run_classifier_azureml.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace 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.
"""BERT finetuning runner."""

from __future__ import absolute_import, division, print_function

import argparse
import csv
import logging
import os
import random
import sys

import sys
sys.path.append("./pretrain/pytorch/")

import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import torch.distributed as dist

from torch.nn import CrossEntropyLoss, MSELoss
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score

from azureml_adapter import set_environment_variables_for_nccl_backend, get_local_rank, get_global_size, get_local_size
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig, WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.optimization import BertAdam
from optimization import LinearWarmupExponentialSchedule, warmup_linear


from azureml.core.run import Run

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__)



def run_evaluation(processor, output_mode, set_type):
    examples = processor.get_dev_examples(args.data_dir) if set_type == "dev" else processor.get_test_examples(args.data_dir)
    features = convert_examples_to_features(
                    examples, processor.get_labels(), args.max_seq_length, tokenizer, output_mode)
    logger.info(f" Running Evaluation on {set_type}")
    logger.info("  Num examples = %d", len(examples))
    logger.info("  Batch size = %d", args.eval_batch_size)
    all_input_ids = torch.tensor(
        [f.input_ids for f in features], dtype=torch.long)
    all_input_mask = torch.tensor(
        [f.input_mask for f in features], dtype=torch.long)
    all_segment_ids = torch.tensor(
        [f.segment_ids for f in features], dtype=torch.long)

    all_label_ids = None
    if output_mode == "classification":
        all_label_ids = torch.tensor(
            [f.label_id for f in features], dtype=torch.long)
    elif output_mode == "regression":
        all_label_ids = torch.tensor(
            [f.label_id for f in features], dtype=torch.float)
    
    data = TensorDataset(
        all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
    # Run prediction for full data
    eval_sampler = SequentialSampler(data)
    dataloader = DataLoader(
        data, sampler=eval_sampler, batch_size=args.eval_batch_size)
    
    model.eval()
    eval_loss = 0
    nb_eval_steps = 0
    preds = []
    
    for input_ids, input_mask, segment_ids, label_ids in tqdm(dataloader, desc="Evaluating"):
        input_ids = input_ids.to(device)
        input_mask = input_mask.to(device)
        segment_ids = segment_ids.to(device)
        label_ids = label_ids.to(device)

        with torch.no_grad():
            logits = model(input_ids, segment_ids,
                        input_mask, labels=None)

        # create eval loss and other metric required by the task
        if set_type == "dev":
            if output_mode == "classification":
                if args.focal:
                    continue
                else:
                    loss_fct = CrossEntropyLoss()
                tmp_eval_loss = loss_fct(
                    logits.view(-1, num_labels), label_ids.view(-1))
            elif output_mode == "regression":
                loss_fct = MSELoss()
                if args.fp16:
                    label_ids = label_ids.half()
                tmp_eval_loss = loss_fct(
                    logits.view(-1), label_ids.view(-1))

            eval_loss += tmp_eval_loss.mean().item()
            nb_eval_steps += 1

        if len(preds) == 0:
            preds.append(logits.detach().cpu().numpy())
        else:
            preds[0] = np.append(
                preds[0], logits.detach().cpu().numpy(), axis=0)

    preds = preds[0]
    if output_mode == "classification":
        preds = np.argmax(preds, axis=1)
    elif output_mode == "regression":
        preds = np.squeeze(preds)


    if set_type == "dev":
        eval_loss = eval_loss / nb_eval_steps
        result = compute_metrics(task_name, preds, all_label_ids.numpy())
        loss = tr_loss/nb_tr_steps if args.do_train else None

        result['eval_loss'] = eval_loss
        result['global_step'] = global_step
        result['loss'] = loss
        logger.info("***** Evaluation results *****")
        for key in sorted(result.keys()):
            logger.info("Epoch %s:  %s = %s", epoch_num,
                        key, str(result[key]))
            if(epoch_num  ==2):
                run.log(key, str(result[key]))
    if set_type == "test":
        output_eval_file = os.path.join(args.output_dir, f"{task_name.upper()}-{args.seed}-{args.learning_rate}-ep-{epoch_num}-tot-epochs-{args.num_train_epochs}.tsv")
        with open(output_eval_file, "w") as writer:
            writer.write("index\tprediction\n")
            for i, sample in enumerate(examples):
                if output_mode == "classification":
                    writer.write(f"{sample.guid}\t{processor.get_labels()[preds[i].item()]}\n")
                elif output_mode == "regression":
                    writer.write(f"{sample.guid}\t{preds[i].item()}\n")

    return
class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()
    
    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the test set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding='utf-8') as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, 'utf-8') for cell in line)
                lines.append(line)
            return lines


class MrpcProcessor(DataProcessor):
    """Processor for the MRPC data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {}".format(
            os.path.join(data_dir, "train.tsv")))
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i) if set_type != "test" else line[0] 
            text_a = line[3]
            text_b = line[4]
            label = line[0] if set_type != "test" else "0"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


class MnliProcessor(DataProcessor):
    """Processor for the MultiNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
            "dev_matched")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["contradiction", "entailment", "neutral"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0]) if set_type != "test" else line[0]
            text_a = line[8]
            text_b = line[9]
            label = line[-1] if set_type != "test" else "entailment"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


class MnliMismatchedProcessor(MnliProcessor):
    """Processor for the MultiNLI Mismatched data set (GLUE version)."""

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")),
            "dev_mismatched")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test_mismatched.tsv")),
            "test")


class ColaProcessor(DataProcessor):
    """Processor for the CoLA data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0 and set_type == "test":
                continue
            guid = "%s-%s" % (set_type, i) if set_type != "test" else line[0]
            text_a = line[3] if set_type != "test" else line[1]
            label = line[1] if set_type != "test" else "0"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples


class Sst2Processor(DataProcessor):
    """Processor for the SST-2 data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i) if set_type != "test" else line[0]
            text_a = line[0] if set_type != "test" else line[1]
            label = line[1] if set_type != "test" else "0"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples


class StsbProcessor(DataProcessor):
    """Processor for the STS-B data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return [None]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0]) if set_type != "test" else line[0]
            text_a = line[7]
            text_b = line[8]
            label = line[-1] if set_type != "test" else "0.0"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


class QqpProcessor(DataProcessor):
    """Processor for the STS-B data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0]) if set_type != "test" else line[0]
            try:
                text_a = line[3] if set_type != "test" else line[1]
                text_b = line[4] if set_type != "test" else line[2]
                label = line[5] if set_type != "test" else "0"
            except IndexError:
                continue
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


class QnliProcessor(DataProcessor):
    """Processor for the STS-B data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")),
            "dev_matched")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["entailment", "not_entailment"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0]) if set_type != "test" else line[0]
            text_a = line[1]
            text_b = line[2]
            label = line[-1] if set_type != "test" else "entailment"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


class RteProcessor(DataProcessor):
    """Processor for the RTE data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
        
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["entailment", "not_entailment"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0]) if set_type != "test" else line[0]
            text_a = line[1]
            text_b = line[2]
            label = line[-1] if set_type != "test" else "entailment"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples

class WnliProcessor(DataProcessor):
    """Processor for the WNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
    
    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0]) if set_type != "test" else line[0]
            text_a = line[1]
            text_b = line[2]
            label = line[-1] if set_type != "test" else "0"
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples


def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, output_mode):
    """Loads a data file into a list of `InputBatch`s."""

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))

        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

        if tokens_b:
            tokens += tokens_b + ["[SEP]"]
            segment_ids += [1] * (len(tokens_b) + 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.
        padding = [0] * (max_seq_length - len(input_ids))
        input_ids += padding
        input_mask += padding
        segment_ids += padding

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        if output_mode == "classification":
            label_id = label_map[example.label]
        elif output_mode == "regression":
            label_id = float(example.label)
        else:
            raise KeyError(output_mode)

        # if ex_index < 5:
        #     logger.info("*** Example ***")
        #     logger.info("guid: %s" % (example.guid))
        #     logger.info("tokens: %s" % " ".join(
        #         [str(x) for x in tokens]))
        #     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]))
        #     logger.info("label: %s (id = %d)" % (example.label, label_id))

        features.append(
            InputFeatures(input_ids=input_ids,
                          input_mask=input_mask,
                          segment_ids=segment_ids,
                          label_id=label_id))
    return features


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def simple_accuracy(preds, labels):
    return (preds == labels).mean()


def acc_and_f1(preds, labels):
    acc = simple_accuracy(preds, labels)
    f1 = f1_score(y_true=labels, y_pred=preds)
    return {
        "acc": acc,
        "f1": f1,
        "acc_and_f1": (acc + f1) / 2,
    }


def pearson_and_spearman(preds, labels):
    pearson_corr = pearsonr(preds, labels)[0]
    spearman_corr = spearmanr(preds, labels)[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }


def compute_metrics(task_name, preds, labels):
    assert len(preds) == len(labels)
    if task_name == "cola":
        return {"mcc": matthews_corrcoef(labels, preds)}
    elif task_name == "sst-2":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "mrpc":
        return acc_and_f1(preds, labels)
    elif task_name == "sts-b":
        return pearson_and_spearman(preds, labels)
    elif task_name == "qqp":
        return acc_and_f1(preds, labels)
    elif task_name == "mnli":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "mnli-mm":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "qnli":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "rte":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "wnli":
        return {"acc": simple_accuracy(preds, labels)}
    else:
        raise KeyError(task_name)

print("The arguments are: " + str(sys.argv))
run = Run.get_context()

parser = argparse.ArgumentParser()

# Required parameters
parser.add_argument("--data_dir",
                    default=None,
                    type=str,
                    required=True,
                    help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
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("--task_name",
                    default=None,
                    type=str,
                    required=True,
                    help="The name of the task to train.")
parser.add_argument("--output_dir",
                    default=None,
                    type=str,
                    required=True,
                    help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--model_file_location", default=None, type=str, required=True,
                    help="The directory in the blob storage which contains data and config files.")
# parser.add_argument("--config_file_location", default=None, type=str, required=True,
#                     help="The directory in the blob storage which contains data and config files.")
# Other parameters
parser.add_argument("--cache_dir",
                    default="",
                    type=str,
                    help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument('--use_pretrain',
                    type=str,
                    default='False',
                    help="Whether to use Bert Pretrain Weights or not")
parser.add_argument("--files_location", default=None, type=str, 
                    help="The directory in the blob storage which contains data and config files.")
parser.add_argument("--max_seq_length",
                    default=128,
                    type=int,
                    help="The maximum total input sequence length after WordPiece tokenization. \n"
                            "Sequences longer than this will be truncated, and sequences shorter \n"
                            "than this will be padded.")
parser.add_argument("--do_train",
                    action='store_true',
                    help="Whether to run training.")
parser.add_argument("--do_test",
                    action="store_true",
                    help="Whether to run tesing.")
parser.add_argument("--do_eval",
                    action='store_true',
                    help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
                    action='store_true',
                    help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
                    default=32,
                    type=int,
                    help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
                    default=8,
                    type=int,
                    help="Total batch size for eval.")
parser.add_argument("--learning_rate",
                    default=5e-5,
                    type=float,
                    help="The initial learning rate for Adam.")
parser.add_argument("--final_learning_rate","--final_lr",
                    default=5e-6,
                    type=float,
                    help="The final learning rate for Adam.")
parser.add_argument("--lr_decay_rate",
                    default=0.99,
                    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("--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('--fp16',
                    action='store_true',
                    help="Whether to use 16-bit float precision instead of 32-bit")
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('--server_ip', type=str, default='',
                    help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='',
                    help="Can be used for distant debugging.")
parser.add_argument("--model_file",
                    type=str,
                    default="0",
                    help="Path to the Pretrained BERT Encoder File.")
parser.add_argument('--random',
                    default=False,
                    action='store_true',
                    help="Whether to fientune for random initialization")
parser.add_argument('--focal',
                    default=False,
                    action='store_true',
                    help="Whether to use Focal Loss for finetuning.")
parser.add_argument('--gamma', type=float, default=0.5,
                    help="Gamma parameter to be used in focal loss.")
args = parser.parse_args()

if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd
    print("Waiting for debugger attach")
    ptvsd.enable_attach(
        address=(args.server_ip, args.server_port), redirect_output=True)
    ptvsd.wait_for_attach()

processors = {
    "cola": ColaProcessor,
    "mnli": MnliProcessor,
    "mnli-mm": MnliMismatchedProcessor,
    "mrpc": MrpcProcessor,
    "sst-2": Sst2Processor,
    "sts-b": StsbProcessor,
    "qqp": QqpProcessor,
    "qnli": QnliProcessor,
    "rte": RteProcessor,
    "wnli": WnliProcessor,
}

output_modes = {
    "cola": "classification",
    "mnli": "classification",
    "mrpc": "classification",
    "sst-2": "classification",
    "sts-b": "regression",
    "qqp": "classification",
    "qnli": "classification",
    "rte": "classification",
    "wnli": "classification",
}

global_size = get_global_size()
local_size = get_local_size()	

set_environment_variables_for_nccl_backend(local_size == global_size)

local_rank = get_local_rank()

is_distributed = bool(local_rank != -1)

# hacky code -- fix
args.model_file = os.path.join(args.model_file_location, args.model_file)
# output_dir = os.path.join(parent_dir, str(run.id))
# os.makedirs(output_dir, exist_ok=True)
args.output_dir = os.path.join(args.output_dir, "saved_models", str(run.id))


# config_path = os.path.join(args.config_file_location, 'bert-large.json')

# config = json.load(open(config_path, 'r', encoding='utf-8'))
# # # Replace placeholder path prefix by path corresponding to "ds.path('data/bert_data/').as_mount()"
# # config['data']['datasets'] = {key: value.replace('placeholder/', args.files_location)
# #                               for (key, value) in config['data']['datasets'].items()}
# # config['validation']['path'] = config['validation']['path'].replace('placeholder/', args.files_location)
# args.config = config

# print("Running Config File: ", config['name'])
# args.logger = logger


if not is_distributed 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(local_rank)
    device = torch.device("cuda", 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(is_distributed), 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 = args.train_batch_size // args.gradient_accumulation_steps
args.seed = random.randint(1, 1000)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
logger.info("seed: {}".format(args.seed))
if n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed)

if not args.do_train and not args.do_eval:
    raise ValueError(
        "At least one of `do_train` or `do_eval` must be True.")

if (torch.distributed.get_rank() == 0):
     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.".format(args.output_dir))
     if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

task_name = args.task_name.lower()

if task_name not in processors:
    raise ValueError("Task not found: %s" % (task_name))

processor = processors[task_name]()
output_mode = output_modes[task_name]

label_list = processor.get_labels()
num_labels = len(label_list)

tokenizer = BertTokenizer.from_pretrained(
    args.bert_model, do_lower_case=args.do_lower_case)

train_examples = None
num_train_optimization_steps = None
if args.do_train:
    train_examples = processor.get_train_examples(args.data_dir)
    num_train_optimization_steps = int(
        len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
    if is_distributed:
        num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()

# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
    str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(local_rank))
model = BertForSequenceClassification.from_pretrained(args.bert_model,
                                                        cache_dir=cache_dir,
                                                        num_labels=num_labels)

#model = BertMultiTask(args)
#logger.info("Model of type: {}".format(type(model)))

if args.model_file is not "0":
    logger.info(f"Loading Pretrained Bert Encoder from: {args.model_file}")
    bert_state_dict = torch.load(args.model_file, map_location=torch.device("cpu"))
    model.bert.load_state_dict(bert_state_dict)
    #model.load(bert_state_dict)
    logger.info(f"Pretrained Bert Encoder Loaded from: {args.model_file}")

if args.random:
    logger.info("USING RANDOM INITIALISATION FOR FINETUNING")
    model.apply(model.init_bert_weights)

if args.fp16:
    model.half()
model.to(device)
if is_distributed:
    try:
        from apex.parallel import DistributedDataParallel as DDP
    except ImportError:
        raise ImportError(
            "Please install apex from https://github.com/nvidia/apex to use distributed and fp16 training.")

    model = DDP(model)
elif n_gpu > 1:
    model = torch.nn.DataParallel(model)

# Prepare optimizer
param_optimizer = list(model.named_parameters())
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}
]
if args.fp16:
    try:
        from apex.optimizers import FP16_Optimizer
        from apex.optimizers import FusedAdam
    except ImportError:
        raise ImportError(
            "Please install apex from https://github.com/nvidia/apex to use distributed and fp16 training.")

    optimizer = FusedAdam(optimizer_grouped_parameters,
                            lr=args.learning_rate,
                            bias_correction=False,
                            max_grad_norm=1.0)
    if args.loss_scale == 0:
        optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
    else:
        optimizer = FP16_Optimizer(
            optimizer, static_loss_scale=args.loss_scale)
    warmup_schedule = LinearWarmupExponentialSchedule(warmup=args.warmup_proportion, t_total=num_train_optimization_steps, initial_lr=args.learning_rate, final_lr=args.final_learning_rate, decay_rate=args.lr_decay_rate)

else:
    optimizer = BertAdam(optimizer_grouped_parameters,
                            lr=args.learning_rate,
                            warmup=args.warmup_proportion,
                            t_total=num_train_optimization_steps)

global_step = 0
nb_tr_steps = 0
tr_loss = 0

if args.do_train:

    if (not is_distributed or torch.distributed.get_rank() == 0):
           run.log('lr', np.float(args.learning_rate))

    train_features = convert_examples_to_features(
        train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(train_examples))
    logger.info("  Batch size = %d", args.train_batch_size)
    logger.info("  Num steps = %d", num_train_optimization_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)

    if output_mode == "classification":
        all_label_ids = torch.tensor(
            [f.label_id for f in train_features], dtype=torch.long)
    elif output_mode == "regression":
        all_label_ids = torch.tensor(
            [f.label_id for f in train_features], dtype=torch.float)

    train_data = TensorDataset(
        all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
    if not is_distributed:
        train_sampler = RandomSampler(train_data)
    else:
        train_sampler = DistributedSampler(train_data)

    for epoch_num in trange(int(args.num_train_epochs), desc="Epoch"):
        model.train()
        train_sampler.set_epoch(epoch_num)
        train_dataloader = DataLoader(
            train_data, sampler=train_sampler, batch_size=args.train_batch_size)
        tr_loss = 0
        nb_tr_examples, nb_tr_steps = 0, 0
        for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
            batch = tuple(t.to(device) for t in batch)
            input_ids, input_mask, segment_ids, label_ids = batch

            # define a new function to compute loss values for both output_modes
            logits = model(input_ids, segment_ids, input_mask, labels=None)

            if output_mode == "classification":
                if args.focal:
                    loss_fct = FocalLoss(
                        class_num=num_labels, gamma=args.gamma, fp16=args.fp16)
                else:
                    loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, num_labels),
                                label_ids.view(-1))
            elif output_mode == "regression":
                loss_fct = MSELoss()
                if args.fp16:
                    label_ids = label_ids.half()
                loss = loss_fct(logits.view(-1), label_ids.view(-1))

            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

            if args.fp16:
                optimizer.backward(loss)
            else:
                loss.backward()

            tr_loss += loss.item()
            nb_tr_examples += input_ids.size(0)
            nb_tr_steps += 1
            if (step + 1) % args.gradient_accumulation_steps == 0:
                if args.fp16:
                    # modify learning rate with special warm up BERT uses
                    # if args.fp16 is False, BertAdam is used that handles this automatically
                    lr_this_step = args.learning_rate * \
                        warmup_linear(
                            global_step/num_train_optimization_steps, args.warmup_proportion)
                    # lr_this_step = args.learning_rate * warmup_schedule.get_lr(global_step)
                    # logger.info(f"LR this step:{lr_this_step}")
                    for param_group in optimizer.param_groups:
                        param_group['lr'] = lr_this_step
                optimizer.step()
                optimizer.zero_grad()
                global_step += 1

        if(args.do_eval) and (not is_distributed or torch.distributed.get_rank() == 0):
            run_evaluation(processor, output_mode, "dev")

            # hack for MNLI-MM
            if task_name == "mnli":
                task_name = "mnli-mm"
                run_evaluation(processors["mnli-mm"](), output_mode, "dev")
                task_name = "mnli"
        if(args.do_test):
           #and (not is_distributed or torch.distributed.get_rank() == 0):
            run_evaluation(processor,output_mode,"test")
            #hack for MNLI-mm
            if task_name == "mnli":
                task_name = "mnli-mm"
                run_evaluation(processors[task_name](),output_mode,"test")
                task_name = "mnli"
        #dist.barrier()  # This is for synchronization between process while 1st GPU evlauates

# if args.do_test and (not is_distributed or torch.distributed.get_rank() == 0):
#     run_evaluation(processor,output_mode,"test")
#      #hack for MNLI-mm
#     if task_name == "mnli":
#         task_name = "mnli-mm"
#         run_evaluation(processors[task_name](),output_mode,"test")




        # # Save a trained model and the associated configuration
        # 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, WEIGHTS_NAME)
        # torch.save(model_to_save.state_dict(), output_model_file)
        # output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
        # with open(output_config_file, 'w') as f:
        #     f.write(model_to_save.config.to_json_string())

    #     # Load a trained model and config that you have fine-tuned
    #     config = BertConfig(output_config_file)
    #     model = BertForSequenceClassification(config, num_labels=num_labels)
    #     model.load_state_dict(torch.load(output_model_file))
    # else:
    #     model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
    # model.to(device)