[tutorial] add cifar10 for diffusion (#1907)

examples/tutorial/stable_diffusion/README.md

@@ -1,5 +1,4 @@
-# Handson 6: Acceleration of Stable Diffusion
+# Stable Diffusion with Colossal-AI
-
 *[Colosssal-AI](https://github.com/hpcaitech/ColossalAI) provides a faster and lower cost solution for pretraining and
 fine-tuning for AIGC (AI-Generated Content) applications such as the model [stable-diffusion](https://github.com/CompVis/stable-diffusion) from [Stability AI](https://stability.ai/).*
 
@@ -55,28 +54,40 @@ pip install -r requirements.txt && pip install .
 > The specified version is due to the interface incompatibility caused by the latest update of [Lightning](https://github.com/Lightning-AI/lightning), which will be fixed in the near future.
 
 ## Dataset
-The DataSet is from [LAION-5B](https://laion.ai/blog/laion-5b/), the subset of [LAION](https://laion.ai/),
+The dataSet is from [LAION-5B](https://laion.ai/blog/laion-5b/), the subset of [LAION](https://laion.ai/),
 you should the change the `data.file_path` in the `config/train_colossalai.yaml`
 
 ## Training
 
-we provide the script `train.sh` to run the training task , and two Stategy in `configs`:`train_colossalai.yaml`, `train_ddp.yaml`
+We provide the script `train.sh` to run the training task , and two Stategy in `configs`:`train_colossalai.yaml`
 
-for example, you can run the training from colossalai by
+For example, you can run the training from colossalai by
 ```
-python main.py --logdir /tmp -t --postfix test -b config/train_colossalai.yaml
+python main.py --logdir /tmp -t --postfix test -b configs/train_colossalai.yaml
 ```
 
 - you can change the `--logdir` the save the log information and the last checkpoint
 
 ### Training config
-you can change the trainging config in the yaml file
+You can change the trainging config in the yaml file
 
 - accelerator: acceleratortype, default 'gpu'
 - devices: device number used for training, default 4
 - max_epochs: max training epochs
 - precision: usefp16 for training or not, default 16, you must use fp16 if you want to apply colossalai
 
+## Example
+
+### Training on cifar10
+
+We provide the finetuning example on CIFAR10 dataset
+
+You can run by config `train_colossalai_cifar10.yaml`
+```
+python main.py --logdir /tmp -t --postfix test -b configs/train_colossalai_cifar10.yaml
+```
+
+
 
 ## Comments
 

examples/tutorial/stable_diffusion/configs/train_colossalai_cifar10.yaml

@@ -0,0 +1,123 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: image
+    cond_stage_key: txt
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1.e-4 ]
+        f_min: [ 1.e-10 ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/unet/diffusion_pytorch_model.bin'
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: False
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/vae/diffusion_pytorch_model.bin'
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      params:
+        use_fp16: True
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 4
+    num_workers: 4
+    train:
+      target: ldm.data.cifar10.hf_dataset
+      params:
+        name: cifar10
+        image_transforms:
+        - target: torchvision.transforms.Resize
+          params:
+            size: 512
+            interpolation: 3
+        - target: torchvision.transforms.RandomCrop
+          params:
+            size: 512
+        - target: torchvision.transforms.RandomHorizontalFlip
+
+lightning:
+  trainer:
+    accelerator: 'gpu' 
+    devices: 2
+    log_gpu_memory: all
+    max_epochs: 2
+    precision: 16
+    auto_select_gpus: False
+    strategy:
+      target: pytorch_lightning.strategies.ColossalAIStrategy
+      params:
+        use_chunk: False
+        enable_distributed_storage: True,
+        placement_policy: cuda
+        force_outputs_fp32: False
+
+    log_every_n_steps: 2
+    logger: True
+    default_root_dir: "/tmp/diff_log/"
+    profiler: pytorch
+
+  logger_config:
+    wandb:
+      target: pytorch_lightning.loggers.WandbLogger
+      params:
+          name: nowname
+          save_dir: "/tmp/diff_log/"
+          offline: opt.debug
+          id: nowname

examples/tutorial/stable_diffusion/environment.yaml

@@ -11,20 +11,21 @@ dependencies:
   - numpy=1.19.2
   - pip:
     - albumentations==0.4.3
+    - datasets
     - diffusers
     - opencv-python==4.6.0.66
     - pudb==2019.2
     - invisible-watermark
     - imageio==2.9.0
     - imageio-ffmpeg==0.4.2
-    - pytorch-lightning==1.4.2
+    - pytorch-lightning==1.8.0
     - omegaconf==2.1.1
     - test-tube>=0.7.5
     - streamlit>=0.73.1
     - einops==0.3.0
     - torch-fidelity==0.3.0
     - transformers==4.19.2
-    - torchmetrics==0.6.0
+    - torchmetrics==0.7.0
     - kornia==0.6
     - prefetch_generator
     - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers

examples/tutorial/stable_diffusion/ldm/data/cifar10.py

@@ -0,0 +1,184 @@
+from typing import Dict
+import numpy as np
+from omegaconf import DictConfig, ListConfig
+import torch
+from torch.utils.data import Dataset
+from pathlib import Path
+import json
+from PIL import Image
+from torchvision import transforms
+from einops import rearrange
+from ldm.util import instantiate_from_config
+from datasets import load_dataset
+
+def make_multi_folder_data(paths, caption_files=None, **kwargs):
+    """Make a concat dataset from multiple folders
+    Don't suport captions yet
+    If paths is a list, that's ok, if it's a Dict interpret it as:
+    k=folder v=n_times to repeat that
+    """
+    list_of_paths = []
+    if isinstance(paths, (Dict, DictConfig)):
+        assert caption_files is None, \
+            "Caption files not yet supported for repeats"
+        for folder_path, repeats in paths.items():
+            list_of_paths.extend([folder_path]*repeats)
+        paths = list_of_paths
+
+    if caption_files is not None:
+        datasets = [FolderData(p, caption_file=c, **kwargs) for (p, c) in zip(paths, caption_files)]
+    else:
+        datasets = [FolderData(p, **kwargs) for p in paths]
+    return torch.utils.data.ConcatDataset(datasets)
+
+class FolderData(Dataset):
+    def __init__(self,
+        root_dir,
+        caption_file=None,
+        image_transforms=[],
+        ext="jpg",
+        default_caption="",
+        postprocess=None,
+        return_paths=False,
+        ) -> None:
+        """Create a dataset from a folder of images.
+        If you pass in a root directory it will be searched for images
+        ending in ext (ext can be a list)
+        """
+        self.root_dir = Path(root_dir)
+        self.default_caption = default_caption
+        self.return_paths = return_paths
+        if isinstance(postprocess, DictConfig):
+            postprocess = instantiate_from_config(postprocess)
+        self.postprocess = postprocess
+        if caption_file is not None:
+            with open(caption_file, "rt") as f:
+                ext = Path(caption_file).suffix.lower()
+                if ext == ".json":
+                    captions = json.load(f)
+                elif ext == ".jsonl":
+                    lines = f.readlines()
+                    lines = [json.loads(x) for x in lines]
+                    captions = {x["file_name"]: x["text"].strip("\n") for x in lines}
+                else:
+                    raise ValueError(f"Unrecognised format: {ext}")
+            self.captions = captions
+        else:
+            self.captions = None
+
+        if not isinstance(ext, (tuple, list, ListConfig)):
+            ext = [ext]
+
+        # Only used if there is no caption file
+        self.paths = []
+        for e in ext:
+            self.paths.extend(list(self.root_dir.rglob(f"*.{e}")))
+        if isinstance(image_transforms, ListConfig):
+            image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
+        image_transforms.extend([transforms.ToTensor(),
+                                 transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
+        image_transforms = transforms.Compose(image_transforms)
+        self.tform = image_transforms
+
+
+    def __len__(self):
+        if self.captions is not None:
+            return len(self.captions.keys())
+        else:
+            return len(self.paths)
+
+    def __getitem__(self, index):
+        data = {}
+        if self.captions is not None:
+            chosen = list(self.captions.keys())[index]
+            caption = self.captions.get(chosen, None)
+            if caption is None:
+                caption = self.default_caption
+            filename = self.root_dir/chosen
+        else:
+            filename = self.paths[index]
+
+        if self.return_paths:
+            data["path"] = str(filename)
+
+        im = Image.open(filename)
+        im = self.process_im(im)
+        data["image"] = im
+
+        if self.captions is not None:
+            data["txt"] = caption
+        else:
+            data["txt"] = self.default_caption
+
+        if self.postprocess is not None:
+            data = self.postprocess(data)
+
+        return data
+
+    def process_im(self, im):
+        im = im.convert("RGB")
+        return self.tform(im)
+
+def hf_dataset(
+    name,
+    image_transforms=[],
+    image_column="img",
+    label_column="label",
+    text_column="txt",
+    split='train',
+    image_key='image',
+    caption_key='txt',
+    ):
+    """Make huggingface dataset with appropriate list of transforms applied
+    """
+    ds = load_dataset(name, split=split)
+    image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
+    image_transforms.extend([transforms.ToTensor(),
+                                transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
+    tform = transforms.Compose(image_transforms)
+
+    assert image_column in ds.column_names, f"Didn't find column {image_column} in {ds.column_names}"
+    assert label_column in ds.column_names, f"Didn't find column {label_column} in {ds.column_names}"
+
+    def pre_process(examples):
+        processed = {}
+        processed[image_key] = [tform(im) for im in examples[image_column]]
+
+        label_to_text_dict = {0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}
+
+        processed[caption_key] = [label_to_text_dict[label] for label in examples[label_column]]
+
+        return processed
+
+    ds.set_transform(pre_process)
+    return ds
+
+class TextOnly(Dataset):
+    def __init__(self, captions, output_size, image_key="image", caption_key="txt", n_gpus=1):
+        """Returns only captions with dummy images"""
+        self.output_size = output_size
+        self.image_key = image_key
+        self.caption_key = caption_key
+        if isinstance(captions, Path):
+            self.captions = self._load_caption_file(captions)
+        else:
+            self.captions = captions
+
+        if n_gpus > 1:
+            # hack to make sure that all the captions appear on each gpu
+            repeated = [n_gpus*[x] for x in self.captions]
+            self.captions = []
+            [self.captions.extend(x) for x in repeated]
+
+    def __len__(self):
+        return len(self.captions)
+
+    def __getitem__(self, index):
+        dummy_im = torch.zeros(3, self.output_size, self.output_size)
+        dummy_im = rearrange(dummy_im * 2. - 1., 'c h w -> h w c')
+        return {self.image_key: dummy_im, self.caption_key: self.captions[index]}
+
+    def _load_caption_file(self, filename):
+        with open(filename, 'rt') as f:
+            captions = f.readlines()
+        return [x.strip('\n') for x in captions]

examples/tutorial/stable_diffusion/requirements.txt

@@ -1,11 +1,12 @@
 albumentations==0.4.3
 diffusers
-opencv-python==4.1.2.30
 pudb==2019.2
+datasets
 invisible-watermark
 imageio==2.9.0
 imageio-ffmpeg==0.4.2
 omegaconf==2.1.1
+multiprocess
 test-tube>=0.7.5
 streamlit>=0.73.1
 einops==0.3.0