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+# coding=utf-8
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+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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+#
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+# Licensed under the Apache License, Version 2.0 (the "License");
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+# you may not use this file except in compliance with the License.
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+# You may obtain a copy of the License at
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+#
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+# http://www.apache.org/licenses/LICENSE-2.0
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+#
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+# Unless required by applicable law or agreed to in writing, software
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+# distributed under the License is distributed on an "AS IS" BASIS,
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+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+# See the License for the specific language governing permissions and
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+# limitations under the License.
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+
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+"""Megatron arguments."""
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+
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+import argparse
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+import os
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+
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+import torch
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+
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+def parse_args(extra_args_provider=None, defaults={},
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+ ignore_unknown_args=False):
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+ """Parse all arguments."""
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+ parser = argparse.ArgumentParser(description='Megatron-LM Arguments',
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+ allow_abbrev=False)
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+
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+ # Standard arguments.
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+ parser = _add_network_size_args(parser)
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+ parser = _add_regularization_args(parser)
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+ parser = _add_training_args(parser)
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+ parser = _add_initialization_args(parser)
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+ parser = _add_learning_rate_args(parser)
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+ parser = _add_checkpointing_args(parser)
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+ parser = _add_mixed_precision_args(parser)
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+ parser = _add_distributed_args(parser)
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+ parser = _add_validation_args(parser)
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+ parser = _add_data_args(parser)
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+ parser = _add_autoresume_args(parser)
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+ parser = _add_biencoder_args(parser)
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+ parser = _add_vit_args(parser)
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+ parser = _add_logging_args(parser)
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+
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+ # Custom arguments.
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+ if extra_args_provider is not None:
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+ parser = extra_args_provider(parser)
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+
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+ # Parse.
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+ if ignore_unknown_args:
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+ args, _ = parser.parse_known_args()
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+ else:
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+ args = parser.parse_args()
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+
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+ # Distributed args.
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+ args.rank = int(os.getenv('RANK', '0'))
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+ args.world_size = int(os.getenv("WORLD_SIZE", '1'))
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+ # Tensor model parallel size.
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+ args.tensor_model_parallel_size = min(
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+ args.tensor_model_parallel_size, args.world_size)
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+ assert args.world_size % args.tensor_model_parallel_size == 0, 'world size'\
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+ ' ({}) is not divisible by tensor model parallel size ({})'.format(
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+ args.world_size, args.tensor_model_parallel_size)
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+ # Pipeline model parallel size.
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+ args.pipeline_model_parallel_size = min(
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+ args.pipeline_model_parallel_size,
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+ (args.world_size // args.tensor_model_parallel_size))
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+ # Checks.
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+ model_parallel_size = args.pipeline_model_parallel_size * \
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+ args.tensor_model_parallel_size
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+ assert args.world_size % model_parallel_size == 0, 'world size is not'\
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+ ' divisible by tensor parallel size ({}) times pipeline parallel ' \
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+ 'size ({})'.format(args.world_size, args.tensor_model_parallel_size,
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+ args.pipeline_model_parallel_size)
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+ args.data_parallel_size = args.world_size // model_parallel_size
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+ if args.rank == 0:
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+ print('using world size: {}, data-parallel-size: {}, '
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+ 'tensor-model-parallel size: {}, '
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+ 'pipeline-model-parallel size: {} '.format(
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+ args.world_size, args.data_parallel_size,
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+ args.tensor_model_parallel_size,
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+ args.pipeline_model_parallel_size), flush=True)
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+
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+ # Deprecated arguments
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+ assert args.batch_size is None, '--batch-size argument is no longer ' \
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+ 'valid, use --micro-batch-size instead'
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+ del args.batch_size
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+ assert args.warmup is None, '--warmup argument is no longer valid, use ' \
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+ '--lr-warmup-fraction instead'
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+ del args.warmup
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+ assert args.model_parallel_size is None, '--model-parallel-size is no ' \
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+ 'longer valid, use --tensor-model-parallel-size instead'
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+ del args.model_parallel_size
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+
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+ # Set input defaults.
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+ for key in defaults:
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+ # For default to be valid, it should not be provided in the
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+ # arguments that are passed to the program. We check this by
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+ # ensuring the arg is set to None.
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+ if getattr(args, key) is not None:
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+ if args.rank == 0:
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+ print('WARNING: overriding default arguments for {key}:{v} \
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+ with {key}:{v2}'.format(key=key, v=defaults[key],
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+ v2=getattr(args, key)),
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+ flush=True)
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+ else:
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+ setattr(args, key, defaults[key])
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+
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+ # Batch size.
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+ assert args.micro_batch_size is not None
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+ assert args.micro_batch_size > 0
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+ if args.global_batch_size is None:
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+ args.global_batch_size = args.micro_batch_size * args.data_parallel_size
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+ if args.rank == 0:
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+ print('setting global batch size to {}'.format(
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+ args.global_batch_size), flush=True)
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+ assert args.global_batch_size > 0
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+ if args.num_layers_per_virtual_pipeline_stage is not None:
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+ assert args.pipeline_model_parallel_size > 2, \
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+ 'pipeline-model-parallel size should be greater than 2 with ' \
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+ 'interleaved schedule'
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+ assert args.num_layers % args.num_layers_per_virtual_pipeline_stage == 0, \
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+ 'number of layers is not divisible by number of layers per virtual ' \
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+ 'pipeline stage'
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+ args.virtual_pipeline_model_parallel_size = \
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+ (args.num_layers // args.pipeline_model_parallel_size) // \
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+ args.num_layers_per_virtual_pipeline_stage
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+ else:
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+ args.virtual_pipeline_model_parallel_size = None
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+
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+ # Parameters dtype.
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+ args.params_dtype = torch.float
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+ if args.fp16:
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+ assert not args.bf16
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+ args.params_dtype = torch.half
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+ if args.bf16:
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+ assert not args.fp16
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+ args.params_dtype = torch.bfloat16
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+ # bfloat16 requires gradient accumulation and all-reduce to
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+ # be done in fp32.
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+ if not args.accumulate_allreduce_grads_in_fp32:
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+ args.accumulate_allreduce_grads_in_fp32 = True
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+ if args.rank == 0:
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+ print('accumulate and all-reduce gradients in fp32 for '
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+ 'bfloat16 data type.', flush=True)
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+
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+ if args.rank == 0:
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+ print('using {} for parameters ...'.format(args.params_dtype),
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+ flush=True)
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+
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+ # If we do accumulation and all-reduces in fp32, we need to have
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+ # local DDP and we should set the use-contiguous-buffers-in-ddp.
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+ if args.accumulate_allreduce_grads_in_fp32:
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+ assert args.DDP_impl == 'local'
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+ args.use_contiguous_buffers_in_ddp = True
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+
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+ if args.dataloader_type is None:
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+ args.dataloader_type = 'single'
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+
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+ # Consumed tokens.
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+ args.consumed_train_samples = 0
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+ args.consumed_valid_samples = 0
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+
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+ # Iteration-based training.
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+ if args.train_iters:
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+ # If we use iteration-based training, make sure the
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+ # sample-based options are off.
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+ assert args.train_samples is None, \
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+ 'expected iteration-based training'
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+ assert args.lr_decay_samples is None, \
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+ 'expected iteration-based learning rate decay'
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+ assert args.lr_warmup_samples == 0, \
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+ 'expected iteration-based learning rate warmup'
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+ assert args.rampup_batch_size is None, \
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+ 'expected no batch-size rampup for iteration-based training'
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+ if args.lr_warmup_fraction is not None:
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+ assert args.lr_warmup_iters == 0, \
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+ 'can only specify one of lr-warmup-fraction and lr-warmup-iters'
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+
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+ # Sample-based training.
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+ if args.train_samples:
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+ # If we use sample-based training, make sure the
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+ # iteration-based options are off.
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+ assert args.train_iters is None, \
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+ 'expected sample-based training'
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+ assert args.lr_decay_iters is None, \
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+ 'expected sample-based learning rate decay'
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+ assert args.lr_warmup_iters == 0, \
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+ 'expected sample-based learnig rate warmup'
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+ if args.lr_warmup_fraction is not None:
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+ assert args.lr_warmup_samples == 0, \
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+ 'can only specify one of lr-warmup-fraction ' \
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+ 'and lr-warmup-samples'
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+
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+ # Check required arguments.
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+ required_args = ['num_layers', 'hidden_size', 'num_attention_heads',
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+ 'max_position_embeddings']
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+ for req_arg in required_args:
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+ _check_arg_is_not_none(args, req_arg)
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+
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+ # Checks.
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+ if args.ffn_hidden_size is None:
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+ args.ffn_hidden_size = 4 * args.hidden_size
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+
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+ if args.kv_channels is None:
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+ assert args.hidden_size % args.num_attention_heads == 0
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+ args.kv_channels = args.hidden_size // args.num_attention_heads
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+
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+ if args.seq_length is not None:
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+ assert args.encoder_seq_length is None
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+ args.encoder_seq_length = args.seq_length
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+ else:
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+ assert args.encoder_seq_length is not None
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+ args.seq_length = args.encoder_seq_length
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+
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+ if args.seq_length is not None:
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+ assert args.max_position_embeddings >= args.seq_length
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+ if args.decoder_seq_length is not None:
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+ assert args.max_position_embeddings >= args.decoder_seq_length
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+ if args.lr is not None:
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+ assert args.min_lr <= args.lr
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+ if args.save is not None:
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+ assert args.save_interval is not None
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+ # Mixed precision checks.
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+ if args.fp16_lm_cross_entropy:
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+ assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.'
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+ if args.fp32_residual_connection:
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+ assert args.fp16 or args.bf16, \
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+ 'residual connection in fp32 only supported when using fp16 or bf16.'
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+ # Activation checkpointing.
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+ if args.distribute_checkpointed_activations:
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+ assert args.checkpoint_activations, \
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+ 'for distribute-checkpointed-activations to work you '\
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+ 'need to enable checkpoint-activations'
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+
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+ _print_args(args)
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+ return args
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+
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+
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+def _print_args(args):
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+ """Print arguments."""
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+ if args.rank == 0:
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+ print('------------------------ arguments ------------------------',
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+ flush=True)
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+ str_list = []
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+ for arg in vars(args):
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+ dots = '.' * (48 - len(arg))
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+ str_list.append(' {} {} {}'.format(arg, dots, getattr(args, arg)))
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+ for arg in sorted(str_list, key=lambda x: x.lower()):
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+ print(arg, flush=True)
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+ print('-------------------- end of arguments ---------------------',
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+ flush=True)
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+
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+
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+def _check_arg_is_not_none(args, arg):
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+ assert getattr(args, arg) is not None, '{} argument is None'.format(arg)
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+
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+
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+def _add_network_size_args(parser):
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+ group = parser.add_argument_group(title='network size')
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+
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+ group.add_argument('--num-layers', type=int, default=None,
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+ help='Number of transformer layers.')
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+ group.add_argument('--hidden-size', type=int, default=None,
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+ help='Tansformer hidden size.')
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+ group.add_argument('--ffn-hidden-size', type=int, default=None,
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+ help='Transformer Feed-Forward Network hidden size. '
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+ 'This is set to 4*hidden-size if not provided')
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+ group.add_argument('--num-attention-heads', type=int, default=None,
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+ help='Number of transformer attention heads.')
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+ group.add_argument('--kv-channels', type=int, default=None,
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+ help='Projection weights dimension in multi-head '
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+ 'attention. This is set to '
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+ ' args.hidden_size // args.num_attention_heads '
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+ 'if not provided.')
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+ group.add_argument('--max-position-embeddings', type=int, default=None,
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+ help='Maximum number of position embeddings to use. '
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+ 'This is the size of position embedding.')
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+ group.add_argument('--make-vocab-size-divisible-by', type=int, default=128,
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+ help='Pad the vocab size to be divisible by this value.'
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+ 'This is added for computational efficieny reasons.')
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+ group.add_argument('--layernorm-epsilon', type=float, default=1e-5,
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+ help='Layer norm epsilon.')
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+ group.add_argument('--apply-residual-connection-post-layernorm',
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+ action='store_true',
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+ help='If set, use original BERT residula connection '
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+ 'ordering.')
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+ group.add_argument('--openai-gelu', action='store_true',
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+ help='Use OpenAIs GeLU implementation. This option'
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+ 'should not be used unless for backward compatibility'
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+ 'reasons.')
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+ group.add_argument('--onnx-safe', type=bool, required=False,
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+ help='Use workarounds for known problems with '
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+ 'Torch ONNX exporter')
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+ group.add_argument('--bert-no-binary-head', action='store_false',
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+ help='Disable BERT binary head.',
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+ dest='bert_binary_head')
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+
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+ return parser
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+
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+
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+def _add_logging_args(parser):
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+ group = parser.add_argument_group(title='logging')
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+
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+ group.add_argument('--log-params-norm', action='store_true',
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+ help='If set, calculate and log parameters norm.')
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+ group.add_argument('--log-num-zeros-in-grad', action='store_true',
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+ help='If set, calculate and log the number of zeros in gradient.')
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+ group.add_argument('--tensorboard-log-interval', type=int, default=1,
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+ help='Report to tensorboard interval.')
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+ group.add_argument('--tensorboard-queue-size', type=int, default=1000,
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+ help='Size of the tensorboard queue for pending events '
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+ 'and summaries before one of the ‘add’ calls forces a '
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+ 'flush to disk.')
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+ group.add_argument('--log-timers-to-tensorboard', action='store_true',
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+ help='If set, write timers to tensorboard.')
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+ group.add_argument('--log-batch-size-to-tensorboard', action='store_true',
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+ help='If set, write batch-size to tensorboard.')
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+ group.add_argument('--no-log-learnig-rate-to-tensorboard',
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+ action='store_false',
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+ help='Disable learning rate logging to tensorboard.',
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+ dest='log_learning_rate_to_tensorboard')
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+ group.add_argument('--no-log-loss-scale-to-tensorboard',
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+ action='store_false',
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+ help='Disable loss-scale logging to tensorboard.',
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+ dest='log_loss_scale_to_tensorboard')
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+ group.add_argument('--log-validation-ppl-to-tensorboard',
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+ action='store_true',
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+ help='If set, write validation perplexity to '
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+ 'tensorboard.')
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+
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+ return parser
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+
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+
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+def _add_regularization_args(parser):
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+ group = parser.add_argument_group(title='regularization')
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+
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+ group.add_argument('--attention-dropout', type=float, default=0.1,
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+ help='Post attention dropout probability.')
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+ group.add_argument('--hidden-dropout', type=float, default=0.1,
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+ help='Dropout probability for hidden state transformer.')
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+ group.add_argument('--weight-decay', type=float, default=0.01,
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+ help='Weight decay coefficient for L2 regularization.')
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+ group.add_argument('--clip-grad', type=float, default=1.0,
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+ help='Gradient clipping based on global L2 norm.')
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+ group.add_argument('--adam-beta1', type=float, default=0.9,
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+ help='First coefficient for computing running averages '
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+ 'of gradient and its square')
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+ group.add_argument('--adam-beta2', type=float, default=0.999,
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+ help='Second coefficient for computing running averages '
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+ 'of gradient and its square')
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+ group.add_argument('--adam-eps', type=float, default=1e-08,
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+ help='Term added to the denominator to improve'
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+ 'numerical stability')
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+ group.add_argument('--sgd-momentum', type=float, default=0.9,
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+ help='Momentum factor for sgd')
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+
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+ return parser
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+
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+
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+def _add_training_args(parser):
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+ group = parser.add_argument_group(title='training')
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+
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+ group.add_argument('--micro-batch-size', type=int, default=None,
|
|
|
+ help='Batch size per model instance (local batch size). '
|
|
|
+ 'Global batch size is local batch size times data '
|
|
|
+ 'parallel size times number of micro batches.')
|
|
|
+ group.add_argument('--batch-size', type=int, default=None,
|
|
|
+ help='Old batch size parameter, do not use. '
|
|
|
+ 'Use --micro-batch-size instead')
|
|
|
+ group.add_argument('--global-batch-size', type=int, default=None,
|
|
|
+ help='Training batch size. If set, it should be a '
|
|
|
+ 'multiple of micro-batch-size times data-parallel-size. '
|
|
|
+ 'If this value is None, then '
|
|
|
+ 'use micro-batch-size * data-parallel-size as the '
|
|
|
+ 'global batch size. This choice will result in 1 for '
|
|
|
+ 'number of micro-batches.')
|
|
|
+ group.add_argument('--rampup-batch-size', nargs='*', default=None,
|
|
|
+ help='Batch size ramp up with the following values:'
|
|
|
+ ' --rampup-batch-size <start batch size> '
|
|
|
+ ' <batch size incerement> '
|
|
|
+ ' <ramp-up samples> '
|
|
|
+ 'For example:'
|
|
|
+ ' --rampup-batch-size 16 8 300000 \ '
|
|
|
+ ' --global-batch-size 1024'
|
|
|
+ 'will start with global batch size 16 and over '
|
|
|
+ ' (1024 - 16) / 8 = 126 intervals will increase'
|
|
|
+ 'the batch size linearly to 1024. In each interval'
|
|
|
+ 'we will use approximately 300000 / 126 = 2380 samples.')
|
|
|
+ group.add_argument('--checkpoint-activations', action='store_true',
|
|
|
+ help='Checkpoint activation to allow for training '
|
|
|
+ 'with larger models, sequences, and batch sizes.')
|
|
|
+ group.add_argument('--distribute-checkpointed-activations',
|
|
|
+ action='store_true',
|
|
|
+ help='If set, distribute checkpointed activations '
|
|
|
+ 'across model parallel group.')
|
|
|
+ group.add_argument('--checkpoint-num-layers', type=int, default=1,
|
|
|
+ help='chunk size (number of layers) for checkpointing.')
|
|
|
+ group.add_argument('--train-iters', type=int, default=None,
|
|
|
+ help='Total number of iterations to train over all '
|
|
|
+ 'training runs. Note that either train-iters or '
|
|
|
+ 'train-samples should be provided.')
|
|
|
+ group.add_argument('--train-samples', type=int, default=None,
|
|
|
+ help='Total number of samples to train over all '
|
|
|
+ 'training runs. Note that either train-iters or '
|
|
|
+ 'train-samples should be provided.')
|
|
|
+ group.add_argument('--log-interval', type=int, default=100,
|
|
|
+ help='Report loss and timing interval.')
|
|
|
+ group.add_argument('--exit-interval', type=int, default=None,
|
|
|
+ help='Exit the program after the iteration is divisible '
|
|
|
+ 'by this value.')
|
|
|
+ group.add_argument('--exit-duration-in-mins', type=int, default=None,
|
|
|
+ help='Exit the program after this many minutes.')
|
|
|
+ group.add_argument('--tensorboard-dir', type=str, default=None,
|
|
|
+ help='Write TensorBoard logs to this directory.')
|
|
|
+ group.add_argument('--no-masked-softmax-fusion',
|
|
|
+ action='store_false',
|
|
|
+ help='Disable fusion of query_key_value scaling, '
|
|
|
+ 'masking, and softmax.',
|
|
|
+ dest='masked_softmax_fusion')
|
|
|
+ group.add_argument('--no-bias-gelu-fusion', action='store_false',
|
|
|
+ help='Disable bias and gelu fusion.',
|
|
|
+ dest='bias_gelu_fusion')
|
|
|
+ group.add_argument('--no-bias-dropout-fusion', action='store_false',
|
|
|
+ help='Disable bias and dropout fusion.',
|
|
|
+ dest='bias_dropout_fusion')
|
|
|
+ group.add_argument('--optimizer', type=str, default='adam',
|
|
|
+ choices=['adam', 'sgd'],
|
|
|
+ help='Optimizer function')
|
|
|
+ group.add_argument('--dataloader-type', type=str, default=None,
|
|
|
+ choices=['single', 'cyclic'],
|
|
|
+ help='Single pass vs multiple pass data loader')
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_initialization_args(parser):
|
|
|
+ group = parser.add_argument_group(title='initialization')
|
|
|
+
|
|
|
+ group.add_argument('--seed', type=int, default=1234,
|
|
|
+ help='Random seed used for python, numpy, '
|
|
|
+ 'pytorch, and cuda.')
|
|
|
+ group.add_argument('--init-method-std', type=float, default=0.02,
|
|
|
+ help='Standard deviation of the zero mean normal '
|
|
|
+ 'distribution used for weight initialization.')
|
|
|
+ group.add_argument('--init-method-xavier-uniform', action='store_true',
|
|
|
+ help='Enable Xavier uniform parameter initialization')
|
|
|
+
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_learning_rate_args(parser):
|
|
|
+ group = parser.add_argument_group(title='learning rate')
|
|
|
+
|
|
|
+ group.add_argument('--lr', type=float, default=None,
|
|
|
+ help='Initial learning rate. Depending on decay style '
|
|
|
+ 'and initial warmup, the learing rate at each '
|
|
|
+ 'iteration would be different.')
|
|
|
+ group.add_argument('--lr-decay-style', type=str, default='linear',
|
|
|
+ choices=['constant', 'linear', 'cosine'],
|
|
|
+ help='Learning rate decay function.')
|
|
|
+ group.add_argument('--lr-decay-iters', type=int, default=None,
|
|
|
+ help='number of iterations to decay learning rate over,'
|
|
|
+ ' If None defaults to `--train-iters`')
|
|
|
+ group.add_argument('--lr-decay-samples', type=int, default=None,
|
|
|
+ help='number of samples to decay learning rate over,'
|
|
|
+ ' If None defaults to `--train-samples`')
|
|
|
+ group.add_argument('--lr-warmup-fraction', type=float, default=None,
|
|
|
+ help='fraction of lr-warmup-(iters/samples) to use '
|
|
|
+ 'for warmup (as a float)')
|
|
|
+ group.add_argument('--lr-warmup-iters', type=int, default=0,
|
|
|
+ help='number of iterations to linearly warmup '
|
|
|
+ 'learning rate over.')
|
|
|
+ group.add_argument('--lr-warmup-samples', type=int, default=0,
|
|
|
+ help='number of samples to linearly warmup '
|
|
|
+ 'learning rate over.')
|
|
|
+ group.add_argument('--warmup', type=int, default=None,
|
|
|
+ help='Old lr warmup argument, do not use. Use one of the'
|
|
|
+ '--lr-warmup-* arguments above')
|
|
|
+ group.add_argument('--min-lr', type=float, default=0.0,
|
|
|
+ help='Minumum value for learning rate. The scheduler'
|
|
|
+ 'clip values below this threshold.')
|
|
|
+ group.add_argument('--override-lr-scheduler', action='store_true',
|
|
|
+ help='Reset the values of the scheduler (learning rate,'
|
|
|
+ 'warmup iterations, minimum learning rate, maximum '
|
|
|
+ 'number of iterations, and decay style from input '
|
|
|
+ 'arguments and ignore values from checkpoints. Note'
|
|
|
+ 'that all the above values will be reset.')
|
|
|
+ group.add_argument('--use-checkpoint-lr-scheduler', action='store_true',
|
|
|
+ help='Use checkpoint to set the values of the scheduler '
|
|
|
+ '(learning rate, warmup iterations, minimum learning '
|
|
|
+ 'rate, maximum number of iterations, and decay style '
|
|
|
+ 'from checkpoint and ignore input arguments.')
|
|
|
+
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_checkpointing_args(parser):
|
|
|
+ group = parser.add_argument_group(title='checkpointing')
|
|
|
+
|
|
|
+ group.add_argument('--save', type=str, default=None,
|
|
|
+ help='Output directory to save checkpoints to.')
|
|
|
+ group.add_argument('--save-interval', type=int, default=None,
|
|
|
+ help='Number of iterations between checkpoint saves.')
|
|
|
+ group.add_argument('--no-save-optim', action='store_true', default=None,
|
|
|
+ help='Do not save current optimizer.')
|
|
|
+ group.add_argument('--no-save-rng', action='store_true', default=None,
|
|
|
+ help='Do not save current rng state.')
|
|
|
+ group.add_argument('--load', type=str, default=None,
|
|
|
+ help='Directory containing a model checkpoint.')
|
|
|
+ group.add_argument('--no-load-optim', action='store_true', default=None,
|
|
|
+ help='Do not load optimizer when loading checkpoint.')
|
|
|
+ group.add_argument('--no-load-rng', action='store_true', default=None,
|
|
|
+ help='Do not load rng state when loading checkpoint.')
|
|
|
+ group.add_argument('--finetune', action='store_true',
|
|
|
+ help='Load model for finetuning. Do not load optimizer '
|
|
|
+ 'or rng state from checkpoint and set iteration to 0. '
|
|
|
+ 'Assumed when loading a release checkpoint.')
|
|
|
+
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_mixed_precision_args(parser):
|
|
|
+ group = parser.add_argument_group(title='mixed precision')
|
|
|
+
|
|
|
+ group.add_argument('--fp16', action='store_true',
|
|
|
+ help='Run model in fp16 mode.')
|
|
|
+ group.add_argument('--bf16', action='store_true',
|
|
|
+ help='Run model in bfloat16 mode.')
|
|
|
+ group.add_argument('--loss-scale', type=float, default=None,
|
|
|
+ help='Static loss scaling, positive power of 2 '
|
|
|
+ 'values can improve fp16 convergence. If None, dynamic'
|
|
|
+ 'loss scaling is used.')
|
|
|
+ group.add_argument('--initial-loss-scale', type=float, default=2**32,
|
|
|
+ help='Initial loss-scale for dynamic loss scaling.')
|
|
|
+ group.add_argument('--min-loss-scale', type=float, default=1.0,
|
|
|
+ help='Minimum loss scale for dynamic loss scale.')
|
|
|
+ group.add_argument('--loss-scale-window', type=float, default=1000,
|
|
|
+ help='Window over which to raise/lower dynamic scale.')
|
|
|
+ group.add_argument('--hysteresis', type=int, default=2,
|
|
|
+ help='hysteresis for dynamic loss scaling')
|
|
|
+ group.add_argument('--fp32-residual-connection', action='store_true',
|
|
|
+ help='Move residual connections to fp32.')
|
|
|
+ group.add_argument('--no-query-key-layer-scaling', action='store_false',
|
|
|
+ help='Do not scale Q * K^T by 1 / layer-number.',
|
|
|
+ dest='apply_query_key_layer_scaling')
|
|
|
+ group.add_argument('--attention-softmax-in-fp32', action='store_true',
|
|
|
+ help='Run attention masking and softmax in fp32. '
|
|
|
+ 'This flag is ignored unless '
|
|
|
+ '--no-query-key-layer-scaling is specified.')
|
|
|
+ group.add_argument('--accumulate-allreduce-grads-in-fp32',
|
|
|
+ action='store_true',
|
|
|
+ help='Gradient accumulation and all-reduce in fp32.')
|
|
|
+ group.add_argument('--fp16-lm-cross-entropy', action='store_true',
|
|
|
+ help='Move the cross entropy unreduced loss calculation'
|
|
|
+ 'for lm head to fp16.')
|
|
|
+
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_distributed_args(parser):
|
|
|
+ group = parser.add_argument_group(title='distributed')
|
|
|
+
|
|
|
+ group.add_argument('--tensor-model-parallel-size', type=int, default=1,
|
|
|
+ help='Degree of tensor model parallelism.')
|
|
|
+ group.add_argument('--pipeline-model-parallel-size', type=int, default=1,
|
|
|
+ help='Degree of pipeline model parallelism.')
|
|
|
+ group.add_argument('--model-parallel-size', type=int, default=None,
|
|
|
+ help='Old model parallel argument, do not use. Use '
|
|
|
+ '--tensor-model-parallel-size instead.')
|
|
|
+ group.add_argument('--num-layers-per-virtual-pipeline-stage', type=int, default=None,
|
|
|
+ help='Number of layers per virtual pipeline stage')
|
|
|
+ group.add_argument('--distributed-backend', default='nccl',
|
|
|
+ choices=['nccl', 'gloo'],
|
|
|
+ help='Which backend to use for distributed training.')
|
|
|
+ group.add_argument('--DDP-impl', default='local',
|
|
|
+ choices=['local', 'torch'],
|
|
|
+ help='which DistributedDataParallel implementation '
|
|
|
+ 'to use.')
|
|
|
+ group.add_argument('--use-contiguous-buffers-in-ddp', action='store_true',
|
|
|
+ help='If set, use contiguous buffer in DDP. Note that '
|
|
|
+ 'this option only works woth local DDP.' )
|
|
|
+ group.add_argument('--no-scatter-gather-tensors-in-pipeline', action='store_false',
|
|
|
+ help='Use scatter/gather to optimize communication of tensors in pipeline',
|
|
|
+ dest='scatter_gather_tensors_in_pipeline')
|
|
|
+ group.add_argument('--local_rank', type=int, default=None,
|
|
|
+ help='local rank passed from distributed launcher.')
|
|
|
+ group.add_argument('--lazy-mpu-init', type=bool, required=False,
|
|
|
+ help='If set to True, initialize_megatron() '
|
|
|
+ 'skips DDP initialization and returns function to '
|
|
|
+ 'complete it instead.Also turns on '
|
|
|
+ '--use-cpu-initialization flag. This is for '
|
|
|
+ 'external DDP manager.' )
|
|
|
+ group.add_argument('--use-cpu-initialization', action='store_true',
|
|
|
+ default=None, help='If set, affine parallel weights '
|
|
|
+ 'initialization uses CPU' )
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_validation_args(parser):
|
|
|
+ group = parser.add_argument_group(title='validation')
|
|
|
+
|
|
|
+ group.add_argument('--eval-iters', type=int, default=100,
|
|
|
+ help='Number of iterations to run for evaluation'
|
|
|
+ 'validation/test for.')
|
|
|
+ group.add_argument('--eval-interval', type=int, default=1000,
|
|
|
+ help='Interval between running evaluation on '
|
|
|
+ 'validation set.')
|
|
|
+
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_data_args(parser):
|
|
|
+ group = parser.add_argument_group(title='data and dataloader')
|
|
|
+
|
|
|
+ group.add_argument('--data-path', nargs='*', default=None,
|
|
|
+ help='Path to the training dataset. Accepted format:'
|
|
|
+ '1) a single data path, 2) multiple datasets in the'
|
|
|
+ 'form: dataset1-weight dataset1-path dataset2-weight '
|
|
|
+ 'dataset2-path ...')
|
|
|
+ group.add_argument('--split', type=str, default='969, 30, 1',
|
|
|
+ help='Comma-separated list of proportions for training,'
|
|
|
+ ' validation, and test split. For example the split '
|
|
|
+ '`90,5,5` will use 90%% of data for training, 5%% for '
|
|
|
+ 'validation and 5%% for test.')
|
|
|
+ group.add_argument('--vocab-file', type=str, default=None,
|
|
|
+ help='Path to the vocab file.')
|
|
|
+ group.add_argument('--merge-file', type=str, default=None,
|
|
|
+ help='Path to the BPE merge file.')
|
|
|
+ group.add_argument('--vocab-extra-ids', type=int, default=0,
|
|
|
+ help='Number of additional vocabulary tokens. '
|
|
|
+ 'They are used for span masking in the T5 model')
|
|
|
+ group.add_argument('--seq-length', type=int, default=None,
|
|
|
+ help='Maximum sequence length to process.')
|
|
|
+ group.add_argument('--encoder-seq-length', type=int, default=None,
|
|
|
+ help='Maximum encoder sequence length to process.'
|
|
|
+ 'This should be exclusive of --seq-length')
|
|
|
+ group.add_argument('--decoder-seq-length', type=int, default=None,
|
|
|
+ help="Maximum decoder sequence length to process.")
|
|
|
+ group.add_argument('--retriever-seq-length', type=int, default=256,
|
|
|
+ help='Maximum sequence length for the biencoder model '
|
|
|
+ ' for retriever')
|
|
|
+ group.add_argument('--sample-rate', type=float, default=1.0,
|
|
|
+ help='sample rate for training data. Supposed to be 0 '
|
|
|
+ ' < sample_rate < 1')
|
|
|
+ group.add_argument('--mask-prob', type=float, default=0.15,
|
|
|
+ help='Probability of replacing a token with mask.')
|
|
|
+ group.add_argument('--short-seq-prob', type=float, default=0.1,
|
|
|
+ help='Probability of producing a short sequence.')
|
|
|
+ group.add_argument('--mmap-warmup', action='store_true',
|
|
|
+ help='Warm up mmap files.')
|
|
|
+ group.add_argument('--num-workers', type=int, default=2,
|
|
|
+ help="Dataloader number of workers.")
|
|
|
+ group.add_argument('--tokenizer-type', type=str,
|
|
|
+ default=None,
|
|
|
+ choices=['BertWordPieceLowerCase',
|
|
|
+ 'BertWordPieceCase',
|
|
|
+ 'GPT2BPETokenizer'],
|
|
|
+ help='What type of tokenizer to use.')
|
|
|
+ group.add_argument('--data-impl', type=str, default='infer',
|
|
|
+ choices=['lazy', 'cached', 'mmap', 'infer'],
|
|
|
+ help='Implementation of indexed datasets.')
|
|
|
+ group.add_argument('--reset-position-ids', action='store_true',
|
|
|
+ help='Reset posistion ids after end-of-document token.')
|
|
|
+ group.add_argument('--reset-attention-mask', action='store_true',
|
|
|
+ help='Reset self attention maske after '
|
|
|
+ 'end-of-document token.')
|
|
|
+ group.add_argument('--eod-mask-loss', action='store_true',
|
|
|
+ help='Mask loss for the end of document tokens.')
|
|
|
+
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_autoresume_args(parser):
|
|
|
+ group = parser.add_argument_group(title='autoresume')
|
|
|
+
|
|
|
+ group.add_argument('--adlr-autoresume', action='store_true',
|
|
|
+ help='Enable autoresume on adlr cluster.')
|
|
|
+ group.add_argument('--adlr-autoresume-interval', type=int, default=1000,
|
|
|
+ help='Intervals over which check for autoresume'
|
|
|
+ 'termination signal')
|
|
|
+
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_biencoder_args(parser):
|
|
|
+ group = parser.add_argument_group(title='biencoder')
|
|
|
+
|
|
|
+ # network size
|
|
|
+ group.add_argument('--ict-head-size', type=int, default=None,
|
|
|
+ help='Size of block embeddings to be used in ICT and '
|
|
|
+ 'REALM (paper default: 128)')
|
|
|
+ group.add_argument('--biencoder-projection-dim', type=int, default=0,
|
|
|
+ help='Size of projection head used in biencoder (paper'
|
|
|
+ ' default: 128)')
|
|
|
+ group.add_argument('--biencoder-shared-query-context-model', action='store_true',
|
|
|
+ help='Whether to share the parameters of the query '
|
|
|
+ 'and context models or not')
|
|
|
+
|
|
|
+ # checkpointing
|
|
|
+ group.add_argument('--ict-load', type=str, default=None,
|
|
|
+ help='Directory containing an ICTBertModel checkpoint')
|
|
|
+ group.add_argument('--bert-load', type=str, default=None,
|
|
|
+ help='Directory containing an BertModel checkpoint '
|
|
|
+ '(needed to start ICT and REALM)')
|
|
|
+
|
|
|
+ # data
|
|
|
+ group.add_argument('--titles-data-path', type=str, default=None,
|
|
|
+ help='Path to titles dataset used for ICT')
|
|
|
+ group.add_argument('--query-in-block-prob', type=float, default=0.1,
|
|
|
+ help='Probability of keeping query in block for '
|
|
|
+ 'ICT dataset')
|
|
|
+ group.add_argument('--use-one-sent-docs', action='store_true',
|
|
|
+ help='Whether to use one sentence documents in ICT')
|
|
|
+ group.add_argument('--evidence-data-path', type=str, default=None,
|
|
|
+ help='Path to Wikipedia Evidence frm DPR paper')
|
|
|
+
|
|
|
+ # training
|
|
|
+ group.add_argument('--retriever-report-topk-accuracies', nargs='+', type=int,
|
|
|
+ default=[], help="Which top-k accuracies to report "
|
|
|
+ "(e.g. '1 5 20')")
|
|
|
+ group.add_argument('--retriever-score-scaling', action='store_true',
|
|
|
+ help='Whether to scale retriever scores by inverse '
|
|
|
+ 'square root of hidden size')
|
|
|
+
|
|
|
+ # faiss index
|
|
|
+ group.add_argument('--block-data-path', type=str, default=None,
|
|
|
+ help='Where to save/load BlockData to/from')
|
|
|
+ group.add_argument('--embedding-path', type=str, default=None,
|
|
|
+ help='Where to save/load Open-Retrieval Embedding'
|
|
|
+ ' data to/from')
|
|
|
+
|
|
|
+ # indexer
|
|
|
+ group.add_argument('--indexer-batch-size', type=int, default=128,
|
|
|
+ help='How large of batches to use when doing indexing '
|
|
|
+ 'jobs')
|
|
|
+ group.add_argument('--indexer-log-interval', type=int, default=1000,
|
|
|
+ help='After how many batches should the indexer '
|
|
|
+ 'report progress')
|
|
|
+ return parser
|
|
|
+
|
|
|
+
|
|
|
+def _add_vit_args(parser):
|
|
|
+ group = parser.add_argument_group(title="vit")
|
|
|
+
|
|
|
+ group.add_argument('--num-classes', type=int, default=1000,
|
|
|
+ help='num of classes in vision classificaiton task')
|
|
|
+ group.add_argument('--img-dim', type=int, default=224,
|
|
|
+ help='Image size for vision classification task')
|
|
|
+ group.add_argument('--num-channels', type=int, default=3,
|
|
|
+ help='Number of channels in input image data')
|
|
|
+ group.add_argument('--patch-dim', type=int, default=16,
|
|
|
+ help='patch dimension used in vit')
|
|
|
+
|
|
|
+ return parser
|