train_bert.py --checkpoint_dir . --num_layers 24 --h_dim 4096
+```
+
+---
+📌 **Note:** Earlier on when we setup `deepspeed.initialize` we chose not to explicitly pass an optimizer and instead let the DeepSpeed engine instantiate one for us. This is especially useful now that we are using ZeRO-Offload. DeepSpeed includes a highly optimized version of Adam that executes purely on CPU. This means that DeepSpeed will detect if you are using ZeRO-Offload w. Adam and switch to optimized CPUAdam variant.
+
+---
+
+## References
+> <a id="1">[1]</a>
+[Vaswani et al. Attention is all you need.
+In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17)](https://arxiv.org/pdf/1706.03762.pdf)
+>
+> <a id="2">[2]</a>
+[J. Devlin et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT'19)](https://aclanthology.org/N19-1423.pdf)
+>
+> <a id="3">[3]</a>
+[P. Micikevicius et al. Mixed Precision Training (ICLR'18)](https://arxiv.org/pdf/1710.03740v3.pdf)
+>
+> <a id="4">[4]></a>
+[S. Rajbhandari, J. Rasley, O. Ruwase, Y. He. ZeRO: memory optimizations toward training trillion parameter models. (SC‘20)](https://arxiv.org/pdf/1910.02054.pdf)
+>
+> <a id="5">[5]</a>
+[J. Ren, S. Rajbhandari, R. Aminabadi, O. Ruwase, S. Yang, M. Zhang, D. Li, Y. He. ZeRO-Offload: Democratizing Billion-Scale Model Training. (ATC'21)](https://www.usenix.org/system/files/atc21-ren-jie.pdf)
+>
+> <a id="1">[6]</a>
+[S. Rajbhandari, O. Ruwase, J. Rasley, S. Smith, Y. He. ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning (SC'21)](https://arxiv.org/abs/2104.07857)

Deepspeed/HelloDeepSpeed/requirements.txt

+datasets==1.13.3
+transformers==4.5.1
+fire==0.4.0
+pytz==2021.1
+loguru==0.5.3
+sh==1.14.2
+pytest==6.2.5
+tqdm==4.62.3
\ No newline at end of file