checkpointing.py

# 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.

"""Input/output checkpointing."""

import os
import random
import sys
import numpy as np

import torch

from megatron import (get_args,
                      mpu,
                      print_rank_0,
                      update_num_microbatches,
                      utils)

_CHECKPOINT_VERSION = None

def set_checkpoint_version(value):
    global _CHECKPOINT_VERSION
    if _CHECKPOINT_VERSION is not None:
        assert _CHECKPOINT_VERSION == value, \
            "checkpoint versions do not match"
    _CHECKPOINT_VERSION = value

def get_checkpoint_version():
    global _CHECKPOINT_VERSION
    return _CHECKPOINT_VERSION

def check_checkpoint_args(checkpoint_args):
    """Ensure fixed arguments for a model are the same for the input
    arguments and the one retrieved from checkpoint."""
    args = get_args()

    def _compare(arg_name, old_arg_name=None):
        if old_arg_name is not None:
            checkpoint_value = getattr(checkpoint_args, old_arg_name)
        else:
            checkpoint_value = getattr(checkpoint_args, arg_name)
        args_value = getattr(args, arg_name)
        error_message = '{} value from checkpoint ({}) is not equal to the ' \
                        'input argument value ({}).'.format(
                            arg_name, checkpoint_value, args_value)
        assert checkpoint_value == args_value, error_message

    _compare('num_layers')
    _compare('hidden_size')
    _compare('num_attention_heads')
    if args.vocab_file:
        _compare('max_position_embeddings')
        _compare('make_vocab_size_divisible_by')
        _compare('padded_vocab_size')
        _compare('tokenizer_type')
    if args.data_parallel_random_init:
        _compare('data_parallel_random_init')
    if get_checkpoint_version() < 3.0:
        _compare('tensor_model_parallel_size',
                 old_arg_name='model_parallel_size')
    if get_checkpoint_version() >= 3.0:
        _compare('tensor_model_parallel_size')
        _compare('pipeline_model_parallel_size')

def ensure_directory_exists(filename):
    """Build filename's path if it does not already exists."""
    dirname = os.path.dirname(filename)
    if not os.path.exists(dirname):
        os.makedirs(dirname)


def get_checkpoint_names(checkpoints_path, iteration, use_distributed_optimizer,
                         release=False):
    """A unified checkpoint name."""
    if release:
        directory = 'release'
    else:
        directory = 'iter_{:07d}'.format(iteration)
    # Use both the tensor and pipeline MP rank. If using the distributed
    # optimizer, then the optimizer's path must additionally include the
    # data parallel rank.
    common_path = os.path.join(
        checkpoints_path,
        directory,
        "mp_rank_%02d_%03d" % (
            mpu.get_tensor_model_parallel_rank(),
            mpu.get_pipeline_model_parallel_rank()))
    model_name = os.path.join(common_path, "model_rng.pt")
    if use_distributed_optimizer:
        optim_name = os.path.join(
            common_path + "_%03d" % mpu.get_data_parallel_rank(),
            "optim.pt")
    else:
        optim_name = os.path.join(common_path, "optim.pt")
    return model_name, optim_name


def get_checkpoint_tracker_filename(checkpoints_path):
    """Tracker file rescords the latest chckpoint during
    training to restart from."""
    return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt')


def read_metadata(tracker_filename):
    # Read the tracker file and either set the iteration or
    # mark it as a release checkpoint.
    iteration = 0
    release = False
    with open(tracker_filename, 'r') as f:
        metastring = f.read().strip()
        try:
            iteration = int(metastring)
        except ValueError:
            release = metastring == 'release'
            if not release:
                print_rank_0('ERROR: Invalid metadata file {}. Exiting'.format(
                    tracker_filename))
                sys.exit()
    assert iteration > 0 or release, 'error parsing metadata file {}'.format(
        tracker_filename)

    # Get the max iteration retrieved across the ranks.
    iters_cuda = torch.cuda.LongTensor([iteration])
    torch.distributed.all_reduce(iters_cuda, op=torch.distributed.ReduceOp.MAX)
    max_iter = iters_cuda[0].item()

    # We should now have all the same iteration.
    # If not, print a warning and chose the maximum
    # iteration across all ranks.
    if iteration != max_iter:
        print('WARNING: on rank {} found iteration {} in the '
              'metadata while max iteration across the ranks '
              'is {}, replacing it with max iteration.'.format(
                  rank, iteration, max_iter), flush=True)
    return max_iter, release


def get_rng_state():
    """ collect rng state across data parallel ranks """
    args = get_args()
    rng_state = {
        'random_rng_state': random.getstate(),
        'np_rng_state': np.random.get_state(),
        'torch_rng_state': torch.get_rng_state(),
        'cuda_rng_state': torch.cuda.get_rng_state(),
        'rng_tracker_states': mpu.get_cuda_rng_tracker().get_states()}

    rng_state_list = None
    if torch.distributed.is_initialized() and \
            mpu.get_data_parallel_world_size() > 1 and \
            args.data_parallel_random_init:
        rng_state_list = \
            [None for i in range(mpu.get_data_parallel_world_size())]
        torch.distributed.all_gather_object(
            rng_state_list,
            rng_state,
            group=mpu.get_data_parallel_group())
    else:
        rng_state_list = [rng_state]

    return rng_state_list


def save_checkpoint(iteration, model, optimizer, opt_param_scheduler):
    """Save a model checkpoint."""
    args = get_args()

    # Only rank zero of the data parallel writes to the disk.
    model = utils.unwrap_model(model)

    print_rank_0('saving checkpoint at iteration {:7d} to {}'.format(
        iteration, args.save))

    # Collect rng state across data parallel ranks.
    rng_state = get_rng_state()

    # Checkpoint file names.
    model_checkpoint_name, optim_checkpoint_name = \
        get_checkpoint_names(args.save, iteration, args.use_distributed_optimizer)

    # Save args, model, RNG.
    if not torch.distributed.is_initialized() \
       or mpu.get_data_parallel_rank() == 0:

        # Arguments, iteration, and model.
        state_dict = {}
        state_dict['args'] = args
        state_dict['checkpoint_version'] = 3.0
        state_dict['iteration'] = iteration
        if len(model) == 1:
            state_dict['model'] = model[0].state_dict_for_save_checkpoint()
        else:
            for i in range(len(model)):
                mpu.set_virtual_pipeline_model_parallel_rank(i)
                state_dict['model%d' % i] = model[i].state_dict_for_save_checkpoint()

        # RNG states.
        if not args.no_save_rng:
            state_dict["rng_state"] = rng_state

        # Save.
        ensure_directory_exists(model_checkpoint_name)
        torch.save(state_dict, model_checkpoint_name)

    # Save optimizer state. (Optimizer is saved separately from the model, due
    # to the conflicting data pattern when using the distributed optimizer.)
    if not args.no_save_optim \
       and (not torch.distributed.is_initialized()
            or mpu.get_data_parallel_rank() == 0
            or args.use_distributed_optimizer):

        # Optimizer stuff.
        state_dict = {}
        if optimizer is not None:
            state_dict['optimizer'] = optimizer.state_dict()
        if opt_param_scheduler is not None:
            state_dict['opt_param_scheduler'] = opt_param_scheduler.state_dict()

        # Save.
        ensure_directory_exists(optim_checkpoint_name)
        torch.save(state_dict, optim_checkpoint_name)

    # Wait so everyone is done (necessary)
    if torch.distributed.is_initialized():
        torch.distributed.barrier()

    print_rank_0('  successfully saved checkpoint at iteration {:7d} to {}'.format(
        iteration, args.save))

    # And update the latest iteration
    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
        tracker_filename = get_checkpoint_tracker_filename(args.save)
        with open(tracker_filename, 'w') as f:
            f.write(str(iteration))

    # Wait so everyone is done (not necessary)
    if torch.distributed.is_initialized():
        torch.distributed.barrier()

def _transpose_first_dim(t, num_splits, num_splits_first, model):
    input_shape = t.size()
    # We use a self_attention module but the values extracted aren't
    # specific to self attention so should work for cross attention as well
    while hasattr(model, 'module'):
        model = model.module
    attention_module = model.language_model.encoder.layers[0].self_attention
    hidden_size_per_attention_head = attention_module.hidden_size_per_attention_head
    num_attention_heads_per_partition = attention_module.num_attention_heads_per_partition
    if num_splits_first:
        """[num_splits * np * hn, h]
        -->(view) [num_splits, np, hn, h]
        -->(tranpose) [np, num_splits, hn, h]
        -->(view) [np * num_splits * hn, h] """

        intermediate_shape = \
            (num_splits, num_attention_heads_per_partition,
             hidden_size_per_attention_head) + input_shape[1:]

        t = t.view(*intermediate_shape)
        t = t.transpose(0, 1).contiguous()
    else:
        """[np * hn * num_splits, h]
        -->(view) [np, hn, num_splits, h]
        -->(tranpose) [np, num_splits, hn, h]
        -->(view) [np * num_splits * hn, h] """

        intermediate_shape = \
            (num_attention_heads_per_partition,
             hidden_size_per_attention_head, num_splits) +\
             input_shape[1:]

        t = t.view(*intermediate_shape)
        t = t.transpose(1, 2).contiguous()
    t = t.view(*input_shape)

    return t

def fix_query_key_value_ordering(model, checkpoint_version):
    """Fix up query/key/value matrix ordering if checkpoint
    version is smaller than 2.0
    """
    if checkpoint_version < 2.0:
        if isinstance(model, list):
            assert len(model)==1
            model = model[0]
        for name, param in model.named_parameters():
            if name.endswith(('.query_key_value.weight', '.query_key_value.bias')):
                if checkpoint_version == 0:
                    fixed_param = _transpose_first_dim(param.data, 3, True, model)
                elif checkpoint_version == 1.0:
                    fixed_param = _transpose_first_dim(param.data, 3, False, model)
                else:
                    print_rank_0(f"Invalid checkpoint version {checkpoint_version}.")
                    sys.exit()
                param.data.copy_(fixed_param)
            if name.endswith(('.key_value.weight', '.key_value.bias')):
                if checkpoint_version == 0:
                    fixed_param = _transpose_first_dim(param.data, 2, True, model)
                elif checkpoint_version == 1.0:
                    fixed_param = _transpose_first_dim(param.data, 2, False, model)
                else:
                    print_rank_0(f"Invalid checkpoint version {checkpoint_version}.")
                    sys.exit()
                param.data.copy_(fixed_param)
        print_rank_0(" succesfully fixed query-key-values ordering for"
                    " checkpoint version {}".format(checkpoint_version))

def load_checkpoint(model, optimizer, opt_param_scheduler, load_arg='load', strict=True):
    """Load a model checkpoint and return the iteration.
    strict (bool): whether to strictly enforce that the keys in
        :attr:`state_dict` of the checkpoint match the names of
        parameters and buffers in model.
    """
    args = get_args()
    load_dir = getattr(args, load_arg)

    model = utils.unwrap_model(model)

    # Read the tracker file and set the iteration.
    tracker_filename = get_checkpoint_tracker_filename(load_dir)

    # If no tracker file, return iretation zero.
    if not os.path.isfile(tracker_filename):
        print_rank_0('WARNING: could not find the metadata file {} '.format(
            tracker_filename))
        print_rank_0('    will not load any checkpoints and will start from '
                     'random')
        return 0

    # Otherwise, read the tracker file and either set the iteration or
    # mark it as a release checkpoint.
    iteration, release = read_metadata(tracker_filename)

    # Checkpoint.
    model_checkpoint_name, optim_checkpoint_name = \
        get_checkpoint_names(load_dir, iteration,
                             args.use_distributed_optimizer,
                             release)
    print_rank_0(f' loading checkpoint from {args.load} at iteration {iteration}')

    # Load the checkpoint.
    try:
        model_state_dict = torch.load(model_checkpoint_name, map_location='cpu')
        optim_state_dict = torch.load(optim_checkpoint_name, map_location='cpu')
    except ModuleNotFoundError:
        from megatron.fp16_deprecated import loss_scaler
        # For backward compatibility.
        print_rank_0(' > deserializing using the old code structure ...')
        sys.modules['fp16.loss_scaler'] = sys.modules[
            'megatron.fp16_deprecated.loss_scaler']
        sys.modules['megatron.fp16.loss_scaler'] = sys.modules[
            'megatron.fp16_deprecated.loss_scaler']
        model_state_dict = torch.load(model_checkpoint_name, map_location='cpu')
        optim_state_dict = torch.load(optim_checkpoint_name, map_location='cpu')
        sys.modules.pop('fp16.loss_scaler', None)
        sys.modules.pop('megatron.fp16.loss_scaler', None)
    except BaseException as e:
        print_rank_0('could not load the checkpoint')
        print_rank_0(e)
        sys.exit()

    # Set checkpoint version.
    set_checkpoint_version(model_state_dict.get('checkpoint_version', 0))

    # Set iteration.
    if args.finetune or release:
        iteration = 0
    else:
        try:
            iteration = model_state_dict['iteration']
        except KeyError:
            try:  # Backward compatible with older checkpoints
                iteration = model_state_dict['total_iters']
            except KeyError:
                print_rank_0('A metadata file exists but unable to load '
                             'iteration from checkpoint {}, exiting'.format(
                                 checkpoint_name))
                sys.exit()

    # Check arguments.
    assert args.consumed_train_samples == 0
    assert args.consumed_valid_samples == 0
    if 'args' in model_state_dict:
        checkpoint_args = model_state_dict['args']
        check_checkpoint_args(checkpoint_args)
        args.consumed_train_samples = getattr(checkpoint_args,
                                              'consumed_train_samples', 0)
        update_num_microbatches(consumed_samples=args.consumed_train_samples)
        args.consumed_valid_samples = getattr(checkpoint_args,
                                              'consumed_valid_samples', 0)
    else:
        print_rank_0('could not find arguments in the checkpoint ...')

    # Model.
    if len(model) == 1:
        model[0].load_state_dict(model_state_dict['model'], strict=strict)
    else:
        for i in range(len(model)):
            mpu.set_virtual_pipeline_model_parallel_rank(i)
            model[i].load_state_dict(model_state_dict['model%d' % i], strict=strict)

    # Fix up query/key/value matrix ordering if needed
    checkpoint_version = get_checkpoint_version()
    print_rank_0(f' checkpoint version {checkpoint_version}')
    fix_query_key_value_ordering(model, checkpoint_version)

    # Optimizer.
    if not release and not args.finetune and not args.no_load_optim:
        try:
            if optimizer is not None:
                optimizer.load_state_dict(optim_state_dict['optimizer'])
            if opt_param_scheduler is not None:
                if 'lr_scheduler' in optim_state_dict: # backward compatbility
                    opt_param_scheduler.load_state_dict(optim_state_dict['lr_scheduler'])
                else:
                    opt_param_scheduler.load_state_dict(optim_state_dict['opt_param_scheduler'])
        except KeyError:
            print_rank_0('Unable to load optimizer from checkpoint {}. '
                         'Specify --no-load-optim or --finetune to prevent '
                         'attempting to load the optimizer state, '
                         'exiting ...'.format(checkpoint_name))
            sys.exit()

    # rng states.
    if not release and not args.finetune and not args.no_load_rng:
        try:
            if 'rng_state' in model_state_dict:
                # access rng_state for data parallel rank
                if args.data_parallel_random_init:

                    rng_state = model_state_dict['rng_state'][mpu.get_data_parallel_rank()]
                else:
                    rng_state = model_state_dict['rng_state'][0]
                random.setstate(rng_state['random_rng_state'])
                np.random.set_state(rng_state['np_rng_state'])
                torch.set_rng_state(rng_state['torch_rng_state'])
                torch.cuda.set_rng_state(rng_state['cuda_rng_state'])
                # Check for empty states array
                if not rng_state['rng_tracker_states']:
                    raise KeyError
                mpu.get_cuda_rng_tracker().set_states(
                    rng_state['rng_tracker_states'])
            else:  # backward compatability
                random.setstate(model_state_dict['random_rng_state'])
                np.random.set_state(model_state_dict['np_rng_state'])
                torch.set_rng_state(model_state_dict['torch_rng_state'])
                torch.cuda.set_rng_state(model_state_dict['cuda_rng_state'])
                # Check for empty states array
                if not model_state_dict['rng_tracker_states']:
                    raise KeyError
                mpu.get_cuda_rng_tracker().set_states(
                    model_state_dict['rng_tracker_states'])
        except KeyError:
            print_rank_0('Unable to load rng state from checkpoint {}. '
                         'Specify --no-load-rng or --finetune to prevent '
                         'attempting to load the rng state, '
                         'exiting ...'.format(checkpoint_name))
            sys.exit()

    # Some utilities want to load a checkpoint without distributed being initialized
    if torch.distributed.is_initialized():
        torch.distributed.barrier()

    print_rank_0(f'  successfully loaded checkpoint from {args.load} '
                 f'at iteration {iteration}')

    return iteration


def load_biencoder_checkpoint(model, only_query_model=False,
        only_context_model=False, custom_load_path=None):
    """
    selectively load retrieval models for indexing/retrieving 
    from saved checkpoints
    """

    args = get_args()

    model = utils.unwrap_model(model)

    load_path = custom_load_path if custom_load_path is not None else args.load

    tracker_filename = get_checkpoint_tracker_filename(load_path)
    with open(tracker_filename, 'r') as f:
        iteration = int(f.read().strip())

    checkpoint_name, _ = get_checkpoint_names(load_path, iteration,
                                              args.use_distributed_optimizer,
                                              False)
    if mpu.get_data_parallel_rank() == 0:
        print('global rank {} is loading checkpoint {}'.format(
            torch.distributed.get_rank(), checkpoint_name))

    state_dict = torch.load(model_checkpoint_name, map_location='cpu')
    ret_state_dict = state_dict['model']

    if only_query_model:
        ret_state_dict.pop('context_model')
    if only_context_model:
        ret_state_dict.pop('query_model')

    assert len(model) == 1
    model[0].load_state_dict(ret_state_dict)
    torch.distributed.barrier()

    if mpu.get_data_parallel_rank() == 0:
        print(' successfully loaded {}'.format(checkpoint_name))

    return model