692 lines
32 KiB
Python
692 lines
32 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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A minimal training script for DiT using PyTorch DDP.
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"""
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import argparse
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import logging
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import math
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import os
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import shutil
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from pathlib import Path
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import gc
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import wandb
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from einops import rearrange
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from tqdm import tqdm
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from diffusers.training_utils import cast_training_params, compute_density_for_timestep_sampling, compute_loss_weighting_for_sd3
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from fastvideo.utils.parallel_states import initialize_sequence_parallel_state, \
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destroy_sequence_parallel_group, get_sequence_parallel_state, set_sequence_parallel_state
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from fastvideo.utils.communications import prepare_parallel_data, broadcast
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import time
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from torch.utils.data import DataLoader
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from copy import deepcopy
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import accelerate
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import torch
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from torch.nn import functional as F
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import transformers
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from accelerate import Accelerator
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from diffusers.utils.torch_utils import is_compiled_module
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from accelerate.logging import get_logger
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from accelerate.utils import DistributedType, ProjectConfiguration, set_seed
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from tqdm.auto import tqdm
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import diffusers
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from diffusers import (
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AutoencoderKLMochi,
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FlowMatchEulerDiscreteScheduler,
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MochiTransformer3DModel,
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MochiPipeline,
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)
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import compute_snr
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from diffusers.utils import check_min_version, is_wandb_available
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from transformers import AutoTokenizer
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from fastvideo.utils.ema import EMAModel
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from fastvideo.dataset.latent_datasets import LatentDataset, latent_collate_function
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.24.0")
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logger = get_logger(__name__)
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import sys
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import pdb
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class ForkedPdb(pdb.Pdb):
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"""A Pdb subclass that may be used
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from a forked multiprocessing child
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"""
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def interaction(self, *args, **kwargs):
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_stdin = sys.stdin
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try:
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sys.stdin = open('/dev/stdin')
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pdb.Pdb.interaction(self, *args, **kwargs)
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finally:
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sys.stdin = _stdin
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@torch.inference_mode()
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def log_validation(args, transformer,vae, accelerator, weight_dtype, global_step, ema=False):
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logger.info(f"Running validation....\n")
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transformer = accelerator.unwrap_model(transformer)
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mochi_pipeline = MochiPipeline.from_pretrained(args.pretrained_model_name_or_path,vae=vae, transformer=transformer, torch_dtype=weight_dtype).to(device=accelerator.device)
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videos = []
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for prompt in args.validaiton_prompt:
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logger.info('Processing the ({}) prompt'.format(prompt))
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video = mochi_pipeline(
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args.prompt,
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num_frames=args.num_frames,
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height=args.max_height,
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width=args.max_width,
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num_inference_steps=args.validation_sampling_steps,
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guidance_scale=args.guidance_scale,
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).images
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videos.append(video[0])
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# import ipdb;ipdb.set_trace()
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gc.collect()
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torch.cuda.empty_cache()
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videos = torch.stack(videos).numpy()
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videos = rearrange(videos, 'b t h w c -> b t c h w')
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for tracker in accelerator.trackers:
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logs = {
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f"{'ema_' if ema else ''}validation": [
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wandb.Video(video, caption=f"{i}: {prompt}", fps=24)
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for i, (video, prompt) in enumerate(zip(videos, args.validaiton_prompt))
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]
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}
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tracker.log(logs, step=global_step)
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del mochi_pipeline
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gc.collect()
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torch.cuda.empty_cache()
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class ProgressInfo:
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def __init__(self, global_step, train_loss=0.0):
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self.global_step = global_step
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self.train_loss = train_loss
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#################################################################################
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# Training Loop #
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#################################################################################
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def main(args):
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logging_dir = Path(args.output_dir, args.logging_dir)
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# use LayerNorm, GeLu, SiLu always as fp32 mode
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# TODO:
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if args.enable_stable_fp32:
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raise NotImplementedError("enable_stable_fp32 is not supported now.")
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accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
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accelerator = Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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mixed_precision=args.mixed_precision,
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log_with="wandb",
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project_config=accelerator_project_config,
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)
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initialize_sequence_parallel_state(args.sp_size)
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if not is_wandb_available():
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raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger.info(accelerator.state, main_process_only=False)
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if accelerator.is_local_main_process:
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transformers.utils.logging.set_verbosity_warning()
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diffusers.utils.logging.set_verbosity_info()
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else:
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transformers.utils.logging.set_verbosity_error()
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diffusers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now. On GPU...
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if args.seed is not None:
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set_seed(args.seed)
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# Handle the repository creation
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if accelerator.is_main_process:
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if args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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# For mixed precision training we cast all non-trainable weigths to half-precision
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# as these weights are only used for inference, keeping weights in full precision is not required.
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weight_dtype = torch.float32
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if accelerator.mixed_precision == "fp16":
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weight_dtype = torch.float16
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elif accelerator.mixed_precision == "bf16":
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weight_dtype = torch.bfloat16
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# Create model:
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vae = AutoencoderKLMochi.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", torch_dtype=weight_dtype)
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if args.enable_tiling:
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vae.enable_tiling()
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vae.tile_sample_stride_height = args.tile_sample_stride
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vae.tile_sample_stride_width = args.tile_sample_stride
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load_dtype = torch.bfloat16 # TODO
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transformer = MochiTransformer3DModel.from_pretrained(
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args.pretrained_model_name_or_path,
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subfolder="transformer",
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torch_dtype=load_dtype,
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)
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if args.gradient_checkpointing:
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transformer.enable_gradient_checkpointing()
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# Freeze vae and text encoders.
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vae.requires_grad_(False)
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# Set model as trainable.
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transformer.train()
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noise_scheduler = FlowMatchEulerDiscreteScheduler()
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# Move unet, vae and text_encoder to device and cast to weight_dtype
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# The VAE is in float32 to avoid NaN losses.
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vae.to(accelerator.device, dtype=weight_dtype)
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# ae.vae.to(accelerator.device, dtype=weight_dtype)
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# transformer.to(accelerator.device, dtype=weight_dtype)
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# Create EMA for the unet.
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if args.use_ema:
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ema_transformer = deepcopy(transformer)
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ema_transformer = EMAModel(ema_transformer.parameters(), decay=args.ema_decay, update_after_step=args.ema_start_step,
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model_cls=MochiTransformer3DModel, model_config=ema_transformer.config)
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def unwrap_model(model):
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model = accelerator.unwrap_model(model)
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model = model._orig_mod if is_compiled_module(model) else model
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return model
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# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
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def save_model_hook(models, weights, output_dir):
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if accelerator.is_main_process:
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if args.use_ema:
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ema_transformer.save_pretrained(os.path.join(output_dir, "model_ema"))
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for model in models:
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if isinstance(unwrap_model(model), type(unwrap_model(transformer))):
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model: MochiTransformer3DModel = unwrap_model(model)
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model.save_pretrained(
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os.path.join(output_dir, "transformer"), safe_serialization=True, max_shard_size="5GB"
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)
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else:
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raise ValueError(f"Unexpected save model: {model.__class__}")
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# make sure to pop weight so that corresponding model is not saved again
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if weights:
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weights.pop()
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def load_model_hook(models, input_dir):
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# TODO
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if args.use_ema:
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load_model = EMAModel.from_pretrained(os.path.join(input_dir, "model_ema"), MochiTransformer3DModel)
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ema_transformer.load_state_dict(load_model.state_dict())
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ema_transformer.to(accelerator.device)
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del load_model
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for i in range(len(models)):
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# pop models so that they are not loaded again
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model = models.pop()
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# load diffusers style into model
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load_model = MochiTransformer3DModel.from_pretrained(input_dir, subfolder="transformer")
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model.register_to_config(**load_model.config)
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model.load_state_dict(load_model.state_dict())
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del load_model
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accelerator.register_save_state_pre_hook(save_model_hook)
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accelerator.register_load_state_pre_hook(load_model_hook)
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# Enable TF32 for faster training on Ampere GPUs,
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# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
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if args.allow_tf32:
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torch.backends.cuda.matmul.allow_tf32 = True
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if args.scale_lr:
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args.learning_rate = (
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args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
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)
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# Make sure the trainable params are in float32.
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# if args.mixed_precision == "fp16" or args.mixed_precision == "bf16":
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# cast_training_params([transformer], dtype=torch.float32)
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params_to_optimize = transformer.parameters()
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# TODO: Other optmizer in cogvideoX-factory
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optimizer = torch.optim.AdamW(
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params_to_optimize,
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lr=args.learning_rate,
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betas=(args.adam_beta1, args.adam_beta2),
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weight_decay=args.adam_weight_decay,
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eps=args.adam_epsilon,
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)
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logger.info(f"optimizer: {optimizer}")
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train_dataset = LatentDataset(args.data_merge_path, args.num_latent_t)
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train_dataloader = DataLoader(
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train_dataset,
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shuffle=True,
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collate_fn=latent_collate_function,
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pin_memory=True,
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batch_size=args.train_batch_size,
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num_workers=args.dataloader_num_workers,
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drop_last=True,
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)
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# Scheduler and math around the number of training steps.
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overrode_max_train_steps = False
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps * args.sp_size / args.train_sp_batch_size)
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if args.max_train_steps is None:
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args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
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overrode_max_train_steps = True
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lr_scheduler = get_scheduler(
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args.lr_scheduler,
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optimizer=optimizer,
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num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
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num_training_steps=args.max_train_steps * accelerator.num_processes,
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)
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# Prepare everything with our `accelerator`.
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# model.requires_grad_(False)
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# model.pos_embed.requires_grad_(True)
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logger.info(f'before accelerator.prepare')
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transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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transformer, optimizer, train_dataloader, lr_scheduler
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)
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logger.info(f'after accelerator.prepare')
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if args.use_ema:
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ema_transformer.to(accelerator.device)
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# We need to recalculate our total training steps as the size of the training dataloader may have changed.
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps * args.sp_size / args.train_sp_batch_size)
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if overrode_max_train_steps:
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args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
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# Afterwards we recalculate our number of training epochs
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args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
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# We need to initialize the trackers we use, and also store our configuration.
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# The trackers initializes automatically on the main process.
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if accelerator.is_main_process:
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tracker_project_name = args.tracker_project_name or "fastvideo"
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accelerator.init_trackers(tracker_project_name, config=vars(args))
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# Train!
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total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
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total_batch_size = total_batch_size // args.sp_size * args.train_sp_batch_size
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logger.info("***** Running training *****")
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logger.info(f" Num examples = {len(train_dataset)}")
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logger.info(f" Num Epochs = {args.num_train_epochs}")
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logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
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logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
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logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
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logger.info(f" Total optimization steps = {args.max_train_steps}")
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logger.info(f" Total training parameters = {sum(p.numel() for p in transformer.parameters() if p.requires_grad) / 1e9} B")
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global_step = 0
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first_epoch = 0
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# Potentially load in the weights and states from a previous save
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if args.resume_from_checkpoint:
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if args.resume_from_checkpoint != "latest":
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path = os.path.basename(args.resume_from_checkpoint)
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else:
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# Get the most recent checkpoint
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dirs = os.listdir(args.output_dir)
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dirs = [d for d in dirs if d.startswith("checkpoint")]
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dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
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path = dirs[-1] if len(dirs) > 0 else None
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if path is None:
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accelerator.print(
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f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
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)
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args.resume_from_checkpoint = None
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initial_global_step = 0
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else:
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accelerator.print(f"Resuming from checkpoint {path}")
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accelerator.load_state(os.path.join(args.output_dir, path))
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global_step = int(path.split("-")[1])
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initial_global_step = global_step
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first_epoch = global_step // num_update_steps_per_epoch
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else:
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initial_global_step = 0
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progress_bar = tqdm(
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range(0, args.max_train_steps),
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initial=initial_global_step,
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desc="Steps",
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# Only show the progress bar once on each machine.
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disable=not accelerator.is_local_main_process,
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)
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progress_info = ProgressInfo(global_step, train_loss=0.0)
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def sync_gradients_info(loss):
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# Checks if the accelerator has performed an optimization step behind the scenes
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if args.use_ema:
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ema_transformer.step(transformer.parameters())
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progress_bar.update(1)
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progress_info.global_step += 1
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end_time = time.time()
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one_step_duration = end_time - start_time
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accelerator.log({"train_loss": progress_info.train_loss}, step=progress_info.global_step)
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progress_info.train_loss = 0.0
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# DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.
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if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:
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if progress_info.global_step % args.checkpointing_steps == 0:
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# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
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if accelerator.is_main_process and args.checkpoints_total_limit is not None:
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checkpoints = os.listdir(args.output_dir)
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checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
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checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
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# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
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if len(checkpoints) >= args.checkpoints_total_limit:
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num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
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removing_checkpoints = checkpoints[0:num_to_remove]
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logger.info(
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f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
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)
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logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
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for removing_checkpoint in removing_checkpoints:
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removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
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shutil.rmtree(removing_checkpoint)
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save_path = os.path.join(args.output_dir, f"checkpoint-{progress_info.global_step}")
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accelerator.save_state(save_path)
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logger.info(f"Saved state to {save_path}")
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# TODO: this is local loss on GPU0
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# The scale is different wandb v.s. cli
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logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
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progress_bar.set_postfix(**logs)
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def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
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sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
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schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
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timesteps = timesteps.to(accelerator.device)
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step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
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sigma = sigmas[step_indices].flatten()
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while len(sigma.shape) < n_dim:
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sigma = sigma.unsqueeze(-1)
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return sigma
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def run(latents, encoder_hidden_states,encoder_attention_mask, prof):
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global start_time
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start_time = time.time()
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bsz = latents.shape[0]
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noise = torch.randn_like(latents)
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u = compute_density_for_timestep_sampling(
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weighting_scheme=args.weighting_scheme,
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batch_size=bsz,
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logit_mean=args.logit_mean,
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logit_std=args.logit_std,
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mode_scale=args.mode_scale,
|
|
)
|
|
|
|
indices = (u * noise_scheduler.config.num_train_timesteps).long()
|
|
timesteps = noise_scheduler.timesteps[indices].to(device=latents.device)
|
|
|
|
# Add noise according to flow matching.
|
|
# zt = (1 - texp) * x + texp * z1
|
|
sigmas = get_sigmas(timesteps, n_dim=latents.ndim, dtype=latents.dtype)
|
|
noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise
|
|
model_pred = transformer(
|
|
noisy_model_input,
|
|
encoder_hidden_states,
|
|
noise_scheduler.config.num_train_timesteps - timesteps, # the timestep for mochi is invereted
|
|
encoder_attention_mask, # B, L
|
|
return_dict= False
|
|
)[0]
|
|
|
|
if args.precondition_outputs:
|
|
model_pred = model_pred * sigmas + noisy_model_input
|
|
|
|
# these weighting schemes use a uniform timestep sampling
|
|
# and instead post-weight the loss
|
|
weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
|
|
|
|
# flow matching loss
|
|
if args.precondition_outputs:
|
|
target = latents
|
|
else:
|
|
target = latents - noise
|
|
|
|
loss = torch.mean(
|
|
(weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),
|
|
1,
|
|
)
|
|
|
|
# Gather the losses across all processes for logging (if we use distributed training).
|
|
# TODO Why repeat here? Weird
|
|
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
|
|
progress_info.train_loss += avg_loss.detach().item() / args.gradient_accumulation_steps
|
|
|
|
# Backpropagate
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = transformer.parameters()
|
|
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
if accelerator.sync_gradients:
|
|
sync_gradients_info(loss)
|
|
|
|
if accelerator.is_main_process:
|
|
|
|
if progress_info.global_step % args.checkpointing_steps == 0:
|
|
|
|
if args.enable_tracker:
|
|
log_validation(args, transformer, vae, accelerator,
|
|
weight_dtype, progress_info.global_step)
|
|
|
|
if args.use_ema:
|
|
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
|
|
ema_transformer.store(transformer.parameters())
|
|
ema_transformer.copy_to(transformer.parameters())
|
|
log_validation(args, transformer,vae, accelerator,
|
|
weight_dtype, progress_info.global_step, ema=True)
|
|
# Switch back to the original UNet parameters.
|
|
ema_transformer.restore(transformer.parameters())
|
|
|
|
if prof is not None:
|
|
prof.step()
|
|
|
|
|
|
return loss
|
|
|
|
def train_one_step( data_item_, prof_=None):
|
|
latents, cond,attn_mask, cond_mask = data_item_
|
|
# TODO
|
|
latents = latents.to(dtype=torch.bfloat16)
|
|
cond = cond.to(dtype=torch.bfloat16)
|
|
latents_mean = (
|
|
torch.tensor(vae.config.latents_mean).view(1, 12, 1, 1, 1).to(latents.device, latents.dtype)
|
|
)
|
|
latents_std = (
|
|
torch.tensor(vae.config.latents_std).view(1, 12, 1, 1, 1).to(latents.device, latents.dtype)
|
|
)
|
|
|
|
latents = (latents - latents_mean) * vae.config.scaling_factor / latents_std
|
|
|
|
|
|
if get_sequence_parallel_state():
|
|
latents, cond, attn_mask, cond_mask = prepare_parallel_data(latents, cond, attn_mask, cond_mask)
|
|
for iter in range(args.train_batch_size * args.sp_size // args.train_sp_batch_size):
|
|
with accelerator.accumulate(transformer):
|
|
st_idx = iter * args.train_sp_batch_size
|
|
ed_idx = (iter + 1) * args.train_sp_batch_size
|
|
encoder_hidden_states=cond[st_idx: ed_idx]
|
|
# TODO
|
|
attention_mask=attn_mask[st_idx: ed_idx]
|
|
encoder_attention_mask=cond_mask[st_idx: ed_idx]
|
|
run(latents[st_idx: ed_idx], encoder_hidden_states, encoder_attention_mask, prof_)
|
|
else:
|
|
with accelerator.accumulate(transformer):
|
|
encoder_hidden_states=cond
|
|
attention_mask=attn_mask,
|
|
encoder_attention_mask=cond_mask
|
|
run(latents, encoder_hidden_states, encoder_attention_mask, prof_)
|
|
|
|
if progress_info.global_step >= args.max_train_steps:
|
|
return True
|
|
|
|
return False
|
|
|
|
def train_all_epoch(prof_=None):
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
progress_info.train_loss = 0.0
|
|
if progress_info.global_step >= args.max_train_steps:
|
|
return True
|
|
|
|
for step, data_item in enumerate(train_dataloader):
|
|
if train_one_step(data_item, prof_):
|
|
break
|
|
|
|
|
|
train_all_epoch()
|
|
accelerator.wait_for_everyone()
|
|
accelerator.end_training()
|
|
if get_sequence_parallel_state():
|
|
destroy_sequence_parallel_group()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
|
|
# dataset & dataloader
|
|
parser.add_argument("--data_merge_path", type=str, required=True)
|
|
parser.add_argument("--num_frames", type=int, default=65)
|
|
parser.add_argument("--dataloader_num_workers", type=int, default=10, help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.")
|
|
parser.add_argument("--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader.")
|
|
parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.")
|
|
parser.add_argument("--group_frame", action="store_true") # TODO
|
|
parser.add_argument("--group_resolution", action="store_true") # TODO
|
|
|
|
# text encoder & vae & diffusion model
|
|
parser.add_argument("--pretrained_model_name_or_path", type=str)
|
|
parser.add_argument('--tile_sample_stride', type=int, default=192)
|
|
parser.add_argument('--enable_tiling', action='store_true')
|
|
parser.add_argument("--downsampler", type=str, default=None)
|
|
parser.add_argument("--cache_dir", type=str, default='./cache_dir')
|
|
parser.add_argument("--pretrained", type=str, default=None)
|
|
parser.add_argument('--enable_stable_fp32', action='store_true') # TODO
|
|
|
|
# diffusion setting
|
|
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
|
|
parser.add_argument("--ema_decay", type=float, default=0.999)
|
|
parser.add_argument("--ema_start_step", type=int, default=0)
|
|
parser.add_argument('--cfg', type=float, default=0.1)
|
|
parser.add_argument("--prediction_type", type=str, default=None, help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.")
|
|
|
|
parser.add_argument(
|
|
"--weighting_scheme",
|
|
type=str,
|
|
default="logit_normal",
|
|
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap"],
|
|
)
|
|
parser.add_argument(
|
|
"--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--mode_scale",
|
|
type=float,
|
|
default=1.29,
|
|
help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
|
|
)
|
|
parser.add_argument("--precondition_outputs", action="store_true", help="Whether to precondition the outputs of the model.")
|
|
|
|
# validation & logs
|
|
parser.add_argument("--validaiton_prompt", nargs='+', help="List of prompts to use for validation.")
|
|
parser.add_argument("--validation_sampling_steps", type=int, default=64)
|
|
parser.add_argument('--guidance_scale', type=float, default=4.5)
|
|
parser.add_argument("--enable_tracker", action="store_true")
|
|
parser.add_argument("--tracker_project_name", type=str, default=None)
|
|
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
|
|
parser.add_argument("--output_dir", type=str, default=None, help="The output directory where the model predictions and checkpoints will be written.")
|
|
parser.add_argument("--checkpoints_total_limit", type=int, default=None, help=("Max number of checkpoints to store."))
|
|
parser.add_argument("--checkpointing_steps", type=int, default=500,
|
|
help=(
|
|
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
|
|
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
|
|
" training using `--resume_from_checkpoint`."
|
|
),
|
|
)
|
|
parser.add_argument("--resume_from_checkpoint", type=str, default=None,
|
|
help=(
|
|
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
|
|
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
|
|
),
|
|
)
|
|
parser.add_argument("--logging_dir", type=str, default="logs",
|
|
help=(
|
|
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
|
|
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
|
|
),
|
|
)
|
|
|
|
# optimizer & scheduler & Training
|
|
parser.add_argument("--num_train_epochs", type=int, default=100)
|
|
parser.add_argument("--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.")
|
|
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.")
|
|
parser.add_argument("--optimizer", type=str, default="adamW", help='The optimizer type to use.')
|
|
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.")
|
|
parser.add_argument("--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.")
|
|
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. Ignored if optimizer is not set to AdamW")
|
|
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers.")
|
|
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers.")
|
|
parser.add_argument("--adam_weight_decay", type=float, default=1e-02, help="Weight decay to use for unet params")
|
|
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer and Prodigy optimizers.")
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
parser.add_argument("--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.")
|
|
parser.add_argument("--lr_scheduler", type=str, default="constant",
|
|
help=(
|
|
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
|
|
' "constant", "constant_with_warmup"]'
|
|
),
|
|
)
|
|
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("--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"],
|
|
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."
|
|
),
|
|
)
|
|
|
|
parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel")
|
|
parser.add_argument("--train_sp_batch_size", type=int, default=1, help="Batch size for sequence parallel training")
|
|
|
|
args = parser.parse_args()
|
|
main(args)
|