utils.py

# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Utilities for logging and serialization"""

import os
import random
import time
import numpy as np
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP

from apex.optimizers import FusedAdam as Adam

from megatron import mpu
from megatron.fp16 import FP16_Module
from megatron.fp16 import FP16_Optimizer
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import get_params_for_weight_decay_optimization


def get_ltor_masks_and_position_ids(data,
                                    eod_token,
                                    reset_position_ids,
                                    reset_attention_mask,
                                    eod_mask_loss):
    """Build masks and position id for left to right model."""

    # Extract batch size and sequence length.
    batch_size, seq_length = data.size()

    # Attention mask (lower triangular).
    if reset_attention_mask:
        att_mask_batch = batch_size
    else:
        att_mask_batch = 1
    attention_mask = torch.tril(torch.ones(
        (att_mask_batch, seq_length, seq_length), device=data.device)).view(
            att_mask_batch, 1, seq_length, seq_length)

    # Loss mask.
    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
    if eod_mask_loss:
        loss_mask[data == eod_token] = 0.0

    # Position ids.
    position_ids = torch.arange(seq_length, dtype=torch.long,
                                device=data.device)
    position_ids = position_ids.unsqueeze(0).expand_as(data)
    # We need to clone as the ids will be modifed based on batch index.
    if reset_position_ids:
        position_ids = position_ids.clone()

    if reset_position_ids or reset_attention_mask:
        # Loop through the batches:
        for b in range(batch_size):

            # Find indecies where EOD token is.
            eod_index = position_ids[b, data[b] == eod_token]
            # Detach indecies from positions if going to modify positions.
            if reset_position_ids:
                eod_index = eod_index.clone()

            # Loop through EOD indecies:
            prev_index = 0
            for j in range(eod_index.size()[0]):
                i = eod_index[j]
                # Mask attention loss.
                if reset_attention_mask:
                    attention_mask[b, 0, (i+1):, :(i+1)] = 0
                # Reset positions.
                if reset_position_ids:
                    position_ids[b, (i+1):] -= (i + 1 - prev_index)
                    prev_index = i + 1

    return attention_mask, loss_mask, position_ids


def reduce_losses(losses):
    reduced_losses = torch.cat(
        [loss.clone().detach().view(1) for loss in losses])
    torch.distributed.all_reduce(reduced_losses)
    reduced_losses = reduced_losses / torch.distributed.get_world_size()

    return reduced_losses


def get_tensorboard_writer(args):
    writer = None
    if hasattr(args, 'tensorboard_dir') and \
       args.tensorboard_dir and args.rank == 0:
        try:
            from torch.utils.tensorboard import SummaryWriter
            writer = SummaryWriter(log_dir=args.tensorboard_dir)
        except ModuleNotFoundError:
            print_rank_0('WARNING: TensorBoard writing requested but is not '
                         'available (are you using PyTorch 1.1.0 or later?), '
                         'no TensorBoard logs will be written.')
            writer = None
    return writer


def print_rank_0(message):
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)


def enable_adlr_autoresume(args):
    print_rank_0('enabling autoresume ...')
    import sys
    sys.path.append(os.environ.get('SUBMIT_SCRIPTS', '.'))
    try:
        from userlib.auto_resume import AutoResume
    except:
        print_rank_0('ADLR autoresume is not available, exiting ...')
        exit()
    args.AutoResume = AutoResume
    args.AutoResume.init()


def check_adlr_autoresume_termination(iteration, model, optimizer,
                                      lr_scheduler, args):
    # Add barrier to ensure consistnecy.
    torch.distributed.barrier()
    if args.AutoResume.termination_requested():
        if args.save:
            save_checkpoint(iteration, model, optimizer, lr_scheduler, args)
        print_rank_0(">>> autoresume termination request found!")
        if torch.distributed.get_rank() == 0:
            args.AutoResume.request_resume()
        print_rank_0(">>> training terminated. Returning")
        exit(0)


def print_args(args, writer=None):
    """Print arguments."""

    print('arguments:', flush=True)
    for arg in vars(args):
        dots = '.' * (29 - len(arg))
        print('  {} {} {}'.format(arg, dots, getattr(args, arg)), flush=True)

        if writer:
            writer.add_text(arg, str(getattr(args, arg)))


def print_params_min_max_norm(optimizer, iteration):
    """Print min, max, and norm of all parameters."""
    index = 0
    rank = torch.distributed.get_rank()
    string = 'iteration, rank, index, model-parallel,min, max, norm\n'
    optimizer_ = optimizer
    if isinstance(optimizer, FP16_Optimizer):
        optimizer_ = optimizer.optimizer
    for param_group in optimizer_.param_groups:
        for param in param_group['params']:
            index += 1
            min_ = param.data.min()
            max_ = param.data.max()
            norm = param.data.norm()
            string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
                iteration, rank, index, int(param.model_parallel))
            string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
    print(string, flush=True)


class Timers:
    """Group of timers."""

    class Timer:
        """Timer."""

        def __init__(self, name):
            self.name_ = name
            self.elapsed_ = 0.0
            self.started_ = False
            self.start_time = time.time()

        def start(self):
            """Start the timer."""
            assert not self.started_, 'timer has already been started'
            torch.cuda.synchronize()
            self.start_time = time.time()
            self.started_ = True

        def stop(self):
            """Stop the timer."""
            assert self.started_, 'timer is not started'
            torch.cuda.synchronize()
            self.elapsed_ += (time.time() - self.start_time)
            self.started_ = False

        def reset(self):
            """Reset timer."""
            self.elapsed_ = 0.0
            self.started_ = False

        def elapsed(self, reset=True):
            """Calculate the elapsed time."""
            started_ = self.started_
            # If the timing in progress, end it first.
            if self.started_:
                self.stop()
            # Get the elapsed time.
            elapsed_ = self.elapsed_
            # Reset the elapsed time
            if reset:
                self.reset()
            # If timing was in progress, set it back.
            if started_:
                self.start()
            return elapsed_

    def __init__(self):
        self.timers = {}

    def __call__(self, name):
        if name not in self.timers:
            self.timers[name] = self.Timer(name)
        return self.timers[name]

    def write(self, names, writer, iteration, normalizer=1.0, reset=False):
        """Write timers to a tensorboard writer"""
        # currently when using add_scalars,
        # torch.utils.add_scalars makes each timer its own run, which
        # polutes the runs list, so we just add each as a scalar
        assert normalizer > 0.0
        for name in names:
            value = self.timers[name].elapsed(reset=reset) / normalizer
            writer.add_scalar(name + '_time', value, iteration)

    def log(self, names, normalizer=1.0, reset=True):
        """Log a group of timers."""
        assert normalizer > 0.0
        string = 'time (ms)'
        for name in names:
            elapsed_time = self.timers[name].elapsed(
                reset=reset) * 1000.0/ normalizer
            string += ' | {}: {:.2f}'.format(name, elapsed_time)
        print_rank_0(string)


def report_memory(name):
    """Simple GPU memory report."""

    mega_bytes = 1024.0 * 1024.0
    string = name + ' memory (MB)'
    string += ' | allocated: {}'.format(
        torch.cuda.memory_allocated() / mega_bytes)
    string += ' | max allocated: {}'.format(
        torch.cuda.max_memory_allocated() / mega_bytes)
    string += ' | cached: {}'.format(torch.cuda.memory_cached() / mega_bytes)
    string += ' | max cached: {}'.format(
        torch.cuda.max_memory_cached()/ mega_bytes)
    print_rank_0(string)


def vocab_size_with_padding(num_tokens, args):

    after = num_tokens
    multiple = args.make_vocab_size_divisible_by * \
               mpu.get_model_parallel_world_size()
    while (after % multiple) != 0:
        after += 1
    print_rank_0('> padded vocab (size: {}) with {} dummy '
                 'tokens (new size: {})'.format(
                     num_tokens, after - num_tokens, after))
    return after


def initialize_distributed(args):
    """Initialize torch.distributed."""

    # Manually set the device ids.
    device = args.rank % torch.cuda.device_count()
    if args.local_rank is not None:
        device = args.local_rank
    torch.cuda.set_device(device)
    # Call the init process
    init_method = 'tcp://'
    master_ip = os.getenv('MASTER_ADDR', 'localhost')
    master_port = os.getenv('MASTER_PORT', '6000')
    init_method += master_ip + ':' + master_port
    torch.distributed.init_process_group(
        backend=args.distributed_backend,
        world_size=args.world_size, rank=args.rank,
        init_method=init_method)

    # Set the model-parallel / data-parallel communicators.
    mpu.initialize_model_parallel(args.model_parallel_size)


def set_random_seed(seed):
    """Set random seed for reproducability."""

    if seed is not None and seed > 0:
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        mpu.model_parallel_cuda_manual_seed(seed)


def get_checkpoint_name(checkpoints_path, iteration, release=False,
                        mp_rank=None):
    if release:
        d = 'release'
    else:
        d = 'iter_{:07d}'.format(iteration)
    return os.path.join(checkpoints_path, d,
                        'mp_rank_{:02d}'.format(
                            mpu.get_model_parallel_rank() if mp_rank is None \
                            else mp_rank),
                        'model_optim_rng.pt')


def ensure_directory_exists(filename):
    dirname = os.path.dirname(filename)
    if not os.path.exists(dirname):
        os.makedirs(dirname)


def get_checkpoint_tracker_filename(checkpoints_path):
    return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt')


def save_checkpoint(iteration, model, optimizer,
                    lr_scheduler, args):
    """Save a model checkpoint."""
    # Only rank zer0 of the data parallel writes to the disk.
    if isinstance(model, torchDDP):
        model = model.module
    if mpu.get_data_parallel_rank() == 0:
        checkpoint_name = get_checkpoint_name(args.save, iteration)
        print('global rank {} is saving checkpoint at iteration {:7d} to {}'.
              format(torch.distributed.get_rank(), iteration, checkpoint_name))

        sd = {}
        sd['iteration'] = iteration
        sd['model'] = model.state_dict_for_save_checkpoint()

        # Optimizer stuff.
        if not args.no_save_optim:
            if optimizer is not None:
                sd['optimizer'] = optimizer.state_dict()
            if lr_scheduler is not None:
                sd['lr_scheduler'] = lr_scheduler.state_dict()

        # rng states.
        if not args.no_save_rng:
            sd['random_rng_state'] = random.getstate()
            sd['np_rng_state'] = np.random.get_state()
            sd['torch_rng_state'] = torch.get_rng_state()
            sd['cuda_rng_state'] = torch.cuda.get_rng_state()
            sd['rng_tracker_states'] = mpu.get_cuda_rng_tracker().get_states()

        ensure_directory_exists(checkpoint_name)
        torch.save(sd, checkpoint_name)
        print('  successfully saved {}'.format(checkpoint_name))

    # Wait so everyone is done (necessary)
    torch.distributed.barrier()
    # And update the latest iteration
    if 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)
    torch.distributed.barrier()


def load_checkpoint(model, optimizer, lr_scheduler, args):
    """Load a model checkpoint."""
    if isinstance(model, torchDDP):
        model = model.module
    # Read the tracker file and set the iteration.
    tracker_filename = get_checkpoint_tracker_filename(args.load)
    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
    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))
                exit()

    assert iteration > 0 or release, 'error parsing metadata file {}'.format(
        tracker_filename)

    # Checkpoint.
    checkpoint_name = get_checkpoint_name(args.load, iteration, release)
    if mpu.get_data_parallel_rank() == 0:
        print('global rank {} is loading checkpoint {}'.format(
            torch.distributed.get_rank(), checkpoint_name))

    # Load the checkpoint.
    try:
        sd = torch.load(checkpoint_name, map_location='cpu')
    except ModuleNotFoundError:
        # For backward compatibility.
        print_rank_0(' > deserializing using the old code structure ...')
        import sys
        sys.modules['fp16.loss_scaler'] = sys.modules[
            'megatron.fp16.loss_scaler']
        sd = torch.load(checkpoint_name, map_location='cpu')
        sys.modules.pop('fp16.loss_scaler', None)
    except:
        print_rank_0('could not load the checkpoint')
        exit()

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

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

    # rng states.
    if not release and not args.finetune and not args.no_load_rng:
        try:
            random.setstate(sd['random_rng_state'])
            np.random.set_state(sd['np_rng_state'])
            torch.set_rng_state(sd['torch_rng_state'])
            torch.cuda.set_rng_state(sd['cuda_rng_state'])
            mpu.get_cuda_rng_tracker().set_states(sd['rng_tracker_states'])
        except KeyError:
            print_rank_0('Unable to load optimizer from checkpoint {}, exiting.'
                         'Specify --no-load-optim or --finetune to prevent '
                         'attempting to load the optimizer '
                         'state.'.format(checkpoint_name))
            exit()

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

    return iteration


def load_weights(src, dst, dst2src=False):
    """
    Loads weights from src to dst via in place copy.
    src is a huggingface gpt2model, while dst is one of our models.
    dst2src=True loads parameters from our models into huggingface's.
    ^dst2src is still untested
    """
    conv_layer = 'Conv1D' in  str(type(src))
    for n, p in src.named_parameters():
        if dst2src:
            data = dst._parameters[n].data
            load = p.data
        else:
            data = p.data
            load = dst._parameters[n].data
        if conv_layer and 'weight' in n:
            data = data.t().contiguous()
        load.copy_(data)
#        dst._parameters[n].data.copy_(data)

def load_mlp(our, oai, dst2src=False):
    load_weights(oai.c_fc, our.dense_h_to_4h, dst2src)
    load_weights(oai.c_proj, our.dense_4h_to_h, dst2src)

def load_attention(our, oai, dst2src=False):
    load_weights(oai.c_attn, our.query_key_value, dst2src)
    load_weights(oai.c_proj, our.dense, dst2src)

def load_transformer_layer(our, oai, dst2src=False):
    load_weights(oai.ln_1, our.input_layernorm, dst2src)
    load_weights(oai.ln_2, our.post_attention_layernorm, dst2src)
    load_mlp(our.mlp, oai.mlp, dst2src)
    load_attention(our.attention, oai.attn, dst2src)

def move_weights(our, oai, dst2src=False):
    """
    Loads weights from `oai` to `our` via in place copy.
    `oai` is a huggingface gpt2model, while `our` is one of our models.
    dst2src=True loads parameters from our models into huggingface's.
    ^dst2src=True is still untested
    """
#    while isinstance(our, (torchDDP, model.distributed.DistributedDataParallel, FP16_Module)):
#        our=our.module
    transformer_model = oai.transformer
    load_weights(transformer_model.ln_f, our.transformer.final_layernorm, dst2src)
    load_weights(transformer_model.wte, our.word_embeddings, dst2src)
    load_weights(transformer_model.wpe, our.position_embeddings, dst2src)

    for our_layer, oai_layer in zip(our.transformer.layers, oai.transformer.h):
        load_transformer_layer(our_layer, oai_layer, dst2src)


def merge_parallel_state_dicts(state_dicts):
    temp_sd = {}
    for sd in state_dicts:
        for k, v in sd.items():
            temp_sd[k].append()
    pass

def merge_parallel_checkpoints(checkpoint_dir, model_parallel_size):
    pass