Merge branch 'lmcafee/retro' into 'main'

Retro

See merge request ADLR/megatron-lm!489

.gitignore

@@ -3,3 +3,4 @@ __pycache__
 build
 .coverage_*
 *.egg-info
+*~

README.md

@@ -42,7 +42,9 @@ The following table shows both model (MFU) and hardware (HFU) FLOPs utilization
       * [Distributed Pretraining](#distributed-pretraining)
       * [Activation Checkpointing and Recomputation](#activation-checkpointing-and-recomputation)
       * [Distributed Optimizer](#distributed-optimizer)
+      * [FlashAttention](#flashattention)
       * [GPT-3 Example](#gpt-3-example)
+      * [Retro](#retro)
    * [Evaluation and Tasks](#evaluation-and-tasks)
       * [GPT Text Generation](#gpt-text-generation)
       * [GPT Evaluation](#gpt-evaluation)
@@ -323,6 +325,19 @@ In `examples/pretrain_gpt3_175B.sh` we have provided an example of how to config
 With full global batch size of 1536 on 1024 A100 GPUs, each iteration takes around 32 seconds resulting in 138 teraFLOPs per GPU which is 44% of the theoretical peak FLOPs.
 
 
+## Retro
+
+See:
+
+- `tools/retro/README.md` for an overview.
+- `tools/retro/examples/get_preprocess_cmd.sh` for an example of common preprocessing arguments.
+- `tools/retro/examples/preprocess_data.sh` for an example of how to preprocess data.
+- `tools/retro/examples/pretrain_model.sh` for an example of how to pretrain a model.
+
+Retro is a retrieval-enhanced model that is based on GPT. As described in [Improving language models by retrieving from trillions of tokens](https://arxiv.org/abs/2112.04426), Retro retrieves from a database of document chunks by performing locality search using a sample's tokens. The retrieval database can be large -- often billions or even trillions of tokens -- and provides a more efficient storage mechanism of factual knowledge, when compared to storing factual knowledge implicitly within the network's parameters.
+
+Using Retro requires two steps: 1) preprocessing the retrieval database and pretraining neighbors, and 2) pretraining a model using this data. Please see `tools/retro/README.md` for a detailed overview.
+
 <!--
 ## REALM Pipeline
 We are working on implementing the [REALM](https://arxiv.org/pdf/2002.08909.pdf) system. The following sections (will) reflect the three stages of training it. For now it's just the ICT code.

examples/detxoify_lm/finetune_gpt.py

@@ -11,10 +11,10 @@ import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
                                              os.path.pardir, os.path.pardir)))
 from megatron import get_args
-from megatron import print_rank_0
 from megatron import get_timers
 from megatron import get_tokenizer
-from megatron import mpu
+from megatron import print_rank_0
+from megatron.core import mpu
 from megatron.data.blendable_dataset import BlendableDataset
 from megatron.data.gpt_dataset import build_train_valid_test_datasets
 from megatron.model import GPTModel, ModelType

examples/detxoify_lm/generate_samples_gpt.py

@@ -10,10 +10,10 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
                                              os.path.pardir, os.path.pardir)))
 import torch
 from megatron import get_args
-from megatron import print_rank_0
 from megatron import get_tokenizer
-from megatron import mpu
+from megatron import print_rank_0
 from megatron.checkpointing import load_checkpoint
+from megatron.core import mpu
 from megatron.initialize import initialize_megatron
 from megatron.model import GPTModel
 from megatron.training import get_model

megatron/__init__.py

 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
 import torch
 
-from .global_vars import get_args
+from .global_vars import get_args, get_retro_args
 from .global_vars import get_current_global_batch_size
 from .global_vars import get_num_microbatches
 from .global_vars import get_signal_handler

megatron/arguments.py

@@ -3,9 +3,14 @@
 """Megatron arguments."""
 
 import argparse
+import json
 import os
-
 import torch
+import types
+
+from megatron.global_vars import set_retro_args, get_retro_args
+from tools.retro.utils import get_args_path as get_retro_args_path
+
 
 def parse_args(extra_args_provider=None, ignore_unknown_args=False):
     """Parse all arguments."""
@@ -29,6 +34,7 @@ def parse_args(extra_args_provider=None, ignore_unknown_args=False):
     parser = _add_logging_args(parser)
     parser = _add_inference_args(parser)
     parser = _add_transformer_engine_args(parser)
+    parser = _add_retro_args(parser)
 
     # Custom arguments.
     if extra_args_provider is not None:
@@ -333,7 +339,6 @@ def validate_args(args, defaults={}):
     if args.sequence_parallel:
         args.async_tensor_model_parallel_allreduce = False
 
-
     if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
         if args.sequence_parallel:
             raise RuntimeError(
@@ -344,15 +349,31 @@ def validate_args(args, defaults={}):
                 "Using async gradient all reduce requires setting the environment "
                 "variable CUDA_DEVICE_MAX_CONNECTIONS to 1")
 
+    # Load retro args.
+    if args.retro_workdir:
+        retro_args_path = get_retro_args_path(args.retro_workdir)
+        if os.path.exists(retro_args_path):
+            with open(retro_args_path) as f:
+                retro_args = types.SimpleNamespace(**json.load(f))
+                retro_args.retro_return_doc_ids = args.retro_return_doc_ids
+                retro_args.retro_gpt_retrieved_length = \
+                    args.retro_num_retrieved_chunks * \
+                    retro_args.retro_gpt_chunk_length
+                set_retro_args(retro_args)
+
+    # Print arguments.
+    _print_args("arguments", args)
+    retro_args = get_retro_args()
+    if retro_args and args != retro_args:
+        _print_args("retro arguments", types.SimpleNamespace(**{k:v for k,v in vars(retro_args).items() if k.startswith("retro")}, rank=args.rank))
 
-    _print_args(args)
     return args
 
 
-def _print_args(args):
+def _print_args(title, args):
     """Print arguments."""
     if args.rank == 0:
-        print('------------------------ arguments ------------------------',
+        print(f'------------------------ {title} ------------------------',
               flush=True)
         str_list = []
         for arg in vars(args):
@@ -360,7 +381,7 @@ def _print_args(args):
             str_list.append('  {} {} {}'.format(arg, dots, getattr(args, arg)))
         for arg in sorted(str_list, key=lambda x: x.lower()):
             print(arg, flush=True)
-        print('-------------------- end of arguments ---------------------',
+        print(f'-------------------- end of {title} ---------------------',
               flush=True)
 
 
@@ -403,15 +424,67 @@ def _add_inference_args(parser):
                        help='During inference, if batch-size times '
                        'sequence-length is smaller than this threshold '
                        'then we will not use pipelining, otherwise we will.')
-    
     group.add_argument('--max-tokens-to-oom',
                        type=int, default=12000,
                        help='Maximum number of tokens during inference'
                        'tokens here is # in prompt + # to generate'
                        'Allows us to throw an error before OOM crashes server')
+    group.add_argument('--output-bert-embeddings', action='store_true',
+                       help='Output Bert embeddings (via mean pooling) from '
+                       'model, rather than its binary head output or entire '
+                       'hidden batch.')
+    group.add_argument('--bert-embedder-type', default="megatron",
+                       choices=["megatron", "huggingface"],
+                       help='Select either Megatron or Huggingface as the '
+                       'Bert embedder.')
+
     return parser
 
-    
+
+def _add_retro_args(parser):
+    group = parser.add_argument_group(title='retro')
+
+    group.add_argument('--retro-workdir', default=None,
+                       help='Retro working directory, which contains the '
+                       'preprocessed data for for pretraining. This directory '
+                       'is built during preprocessing (see '
+                       'tools/retro/README.md), and contains subdirectories '
+                       'for the chunk database and pretraining neighbors.')
+    group.add_argument('--retro-add-retriever',
+                       action='store_true', default=False,
+                       help='Add a retriever to the transformer, for use in '
+                       'pretraining a Retro model.')
+    group.add_argument('--retro-cyclic-train-iters', type=int, default=None,
+                       help='Set number of training iterations for cyclic '
+                       'Retro training.')
+    group.add_argument('--retro-encoder-layers', type=int, default=2,
+                       help='Number of layers to use for the retrieval '
+                       'encoder.')
+    group.add_argument('--retro-encoder-hidden-dropout',
+                       type=float, default=0.1, help='Hidden dropout for '
+                       'retrieval encoder.')
+    group.add_argument('--retro-encoder-attention-dropout',
+                       type=float, default=0.1, help='Attention dropout for '
+                       'retrieval encoder.')
+    group.add_argument("--retro-num-neighbors", type=int, default=2,
+                       help='Number of neighbors to retrieve during '
+                       'pretraining.')
+    group.add_argument("--retro-num-retrieved-chunks", type=int, default=2,
+                       help='Number of chunks to retrieve from the retrieval '
+                       'database.')
+    group.add_argument("--retro-return-doc-ids", action="store_true",
+                       help="Turn this on when preprocessing retro data.")
+
+    # Enforce argument naming convention.
+    for action in group._group_actions:
+        prefix = action.dest.split("_")[0]
+        assert prefix == "retro", \
+            "Retro args must be prefixed with '--retro-*', for consistent " \
+            "styling. Please fix '%s'." % ", ".join(action.option_strings)
+
+    return parser
+
+
 def _add_network_size_args(parser):
     group = parser.add_argument_group(title='network size')
 
@@ -775,6 +848,10 @@ def _add_checkpointing_args(parser):
     group.add_argument('--use-checkpoint-args', action='store_true',
                        help='Override any command line arguments with arguments '
                        'from the checkpoint')
+    group.add_argument('--exit-on-missing-checkpoint', action='store_true',
+                       help="If '--load' is set, but checkpoint is not found "
+                       "(e.g., path typo), then exit instead of random "
+                       "initialization.")
 
     return parser
 
@@ -835,6 +912,8 @@ def _add_distributed_args(parser):
     group.add_argument('--distributed-backend', default='nccl',
                        choices=['nccl', 'gloo'],
                        help='Which backend to use for distributed training.')
+    group.add_argument('--distributed-timeout-minutes', type=int, default=10,
+                       help='Timeout minutes for torch.distributed.')
     group.add_argument('--DDP-impl', default='local',
                        choices=['local', 'torch'],
                        help='which DistributedDataParallel implementation '

megatron/checkpointing.py

@@ -508,7 +508,16 @@ def load_checkpoint(model, optimizer, opt_param_scheduler, load_arg='load', stri
                               use_distributed_optimizer=args.use_distributed_optimizer,
                               rank0=False)
 
+    # Checkpoint not loaded.
     if model_state_dict is None:
+
+        # Conditionally exit at this point.
+        if args.exit_on_missing_checkpoint:
+            print_rank_0(">> '--exit-on-missing-checkpoint' set ... exiting. <<")
+            torch.distributed.barrier()
+            sys.exit()
+
+        # Iteration defaults to 0.
         return 0
 
     # set checkpoint version

megatron/data/bert_dataset.py

@@ -19,7 +19,6 @@ from megatron.data.dataset_utils import (
     create_masked_lm_predictions
 )
 
-
 class BertDataset(torch.utils.data.Dataset):
 
     def __init__(self, name, indexed_dataset, data_prefix,
@@ -156,7 +155,9 @@ def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
     # Some checks.
     num_tokens = len(tokens)
     padding_length = max_seq_length - num_tokens
-    assert padding_length >= 0
+    assert padding_length >= 0, \
+        f"num_tokens ({num_tokens}) is greater than " \
+        "max_seq_length ({max_seq_length})."
     assert len(tokentypes) == num_tokens
     assert len(masked_positions) == len(masked_labels)
 

megatron/data/blendable_dataset.py

@@ -50,4 +50,7 @@ class BlendableDataset(torch.utils.data.Dataset):
     def __getitem__(self, idx):
         dataset_idx = self.dataset_index[idx]
         sample_idx = self.dataset_sample_index[idx]
-        return self.datasets[dataset_idx][sample_idx]
+        return {
+            "dataset_idx" : dataset_idx,
+            **self.datasets[dataset_idx][sample_idx],
+        }

megatron/data/dataset_utils.py

@@ -452,7 +452,8 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
             prefixes[i], data_impl, splits_string,
             datasets_train_valid_test_num_samples[i],
             max_seq_length, masked_lm_prob, short_seq_prob,
-            seed, skip_warmup, binary_head, dataset_type=dataset_type)
+            seed, skip_warmup, binary_head, max_seq_length_dec,
+            dataset_type=dataset_type)
         if train_ds:
             train_datasets.append(train_ds)
         if valid_ds:
@@ -460,7 +461,7 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
         if test_ds:
             test_datasets.append(test_ds)
 
-        # Blend.
+    # Blend.
     blending_train_dataset = None
     if train_datasets:
         blending_train_dataset = BlendableDataset(train_datasets, weights)

megatron/data/gpt_dataset.py

@@ -16,15 +16,18 @@ from megatron.data.dataset_utils import get_train_valid_test_split_
 from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset
 
 
-def build_train_valid_test_datasets(data_prefix, data_impl,
-                                    splits_string, train_valid_test_num_samples,
+def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
+                                    train_valid_test_num_samples,
                                     seq_length, seed, skip_warmup,
-                                    train_data_prefix=None, valid_data_prefix=None,
-                                    test_data_prefix=None,):
+                                    train_data_prefix=None,
+                                    valid_data_prefix=None,
+                                    test_data_prefix=None,
+                                    return_doc_ids=False):
     """Build train, valid, and test datasets."""
 
     if data_prefix:
         print_rank_0("Single data path provided for train, valid & test")
+
         # Single dataset.
         if len(data_prefix) == 1:
             return _build_train_valid_test_datasets(data_prefix[0],
@@ -35,7 +38,7 @@ def build_train_valid_test_datasets(data_prefix, data_impl,
         # Blending dataset.
         # Parse the values.
         output = get_datasets_weights_and_num_samples(data_prefix,
-                                                    train_valid_test_num_samples)
+                                                      train_valid_test_num_samples)
         prefixes, weights, datasets_train_valid_test_num_samples = output
 
         # Build individual datasets.
@@ -46,7 +49,8 @@ def build_train_valid_test_datasets(data_prefix, data_impl,
             train_ds, valid_ds, test_ds = _build_train_valid_test_datasets(
                 prefixes[i], data_impl, splits_string,
                 datasets_train_valid_test_num_samples[i],
-                seq_length, seed, skip_warmup)
+                seq_length, seed, skip_warmup,
+                return_doc_ids)
             if train_ds:
                 train_datasets.append(train_ds)
             if valid_ds:
@@ -67,6 +71,7 @@ def build_train_valid_test_datasets(data_prefix, data_impl,
 
         return (blending_train_dataset, blending_valid_dataset,
                 blending_test_dataset)
+
     else:
         print_rank_0("Separate data paths provided for train, valid & test. Split string will be ignored.")
 
@@ -74,23 +79,69 @@ def build_train_valid_test_datasets(data_prefix, data_impl,
         # Single dataset.
         if train_data_prefix is not None:
             train_dataset = build_dataset("train", train_data_prefix, data_impl,
-                                        train_valid_test_num_samples[0], seq_length, seed,
-                                        skip_warmup)
+                                          train_valid_test_num_samples[0],
+                                          seq_length, seed, skip_warmup)
 
         if valid_data_prefix is not None:
             valid_dataset = build_dataset("valid", valid_data_prefix, data_impl,
-                                    train_valid_test_num_samples[1], seq_length, seed,
-                                    False)
+                                          train_valid_test_num_samples[1],
+                                          seq_length, seed, False)
 
         if test_data_prefix is not None:
             test_dataset = build_dataset("test", test_data_prefix, data_impl,
-                                    train_valid_test_num_samples[2], seq_length, seed,
-                                    False)
+                                         train_valid_test_num_samples[2],
+                                         seq_length, seed, False)
 
         return (train_dataset, valid_dataset, test_dataset)
 
 
-def build_dataset(dataset_name, data_prefix, data_impl, num_samples, seq_length, seed, skip_warmup):
+def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
+                                     train_valid_test_num_samples,
+                                     seq_length, seed, skip_warmup,
+                                     return_doc_ids=False):
+    """Build train, valid, and test datasets."""
+
+    # Indexed dataset.
+    indexed_dataset = get_indexed_dataset_(data_prefix,
+                                           data_impl,
+                                           skip_warmup)
+
+    total_num_of_documents = indexed_dataset.sizes.shape[0]
+    splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
+
+    # Print stats about the splits.
+    print_rank_0(' > dataset split:')
+
+    def print_split_stats(name, index):
+        print_rank_0('    {}:'.format(name))
+        print_rank_0('     document indices in [{}, {}) total of {} '
+                     'documents'.format(splits[index], splits[index + 1],
+                                        splits[index + 1] - splits[index]))
+    print_split_stats('train', 0)
+    print_split_stats('validation', 1)
+    print_split_stats('test', 2)
+
+    def build_dataset(index, name):
+        dataset = None
+        if splits[index + 1] > splits[index]:
+            documents = np.arange(start=splits[index], stop=splits[index + 1],
+                                  step=1, dtype=np.int32)
+            dataset = GPTDataset(name, data_prefix,
+                                 documents, indexed_dataset,
+                                 train_valid_test_num_samples[index],
+                                 seq_length, seed,
+                                 return_doc_ids)
+        return dataset
+
+    train_dataset = build_dataset(0, 'train')
+    valid_dataset = build_dataset(1, 'valid')
+    test_dataset = build_dataset(2, 'test')
+
+    return (train_dataset, valid_dataset, test_dataset)
+
+
+def build_dataset(dataset_name, data_prefix, data_impl, num_samples,
+                  seq_length, seed, skip_warmup):
     dataset = None
     if len(data_prefix) == 1:
         dataset = _build_dataset(dataset_name,
@@ -119,7 +170,7 @@ def build_dataset(dataset_name, data_prefix, data_impl, num_samples, seq_length,
 
 
 def _build_dataset(dataset_name, data_prefix, data_impl,
-                num_samples, seq_length, seed, skip_warmup):
+                   num_samples, seq_length, seed, skip_warmup):
     """
     Build dataset. This method is called when individual
     train, valid, test datasets are provided
@@ -146,49 +197,6 @@ def _build_dataset(dataset_name, data_prefix, data_impl,
     return dataset
 
 
-def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
-                                     train_valid_test_num_samples,
-                                     seq_length, seed, skip_warmup):
-    """Build train, valid, and test datasets."""
-
-    # Indexed dataset.
-    indexed_dataset = get_indexed_dataset_(data_prefix,
-                                           data_impl,
-                                           skip_warmup)
-
-    total_num_of_documents = indexed_dataset.sizes.shape[0]
-    splits = get_train_valid_test_split_(splits_string, total_num_of_documents)
-
-    # Print stats about the splits.
-    print_rank_0(' > dataset split:')
-
-    def print_split_stats(name, index):
-        print_rank_0('    {}:'.format(name))
-        print_rank_0('     document indices in [{}, {}) total of {} '
-                     'documents'.format(splits[index], splits[index + 1],
-                                        splits[index + 1] - splits[index]))
-    print_split_stats('train', 0)
-    print_split_stats('validation', 1)
-    print_split_stats('test', 2)
-
-    def build_dataset(index, name):
-        dataset = None
-        if splits[index + 1] > splits[index]:
-            documents = np.arange(start=splits[index], stop=splits[index + 1],
-                                  step=1, dtype=np.int32)
-            dataset = GPTDataset(name, data_prefix,
-                                  documents, indexed_dataset,
-                                  train_valid_test_num_samples[index],
-                                  seq_length, seed)
-        return dataset
-
-    train_dataset = build_dataset(0, 'train')
-    valid_dataset = build_dataset(1, 'valid')
-    test_dataset = build_dataset(2, 'test')
-
-    return (train_dataset, valid_dataset, test_dataset)
-
-
 def get_indexed_dataset_(data_prefix, data_impl, skip_warmup):
     """Build indexed dataset."""
     print_rank_0(' > building dataset index ...')
@@ -208,19 +216,23 @@ def get_indexed_dataset_(data_prefix, data_impl, skip_warmup):
 class GPTDataset(torch.utils.data.Dataset):
 
     def __init__(self, name, data_prefix, documents, indexed_dataset,
-                 num_samples, seq_length, seed):
+                 num_samples, seq_length, seed,
+                 return_doc_ids=False):
 
         self.name = name
         self.indexed_dataset = indexed_dataset
+        self.return_doc_ids = return_doc_ids
 
         # Checks
         assert np.min(documents) >= 0
         assert np.max(documents) < indexed_dataset.sizes.shape[0]
 
         # Build index mappings.
-        self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings(
-            self.name, data_prefix, documents, self.indexed_dataset.sizes,
-            num_samples, seq_length, seed)
+        self.doc_idx, self.sample_idx, self.shuffle_idx, self.index_prefix = \
+            _build_index_mappings(self.name, data_prefix,
+                                  documents, self.indexed_dataset.sizes,
+                                  num_samples, seq_length, seed)
+
 
     def __len__(self):
         # -1 is due to data structure used to retieve the index:
@@ -236,24 +248,33 @@ class GPTDataset(torch.utils.data.Dataset):
         offset_f = self.sample_idx[idx][1]
         offset_l = self.sample_idx[idx + 1][1]
         # If we are within the same document, just extract the chunk.
+        doc_ids = []
         if doc_index_f == doc_index_l:
+            doc_ids.append(self.doc_idx[doc_index_f])
             sample = self.indexed_dataset.get(self.doc_idx[doc_index_f],
                                               offset=offset_f,
                                               length=offset_l - offset_f + 1)
         else:
             # Otherwise, get the rest of the initial document.
+            doc_ids.append(self.doc_idx[doc_index_f])
             sample_list = [self.indexed_dataset.get(self.doc_idx[doc_index_f],
                                                     offset=offset_f)]
             # Loop over all in between documents and add the entire document.
             for i in range(doc_index_f + 1, doc_index_l):
+                doc_ids.append(self.doc_idx[i])
                 sample_list.append(self.indexed_dataset.get(self.doc_idx[i]))
             # And finally add the relevant portion of last document.
+            doc_ids.append(self.doc_idx[doc_index_l])
             sample_list.append(self.indexed_dataset.get(
                 self.doc_idx[doc_index_l],
                 length=offset_l + 1))
             sample = np.concatenate(sample_list)
 
-        return {'text': np.array(sample, dtype=np.int64)}
+        if self.return_doc_ids: # for retro preprocessing
+            return {'text': np.array(sample, dtype=np.int64),
+                    'doc_ids': np.array(doc_ids, dtype=np.int64)}
+        else:
+            return {'text': np.array(sample, dtype=np.int64)}
 
 
 def _build_index_mappings(name, data_prefix, documents, sizes,
@@ -267,15 +288,16 @@ def _build_index_mappings(name, data_prefix, documents, sizes,
     # Number of tokens in each epoch and number of required epochs.
     tokens_per_epoch = _num_tokens(documents, sizes)
     num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples)
+
     # rng state
     np_rng = np.random.RandomState(seed=seed)
 
     # Filename of the index mappings.
-    _filename = data_prefix
-    _filename += '_{}_indexmap'.format(name)
-    _filename += '_{}ns'.format(num_samples)
-    _filename += '_{}sl'.format(seq_length)
-    _filename += '_{}s'.format(seed)
+    index_prefix = '{}_indexmap'.format(name)
+    index_prefix += '_{}ns'.format(num_samples)
+    index_prefix += '_{}sl'.format(seq_length)
+    index_prefix += '_{}s'.format(seed)
+    _filename = data_prefix + '_' + index_prefix
     doc_idx_filename = _filename + '_doc_idx.npy'
     sample_idx_filename = _filename + '_sample_idx.npy'
     shuffle_idx_filename = _filename + '_shuffle_idx.npy'
@@ -343,8 +365,6 @@ def _build_index_mappings(name, data_prefix, documents, sizes,
             assert sizes.dtype == np.int32
             sample_idx = helpers.build_sample_idx(sizes, doc_idx, seq_length,
                                                   num_epochs, tokens_per_epoch)
-            # sample_idx = _build_sample_idx(sizes, doc_idx, seq_length,
-            #                               num_epochs, tokens_per_epoch)
             np.save(sample_idx_filename, sample_idx, allow_pickle=True)
             print_rank_0(' > elasped time to build and save sample-idx mapping '
                          '(seconds): {:4f}'.format(time.time() - start_time))
@@ -389,7 +409,7 @@ def _build_index_mappings(name, data_prefix, documents, sizes,
         sample_idx.shape[0]))
     print_rank_0('    total number of epochs: {}'.format(num_epochs))
 
-    return doc_idx, sample_idx, shuffle_idx
+    return doc_idx, sample_idx, shuffle_idx, index_prefix
 
 
 def _num_tokens(documents, sizes):
@@ -481,7 +501,7 @@ def _build_shuffle_idx(num_samples, total_size, np_rng):
     """Build the range [0, size) and shuffle."""
     print(' > building shuffle index with split [0, {}) and [{}, {}) '
           '...'.format(num_samples, num_samples, total_size), flush=True)
-    
+
     dtype_ = np.uint32
     if total_size >= (np.iinfo(np.uint32).max - 1):
         dtype_ = np.int64

megatron/data/indexed_dataset.py

@@ -460,7 +460,7 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
         return self._path
 
     def __setstate__(self, state):
-        self._do_init(state)
+        self._do_init(state, skip_warmup=True)
 
     def _do_init(self, path, skip_warmup):
         self._path = path

megatron/global_vars.py

@@ -12,6 +12,7 @@ from .microbatches import build_num_microbatches_calculator
 from .timers import Timers
 
 _GLOBAL_ARGS = None
+_GLOBAL_RETRO_ARGS = None
 _GLOBAL_NUM_MICROBATCHES_CALCULATOR = None
 _GLOBAL_TOKENIZER = None
 _GLOBAL_TENSORBOARD_WRITER = None
@@ -25,6 +26,11 @@ def get_args():
     return _GLOBAL_ARGS
 
 
+def get_retro_args():
+    """Return retro arguments."""
+    return _GLOBAL_RETRO_ARGS
+
+
 def get_num_microbatches():
     return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get()
 
@@ -98,6 +104,11 @@ def set_args(args):
     _GLOBAL_ARGS = args
 
 
+def set_retro_args(retro_args):
+    global _GLOBAL_RETRO_ARGS
+    _GLOBAL_RETRO_ARGS = retro_args
+
+
 def _build_num_microbatches_calculator(args):
 
     global _GLOBAL_NUM_MICROBATCHES_CALCULATOR

megatron/initialize.py

@@ -174,7 +174,7 @@ def _initialize_distributed():
     torch.distributed.init_process_group(
         backend=args.distributed_backend,
         world_size=args.world_size, rank=args.rank,
-        timeout=timedelta(minutes=10))
+        timeout=timedelta(minutes=args.distributed_timeout_minutes))
 
     # Set the tensor model-parallel, pipeline model-parallel, and
     # data-parallel communicators.

megatron/model/bert_model.py

@@ -16,6 +16,7 @@ from megatron.model.utils import init_method_normal
 from megatron.model.utils import scaled_init_method_normal
 from .module import MegatronModule
 
+
 def bert_extended_attention_mask(attention_mask):
     # We create a 3D attention mask from a 2D tensor mask.
     # [b, 1, s]
@@ -137,6 +138,10 @@ class BertModel(MegatronModule):
         self.pre_process = pre_process
         self.post_process = post_process
 
+        self.return_embeddings = args.output_bert_embeddings
+        if self.return_embeddings:
+            assert self.post_process and self.add_binary_head
+
         init_method = init_method_normal(args.init_method_std)
         scaled_init_method = scaled_init_method_normal(args.init_method_std,
                                                        args.num_layers)
@@ -182,6 +187,24 @@ class BertModel(MegatronModule):
 
         if self.post_process and self.add_binary_head:
             lm_output, pooled_output = lm_output
+
+            # Return pooled output (e.g., when computing Bert embeddings).
+            if self.return_embeddings:
+
+                # Sum attention mask.
+                embeddings = torch.transpose(lm_output, 0, 1)
+                masks = torch.sum(attention_mask, dim=1)
+
+                # Collect masked embeddings.
+                output = torch.zeros(
+                    size=(embeddings.shape[0], embeddings.shape[2]),
+                    dtype=torch.float32,
+                    device=torch.cuda.current_device())
+                for i, (embedding, mask) in enumerate(zip(embeddings, masks)):
+                    output[i, :] = torch.mean(embedding[1: mask - 1], dim=0)
+
+                return output
+
         else:
             pooled_output = None
 

megatron/model/gpt_model.py

@@ -74,13 +74,17 @@ class GPTModel(MegatronModule):
         """See megatron.model.transformer.set_input_tensor()"""
         self.language_model.set_input_tensor(input_tensor)
 
-    def forward(self, input_ids, position_ids, attention_mask, labels=None,
-                tokentype_ids=None, inference_params=None):
+    def forward(self, input_ids, position_ids, attention_mask,
+                ret_input_ids=None, ret_position_ids=None, ret_attn_mask=None,
+                labels=None, tokentype_ids=None, inference_params=None):
 
         lm_output = self.language_model(
             input_ids,
             position_ids,
             attention_mask,
+            ret_input_ids=ret_input_ids,
+            ret_position_ids=ret_position_ids,
+            ret_attn_mask=ret_attn_mask,
             inference_params=inference_params)
 
         if self.post_process:

megatron/model/language_model.py

@@ -7,11 +7,13 @@ import torch.nn.functional as F
 
 from megatron import get_args
 from megatron.core import mpu, tensor_parallel
+
+from .enums import LayerType, AttnMaskType
 from .module import MegatronModule
-from megatron.model.enums import LayerType, AttnMaskType
-from megatron.model.transformer import ParallelTransformer
-from megatron.model.utils import get_linear_layer
-from megatron.model.utils import init_method_normal, scaled_init_method_normal
+from .retro_transformer import ParallelRetroEncoder, ParallelRetroTransformer
+from .transformer import ParallelTransformer
+from .utils import get_linear_layer
+from .utils import init_method_normal, scaled_init_method_normal
 
 
 def parallel_lm_logits(input_, word_embeddings_weight, parallel_output,
@@ -349,17 +351,39 @@ class TransformerLanguageModel(MegatronModule):
                                        self.num_tokentypes)
             self._embedding_key = 'embedding'
 
-        # Transformer.
-        # Encoder (usually set to True, False if part of an encoder-decoder
-        # architecture and in encoder-only stage).
-        if self.add_encoder:
-            self.encoder = ParallelTransformer(
+        # Retriever (bi-directional transformer with cross attention)
+        if args.retro_add_retriever:
+            self.retriever = ParallelRetroEncoder(
                 self.init_method,
                 output_layer_init_method,
-                self_attn_mask_type=self.encoder_attn_mask_type,
+                self_attn_mask_type=AttnMaskType.padding,
                 pre_process=self.pre_process,
-                post_process=self.post_process
+                post_process=False,
             )
+            self._retriever_key = 'retriever'
+        else:
+            self.retriever = None
+
+        # Encoder (usually set to True, False if part of an encoder-decoder
+        # architecture and in encoder-only stage).
+        if self.add_encoder:
+            if args.retro_add_retriever:
+                self.encoder = ParallelRetroTransformer(
+                    self.init_method,
+                    output_layer_init_method,
+                    self_attn_mask_type=self.encoder_attn_mask_type,
+                    pre_process=self.pre_process,
+                    post_process=self.post_process,
+                    retriever=self.retriever,
+                )
+            else:
+                self.encoder = ParallelTransformer(
+                    self.init_method,
+                    output_layer_init_method,
+                    self_attn_mask_type=self.encoder_attn_mask_type,
+                    pre_process=self.pre_process,
+                    post_process=self.post_process,
+                )
             self._encoder_key = 'encoder'
         else:
             self.encoder = None
@@ -414,11 +438,19 @@ class TransformerLanguageModel(MegatronModule):
 
     def forward(self, enc_input_ids, enc_position_ids, enc_attn_mask,
                 dec_input_ids=None, dec_position_ids=None, dec_attn_mask=None,
+                ret_input_ids=None, ret_position_ids=None, ret_attn_mask=None,
                 enc_dec_attn_mask=None, tokentype_ids=None,
                 inference_params=None,
                 pooling_sequence_index=0,
                 enc_hidden_states=None, output_enc_hidden=False):
 
+        # Retriever embedding.
+        if self.retriever and self.pre_process:
+            retriever_input = self.embedding(ret_input_ids, ret_position_ids,
+                                             tokentype_ids=tokentype_ids)
+        else:
+            retriever_input = None
+
         # Encoder embedding.
         if self.pre_process:
             encoder_input = self.embedding(enc_input_ids, enc_position_ids,
@@ -429,10 +461,18 @@ class TransformerLanguageModel(MegatronModule):
         # Run encoder.
         if enc_hidden_states is None:
             if self.encoder is not None:
-                encoder_output = self.encoder(
-                    encoder_input,
-                    enc_attn_mask,
-                    inference_params=inference_params)
+                if self.retriever:
+                    encoder_output = self.encoder(
+                        encoder_input,
+                        enc_attn_mask,
+                        retriever_output=retriever_input,
+                        retriever_attn_mask=ret_attn_mask,
+                        inference_params=inference_params)
+                else:
+                    encoder_output = self.encoder(
+                        encoder_input,
+                        enc_attn_mask,
+                        inference_params=inference_params)
             else:
                 encoder_output = self.encoder_hidden_state
         else:

megatron/model/transformer.py

@@ -270,7 +270,6 @@ class CoreAttention(MegatronModule):
 
         # This is actually dropping out entire tokens to attend to, which might
         # seem a bit unusual, but is taken from the original Transformer paper.
-
         if not self.sequence_parallel:
             with tensor_parallel.get_cuda_rng_tracker().fork():
                 attention_probs = self.attention_dropout(attention_probs)
@@ -477,6 +476,7 @@ class ParallelAttention(MegatronModule):
         # =================================================
         # Pre-allocate memory for key-values for inference.
         # =================================================
+
         if inference_params:
             if self.layer_number not in inference_params.key_value_memory_dict:
                 inf_max_seq_len = inference_params.max_sequence_len

megatron/schedules.py

@@ -244,6 +244,7 @@ def forward_backward_no_pipelining(forward_step_func,
     output_tensor = forward_step(forward_step_func, data_iterator,
                                  model, input_tensor, forward_data_store,
                                  timers, collect_non_loss_data)
+
     if not forward_only:
         backward_step(optimizer, input_tensor, output_tensor,
                       output_tensor_grad, timers)