439 lines
20 KiB
Python
Executable File
439 lines
20 KiB
Python
Executable File
import os
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import copy
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from functools import partial
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from contextlib import nullcontext
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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import numpy as np
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from .utils import *
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from .base import Trainer
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from ..utils.general_utils import *
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from ..utils.dist_utils import *
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from ..utils import grad_clip_utils, elastic_utils
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class BasicTrainer(Trainer):
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"""
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Trainer for basic training loop.
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Args:
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models (dict[str, nn.Module]): Models to train.
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dataset (torch.utils.data.Dataset): Dataset.
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output_dir (str): Output directory.
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load_dir (str): Load directory.
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step (int): Step to load.
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batch_size (int): Batch size.
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batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
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batch_split (int): Split batch with gradient accumulation.
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max_steps (int): Max steps.
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optimizer (dict): Optimizer config.
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lr_scheduler (dict): Learning rate scheduler config.
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elastic (dict): Elastic memory management config.
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grad_clip (float or dict): Gradient clip config.
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ema_rate (float or list): Exponential moving average rates.
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fp16_mode (str): FP16 mode.
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- None: No FP16.
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- 'inflat_all': Hold a inflated fp32 master param for all params.
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- 'amp': Automatic mixed precision.
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fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
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finetune_ckpt (dict): Finetune checkpoint.
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log_param_stats (bool): Log parameter stats.
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i_print (int): Print interval.
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i_log (int): Log interval.
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i_sample (int): Sample interval.
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i_save (int): Save interval.
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i_ddpcheck (int): DDP check interval.
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"""
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def __str__(self):
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lines = []
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lines.append(self.__class__.__name__)
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lines.append(f' - Models:')
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for name, model in self.models.items():
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lines.append(f' - {name}: {model.__class__.__name__}')
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lines.append(f' - Dataset: {indent(str(self.dataset), 2)}')
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lines.append(f' - Dataloader:')
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lines.append(f' - Sampler: {self.dataloader.sampler.__class__.__name__}')
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lines.append(f' - Num workers: {self.dataloader.num_workers}')
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lines.append(f' - Number of steps: {self.max_steps}')
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lines.append(f' - Number of GPUs: {self.world_size}')
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lines.append(f' - Batch size: {self.batch_size}')
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lines.append(f' - Batch size per GPU: {self.batch_size_per_gpu}')
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lines.append(f' - Batch split: {self.batch_split}')
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lines.append(f' - Optimizer: {self.optimizer.__class__.__name__}')
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lines.append(f' - Learning rate: {self.optimizer.param_groups[0]["lr"]}')
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if self.lr_scheduler_config is not None:
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lines.append(f' - LR scheduler: {self.lr_scheduler.__class__.__name__}')
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if self.elastic_controller_config is not None:
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lines.append(f' - Elastic memory: {indent(str(self.elastic_controller), 2)}')
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if self.grad_clip is not None:
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lines.append(f' - Gradient clip: {indent(str(self.grad_clip), 2)}')
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lines.append(f' - EMA rate: {self.ema_rate}')
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lines.append(f' - FP16 mode: {self.fp16_mode}')
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return '\n'.join(lines)
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def init_models_and_more(self, **kwargs):
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"""
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Initialize models and more.
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"""
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if self.world_size > 1:
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# Prepare distributed data parallel
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self.training_models = {
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name: DDP(
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model,
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device_ids=[self.local_rank],
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output_device=self.local_rank,
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bucket_cap_mb=128,
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find_unused_parameters=False
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)
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for name, model in self.models.items()
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}
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else:
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self.training_models = self.models
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# Build master params
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self.model_params = sum(
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[[p for p in model.parameters() if p.requires_grad] for model in self.models.values()]
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, [])
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if self.fp16_mode == 'amp':
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self.master_params = self.model_params
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self.scaler = torch.GradScaler() if self.fp16_mode == 'amp' else None
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elif self.fp16_mode == 'inflat_all':
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self.master_params = make_master_params(self.model_params)
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self.fp16_scale_growth = self.fp16_scale_growth
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self.log_scale = 20.0
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elif self.fp16_mode is None:
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self.master_params = self.model_params
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else:
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raise NotImplementedError(f'FP16 mode {self.fp16_mode} is not implemented.')
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# Build EMA params
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if self.is_master:
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self.ema_params = [copy.deepcopy(self.master_params) for _ in self.ema_rate]
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# Initialize optimizer
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if hasattr(torch.optim, self.optimizer_config['name']):
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self.optimizer = getattr(torch.optim, self.optimizer_config['name'])(self.master_params, **self.optimizer_config['args'])
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else:
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self.optimizer = globals()[self.optimizer_config['name']](self.master_params, **self.optimizer_config['args'])
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# Initalize learning rate scheduler
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if self.lr_scheduler_config is not None:
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if hasattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name']):
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self.lr_scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name'])(self.optimizer, **self.lr_scheduler_config['args'])
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else:
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self.lr_scheduler = globals()[self.lr_scheduler_config['name']](self.optimizer, **self.lr_scheduler_config['args'])
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# Initialize elastic memory controller
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if self.elastic_controller_config is not None:
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assert any([isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)) for model in self.models.values()]), \
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'No elastic module found in models, please inherit from ElasticModule or ElasticModuleMixin'
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self.elastic_controller = getattr(elastic_utils, self.elastic_controller_config['name'])(**self.elastic_controller_config['args'])
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for model in self.models.values():
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if isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)):
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model.register_memory_controller(self.elastic_controller)
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# Initialize gradient clipper
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if self.grad_clip is not None:
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if isinstance(self.grad_clip, (float, int)):
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self.grad_clip = float(self.grad_clip)
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else:
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self.grad_clip = getattr(grad_clip_utils, self.grad_clip['name'])(**self.grad_clip['args'])
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def _master_params_to_state_dicts(self, master_params):
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"""
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Convert master params to dict of state_dicts.
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"""
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if self.fp16_mode == 'inflat_all':
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master_params = unflatten_master_params(self.model_params, master_params)
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state_dicts = {name: model.state_dict() for name, model in self.models.items()}
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master_params_names = sum(
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[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
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, [])
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for i, (model_name, param_name) in enumerate(master_params_names):
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state_dicts[model_name][param_name] = master_params[i]
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return state_dicts
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def _state_dicts_to_master_params(self, master_params, state_dicts):
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"""
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Convert a state_dict to master params.
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"""
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master_params_names = sum(
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[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
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, [])
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params = [state_dicts[name][param_name] for name, param_name in master_params_names]
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if self.fp16_mode == 'inflat_all':
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model_params_to_master_params(params, master_params)
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else:
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for i, param in enumerate(params):
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master_params[i].data.copy_(param.data)
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def load(self, load_dir, step=0):
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"""
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Load a checkpoint.
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Should be called by all processes.
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"""
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if self.is_master:
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print(f'\nLoading checkpoint from step {step}...', end='')
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model_ckpts = {}
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for name, model in self.models.items():
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model_ckpt = torch.load(read_file_dist(os.path.join(load_dir, 'ckpts', f'{name}_step{step:07d}.pt')), map_location=self.device, weights_only=True)
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model_ckpts[name] = model_ckpt
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model.load_state_dict(model_ckpt)
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if self.fp16_mode == 'inflat_all':
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model.convert_to_fp16()
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self._state_dicts_to_master_params(self.master_params, model_ckpts)
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del model_ckpts
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if self.is_master:
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for i, ema_rate in enumerate(self.ema_rate):
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ema_ckpts = {}
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for name, model in self.models.items():
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ema_ckpt = torch.load(os.path.join(load_dir, 'ckpts', f'{name}_ema{ema_rate}_step{step:07d}.pt'), map_location=self.device, weights_only=True)
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ema_ckpts[name] = ema_ckpt
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self._state_dicts_to_master_params(self.ema_params[i], ema_ckpts)
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del ema_ckpts
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misc_ckpt = torch.load(read_file_dist(os.path.join(load_dir, 'ckpts', f'misc_step{step:07d}.pt')), map_location=torch.device('cpu'), weights_only=False)
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self.optimizer.load_state_dict(misc_ckpt['optimizer'])
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self.step = misc_ckpt['step']
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self.data_sampler.load_state_dict(misc_ckpt['data_sampler'])
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if self.fp16_mode == 'amp':
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self.scaler.load_state_dict(misc_ckpt['scaler'])
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elif self.fp16_mode == 'inflat_all':
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self.log_scale = misc_ckpt['log_scale']
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if self.lr_scheduler_config is not None:
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self.lr_scheduler.load_state_dict(misc_ckpt['lr_scheduler'])
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if self.elastic_controller_config is not None:
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self.elastic_controller.load_state_dict(misc_ckpt['elastic_controller'])
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if self.grad_clip is not None and not isinstance(self.grad_clip, float):
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self.grad_clip.load_state_dict(misc_ckpt['grad_clip'])
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del misc_ckpt
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if self.world_size > 1:
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dist.barrier()
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if self.is_master:
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print(' Done.')
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if self.world_size > 1:
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self.check_ddp()
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def save(self):
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"""
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Save a checkpoint.
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Should be called only by the rank 0 process.
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"""
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assert self.is_master, 'save() should be called only by the rank 0 process.'
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print(f'\nSaving checkpoint at step {self.step}...', end='')
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model_ckpts = self._master_params_to_state_dicts(self.master_params)
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for name, model_ckpt in model_ckpts.items():
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torch.save(model_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_step{self.step:07d}.pt'))
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for i, ema_rate in enumerate(self.ema_rate):
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ema_ckpts = self._master_params_to_state_dicts(self.ema_params[i])
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for name, ema_ckpt in ema_ckpts.items():
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torch.save(ema_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_ema{ema_rate}_step{self.step:07d}.pt'))
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misc_ckpt = {
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'optimizer': self.optimizer.state_dict(),
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'step': self.step,
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'data_sampler': self.data_sampler.state_dict(),
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}
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if self.fp16_mode == 'amp':
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misc_ckpt['scaler'] = self.scaler.state_dict()
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elif self.fp16_mode == 'inflat_all':
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misc_ckpt['log_scale'] = self.log_scale
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if self.lr_scheduler_config is not None:
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misc_ckpt['lr_scheduler'] = self.lr_scheduler.state_dict()
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if self.elastic_controller_config is not None:
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misc_ckpt['elastic_controller'] = self.elastic_controller.state_dict()
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if self.grad_clip is not None and not isinstance(self.grad_clip, float):
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misc_ckpt['grad_clip'] = self.grad_clip.state_dict()
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torch.save(misc_ckpt, os.path.join(self.output_dir, 'ckpts', f'misc_step{self.step:07d}.pt'))
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print(' Done.')
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def finetune_from(self, finetune_ckpt):
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"""
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Finetune from a checkpoint.
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Should be called by all processes.
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"""
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if self.is_master:
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print('\nFinetuning from:')
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for name, path in finetune_ckpt.items():
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print(f' - {name}: {path}')
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model_ckpts = {}
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for name, model in self.models.items():
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model_state_dict = model.state_dict()
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if name in finetune_ckpt:
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model_ckpt = torch.load(read_file_dist(finetune_ckpt[name]), map_location=self.device, weights_only=True)
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for k, v in model_ckpt.items():
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if model_ckpt[k].shape != model_state_dict[k].shape:
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if self.is_master:
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print(f'Warning: {k} shape mismatch, {model_ckpt[k].shape} vs {model_state_dict[k].shape}, skipped.')
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model_ckpt[k] = model_state_dict[k]
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model_ckpts[name] = model_ckpt
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model.load_state_dict(model_ckpt)
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if self.fp16_mode == 'inflat_all':
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model.convert_to_fp16()
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else:
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if self.is_master:
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print(f'Warning: {name} not found in finetune_ckpt, skipped.')
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model_ckpts[name] = model_state_dict
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self._state_dicts_to_master_params(self.master_params, model_ckpts)
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del model_ckpts
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if self.world_size > 1:
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dist.barrier()
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if self.is_master:
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print('Done.')
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if self.world_size > 1:
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self.check_ddp()
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def update_ema(self):
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"""
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Update exponential moving average.
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Should only be called by the rank 0 process.
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"""
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assert self.is_master, 'update_ema() should be called only by the rank 0 process.'
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for i, ema_rate in enumerate(self.ema_rate):
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for master_param, ema_param in zip(self.master_params, self.ema_params[i]):
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ema_param.detach().mul_(ema_rate).add_(master_param, alpha=1.0 - ema_rate)
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def check_ddp(self):
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"""
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Check if DDP is working properly.
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Should be called by all process.
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"""
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if self.is_master:
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print('\nPerforming DDP check...')
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if self.is_master:
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print('Checking if parameters are consistent across processes...')
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dist.barrier()
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try:
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for p in self.master_params:
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# split to avoid OOM
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for i in range(0, p.numel(), 10000000):
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sub_size = min(10000000, p.numel() - i)
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sub_p = p.detach().view(-1)[i:i+sub_size]
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# gather from all processes
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sub_p_gather = [torch.empty_like(sub_p) for _ in range(self.world_size)]
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dist.all_gather(sub_p_gather, sub_p)
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# check if equal
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assert all([torch.equal(sub_p, sub_p_gather[i]) for i in range(self.world_size)]), 'parameters are not consistent across processes'
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except AssertionError as e:
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if self.is_master:
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print(f'\n\033[91mError: {e}\033[0m')
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print('DDP check failed.')
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raise e
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dist.barrier()
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if self.is_master:
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print('Done.')
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def run_step(self, data_list):
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"""
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Run a training step.
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"""
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step_log = {'loss': {}, 'status': {}}
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amp_context = partial(torch.autocast, device_type='cuda') if self.fp16_mode == 'amp' else nullcontext
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elastic_controller_context = self.elastic_controller.record if self.elastic_controller_config is not None else nullcontext
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# Train
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losses = []
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statuses = []
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elastic_controller_logs = []
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zero_grad(self.model_params)
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for i, mb_data in enumerate(data_list):
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## sync at the end of each batch split
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sync_contexts = [self.training_models[name].no_sync for name in self.training_models] if i != len(data_list) - 1 and self.world_size > 1 else [nullcontext]
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with nested_contexts(*sync_contexts), elastic_controller_context():
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with amp_context():
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loss, status = self.training_losses(**mb_data)
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l = loss['loss'] / len(data_list)
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## backward
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if self.fp16_mode == 'amp':
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self.scaler.scale(l).backward()
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elif self.fp16_mode == 'inflat_all':
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scaled_l = l * (2 ** self.log_scale)
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scaled_l.backward()
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else:
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l.backward()
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## log
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losses.append(dict_foreach(loss, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
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statuses.append(dict_foreach(status, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
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if self.elastic_controller_config is not None:
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elastic_controller_logs.append(self.elastic_controller.log())
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## gradient clip
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if self.grad_clip is not None:
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if self.fp16_mode == 'amp':
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self.scaler.unscale_(self.optimizer)
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elif self.fp16_mode == 'inflat_all':
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model_grads_to_master_grads(self.model_params, self.master_params)
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self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
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if isinstance(self.grad_clip, float):
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grad_norm = torch.nn.utils.clip_grad_norm_(self.master_params, self.grad_clip)
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else:
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grad_norm = self.grad_clip(self.master_params)
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if torch.isfinite(grad_norm):
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statuses[-1]['grad_norm'] = grad_norm.item()
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## step
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if self.fp16_mode == 'amp':
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prev_scale = self.scaler.get_scale()
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self.scaler.step(self.optimizer)
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self.scaler.update()
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elif self.fp16_mode == 'inflat_all':
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prev_scale = 2 ** self.log_scale
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if not any(not p.grad.isfinite().all() for p in self.model_params):
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if self.grad_clip is None:
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model_grads_to_master_grads(self.model_params, self.master_params)
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self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
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self.optimizer.step()
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master_params_to_model_params(self.model_params, self.master_params)
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self.log_scale += self.fp16_scale_growth
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else:
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self.log_scale -= 1
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else:
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prev_scale = 1.0
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if not any(not p.grad.isfinite().all() for p in self.model_params):
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self.optimizer.step()
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else:
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print('\n\033[93mWarning: NaN detected in gradients. Skipping update.\033[0m')
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## adjust learning rate
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if self.lr_scheduler_config is not None:
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statuses[-1]['lr'] = self.lr_scheduler.get_last_lr()[0]
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self.lr_scheduler.step()
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|
|
|
# Logs
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step_log['loss'] = dict_reduce(losses, lambda x: np.mean(x))
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|
step_log['status'] = dict_reduce(statuses, lambda x: np.mean(x), special_func={'min': lambda x: np.min(x), 'max': lambda x: np.max(x)})
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|
if self.elastic_controller_config is not None:
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step_log['elastic'] = dict_reduce(elastic_controller_logs, lambda x: np.mean(x))
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|
if self.grad_clip is not None:
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|
step_log['grad_clip'] = self.grad_clip if isinstance(self.grad_clip, float) else self.grad_clip.log()
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|
|
|
# Check grad and norm of each param
|
|
if self.log_param_stats:
|
|
param_norms = {}
|
|
param_grads = {}
|
|
for name, param in self.backbone.named_parameters():
|
|
if param.requires_grad:
|
|
param_norms[name] = param.norm().item()
|
|
if param.grad is not None and torch.isfinite(param.grad).all():
|
|
param_grads[name] = param.grad.norm().item() / prev_scale
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|
step_log['param_norms'] = param_norms
|
|
step_log['param_grads'] = param_grads
|
|
|
|
# Update exponential moving average
|
|
if self.is_master:
|
|
self.update_ema()
|
|
|
|
return step_log
|