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Running
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A10G
| import os | |
| import math | |
| import torch | |
| import logging | |
| import subprocess | |
| import numpy as np | |
| import torch.distributed as dist | |
| # from torch._six import inf | |
| from torch import inf | |
| from PIL import Image | |
| from typing import Union, Iterable | |
| from collections import OrderedDict | |
| _tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]] | |
| ################################################################################# | |
| # Training Helper Functions # | |
| ################################################################################# | |
| ################################################################################# | |
| # Training Clip Gradients # | |
| ################################################################################# | |
| def get_grad_norm( | |
| parameters: _tensor_or_tensors, norm_type: float = 2.0) -> torch.Tensor: | |
| r""" | |
| Copy from torch.nn.utils.clip_grad_norm_ | |
| Clips gradient norm of an iterable of parameters. | |
| The norm is computed over all gradients together, as if they were | |
| concatenated into a single vector. Gradients are modified in-place. | |
| Args: | |
| parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a | |
| single Tensor that will have gradients normalized | |
| max_norm (float or int): max norm of the gradients | |
| norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for | |
| infinity norm. | |
| error_if_nonfinite (bool): if True, an error is thrown if the total | |
| norm of the gradients from :attr:`parameters` is ``nan``, | |
| ``inf``, or ``-inf``. Default: False (will switch to True in the future) | |
| Returns: | |
| Total norm of the parameter gradients (viewed as a single vector). | |
| """ | |
| if isinstance(parameters, torch.Tensor): | |
| parameters = [parameters] | |
| grads = [p.grad for p in parameters if p.grad is not None] | |
| norm_type = float(norm_type) | |
| if len(grads) == 0: | |
| return torch.tensor(0.) | |
| device = grads[0].device | |
| if norm_type == inf: | |
| norms = [g.detach().abs().max().to(device) for g in grads] | |
| total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms)) | |
| else: | |
| total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type) | |
| return total_norm | |
| def clip_grad_norm_( | |
| parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0, | |
| error_if_nonfinite: bool = False, clip_grad = True) -> torch.Tensor: | |
| r""" | |
| Copy from torch.nn.utils.clip_grad_norm_ | |
| Clips gradient norm of an iterable of parameters. | |
| The norm is computed over all gradients together, as if they were | |
| concatenated into a single vector. Gradients are modified in-place. | |
| Args: | |
| parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a | |
| single Tensor that will have gradients normalized | |
| max_norm (float or int): max norm of the gradients | |
| norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for | |
| infinity norm. | |
| error_if_nonfinite (bool): if True, an error is thrown if the total | |
| norm of the gradients from :attr:`parameters` is ``nan``, | |
| ``inf``, or ``-inf``. Default: False (will switch to True in the future) | |
| Returns: | |
| Total norm of the parameter gradients (viewed as a single vector). | |
| """ | |
| if isinstance(parameters, torch.Tensor): | |
| parameters = [parameters] | |
| grads = [p.grad for p in parameters if p.grad is not None] | |
| max_norm = float(max_norm) | |
| norm_type = float(norm_type) | |
| if len(grads) == 0: | |
| return torch.tensor(0.) | |
| device = grads[0].device | |
| if norm_type == inf: | |
| norms = [g.detach().abs().max().to(device) for g in grads] | |
| total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms)) | |
| else: | |
| total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type) | |
| # print(total_norm) | |
| if clip_grad: | |
| if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()): | |
| raise RuntimeError( | |
| f'The total norm of order {norm_type} for gradients from ' | |
| '`parameters` is non-finite, so it cannot be clipped. To disable ' | |
| 'this error and scale the gradients by the non-finite norm anyway, ' | |
| 'set `error_if_nonfinite=False`') | |
| clip_coef = max_norm / (total_norm + 1e-6) | |
| # Note: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so | |
| # avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization | |
| # when the gradients do not reside in CPU memory. | |
| clip_coef_clamped = torch.clamp(clip_coef, max=1.0) | |
| for g in grads: | |
| g.detach().mul_(clip_coef_clamped.to(g.device)) | |
| # gradient_cliped = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type) | |
| # print(gradient_cliped) | |
| return total_norm | |
| ################################################################################# | |
| # Training Logger # | |
| ################################################################################# | |
| def create_logger(logging_dir): | |
| """ | |
| Create a logger that writes to a log file and stdout. | |
| """ | |
| if dist.get_rank() == 0: # real logger | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| # format='[\033[34m%(asctime)s\033[0m] %(message)s', | |
| format='[%(asctime)s] %(message)s', | |
| datefmt='%Y-%m-%d %H:%M:%S', | |
| handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")] | |
| ) | |
| logger = logging.getLogger(__name__) | |
| else: # dummy logger (does nothing) | |
| logger = logging.getLogger(__name__) | |
| logger.addHandler(logging.NullHandler()) | |
| return logger | |
| def create_accelerate_logger(logging_dir, is_main_process=False): | |
| """ | |
| Create a logger that writes to a log file and stdout. | |
| """ | |
| if is_main_process: # real logger | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| # format='[\033[34m%(asctime)s\033[0m] %(message)s', | |
| format='[%(asctime)s] %(message)s', | |
| datefmt='%Y-%m-%d %H:%M:%S', | |
| handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")] | |
| ) | |
| logger = logging.getLogger(__name__) | |
| else: # dummy logger (does nothing) | |
| logger = logging.getLogger(__name__) | |
| logger.addHandler(logging.NullHandler()) | |
| return logger | |
| def create_tensorboard(tensorboard_dir): | |
| """ | |
| Create a tensorboard that saves losses. | |
| """ | |
| if dist.get_rank() == 0: # real tensorboard | |
| # tensorboard | |
| writer = SummaryWriter(tensorboard_dir) | |
| return writer | |
| def write_tensorboard(writer, *args): | |
| ''' | |
| write the loss information to a tensorboard file. | |
| Only for pytorch DDP mode. | |
| ''' | |
| if dist.get_rank() == 0: # real tensorboard | |
| writer.add_scalar(args[0], args[1], args[2]) | |
| ################################################################################# | |
| # EMA Update/ DDP Training Utils # | |
| ################################################################################# | |
| def update_ema(ema_model, model, decay=0.9999): | |
| """ | |
| Step the EMA model towards the current model. | |
| """ | |
| ema_params = OrderedDict(ema_model.named_parameters()) | |
| model_params = OrderedDict(model.named_parameters()) | |
| for name, param in model_params.items(): | |
| # TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed | |
| ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay) | |
| def requires_grad(model, flag=True): | |
| """ | |
| Set requires_grad flag for all parameters in a model. | |
| """ | |
| for p in model.parameters(): | |
| p.requires_grad = flag | |
| def cleanup(): | |
| """ | |
| End DDP training. | |
| """ | |
| dist.destroy_process_group() | |
| def setup_distributed(backend="nccl", port=None): | |
| """Initialize distributed training environment. | |
| support both slurm and torch.distributed.launch | |
| see torch.distributed.init_process_group() for more details | |
| """ | |
| num_gpus = torch.cuda.device_count() | |
| print(f'Hahahahahaha') | |
| if "SLURM_JOB_ID" in os.environ: | |
| rank = int(os.environ["SLURM_PROCID"]) | |
| world_size = int(os.environ["SLURM_NTASKS"]) | |
| node_list = os.environ["SLURM_NODELIST"] | |
| addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1") | |
| # specify master port | |
| if port is not None: | |
| os.environ["MASTER_PORT"] = str(port) | |
| elif "MASTER_PORT" not in os.environ: | |
| # os.environ["MASTER_PORT"] = "29566" | |
| os.environ["MASTER_PORT"] = str(29566 + num_gpus) | |
| if "MASTER_ADDR" not in os.environ: | |
| os.environ["MASTER_ADDR"] = addr | |
| os.environ["WORLD_SIZE"] = str(world_size) | |
| os.environ["LOCAL_RANK"] = str(rank % num_gpus) | |
| os.environ["RANK"] = str(rank) | |
| else: | |
| rank = int(os.environ["RANK"]) | |
| world_size = int(os.environ["WORLD_SIZE"]) | |
| # torch.cuda.set_device(rank % num_gpus) | |
| print(f'before dist.init_process_group') | |
| dist.init_process_group( | |
| backend=backend, | |
| world_size=world_size, | |
| rank=rank, | |
| ) | |
| print(f'after dist.init_process_group') | |
| ################################################################################# | |
| # Testing Utils # | |
| ################################################################################# | |
| def save_video_grid(video, nrow=None): | |
| b, t, h, w, c = video.shape | |
| if nrow is None: | |
| nrow = math.ceil(math.sqrt(b)) | |
| ncol = math.ceil(b / nrow) | |
| padding = 1 | |
| video_grid = torch.zeros((t, (padding + h) * nrow + padding, | |
| (padding + w) * ncol + padding, c), dtype=torch.uint8) | |
| print(video_grid.shape) | |
| for i in range(b): | |
| r = i // ncol | |
| c = i % ncol | |
| start_r = (padding + h) * r | |
| start_c = (padding + w) * c | |
| video_grid[:, start_r:start_r + h, start_c:start_c + w] = video[i] | |
| return video_grid | |
| ################################################################################# | |
| # MMCV Utils # | |
| ################################################################################# | |
| def collect_env(): | |
| # Copyright (c) OpenMMLab. All rights reserved. | |
| from mmcv.utils import collect_env as collect_base_env | |
| from mmcv.utils import get_git_hash | |
| """Collect the information of the running environments.""" | |
| env_info = collect_base_env() | |
| env_info['MMClassification'] = get_git_hash()[:7] | |
| for name, val in env_info.items(): | |
| print(f'{name}: {val}') | |
| print(torch.cuda.get_arch_list()) | |
| print(torch.version.cuda) | |
| ################################################################################# | |
| # Long video generation Utils # | |
| ################################################################################# | |
| def mask_generation(mask_type, shape, dtype, device): | |
| b, c, f, h, w = shape | |
| if mask_type.startswith('random'): | |
| num = float(mask_type.split('random')[-1]) | |
| mask_f = torch.ones(1, 1, f, 1, 1, dtype=dtype, device=device) | |
| indices = torch.randperm(f, device=device)[:int(f*num)] | |
| mask_f[0, 0, indices, :, :] = 0 | |
| mask = mask_f.expand(b, c, -1, h, w) | |
| elif mask_type.startswith('first'): | |
| num = int(mask_type.split('first')[-1]) | |
| mask_f = torch.cat([torch.zeros(1, 1, num, 1, 1, dtype=dtype, device=device), | |
| torch.ones(1, 1, f-num, 1, 1, dtype=dtype, device=device)], dim=2) | |
| mask = mask_f.expand(b, c, -1, h, w) | |
| else: | |
| raise ValueError(f"Invalid mask type: {mask_type}") | |
| return mask | |
| def mask_generation_before(mask_type, shape, dtype, device): | |
| b, f, c, h, w = shape | |
| if mask_type.startswith('random'): | |
| num = float(mask_type.split('random')[-1]) | |
| mask_f = torch.ones(1, f, 1, 1, 1, dtype=dtype, device=device) | |
| indices = torch.randperm(f, device=device)[:int(f*num)] | |
| mask_f[0, indices, :, :, :] = 0 | |
| mask = mask_f.expand(b, -1, c, h, w) | |
| elif mask_type.startswith('first'): | |
| num = int(mask_type.split('first')[-1]) | |
| mask_f = torch.cat([torch.zeros(1, num, 1, 1, 1, dtype=dtype, device=device), | |
| torch.ones(1, f-num, 1, 1, 1, dtype=dtype, device=device)], dim=1) | |
| mask = mask_f.expand(b, -1, c, h, w) | |
| elif mask_type.startswith('uniform'): | |
| p = float(mask_type.split('uniform')[-1]) | |
| mask_f = torch.ones(1, f, 1, 1, 1, dtype=dtype, device=device) | |
| mask_f[0, torch.rand(f, device=device) < p, :, :, :] = 0 | |
| print(f'mask_f: = {mask_f}') | |
| mask = mask_f.expand(b, -1, c, h, w) | |
| print(f'mask.shape: = {mask.shape}, mask: = {mask}') | |
| elif mask_type.startswith('all'): | |
| mask = torch.ones(b,f,c,h,w,dtype=dtype,device=device) | |
| elif mask_type.startswith('onelast'): | |
| num = int(mask_type.split('onelast')[-1]) | |
| mask_one = torch.zeros(1,1,1,1,1, dtype=dtype, device=device) | |
| mask_mid = torch.ones(1,f-2*num,1,1,1,dtype=dtype, device=device) | |
| mask_last = torch.zeros_like(mask_one) | |
| mask = torch.cat([mask_one]*num + [mask_mid] + [mask_last]*num, dim=1) | |
| # breakpoint() | |
| mask = mask.expand(b, -1, c, h, w) | |
| elif mask_type.startswith('interpolate'): | |
| mask_f = [] | |
| for i in range(4): | |
| mask_zero = torch.zeros(1,1,1,1,1, dtype=dtype, device=device) | |
| mask_f.append(mask_zero) | |
| mask_one = torch.ones(1,3,1,1,1, dtype=dtype, device=device) | |
| mask_f.append(mask_one) | |
| mask = torch.cat(mask_f, dim=1) | |
| print(f'mask={mask}') | |
| elif mask_type.startswith('tsr'): | |
| mask_f = [] | |
| mask_zero = torch.zeros(1,1,1,1,1, dtype=dtype, device=device) | |
| mask_one = torch.ones(1,3,1,1,1, dtype=dtype, device=device) | |
| for i in range(15): | |
| mask_f.append(mask_zero) # not masked | |
| mask_f.append(mask_one) # masked | |
| mask_f.append(mask_zero) # not masked | |
| mask = torch.cat(mask_f, dim=1) | |
| # print(f'before mask.shape = {mask.shape}, mask = {mask}') # [1, 61, 1, 1, 1] | |
| mask = mask.expand(b, -1, c, h, w) | |
| # print(f'after mask.shape = {mask.shape}, mask = {mask}') # [4, 61, 3, 256, 256] | |
| else: | |
| raise ValueError(f"Invalid mask type: {mask_type}") | |
| return mask | |