diff --git a/examples/run_glue.py b/examples/run_glue.py index 550a0b81..601e9a34 100644 --- a/examples/run_glue.py +++ b/examples/run_glue.py @@ -22,6 +22,7 @@ import glob import logging import os import random +import json import numpy as np import torch @@ -176,15 +177,23 @@ def train(args, train_dataset, model, tokenizer): global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: - # Log metrics + logs = {} if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): - tb_writer.add_scalar('eval_{}'.format(key), value, global_step) - tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step) - tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) + eval_key = 'eval_{}'.format(key) + logs[eval_key] = value + + loss_scalar = (tr_loss - logging_loss) / args.logging_steps + learning_rate_scalar = scheduler.get_lr()[0] + logs['learning_rate'] = learning_rate_scalar + logs['loss'] = loss_scalar logging_loss = tr_loss + for key, value in logs.items(): + tb_writer.add_scalar(key, value, global_step) + print(json.dumps({**logs, **{'step': global_step}})) + if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step)) diff --git a/utils/download_glue_data.py b/utils/download_glue_data.py new file mode 100644 index 00000000..de8cfa9e --- /dev/null +++ b/utils/download_glue_data.py @@ -0,0 +1,142 @@ +''' Script for downloading all GLUE data. +Original source: https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e + +Note: for legal reasons, we are unable to host MRPC. +You can either use the version hosted by the SentEval team, which is already tokenized, +or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. +For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). +You should then rename and place specific files in a folder (see below for an example). + +mkdir MRPC +cabextract MSRParaphraseCorpus.msi -d MRPC +cat MRPC/_2DEC3DBE877E4DB192D17C0256E90F1D | tr -d $'\r' > MRPC/msr_paraphrase_train.txt +cat MRPC/_D7B391F9EAFF4B1B8BCE8F21B20B1B61 | tr -d $'\r' > MRPC/msr_paraphrase_test.txt +rm MRPC/_* +rm MSRParaphraseCorpus.msi + +1/30/19: It looks like SentEval is no longer hosting their extracted and tokenized MRPC data, so you'll need to download the data from the original source for now. +2/11/19: It looks like SentEval actually *is* hosting the extracted data. Hooray! +''' + +import os +import sys +import shutil +import argparse +import tempfile +import urllib.request +import zipfile + +TASKS = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"] +TASK2PATH = {"CoLA":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4', + "SST":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8', + "MRPC":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc', + "QQP":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5', + "STS":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5', + "MNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce', + "SNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df', + "QNLI": 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601', + "RTE":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb', + "WNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf', + "diagnostic":'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D'} + +MRPC_TRAIN = 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt' +MRPC_TEST = 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt' + +def download_and_extract(task, data_dir): + print("Downloading and extracting %s..." % task) + data_file = "%s.zip" % task + urllib.request.urlretrieve(TASK2PATH[task], data_file) + with zipfile.ZipFile(data_file) as zip_ref: + zip_ref.extractall(data_dir) + os.remove(data_file) + print("\tCompleted!") + +def format_mrpc(data_dir, path_to_data): + print("Processing MRPC...") + mrpc_dir = os.path.join(data_dir, "MRPC") + if not os.path.isdir(mrpc_dir): + os.mkdir(mrpc_dir) + if path_to_data: + mrpc_train_file = os.path.join(path_to_data, "msr_paraphrase_train.txt") + mrpc_test_file = os.path.join(path_to_data, "msr_paraphrase_test.txt") + else: + print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN) + mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt") + mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt") + urllib.request.urlretrieve(MRPC_TRAIN, mrpc_train_file) + urllib.request.urlretrieve(MRPC_TEST, mrpc_test_file) + assert os.path.isfile(mrpc_train_file), "Train data not found at %s" % mrpc_train_file + assert os.path.isfile(mrpc_test_file), "Test data not found at %s" % mrpc_test_file + urllib.request.urlretrieve(TASK2PATH["MRPC"], os.path.join(mrpc_dir, "dev_ids.tsv")) + + dev_ids = [] + with open(os.path.join(mrpc_dir, "dev_ids.tsv"), encoding="utf8") as ids_fh: + for row in ids_fh: + dev_ids.append(row.strip().split('\t')) + + with open(mrpc_train_file, encoding="utf8") as data_fh, \ + open(os.path.join(mrpc_dir, "train.tsv"), 'w', encoding="utf8") as train_fh, \ + open(os.path.join(mrpc_dir, "dev.tsv"), 'w', encoding="utf8") as dev_fh: + header = data_fh.readline() + train_fh.write(header) + dev_fh.write(header) + for row in data_fh: + label, id1, id2, s1, s2 = row.strip().split('\t') + if [id1, id2] in dev_ids: + dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) + else: + train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) + + with open(mrpc_test_file, encoding="utf8") as data_fh, \ + open(os.path.join(mrpc_dir, "test.tsv"), 'w', encoding="utf8") as test_fh: + header = data_fh.readline() + test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n") + for idx, row in enumerate(data_fh): + label, id1, id2, s1, s2 = row.strip().split('\t') + test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, id1, id2, s1, s2)) + print("\tCompleted!") + +def download_diagnostic(data_dir): + print("Downloading and extracting diagnostic...") + if not os.path.isdir(os.path.join(data_dir, "diagnostic")): + os.mkdir(os.path.join(data_dir, "diagnostic")) + data_file = os.path.join(data_dir, "diagnostic", "diagnostic.tsv") + urllib.request.urlretrieve(TASK2PATH["diagnostic"], data_file) + print("\tCompleted!") + return + +def get_tasks(task_names): + task_names = task_names.split(',') + if "all" in task_names: + tasks = TASKS + else: + tasks = [] + for task_name in task_names: + assert task_name in TASKS, "Task %s not found!" % task_name + tasks.append(task_name) + return tasks + +def main(arguments): + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', help='directory to save data to', type=str, default='glue_data') + parser.add_argument('--tasks', help='tasks to download data for as a comma separated string', + type=str, default='all') + parser.add_argument('--path_to_mrpc', help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt', + type=str, default='') + args = parser.parse_args(arguments) + + if not os.path.isdir(args.data_dir): + os.mkdir(args.data_dir) + tasks = get_tasks(args.tasks) + + for task in tasks: + if task == 'MRPC': + format_mrpc(args.data_dir, args.path_to_mrpc) + elif task == 'diagnostic': + download_diagnostic(args.data_dir) + else: + download_and_extract(task, args.data_dir) + + +if __name__ == '__main__': + sys.exit(main(sys.argv[1:])) diff --git a/valohai.yaml b/valohai.yaml new file mode 100644 index 00000000..2573551b --- /dev/null +++ b/valohai.yaml @@ -0,0 +1,94 @@ +--- + +- step: + name: Execute python examples/run_glue.py + image: pytorch/pytorch:nightly-devel-cuda10.0-cudnn7 + command: + - python /valohai/repository/utils/download_glue_data.py --data_dir=/glue_data + - pip install -e . + - pip install -r examples/requirements.txt + - python examples/run_glue.py --do_train --data_dir=/glue_data/{parameter-value:task_name} {parameters} + parameters: + - name: model_type + pass-as: --model_type={v} + type: string + default: bert + - name: model_name_or_path + pass-as: --model_name_or_path={v} + type: string + default: bert-base-uncased + - name: task_name + pass-as: --task_name={v} + type: string + default: MRPC + - name: max_seq_length + pass-as: --max_seq_length={v} + description: The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded. + type: integer + default: 128 + - name: per_gpu_train_batch_size + pass-as: --per_gpu_train_batch_size={v} + description: Batch size per GPU/CPU for training. + type: integer + default: 8 + - name: per_gpu_eval_batch_size + pass-as: --per_gpu_eval_batch_size={v} + description: Batch size per GPU/CPU for evaluation. + type: integer + default: 8 + - name: gradient_accumulation_steps + pass-as: --gradient_accumulation_steps={v} + description: Number of updates steps to accumulate before performing a backward/update pass. + type: integer + default: 1 + - name: learning_rate + pass-as: --learning_rate={v} + description: The initial learning rate for Adam. + type: float + default: 0.00005 + - name: adam_epsilon + pass-as: --adam_epsilon={v} + description: Epsilon for Adam optimizer. + type: float + default: 0.00000001 + - name: max_grad_norm + pass-as: --max_grad_norm={v} + description: Max gradient norm. + type: float + default: 1.0 + - name: num_train_epochs + pass-as: --num_train_epochs={v} + description: Total number of training epochs to perform. + type: integer + default: 3 + - name: max_steps + pass-as: --max_steps={v} + description: If > 0, set total number of training steps to perform. Override num_train_epochs. + type: integer + default: -1 + - name: warmup_steps + pass-as: --warmup_steps={v} + description: Linear warmup over warmup_steps. + type: integer + default: -1 + - name: logging_steps + pass-as: --logging_steps={v} + description: Log every X updates steps. + type: integer + default: 25 + - name: save_steps + pass-as: --save_steps={v} + description: Save checkpoint every X updates steps. + type: integer + default: -1 + - name: output_dir + pass-as: --output_dir={v} + type: string + default: /valohai/outputs + - name: evaluate_during_training + description: Run evaluation during training at each logging step. + type: flag + default: true + - name: do_lower_case + description: Set this flag if you are using an uncased model. + type: flag