"""
Supervised fine-tuning (SFT) the model.
Run as:

python -m scripts.chat_sft

Or torchrun for training:

torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --device-batch-size=16
"""

import gc
import math
import argparse
import os
os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
import time
import wandb
import torch
from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir, autodetect_device_type, get_peak_flops, COMPUTE_DTYPE, COMPUTE_DTYPE_REASON, is_ddp_initialized
from nanochat.tokenizer import get_token_bytes
from nanochat.checkpoint_manager import save_checkpoint, load_model, load_optimizer_state
from nanochat.loss_eval import evaluate_bpb
import torch.distributed as dist
from nanochat.flash_attention import HAS_FA
from nanochat.dataloader import sft_data_loader_varlen
from nanochat.engine import Engine
from scripts.chat_eval import run_chat_eval

from tasks.common import TaskMixture
from tasks.gsm8k import GSM8K
from tasks.mmlu import MMLU
from tasks.smoltalk import SmolTalk
from tasks.customjson import CustomJSON
from tasks.spellingbee import SimpleSpelling, SpellingBee

# -----------------------------------------------------------------------------
# CLI arguments
parser = argparse.ArgumentParser(description="Supervised fine-tuning (SFT) the model")
# Logging
parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('dummy' disables wandb logging)")
# Runtime
parser.add_argument("--device-type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
# Model loading
parser.add_argument("--model-tag", type=str, default=None, help="model tag to load from")
parser.add_argument("--model-step", type=int, default=None, help="model step to load from")
parser.add_argument("--load-optimizer", type=int, default=1, help="warm-start optimizer from pretrained checkpoint (0=no, 1=yes)")
# Training horizon
parser.add_argument("--num-iterations", type=int, default=-1, help="number of optimization steps (-1 = full epoch)")
# Batch sizes (default: inherit from pretrained checkpoint)
parser.add_argument("--max-seq-len", type=int, default=None, help="max context length (default: inherit from pretrain)")
parser.add_argument("--device-batch-size", type=int, default=None, help="per-device batch size (default: inherit from pretrain)")
parser.add_argument("--total-batch-size", type=int, default=None, help="total batch size in tokens (default: inherit from pretrain)")
# Optimization (default: inherit from pretrained checkpoint)
parser.add_argument("--embedding-lr", type=float, default=None, help="learning rate for embedding parameters (Adam) (default: inherit from pretrain)")
parser.add_argument("--unembedding-lr", type=float, default=None, help="learning rate for unembedding parameters (Adam) (default: inherit from pretrain)")
parser.add_argument("--matrix-lr", type=float, default=None, help="learning rate for matrix parameters (Muon) (default: inherit from pretrain)")
parser.add_argument("--init-lr-frac", type=float, default=0.8, help="initial LR as fraction of base LR")
parser.add_argument("--warmup-ratio", type=float, default=0.0, help="ratio of iterations for LR warmup")
parser.add_argument("--warmdown-ratio", type=float, default=0.5, help="ratio of iterations for LR warmdown")
parser.add_argument("--final-lr-frac", type=float, default=0.0, help="final LR as fraction of initial LR")
# Evaluation
parser.add_argument("--eval-every", type=int, default=200, help="evaluate val bpb every N steps (-1 = disable)")
parser.add_argument("--eval-tokens", type=int, default=40*524288, help="number of tokens to evaluate val loss on")
parser.add_argument("--chatcore-every", type=int, default=200, help="evaluate ChatCORE metric every N steps (-1 = disable)")
parser.add_argument("--chatcore-max-cat", type=int, default=-1, help="max problems per categorical task for ChatCORE")
parser.add_argument("--chatcore-max-sample", type=int, default=24, help="max problems per generative task for ChatCORE")
# Data mixture
parser.add_argument("--mmlu-epochs", type=int, default=3, help="number of epochs of MMLU in training mixture (teaches Multiple Choice)")
parser.add_argument("--gsm8k-epochs", type=int, default=4, help="number of epochs of GSM8K in training mixture (teaches Math and Tool Use)")
args = parser.parse_args()
user_config = vars(args).copy()
# -----------------------------------------------------------------------------

# Compute init
device_type = autodetect_device_type() if args.device_type == "" else args.device_type
ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
master_process = ddp_rank == 0
print0(f"COMPUTE_DTYPE: {COMPUTE_DTYPE} ({COMPUTE_DTYPE_REASON})")
synchronize = torch.cuda.synchronize if device_type == "cuda" else lambda: None
get_max_memory = torch.cuda.max_memory_allocated if device_type == "cuda" else lambda: 0
if device_type == "cuda":
    gpu_device_name = torch.cuda.get_device_name(0)
    gpu_peak_flops = get_peak_flops(gpu_device_name)
    print0(f"GPU: {gpu_device_name} | Peak FLOPS (BF16): {gpu_peak_flops:.2e}")
else:
    gpu_peak_flops = float('inf')  # MFU not meaningful for CPU/MPS

# wandb logging init
use_dummy_wandb = args.run == "dummy" or not master_process
wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat-sft", name=args.run, config=user_config)

# Flash Attention status
if not HAS_FA:
    print0("WARNING: Flash Attention not available, using PyTorch SDPA fallback. Training will be less efficient.")

# Load the model and tokenizer
model, tokenizer, meta = load_model("base", device, phase="train", model_tag=args.model_tag, step=args.model_step)

# Inherit training hyperparameters from pretrained checkpoint (None = inherit, explicit value = override)
pretrain_user_config = meta.get("user_config", {})
for name, fallback, source in [
    ("max_seq_len",       2048,  meta),
    ("device_batch_size", 32,    meta),
    ("total_batch_size",  524288, meta),
    ("embedding_lr",      0.3,   pretrain_user_config),
    ("unembedding_lr",    0.004, pretrain_user_config),
    ("matrix_lr",         0.02,  pretrain_user_config),
]:
    arg_val = getattr(args, name)
    pretrain_val = source.get(name)
    if arg_val is None:
        resolved = pretrain_val if pretrain_val is not None else fallback
        setattr(args, name, resolved)
        print0(f"Inherited {name}={resolved} from pretrained checkpoint")
    elif pretrain_val is not None and arg_val != pretrain_val:
        print0(f"NOTE: --{name.replace('_', '-')}={arg_val} overrides pretrained value of {pretrain_val}")
    else:
        print0(f"Using {name}={arg_val}")

orig_model = model
model = torch.compile(model, dynamic=False)
depth = model.config.n_layer
num_flops_per_token = model.estimate_flops()
tokens_per_fwdbwd = args.device_batch_size * args.max_seq_len # tokens per iteration for a single rank
world_tokens_per_fwdbwd = tokens_per_fwdbwd * ddp_world_size # total tokens per iteration for all ranks
assert args.total_batch_size % world_tokens_per_fwdbwd == 0
grad_accum_steps = args.total_batch_size // world_tokens_per_fwdbwd
print0(f"Tokens / micro-batch / rank: {args.device_batch_size} x {args.max_seq_len} = {tokens_per_fwdbwd:,}")
print0(f"Tokens / micro-batch: {world_tokens_per_fwdbwd:,}")
print0(f"Total batch size {args.total_batch_size:,} => gradient accumulation steps: {grad_accum_steps}")
token_bytes = get_token_bytes(device=device)

# Initialize the Optimizer (combined MuonAdamW: Muon for matrix params, AdamW for rest)
# Note that pretraining ramps weight_decay to zero by end of pretraining, so SFT continues with zero
optimizer = model.setup_optimizer(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=0.0)

# Optionally warm-start optimizer from pretrained checkpoint (momentum buffers etc.)
# Note: load_state_dict overwrites param_group metadata (LRs, betas, etc.) with the
# pretrained values. Since pretraining warmdown brings LRs to ~0, we must save and
# restore our fresh SFT LRs after loading.
base_dir = get_base_dir()
if args.load_optimizer:
    optimizer_data = load_optimizer_state("base", device, rank=ddp_rank, model_tag=args.model_tag, step=args.model_step)
    if optimizer_data is not None:
        base_lrs = [group["lr"] for group in optimizer.param_groups]
        optimizer.load_state_dict(optimizer_data)
        del optimizer_data
        for group, base_lr in zip(optimizer.param_groups, base_lrs):
            group["lr"] = base_lr
        print0("Loaded optimizer state from pretrained checkpoint (momentum buffers only, LRs reset)")
    else:
        print0("WARNING: optimizer checkpoint not found, starting with fresh optimizer (slightly worse)")

# GradScaler for fp16 training (bf16/fp32 don't need it)
scaler = torch.amp.GradScaler() if COMPUTE_DTYPE == torch.float16 else None
if scaler is not None:
    print0("GradScaler enabled for fp16 training")

# Override the initial learning rate as a fraction of the base learning rate
for group in optimizer.param_groups:
    group["lr"] = group["lr"] * args.init_lr_frac
    group["initial_lr"] = group["lr"]

# SFT data mixture and DataLoader
identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
train_tasks = [
    SmolTalk(split="train"), # 460K rows of general conversations
    CustomJSON(filepath=identity_conversations_filepath), # 1000 rows of synthetic identity conversations
    CustomJSON(filepath=identity_conversations_filepath), # 2 epochs of these
    *[MMLU(subset="auxiliary_train", split="train") for _ in range(args.mmlu_epochs)], # 100K rows per epoch
    *[GSM8K(subset="main", split="train") for _ in range(args.gsm8k_epochs)], # 8K rows per epoch
    SimpleSpelling(size=200000, split="train"), # 200K rows of Simple Spelling (e.g. spell the word 'apple')
    SpellingBee(size=80000, split="train"), # 80K rows of Spelling Bee (e.g. how many 'r' are in 'strawberry'?)
]
train_dataset = TaskMixture(train_tasks)
print0(f"Training mixture: {len(train_dataset):,} rows (MMLU x{args.mmlu_epochs}, GSM8K x{args.gsm8k_epochs})")
val_dataset = TaskMixture([
    SmolTalk(split="test"), # 24K rows in test set
    MMLU(subset="all", split="test", stop=5200), # 14K rows in test set, use only 5.2K to match the train ratios
    GSM8K(subset="main", split="test", stop=420), # 1.32K rows in test set, use only 420 to match the train ratios
]) # total: 24K + 14K + 1.32K ~= 39K rows

# Pre-tokenize and pre-pack all conversations into batch plans.
# This runs the same best-fit packing algorithm offline at startup, so we know
# num_iterations and max_num_docs exactly before training starts.
def tokenize_and_pack_sft(dataset, tokenizer, B, T, bos_token, ddp_rank, ddp_world_size, buffer_size=100):
    """
    Pre-tokenize and pre-pack SFT conversations using best-fit packing.

    Preserves TaskMixture's shuffled ordering (no length sorting) and uses the
    same buffer-based best-fit algorithm as the original inline dataloader.

    Returns (conversations, batch_plans, total_micro_batches, max_num_docs).
    """
    dataset_size = len(dataset)
    buffer_capacity = B * T + 1

    conversations = []
    num_convs = (dataset_size - ddp_rank + ddp_world_size - 1) // ddp_world_size
    cursor = ddp_rank
    while cursor < dataset_size:
        ids, mask = tokenizer.render_conversation(dataset[cursor])
        conversations.append((ids, mask))
        cursor += ddp_world_size
        if len(conversations) % 5000 == 0:
            print0(f"\r\033[KTokenizing: {len(conversations):,}/{num_convs:,} ({100*len(conversations)/num_convs:.0f}%)", end='', flush=True)
    print0(f"\r\033[KTokenized {len(conversations):,} conversations", flush=True)

    batch_plans = []
    conv_buffer = []
    fetch_cursor = 0
    max_doc_count = 0

    def refill():
        nonlocal fetch_cursor
        while len(conv_buffer) < buffer_size and fetch_cursor < len(conversations):
            conv_buffer.append(fetch_cursor)
            fetch_cursor += 1

    while True:
        refill()
        if not conv_buffer:
            break
        batch_indices = []
        pos = 0
        while pos < buffer_capacity:
            refill()
            if not conv_buffer:
                break
            remaining = buffer_capacity - pos
            best_buf_idx = -1
            best_len = 0
            for i, conv_idx in enumerate(conv_buffer):
                conv_len = len(conversations[conv_idx][0])
                if conv_len <= remaining and conv_len > best_len:
                    best_buf_idx = i
                    best_len = conv_len
            if best_buf_idx >= 0:
                batch_indices.append(conv_buffer.pop(best_buf_idx))
                pos += best_len
            else:
                break
        if batch_indices:
            doc_count = len(batch_indices) + (1 if pos < buffer_capacity else 0)
            max_doc_count = max(max_doc_count, doc_count)
            batch_plans.append(batch_indices)

    max_num_docs = math.ceil((max_doc_count + 1) / 16) * 16
    return conversations, batch_plans, len(batch_plans), max(max_num_docs, 16)

bos_token = tokenizer.get_bos_token_id()
t_pack_start = time.time()
train_convs, train_plans, train_micro_batches, train_max_docs = tokenize_and_pack_sft(
    train_dataset, tokenizer, args.device_batch_size, args.max_seq_len,
    bos_token, ddp_rank, ddp_world_size)
t_pack_train = time.time()
val_convs, val_plans, val_micro_batches, val_max_docs = tokenize_and_pack_sft(
    val_dataset, tokenizer, args.device_batch_size, args.max_seq_len,
    bos_token, ddp_rank, ddp_world_size)
t_pack_val = time.time()
max_num_docs = max(train_max_docs, val_max_docs)
print0(f"Pre-tokenize & pack: train {t_pack_train - t_pack_start:.1f}s, val {t_pack_val - t_pack_train:.1f}s, total {t_pack_val - t_pack_start:.1f}s")

# Document length and packing statistics
import numpy as np
train_doc_lens = [len(ids) for ids, _ in train_convs]
train_docs_per_batch = [len(plan) for plan in train_plans]
train_tokens_per_batch = [sum(len(train_convs[i][0]) for i in plan) for plan in train_plans]
buffer_capacity = args.device_batch_size * args.max_seq_len + 1
train_packing_eff = [t / buffer_capacity for t in train_tokens_per_batch]
dl = np.array(train_doc_lens)
dpb = np.array(train_docs_per_batch)
pe = np.array(train_packing_eff)
print0(f"Train doc lengths: n={len(dl):,} | mean={dl.mean():.0f} median={np.median(dl):.0f} "
       f"min={dl.min()} max={dl.max()} p5={np.percentile(dl,5):.0f} p95={np.percentile(dl,95):.0f}")
print0(f"Train docs/batch:  n={len(dpb):,} | mean={dpb.mean():.1f} median={np.median(dpb):.0f} "
       f"min={dpb.min()} max={dpb.max()} p5={np.percentile(dpb,5):.0f} p95={np.percentile(dpb,95):.0f}")
print0(f"Train packing eff: mean={pe.mean():.3f} median={np.median(pe):.3f} "
       f"min={pe.min():.3f} max={pe.max():.3f}")

# num_iterations: exact count of optimization steps. The -1 accounts for the
# prefetch batch that the training loop requests but never trains on.
data_num_iterations = (train_micro_batches - 1) // grad_accum_steps
if args.num_iterations > 0:
    num_iterations = min(args.num_iterations, data_num_iterations)
else:
    num_iterations = data_num_iterations
if ddp:
    num_iter_tensor = torch.tensor([num_iterations], dtype=torch.long, device=device)
    dist.all_reduce(num_iter_tensor, op=dist.ReduceOp.MIN)
    num_iterations = num_iter_tensor.item()
print0(f"Pre-packed {len(train_convs):,} train conversations into {train_micro_batches:,} micro-batches "
       f"=> {num_iterations:,} optimization steps (max {max_num_docs} docs/batch)")

train_loader = sft_data_loader_varlen(
    train_convs, train_plans, args.device_batch_size, args.max_seq_len,
    max_num_docs, bos_token, device=device)
build_val_loader = lambda: sft_data_loader_varlen(
    val_convs, val_plans, args.device_batch_size, args.max_seq_len,
    max_num_docs, bos_token, device=device, cycle=True)

# Learning rate schedule (linear warmup, constant, linear warmdown)
def get_lr_multiplier(it):
    warmup_iters = round(args.warmup_ratio * num_iterations)
    warmdown_iters = round(args.warmdown_ratio * num_iterations)
    if it < warmup_iters:
        return (it + 1) / warmup_iters
    elif it <= num_iterations - warmdown_iters:
        return 1.0
    else:
        progress = (num_iterations - it) / warmdown_iters
        return progress * 1.0 + (1 - progress) * args.final_lr_frac

# Momentum scheduler for Muon optimizer
def get_muon_momentum(it):
    frac = min(it / 300, 1)
    momentum = (1 - frac) * 0.85 + frac * 0.95
    return momentum

# -----------------------------------------------------------------------------
# Training loop
x, y, cu_seqlens = next(train_loader) # prefetch the very first batch of data
min_val_bpb = float("inf")
smooth_train_loss = 0 # EMA of training loss
ema_beta = 0.9 # EMA decay factor
total_training_time = 0 # total wall-clock time of training
step = 0
while True:
    last_step = step == num_iterations
    flops_so_far = num_flops_per_token * args.total_batch_size * step

    # once in a while: evaluate the val bpb (all ranks participate)
    if last_step or (args.eval_every > 0 and step % args.eval_every == 0):
        model.eval()
        val_loader = build_val_loader()
        eval_steps = args.eval_tokens // (args.device_batch_size * args.max_seq_len * ddp_world_size)
        val_bpb = evaluate_bpb(model, val_loader, eval_steps, token_bytes)
        print0(f"Step {step:05d} | Validation bpb: {val_bpb:.4f}")
        if val_bpb < min_val_bpb:
            min_val_bpb = val_bpb
        wandb_run.log({
            "step": step,
            "total_training_flops": flops_so_far,
            "total_training_time": total_training_time,
            "val/bpb": val_bpb,
        })
        model.train()

    # once in a while: estimate the ChatCORE metric (all ranks participate)
    # use the original uncompiled model because the inputs keep changing shape
    chatcore_results = {}
    if args.chatcore_every > 0 and (last_step or (step > 0 and step % args.chatcore_every == 0)):
        model.eval()
        engine = Engine(orig_model, tokenizer)
        all_tasks = ['ARC-Easy', 'ARC-Challenge', 'MMLU', 'GSM8K', 'HumanEval', 'SpellingBee']
        categorical_tasks = {'ARC-Easy', 'ARC-Challenge', 'MMLU'}
        baseline_accuracies = {
            'ARC-Easy': 0.25, 'ARC-Challenge': 0.25, 'MMLU': 0.25,
            'GSM8K': 0.0, 'HumanEval': 0.0, 'SpellingBee': 0.0,
        }
        task_results = {}
        for task_name in all_tasks:
            limit = args.chatcore_max_cat if task_name in categorical_tasks else args.chatcore_max_sample
            max_problems = None if limit < 0 else limit  # -1 means no limit
            acc = run_chat_eval(task_name, orig_model, tokenizer, engine,
                                batch_size=args.device_batch_size, max_problems=max_problems)
            task_results[task_name] = acc
            print0(f"  {task_name}: {100*acc:.2f}%")
        # Compute ChatCORE metrics (mean centered accuracy, ranges from 0=random to 1=perfect)
        def centered_mean(tasks):
            return sum((task_results[t] - baseline_accuracies[t]) / (1.0 - baseline_accuracies[t]) for t in tasks) / len(tasks)
        chatcore = centered_mean(all_tasks)
        chatcore_cat = centered_mean(categorical_tasks)
        print0(f"Step {step:05d} | ChatCORE: {chatcore:.4f} | ChatCORE_cat: {chatcore_cat:.4f}")
        wandb_run.log({
            "step": step,
            "total_training_flops": flops_so_far,
            "chatcore_metric": chatcore,
            "chatcore_cat": chatcore_cat,
            **{f"chatcore/{task_name}": acc for task_name, acc in task_results.items()},
        })
        model.train()

    # save checkpoint at the end of the run (all ranks participate so each saves its optimizer shard)
    if last_step:
        output_dirname = args.model_tag if args.model_tag else f"d{depth}" # e.g. d12
        checkpoint_dir = os.path.join(base_dir, "chatsft_checkpoints", output_dirname)
        save_checkpoint(
            checkpoint_dir,
            step,
            orig_model.state_dict(),
            optimizer.state_dict(),
            {
                "step": step,
                "val_bpb": val_bpb, # loss at last step
                "model_config": {
                    "sequence_len": args.max_seq_len,
                    "vocab_size": tokenizer.get_vocab_size(),
                    "n_layer": depth,
                    "n_head": model.config.n_head,
                    "n_kv_head": model.config.n_kv_head,
                    "n_embd": model.config.n_embd,
                    "window_pattern": model.config.window_pattern,
                },
                "user_config": user_config, # inputs to the training script
            },
            rank=ddp_rank,
        )

    if last_step:
        break

    # -------------------------------------------------------------------------
    # single training step
    # evaluate the gradient
    synchronize()
    t0 = time.time()
    for micro_step in range(grad_accum_steps):
        loss = model(x, y, cu_seqlens=cu_seqlens)
        train_loss = loss.detach() # for logging
        loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
        if scaler is not None:
            scaler.scale(loss).backward()
        else:
            loss.backward()
        x, y, cu_seqlens = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
    # step the optimizer
    lrm = get_lr_multiplier(step)
    muon_momentum = get_muon_momentum(step)
    for group in optimizer.param_groups:
        group["lr"] = group["initial_lr"] * lrm
        if group['kind'] == 'muon':
            group["momentum"] = muon_momentum
    if scaler is not None:
        scaler.unscale_(optimizer)
        if is_ddp_initialized():
            for v in scaler._found_inf_per_device(optimizer).values():
                dist.all_reduce(v, op=dist.ReduceOp.MAX)
        scaler.step(optimizer)
        scaler.update()
    else:
        optimizer.step()
    model.zero_grad(set_to_none=True)
    synchronize()
    t1 = time.time()
    dt = t1 - t0
    # -------------------------------------------------------------------------

    # State
    step += 1

    # logging
    smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss.item() # EMA the training loss
    debiased_smooth_loss = smooth_train_loss / (1 - ema_beta**(step + 1)) # debias the EMA
    pct_done = 100 * step / num_iterations
    tok_per_sec = int(args.total_batch_size / dt)
    flops_per_sec = num_flops_per_token * args.total_batch_size / dt
    mfu = 100 * flops_per_sec / (gpu_peak_flops * ddp_world_size)
    if step > 10:
        total_training_time += dt # only count the time after the first 10 steps
    print0(f"step {step:05d}/{num_iterations:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | total time: {total_training_time/60:.2f}m")
    if step % 10 == 0:
        wandb_run.log({
            "step": step,
            "total_training_flops": flops_so_far,
            "total_training_time": total_training_time,
            "train/loss": debiased_smooth_loss,
            "train/lrm": lrm,
            "train/dt": dt,
            "train/tok_per_sec": tok_per_sec,
            "train/mfu": mfu,
        })

    # The garbage collector spends ~500ms scanning for cycles quite frequently.
    # We manually manage it to avoid these pauses during training.
    if step == 1:
        gc.collect() # manually collect a lot of garbage from setup
        gc.freeze() # freeze all currently surviving objects and exclude them from GC
        gc.disable() # disable GC entirely except:
    elif step % 5000 == 0: # every 5000 steps...
        gc.collect() # manually collect, just to be safe for very long runs

# print a few more stats
print0(f"Peak memory usage: {get_max_memory() / 1024 / 1024:.2f}MiB")
print0(f"Total training time: {total_training_time/60:.2f}m")
print0(f"Minimum validation bpb: {min_val_bpb:.4f}")

# Log to report
from nanochat.report import get_report
get_report().log(section="SFT", data=[
    user_config, # CLI args
    { # stats about the training setup
        "Number of iterations": step,
        "DDP world size": ddp_world_size,
    },
    { # stats about training outcomes
        "Minimum validation bpb": min_val_bpb,
    }
])

# cleanup
wandb_run.finish() # wandb run finish
compute_cleanup()