merge_mp_partitions.py

import os
import torch

from arguments import get_args
from megatron import mpu
from megatron.utils import ensure_directory_exists
from megatron.utils import get_checkpoint_name
from megatron.utils import get_checkpoint_tracker_filename
from megatron.utils import vocab_size_with_padding


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, args):

    if model_type == 'BERT':
        from pretrain_albert import model_provider
        args.tokentype_size = 2
    elif  model_type == 'GPT':
        from pretrain_gpt2 import model_provider
    else:
        raise Exception('unrecognized model type: {}'.format(model_type))

    orig_vocab_size = args.vocab_size
    args.vocab_size = vocab_size_with_padding(args.vocab_size, args)
    model = model_provider(args)
    model = model.half()
    args.vocab_size = orig_vocab_size

    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 main(model_type):

    # Args
    args = get_args()

    print('\n merging model parallel partitions ...')
    assert args.vocab_size is not None
    print(' > number of partitions: {}'.format(args.model_parallel_size))
    print(' > checkpoint path: {}'.format(args.load))
    print(' > model parameters:')
    print('    number of tokens ................ {} '.format(args.vocab_size))
    print('    number of layers ................ {}'.format(args.num_layers))
    print('    hidden sise ..................... {}'.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_model_parallel_world_size(1)
    mpu.initialize.set_model_parallel_rank(0)
    merged_model = get_model(model_type, args)

    # Build and load partitions.
    partitions = []
    iteration = 0
    mpu.initialize.set_model_parallel_world_size(args.model_parallel_size)
    for rank in range(args.model_parallel_size):
        mpu.initialize.set_model_parallel_rank(rank)
        checkpoint_name, iteration = get_parallel_checkpoint_name(args.load)
        print('> loading {} ...'.format(checkpoint_name))
        model_ = get_model(model_type, args)
        sd = torch.load(checkpoint_name, map_location='cpu')
        model_.load_state_dict(sd['model'])
        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, '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:
                print('     parallel parameter merge with stride {} along '
                      'dimention {}'.format(merged_param.stride,
                                            merged_param.partition_dim))
                merge_partitions(merged_param,
                                 partitions_param,
                                 merged_param.partition_dim,
                                 merged_param.stride)

        except StopIteration:
            break


    # Save the model.
    mpu.initialize.set_model_parallel_rank(0)
    sd = {}
    sd['model'] = merged_model.state_dict_for_save_checkpoint()
    sd['iteration'] = iteration
    merged_path = os.path.join(args.load, 'merged')
    checkpoint_name = get_checkpoint_name(merged_path, iteration)
    ensure_directory_exists(checkpoint_name)
    print('> saving merged model to {}'.format(checkpoint_name))
    torch.save(sd, checkpoint_name)

    print('done :-)')


if __name__ == '__main__':

    main('BERT')