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 fp16 import FP16_Optimizer
import mpu
import model
from tensorboardX import SummaryWriter

SUMMARY_WRITER_DIR_NAME = 'runs'


def get_sample_writer(name, base="..", iteration=0):
    """Returns a tensorboard summary writer
    """
    return SummaryWriter(
        log_dir=os.path.join(base, SUMMARY_WRITER_DIR_NAME, name), purge_step=iteration)


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 print_args(args):
    """Print arguments."""

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


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 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.memory_reserved() / mega_bytes)
    print_rank_0(string)


def get_checkpoint_name(checkpoints_path, iteration, release=False, zero=False):
    if release:
        d = 'release'
    else:
        d = '{:d}'.format(iteration)
    if zero:
        dp_rank = mpu.get_data_parallel_rank()
        d += '_zero_dp_rank_{}'.format(dp_rank)
    return os.path.join(checkpoints_path, d, 'mp_rank_{:02d}_model_states.pt'.format(mpu.get_model_parallel_rank()))


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_zero_checkpoint(args, iteration, optimizer):
    zero_sd = {'iteration': iteration,
               'optimizer_state_dict': optimizer.state_dict()}
    zero_checkpoint_name = get_checkpoint_name(args.save, iteration, zero=True)
    ensure_directory_exists(zero_checkpoint_name)
    torch.save(zero_sd, zero_checkpoint_name)
    print('  successfully saved {}'.format(zero_checkpoint_name))


def save_checkpoint(iteration, model, optimizer,
                    lr_scheduler, args):
    """Save a model checkpoint."""
    if args.deepspeed:
        save_ds_checkpoint(iteration, model, lr_scheduler, args)
    else:
        # 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['module'] = model.state_dict()

            # 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 save_ds_checkpoint(iteration, model, lr_scheduler, args):
    """Save a model checkpoint."""

    sd = {}
    sd['iteration'] = iteration
    if lr_scheduler is not None:
        sd['client_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()

    model.save_checkpoint(args.save, str(iteration), client_state=sd)


def get_checkpoint_iteration(args):
    # 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, False, False
    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)

    return iteration, release, True


def extend_position_embedding(weight, length):
    ori_length, hidden_size = weight.shape
    assert length % ori_length == 0
    position_embeddings = weight.expand(length // ori_length, -1, -1).reshape(length, hidden_size)
    return position_embeddings

def load_checkpoint(model, optimizer, lr_scheduler, args, load_optimizer_states=True):
    """Load a model checkpoint."""

    iteration, release, success = get_checkpoint_iteration(args)

    if not success:
        return 0

    if args.deepspeed:
        
        checkpoint_name, sd = model.load_checkpoint(args.load, iteration, load_optimizer_states=not args.no_load_optim)
        if args.fp16 and args.no_load_optim:
            model.optimizer.refresh_fp32_params()
            
        if "client_lr_scheduler" in sd:
            lr_scheduler.load_state_dict(sd["client_lr_scheduler"])
            print_rank_0("Load lr scheduler state")
        if checkpoint_name is None:
            if mpu.get_data_parallel_rank() == 0:
                print("Unable to load checkpoint.")
            return iteration

    else:

        # 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.
        sd = torch.load(checkpoint_name, map_location='cpu')

        if isinstance(model, torchDDP):
            model = model.module

        # Model.
        try:
            model.load_state_dict(sd['module'])
        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 and load_optimizer_states:
                    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()

    # 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()

    # 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-rng or --finetune to prevent '
                         'attempting to load the random '
                         'state.'.format(checkpoint_name))
            exit()

    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)