add fp16 training
This commit is contained in:
harubaru
parent
9581fbc226
commit
f980137430
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@ -47,6 +47,7 @@ model:
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ckpt_path: "../latent-diffusion/logs/original/checkpoints/last.ckpt"
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ddconfig:
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double_z: true
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z_channels: 4
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@ -69,22 +70,25 @@ model:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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params:
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penultimate: true # use 2nd last layer - https://arxiv.org/pdf/2205.11487.pdf D.1
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extended_mode: 3 # extend clip context to 225 tokens - as per NAI blogpost
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 4
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num_workers: 4
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batch_size: 2
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num_workers: 2
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wrap: false
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train:
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target: ldm.data.local.LocalDanbooruBase
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target: ldm.data.localdanboorubase.LocalDanbooruBase
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params:
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data_root: '../dataset'
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size: 512
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mode: "train"
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ucg: 0.1 # unconditional guidance training
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validation:
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target: ldm.data.local.LocalDanbooruBase
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target: ldm.data.localdanboorubase.LocalDanbooruBase
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params:
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data_root: '../dataset'
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size: 512
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mode: "val"
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val_split: 64
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@ -109,9 +113,11 @@ lightning:
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plot_diffusion_rows: False
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N: 4
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ddim_steps: 50
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trainer:
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benchmark: True
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val_check_interval: 5000000
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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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trainer:
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precision: 16
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amp_backend: "native"
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strategy: "fsdp"
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benchmark: True
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limit_val_batches: 0
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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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@ -0,0 +1,182 @@
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import os
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import numpy as np
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import PIL
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from PIL import Image, ImageOps
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from torch.utils.data import Dataset
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from torchvision import transforms
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import torchvision.transforms.functional as TF
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from functools import partial
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import copy
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import glob
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import random
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PIL.Image.MAX_IMAGE_PIXELS = 933120000
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import torchvision
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import pytorch_lightning as pl
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import torch
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import re
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import json
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import io
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def resize_image(image: Image, max_size=(768,768)):
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image = ImageOps.contain(image, max_size, Image.LANCZOS)
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# resize to integer multiple of 64
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w, h = image.size
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w, h = map(lambda x: x - x % 64, (w, h))
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ratio = w / h
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src_ratio = image.width / image.height
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src_w = w if ratio > src_ratio else image.width * h // image.height
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src_h = h if ratio <= src_ratio else image.height * w // image.width
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resized = image.resize((src_w, src_h), resample=Image.LANCZOS)
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res = Image.new("RGB", (w, h))
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res.paste(resized, box=(w // 2 - src_w // 2, h // 2 - src_h // 2))
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return res
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class CaptionProcessor(object):
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def __init__(self, transforms, max_size, resize, random_order, LR_size):
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self.transforms = transforms
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self.max_size = max_size
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self.resize = resize
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self.random_order = random_order
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self.degradation_process = partial(TF.resize, size=LR_size, interpolation=TF.InterpolationMode.NEAREST)
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def __call__(self, sample):
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# preprocess caption
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pass
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# preprocess image
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image = sample['image']
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image = Image.open(io.BytesIO(image))
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if self.resize:
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image = resize_image(image, max_size=(self.max_size, self.max_size))
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image = self.transforms(image)
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lr_image = copy.deepcopy(image)
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image = np.array(image).astype(np.uint8)
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sample['image'] = (image / 127.5 - 1.0).astype(np.float32)
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# preprocess LR image
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lr_image = self.degradation_process(lr_image)
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lr_image = np.array(lr_image).astype(np.uint8)
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sample['LR_image'] = (lr_image/127.5 - 1.0).astype(np.float32)
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return sample
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class LocalDanbooruBaseVAE(Dataset):
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def __init__(self,
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data_root='./danbooru-aesthetic',
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size=256,
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interpolation="bicubic",
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flip_p=0.5,
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crop=True,
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shuffle=False,
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mode='train',
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val_split=64,
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downscale_f=8
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):
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super().__init__()
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self.shuffle=shuffle
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self.crop = crop
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print('Fetching data.')
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ext = ['image']
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self.image_files = []
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[self.image_files.extend(glob.glob(f'{data_root}' + '/*.' + e)) for e in ext]
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if mode == 'val':
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self.image_files = self.image_files[:len(self.image_files)//val_split]
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print(f'Constructing image map. Found {len(self.image_files)} images')
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self.examples = {}
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self.hashes = []
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for i in self.image_files:
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hash = i[len(f'{data_root}/'):].split('.')[0]
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self.examples[hash] = {
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'image': i
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}
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self.hashes.append(hash)
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print(f'image map has {len(self.examples.keys())} examples')
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self.size = size
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self.interpolation = {"linear": PIL.Image.LINEAR,
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"bilinear": PIL.Image.BILINEAR,
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"bicubic": PIL.Image.BICUBIC,
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"lanczos": PIL.Image.LANCZOS,
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}[interpolation]
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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image_transforms = []
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image_transforms.extend([torchvision.transforms.RandomHorizontalFlip(flip_p)],)
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image_transforms = torchvision.transforms.Compose(image_transforms)
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self.captionprocessor = CaptionProcessor(image_transforms, self.size, True, True, int(size / downscale_f))
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def random_sample(self):
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return self.__getitem__(random.randint(0, self.__len__() - 1))
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def sequential_sample(self, i):
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if i >= self.__len__() - 1:
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return self.__getitem__(0)
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return self.__getitem__(i + 1)
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def skip_sample(self, i):
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return None
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def __len__(self):
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return len(self.image_files)
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def __getitem__(self, i):
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return self.get_image(i)
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def get_image(self, i):
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image = {}
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try:
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image_file = self.examples[self.hashes[i]]['image']
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with open(image_file, 'rb') as f:
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image['image'] = f.read()
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image = self.captionprocessor(image)
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except Exception as e:
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print(f'Error with {self.examples[self.hashes[i]]["image"]} -- {e} -- skipping {i}')
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return self.skip_sample(i)
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return image
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"""
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if __name__ == "__main__":
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dataset = LocalBase('./danbooru-aesthetic', size=512, crop=False, mode='val')
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print(dataset.__len__())
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example = dataset.__getitem__(0)
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print(dataset.hashes[0])
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print(example['caption'])
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image = example['image']
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image = ((image + 1) * 127.5).astype(np.uint8)
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image = Image.fromarray(image)
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image.save('example.png')
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"""
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"""
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from tqdm import tqdm
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if __name__ == "__main__":
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dataset = LocalDanbooruBase('./links', size=768)
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import time
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a = time.process_time()
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for i in range(8):
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example = dataset.get_image(i)
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image = example['image']
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image = ((image + 1) * 127.5).astype(np.uint8)
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image = Image.fromarray(image)
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image.save(f'example-{i}.png')
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print(example['caption'])
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print('time:', time.process_time()-a)
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"""
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@ -340,17 +340,18 @@ class DDPM(pl.LightningModule):
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return loss, loss_dict
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def training_step(self, batch, batch_idx):
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loss, loss_dict = self.shared_step(batch)
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with torch.autocast('cuda'):
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loss, loss_dict = self.shared_step(batch)
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self.log_dict(loss_dict, prog_bar=True,
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logger=True, on_step=True, on_epoch=True)
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self.log_dict(loss_dict, prog_bar=True,
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logger=True, on_step=True, on_epoch=True)
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self.log("global_step", self.global_step,
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prog_bar=True, logger=True, on_step=True, on_epoch=False)
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self.log("global_step", self.global_step,
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prog_bar=True, logger=True, on_step=True, on_epoch=False)
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if self.use_scheduler:
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lr = self.optimizers().param_groups[0]['lr']
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self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
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if self.use_scheduler:
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lr = self.optimizers().param_groups[0]['lr']
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self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
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return loss
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@ -475,7 +476,7 @@ class LatentDiffusion(DDPM):
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@rank_zero_only
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@torch.no_grad()
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def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
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def on_train_batch_start(self, batch, batch_idx):
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# only for very first batch
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if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
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assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
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@ -119,33 +119,35 @@ def checkpoint(func, inputs, params, flag):
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class CheckpointFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, run_function, length, *args):
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ctx.run_function = run_function
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ctx.input_tensors = list(args[:length])
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ctx.input_params = list(args[length:])
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with torch.autocast('cuda'):
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ctx.run_function = run_function
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ctx.input_tensors = list(args[:length])
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ctx.input_params = list(args[length:])
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with torch.no_grad():
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output_tensors = ctx.run_function(*ctx.input_tensors)
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return output_tensors
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with torch.no_grad():
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output_tensors = ctx.run_function(*ctx.input_tensors)
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return output_tensors
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@staticmethod
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def backward(ctx, *output_grads):
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ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
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with torch.enable_grad():
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# Fixes a bug where the first op in run_function modifies the
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# Tensor storage in place, which is not allowed for detach()'d
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# Tensors.
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shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
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output_tensors = ctx.run_function(*shallow_copies)
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input_grads = torch.autograd.grad(
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output_tensors,
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ctx.input_tensors + ctx.input_params,
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output_grads,
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allow_unused=True,
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)
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del ctx.input_tensors
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del ctx.input_params
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del output_tensors
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return (None, None) + input_grads
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with torch.autocast('cuda'):
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ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
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with torch.enable_grad():
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# Fixes a bug where the first op in run_function modifies the
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# Tensor storage in place, which is not allowed for detach()'d
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# Tensors.
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shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
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output_tensors = ctx.run_function(*shallow_copies)
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input_grads = torch.autograd.grad(
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output_tensors,
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ctx.input_tensors + ctx.input_params,
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output_grads,
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allow_unused=True,
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)
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del ctx.input_tensors
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del ctx.input_params
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del output_tensors
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return (None, None) + input_grads
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def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
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@ -136,8 +136,7 @@ class SpatialRescaler(nn.Module):
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return self(x)
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class FrozenCLIPEmbedder(AbstractEncoder):
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"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
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def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, penultimate=True, max_chunks=3, extended_mode=True):
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def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, penultimate=True, extended_mode=None):
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super().__init__()
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self.tokenizer = CLIPTokenizer.from_pretrained(version)
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self.transformer = CLIPTextModel.from_pretrained(version)
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@ -145,7 +144,6 @@ class FrozenCLIPEmbedder(AbstractEncoder):
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self.max_length = max_length
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self.penultimate = penultimate # return embeddings from 2nd to last layer, see https://arxiv.org/pdf/2205.11487.pdf
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self.extended_mode = extended_mode
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self.max_chunks = max_chunks
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self.freeze()
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def freeze(self):
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@ -168,7 +166,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
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if self.extended_mode:
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max_standard_tokens = self.max_length - 2
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batch_encoding = self.tokenizer(text, truncation=True, max_length=(self.max_length * self.max_chunks) - (self.max_chunks * 2), return_length=True, return_overflowing_tokens=False, padding=False,
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batch_encoding = self.tokenizer(text, truncation=True, max_length=(self.max_length * self.extended_mode) - (self.extended_mode * 2), return_length=True, return_overflowing_tokens=False, padding=False,
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add_special_tokens=False)
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# get the max length aligned to chunk size.
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17
main.py
17
main.py
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@ -295,7 +295,7 @@ class ImageLogger(Callback):
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self.batch_freq = batch_frequency
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self.max_images = max_images
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self.logger_log_images = {
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pl.loggers.TestTubeLogger: self._testtube,
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pl.loggers.WandbLogger: self._testtube,
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}
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self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
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if not increase_log_steps:
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@ -350,7 +350,8 @@ class ImageLogger(Callback):
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pl_module.eval()
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with torch.no_grad():
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images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
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with torch.autocast('cuda'):
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images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
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for k in images:
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N = min(images[k].shape[0], self.max_images)
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@ -380,7 +381,7 @@ class ImageLogger(Callback):
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return True
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return False
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
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if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
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self.log_img(pl_module, batch, batch_idx, split="train")
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@ -518,7 +519,7 @@ if __name__ == "__main__":
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# merge trainer cli with config
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trainer_config = lightning_config.get("trainer", OmegaConf.create())
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# default to ddp
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trainer_config["accelerator"] = "ddp"
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trainer_config["accelerator"] = "gpu"
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for k in nondefault_trainer_args(opt):
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trainer_config[k] = getattr(opt, k)
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if not "gpus" in trainer_config:
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@ -556,7 +557,7 @@ if __name__ == "__main__":
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}
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},
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}
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default_logger_cfg = default_logger_cfgs["testtube"]
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default_logger_cfg = default_logger_cfgs["wandb"]
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if "logger" in lightning_config:
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logger_cfg = lightning_config.logger
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else:
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@ -656,9 +657,11 @@ if __name__ == "__main__":
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trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
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trainer_kwargs["plugins"] = list()
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from pytorch_lightning.plugins import DDPPlugin
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trainer_kwargs["plugins"].append(DDPPlugin(find_unused_parameters=False))
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from pytorch_lightning.plugins import DDPPlugin, NativeMixedPrecisionPlugin
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#trainer_kwargs["plugins"].append(DDPPlugin(find_unused_parameters=False))
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trainer_kwargs["plugins"].append(NativeMixedPrecisionPlugin(16, 'cuda', torch.cuda.amp.GradScaler(enabled=True)))
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trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
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#trainer = Trainer(gpus=1, precision=16, amp_backend="native", strategy="deepspeed_stage_2_offload", benchmark=True, limit_val_batches=0, num_sanity_val_steps=0, accumulate_grad_batches=1)
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trainer.logdir = logdir ###
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# data
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|
|
@ -4,7 +4,7 @@ opencv-python
|
|||
pudb==2019.2
|
||||
imageio==2.9.0
|
||||
imageio-ffmpeg==0.4.2
|
||||
pytorch-lightning==1.6.0
|
||||
pytorch-lightning==1.7.7
|
||||
omegaconf==2.1.1
|
||||
test-tube>=0.7.5
|
||||
streamlit>=0.73.1
|
||||
|
|
@ -17,4 +17,6 @@ gradio
|
|||
git+https://github.com/illeatmyhat/taming-transformers.git@master#egg=taming-transformers
|
||||
git+https://github.com/openai/CLIP.git@main#egg=clip
|
||||
git+https://github.com/hlky/k-diffusion-sd#egg=k_diffusion
|
||||
webdataset
|
||||
webdataset
|
||||
wandb
|
||||
fairscale
|
||||
Loading…
Reference in New Issue