Working non-lora training; other changes
This commit is contained in:
Kevin Black
196
scripts/train.py
196
scripts/train.py
@@ -1,4 +1,6 @@
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from collections import defaultdict
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import contextlib
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import os
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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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@@ -17,6 +19,8 @@ import torch
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import wandb
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from functools import partial
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import tqdm
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import tempfile
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from PIL import Image
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tqdm = partial(tqdm.tqdm, dynamic_ncols=True)
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@@ -46,9 +50,9 @@ def main(_):
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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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pipeline.unet.requires_grad_(not config.use_lora)
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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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@@ -56,40 +60,47 @@ def main(_):
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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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desc="Timestep",
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dynamic_ncols=True,
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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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inference_dtype = torch.float32
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if accelerator.mixed_precision == "fp16":
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weight_dtype = torch.float16
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inference_dtype = torch.float16
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elif accelerator.mixed_precision == "bf16":
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weight_dtype = torch.bfloat16
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inference_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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# Move unet, vae and text_encoder to device and cast to inference_dtype
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pipeline.vae.to(accelerator.device, dtype=inference_dtype)
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pipeline.text_encoder.to(accelerator.device, dtype=inference_dtype)
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if config.use_lora:
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pipeline.unet.to(accelerator.device, dtype=inference_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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if config.use_lora:
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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 = (
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None if name.endswith("attn1.processor") else pipeline.unet.config.cross_attention_dim
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)
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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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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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trainable_layers = AttnProcsLayers(pipeline.unet.attn_processors)
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else:
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trainable_layers = pipeline.unet
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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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@@ -110,7 +121,7 @@ def main(_):
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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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trainable_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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@@ -121,8 +132,31 @@ def main(_):
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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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# generate negative prompt embeddings
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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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# initialize stat tracker
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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 some reason, autocast is necessary for non-lora training but for lora training it uses more memory
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autocast = contextlib.nullcontext if config.use_lora else accelerator.autocast
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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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trainable_layers, optimizer = accelerator.prepare(trainable_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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@@ -144,27 +178,10 @@ def main(_):
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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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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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global_step = 0
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for epoch in range(config.num_epochs):
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#################### SAMPLING ####################
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pipeline.unet.eval()
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samples = []
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prompts = []
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for i in tqdm(
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@@ -189,17 +206,16 @@ def main(_):
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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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with autocast():
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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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@@ -226,14 +242,26 @@ def main(_):
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# gather rewards across processes
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rewards = accelerator.gather(samples["rewards"]).cpu().numpy()
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# log sample-related stuff
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accelerator.log({"reward": rewards, "epoch": epoch}, step=global_step)
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# log rewards and images
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accelerator.log(
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{"images": [wandb.Image(image, caption=prompt) for image, prompt in zip(images, prompts)]},
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{"reward": rewards, "epoch": epoch, "reward_mean": rewards.mean(), "reward_std": rewards.std()},
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step=global_step,
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)
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# from PIL import Image
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# Image.fromarray((images[0].cpu().numpy().transpose(1, 2, 0) * 255).astype(np.uint8)).save(f"test.png")
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# this is a hack to force wandb to log the images as JPEGs instead of PNGs
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with tempfile.TemporaryDirectory() as tmpdir:
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for i, image in enumerate(images):
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pil = Image.fromarray((image.cpu().numpy().transpose(1, 2, 0) * 255).astype(np.uint8))
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pil = pil.resize((256, 256))
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pil.save(os.path.join(tmpdir, f"{i}.jpg"))
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accelerator.log(
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{
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"images": [
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wandb.Image(os.path.join(tmpdir, f"{i}.jpg"), caption=prompt)
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for i, prompt in enumerate(prompts)
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],
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},
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step=global_step,
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)
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# per-prompt mean/std tracking
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if config.per_prompt_stat_tracking:
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@@ -271,6 +299,7 @@ def main(_):
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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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pipeline.unet.train()
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for i, sample in tqdm(
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list(enumerate(samples_batched)),
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desc=f"Epoch {epoch}.{inner_epoch}: training",
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@@ -286,34 +315,35 @@ def main(_):
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info = defaultdict(list)
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for j in tqdm(
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range(num_timesteps),
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desc=f"Timestep",
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desc="Timestep",
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position=1,
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leave=False,
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disable=not accelerator.is_local_main_process,
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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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with autocast():
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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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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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@@ -337,7 +367,7 @@ def main(_):
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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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accelerator.clip_grad_norm_(trainable_layers.parameters(), config.train.max_grad_norm)
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optimizer.step()
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optimizer.zero_grad()
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