[train_text_to_image] allow using non-ema weights for training

examples/text_to_image/train_text_to_image.py

@@ -1,4 +1,5 @@
 import argparse
+import copy
 import logging
 import math
 import os
@@ -11,6 +12,9 @@
 import torch.nn.functional as F
 import torch.utils.checkpoint
 
+import datasets
+import diffusers
+import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
@@ -28,7 +32,7 @@
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.10.0.dev0")
 
-logger = get_logger(__name__)
+logger = get_logger(__name__, log_level="INFO")
 
 
 def parse_args():
@@ -171,7 +175,25 @@ def parse_args():
     parser.add_argument(
         "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
     )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
     parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
+    parser.add_argument(
+        "--non_ema_revision",
+        type=str,
+        default=None,
+        required=False,
+        help=(
+            "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
+            " remote repository specified with --pretrained_model_name_or_path."
+        ),
+    )
     parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
     parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
     parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
@@ -247,6 +269,10 @@ def parse_args():
     if args.dataset_name is None and args.train_data_dir is None:
         raise ValueError("Need either a dataset name or a training folder.")
 
+    # default to using the same revision for the non-ema model if not specified
+    if args.non_ema_revision is None:
+        args.non_ema_revision = args.revision
+
     return args
 
 
@@ -275,6 +301,8 @@ def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):
         parameters = list(parameters)
         self.shadow_params = [p.clone().detach() for p in parameters]
 
+        self.collected_params = None
+
         self.decay = decay
         self.optimization_step = 0
 
@@ -322,6 +350,55 @@ def to(self, device=None, dtype=None) -> None:
             for p in self.shadow_params
         ]
 
+    def state_dict(self) -> dict:
+        r"""
+        Returns the state of the ExponentialMovingAverage as a dict.
+        This method is used by accelerate during checkpointing to save the ema state dict.
+        """
+        # Following PyTorch conventions, references to tensors are returned:
+        # "returns a reference to the state and not its copy!" -
+        # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
+        return {
+            "decay": self.decay,
+            "optimization_step": self.optimization_step,
+            "shadow_params": self.shadow_params,
+            "collected_params": self.collected_params,
+        }
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        r"""
+        Loads the ExponentialMovingAverage state.
+        This method is used by accelerate during checkpointing to save the ema state dict.
+        Args:
+            state_dict (dict): EMA state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        # deepcopy, to be consistent with module API
+        state_dict = copy.deepcopy(state_dict)
+
+        self.decay = state_dict["decay"]
+        if self.decay < 0.0 or self.decay > 1.0:
+            raise ValueError("Decay must be between 0 and 1")
+
+        self.optimization_step = state_dict["optimization_step"]
+        if not isinstance(self.optimization_step, int):
+            raise ValueError("Invalid optimization_step")
+
+        self.shadow_params = state_dict["shadow_params"]
+        if not isinstance(self.shadow_params, list):
+            raise ValueError("shadow_params must be a list")
+        if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):
+            raise ValueError("shadow_params must all be Tensors")
+
+        self.collected_params = state_dict["collected_params"]
+        if self.collected_params is not None:
+            if not isinstance(self.collected_params, list):
+                raise ValueError("collected_params must be a list")
+            if not all(isinstance(p, torch.Tensor) for p in self.collected_params):
+                raise ValueError("collected_params must all be Tensors")
+            if len(self.collected_params) != len(self.shadow_params):
+                raise ValueError("collected_params and shadow_params must have the same length")
+
 
 def main():
     args = parse_args()
@@ -339,6 +416,15 @@ def main():
         datefmt="%m/%d/%Y %H:%M:%S",
         level=logging.INFO,
     )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
 
     # If passed along, set the training seed now.
     if args.seed is not None:
@@ -361,39 +447,44 @@ def main():
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
 
-    # Load models and create wrapper for stable diffusion
+    # Load scheduler, tokenizer and models.
+    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     tokenizer = CLIPTokenizer.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
     )
     text_encoder = CLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        revision=args.revision,
-    )
-    vae = AutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="vae",
-        revision=args.revision,
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="unet",
-        revision=args.revision,
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
     )
 
+    # Freeze vae and text_encoder
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+
+    # Create EMA for the unet.
+    if args.use_ema:
+        ema_unet = UNet2DConditionModel.from_pretrained(
+            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
+        )
+        ema_unet = EMAModel(ema_unet.parameters())
+
     if args.enable_xformers_memory_efficient_attention:
         if is_xformers_available():
             unet.enable_xformers_memory_efficient_attention()
         else:
             raise ValueError("xformers is not available. Make sure it is installed correctly")
 
-    # Freeze vae and text_encoder
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
 
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
     if args.scale_lr:
         args.learning_rate = (
             args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
@@ -419,7 +510,6 @@ def main():
         weight_decay=args.adam_weight_decay,
         eps=args.adam_epsilon,
     )
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
 
     # Get the datasets: you can either provide your own training and evaluation files (see below)
     # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
@@ -482,9 +572,10 @@ def tokenize_captions(examples, is_train=True):
                 raise ValueError(
                     f"Caption column `{caption_column}` should contain either strings or lists of strings."
                 )
-        inputs = tokenizer(captions, max_length=tokenizer.model_max_length, padding="do_not_pad", truncation=True)
-        input_ids = inputs.input_ids
-        return input_ids
+        inputs = tokenizer(
+            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+        )
+        return inputs.input_ids
 
     train_transforms = transforms.Compose(
         [
@@ -500,7 +591,6 @@ def preprocess_train(examples):
         images = [image.convert("RGB") for image in examples[image_column]]
         examples["pixel_values"] = [train_transforms(image) for image in images]
         examples["input_ids"] = tokenize_captions(examples)
-
         return examples
 
     with accelerator.main_process_first():
@@ -512,13 +602,8 @@ def preprocess_train(examples):
     def collate_fn(examples):
         pixel_values = torch.stack([example["pixel_values"] for example in examples])
         pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-        input_ids = [example["input_ids"] for example in examples]
-        padded_tokens = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt")
-        return {
-            "pixel_values": pixel_values,
-            "input_ids": padded_tokens.input_ids,
-            "attention_mask": padded_tokens.attention_mask,
-        }
+        input_ids = torch.stack([example["input_ids"] for example in examples])
+        return {"pixel_values": pixel_values, "input_ids": input_ids}
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size
@@ -541,7 +626,8 @@ def collate_fn(examples):
     unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
         unet, optimizer, train_dataloader, lr_scheduler
     )
-    accelerator.register_for_checkpointing(lr_scheduler)
+    if args.use_ema:
+        accelerator.register_for_checkpointing(ema_unet)
 
     weight_dtype = torch.float32
     if accelerator.mixed_precision == "fp16":
@@ -554,10 +640,8 @@ def collate_fn(examples):
     # as these models are only used for inference, keeping weights in full precision is not required.
     text_encoder.to(accelerator.device, dtype=weight_dtype)
     vae.to(accelerator.device, dtype=weight_dtype)
-
-    # Create EMA for the unet.
     if args.use_ema:
-        ema_unet = EMAModel(unet.parameters())
+        ema_unet.to(accelerator.device)
 
     # 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)