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63
trellis/trainers/__init__.py
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63
trellis/trainers/__init__.py
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import importlib
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__attributes = {
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'BasicTrainer': 'basic',
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'SparseStructureVaeTrainer': 'vae.sparse_structure_vae',
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'SLatVaeGaussianTrainer': 'vae.structured_latent_vae_gaussian',
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'SLatVaeRadianceFieldDecoderTrainer': 'vae.structured_latent_vae_rf_dec',
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'SLatVaeMeshDecoderTrainer': 'vae.structured_latent_vae_mesh_dec',
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'FlowMatchingTrainer': 'flow_matching.flow_matching',
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'FlowMatchingCFGTrainer': 'flow_matching.flow_matching',
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'TextConditionedFlowMatchingCFGTrainer': 'flow_matching.flow_matching',
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'ImageConditionedFlowMatchingCFGTrainer': 'flow_matching.flow_matching',
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'SparseFlowMatchingTrainer': 'flow_matching.sparse_flow_matching',
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'SparseFlowMatchingCFGTrainer': 'flow_matching.sparse_flow_matching',
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'TextConditionedSparseFlowMatchingCFGTrainer': 'flow_matching.sparse_flow_matching',
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'ImageConditionedSparseFlowMatchingCFGTrainer': 'flow_matching.sparse_flow_matching',
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}
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__submodules = []
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__all__ = list(__attributes.keys()) + __submodules
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def __getattr__(name):
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if name not in globals():
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if name in __attributes:
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module_name = __attributes[name]
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module = importlib.import_module(f".{module_name}", __name__)
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globals()[name] = getattr(module, name)
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elif name in __submodules:
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module = importlib.import_module(f".{name}", __name__)
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globals()[name] = module
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else:
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raise AttributeError(f"module {__name__} has no attribute {name}")
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return globals()[name]
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# For Pylance
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if __name__ == '__main__':
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from .basic import BasicTrainer
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from .vae.sparse_structure_vae import SparseStructureVaeTrainer
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from .vae.structured_latent_vae_gaussian import SLatVaeGaussianTrainer
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from .vae.structured_latent_vae_rf_dec import SLatVaeRadianceFieldDecoderTrainer
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from .vae.structured_latent_vae_mesh_dec import SLatVaeMeshDecoderTrainer
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from .flow_matching.flow_matching import (
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FlowMatchingTrainer,
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FlowMatchingCFGTrainer,
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TextConditionedFlowMatchingCFGTrainer,
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ImageConditionedFlowMatchingCFGTrainer,
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)
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from .flow_matching.sparse_flow_matching import (
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SparseFlowMatchingTrainer,
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SparseFlowMatchingCFGTrainer,
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TextConditionedSparseFlowMatchingCFGTrainer,
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ImageConditionedSparseFlowMatchingCFGTrainer,
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)
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451
trellis/trainers/base.py
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451
trellis/trainers/base.py
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@@ -0,0 +1,451 @@
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from abc import abstractmethod
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import os
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import time
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import json
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import torch
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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import numpy as np
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from torchvision import utils
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from torch.utils.tensorboard import SummaryWriter
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from .utils import *
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from ..utils.general_utils import *
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from ..utils.data_utils import recursive_to_device, cycle, ResumableSampler
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class Trainer:
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"""
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Base class for training.
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"""
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def __init__(self,
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models,
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dataset,
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*,
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output_dir,
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load_dir,
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step,
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max_steps,
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batch_size=None,
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batch_size_per_gpu=None,
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batch_split=None,
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optimizer={},
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lr_scheduler=None,
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elastic=None,
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grad_clip=None,
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ema_rate=0.9999,
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fp16_mode='inflat_all',
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fp16_scale_growth=1e-3,
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finetune_ckpt=None,
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log_param_stats=False,
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prefetch_data=True,
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i_print=1000,
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i_log=500,
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i_sample=10000,
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i_save=10000,
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i_ddpcheck=10000,
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**kwargs
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):
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assert batch_size is not None or batch_size_per_gpu is not None, 'Either batch_size or batch_size_per_gpu must be specified.'
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self.models = models
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self.dataset = dataset
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self.batch_split = batch_split if batch_split is not None else 1
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self.max_steps = max_steps
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self.optimizer_config = optimizer
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self.lr_scheduler_config = lr_scheduler
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self.elastic_controller_config = elastic
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self.grad_clip = grad_clip
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self.ema_rate = [ema_rate] if isinstance(ema_rate, float) else ema_rate
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self.fp16_mode = fp16_mode
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self.fp16_scale_growth = fp16_scale_growth
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self.log_param_stats = log_param_stats
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self.prefetch_data = prefetch_data
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if self.prefetch_data:
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self._data_prefetched = None
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self.output_dir = output_dir
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self.i_print = i_print
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self.i_log = i_log
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self.i_sample = i_sample
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self.i_save = i_save
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self.i_ddpcheck = i_ddpcheck
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if dist.is_initialized():
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# Multi-GPU params
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self.world_size = dist.get_world_size()
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self.rank = dist.get_rank()
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self.local_rank = dist.get_rank() % torch.cuda.device_count()
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self.is_master = self.rank == 0
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else:
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# Single-GPU params
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self.world_size = 1
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self.rank = 0
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self.local_rank = 0
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self.is_master = True
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self.batch_size = batch_size if batch_size_per_gpu is None else batch_size_per_gpu * self.world_size
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self.batch_size_per_gpu = batch_size_per_gpu if batch_size_per_gpu is not None else batch_size // self.world_size
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assert self.batch_size % self.world_size == 0, 'Batch size must be divisible by the number of GPUs.'
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assert self.batch_size_per_gpu % self.batch_split == 0, 'Batch size per GPU must be divisible by batch split.'
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self.init_models_and_more(**kwargs)
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self.prepare_dataloader(**kwargs)
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# Load checkpoint
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self.step = 0
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if load_dir is not None and step is not None:
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self.load(load_dir, step)
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elif finetune_ckpt is not None:
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self.finetune_from(finetune_ckpt)
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if self.is_master:
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os.makedirs(os.path.join(self.output_dir, 'ckpts'), exist_ok=True)
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os.makedirs(os.path.join(self.output_dir, 'samples'), exist_ok=True)
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self.writer = SummaryWriter(os.path.join(self.output_dir, 'tb_logs'))
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if self.world_size > 1:
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self.check_ddp()
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if self.is_master:
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print('\n\nTrainer initialized.')
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print(self)
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@property
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def device(self):
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for _, model in self.models.items():
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if hasattr(model, 'device'):
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return model.device
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return next(list(self.models.values())[0].parameters()).device
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@abstractmethod
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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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pass
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def prepare_dataloader(self, **kwargs):
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"""
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Prepare dataloader.
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"""
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self.data_sampler = ResumableSampler(
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self.dataset,
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shuffle=True,
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)
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self.dataloader = DataLoader(
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self.dataset,
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batch_size=self.batch_size_per_gpu,
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num_workers=int(np.ceil(os.cpu_count() / torch.cuda.device_count())),
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pin_memory=True,
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drop_last=True,
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persistent_workers=True,
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collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
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sampler=self.data_sampler,
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)
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self.data_iterator = cycle(self.dataloader)
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@abstractmethod
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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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pass
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@abstractmethod
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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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pass
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@abstractmethod
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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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pass
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@abstractmethod
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def run_snapshot(self, num_samples, batch_size=4, verbose=False, **kwargs):
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"""
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Run a snapshot of the model.
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"""
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pass
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@torch.no_grad()
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def visualize_sample(self, sample):
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"""
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Convert a sample to an image.
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"""
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if hasattr(self.dataset, 'visualize_sample'):
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return self.dataset.visualize_sample(sample)
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else:
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return sample
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@torch.no_grad()
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def snapshot_dataset(self, num_samples=100):
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"""
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Sample images from the dataset.
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"""
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dataloader = torch.utils.data.DataLoader(
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self.dataset,
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batch_size=num_samples,
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num_workers=0,
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shuffle=True,
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collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
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)
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data = next(iter(dataloader))
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data = recursive_to_device(data, self.device)
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vis = self.visualize_sample(data)
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if isinstance(vis, dict):
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save_cfg = [(f'dataset_{k}', v) for k, v in vis.items()]
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else:
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save_cfg = [('dataset', vis)]
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for name, image in save_cfg:
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utils.save_image(
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image,
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os.path.join(self.output_dir, 'samples', f'{name}.jpg'),
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nrow=int(np.sqrt(num_samples)),
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normalize=True,
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value_range=self.dataset.value_range,
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)
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@torch.no_grad()
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def snapshot(self, suffix=None, num_samples=64, batch_size=4, verbose=False):
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"""
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Sample images from the model.
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NOTE: This function should be called by all processes.
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"""
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if self.is_master:
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print(f'\nSampling {num_samples} images...', end='')
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if suffix is None:
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suffix = f'step{self.step:07d}'
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# Assign tasks
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num_samples_per_process = int(np.ceil(num_samples / self.world_size))
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samples = self.run_snapshot(num_samples_per_process, batch_size=batch_size, verbose=verbose)
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# Preprocess images
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for key in list(samples.keys()):
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if samples[key]['type'] == 'sample':
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vis = self.visualize_sample(samples[key]['value'])
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if isinstance(vis, dict):
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for k, v in vis.items():
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samples[f'{key}_{k}'] = {'value': v, 'type': 'image'}
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del samples[key]
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else:
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samples[key] = {'value': vis, 'type': 'image'}
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# Gather results
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if self.world_size > 1:
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for key in samples.keys():
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samples[key]['value'] = samples[key]['value'].contiguous()
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if self.is_master:
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all_images = [torch.empty_like(samples[key]['value']) for _ in range(self.world_size)]
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else:
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all_images = []
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dist.gather(samples[key]['value'], all_images, dst=0)
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if self.is_master:
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samples[key]['value'] = torch.cat(all_images, dim=0)[:num_samples]
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# Save images
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if self.is_master:
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os.makedirs(os.path.join(self.output_dir, 'samples', suffix), exist_ok=True)
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for key in samples.keys():
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if samples[key]['type'] == 'image':
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utils.save_image(
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samples[key]['value'],
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os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg'),
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nrow=int(np.sqrt(num_samples)),
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normalize=True,
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value_range=self.dataset.value_range,
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)
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elif samples[key]['type'] == 'number':
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min = samples[key]['value'].min()
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max = samples[key]['value'].max()
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images = (samples[key]['value'] - min) / (max - min)
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images = utils.make_grid(
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images,
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nrow=int(np.sqrt(num_samples)),
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normalize=False,
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)
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save_image_with_notes(
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images,
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os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg'),
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notes=f'{key} min: {min}, max: {max}',
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)
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if self.is_master:
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print(' Done.')
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@abstractmethod
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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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pass
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@abstractmethod
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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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pass
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@abstractmethod
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def training_losses(**mb_data):
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"""
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Compute training losses.
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"""
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pass
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def load_data(self):
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"""
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Load data.
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"""
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if self.prefetch_data:
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if self._data_prefetched is None:
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self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
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data = self._data_prefetched
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self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
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else:
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data = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
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# if the data is a dict, we need to split it into multiple dicts with batch_size_per_gpu
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if isinstance(data, dict):
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if self.batch_split == 1:
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data_list = [data]
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else:
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batch_size = list(data.values())[0].shape[0]
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data_list = [
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{k: v[i * batch_size // self.batch_split:(i + 1) * batch_size // self.batch_split] for k, v in data.items()}
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for i in range(self.batch_split)
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]
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elif isinstance(data, list):
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data_list = data
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else:
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raise ValueError('Data must be a dict or a list of dicts.')
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return data_list
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@abstractmethod
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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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pass
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def run(self):
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"""
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Run training.
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"""
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if self.is_master:
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print('\nStarting training...')
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self.snapshot_dataset()
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if self.step == 0:
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self.snapshot(suffix='init')
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else: # resume
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self.snapshot(suffix=f'resume_step{self.step:07d}')
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log = []
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time_last_print = 0.0
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time_elapsed = 0.0
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while self.step < self.max_steps:
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time_start = time.time()
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data_list = self.load_data()
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step_log = self.run_step(data_list)
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time_end = time.time()
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time_elapsed += time_end - time_start
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self.step += 1
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# Print progress
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if self.is_master and self.step % self.i_print == 0:
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speed = self.i_print / (time_elapsed - time_last_print) * 3600
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columns = [
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f'Step: {self.step}/{self.max_steps} ({self.step / self.max_steps * 100:.2f}%)',
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f'Elapsed: {time_elapsed / 3600:.2f} h',
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f'Speed: {speed:.2f} steps/h',
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f'ETA: {(self.max_steps - self.step) / speed:.2f} h',
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]
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print(' | '.join([c.ljust(25) for c in columns]), flush=True)
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time_last_print = time_elapsed
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|
||||
# Check ddp
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if self.world_size > 1 and self.i_ddpcheck is not None and self.step % self.i_ddpcheck == 0:
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self.check_ddp()
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|
||||
# Sample images
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if self.step % self.i_sample == 0:
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self.snapshot()
|
||||
|
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if self.is_master:
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log.append((self.step, {}))
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||||
# Log time
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||||
log[-1][1]['time'] = {
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'step': time_end - time_start,
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'elapsed': time_elapsed,
|
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}
|
||||
|
||||
# Log losses
|
||||
if step_log is not None:
|
||||
log[-1][1].update(step_log)
|
||||
|
||||
# Log scale
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||||
if self.fp16_mode == 'amp':
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||||
log[-1][1]['scale'] = self.scaler.get_scale()
|
||||
elif self.fp16_mode == 'inflat_all':
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||||
log[-1][1]['log_scale'] = self.log_scale
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||||
|
||||
# Save log
|
||||
if self.step % self.i_log == 0:
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||||
## save to log file
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||||
log_str = '\n'.join([
|
||||
f'{step}: {json.dumps(log)}' for step, log in log
|
||||
])
|
||||
with open(os.path.join(self.output_dir, 'log.txt'), 'a') as log_file:
|
||||
log_file.write(log_str + '\n')
|
||||
|
||||
# show with mlflow
|
||||
log_show = [l for _, l in log if not dict_any(l, lambda x: np.isnan(x))]
|
||||
log_show = dict_reduce(log_show, lambda x: np.mean(x))
|
||||
log_show = dict_flatten(log_show, sep='/')
|
||||
for key, value in log_show.items():
|
||||
self.writer.add_scalar(key, value, self.step)
|
||||
log = []
|
||||
|
||||
# Save checkpoint
|
||||
if self.step % self.i_save == 0:
|
||||
self.save()
|
||||
|
||||
if self.is_master:
|
||||
self.snapshot(suffix='final')
|
||||
self.writer.close()
|
||||
print('Training finished.')
|
||||
|
||||
def profile(self, wait=2, warmup=3, active=5):
|
||||
"""
|
||||
Profile the training loop.
|
||||
"""
|
||||
with torch.profiler.profile(
|
||||
schedule=torch.profiler.schedule(wait=wait, warmup=warmup, active=active, repeat=1),
|
||||
on_trace_ready=torch.profiler.tensorboard_trace_handler(os.path.join(self.output_dir, 'profile')),
|
||||
profile_memory=True,
|
||||
with_stack=True,
|
||||
) as prof:
|
||||
for _ in range(wait + warmup + active):
|
||||
self.run_step()
|
||||
prof.step()
|
||||
|
||||
438
trellis/trainers/basic.py
Normal file
438
trellis/trainers/basic.py
Normal file
@@ -0,0 +1,438 @@
|
||||
import os
|
||||
import copy
|
||||
from functools import partial
|
||||
from contextlib import nullcontext
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
import numpy as np
|
||||
|
||||
from .utils import *
|
||||
from .base import Trainer
|
||||
from ..utils.general_utils import *
|
||||
from ..utils.dist_utils import *
|
||||
from ..utils import grad_clip_utils, elastic_utils
|
||||
|
||||
|
||||
class BasicTrainer(Trainer):
|
||||
"""
|
||||
Trainer for basic training loop.
|
||||
|
||||
Args:
|
||||
models (dict[str, nn.Module]): Models to train.
|
||||
dataset (torch.utils.data.Dataset): Dataset.
|
||||
output_dir (str): Output directory.
|
||||
load_dir (str): Load directory.
|
||||
step (int): Step to load.
|
||||
batch_size (int): Batch size.
|
||||
batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
|
||||
batch_split (int): Split batch with gradient accumulation.
|
||||
max_steps (int): Max steps.
|
||||
optimizer (dict): Optimizer config.
|
||||
lr_scheduler (dict): Learning rate scheduler config.
|
||||
elastic (dict): Elastic memory management config.
|
||||
grad_clip (float or dict): Gradient clip config.
|
||||
ema_rate (float or list): Exponential moving average rates.
|
||||
fp16_mode (str): FP16 mode.
|
||||
- None: No FP16.
|
||||
- 'inflat_all': Hold a inflated fp32 master param for all params.
|
||||
- 'amp': Automatic mixed precision.
|
||||
fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
|
||||
finetune_ckpt (dict): Finetune checkpoint.
|
||||
log_param_stats (bool): Log parameter stats.
|
||||
i_print (int): Print interval.
|
||||
i_log (int): Log interval.
|
||||
i_sample (int): Sample interval.
|
||||
i_save (int): Save interval.
|
||||
i_ddpcheck (int): DDP check interval.
|
||||
"""
|
||||
|
||||
def __str__(self):
|
||||
lines = []
|
||||
lines.append(self.__class__.__name__)
|
||||
lines.append(f' - Models:')
|
||||
for name, model in self.models.items():
|
||||
lines.append(f' - {name}: {model.__class__.__name__}')
|
||||
lines.append(f' - Dataset: {indent(str(self.dataset), 2)}')
|
||||
lines.append(f' - Dataloader:')
|
||||
lines.append(f' - Sampler: {self.dataloader.sampler.__class__.__name__}')
|
||||
lines.append(f' - Num workers: {self.dataloader.num_workers}')
|
||||
lines.append(f' - Number of steps: {self.max_steps}')
|
||||
lines.append(f' - Number of GPUs: {self.world_size}')
|
||||
lines.append(f' - Batch size: {self.batch_size}')
|
||||
lines.append(f' - Batch size per GPU: {self.batch_size_per_gpu}')
|
||||
lines.append(f' - Batch split: {self.batch_split}')
|
||||
lines.append(f' - Optimizer: {self.optimizer.__class__.__name__}')
|
||||
lines.append(f' - Learning rate: {self.optimizer.param_groups[0]["lr"]}')
|
||||
if self.lr_scheduler_config is not None:
|
||||
lines.append(f' - LR scheduler: {self.lr_scheduler.__class__.__name__}')
|
||||
if self.elastic_controller_config is not None:
|
||||
lines.append(f' - Elastic memory: {indent(str(self.elastic_controller), 2)}')
|
||||
if self.grad_clip is not None:
|
||||
lines.append(f' - Gradient clip: {indent(str(self.grad_clip), 2)}')
|
||||
lines.append(f' - EMA rate: {self.ema_rate}')
|
||||
lines.append(f' - FP16 mode: {self.fp16_mode}')
|
||||
return '\n'.join(lines)
|
||||
|
||||
def init_models_and_more(self, **kwargs):
|
||||
"""
|
||||
Initialize models and more.
|
||||
"""
|
||||
if self.world_size > 1:
|
||||
# Prepare distributed data parallel
|
||||
self.training_models = {
|
||||
name: DDP(
|
||||
model,
|
||||
device_ids=[self.local_rank],
|
||||
output_device=self.local_rank,
|
||||
bucket_cap_mb=128,
|
||||
find_unused_parameters=False
|
||||
)
|
||||
for name, model in self.models.items()
|
||||
}
|
||||
else:
|
||||
self.training_models = self.models
|
||||
|
||||
# Build master params
|
||||
self.model_params = sum(
|
||||
[[p for p in model.parameters() if p.requires_grad] for model in self.models.values()]
|
||||
, [])
|
||||
if self.fp16_mode == 'amp':
|
||||
self.master_params = self.model_params
|
||||
self.scaler = torch.GradScaler() if self.fp16_mode == 'amp' else None
|
||||
elif self.fp16_mode == 'inflat_all':
|
||||
self.master_params = make_master_params(self.model_params)
|
||||
self.fp16_scale_growth = self.fp16_scale_growth
|
||||
self.log_scale = 20.0
|
||||
elif self.fp16_mode is None:
|
||||
self.master_params = self.model_params
|
||||
else:
|
||||
raise NotImplementedError(f'FP16 mode {self.fp16_mode} is not implemented.')
|
||||
|
||||
# Build EMA params
|
||||
if self.is_master:
|
||||
self.ema_params = [copy.deepcopy(self.master_params) for _ in self.ema_rate]
|
||||
|
||||
# Initialize optimizer
|
||||
if hasattr(torch.optim, self.optimizer_config['name']):
|
||||
self.optimizer = getattr(torch.optim, self.optimizer_config['name'])(self.master_params, **self.optimizer_config['args'])
|
||||
else:
|
||||
self.optimizer = globals()[self.optimizer_config['name']](self.master_params, **self.optimizer_config['args'])
|
||||
|
||||
# Initalize learning rate scheduler
|
||||
if self.lr_scheduler_config is not None:
|
||||
if hasattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name']):
|
||||
self.lr_scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name'])(self.optimizer, **self.lr_scheduler_config['args'])
|
||||
else:
|
||||
self.lr_scheduler = globals()[self.lr_scheduler_config['name']](self.optimizer, **self.lr_scheduler_config['args'])
|
||||
|
||||
# Initialize elastic memory controller
|
||||
if self.elastic_controller_config is not None:
|
||||
assert any([isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)) for model in self.models.values()]), \
|
||||
'No elastic module found in models, please inherit from ElasticModule or ElasticModuleMixin'
|
||||
self.elastic_controller = getattr(elastic_utils, self.elastic_controller_config['name'])(**self.elastic_controller_config['args'])
|
||||
for model in self.models.values():
|
||||
if isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)):
|
||||
model.register_memory_controller(self.elastic_controller)
|
||||
|
||||
# Initialize gradient clipper
|
||||
if self.grad_clip is not None:
|
||||
if isinstance(self.grad_clip, (float, int)):
|
||||
self.grad_clip = float(self.grad_clip)
|
||||
else:
|
||||
self.grad_clip = getattr(grad_clip_utils, self.grad_clip['name'])(**self.grad_clip['args'])
|
||||
|
||||
def _master_params_to_state_dicts(self, master_params):
|
||||
"""
|
||||
Convert master params to dict of state_dicts.
|
||||
"""
|
||||
if self.fp16_mode == 'inflat_all':
|
||||
master_params = unflatten_master_params(self.model_params, master_params)
|
||||
state_dicts = {name: model.state_dict() for name, model in self.models.items()}
|
||||
master_params_names = sum(
|
||||
[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
|
||||
, [])
|
||||
for i, (model_name, param_name) in enumerate(master_params_names):
|
||||
state_dicts[model_name][param_name] = master_params[i]
|
||||
return state_dicts
|
||||
|
||||
def _state_dicts_to_master_params(self, master_params, state_dicts):
|
||||
"""
|
||||
Convert a state_dict to master params.
|
||||
"""
|
||||
master_params_names = sum(
|
||||
[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
|
||||
, [])
|
||||
params = [state_dicts[name][param_name] for name, param_name in master_params_names]
|
||||
if self.fp16_mode == 'inflat_all':
|
||||
model_params_to_master_params(params, master_params)
|
||||
else:
|
||||
for i, param in enumerate(params):
|
||||
master_params[i].data.copy_(param.data)
|
||||
|
||||
def load(self, load_dir, step=0):
|
||||
"""
|
||||
Load a checkpoint.
|
||||
Should be called by all processes.
|
||||
"""
|
||||
if self.is_master:
|
||||
print(f'\nLoading checkpoint from step {step}...', end='')
|
||||
|
||||
model_ckpts = {}
|
||||
for name, model in self.models.items():
|
||||
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)
|
||||
model_ckpts[name] = model_ckpt
|
||||
model.load_state_dict(model_ckpt)
|
||||
if self.fp16_mode == 'inflat_all':
|
||||
model.convert_to_fp16()
|
||||
self._state_dicts_to_master_params(self.master_params, model_ckpts)
|
||||
del model_ckpts
|
||||
|
||||
if self.is_master:
|
||||
for i, ema_rate in enumerate(self.ema_rate):
|
||||
ema_ckpts = {}
|
||||
for name, model in self.models.items():
|
||||
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)
|
||||
ema_ckpts[name] = ema_ckpt
|
||||
self._state_dicts_to_master_params(self.ema_params[i], ema_ckpts)
|
||||
del ema_ckpts
|
||||
|
||||
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)
|
||||
self.optimizer.load_state_dict(misc_ckpt['optimizer'])
|
||||
self.step = misc_ckpt['step']
|
||||
self.data_sampler.load_state_dict(misc_ckpt['data_sampler'])
|
||||
if self.fp16_mode == 'amp':
|
||||
self.scaler.load_state_dict(misc_ckpt['scaler'])
|
||||
elif self.fp16_mode == 'inflat_all':
|
||||
self.log_scale = misc_ckpt['log_scale']
|
||||
if self.lr_scheduler_config is not None:
|
||||
self.lr_scheduler.load_state_dict(misc_ckpt['lr_scheduler'])
|
||||
if self.elastic_controller_config is not None:
|
||||
self.elastic_controller.load_state_dict(misc_ckpt['elastic_controller'])
|
||||
if self.grad_clip is not None and not isinstance(self.grad_clip, float):
|
||||
self.grad_clip.load_state_dict(misc_ckpt['grad_clip'])
|
||||
del misc_ckpt
|
||||
|
||||
if self.world_size > 1:
|
||||
dist.barrier()
|
||||
if self.is_master:
|
||||
print(' Done.')
|
||||
|
||||
if self.world_size > 1:
|
||||
self.check_ddp()
|
||||
|
||||
def save(self):
|
||||
"""
|
||||
Save a checkpoint.
|
||||
Should be called only by the rank 0 process.
|
||||
"""
|
||||
assert self.is_master, 'save() should be called only by the rank 0 process.'
|
||||
print(f'\nSaving checkpoint at step {self.step}...', end='')
|
||||
|
||||
model_ckpts = self._master_params_to_state_dicts(self.master_params)
|
||||
for name, model_ckpt in model_ckpts.items():
|
||||
torch.save(model_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_step{self.step:07d}.pt'))
|
||||
|
||||
for i, ema_rate in enumerate(self.ema_rate):
|
||||
ema_ckpts = self._master_params_to_state_dicts(self.ema_params[i])
|
||||
for name, ema_ckpt in ema_ckpts.items():
|
||||
torch.save(ema_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_ema{ema_rate}_step{self.step:07d}.pt'))
|
||||
|
||||
misc_ckpt = {
|
||||
'optimizer': self.optimizer.state_dict(),
|
||||
'step': self.step,
|
||||
'data_sampler': self.data_sampler.state_dict(),
|
||||
}
|
||||
if self.fp16_mode == 'amp':
|
||||
misc_ckpt['scaler'] = self.scaler.state_dict()
|
||||
elif self.fp16_mode == 'inflat_all':
|
||||
misc_ckpt['log_scale'] = self.log_scale
|
||||
if self.lr_scheduler_config is not None:
|
||||
misc_ckpt['lr_scheduler'] = self.lr_scheduler.state_dict()
|
||||
if self.elastic_controller_config is not None:
|
||||
misc_ckpt['elastic_controller'] = self.elastic_controller.state_dict()
|
||||
if self.grad_clip is not None and not isinstance(self.grad_clip, float):
|
||||
misc_ckpt['grad_clip'] = self.grad_clip.state_dict()
|
||||
torch.save(misc_ckpt, os.path.join(self.output_dir, 'ckpts', f'misc_step{self.step:07d}.pt'))
|
||||
print(' Done.')
|
||||
|
||||
def finetune_from(self, finetune_ckpt):
|
||||
"""
|
||||
Finetune from a checkpoint.
|
||||
Should be called by all processes.
|
||||
"""
|
||||
if self.is_master:
|
||||
print('\nFinetuning from:')
|
||||
for name, path in finetune_ckpt.items():
|
||||
print(f' - {name}: {path}')
|
||||
|
||||
model_ckpts = {}
|
||||
for name, model in self.models.items():
|
||||
model_state_dict = model.state_dict()
|
||||
if name in finetune_ckpt:
|
||||
model_ckpt = torch.load(read_file_dist(finetune_ckpt[name]), map_location=self.device, weights_only=True)
|
||||
for k, v in model_ckpt.items():
|
||||
if model_ckpt[k].shape != model_state_dict[k].shape:
|
||||
if self.is_master:
|
||||
print(f'Warning: {k} shape mismatch, {model_ckpt[k].shape} vs {model_state_dict[k].shape}, skipped.')
|
||||
model_ckpt[k] = model_state_dict[k]
|
||||
model_ckpts[name] = model_ckpt
|
||||
model.load_state_dict(model_ckpt)
|
||||
if self.fp16_mode == 'inflat_all':
|
||||
model.convert_to_fp16()
|
||||
else:
|
||||
if self.is_master:
|
||||
print(f'Warning: {name} not found in finetune_ckpt, skipped.')
|
||||
model_ckpts[name] = model_state_dict
|
||||
self._state_dicts_to_master_params(self.master_params, model_ckpts)
|
||||
del model_ckpts
|
||||
|
||||
if self.world_size > 1:
|
||||
dist.barrier()
|
||||
if self.is_master:
|
||||
print('Done.')
|
||||
|
||||
if self.world_size > 1:
|
||||
self.check_ddp()
|
||||
|
||||
def update_ema(self):
|
||||
"""
|
||||
Update exponential moving average.
|
||||
Should only be called by the rank 0 process.
|
||||
"""
|
||||
assert self.is_master, 'update_ema() should be called only by the rank 0 process.'
|
||||
for i, ema_rate in enumerate(self.ema_rate):
|
||||
for master_param, ema_param in zip(self.master_params, self.ema_params[i]):
|
||||
ema_param.detach().mul_(ema_rate).add_(master_param, alpha=1.0 - ema_rate)
|
||||
|
||||
def check_ddp(self):
|
||||
"""
|
||||
Check if DDP is working properly.
|
||||
Should be called by all process.
|
||||
"""
|
||||
if self.is_master:
|
||||
print('\nPerforming DDP check...')
|
||||
|
||||
if self.is_master:
|
||||
print('Checking if parameters are consistent across processes...')
|
||||
dist.barrier()
|
||||
try:
|
||||
for p in self.master_params:
|
||||
# split to avoid OOM
|
||||
for i in range(0, p.numel(), 10000000):
|
||||
sub_size = min(10000000, p.numel() - i)
|
||||
sub_p = p.detach().view(-1)[i:i+sub_size]
|
||||
# gather from all processes
|
||||
sub_p_gather = [torch.empty_like(sub_p) for _ in range(self.world_size)]
|
||||
dist.all_gather(sub_p_gather, sub_p)
|
||||
# check if equal
|
||||
assert all([torch.equal(sub_p, sub_p_gather[i]) for i in range(self.world_size)]), 'parameters are not consistent across processes'
|
||||
except AssertionError as e:
|
||||
if self.is_master:
|
||||
print(f'\n\033[91mError: {e}\033[0m')
|
||||
print('DDP check failed.')
|
||||
raise e
|
||||
|
||||
dist.barrier()
|
||||
if self.is_master:
|
||||
print('Done.')
|
||||
|
||||
def run_step(self, data_list):
|
||||
"""
|
||||
Run a training step.
|
||||
"""
|
||||
step_log = {'loss': {}, 'status': {}}
|
||||
amp_context = partial(torch.autocast, device_type='cuda') if self.fp16_mode == 'amp' else nullcontext
|
||||
elastic_controller_context = self.elastic_controller.record if self.elastic_controller_config is not None else nullcontext
|
||||
|
||||
# Train
|
||||
losses = []
|
||||
statuses = []
|
||||
elastic_controller_logs = []
|
||||
zero_grad(self.model_params)
|
||||
for i, mb_data in enumerate(data_list):
|
||||
## sync at the end of each batch split
|
||||
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]
|
||||
with nested_contexts(*sync_contexts), elastic_controller_context():
|
||||
with amp_context():
|
||||
loss, status = self.training_losses(**mb_data)
|
||||
l = loss['loss'] / len(data_list)
|
||||
## backward
|
||||
if self.fp16_mode == 'amp':
|
||||
self.scaler.scale(l).backward()
|
||||
elif self.fp16_mode == 'inflat_all':
|
||||
scaled_l = l * (2 ** self.log_scale)
|
||||
scaled_l.backward()
|
||||
else:
|
||||
l.backward()
|
||||
## log
|
||||
losses.append(dict_foreach(loss, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
|
||||
statuses.append(dict_foreach(status, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
|
||||
if self.elastic_controller_config is not None:
|
||||
elastic_controller_logs.append(self.elastic_controller.log())
|
||||
## gradient clip
|
||||
if self.grad_clip is not None:
|
||||
if self.fp16_mode == 'amp':
|
||||
self.scaler.unscale_(self.optimizer)
|
||||
elif self.fp16_mode == 'inflat_all':
|
||||
model_grads_to_master_grads(self.model_params, self.master_params)
|
||||
self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
|
||||
if isinstance(self.grad_clip, float):
|
||||
grad_norm = torch.nn.utils.clip_grad_norm_(self.master_params, self.grad_clip)
|
||||
else:
|
||||
grad_norm = self.grad_clip(self.master_params)
|
||||
if torch.isfinite(grad_norm):
|
||||
statuses[-1]['grad_norm'] = grad_norm.item()
|
||||
## step
|
||||
if self.fp16_mode == 'amp':
|
||||
prev_scale = self.scaler.get_scale()
|
||||
self.scaler.step(self.optimizer)
|
||||
self.scaler.update()
|
||||
elif self.fp16_mode == 'inflat_all':
|
||||
prev_scale = 2 ** self.log_scale
|
||||
if not any(not p.grad.isfinite().all() for p in self.model_params):
|
||||
if self.grad_clip is None:
|
||||
model_grads_to_master_grads(self.model_params, self.master_params)
|
||||
self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
|
||||
self.optimizer.step()
|
||||
master_params_to_model_params(self.model_params, self.master_params)
|
||||
self.log_scale += self.fp16_scale_growth
|
||||
else:
|
||||
self.log_scale -= 1
|
||||
else:
|
||||
prev_scale = 1.0
|
||||
if not any(not p.grad.isfinite().all() for p in self.model_params):
|
||||
self.optimizer.step()
|
||||
else:
|
||||
print('\n\033[93mWarning: NaN detected in gradients. Skipping update.\033[0m')
|
||||
## adjust learning rate
|
||||
if self.lr_scheduler_config is not None:
|
||||
statuses[-1]['lr'] = self.lr_scheduler.get_last_lr()[0]
|
||||
self.lr_scheduler.step()
|
||||
|
||||
# Logs
|
||||
step_log['loss'] = dict_reduce(losses, lambda x: np.mean(x))
|
||||
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)})
|
||||
if self.elastic_controller_config is not None:
|
||||
step_log['elastic'] = dict_reduce(elastic_controller_logs, lambda x: np.mean(x))
|
||||
if self.grad_clip is not None:
|
||||
step_log['grad_clip'] = self.grad_clip if isinstance(self.grad_clip, float) else self.grad_clip.log()
|
||||
|
||||
# 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
|
||||
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
|
||||
@@ -0,0 +1,59 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
from ....utils.general_utils import dict_foreach
|
||||
from ....pipelines import samplers
|
||||
|
||||
|
||||
class ClassifierFreeGuidanceMixin:
|
||||
def __init__(self, *args, p_uncond: float = 0.1, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.p_uncond = p_uncond
|
||||
|
||||
def get_cond(self, cond, neg_cond=None, **kwargs):
|
||||
"""
|
||||
Get the conditioning data.
|
||||
"""
|
||||
assert neg_cond is not None, "neg_cond must be provided for classifier-free guidance"
|
||||
|
||||
if self.p_uncond > 0:
|
||||
# randomly drop the class label
|
||||
def get_batch_size(cond):
|
||||
if isinstance(cond, torch.Tensor):
|
||||
return cond.shape[0]
|
||||
elif isinstance(cond, list):
|
||||
return len(cond)
|
||||
else:
|
||||
raise ValueError(f"Unsupported type of cond: {type(cond)}")
|
||||
|
||||
ref_cond = cond if not isinstance(cond, dict) else cond[list(cond.keys())[0]]
|
||||
B = get_batch_size(ref_cond)
|
||||
|
||||
def select(cond, neg_cond, mask):
|
||||
if isinstance(cond, torch.Tensor):
|
||||
mask = torch.tensor(mask, device=cond.device).reshape(-1, *[1] * (cond.ndim - 1))
|
||||
return torch.where(mask, neg_cond, cond)
|
||||
elif isinstance(cond, list):
|
||||
return [nc if m else c for c, nc, m in zip(cond, neg_cond, mask)]
|
||||
else:
|
||||
raise ValueError(f"Unsupported type of cond: {type(cond)}")
|
||||
|
||||
mask = list(np.random.rand(B) < self.p_uncond)
|
||||
if not isinstance(cond, dict):
|
||||
cond = select(cond, neg_cond, mask)
|
||||
else:
|
||||
cond = dict_foreach([cond, neg_cond], lambda x: select(x[0], x[1], mask))
|
||||
|
||||
return cond
|
||||
|
||||
def get_inference_cond(self, cond, neg_cond=None, **kwargs):
|
||||
"""
|
||||
Get the conditioning data for inference.
|
||||
"""
|
||||
assert neg_cond is not None, "neg_cond must be provided for classifier-free guidance"
|
||||
return {'cond': cond, 'neg_cond': neg_cond, **kwargs}
|
||||
|
||||
def get_sampler(self, **kwargs) -> samplers.FlowEulerCfgSampler:
|
||||
"""
|
||||
Get the sampler for the diffusion process.
|
||||
"""
|
||||
return samplers.FlowEulerCfgSampler(self.sigma_min)
|
||||
Reference in New Issue
Block a user