__init__.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.

import torch
from apex.optimizers import FusedAdam as Adam
from apex.optimizers import FusedSGD as SGD

from megatron import get_args

from .grad_scaler import ConstantGradScaler, DynamicGradScaler
from .optimizer import Float16OptimizerWithFloat16Params, FP32Optimizer


def get_param_groups(modules,
                     no_weight_decay_cond,
                     scale_lr_cond,
                     lr_mult):
    """creates param groups based on weight decay condition (regularized vs non regularized)
       and learning rate scale condition (args.lr vs lr_mult * args.lr)
       scale_lr_cond is used during finetuning where head of the network requires a scaled
       version of the base learning rate. 
    """
    wd_no_scale_lr = []
    wd_scale_lr = []
    no_wd_no_scale_lr = []
    no_wd_scale_lr = []
    for module in modules:
        for name, param in module.named_parameters():
            if not param.requires_grad:
                continue

            if no_weight_decay_cond is not None:
                no_wd = no_weight_decay_cond(name, param)
            else:
                # do not regularize biases nor Norm parameters
                no_wd = name.endswith(".bias") or len(param.shape) == 1

            if scale_lr_cond is not None:
                scale_lr = scale_lr_cond(name, param)
            else:
                scale_lr = False

            if not no_wd and not scale_lr:
                wd_no_scale_lr.append(param)
            elif not no_wd and scale_lr:
                wd_scale_lr.append(param)
            elif no_wd and not scale_lr:
                no_wd_no_scale_lr.append(param)
            else:
                no_wd_scale_lr.append(param)

    param_groups = []
    if len(wd_no_scale_lr):
        param_groups.append({'params': wd_no_scale_lr, 'wd_mult': 1.0, 'lr_mult': 1.0})
    if len(wd_scale_lr):
        param_groups.append({'params': wd_scale_lr, 'wd_mult': 1.0, 'lr_mult': lr_mult})
    if len(no_wd_no_scale_lr):
        param_groups.append({'params': no_wd_no_scale_lr, 'wd_mult': 0.0, 'lr_mult': 1.0})
    if len(no_wd_scale_lr):
        param_groups.append({'params': no_wd_scale_lr, 'wd_mult': 0.0, 'lr_mult': lr_mult})

    return param_groups

def get_megatron_optimizer(model,
                           no_weight_decay_cond=None,
                           scale_lr_cond=None,
                           lr_mult=1.0):
    args = get_args()

    # Base optimizer.
    param_groups = get_param_groups(model,
                                    no_weight_decay_cond,
                                    scale_lr_cond,
                                    lr_mult)

    if args.optimizer == 'adam':
        optimizer = Adam(param_groups,
                         lr=args.lr,
                         weight_decay=args.weight_decay,
                         betas=(args.adam_beta1, args.adam_beta2),
                         eps=args.adam_eps)

        # preallocating state tensors to avoid fragmentation
        for param_group in optimizer.param_groups:
            for i, param in enumerate(param_group['params']):
                if param.requires_grad:
                    state = optimizer.state[param]
                    if len(state) == 0:
                        # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(param.data, dtype=torch.float)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(param.data, dtype=torch.float)

    elif args.optimizer == 'sgd':
        optimizer = SGD(param_groups,
                        lr=args.lr,
                        weight_decay=args.weight_decay,
                        momentum=args.sgd_momentum)
    else:
        raise Exception('{} optimizer is not supported.'.format(
            args.optimizer))

    # Determine whether the params have main-grad field.
    params_have_main_grad = False
    if args.DDP_impl == 'local':
        params_have_main_grad = True

    if args.fp16 or args.bf16:

        # Grad scaler:
        #    if loss-scale is provided, instantiate the constant scaler.
        #    if we are using fp16 and loss-scale is not present, use a
        #       dynamic scaler.
        #    otherwise we are running in bf16 with no loss-scale so
        #       leave it as None.
        grad_scaler = None
        # Constant loss scale.
        if args.loss_scale:
            grad_scaler = ConstantGradScaler(args.loss_scale)
        # Dynamic loss scale.
        else:
            if args.fp16:
                grad_scaler = DynamicGradScaler(
                    initial_scale=args.initial_loss_scale,
                    min_scale=args.min_loss_scale,
                    growth_factor=2.0,
                    backoff_factor=0.5,
                    growth_interval=args.loss_scale_window,
                    hysteresis=args.hysteresis)

        # Megatron optimizer.
        return Float16OptimizerWithFloat16Params(optimizer,
                                                 args.clip_grad,
                                                 args.log_num_zeros_in_grad,
                                                 params_have_main_grad,
                                                 args.use_contiguous_buffers_in_local_ddp,
                                                 args.bf16,
                                                 grad_scaler)

    # FP32.
    return FP32Optimizer(optimizer, args.clip_grad,
                         args.log_num_zeros_in_grad,
                         params_have_main_grad,
                         args.use_contiguous_buffers_in_local_ddp)