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Merge pull request #3905 from MaruyamaAya/dreambooth

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[example] Adding an example of training dreambooth with the new booster API
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Liu Ziming 2023-06-09 08:44:18 +08:00 committed by GitHub
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23
examples/images/dreambooth/README.md
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@ -92,6 +92,29 @@ torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
--placement="cuda"
```
## New API
We have modified our previous implementation of Dreambooth with our new Booster API, which offers a more flexible and efficient way to train your model. The new API is more user-friendly and easy to use. You can find the new API in `train_dreambooth_colossalai.py`.
We have also offer a shell script `test_ci.sh` for you to go through all our plugins for the booster.
For more information about the booster API you can refer to https://colossalai.org/docs/basics/booster_api/.
## Performance
| Strategy | #GPU | Batch Size | GPU RAM(GB) | speedup |
|:--------------:|:----:|:----------:|:-----------:|:-------:|
| Traditional | 1 | 16 | oom | \ |
| Traditional | 1 | 8 | 61.81 | 1 |
| torch_ddp | 4 | 16 | oom | \ |
| torch_ddp | 4 | 8 | 41.97 | 0.97 |
| gemini | 4 | 16 | 53.29 | \ |
| gemini | 4 | 8 | 29.36 | 2.00 |
| low_level_zero | 4 | 16 | 52.80 | \ |
| low_level_zero | 4 | 8 | 28.87 | 2.02 |
The evaluation is performed on 4 Nvidia A100 GPUs with 80GB memory each, with GPU 0 & 1, 2 & 3 connected with NVLink.
We finetuned the [stable-diffusion-v1-4](https://huggingface.co/stabilityai/stable-diffusion-v1-4) model with 512x512 resolution on the [Teyvat](https://huggingface.co/datasets/Fazzie/Teyvat) dataset and compared
the memory cost and the throughput for the plugins.
## Inference
Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. `--instance_prompt="a photo of sks dog" ` in the above example) in your prompt.

24
examples/images/dreambooth/colossalai.sh
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@ -1,22 +1,18 @@
export MODEL_NAME= <Your Pretrained Model Path>
export INSTANCE_DIR= <Your Input Pics Path>
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
HF_DATASETS_OFFLINE=1
TRANSFORMERS_OFFLINE=1
HF_DATASETS_OFFLINE=1
TRANSFORMERS_OFFLINE=1
DIFFUSERS_OFFLINE=1
torchrun --nproc_per_node 2 --master_port=25641 train_dreambooth_colossalai.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of a dog" \
torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \
--pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \
--instance_data_dir="/data/dreambooth/Teyvat/data" \
--output_dir="./weight_output" \
--instance_prompt="a picture of a dog" \
--resolution=512 \
--plugin="gemini" \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--placement="cuda" \
--test_run=True \
--placement="auto" \

6
examples/images/dreambooth/dreambooth.sh
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@ -1,7 +1,7 @@
python train_dreambooth.py \
--pretrained_model_name_or_path= ## Your Model Path \
--instance_data_dir= ## Your Training Input Pics Path \
--output_dir="path-to-save-model" \
--pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \
--instance_data_dir="/data/dreambooth/Teyvat/data" \
--output_dir="./weight_output" \
--instance_prompt="a photo of a dog" \
--resolution=512 \
--train_batch_size=1 \

25
examples/images/dreambooth/test_ci.sh
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@ -0,0 +1,25 @@
#!/bin/bash
set -xe
pip install -r requirements.txt
HF_DATASETS_OFFLINE=1
TRANSFORMERS_OFFLINE=1
DIFFUSERS_OFFLINE=1
# "torch_ddp" "torch_ddp_fp16" "low_level_zero"
for plugin in "gemini"; do
torchrun --nproc_per_node 4 --standalone train_dreambooth_colossalai.py \
--pretrained_model_name_or_path="/data/dreambooth/diffuser/stable-diffusion-v1-4" \
--instance_data_dir="/data/dreambooth/Teyvat/data" \
--output_dir="./weight_output" \
--instance_prompt="a picture of a dog" \
--resolution=512 \
--plugin=$plugin \
--train_batch_size=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--test_run=True \
--num_class_images=200 \
--placement="auto" # "cuda"
done

100
examples/images/dreambooth/train_dreambooth_colossalai.py
View File

@ -4,6 +4,7 @@ import math
import os
from pathlib import Path
from typing import Optional
import shutil
import torch
import torch.nn.functional as F
@ -21,9 +22,12 @@ import colossalai
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import disable_existing_loggers, get_dist_logger
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device
from colossalai.zero import ColoInitContext, GeminiAdamOptimizer
from colossalai.zero import ColoInitContext
from colossalai.zero.gemini import get_static_torch_model
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
disable_existing_loggers()
logger = get_dist_logger()
@ -58,6 +62,13 @@ def parse_args(input_args=None):
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--externel_unet_path",
type=str,
default=None,
required=False,
help="Path to the externel unet model.",
)
parser.add_argument(
"--revision",
type=str,
@ -187,12 +198,19 @@ def parse_args(input_args=None):
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument("--test_run", default=False, help="Whether to use a smaller dataset for test run.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument('-p',
'--plugin',
type=str,
default='torch_ddp',
choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'],
help="plugin to use")
parser.add_argument(
"--logging_dir",
type=str,
@ -250,6 +268,7 @@ class DreamBoothDataset(Dataset):
class_prompt=None,
size=512,
center_crop=False,
test=False,
):
self.size = size
self.center_crop = center_crop
@ -260,6 +279,8 @@ class DreamBoothDataset(Dataset):
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
if test:
self.instance_images_path = self.instance_images_path[:10]
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
@ -339,18 +360,6 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
return f"{organization}/{model_id}"
# Gemini + ZeRO DDP
def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"):
from colossalai.nn.parallel import GeminiDDP
model = GeminiDDP(model,
device=get_current_device(),
placement_policy=placement_policy,
pin_memory=True,
search_range_mb=64)
return model
def main(args):
if args.seed is None:
colossalai.launch_from_torch(config={})
@ -392,7 +401,7 @@ def main(args):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
hash_image = hashlib.sha256(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
@ -452,12 +461,18 @@ def main(args):
revision=args.revision,
)
logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
with ColoInitContext(device=get_current_device()):
if args.externel_unet_path is None:
logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
low_cpu_mem_usage=False)
subfolder="unet",
revision=args.revision,
low_cpu_mem_usage=False)
else:
logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0])
unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path,
revision=args.revision,
low_cpu_mem_usage=False)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
@ -468,10 +483,22 @@ def main(args):
if args.scale_lr:
args.learning_rate = args.learning_rate * args.train_batch_size * world_size
unet = gemini_zero_dpp(unet, args.placement)
# Use Booster API to use Gemini/Zero with ColossalAI
booster_kwargs = {}
if args.plugin == 'torch_ddp_fp16':
booster_kwargs['mixed_precision'] = 'fp16'
if args.plugin.startswith('torch_ddp'):
plugin = TorchDDPPlugin()
elif args.plugin == 'gemini':
plugin = GeminiPlugin(placement_policy=args.placement, strict_ddp_mode=True, initial_scale=2 ** 5)
elif args.plugin == 'low_level_zero':
plugin = LowLevelZeroPlugin(initial_scale=2 ** 5)
booster = Booster(plugin=plugin, **booster_kwargs)
# config optimizer for colossalai zero
optimizer = GeminiAdamOptimizer(unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm)
optimizer = HybridAdam(unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm)
# load noise_scheduler
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
@ -486,6 +513,7 @@ def main(args):
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
test=args.test_run
)
def collate_fn(examples):
@ -554,6 +582,8 @@ def main(args):
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler)
# Train!
total_batch_size = args.train_batch_size * world_size
@ -642,36 +672,24 @@ def main(args):
if global_step % args.save_steps == 0:
torch.cuda.synchronize()
torch_unet = get_static_torch_model(unet)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin"))
if local_rank == 0:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=torch_unet,
revision=args.revision,
)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
pipeline.save_pretrained(save_path)
if not os.path.exists(os.path.join(save_path, "config.json")):
shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path)
logger.info(f"Saving model checkpoint to {save_path}", ranks=[0])
if global_step >= args.max_train_steps:
break
torch.cuda.synchronize()
unet = get_static_torch_model(unet)
booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin"))
logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}")
if local_rank == 0:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=unet,
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0])
if not os.path.exists(os.path.join(args.output_dir, "config.json")):
shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
if __name__ == "__main__":
args = parse_args()
main(args)

122
examples/images/dreambooth/train_dreambooth_colossalai_lora.py
View File

@ -4,6 +4,7 @@ import math
import os
from pathlib import Path
from typing import Optional
import shutil
import torch
import torch.nn.functional as F
@ -23,9 +24,12 @@ import colossalai
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import disable_existing_loggers, get_dist_logger
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device
from colossalai.zero import ColoInitContext, GeminiAdamOptimizer
from colossalai.zero.gemini import get_static_torch_model
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
disable_existing_loggers()
logger = get_dist_logger()
@ -60,6 +64,13 @@ def parse_args(input_args=None):
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--externel_unet_path",
type=str,
default=None,
required=False,
help="Path to the externel unet model.",
)
parser.add_argument(
"--revision",
type=str,
@ -195,6 +206,12 @@ def parse_args(input_args=None):
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument('-p',
'--plugin',
type=str,
default='torch_ddp',
choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'],
help="plugin to use")
parser.add_argument(
"--logging_dir",
type=str,
@ -341,18 +358,6 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
return f"{organization}/{model_id}"
# Gemini + ZeRO DDP
def gemini_zero_dpp(model: torch.nn.Module, placement_policy: str = "auto"):
from colossalai.nn.parallel import GeminiDDP
model = GeminiDDP(model,
device=get_current_device(),
placement_policy=placement_policy,
pin_memory=True,
search_range_mb=64)
return model
def main(args):
if args.seed is None:
colossalai.launch_from_torch(config={})
@ -394,7 +399,7 @@ def main(args):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
hash_image = hashlib.sha256(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
@ -454,32 +459,42 @@ def main(args):
revision=args.revision,
)
logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
with ColoInitContext(device=get_current_device()):
if args.externel_unet_path is None:
logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
low_cpu_mem_usage=False)
unet.requires_grad_(False)
subfolder="unet",
revision=args.revision,
low_cpu_mem_usage=False)
else:
logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0])
unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path,
revision=args.revision,
low_cpu_mem_usage=False)
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
low_cpu_mem_usage=False)
unet.requires_grad_(False)
# Set correct lora layers
lora_attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
# Set correct lora layers
lora_attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
lora_attn_procs[name] = LoRACrossAttnProcessor(hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim)
lora_attn_procs[name] = LoRACrossAttnProcessor(hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim)
unet.set_attn_processor(lora_attn_procs)
lora_layers = AttnProcsLayers(unet.attn_processors)
unet.set_attn_processor(lora_attn_procs)
lora_layers = AttnProcsLayers(unet.attn_processors)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
@ -490,10 +505,22 @@ def main(args):
if args.scale_lr:
args.learning_rate = args.learning_rate * args.train_batch_size * world_size
unet = gemini_zero_dpp(unet, args.placement)
# Use Booster API to use Gemini/Zero with ColossalAI
booster_kwargs = {}
if args.plugin == 'torch_ddp_fp16':
booster_kwargs['mixed_precision'] = 'fp16'
if args.plugin.startswith('torch_ddp'):
plugin = TorchDDPPlugin()
elif args.plugin == 'gemini':
plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2 ** 5)
elif args.plugin == 'low_level_zero':
plugin = LowLevelZeroPlugin(initial_scale=2 ** 5)
booster = Booster(plugin=plugin, **booster_kwargs)
# config optimizer for colossalai zero
optimizer = GeminiAdamOptimizer(unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm)
optimizer = HybridAdam(unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm)
# load noise_scheduler
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
@ -576,6 +603,8 @@ def main(args):
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler)
# Train!
total_batch_size = args.train_batch_size * world_size
@ -664,27 +693,24 @@ def main(args):
if global_step % args.save_steps == 0:
torch.cuda.synchronize()
torch_unet = get_static_torch_model(unet)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin"))
if local_rank == 0:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
torch_unet = torch_unet.to(torch.float32)
torch_unet.save_attn_procs(save_path)
if not os.path.exists(os.path.join(save_path, "config.json")):
shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path)
logger.info(f"Saving model checkpoint to {save_path}", ranks=[0])
if global_step >= args.max_train_steps:
break
torch.cuda.synchronize()
torch_unet = get_static_torch_model(unet)
booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin"))
logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}")
if local_rank == 0:
torch_unet = torch_unet.to(torch.float32)
torch_unet.save_attn_procs(save_path)
logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0])
if not os.path.exists(os.path.join(args.output_dir, "config.json")):
shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
if __name__ == "__main__":
args = parse_args()
main(args)