dtk23.04初始化

tasks/vision/classification.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""Vision-classification finetuning/evaluation."""
+
+from megatron import get_args
+from megatron import print_rank_0
+from megatron.model.vit_model import VitModel
+from megatron.data.vit_dataset import build_train_valid_datasets
+from tasks.vision.eval_utils import accuracy_func_provider
+from tasks.vision.finetune_utils import finetune
+
+
+def classification():
+    def train_valid_datasets_provider():
+        """Build train and validation dataset."""
+        args = get_args()
+
+        train_ds, valid_ds = build_train_valid_datasets(
+            data_path=args.data_path,
+            crop_size=args.img_dim,
+        )
+        return train_ds, valid_ds
+
+    def model_provider():
+        """Build the model."""
+        args = get_args()
+
+        print_rank_0("building classification model for ImageNet ...")
+
+        return VitModel(num_classes=args.num_classes, finetune=True)
+
+    """Finetune/evaluate."""
+    finetune(
+        train_valid_datasets_provider,
+        model_provider,
+        end_of_epoch_callback_provider=accuracy_func_provider,
+    )
+
+
+def main():
+    classification()

tasks/vision/eval_utils.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""Evaluation utilities."""
+
+import os
+import torch
+from megatron import get_args
+from megatron import print_rank_0
+from megatron import mpu
+from tasks.vision.finetune_utils import build_data_loader
+from tasks.vision.finetune_utils import process_batch
+from torchvision import datasets, transforms
+
+
+def accuracy_func_provider():
+    """Provide function that calculates accuracies."""
+    args = get_args()
+    data_path = args.data_path
+    crop_size = args.img_dim
+
+    # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+    # Build dataloaders.
+    val_data_path = os.path.join(data_path[0], "val")
+    normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    transform_val = transforms.Compose(
+        [
+            transforms.Resize(crop_size),
+            transforms.CenterCrop(crop_size),
+            transforms.ToTensor(),
+            normalize,
+        ]
+    )
+    dataset = datasets.ImageFolder(root=val_data_path, transform=transform_val)
+
+    dataloader = build_data_loader(
+        dataset,
+        args.micro_batch_size,
+        num_workers=args.num_workers,
+        drop_last=(mpu.get_data_parallel_world_size() > 1),
+    )
+
+    def metrics_func(model, epoch):
+        print_rank_0("calculating metrics ...")
+        correct, total = calculate_correct_answers(model, dataloader, epoch)
+        percent = float(correct) * 100.0 / float(total)
+        print_rank_0(
+            " >> |epoch: {}| overall: correct / total = {} / {} = "
+            "{:.4f} %".format(epoch, correct, total, percent)
+        )
+
+    return metrics_func
+
+
+def calculate_correct_answers(model, dataloader, epoch):
+    """Calculate correct over total answers"""
+
+    model.eval()
+    with torch.no_grad():
+        # For all the batches in the dataset.
+        total = 0
+        correct = 0
+        for _, batch in enumerate(dataloader):
+            # Run the model forward.
+            images, labels = process_batch(batch)
+            logits = model(images).contiguous().float()
+            # Add output predictions.
+            # Compute the correct answers.
+            predicted = torch.argmax(logits, dim=-1)
+            corrects = (predicted == labels).float()
+            # Add to the counters.
+            total += labels.size(0)
+            correct += corrects.sum().item()
+    model.train()
+
+    # Reduce.
+    unreduced = torch.cuda.LongTensor([correct, total])
+    torch.distributed.all_reduce(unreduced, group=mpu.get_data_parallel_group())
+
+    # Print on screen.
+    correct_ans = unreduced[0].item()
+    total_count = unreduced[1].item()
+    return correct_ans, total_count

tasks/vision/finetune_utils.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""Finetune utilities."""
+
+import torch
+import torch.nn.functional as F
+from megatron import get_args
+from megatron import print_rank_0
+from megatron import get_timers
+from megatron import mpu
+from megatron.checkpointing import load_checkpoint
+from megatron.checkpointing import save_checkpoint
+from megatron.training import evaluate_and_print_results
+from megatron.training import setup_model_and_optimizer
+from megatron.training import train_step
+from megatron.training import training_log
+from megatron.utils import check_adlr_autoresume_termination
+from megatron.utils import average_losses_across_data_parallel_group
+
+
+def process_batch(batch):
+    """Process batch and produce inputs for the model."""
+    images = batch[0].cuda().contiguous()
+    labels = batch[1].cuda().contiguous()
+    return images, labels
+
+
+def _cross_entropy_forward_step(batch, model, input_tensor):
+    """Simple forward step with cross-entropy loss."""
+    timers = get_timers()
+    assert input_tensor is None
+
+    # Get the batch.
+    timers("batch generator").start()
+    try:
+        batch_ = next(batch)
+    except BaseException:
+        batch_ = batch
+    images, labels = process_batch(batch_)
+    timers("batch generator").stop()
+
+    # Forward model.
+    logits = model(images).contiguous().float()
+
+    # Cross-entropy loss.
+    loss = F.cross_entropy(logits, labels)
+
+    # Reduce loss for logging.
+    average_loss = average_losses_across_data_parallel_group([loss])
+
+    return loss, {"lm loss": average_loss[0]}
+
+
+def build_data_loader(dataset, micro_batch_size, num_workers, drop_last):
+    """Data loader. Note that batch-size is the local (per GPU) batch-size."""
+
+    # Sampler.
+    world_size = mpu.get_data_parallel_world_size()
+    rank = mpu.get_data_parallel_rank()
+    sampler = torch.utils.data.distributed.DistributedSampler(
+        dataset, num_replicas=world_size, rank=rank
+    )
+
+    # Data loader. Note that batch size is the per GPU batch size.
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=micro_batch_size,
+        sampler=sampler,
+        shuffle=False,
+        num_workers=num_workers,
+        drop_last=drop_last,
+        pin_memory=True,
+    )
+
+    return data_loader
+
+
+def _build_infinite_size_dataloader(dataloader):
+    """Build a looped dataloader with infinite size."""
+
+    iterator = dataloader.__iter__()
+    while True:
+        try:
+            yield iterator.__next__()
+        except StopIteration:
+            iterator = dataloader.__iter__()
+
+
+def _build_train_valid_dataloaders(train_dataset, valid_dataset):
+    """Traing and validation dataloaders."""
+    args = get_args()
+
+    print_rank_0("building train and validation dataloaders ...")
+    # Training dataset.
+    train_dataloader = build_data_loader(
+        train_dataset, args.micro_batch_size, args.num_workers, not args.keep_last
+    )
+    # Set the training iterations.
+    args.train_iters_per_epoch = len(train_dataloader)
+    args.train_iters = args.epochs * args.train_iters_per_epoch
+    # Validation dataset. For this dataset, we do not need to set up
+    # shuffling so we can just use a simple infinite loop.
+    valid_dataloader_ = build_data_loader(
+        valid_dataset, args.micro_batch_size, args.num_workers, not args.keep_last
+    )
+    valid_dataloader = _build_infinite_size_dataloader(valid_dataloader_)
+
+    return train_dataloader, valid_dataloader
+
+
+def _train(
+    model,
+    optimizer,
+    lr_scheduler,
+    forward_step,
+    train_dataloader,
+    valid_dataloader,
+    end_of_epoch_callback,
+):
+    """Train the model."""
+    args = get_args()
+    timers = get_timers()
+
+    # Turn on training mode which enables dropout.
+    model.train()
+
+    # Tracking loss.
+    losses_dict_sum = {}
+
+    # Starting epoch and iteration
+    start_epoch = args.iteration // args.train_iters_per_epoch
+    start_iteration = args.iteration % args.train_iters_per_epoch
+    iteration = args.iteration
+
+    # Memory reporting flag.
+    report_memory_flag = True
+
+    # For each remaining epoch
+    timers("interval-time").start()
+    for epoch in range(start_epoch, args.epochs):
+        print_rank_0("working on epoch {} ...".format(epoch + 1))
+
+        # Set the data loader epoch to shuffle the index iterator.
+        train_dataloader.sampler.set_epoch(args.seed + epoch)
+
+        # For all the batches in the dataset.
+        for iteration_, batch in enumerate(train_dataloader):
+
+            # Ignore the iterations before starting value
+            if iteration_ < start_iteration:
+                continue
+            # Set to zero so the next epoch does not skip any batches.
+            start_iteration = 0
+
+            # Train for one step.
+            losses_dict, skipped_iter = train_step(
+                forward_step, batch, model, optimizer, lr_scheduler
+            )
+            iteration += 1
+
+            # Logging.
+            report_memory_flag = training_log(
+                losses_dict,
+                losses_dict_sum,
+                optimizer.param_groups[0]["lr"],
+                iteration,
+                optimizer.get_loss_scale().item(),
+                report_memory_flag,
+                skipped_iter,
+            )
+
+            # Autoresume
+            if args.adlr_autoresume and (
+                iteration % args.adlr_autoresume_interval == 0
+            ):
+                check_adlr_autoresume_termination(
+                    iteration, model, optimizer, lr_scheduler
+                )
+
+            # Checkpointing
+            if (
+                args.save
+                and args.save_interval
+                and iteration % args.save_interval == 0
+            ):
+                save_checkpoint(iteration, model, optimizer, lr_scheduler)
+
+            # Evaluation
+            if args.eval_interval and iteration % args.eval_interval == 0:
+                prefix = "iteration {}".format(iteration)
+                evaluate_and_print_results(
+                    prefix,
+                    forward_step,
+                    valid_dataloader,
+                    model,
+                    iteration,
+                    False,
+                )
+
+        # Checkpointing at the end of each epoch.
+        if args.save:
+            save_checkpoint(iteration, model, optimizer, lr_scheduler)
+
+        # Callback at the end of each epoch.
+        if end_of_epoch_callback is not None:
+            end_of_epoch_callback(model, epoch)
+
+
+def finetune(
+    train_valid_datasets_provider,
+    model_provider,
+    forward_step=_cross_entropy_forward_step,
+    end_of_epoch_callback_provider=None,
+):
+    """Main finetune function used across all tasks."""
+    args = get_args()
+    timers = get_timers()
+
+    # Train and validation data loaders.
+    timers("train/valid/test dataset/dataloder").start()
+    if args.epochs > 0:
+        train_dataset, valid_dataset = train_valid_datasets_provider()
+        train_dataloader, valid_dataloader = _build_train_valid_dataloaders(
+            train_dataset, valid_dataset
+        )
+    timers("train/valid/test dataset/dataloder").stop()
+
+    # Build calback function.
+    timers("callback function").start()
+    end_of_epoch_callback = None
+    if end_of_epoch_callback_provider is not None:
+        end_of_epoch_callback = end_of_epoch_callback_provider()
+    timers("callback function").stop()
+
+    # Build model, optimizer and learning rate scheduler.
+    timers("model and optimizer").start()
+    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider)
+    timers("model and optimizer").stop()
+
+    # If pretrained checkpoint is provided and we have not trained for
+    # any iteration (i.e., iteration is zero), then load the pretrained
+    # checkpoint.
+    timers("pretrained checkpoint").start()
+    if args.iteration == 0 and args.pretrained_checkpoint is not None:
+        original_load = args.load
+        args.load = args.pretrained_checkpoint
+        _ = load_checkpoint(model, None, None, strict=False)
+        args.load = original_load
+        # This is critical when only model is loaded. We should make sure
+        # master parameters are also updated.
+        optimizer.reload_model_params()
+    timers("pretrained checkpoint").stop()
+
+    # Print setup timing.
+    print_rank_0("done with setups ...")
+    timers.log(
+        [
+            "train/valid/test dataset/dataloder",
+            "callback function",
+            "model and optimizer",
+            "pretrained checkpoint",
+        ]
+    )
+    print_rank_0("training ...")
+
+    # Finetune the model.
+    if args.epochs > 0:
+        _train(
+            model,
+            optimizer,
+            lr_scheduler,
+            forward_step,
+            train_dataloader,
+            valid_dataloader,
+            end_of_epoch_callback,
+        )
+    # Or just evaluate.
+    else:
+        if end_of_epoch_callback is not None:
+            print_rank_0("evaluation only mode, setting epoch to -1")
+            end_of_epoch_callback(model, epoch=-1, output_predictions=True)
+
+    print_rank_0("done :-)")

tasks/vision/main.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""Main tasks functionality."""
+
+import os
+import sys
+
+sys.path.append(
+    os.path.abspath(
+        os.path.join(
+            os.path.join(os.path.dirname(__file__), os.path.pardir),
+            os.path.pardir,
+        )
+    )
+)
+from megatron import get_args
+from megatron.initialize import initialize_megatron
+from classification import main
+
+
+def get_tasks_args(parser):
+    """Provide extra arguments required for tasks."""
+    group = parser.add_argument_group(title="tasks")
+
+    group.add_argument(
+        "--epochs",
+        type=int,
+        default=None,
+        help="Number of finetunning epochs. Zero results in "
+        "evaluation only.",
+    )
+    group.add_argument(
+        "--pretrained-checkpoint",
+        type=str,
+        default=None,
+        help="Pretrained checkpoint used for finetunning.",
+    )
+    group.add_argument(
+        "--keep-last",
+        action="store_true",
+        help="Keep the last batch (maybe incomplete) in" "the data loader",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+
+    initialize_megatron(extra_args_provider=get_tasks_args)
+    args = get_args()
+    main()

tasks/zeroshot_gpt/datasets.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""Zero-shot datasets."""
+
+import json
+import math
+
+import numpy as np
+import torch
+
+from megatron import get_args
+from megatron import print_rank_0
+from megatron import get_tokenizer
+from .detokenizer import get_detokenizer
+
+
+def build_dataset(task):
+    """Helper function to select and build dataset."""
+
+    if task == 'LAMBADA':
+        return _build_lambada_dataset()
+    if task == 'WIKITEXT103':
+        return _build_wikitext103_dataset()
+
+    raise NotImplementedError('dataset for {} task is not '
+                              'implemented.'.format(task))
+
+
+class _LMDataset(torch.utils.data.Dataset):
+
+    def __init__(self, tokens, seq_len, pad_idx, num_original_tokens,
+                 num_tokenized_tokens, overalapping_eval=None):
+        self.tokens = tokens
+        self.seq_len = seq_len
+        self.pad_idx = pad_idx
+        self.overalapping_eval = overalapping_eval
+        if self.overalapping_eval is None:
+            self.overalapping_eval = self.seq_len
+        self.overalapping_eval = max(1, self.overalapping_eval)
+        self.num_original_tokens = num_original_tokens
+        self.num_tokenized_tokens = num_tokenized_tokens
+        self.total_targets = len(self.tokens) - 1
+        # remove first sequence tokens
+        targets = max(self.total_targets - self.overalapping_eval, 0)
+        self.total_sequences = max(
+            math.ceil(targets / self.overalapping_eval) + 1, 1)
+
+    def __len__(self):
+        return self.total_sequences
+
+    def __getitem__(self, idx):
+        start_idx = idx * self.overalapping_eval
+        end_idx = start_idx + self.seq_len
+        tokens = self.tokens[start_idx:end_idx + 1]
+        num_tokens = len(tokens)
+        pad_mask = [1] * num_tokens
+        if num_tokens < self.seq_len + 1:
+            num_pad = (self.seq_len + 1 - num_tokens)
+            pad_mask += [0] * (num_pad)
+            tokens += [self.pad_idx] * num_pad
+        pad_mask = np.array(pad_mask[1:])
+        if self.overalapping_eval != self.seq_len and idx != 0:
+            pad_mask[:-self.overalapping_eval] *= 0
+
+        return {'text': np.array(tokens), 'pad_mask': pad_mask}
+
+
+class _LambadaDataset(torch.utils.data.Dataset):
+
+    def __init__(self, path, pad_idx, tokenizer, seq_len, strict=False):
+        print_rank_0('> building lambada dataset from {} ...'.format(path))
+        self.seq_len = seq_len
+        self.pad_idx = pad_idx
+        self.tokenizer = tokenizer
+        self.strict = strict
+
+        self.tokens = []
+        self.labels = []
+        with open(path, 'r') as f:
+            for line in f.readlines():
+                text = json.loads(line)['text']
+                tokens, labels = self.get_tokens(text)
+                self.tokens.append(tokens)
+                self.labels.append(labels)
+
+    def get_tokens(self, text):
+        if not self.strict:
+            tokens = self.tokenizer.tokenize(text)
+            return tokens[:-1], [tokens[-1]]
+        last_token = text.split()[-1]
+        start_idx = text.rfind(last_token)
+        beginning_tokens = self.tokenizer.tokenize(text[:start_idx].strip())
+        last_token = self.tokenizer.tokenize(' ' + last_token)
+        return beginning_tokens, last_token
+
+    def __len__(self):
+        return len(self.tokens)
+
+    def __getitem__(self, idx):
+        tokens = self.tokens[idx]
+        num_tokens = len(tokens)
+        pad_mask = [0] * num_tokens
+        labels = self.labels[idx]
+        pad_mask += [1] * len(labels)
+        tokens = tokens + labels
+        num_tokens = len(tokens)
+        if num_tokens < self.seq_len + 1:
+            num_pad = (self.seq_len + 1 - num_tokens)
+            pad_mask += [0] * (num_pad)
+            tokens += [self.pad_idx] * num_pad
+        pad_mask = np.array(pad_mask[1:])
+
+        return {'text': np.array(tokens), 'pad_mask': pad_mask}
+
+
+def _build_lambada_dataset():
+    """Build lambada dataset."""
+    args = get_args()
+    tokenizer = get_tokenizer()
+
+    assert len(args.valid_data) == 1
+    val_dataset = _LambadaDataset(args.valid_data[0], tokenizer.eod, tokenizer,
+                                  args.seq_length, args.strict_lambada)
+    print_rank_0(' > found {} samples.'.format(len(val_dataset)))
+
+    return val_dataset
+
+
+def _build_wikitext103_dataset():
+    """"""
+    args = get_args()
+    tokenizer = get_tokenizer()
+
+    assert len(args.valid_data) == 1
+    with open(args.valid_data[0], "rb") as reader:
+        entire_data = reader.read().decode('utf-8')
+    num_original_tokens = len(entire_data.strip().split(" "))
+    entire_data = get_detokenizer(args.valid_data[0])(entire_data)
+    tokenized_data = tokenizer.tokenize(entire_data)
+    num_tokenized_tokens = len(tokenized_data)
+
+    val_dataset = _LMDataset(tokenized_data, args.seq_length, tokenizer.eod,
+                             num_original_tokens, num_tokenized_tokens,
+                             args.overlapping_eval)
+    print_rank_0(' > number of original tokens: {}, number of detokenized '
+                 'tokens: {}'.format(num_original_tokens, num_tokenized_tokens))
+
+    return val_dataset

tasks/zeroshot_gpt/detokenizer.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""Detokenization."""
+
+import re
+
+
+def ptb_detokenizer(string):
+    string = string.replace(" '", "'")
+    string = string.replace(" \n", "\n")
+    string = string.replace("\n ", "\n")
+    string = string.replace(" n't", "n't")
+    string = string.replace(" N ", "1 ")
+    string = string.replace("$ 1", "$1")
+    string = string.replace("# 1", "#1")
+    return string
+
+
+def wikitext_detokenizer(string):
+    # contractions
+    string = string.replace("s '", "s'")
+    string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string)
+    # number separators
+    string = string.replace(" @-@ ", "-")
+    string = string.replace(" @,@ ", ",")
+    string = string.replace(" @.@ ", ".")
+    # punctuation
+    string = string.replace(" : ", ": ")
+    string = string.replace(" ; ", "; ")
+    string = string.replace(" . ", ". ")
+    string = string.replace(" ! ", "! ")
+    string = string.replace(" ? ", "? ")
+    string = string.replace(" , ", ", ")
+    # double brackets
+    string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string)
+    string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string)
+    string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string)
+    string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string)
+    string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string)
+    # miscellaneous
+    string = string.replace("= = = =", "====")
+    string = string.replace("= = =", "===")
+    string = string.replace("= =", "==")
+    string = string.replace(" " + chr(176) + " ", chr(176))
+    string = string.replace(" \n", "\n")
+    string = string.replace("\n ", "\n")
+    string = string.replace(" N ", " 1 ")
+    string = string.replace(" 's", "'s")
+
+    return string
+
+
+def lambada_detokenizer(string):
+    return string
+
+
+_DETOKENIZERS = {
+    'ptb': ptb_detokenizer,
+    'wiki': wikitext_detokenizer,
+    'lambada': lambada_detokenizer,
+}
+
+
+def get_detokenizer(path):
+    for key in _DETOKENIZERS.keys():
+        if key in path:
+            return _DETOKENIZERS[key]

tasks/zeroshot_gpt/evaluate.py

+# coding=utf-8
+# Copyright (c) 2020, 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.
+
+"""GPT zero-shot evaluation."""
+
+import math
+
+import torch
+
+from megatron import get_args
+from megatron import print_rank_0, is_last_rank
+from megatron import get_tokenizer
+from megatron import mpu
+from megatron.checkpointing import load_checkpoint
+from megatron.model.gpt_model import GPTModel
+from megatron.training import get_model
+from megatron.utils import get_ltor_masks_and_position_ids, unwrap_model
+from megatron.p2p_communication import recv_forward, send_forward
+from tasks.finetune_utils import build_data_loader
+
+from .datasets import build_dataset
+
+# These are needed to unwrap the model, would be nice to put these in megatron.utils if possible?
+from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
+from megatron.model.distributed import DistributedDataParallel as LocalDDP
+from megatron.model.module import Float16Module
+
+def get_model_provider(eval_metric):
+    """Based on evaluation metric set the parallel-output flag and
+    return the model provider."""
+
+    def model_provider(pre_process=True, post_process=True):
+        """Build the model."""
+
+        if eval_metric == 'loss':
+            parallel_output = True
+        elif eval_metric == 'accuracy':
+            parallel_output = False
+        else:
+            raise NotImplementedError('output type for {} evaluation metric '
+                                      'is not supported.'.format(eval_metric))
+
+        print_rank_0('building GPT model ...')
+        model = GPTModel(num_tokentypes=0, parallel_output=parallel_output,
+                         pre_process=pre_process, post_process=post_process)
+
+        return model
+
+    return model_provider
+
+
+def process_batch(batch):
+    """Process batch and produce inputs for the model."""
+    args = get_args()
+    tokenizer = get_tokenizer()
+
+    loss_mask = batch['pad_mask'].long().cuda().contiguous().byte()
+    tokens_ = batch['text'].long().cuda().contiguous()
+    labels = tokens_[:, 1:].contiguous()
+    tokens = tokens_[:, :-1].contiguous()
+
+    # Get the masks and position ids.
+    attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
+        tokens,
+        tokenizer.eod,
+        args.reset_position_ids,
+        args.reset_attention_mask,
+        args.eod_mask_loss,
+        prefix_indices=None,
+        loss_on_targets_only=args.loss_on_targets_only
+    )
+
+    return tokens, labels, attention_mask, position_ids, loss_mask
+
+
+def forward_step(batch, model, eval_metric):
+    """Forward step."""
+
+    # Get the batch.
+    tokens, labels, attention_mask, position_ids, loss_mask = process_batch(
+        batch)
+
+    # Tell the model what our actual batch size will be
+    args = get_args()
+    args.micro_batch_size = len(labels)
+
+    input_tensor = recv_forward()
+
+    # Forward pass through the model.
+    unwrapped_model = unwrap_model(
+        model, (torchDDP, LocalDDP, Float16Module))
+    unwrapped_model.set_input_tensor(input_tensor)
+    output = model(tokens, position_ids, attention_mask)
+
+    send_forward(output)
+
+    if mpu.is_pipeline_last_stage():
+        # For loss, return the unreduced loss.
+        if eval_metric == 'loss':
+            losses = mpu.vocab_parallel_cross_entropy(
+                output.contiguous().float(), labels.contiguous())
+            loss = torch.sum(
+                losses.view(-1) * loss_mask.contiguous().view(-1).float())
+            return loss
+
+        # For accuracy, return the number of correctly predicted samples.
+        if eval_metric == 'accuracy':
+            outputs = torch.argmax(output, -1)
+            correct = (outputs == labels).float()
+            correct[(1 - loss_mask).bool()] = 1
+            correct = correct.prod(-1)
+            return correct.sum()
+
+        raise NotImplementedError('forward method for evaluation metric {} '
+                                  'is not implemented.'.format(eval_metric))
+    return None
+
+
+def evaluate(data_loader, model, eval_metric):
+    """Evaluation."""
+    args = get_args()
+
+    # Turn on evaluation mode which disables dropout.
+    model.eval()
+
+    total_output = 0.0
+    with torch.no_grad():
+        # For all the batches in the dataset.
+        for iteration, batch in enumerate(data_loader):
+            if iteration % args.log_interval == 0:
+                print_rank_0('> working on iteration: {}'.format(iteration))
+            # Forward evaluation.
+            output = forward_step(batch, model, eval_metric)
+
+            # Reduce across processes.
+            if mpu.is_pipeline_last_stage():
+                torch.distributed.all_reduce(output,
+                                             group=mpu.get_data_parallel_group())
+
+                total_output += output
+
+    return total_output
+
+
+def evaluate_and_print_results(task, data_loader, model, eval_metric):
+    """Evaluate and print results on screen."""
+
+    # Evaluate and get results.
+    output = evaluate(data_loader, model, eval_metric)
+
+    string = ' validation results on {} | '.format(task)
+    if is_last_rank():
+        if eval_metric == 'loss':
+            num_tokenized_tokens = data_loader.dataset.num_tokenized_tokens
+            num_original_tokens = data_loader.dataset.num_original_tokens
+            val_loss = output / (num_tokenized_tokens - 1)
+            ppl = math.exp(min(20, val_loss))
+            token_ratio = (num_tokenized_tokens - 1) / (num_original_tokens - 1)
+            adjusted_ppl = math.exp(min(20, val_loss * token_ratio))
+            string += 'avg loss: {:.4E} | '.format(val_loss)
+            string += 'ppl: {:.4E} | '.format(ppl)
+            string += 'adjusted ppl: {:.4E} | '.format(adjusted_ppl)
+            string += 'token ratio: {} |'.format(token_ratio)
+
+        elif eval_metric == 'accuracy':
+            num_examples = len(data_loader.dataset)
+            acc = output / num_examples
+            string += 'number correct: {:.4E} | '.format(output)
+            string += 'total examples: {:.4E} | '.format(num_examples)
+            string += 'avg accuracy: {:.4E}'.format(acc)
+
+        else:
+            raise NotImplementedError('evaluation method for {} metric is not '
+                                      'implemented yet.'.format(eval_metric))
+
+        length = len(string) + 1
+        print('-' * length)
+        print(string)
+        print('-' * length)
+
+
+def main():
+    """Main program."""
+    args = get_args()
+
+    if args.num_layers_per_virtual_pipeline_stage is not None:
+        print("Interleaved pipeline schedule is not yet supported for text generation.")
+        exit()
+
+    if args.task == 'LAMBADA':
+        eval_metric = 'accuracy'
+    elif args.task == 'WIKITEXT103':
+        eval_metric = 'loss'
+    else:
+        raise NotImplementedError('{} task is not implemented.'.format(
+            args.task))
+
+    # Set up model and load checkpoint.
+    model = get_model(get_model_provider(eval_metric))
+    if args.load is not None:
+        _ = load_checkpoint(model, None, None)
+
+    assert len(model) == 1, "Above condition should have caught this"
+    model = model[0]
+
+    # Data stuff.
+    dataset = build_dataset(args.task)
+    dataloader = build_data_loader(dataset, args.micro_batch_size,
+                                   args.num_workers, drop_last=False)
+
+    # Run evaluation.
+    evaluate_and_print_results(args.task, dataloader, model, eval_metric)
+
+    print_rank_0('done :-)')

tests/README.md

+# Testing
+
+This test suite heavily borrows from [HF Transformers](https://github.com/huggingface/transformers/), therefore you can refer to the its [testing docs](https://huggingface.co/transformers/testing.html) for in-depth details. In particular wrt writing new tests, as we have access a lot of helper classes and functions, so you can write tests very quickly and not need to reinvent the wheel.
+
+The foundation is `pytest`, which allows you to write normal `pytest` tests, but we also use a lot of unit tests in particular via `TestCasePlus` which extends `unittest` and provides additional rich functionality.
+
+## Running testing
+
+```
+make test
+```
+or:
+
+```
+pytest tests
+```
+
+Important: the first time you run this it can take some minutes to build all the Megatron cuda kernels and deepspeed kernels if you haven't pre-built the latter.
+
+For various other options please see the doc mentioned at the very top.
+
+You will want to have at least 1 gpu available, best 2 to run the tests.
+
+## CI
+
+The CI setup is documented [here](../.github/workflows/ci.md).

tests/conftest.py

+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# tests directory-specific settings - this file is run automatically
+# by pytest before any tests are run
+
+import sys
+import warnings
+from os.path import abspath, dirname, join
+
+
+# allow having multiple repository checkouts and not needing to remember to rerun
+# 'pip install -e .[dev]' when switching between checkouts and running tests.
+git_repo_path = abspath(join(dirname(dirname(__file__))))
+sys.path.insert(1, git_repo_path)
+
+# silence FutureWarning warnings in tests since often we can't act on them until
+# they become normal warnings - i.e. the tests still need to test the current functionality
+warnings.simplefilter(action="ignore", category=FutureWarning)
+
+
+def pytest_sessionfinish(session, exitstatus):
+    # If no tests are collected, pytest exists with code 5, which makes the CI fail.
+    if exitstatus == 5:
+        session.exitstatus = 0

tests/data/gpt2/README.md

+Dataset used for testing.
+
+`ag_news_prompt*`: manually generated from dataset available at https://huggingface.co/datasets/TimeRobber/ag_news_classify_question_first_100
\ No newline at end of file

tests/data/gpt2/generate_ag_news_mtf_dataset.sh

+python -c "from datasets import load_dataset; load_dataset('TimeRobber/ag_news_classify_question_first_100', split='train').to_json('ag_news_classify_question_first_100.jsonl')"
+
+python tools/preprocess_data.py \
+    --input ag_news_classify_question_first_100.jsonl \
+    --output-prefix tests/data/gpt2/ag_news_prompt \
+    --dataset-impl mmap \
+    --json-key targets \
+    --tokenizer-type PretrainedFromHF \
+    --tokenizer-name-or-path bigscience/tokenizer \
+    --append-eod \
+    --workers 8
+
+python tools/preprocess_data.py \
+    --input ag_news_classify_question_first_100.jsonl \
+    --output-prefix tests/data/gpt2/ag_news_prompt \
+    --dataset-impl mmap \
+    --json-key inputs \
+    --tokenizer-type PretrainedFromHF \
+    --tokenizer-name-or-path bigscience/tokenizer \
+    --workers 8
+
+rm ag_news_classify_question_first_100.jsonl

tests/data/gpt2/gpt2-tiny-merges.txt

+#version: 0.2 - Trained by `huggingface/tokenizers`
+Ġ t
+Ġ a
+h e
+i n
+r e
+o n
+Ġt he
+e r
+Ġ s
+a t
+Ġ w
+Ġ o
+e n
+Ġ c
+i t
+i s
+a n
+o r
+e s
+Ġ b
+e d
+Ġ f
+in g
+Ġ p
+o u
+Ġa n
+a l
+a r
+Ġt o
+Ġ m
+Ġo f
+Ġ in
+Ġ d
+Ġ h
+Ġan d
+i c
+a s
+l e
+Ġt h
+i on
+o m
+l l
+en t
+Ġ n
+Ġ l
+s t
+Ġ re
+v e
+Ġ e
+r o
+l y
+Ġb e
+Ġ g
+Ġ T
+c t
+Ġ S
+i d
+o t
+Ġ I
+u t
+e t
+Ġ A
+Ġ is
+Ġ on
+i m
+a m
+o w
+a y
+a d
+s e
+Ġth at
+Ġ C
+i g
+Ġf or
+a c
+Ġ y
+v er
+u r
+Ġ u
+l d
+Ġs t
+Ġ M
+' s
+Ġ he
+Ġ it
+at ion
+it h
+i r
+c e
+Ġy ou
+i l
+Ġ B
+Ġw h
+o l
+Ġ P
+Ġw ith
+Ġ 1
+t er
+c h
+Ġa s
+Ġw e
+Ġ (
+n d
+i ll
+Ġ D
+i f
+Ġ 2
+a g
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+k e
+Ġ "
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+e m
+Ġc on
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+Ġ R
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+Ġ N
+t h
+Ġc om
+Ġ G
+u n
+o p
+0 0
+Ġ L
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+Ġe x
+Ġ v
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+Ġb y
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+o s
+or t
+o c
+q u
+Ġf rom
+Ġha ve
+Ġs u
+i ve
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+Ġs h
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+r a
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+igh t
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+m ent
+Ġa l
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+- -
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+Ġ O
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+Ġc h
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+Ġ J
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+p t
+Ġp l
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+om e
+u d
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+Ġg o
+Ġh as
+g e
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+Ġ U
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+a re
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+i a
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+ver y
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+, "
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+as s
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+a il
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+ic al
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+a v
+Ġm y
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+Ġwhe n
+ou gh
+ac h
+Ġth an
+r u
+on d
+ic k
+Ġo ver
+ve l
+Ġ qu
+Ċ Ċ
+Ġs c
+re at
+re e
+ĠI t
+ou nd
+p ort
+Ġal so
+Ġp art
+f ter
+Ġk n
+Ġbe c
+Ġt ime
+en s
+Ġ 5
+op le
+Ġwh at
+Ġn o
+d u
+m er
+an g
+Ġn ew
+-- --
+Ġg et
+or y
+it ion
+ing s
+Ġj ust
+Ġint o
+Ġ 0
+ent s
+o ve
+t e
+Ġpe ople
+Ġp re
+Ġit s
+Ġre c
+Ġt w
+i an
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+ar k
+or s
+Ġwor k
+ad e
+o b
+Ġs he
+Ġo ur
+w n
+in k
+l ic
+Ġ1 9
+ĠH e
+is h
+nd er
+au se
+Ġh im
+on s
+Ġ [
+Ġ ro
+f orm
+i ld
+at es
+ver s
+Ġon ly
+o ll
+Ġs pe
+c k
+e ll
+am p
+Ġa cc
+Ġb l
+i ous
+ur n
+f t
+o od
+Ġh ow
+he d
+Ġ '
+Ġa fter
+a w
+Ġat t
+o v
+n e
+Ġpl ay
+er v
+ic t
+Ġc ould
+it t
+Ġa m
+Ġf irst
+Ġ 6
+Ġa ct
+Ġ $
+e c
+h ing
+u al
+u ll
+Ġcom m
+o y
+o ld
+c es
+at er
+Ġf e
+Ġbe t
+w e
+if f
+Ġtw o
+oc k
+Ġb ack
+) .
+id ent
+Ġu nder
+rou gh
+se l
+x t
+Ġm ay
+rou nd
+Ġp o
+p h
+is s
+Ġd es
+Ġm ost
+Ġd id
+Ġad d
+j ect
+Ġin c
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+Ġp ol
+on t
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+Ġne ed
+Ġcon s
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+Ġw ant
+Ġse e
+Ġ 7
+n ing
+i ew
+ĠTh is
+c ed
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+Ġin d
+t y
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+at h
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+Ġp r
+Ġu se
+Ġbec ause
+Ġf l
+n g
+Ġn ow
+ĠâĢ ĵ
+c om
+is e
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+Ġthe n
+ow er
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+os s
+u ch
+Ġe m
+Ġ =
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+i ed
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+le ct
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+at ing
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+m an
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+i ly
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+Ġsh ould
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+Ġe v
+d ay
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+) ,
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