342 lines
15 KiB
Python
342 lines
15 KiB
Python
from absl import app, flags, logging
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from ml_collections import config_flags
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from accelerate import Accelerator
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from accelerate.utils import set_seed
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from accelerate.logging import get_logger
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from diffusers import StableDiffusionPipeline, DDIMScheduler
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from diffusers.loaders import AttnProcsLayers
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from diffusers.models.attention_processor import LoRAAttnProcessor
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import ddpo_pytorch.prompts
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import ddpo_pytorch.rewards
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from ddpo_pytorch.stat_tracking import PerPromptStatTracker
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from ddpo_pytorch.diffusers_patch.pipeline_with_logprob import pipeline_with_logprob
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from ddpo_pytorch.diffusers_patch.ddim_with_logprob import ddim_step_with_logprob
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import torch
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import tqdm
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FLAGS = flags.FLAGS
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config_flags.DEFINE_config_file("config", "config/base.py", "Training configuration.")
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logger = get_logger(__name__)
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def main(_):
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# basic Accelerate and logging setup
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config = FLAGS.config
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accelerator = Accelerator(
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log_with="all",
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mixed_precision=config.mixed_precision,
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project_dir=config.logdir,
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)
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if accelerator.is_main_process:
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accelerator.init_trackers(project_name="ddpo-pytorch", config=config)
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logger.info(config)
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# set seed
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set_seed(config.seed)
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# load scheduler, tokenizer and models.
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pipeline = StableDiffusionPipeline.from_pretrained(config.pretrained.model, revision=config.pretrained.revision)
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# freeze parameters of models to save more memory
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pipeline.unet.requires_grad_(False)
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pipeline.vae.requires_grad_(False)
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pipeline.text_encoder.requires_grad_(False)
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# disable safety checker
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pipeline.safety_checker = None
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# make the progress bar nicer
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pipeline.set_progress_bar_config(
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position=1,
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disable=not accelerator.is_local_main_process,
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leave=False,
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)
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# switch to DDIM scheduler
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pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
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# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) 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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# Move unet, vae and text_encoder to device and cast to weight_dtype
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pipeline.unet.to(accelerator.device, dtype=weight_dtype)
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pipeline.vae.to(accelerator.device, dtype=weight_dtype)
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pipeline.text_encoder.to(accelerator.device, dtype=weight_dtype)
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# Set correct lora layers
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lora_attn_procs = {}
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for name in pipeline.unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else pipeline.unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = pipeline.unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(pipeline.unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = pipeline.unet.config.block_out_channels[block_id]
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lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
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pipeline.unet.set_attn_processor(lora_attn_procs)
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lora_layers = AttnProcsLayers(pipeline.unet.attn_processors)
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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 config.allow_tf32:
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torch.backends.cuda.matmul.allow_tf32 = True
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if config.train.scale_lr:
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config.train.learning_rate = (
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config.train.learning_rate
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* config.train.gradient_accumulation_steps
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* config.train.batch_size
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* accelerator.num_processes
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)
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# Initialize the optimizer
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if config.train.use_8bit_adam:
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try:
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import bitsandbytes as bnb
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except ImportError:
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raise ImportError(
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"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
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)
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optimizer_cls = bnb.optim.AdamW8bit
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else:
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optimizer_cls = torch.optim.AdamW
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optimizer = optimizer_cls(
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lora_layers.parameters(),
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lr=config.train.learning_rate,
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betas=(config.train.adam_beta1, config.train.adam_beta2),
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weight_decay=config.train.adam_weight_decay,
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eps=config.train.adam_epsilon,
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)
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# prepare prompt and reward fn
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prompt_fn = getattr(ddpo_pytorch.prompts, config.prompt_fn)
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reward_fn = getattr(ddpo_pytorch.rewards, config.reward_fn)()
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# Prepare everything with our `accelerator`.
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lora_layers, optimizer = accelerator.prepare(lora_layers, optimizer)
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# Train!
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samples_per_epoch = config.sample.batch_size * accelerator.num_processes * config.sample.num_batches_per_epoch
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total_train_batch_size = (
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config.train.batch_size * accelerator.num_processes * config.train.gradient_accumulation_steps
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)
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assert config.sample.batch_size % config.train.batch_size == 0
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assert samples_per_epoch % total_train_batch_size == 0
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logger.info("***** Running training *****")
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logger.info(f" Num Epochs = {config.num_epochs}")
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logger.info(f" Sample batch size per device = {config.sample.batch_size}")
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logger.info(f" Train batch size per device = {config.train.batch_size}")
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logger.info(f" Gradient Accumulation steps = {config.train.gradient_accumulation_steps}")
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logger.info("")
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logger.info(f" Total number of samples per epoch = {samples_per_epoch}")
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logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
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logger.info(f" Number of gradient updates per inner epoch = {samples_per_epoch // total_train_batch_size}")
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logger.info(f" Number of inner epochs = {config.train.num_inner_epochs}")
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neg_prompt_embed = pipeline.text_encoder(
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pipeline.tokenizer(
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[""],
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return_tensors="pt",
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padding="max_length",
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truncation=True,
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max_length=pipeline.tokenizer.model_max_length,
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).input_ids.to(accelerator.device)
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)[0]
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sample_neg_prompt_embeds = neg_prompt_embed.repeat(config.sample.batch_size, 1, 1)
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train_neg_prompt_embeds = neg_prompt_embed.repeat(config.train.batch_size, 1, 1)
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if config.per_prompt_stat_tracking:
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stat_tracker = PerPromptStatTracker(
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config.per_prompt_stat_tracking.buffer_size,
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config.per_prompt_stat_tracking.min_count,
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)
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for epoch in range(config.num_epochs):
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#################### SAMPLING ####################
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samples = []
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prompts = []
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for i in tqdm.tqdm(
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range(config.sample.num_batches_per_epoch),
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desc=f"Epoch {epoch}: sampling",
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disable=not accelerator.is_local_main_process,
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position=0,
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):
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# generate prompts
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prompts, prompt_metadata = zip(
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*[prompt_fn(**config.prompt_fn_kwargs) for _ in range(config.sample.batch_size)]
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)
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# encode prompts
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prompt_ids = pipeline.tokenizer(
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prompts,
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return_tensors="pt",
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padding="max_length",
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truncation=True,
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max_length=pipeline.tokenizer.model_max_length,
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).input_ids.to(accelerator.device)
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prompt_embeds = pipeline.text_encoder(prompt_ids)[0]
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# sample
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pipeline.unet.eval()
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pipeline.vae.eval()
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images, _, latents, log_probs = pipeline_with_logprob(
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pipeline,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=sample_neg_prompt_embeds,
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num_inference_steps=config.sample.num_steps,
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guidance_scale=config.sample.guidance_scale,
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eta=config.sample.eta,
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output_type="pt",
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)
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latents = torch.stack(latents, dim=1) # (batch_size, num_steps + 1, 4, 64, 64)
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log_probs = torch.stack(log_probs, dim=1) # (batch_size, num_steps, 1)
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timesteps = pipeline.scheduler.timesteps.repeat(config.sample.batch_size, 1) # (batch_size, num_steps)
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# compute rewards
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rewards, reward_metadata = reward_fn(images, prompts, prompt_metadata)
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samples.append(
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{
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"prompt_ids": prompt_ids,
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"prompt_embeds": prompt_embeds,
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"timesteps": timesteps,
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"latents": latents[:, :-1], # each entry is the latent before timestep t
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"next_latents": latents[:, 1:], # each entry is the latent after timestep t
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"log_probs": log_probs,
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"rewards": torch.as_tensor(rewards),
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}
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)
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# collate samples into dict where each entry has shape (num_batches_per_epoch * sample.batch_size, ...)
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samples = {k: torch.cat([s[k] for s in samples]) for k in samples[0].keys()}
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# gather rewards across processes
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rewards = accelerator.gather(samples["rewards"]).cpu().numpy()
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# per-prompt mean/std tracking
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if config.per_prompt_stat_tracking:
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# gather the prompts across processes
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prompt_ids = accelerator.gather(samples["prompt_ids"]).cpu().numpy()
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prompts = pipeline.tokenizer.batch_decode(prompt_ids, skip_special_tokens=True)
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advantages = stat_tracker.update(prompts, rewards)
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else:
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advantages = (rewards - rewards.mean()) / (rewards.std() + 1e-8)
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# ungather advantages; we only need to keep the entries corresponding to the samples on this process
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samples["advantages"] = (
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torch.as_tensor(advantages)
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.reshape(accelerator.num_processes, -1)[accelerator.process_index]
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.to(accelerator.device)
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)
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del samples["rewards"]
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del samples["prompt_ids"]
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total_batch_size, num_timesteps = samples["timesteps"].shape
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assert total_batch_size == config.sample.batch_size * config.sample.num_batches_per_epoch
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assert num_timesteps == config.sample.num_steps
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#################### TRAINING ####################
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for inner_epoch in range(config.train.num_inner_epochs):
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# shuffle samples along batch dimension
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indices = torch.randperm(total_batch_size, device=accelerator.device)
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samples = {k: v[indices] for k, v in samples.items()}
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# shuffle along time dimension, independently for each sample
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for i in range(total_batch_size):
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indices = torch.randperm(num_timesteps, device=accelerator.device)
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for key in ["timesteps", "latents", "next_latents"]:
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samples[key][i] = samples[key][i][indices]
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# rebatch for training
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samples_batched = {k: v.reshape(-1, config.train.batch_size, *v.shape[1:]) for k, v in samples.items()}
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# dict of lists -> list of dicts for easier iteration
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samples_batched = [dict(zip(samples_batched, x)) for x in zip(*samples_batched.values())]
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# train
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for i, sample in tqdm.tqdm(
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list(enumerate(samples_batched)),
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desc=f"Outer epoch {epoch}, inner epoch {inner_epoch}: training",
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position=0,
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):
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if config.train.cfg:
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# concat negative prompts to sample prompts to avoid two forward passes
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embeds = torch.cat([train_neg_prompt_embeds, sample["prompt_embeds"]])
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else:
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embeds = sample["prompt_embeds"]
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for j in tqdm.trange(
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num_timesteps,
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desc=f"Timestep",
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position=1,
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leave=False,
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):
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with accelerator.accumulate(pipeline.unet):
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if config.train.cfg:
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noise_pred = pipeline.unet(
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torch.cat([sample["latents"][:, j]] * 2),
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torch.cat([sample["timesteps"][:, j]] * 2),
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embeds,
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).sample
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + config.sample.guidance_scale * (
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noise_pred_text - noise_pred_uncond
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)
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else:
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noise_pred = pipeline.unet(
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sample["latents"][:, j], sample["timesteps"][:, j], embeds
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).sample
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_, log_prob = ddim_step_with_logprob(
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pipeline.scheduler,
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noise_pred,
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sample["timesteps"][:, j],
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sample["latents"][:, j],
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eta=config.sample.eta,
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prev_sample=sample["next_latents"][:, j],
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)
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# ppo logic
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advantages = torch.clamp(
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sample["advantages"][:, j], -config.train.adv_clip_max, config.train.adv_clip_max
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)
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ratio = torch.exp(log_prob - sample["log_probs"][:, j])
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unclipped_loss = -advantages * ratio
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clipped_loss = -advantages * torch.clamp(
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ratio, 1.0 - config.train.clip_range, 1.0 + config.train.clip_range
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)
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loss = torch.mean(torch.maximum(unclipped_loss, clipped_loss))
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# debugging values
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info = {}
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# John Schulman says that (ratio - 1) - log(ratio) is a better
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# estimator, but most existing code uses this so...
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# http://joschu.net/blog/kl-approx.html
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info["approx_kl"] = 0.5 * torch.mean((log_prob - sample["log_probs"][:, j]) ** 2)
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info["clipfrac"] = torch.mean(torch.abs(ratio - 1.0) > config.train.clip_range)
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info["loss"] = loss
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# backward pass
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accelerator.backward(loss)
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if accelerator.sync_gradients:
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accelerator.clip_grad_norm_(lora_layers.parameters(), config.train.max_grad_norm)
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optimizer.step()
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optimizer.zero_grad()
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if __name__ == "__main__":
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app.run(main)
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