train/train.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import copy
import logging
import math
import os
from pathlib import Path

import diffusers
import torch
import torch.utils.checkpoint
import transformers
import yaml
from accelerate import Accelerator
from accelerate.utils import DeepSpeedPlugin, ProjectConfiguration, set_seed
from diffusers.optimization import get_scheduler
from diffusers.utils import is_wandb_available
from huggingface_hub import create_repo, upload_folder
from tqdm.auto import tqdm
from safetensors.torch import load_model

from models.ema_model import EMAModel
from models.multimodal_encoder.siglip_encoder import SiglipVisionTower
from models.multimodal_encoder.t5_encoder import T5Embedder
from models.rdt_runner import RDTRunner
from train.dataset import DataCollatorForVLAConsumerDataset, VLAConsumerDataset
from train.sample import log_sample_res


if is_wandb_available():
    import wandb


def save_model_card(repo_id: str, base_model=str, repo_folder=None):
    yaml = f"""
---
license: mit
base_model: {base_model}
language:
- en
pipeline_tag: robotics
library_name: transformers
tags:
- robotics
- pytorch
- multimodal
- pretraining
- vla
- diffusion
- rdt
---
    """
    model_card = f"""
# RDT - {repo_id}

This is a RDT model derived from {base_model}. The weights were trained using [RDT](https://rdt-robotics.github.io/rdt-robotics/).
"""
    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


def train(args, logger):
    # Read the config
    with open(args.config_path, "r") as fp:
        config = yaml.safe_load(fp)

    logging_dir = Path(args.output_dir, args.logging_dir)

    accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
    accelerator = Accelerator(
        deepspeed_plugin=DeepSpeedPlugin(
            hf_ds_config=args.deepspeed
        ) if args.deepspeed is not None else None,
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with=args.report_to,
        project_dir=logging_dir,
        project_config=accelerator_project_config,
    )

    if args.report_to == "wandb":
        if not is_wandb_available():
            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()
        diffusers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

        if args.push_to_hub:
            repo_id = create_repo(
                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
            ).repo_id

    # For mixed precision training we cast the text_encoder and vae weights to half-precision
    # as these models are only used for inference, keeping weights in full precision is not required.
    weight_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16
    
    if args.precomp_lang_embed:
        tokenizer, text_encoder = None, None
    else:
        text_embedder = T5Embedder(from_pretrained=args.pretrained_text_encoder_name_or_path, 
                                model_max_length=config["dataset"]["tokenizer_max_length"], device=accelerator.device)
        tokenizer, text_encoder = text_embedder.tokenizer, text_embedder.model

    vision_encoder = SiglipVisionTower(vision_tower=args.pretrained_vision_encoder_name_or_path, args=None)
    image_processor = vision_encoder.image_processor

    # Load from a pretrained checkpoint
    if (
        args.pretrained_model_name_or_path is not None
        and not os.path.isfile(args.pretrained_model_name_or_path)
    ):
        logger.info("Constructing model from pretrained checkpoint.")
        rdt = RDTRunner.from_pretrained(args.pretrained_model_name_or_path)
    else:
        logger.info("Constructing model from provided config.")
        # Calculate the image condition length
        img_cond_len = (config["common"]["img_history_size"] 
                        * config["common"]["num_cameras"] 
                        * vision_encoder.num_patches)
        rdt = RDTRunner(
            action_dim=config["common"]["state_dim"],
            pred_horizon=config["common"]["action_chunk_size"],
            config=config["model"],
            lang_token_dim=config["model"]["lang_token_dim"],
            img_token_dim=config["model"]["img_token_dim"],
            state_token_dim=config["model"]["state_token_dim"],
            max_lang_cond_len=config["dataset"]["tokenizer_max_length"],
            img_cond_len=img_cond_len,
            img_pos_embed_config=[
                # No initial pos embed in the last grid size
                # since we've already done in ViT
                ("image", (config["common"]["img_history_size"], 
                    config["common"]["num_cameras"], 
                    -vision_encoder.num_patches)),  
            ],
            lang_pos_embed_config=[
                # Similarly, no initial pos embed for language
                ("lang", -config["dataset"]["tokenizer_max_length"]),
            ],
            dtype=weight_dtype,
        )
        
                                                                       
    ema_rdt = copy.deepcopy(rdt)
    ema_model = EMAModel(
        ema_rdt,
        update_after_step=config["model"]["ema"]["update_after_step"],
        inv_gamma=config["model"]["ema"]["inv_gamma"],
        power=config["model"]["ema"]["power"],
        min_value=config["model"]["ema"]["min_value"],
        max_value=config["model"]["ema"]["max_value"]
    )

    # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
    # which ensure saving model in huggingface format (config.json + pytorch_model.bin)
    def save_model_hook(models, weights, output_dir):
        if accelerator.is_main_process:
            for model in models:
                model_to_save = model.module if hasattr(model, "module") else model  # type: ignore
                if isinstance(model_to_save, type(accelerator.unwrap_model(rdt))):
                    model_to_save.save_pretrained(output_dir)

    accelerator.register_save_state_pre_hook(save_model_hook)
    
    if args.gradient_checkpointing:
        # TODO: 
        raise NotImplementedError("Gradient checkpointing is not yet implemented.")

    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if args.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
        )

    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
    if args.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
            )

        optimizer_class = bnb.optim.AdamW8bit
    else:
        optimizer_class = torch.optim.AdamW

    # Optimizer creation
    params_to_optimize = rdt.parameters()
    optimizer = optimizer_class(
        params_to_optimize,
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )
    
    # Dataset and DataLoaders creation:                                                           
    train_dataset = VLAConsumerDataset(
        config=config["dataset"],
        tokenizer=tokenizer,
        image_processor=image_processor,
        num_cameras=config["common"]["num_cameras"],
        img_history_size=config["common"]["img_history_size"],
        dataset_type=args.dataset_type,
        image_aug=args.image_aug,
        cond_mask_prob=args.cond_mask_prob,
        cam_ext_mask_prob=args.cam_ext_mask_prob,
        state_noise_snr=args.state_noise_snr,
        use_hdf5=args.load_from_hdf5,
        use_precomp_lang_embed=args.precomp_lang_embed,
    )
    sample_dataset = VLAConsumerDataset(
        config=config["dataset"],
        tokenizer=tokenizer,
        image_processor=image_processor,
        num_cameras=config["common"]["num_cameras"],
        img_history_size=config["common"]["img_history_size"],
        dataset_type=args.dataset_type,
        image_aug=False,
        cond_mask_prob=0,
        cam_ext_mask_prob=-1,
        state_noise_snr=None,
        use_hdf5=args.load_from_hdf5,
        use_precomp_lang_embed=args.precomp_lang_embed,
    )                              
    
    data_collator = DataCollatorForVLAConsumerDataset(tokenizer)                                                        
    
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=args.train_batch_size,
        shuffle=True,
        collate_fn=data_collator,
        num_workers=args.dataloader_num_workers,
        pin_memory=True,
        persistent_workers=True
    )
    sample_dataloader = torch.utils.data.DataLoader(
        sample_dataset,
        batch_size=args.sample_batch_size,
        shuffle=True,
        collate_fn=data_collator,
        num_workers=args.dataloader_num_workers,
        pin_memory=True,
        persistent_workers=True
    )
    
    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
        num_cycles=args.lr_num_cycles,
        power=args.lr_power,
    )

    # Prepare everything with our `accelerator`.
    rdt, optimizer, train_dataloader, sample_dataloader, lr_scheduler = accelerator.prepare(
        rdt, optimizer, train_dataloader, sample_dataloader, lr_scheduler                   
    )

    ema_rdt.to(accelerator.device, dtype=weight_dtype)                                                                             

    if text_encoder is not None:
        text_encoder.to(accelerator.device, dtype=weight_dtype)
    
    if vision_encoder is not None:
        vision_encoder.vision_tower.to(accelerator.device, dtype=weight_dtype)

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        accelerator.init_trackers("roboticDiffusionTransformer", config=vars(args))

    # Train!
    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    global_step = 0
    first_epoch = 0
    
    # Load from a pretrained checkpoint
    if (
        args.resume_from_checkpoint is None 
        and args.pretrained_model_name_or_path is not None
        and os.path.isfile(args.pretrained_model_name_or_path)
    ):
        # Since EMA is deprecated, we do not load EMA from the pretrained checkpoint
        logger.info("Loading from a pretrained checkpoint.")
        checkpoint = torch.load(args.pretrained_model_name_or_path)
        rdt.module.load_state_dict(checkpoint["module"])
   
    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint != "latest":
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the mos recent checkpoint
            dirs = os.listdir(args.output_dir)
            dirs = [d for d in dirs if d.startswith("checkpoint")]
            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
            path = dirs[-1] if len(dirs) > 0 else None

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            try:
                accelerator.load_state(os.path.join(args.output_dir, path)) # load_module_strict=False
            except:
                # load deepspeed's state_dict
                logger.info("Resuming training state failed. Attempting to only load from model checkpoint.")
                checkpoint = torch.load(os.path.join(args.output_dir, path, "pytorch_model", "mp_rank_00_model_states.pt"))
                rdt.module.load_state_dict(checkpoint["module"])
                
            load_model(ema_rdt, os.path.join(args.output_dir, path, "ema", "model.safetensors"))
            global_step = int(path.split("-")[1])

            resume_global_step = global_step * args.gradient_accumulation_steps
            first_epoch = global_step // num_update_steps_per_epoch
            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)

    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
    progress_bar.set_description("Steps")

    loss_for_log = {}
    for epoch in range(first_epoch, args.num_train_epochs):

        rdt.train()
        
        # Set the progress_bar to correct position
        if args.resume_from_checkpoint and epoch == first_epoch:
            progress_bar.update(resume_step // args.gradient_accumulation_steps)
        
        # Forward and backward...
        for batch in train_dataloader:
            with accelerator.accumulate(rdt):
                images = batch["images"].to(dtype=weight_dtype)
                states = batch["states"].to(dtype=weight_dtype) # (B, T, D_a)
                # We only use the last state as input
                states = states[:, -1:, :]
                actions = batch["actions"].to(dtype=weight_dtype)
                state_elem_mask = batch["state_elem_mask"].to(dtype=weight_dtype)
                ctrl_freqs = batch["ctrl_freqs"]
                    
                with torch.no_grad():
                    batch_size, _, C, H, W = images.shape
                    image_embeds = vision_encoder(images.reshape(-1, C, H, W)).detach()
                    image_embeds = image_embeds.reshape((batch_size, -1, vision_encoder.hidden_size))

                    lang_attn_mask = batch["lang_attn_mask"]
                    text_embeds = batch["lang_embeds"].to(dtype=weight_dtype) \
                        if args.precomp_lang_embed \
                        else text_encoder(
                            input_ids=batch["input_ids"],
                            attention_mask=lang_attn_mask
                        )["last_hidden_state"].detach()
                
                state_elem_mask = state_elem_mask.unsqueeze(1)
                loss = rdt(
                    lang_tokens=text_embeds,
                    lang_attn_mask=lang_attn_mask,
                    img_tokens=image_embeds,
                    state_tokens=states,
                    action_gt=actions,
                    action_mask=state_elem_mask,
                    ctrl_freqs=ctrl_freqs
                )

                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    params_to_clip = rdt.parameters()
                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
            
            ema_model.step(accelerator.unwrap_model(rdt))

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1

                if global_step % args.checkpointing_period == 0:
                    save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                    accelerator.save_state(save_path)
                    ema_save_path = os.path.join(save_path, f"ema")
                    accelerator.save_model(ema_rdt, ema_save_path)
                    logger.info(f"Saved state to {save_path}")

                if args.sample_period > 0 and global_step % args.sample_period == 0:
                    sample_loss_for_log = log_sample_res(
                        text_encoder,
                        vision_encoder,
                        rdt,    # We do not use EMA currently
                        args,
                        accelerator,
                        weight_dtype,
                        sample_dataset.get_dataset_id2name(),
                        sample_dataloader,
                        logger,
                    )
                    logger.info(sample_loss_for_log)
                    accelerator.log(sample_loss_for_log, step=global_step)

            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
            progress_bar.set_postfix(**logs)
            logs.update(loss_for_log)
            # logger.info(logs)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

    # Create the pipeline using using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        accelerator.unwrap_model(rdt).save_pretrained(args.output_dir)
        ema_save_path = os.path.join(args.output_dir, f"ema")
        accelerator.save_model(ema_rdt, ema_save_path)
        
        logger.info(f"Saved Model to {args.output_dir}")

        if args.push_to_hub:
            save_model_card(
                repo_id,
                base_model=args.pretrained_model_name_or_path,
                repo_folder=args.output_dir,
            )
            upload_folder(
                repo_id=repo_id,
                folder_path=args.output_dir,
                commit_message="End of training",
                token=args.hub_token,
                allow_patterns=["pytorch_model.bin", "*.json", "*.md"],
                # ignore_patterns=["step_*", "epoch_*"],
            )
            
    accelerator.end_training()