reformatted

Author: Fazziekey

examples/dreambooth/train_dreambooth_colossalai.py

@@ -1,27 +1,14 @@
 import argparse
 import hashlib
-import itertools
 import math
 import os
 from pathlib import Path
 from typing import Optional
 
-import numpy as np
 import torch
-import torch.distributed as dist
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from copy import deepcopy
-from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from huggingface_hub import HfFolder, Repository, whoami
-from packaging import version
-from PIL import Image
-from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
 
 import colossalai
 from colossalai.context.parallel_mode import ParallelMode
@@ -30,10 +17,20 @@
 from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
 from colossalai.nn.parallel import ZeroDDP
 from colossalai.nn.parallel.utils import convert_to_torch_module
-from colossalai.tensor import ColoTensor, ProcessGroup
+from colossalai.tensor import ProcessGroup
 from colossalai.utils import get_current_device
 from colossalai.utils.model.colo_init_context import ColoInitContext
 
+from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from huggingface_hub import HfFolder, Repository, whoami
+from packaging import version
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, PretrainedConfig
+
+
 disable_existing_loggers()
 logger = get_dist_logger()
 
@@ -118,8 +115,10 @@ def parse_args(input_args=None):
         "--num_class_images",
         type=int,
         default=100,
-        help=("Minimal class images for prior preservation loss. If there are not enough images already present in"
-              " class_data_dir, additional images will be sampled with class_prompt."),
+        help=(
+            "Minimal class images for prior preservation loss. If there are not enough images already present in"
+            " class_data_dir, additional images will be sampled with class_prompt."
+        ),
     )
     parser.add_argument(
         "--output_dir",
@@ -132,23 +131,26 @@ def parse_args(input_args=None):
         "--resolution",
         type=int,
         default=512,
-        help=("The resolution for input images, all the images in the train/validation dataset will be resized to this"
-              " resolution"),
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
     )
     parser.add_argument(
         "--placement",
         type=str,
-        default='cpu',
+        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("--train_batch_size",
-                        type=int,
-                        default=4,
-                        help="Batch size (per device) for the training dataloader.")
-    parser.add_argument("--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.")
+    parser.add_argument(
+        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
+        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
+    )
     parser.add_argument("--num_train_epochs", type=int, default=1)
     parser.add_argument(
         "--max_train_steps",
@@ -184,16 +186,17 @@ def parse_args(input_args=None):
         "--lr_scheduler",
         type=str,
         default="constant",
-        help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-              ' "constant", "constant_with_warmup"]'),
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
     )
-    parser.add_argument("--lr_warmup_steps",
-                        type=int,
-                        default=500,
-                        help="Number of steps for the warmup in the lr scheduler.")
-    parser.add_argument("--use_8bit_adam",
-                        action="store_true",
-                        help="Whether or not to use 8-bit Adam from bitsandbytes.")
 
     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.")
@@ -208,8 +211,10 @@ def parse_args(input_args=None):
         "--logging_dir",
         type=str,
         default="logs",
-        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
     )
     parser.add_argument(
         "--mixed_precision",
@@ -219,7 +224,8 @@ def parse_args(input_args=None):
         help=(
             "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
             " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."),
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
     )
     parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
 
@@ -285,12 +291,14 @@ def __init__(
         else:
             self.class_data_root = None
 
-        self.image_transforms = transforms.Compose([
-            transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ])
+        self.image_transforms = transforms.Compose(
+            [
+                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
 
     def __len__(self):
         return self._length
@@ -355,20 +363,24 @@ def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy:
     cai_version = colossalai.__version__
     if version.parse(cai_version) > version.parse("0.1.10"):
         from colossalai.nn.parallel import GeminiDDP
-        model = GeminiDDP(model,
-                          device=get_current_device(),
-                          placement_policy=placememt_policy,
-                          pin_memory=True,
-                          search_range_mb=32)
-        
-    elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
+
+        model = GeminiDDP(
+            model, device=get_current_device(), placement_policy=placememt_policy, pin_memory=True, search_range_mb=32
+        )
+
+    elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse(
+        "0.1.9"
+    ):
         from colossalai.gemini import ChunkManager, GeminiManager
+
         chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
         gemini_manager = GeminiManager(placememt_policy, chunk_manager)
-        chunk_manager = ChunkManager(chunk_size,
-                                     pg,
-                                     enable_distributed_storage=True,
-                                     init_device=GeminiManager.get_default_device(placememt_policy))
+        chunk_manager = ChunkManager(
+            chunk_size,
+            pg,
+            enable_distributed_storage=True,
+            init_device=GeminiManager.get_default_device(placememt_policy),
+        )
         model = ZeroDDP(model, gemini_manager)
     else:
         raise NotImplemented(f"CAI version {cai_version} is not supported")
@@ -383,7 +395,7 @@ def main(args):
         "gradient_accumulation_steps": args.gradient_accumulation_steps,
         "clip_grad_norm": args.max_grad_norm,
     }
-    
+
     colossalai.launch_from_torch(config=config)
     pg = ProcessGroup()
 
@@ -414,9 +426,11 @@ def main(args):
 
             pipeline.to(get_current_device())
 
-            for example in tqdm(sample_dataloader,
-                                desc="Generating class images",
-                                disable=not gpc.get_local_rank(ParallelMode.DATA) == 0):
+            for example in tqdm(
+                sample_dataloader,
+                desc="Generating class images",
+                disable=not gpc.get_local_rank(ParallelMode.DATA) == 0,
+            ):
                 images = pipeline(example["prompt"]).images
 
                 for i, image in enumerate(images):
@@ -466,23 +480,24 @@ def main(args):
 
     logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0])
 
-    text_encoder = text_encoder_cls.from_pretrained(args.pretrained_model_name_or_path,
-                                                    subfolder="text_encoder",
-                                                    revision=args.revision,)
+    text_encoder = text_encoder_cls.from_pretrained(
+        args.pretrained_model_name_or_path,
+        subfolder="text_encoder",
+        revision=args.revision,
+    )
 
     logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0])
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path,
-                                        subfolder="vae",
-                                        revision=args.revision,)
+    vae = AutoencoderKL.from_pretrained(
+        args.pretrained_model_name_or_path,
+        subfolder="vae",
+        revision=args.revision,
+    )
 
-    
     logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
     with ColoInitContext():
-        unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
-                                                subfolder="unet",
-                                                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
+        )
 
     vae.requires_grad_(False)
     text_encoder.requires_grad_(False)
@@ -491,7 +506,7 @@ def main(args):
         unet.enable_gradient_checkpointing()
 
     if args.scale_lr:
-        args.learning_rate = (args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * 2)
+        args.learning_rate = args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * 2
 
     unet = gemini_zero_dpp(unet, pg, args.placement)
 
@@ -527,9 +542,7 @@ def collate_fn(examples):
         pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
 
         input_ids = tokenizer.pad(
-            {
-                "input_ids": input_ids
-            },
+            {"input_ids": input_ids},
             padding="max_length",
             max_length=tokenizer.model_max_length,
             return_tensors="pt",
@@ -541,11 +554,9 @@ def collate_fn(examples):
         }
         return batch
 
-    train_dataloader = torch.utils.data.DataLoader(train_dataset,
-                                                   batch_size=args.train_batch_size,
-                                                   shuffle=True,
-                                                   collate_fn=collate_fn,
-                                                   num_workers=1)
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1
+    )
 
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
@@ -662,8 +673,8 @@ def collate_fn(examples):
             global_step += 1
             logs = {
                 "loss": loss.detach().item(),
-                "lr": optimizer.param_groups[0]['lr']
-            }    #lr_scheduler.get_last_lr()[0]}
+                "lr": optimizer.param_groups[0]["lr"],
+            }  # lr_scheduler.get_last_lr()[0]}
             progress_bar.set_postfix(**logs)
 
             if global_step % args.save_steps == 0:
@@ -681,15 +692,15 @@ def collate_fn(examples):
                 break
 
     torch.cuda.synchronize()
-    unet=convert_to_torch_module(unet)
-        
+    unet = convert_to_torch_module(unet)
+
     if gpc.get_local_rank(ParallelMode.DATA) == 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])