fix/transformer-version

examples/images/diffusion/README.md

@@ -52,7 +52,7 @@ You can also update an existing [latent diffusion](https://github.com/CompVis/la
 
 ```
 conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
-pip install transformers==4.19.2 diffusers invisible-watermark
+pip install transformers diffusers invisible-watermark
 ```
 
 #### Step 2: install lightning

examples/images/diffusion/environment.yaml

@@ -18,7 +18,7 @@ dependencies:
     - test-tube>=0.7.5
     - streamlit==1.12.1
     - einops==0.3.0
-    - transformers==4.19.2
+    - transformers
     - webdataset==0.2.5
     - kornia==0.6
     - open_clip_torch==2.0.2

examples/images/diffusion/requirements.txt

@@ -9,7 +9,7 @@ omegaconf==2.1.1
 test-tube>=0.7.5
 streamlit>=0.73.1
 einops==0.3.0
-transformers==4.19.2
+transformers
 webdataset==0.2.5
 open-clip-torch==2.7.0
 gradio==3.11

examples/images/dreambooth/train_dreambooth_colossalai.py

@@ -10,7 +10,7 @@
 import torch.utils.checkpoint
 from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
 from diffusers.optimization import get_scheduler
-from huggingface_hub import HfFolder, Repository, whoami
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from PIL import Image
 from torch.utils.data import Dataset
 from torchvision import transforms
@@ -133,9 +133,13 @@ def parse_args(input_args=None):
         default="cpu",
         help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
     )
-    parser.add_argument("--center_crop",
-                        action="store_true",
-                        help="Whether to center crop images before resizing to resolution")
+    parser.add_argument(
+        "--center_crop",
+        default=False,
+        action="store_true",
+        help=("Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+              " cropped. The images will be resized to the resolution first before cropping."),
+    )
     parser.add_argument("--train_batch_size",
                         type=int,
                         default=4,
@@ -149,13 +153,6 @@ def parse_args(input_args=None):
         help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help=
-        "Number of updates steps to accumulate before performing a backward/update pass. If using Gemini, it must be 1",
-    )
     parser.add_argument(
         "--gradient_checkpointing",
         action="store_true",
@@ -356,7 +353,6 @@ def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"):
 
 
 def main(args):
-
     if args.seed is None:
         colossalai.launch_from_torch(config={})
     else:
@@ -410,7 +406,8 @@ def main(args):
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
@@ -469,9 +466,8 @@ def main(args):
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
 
-    assert args.gradient_accumulation_steps == 1, "if using ColossalAI gradient_accumulation_steps must be set to 1."
     if args.scale_lr:
-        args.learning_rate = args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * world_size
+        args.learning_rate = args.learning_rate * args.train_batch_size * world_size
 
     unet = gemini_zero_dpp(unet, args.placement)
 
@@ -529,16 +525,16 @@ def collate_fn(examples):
 
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
     if args.max_train_steps is None:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+        num_warmup_steps=args.lr_warmup_steps,
+        num_training_steps=args.max_train_steps,
     )
     weight_dtype = torch.float32
     if args.mixed_precision == "fp16":
@@ -553,22 +549,21 @@ def collate_fn(examples):
     text_encoder.to(get_current_device(), dtype=weight_dtype)
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
     if overrode_max_train_steps:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
     # Afterwards we recalculate our number of training epochs
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
 
     # Train!
-    total_batch_size = args.train_batch_size * world_size * args.gradient_accumulation_steps
+    total_batch_size = args.train_batch_size * world_size
 
     logger.info("***** Running training *****", ranks=[0])
     logger.info(f"  Num examples = {len(train_dataset)}", ranks=[0])
     logger.info(f"  Num batches each epoch = {len(train_dataloader)}", ranks=[0])
     logger.info(f"  Num Epochs = {args.num_train_epochs}", ranks=[0])
     logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}", ranks=[0])
     logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}", ranks=[0])
     logger.info(f"  Total optimization steps = {args.max_train_steps}", ranks=[0])
 
     # Only show the progress bar once on each machine.