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- # coding=utf-8
- # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- """Merge model parallel partitions."""
- import os
- import re
- import sys
- sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
- os.path.pardir)))
- import torch
- from megatron import mpu
- from megatron.checkpointing import load_checkpoint, save_checkpoint
- from megatron.checkpointing import ensure_directory_exists
- from megatron.checkpointing import get_checkpoint_name
- from megatron.checkpointing import get_checkpoint_version
- from megatron.checkpointing import get_checkpoint_tracker_filename
- from megatron.global_vars import set_global_variables, get_args
- from megatron.global_vars import rebuild_tokenizer
- def split_into_partitions(tensor, num_partitions, partition_dim, stride):
- per_partition_size = mpu.utils.divide(tensor.size(partition_dim),
- num_partitions)
- per_partition_per_stride_size = mpu.utils.divide(per_partition_size, stride)
- partitions_list = torch.split(tensor,
- per_partition_per_stride_size,
- dim=partition_dim)
- partitions = []
- for i in range(num_partitions):
- partition = torch.cat(partitions_list[i::num_partitions],
- dim=partition_dim)
- partitions.append(partition)
- return partitions
- def merge_partitions(merged, partitions, partition_dim, stride):
- # Number and size of each partition.
- num_partitions = len(partitions)
- per_partition_size = None
- for partition in partitions:
- if per_partition_size is None:
- per_partition_size = partition.size(partition_dim)
- else:
- assert per_partition_size == partition.size(partition_dim)
- def concat_partitions(partitions_):
- with torch.no_grad():
- if (per_partition_size * num_partitions) == merged.size(
- partition_dim):
- torch.cat(partitions_, dim=partition_dim, out=merged)
- else:
- print(' ***WARNING*** sizes do not match. Will cut '
- 'the merged partitions by {} along dimension {} '
- 'to reduce the size from {} to {} ...'.format(
- (per_partition_size * num_partitions) - \
- merged.size(partition_dim), partition_dim,
- per_partition_size * num_partitions,
- merged.size(partition_dim)))
- merged_ = torch.cat(partitions_, dim=partition_dim)
- merged_split = torch.split(merged_, merged.size(partition_dim),
- dim=partition_dim)
- merged_ = merged_split[0]
- assert merged_.size(partition_dim) == merged.size(partition_dim)
- merged.data.copy_(merged_.data)
- # If stride is 1, then do simple concatination.
- if stride == 1:
- concat_partitions(partitions)
- return
- # For none unity strides, first split based on stride and then group.
- per_partition_per_stride_size = mpu.utils.divide(per_partition_size, stride)
- # Chunk and build a list.
- chunks = None
- for i, partition in enumerate(partitions):
- chunk = torch.split(partition,
- per_partition_per_stride_size,
- dim=partition_dim)
- if chunks is None:
- chunks = [0]*(num_partitions*len(chunk))
- chunks[i::num_partitions] = chunk
- # Concatinate.
- concat_partitions(chunks)
- return
- def get_model(model_type):
- if model_type == 'BERT':
- from pretrain_bert import model_provider
- elif model_type == 'GPT':
- from pretrain_gpt import model_provider
- elif model_type == 'RACE':
- from tasks.race.finetune import model_provider
- elif model_type == ['MNLI', 'QQP']:
- num_classes = 2
- if model_type == 'MNLI':
- num_classes = 3
- from megatron.model.classification import Classification
- def model_provider():
- return Classification(num_classes=num_classes, num_tokentypes=2)
- else:
- raise Exception('unrecognized model type: {}'.format(model_type))
- model = model_provider()
- model = model.half()
- return model
- def get_parallel_checkpoint_name(path):
- tracker_filename = get_checkpoint_tracker_filename(path)
- iteration = 0
- with open(tracker_filename, 'r') as f:
- metastring = f.read().strip()
- iteration = int(metastring)
- assert iteration > 0
- checkpoint_name = get_checkpoint_name(path, iteration)
- return checkpoint_name, iteration
- def test_split_merge():
- print('testing split and merge ...')
- #[QKV.ROW-COL]
- tensor = torch.FloatTensor([[1.11, 1.12, 1.13, 1.14, 1.15],
- [1.21, 1.22, 1.23, 1.24, 1.25],
- [1.31, 1.32, 1.33, 1.34, 1.35],
- [1.41, 1.42, 1.43, 1.44, 1.45],
- [2.11, 2.12, 2.13, 2.14, 2.15],
- [2.21, 2.22, 2.23, 2.24, 2.25],
- [2.31, 2.32, 2.33, 2.34, 2.35],
- [2.41, 2.42, 2.43, 2.44, 2.45],
- [3.11, 3.12, 3.13, 3.14, 3.15],
- [3.21, 3.22, 3.23, 3.24, 3.25],
- [3.31, 3.32, 3.33, 3.34, 3.35],
- [3.41, 3.42, 3.43, 3.44, 3.45]])
- num_partitions = 2
- partition_dim = 0
- stride = 3
- partitions = split_into_partitions(tensor, num_partitions,
- partition_dim, stride)
- merged = torch.zeros_like(tensor)
- merge_partitions(merged, partitions, partition_dim, stride)
- max_error = (merged - tensor).abs().max()
- print(' > max error (should be zero): {}'.format(max_error))
- def get_mp_merge_args(parser):
- """Provide extra arguments required for merging."""
- group = parser.add_argument_group(title='mp merge')
- group.add_argument('--model-type', type=str, required=True,
- choices=['BERT', 'GPT', 'RACE', 'MNLI', 'QQP'],
- help='Type of the mdoel.')
- group.add_argument('--target-pipeline-model-parallel-size', type=int, default=1,
- help='Degree of pipeline model parallelism in output model.')
- return parser
- def main():
- # Arguments do sanity checks on the world size, but we don't care,
- # so trick it into thinking we are plenty of processes
- os.environ["WORLD_SIZE"] = f'{2**31}'
- # Args
- set_global_variables(extra_args_provider=get_mp_merge_args,
- args_defaults = {'use_cpu_initialization': True,
- 'micro_batch_size': 1,
- 'no_load_optim': True,
- 'no_load_rng': True,
- 'no_save_optim': True,
- 'no_save_rng': True,
- 'save_interval': 1})
- args = get_args()
- if args.pipeline_model_parallel_size > 1:
- print("Checkpoints with pipeline model parallelism are not currently supported.")
- exit()
- model_type = args.model_type
- orig_tensor_model_parallel_size = args.tensor_model_parallel_size
- args.tensor_model_parallel_size = 1
- tokenizer = rebuild_tokenizer(args)
- print('\n merging model parallel partitions ...')
- print(' > number of partitions: {}'.format(orig_tensor_model_parallel_size))
- print(' > checkpoint path: {}'.format(args.load))
- print(' > model parameters:')
- print(' number of tokens ................ {} '.format(
- tokenizer.vocab_size))
- print(' number of layers ................ {}'.format(args.num_layers))
- print(' hidden size ..................... {}'.format(args.hidden_size))
- print(' number of attention heads ....... {}'.format(
- args.num_attention_heads))
- print(' maximum position embeddings ..... {}'.format(
- args.max_position_embeddings))
- # Full model.
- print('> building the full model ...')
- mpu.initialize.set_tensor_model_parallel_world_size(1)
- mpu.initialize.set_tensor_model_parallel_rank(0)
- mpu.initialize.set_pipeline_model_parallel_world_size(1)
- mpu.initialize.set_pipeline_model_parallel_rank(0)
- merged_model = get_model(model_type)
- # Build and load partitions.
- partitions = []
- iteration = 0
- args.tensor_model_parallel_size = orig_tensor_model_parallel_size
- tokenizer = rebuild_tokenizer(args)
- mpu.initialize.set_tensor_model_parallel_world_size(args.tensor_model_parallel_size)
- for rank in range(args.tensor_model_parallel_size):
- # Reset these since load_checkpoint asserts they are 0, but we are loading
- # multiple checkpoints in the same process and they get set each time
- args.consumed_train_samples = 0
- args.consumed_valid_samples = 0
- mpu.initialize.set_tensor_model_parallel_rank(rank)
- checkpoint_name, iteration = get_parallel_checkpoint_name(args.load)
- model_ = get_model(model_type)
- print(f'> loading {checkpoint_name} ...')
- load_checkpoint(model_, None, None)
- print(f'> checkpoint version {get_checkpoint_version()}')
- partitions.append(model_)
- # Parameter generators so we can loop through them semiltaneouly.
- merged_params_gen = merged_model.named_parameters()
- partitions_params_gen = [partition.named_parameters()
- for partition in partitions]
- while True:
- try:
- # Get the params and check names.
- name, merged_param = next(merged_params_gen)
- print(' > working on {} ...'.format(name))
- print(' merged type: {}, size: {}'.format(
- merged_param.dtype, list(merged_param.size())))
- partitions_param = []
- for rank, partition_params_gen in enumerate(partitions_params_gen):
- partition_name, partition_param = next(partition_params_gen)
- assert partition_name == name
- partitions_param.append(partition_param)
- print(' partition {} type: {}, size: {}'.format(
- rank, partition_param.dtype, list(partition_param.size())))
- # For the non-parallel parameters, simply copy the rank 0 values.
- if not hasattr(merged_param, 'tensor_model_parallel'):
- print(' none-parallel parameter, simple copy from rank 0')
- with torch.no_grad():
- merged_param.data.copy_(partitions_param[0].data)
- # For parallel parameters, merge the values
- else:
- dim = merged_param.partition_dim
- stride = merged_param.partition_stride
- print(f' parallel parameter merge with stride {stride} along '
- f'dimention {dim}')
- merge_partitions(merged_param,
- partitions_param,
- dim,
- stride)
- except StopIteration:
- break
- partitions = []
- args.tensor_model_parallel_size = 1
- args.pipeline_model_parallel_size = args.target_pipeline_model_parallel_size
- assert args.num_layers % args.pipeline_model_parallel_size == 0, \
- 'num_layers must be divisible by target pipeline model parallel size'
- layers_per_part = args.num_layers // args.pipeline_model_parallel_size
- tokenizer = rebuild_tokenizer(args)
- mpu.initialize.set_tensor_model_parallel_world_size(args.tensor_model_parallel_size)
- mpu.initialize.set_tensor_model_parallel_rank(0)
- mpu.initialize.set_pipeline_model_parallel_world_size(args.pipeline_model_parallel_size)
- # regex to parse out layer number from param name
- layer_re = re.compile('layers\.([0-9]+)')
- if args.pipeline_model_parallel_size > 1:
- merged_params = {}
- for name, merged_param in merged_model.named_parameters():
- merged_params[name] = merged_param
- for rank in range(args.pipeline_model_parallel_size):
- mpu.initialize.set_pipeline_model_parallel_rank(rank)
- model = get_model(model_type)
- def update_layer_num(m):
- # TODO! This assumes no interleaved pipeline execution
- layer = int(m.group(1))
- layer += rank * layers_per_part
- return f'layers.{layer}'
- for dst_name, partition_param in model.named_parameters():
- if dst_name == "word_embeddings.weight":
- # See comment in MegatronModule.initialize_word_embeddings()
- src_name = "language_model.embedding.word_embeddings.weight"
- else:
- # Translate destination layer number (0-N for each partition)
- # to source layer number (single-model layer number)
- src_name = re.sub(layer_re, update_layer_num, dst_name)
- print(f" > copying {src_name} to {dst_name} in rank {rank}'s model")
- partition_param.data.copy_(merged_params[src_name].data)
- partitions.append(model)
- else:
- partitions = [merged_model]
- for rank, model in enumerate(partitions):
- mpu.initialize.set_pipeline_model_parallel_rank(rank)
- print(f"> saving rank {rank}'s model")
- save_checkpoint(iteration, model, None, None)
- print('done :-)')
- if __name__ == '__main__':
- main()
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