Merge branch 'ict-merge' into 'master'

ICT code

See merge request ADLR/megatron-lm!90

pretrain_ict.py

+# coding=utf-8
+# Copyright (c) 2019, 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.
+
+"""Pretrain BERT for Inverse Cloze Task"""
+
+import torch
+import torch.distributed as dist
+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.data.dataset_utils import build_train_valid_test_datasets
+from megatron.model import ICTBertModel
+from megatron.training import pretrain
+from megatron.utils import reduce_losses
+
+num_batches = 0
+
+
+def general_model_provider(only_query_model=False, only_block_model=False):
+    """Build the model."""
+    args = get_args()
+    assert args.ict_head_size is not None, \
+        "Need to specify --ict-head-size to provide an ICTBertModel"
+
+    assert args.model_parallel_size == 1, \
+        "Model parallel size > 1 not supported for ICT"
+
+    print_rank_0('building ICTBertModel...')
+
+    # simpler to just keep using 2 tokentypes since the LM we initialize with has 2 tokentypes
+    model = ICTBertModel(
+        ict_head_size=args.ict_head_size,
+        num_tokentypes=2,
+        parallel_output=True,
+        only_query_model=only_query_model,
+        only_block_model=only_block_model)
+
+    return model
+
+
+def model_provider():
+    return general_model_provider(False, False)
+
+
+def get_group_world_size_rank():
+
+    group = mpu.get_data_parallel_group()
+    rank = torch.distributed.get_rank(group=group)
+    world_size = torch.distributed.get_world_size(group=group)
+
+    return group, rank, world_size
+
+
+class AllgatherFromDataParallelRegion(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, input_):
+        assert input_.dim() == 2
+        group, rank, world_size = get_group_world_size_rank()
+
+        tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
+        tensor_list[rank] = input_
+        torch.distributed.all_gather(tensor_list, input_, group=group)
+
+        output = torch.cat(tensor_list, dim=0).contiguous()
+
+        return output
+
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        group, rank, world_size = get_group_world_size_rank()
+
+        assert grad_output.shape[0] % world_size == 0
+        dim_size = grad_output.shape[0] // world_size
+        output_list = torch.split(grad_output, dim_size, dim=0)
+
+        # get chunk from this rank
+        output = output_list[rank].contiguous()
+        return output
+
+
+def get_batch(data_iterator):
+    # Items and their type.
+    keys = ['query_tokens', 'query_pad_mask',
+            'block_tokens', 'block_pad_mask', 'block_data']
+    datatype = torch.int64
+
+    # Broadcast data.
+    if data_iterator is None:
+        data = None
+    else:
+        data = next(data_iterator)
+    data_b = mpu.broadcast_data(keys, data, datatype)
+
+    # Unpack.
+    query_tokens = data_b['query_tokens'].long()
+    query_pad_mask = data_b['query_pad_mask'].long()
+    block_tokens = data_b['block_tokens'].long()
+    block_pad_mask = data_b['block_pad_mask'].long()
+    block_indices = data_b['block_data'].long()
+
+    return query_tokens, query_pad_mask,\
+           block_tokens, block_pad_mask, block_indices
+
+
+def forward_step(data_iterator, model):
+    """Forward step."""
+    args = get_args()
+    timers = get_timers()
+
+    # Get the batch.
+    timers('batch generator').start()
+    query_tokens, query_pad_mask, \
+    block_tokens, block_pad_mask, block_indices = get_batch(data_iterator)
+    timers('batch generator').stop()
+
+
+    # Forward model.
+    query_logits, block_logits = model(query_tokens, query_pad_mask, block_tokens, block_pad_mask)
+    local_batch_size = query_logits.shape[0]
+    global_batch_size = dist.get_world_size() * local_batch_size  # recall we assert that model_parallel_size == 1
+
+    all_query_logits = AllgatherFromDataParallelRegion.apply(query_logits)
+    all_block_logits = AllgatherFromDataParallelRegion.apply(block_logits)
+
+    # scores are inner products between query and block embeddings
+    retrieval_scores = all_query_logits.float().matmul(torch.transpose(all_block_logits, 0, 1).float())
+    softmaxed = F.softmax(retrieval_scores, dim=1)
+    sorted_vals, sorted_indices = torch.topk(softmaxed, k=softmaxed.shape[1], sorted=True)
+
+    def topk_accuracy(k):
+        return torch.cuda.FloatTensor([sum([int(i in sorted_indices[i, :k]) for i in range(global_batch_size)]) / global_batch_size])
+
+    topk_accs = [topk_accuracy(int(k)) for k in args.report_topk_accuracies]
+    retrieval_loss = torch.nn.CrossEntropyLoss()(retrieval_scores, torch.arange(global_batch_size).long().cuda())
+    reduced_losses = reduce_losses([retrieval_loss, *topk_accs])
+
+    # create stats_dict with retrieval loss and all specified top-k accuracies
+    topk_acc_dict = {'top{}_acc'.format(k): v for k, v in zip(args.report_topk_accuracies, reduced_losses[1:])}
+    stats_dict = dict(retrieval_loss=reduced_losses[0], **topk_acc_dict)
+
+    return retrieval_loss, stats_dict
+
+
+def train_valid_test_datasets_provider(train_val_test_num_samples):
+    """Build train, valid and test datasets."""
+    args = get_args()
+    print_rank_0('> building train, validation, and test datasets '
+                 'for BERT ICT...')
+
+    train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
+        data_prefix=args.data_path,
+        data_impl=args.data_impl,
+        splits_string=args.split,
+        train_valid_test_num_samples=train_val_test_num_samples,
+        max_seq_length=args.seq_length,
+        masked_lm_prob=args.mask_prob,
+        short_seq_prob=args.short_seq_prob,
+        seed=args.seed,
+        skip_warmup=(not args.mmap_warmup),
+        dataset_type='ict')
+    print_rank_0("> finished creating BERT ICT datasets ...")
+
+    return train_ds, valid_ds, test_ds
+
+
+if __name__ == "__main__":
+    pretrain(train_valid_test_datasets_provider, model_provider, forward_step,
+             args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})

tasks/eval_utils.py

@@ -21,8 +21,8 @@ import time
 import torch
 
 from megatron import get_args
-from megatron import mpu
 from megatron import print_rank_0
+from megatron import mpu
 from tasks.finetune_utils import build_data_loader
 from tasks.finetune_utils import process_batch
 

tasks/finetune_utils.py

@@ -18,9 +18,9 @@
 import torch
 
 from megatron import get_args
+from megatron import print_rank_0
 from megatron import get_timers
 from megatron import mpu
-from megatron import print_rank_0
 from megatron.checkpointing import load_checkpoint
 from megatron.checkpointing import save_checkpoint
 from megatron.training import evaluate_and_print_results

tasks/glue/finetune.py

@@ -16,8 +16,8 @@
 """GLUE finetuning/evaluation."""
 
 from megatron import get_args
-from megatron import get_tokenizer
 from megatron import print_rank_0
+from megatron import get_tokenizer
 from megatron.model.classification import Classification
 from tasks.eval_utils import accuracy_func_provider
 from tasks.finetune_utils import finetune

tasks/race/finetune.py

@@ -16,8 +16,8 @@
 """Race."""
 
 from megatron import get_args
-from megatron import get_tokenizer
 from megatron import print_rank_0
+from megatron import get_tokenizer
 from megatron.model.multiple_choice import MultipleChoice
 from tasks.eval_utils import accuracy_func_provider
 from tasks.finetune_utils import finetune

tasks/zeroshot_gpt2/datasets.py

@@ -22,8 +22,8 @@ import numpy as np
 import torch
 
 from megatron import get_args
-from megatron import get_tokenizer
 from megatron import print_rank_0
+from megatron import get_tokenizer
 from .detokenizer import get_detokenizer
 
 

tasks/zeroshot_gpt2/evaluate.py

@@ -20,9 +20,9 @@ import math
 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.model import GPT2Model
 from megatron.training import get_model

tools/generate_samples_gpt2.py

@@ -21,8 +21,8 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
                                              os.path.pardir)))
 
 from megatron import get_args
-from megatron import get_tokenizer
 from megatron import print_rank_0
+from megatron import get_tokenizer
 from megatron.checkpointing import load_checkpoint
 from megatron.initialize import initialize_megatron
 from megatron.model import GPT2Model