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"""Training loop (Phase 3, hyperparameters in DECISIONS.md D-019).
- Step 3.2: AdamW, warmup + cosine LR, gradient accumulation, clipping, bf16 autocast.
- Step 3.3: val loss every eval_every steps, checkpoint save/resume, sample text.
The logging backend (3.4) and MFU (3.5) are added on top.
    uv run python -m tinyllm.train --preset smoke --max_steps 2000
    uv run python -m tinyllm.train --preset main --stop_at 3000      # train in chunks...
    uv run python -m tinyllm.train --preset main --resume            # ...and continue later
"""

import argparse
import math
import os
import time
from dataclasses import asdict, dataclass, fields
from pathlib import Path

import numpy as np
import torch
from torch import Tensor, nn

from tinyllm.config import ModelConfig, get_config
from tinyllm.data import DATA_DIR, get_batch, load_tokens
from tinyllm.generate import sample
from tinyllm.logger import Logger, make_logger
from tinyllm.model import Transformer
from tinyllm.tokenizer import EOS, load_tokenizer

REPO = DATA_DIR.parent
EVAL_SEED = 1234                       # same val windows at every eval, so losses are comparable
SAMPLE_PROMPT = "Once upon a time"


@dataclass
class TrainConfig:
    preset: str = "smoke"
    seq_len: int = 512
    micro_batch: int = 32            # sequences per forward/backward
    grad_accum: int = 8              # micro-batches per optimizer step: 32 x 512 x 8 = 131,072 tokens/step
    max_steps: int = 2000
    lr: float = 1e-3                 # peak LR, reached at the end of warmup
    min_lr: float = 1e-4             # cosine floor (10% of peak)
    warmup_steps: int = 200
    weight_decay: float = 0.1        # matrices and embeddings only, not norm weights
    beta1: float = 0.9
    beta2: float = 0.95
    grad_clip: float = 1.0
    seed: int = 0
    device: str = "cuda"
    log_every: int = 10
    eval_every: int = 200            # val loss + checkpoint (+ sample) every N steps and at the end
    eval_batches: int = 20           # 20 x 32 x 512 = 327,680 val tokens per eval
    sample_tokens: int = 200         # tokens generated at each eval; 0 = no samples
    out_dir: str = ""                # default: checkpoints/<preset>
    resume: bool = False             # continue from <out_dir>/ckpt.pt
    stop_at: int = 0                 # stop after this many steps (0 = max_steps); the LR schedule still uses max_steps
    logger: str = "wandb"            # "wandb" or "none" (D-013)
    wandb_project: str = "tinyllm"
    wandb_mode: str = "online"       # "offline" writes to ./wandb; upload later with `wandb sync`
    run_name: str = ""               # default: <preset>
    peak_tflops: float = 42.0        # MFU denominator: theoretical bf16 dense peak (docs/HARDWARE.md)
    compile: bool = True             # torch.compile the training step, +57% tok/s on main (D-020); eval + samples stay eager

    @property
    def tokens_per_step(self) -> int:
        return self.micro_batch * self.grad_accum * self.seq_len

    @property
    def run_dir(self) -> Path:
        return Path(self.out_dir) if self.out_dir else REPO / "checkpoints" / self.preset


def lr_at(step: int, cfg: TrainConfig) -> float:
    """Linear warmup to cfg.lr, then cosine decay to cfg.min_lr at cfg.max_steps."""
    if step < cfg.warmup_steps:
        return cfg.lr * (step + 1) / cfg.warmup_steps
    if step >= cfg.max_steps:
        return cfg.min_lr
    progress = (step - cfg.warmup_steps) / max(1, cfg.max_steps - cfg.warmup_steps)
    return cfg.min_lr + 0.5 * (1 + math.cos(math.pi * progress)) * (cfg.lr - cfg.min_lr)


def make_optimizer(model: nn.Module, cfg: TrainConfig) -> torch.optim.AdamW:
    """AdamW with weight decay on 2-D weights only. Norm gains (1-D) are not decayed."""
    decay = [p for p in model.parameters() if p.dim() >= 2]
    no_decay = [p for p in model.parameters() if p.dim() < 2]
    groups = [
        {"params": decay, "weight_decay": cfg.weight_decay},
        {"params": no_decay, "weight_decay": 0.0},
    ]
    fused = torch.device(cfg.device).type == "cuda"  # one kernel for all params instead of a loop
    return torch.optim.AdamW(groups, lr=cfg.lr, betas=(cfg.beta1, cfg.beta2), eps=1e-8, fused=fused)


def train_step(
    model: nn.Module,
    opt: torch.optim.Optimizer,
    batches: list[tuple[Tensor, Tensor]],
    lr: float,
    grad_clip: float,
) -> tuple[Tensor, Tensor]:
    """One optimizer step over len(batches) micro-batches. Returns (mean loss, pre-clip grad norm).
    Each micro-batch loss is divided by the number of micro-batches, so the summed
    gradient equals the gradient of one big batch. Results stay on the device;
    calling .item() forces a GPU sync, so the caller only does it when logging.
    """
    for g in opt.param_groups:
        g["lr"] = lr
    device_type = batches[0][0].device.type
    total = torch.zeros((), device=batches[0][0].device)
    for x, y in batches:
        with torch.autocast(device_type, dtype=torch.bfloat16, enabled=device_type == "cuda"):
            _, loss = model(x, targets=y)
        (loss / len(batches)).backward()
        total += loss.detach()
    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
    opt.step()
    opt.zero_grad(set_to_none=True)
    return total / len(batches), grad_norm


@torch.no_grad()
def evaluate(model: nn.Module, tokens: np.ndarray, cfg: TrainConfig) -> float:
    """Mean loss over cfg.eval_batches fixed val batches (same windows every call)."""
    model.eval()
    rng = np.random.default_rng(EVAL_SEED)
    device_type = torch.device(cfg.device).type
    total = torch.zeros((), device=cfg.device)
    for _ in range(cfg.eval_batches):
        x, y = get_batch(tokens, cfg.micro_batch, cfg.seq_len, rng, cfg.device)
        with torch.autocast(device_type, dtype=torch.bfloat16, enabled=device_type == "cuda"):
            _, loss = model(x, targets=y)
        total += loss
    model.train()
    return (total / cfg.eval_batches).item()


def save_checkpoint(path: Path, state: dict) -> None:
    """Write to a temp file, then rename, so a crash mid-save never corrupts the last checkpoint."""
    path.parent.mkdir(parents=True, exist_ok=True)
    tmp = path.with_suffix(".tmp")
    torch.save(state, tmp)
    os.replace(tmp, path)


def load_checkpoint(path: Path, device: str) -> dict:
    return torch.load(path, map_location=device, weights_only=True)


def train(
    cfg: TrainConfig,
    model_cfg: ModelConfig | None = None,
    tokens: np.ndarray | None = None,
    val_tokens: np.ndarray | None = None,
    log=print,
    logger: Logger | None = None,
) -> tuple[Transformer, list[dict]]:
    """Run optimizer steps up to cfg.stop_at (or cfg.max_steps). Returns the model and the history.
    History rows are {"step", "loss", "lr", "grad_norm", "tok_per_s"} at log steps and
    {"step", "val_loss"} at eval steps. `log` prints to the console; `logger` (built
    from cfg.logger when not given) sends the same numbers to W&B.
    """
    run_dir = cfg.run_dir
    ckpt = load_checkpoint(run_dir / "ckpt.pt", cfg.device) if cfg.resume else None
    if ckpt is not None:
        model_cfg = ModelConfig.from_dict(ckpt["model_cfg"])  # the checkpoint defines the model
    model_cfg = model_cfg or get_config(cfg.preset)
    if cfg.seq_len > model_cfg.max_seq_len:
        raise ValueError(f"seq_len {cfg.seq_len} > model max_seq_len {model_cfg.max_seq_len}")
    torch.manual_seed(cfg.seed)
    torch.set_float32_matmul_precision("high")  # TF32 for any matmul left in fp32
    tokens = load_tokens("train") if tokens is None else tokens
    val_tokens = load_tokens("validation") if val_tokens is None else val_tokens
    tok = load_tokenizer() if cfg.sample_tokens else None
    rng = np.random.default_rng(cfg.seed)

    model = Transformer(model_cfg).to(cfg.device)
    opt = make_optimizer(model, cfg)
    start, best_val = 0, math.inf
    if ckpt is not None:
        model.load_state_dict(ckpt["model"])
        opt.load_state_dict(ckpt["optimizer"])
        rng.bit_generator.state = ckpt["rng"]
        start, best_val = ckpt["step"] + 1, ckpt["best_val"]
        log(f"resumed from {run_dir / 'ckpt.pt'} at step {start} (best val {best_val:.4f})")
    end = min(cfg.stop_at or cfg.max_steps, cfg.max_steps)
    log(f"params {model.num_params():,} | {cfg.tokens_per_step:,} tokens/step "
        f"({cfg.micro_batch}x{cfg.seq_len} x {cfg.grad_accum} accum) | steps {start}..{end - 1} "
        f"of {cfg.max_steps} ({cfg.tokens_per_step * cfg.max_steps / 1e6:.1f}M tokens total) | {run_dir}")
    if logger is None:
        logger = make_logger(
            cfg.logger, project=cfg.wandb_project, name=cfg.run_name or cfg.preset,
            config={**asdict(cfg), "model": model_cfg.to_dict(), "params": model.num_params()},
            mode=cfg.wandb_mode, run_id=ckpt.get("wandb_id") if ckpt else None, dir=str(REPO))
    try:
        history = _loop(cfg, model, opt, rng, tokens, val_tokens, tok, model_cfg, run_dir,
                        start, end, best_val, log, logger)
    finally:
        logger.finish()  # flush W&B even if training crashes or is interrupted
    return model, history


def _loop(cfg, model, opt, rng, tokens, val_tokens, tok, model_cfg, run_dir,
          start, end, best_val, log, logger) -> list[dict]:
    history: list[dict] = []
    flops_per_token = model_cfg.flops_per_token(cfg.seq_len)
    # The compiled wrapper shares the model's weights. Only the fixed-shape training step uses it:
    # eval and sampling (a new length every token) would trigger recompiles, and checkpoints
    # save `model` so state_dict keys never get the `_orig_mod.` prefix.
    step_model = torch.compile(model) if cfg.compile else model
    t_last, step_last = time.perf_counter(), start - 1
    for step in range(start, end):
        lr = lr_at(step, cfg)
        batches = [get_batch(tokens, cfg.micro_batch, cfg.seq_len, rng, cfg.device)
                   for _ in range(cfg.grad_accum)]
        loss, grad_norm = train_step(step_model, opt, batches, lr, cfg.grad_clip)

        if step % cfg.log_every == 0 or step == end - 1:
            rec = {"step": step, "loss": loss.item(), "lr": lr, "grad_norm": grad_norm.item()}
            now = time.perf_counter()  # .item() above synced the GPU, so this timing is real
            rec["tok_per_s"] = (step - step_last) * cfg.tokens_per_step / (now - t_last)
            rec["tflops"] = rec["tok_per_s"] * flops_per_token / 1e12   # achieved model TFLOPS
            rec["mfu"] = rec["tflops"] / cfg.peak_tflops
            t_last, step_last = now, step
            history.append(rec)
            log(f"step {step:5d} | loss {rec['loss']:.4f} | lr {lr:.2e} | "
                f"grad {rec['grad_norm']:.3f} | {rec['tok_per_s'] / 1e3:.1f}K tok/s | "
                f"{rec['tflops']:.1f} TFLOPS | MFU {rec['mfu']:.1%}")
            logger.log({"train/loss": rec["loss"], "train/lr": lr, "train/grad_norm": rec["grad_norm"],
                        "train/tok_per_s": rec["tok_per_s"], "train/tflops": rec["tflops"],
                        "train/mfu": rec["mfu"],
                        "train/tokens": (step + 1) * cfg.tokens_per_step}, step=step)

        if step % cfg.eval_every == 0 or step == end - 1:
            val = evaluate(model, val_tokens, cfg)
            history.append({"step": step, "val_loss": val})
            logger.log({"val/loss": val, "val/best": min(val, best_val)}, step=step)
            saved = "ckpt.pt"
            if val < best_val:
                best_val = val
                # Weights only, for sampling / export later.
                save_checkpoint(run_dir / "best.pt", {"model": model.state_dict(),
                                "model_cfg": model_cfg.to_dict(), "step": step, "val_loss": val})
                saved += " + best.pt"
            save_checkpoint(run_dir / "ckpt.pt", {
                "model": model.state_dict(), "optimizer": opt.state_dict(), "rng": rng.bit_generator.state,
                "step": step, "best_val": best_val, "model_cfg": model_cfg.to_dict(), "train_cfg": asdict(cfg),
                "wandb_id": logger.run_id,
            })
            log(f"eval  {step:5d} | val {val:.4f} | best {best_val:.4f} | saved {saved}")
            if tok is not None:
                gen = torch.Generator(cfg.device).manual_seed(cfg.seed)  # same sampling noise each eval
                prompt = torch.tensor([tok.encode(SAMPLE_PROMPT).ids], device=cfg.device)
                out = sample(model, prompt, cfg.sample_tokens, temperature=0.8, top_k=50,
                             eos_id=tok.token_to_id(EOS), generator=gen)
                text = tok.decode(out[0].tolist())
                log("sample | " + text.replace("\n", " "))
                logger.log_sample(text, step=step)
            t_last, step_last = time.perf_counter(), step  # eval time doesn't count toward tok/s
    return history


def main() -> None:
    p = argparse.ArgumentParser(description="Train a tinyllm model")
    for f in fields(TrainConfig):
        if isinstance(f.default, bool):
            p.add_argument(f"--{f.name}", action=argparse.BooleanOptionalAction, default=f.default)
        else:
            p.add_argument(f"--{f.name}", type=type(f.default), default=f.default)
    train(TrainConfig(**vars(p.parse_args())))


if __name__ == "__main__":
    main()

	"""Training loop (Phase 3, hyperparameters in DECISIONS.md D-019).

	- Step 3.2: AdamW, warmup + cosine LR, gradient accumulation, clipping, bf16 autocast.
	- Step 3.3: val loss every eval_every steps, checkpoint save/resume, sample text.

	The logging backend (3.4) and MFU (3.5) are added on top.

	uv run python -m tinyllm.train --preset smoke --max_steps 2000
	uv run python -m tinyllm.train --preset main --stop_at 3000 # train in chunks...
	uv run python -m tinyllm.train --preset main --resume # ...and continue later
	"""

	import argparse
	import math
	import os
	import time
	from dataclasses import asdict, dataclass, fields
	from pathlib import Path

	import numpy as np
	import torch
	from torch import Tensor, nn

	from tinyllm.config import ModelConfig, get_config
	from tinyllm.data import DATA_DIR, get_batch, load_tokens
	from tinyllm.generate import sample
	from tinyllm.logger import Logger, make_logger
	from tinyllm.model import Transformer
	from tinyllm.tokenizer import EOS, load_tokenizer

	REPO = DATA_DIR.parent
	EVAL_SEED = 1234 # same val windows at every eval, so losses are comparable
	SAMPLE_PROMPT = "Once upon a time"


	@dataclass
	class TrainConfig:
	preset: str = "smoke"
	seq_len: int = 512
	micro_batch: int = 32 # sequences per forward/backward
	grad_accum: int = 8 # micro-batches per optimizer step: 32 x 512 x 8 = 131,072 tokens/step
	max_steps: int = 2000
	lr: float = 1e-3 # peak LR, reached at the end of warmup
	min_lr: float = 1e-4 # cosine floor (10% of peak)
	warmup_steps: int = 200
	weight_decay: float = 0.1 # matrices and embeddings only, not norm weights
	beta1: float = 0.9
	beta2: float = 0.95
	grad_clip: float = 1.0
	seed: int = 0
	device: str = "cuda"
	log_every: int = 10
	eval_every: int = 200 # val loss + checkpoint (+ sample) every N steps and at the end
	eval_batches: int = 20 # 20 x 32 x 512 = 327,680 val tokens per eval
	sample_tokens: int = 200 # tokens generated at each eval; 0 = no samples
	out_dir: str = "" # default: checkpoints/<preset>
	resume: bool = False # continue from <out_dir>/ckpt.pt
	stop_at: int = 0 # stop after this many steps (0 = max_steps); the LR schedule still uses max_steps
	logger: str = "wandb" # "wandb" or "none" (D-013)
	wandb_project: str = "tinyllm"
	wandb_mode: str = "online" # "offline" writes to ./wandb; upload later with `wandb sync`
	run_name: str = "" # default: <preset>
	peak_tflops: float = 42.0 # MFU denominator: theoretical bf16 dense peak (docs/HARDWARE.md)
	compile: bool = True # torch.compile the training step, +57% tok/s on main (D-020); eval + samples stay eager

	@property
	def tokens_per_step(self) -> int:
	return self.micro_batch * self.grad_accum * self.seq_len

	@property
	def run_dir(self) -> Path:
	return Path(self.out_dir) if self.out_dir else REPO / "checkpoints" / self.preset


	def lr_at(step: int, cfg: TrainConfig) -> float:
	"""Linear warmup to cfg.lr, then cosine decay to cfg.min_lr at cfg.max_steps."""
	if step < cfg.warmup_steps:
	return cfg.lr * (step + 1) / cfg.warmup_steps
	if step >= cfg.max_steps:
	return cfg.min_lr
	progress = (step - cfg.warmup_steps) / max(1, cfg.max_steps - cfg.warmup_steps)
	return cfg.min_lr + 0.5 * (1 + math.cos(math.pi * progress)) * (cfg.lr - cfg.min_lr)


	def make_optimizer(model: nn.Module, cfg: TrainConfig) -> torch.optim.AdamW:
	"""AdamW with weight decay on 2-D weights only. Norm gains (1-D) are not decayed."""
	decay = [p for p in model.parameters() if p.dim() >= 2]
	no_decay = [p for p in model.parameters() if p.dim() < 2]
	groups = [
	{"params": decay, "weight_decay": cfg.weight_decay},
	{"params": no_decay, "weight_decay": 0.0},
	]
	fused = torch.device(cfg.device).type == "cuda" # one kernel for all params instead of a loop
	return torch.optim.AdamW(groups, lr=cfg.lr, betas=(cfg.beta1, cfg.beta2), eps=1e-8, fused=fused)


	def train_step(
	model: nn.Module,
	opt: torch.optim.Optimizer,
	batches: list[tuple[Tensor, Tensor]],
	lr: float,
	grad_clip: float,
	) -> tuple[Tensor, Tensor]:
	"""One optimizer step over len(batches) micro-batches. Returns (mean loss, pre-clip grad norm).

	Each micro-batch loss is divided by the number of micro-batches, so the summed
	gradient equals the gradient of one big batch. Results stay on the device;
	calling .item() forces a GPU sync, so the caller only does it when logging.
	"""
	for g in opt.param_groups:
	g["lr"] = lr
	device_type = batches[0][0].device.type
	total = torch.zeros((), device=batches[0][0].device)
	for x, y in batches:
	with torch.autocast(device_type, dtype=torch.bfloat16, enabled=device_type == "cuda"):
	_, loss = model(x, targets=y)
	(loss / len(batches)).backward()
	total += loss.detach()
	grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
	opt.step()
	opt.zero_grad(set_to_none=True)
	return total / len(batches), grad_norm


	@torch.no_grad()
	def evaluate(model: nn.Module, tokens: np.ndarray, cfg: TrainConfig) -> float:
	"""Mean loss over cfg.eval_batches fixed val batches (same windows every call)."""
	model.eval()
	rng = np.random.default_rng(EVAL_SEED)
	device_type = torch.device(cfg.device).type
	total = torch.zeros((), device=cfg.device)
	for _ in range(cfg.eval_batches):
	x, y = get_batch(tokens, cfg.micro_batch, cfg.seq_len, rng, cfg.device)
	with torch.autocast(device_type, dtype=torch.bfloat16, enabled=device_type == "cuda"):
	_, loss = model(x, targets=y)
	total += loss
	model.train()
	return (total / cfg.eval_batches).item()


	def save_checkpoint(path: Path, state: dict) -> None:
	"""Write to a temp file, then rename, so a crash mid-save never corrupts the last checkpoint."""
	path.parent.mkdir(parents=True, exist_ok=True)
	tmp = path.with_suffix(".tmp")
	torch.save(state, tmp)
	os.replace(tmp, path)


	def load_checkpoint(path: Path, device: str) -> dict:
	return torch.load(path, map_location=device, weights_only=True)


	def train(
	cfg: TrainConfig,
	model_cfg: ModelConfig \| None = None,
	tokens: np.ndarray \| None = None,
	val_tokens: np.ndarray \| None = None,
	log=print,
	logger: Logger \| None = None,
	) -> tuple[Transformer, list[dict]]:
	"""Run optimizer steps up to cfg.stop_at (or cfg.max_steps). Returns the model and the history.

	History rows are {"step", "loss", "lr", "grad_norm", "tok_per_s"} at log steps and
	{"step", "val_loss"} at eval steps. `log` prints to the console; `logger` (built
	from cfg.logger when not given) sends the same numbers to W&B.
	"""
	run_dir = cfg.run_dir
	ckpt = load_checkpoint(run_dir / "ckpt.pt", cfg.device) if cfg.resume else None
	if ckpt is not None:
	model_cfg = ModelConfig.from_dict(ckpt["model_cfg"]) # the checkpoint defines the model
	model_cfg = model_cfg or get_config(cfg.preset)
	if cfg.seq_len > model_cfg.max_seq_len:
	raise ValueError(f"seq_len {cfg.seq_len} > model max_seq_len {model_cfg.max_seq_len}")
	torch.manual_seed(cfg.seed)
	torch.set_float32_matmul_precision("high") # TF32 for any matmul left in fp32
	tokens = load_tokens("train") if tokens is None else tokens
	val_tokens = load_tokens("validation") if val_tokens is None else val_tokens
	tok = load_tokenizer() if cfg.sample_tokens else None
	rng = np.random.default_rng(cfg.seed)

	model = Transformer(model_cfg).to(cfg.device)
	opt = make_optimizer(model, cfg)
	start, best_val = 0, math.inf
	if ckpt is not None:
	model.load_state_dict(ckpt["model"])
	opt.load_state_dict(ckpt["optimizer"])
	rng.bit_generator.state = ckpt["rng"]
	start, best_val = ckpt["step"] + 1, ckpt["best_val"]
	log(f"resumed from {run_dir / 'ckpt.pt'} at step {start} (best val {best_val:.4f})")
	end = min(cfg.stop_at or cfg.max_steps, cfg.max_steps)
	log(f"params {model.num_params():,} \| {cfg.tokens_per_step:,} tokens/step "
	f"({cfg.micro_batch}x{cfg.seq_len} x {cfg.grad_accum} accum) \| steps {start}..{end - 1} "
	f"of {cfg.max_steps} ({cfg.tokens_per_step * cfg.max_steps / 1e6:.1f}M tokens total) \| {run_dir}")
	if logger is None:
	logger = make_logger(
	cfg.logger, project=cfg.wandb_project, name=cfg.run_name or cfg.preset,
	config={**asdict(cfg), "model": model_cfg.to_dict(), "params": model.num_params()},
	mode=cfg.wandb_mode, run_id=ckpt.get("wandb_id") if ckpt else None, dir=str(REPO))
	try:
	history = _loop(cfg, model, opt, rng, tokens, val_tokens, tok, model_cfg, run_dir,
	start, end, best_val, log, logger)
	finally:
	logger.finish() # flush W&B even if training crashes or is interrupted
	return model, history


	def _loop(cfg, model, opt, rng, tokens, val_tokens, tok, model_cfg, run_dir,
	start, end, best_val, log, logger) -> list[dict]:
	history: list[dict] = []
	flops_per_token = model_cfg.flops_per_token(cfg.seq_len)
	# The compiled wrapper shares the model's weights. Only the fixed-shape training step uses it:
	# eval and sampling (a new length every token) would trigger recompiles, and checkpoints
	# save `model` so state_dict keys never get the `_orig_mod.` prefix.
	step_model = torch.compile(model) if cfg.compile else model
	t_last, step_last = time.perf_counter(), start - 1
	for step in range(start, end):
	lr = lr_at(step, cfg)
	batches = [get_batch(tokens, cfg.micro_batch, cfg.seq_len, rng, cfg.device)
	for _ in range(cfg.grad_accum)]
	loss, grad_norm = train_step(step_model, opt, batches, lr, cfg.grad_clip)

	if step % cfg.log_every == 0 or step == end - 1:
	rec = {"step": step, "loss": loss.item(), "lr": lr, "grad_norm": grad_norm.item()}
	now = time.perf_counter() # .item() above synced the GPU, so this timing is real
	rec["tok_per_s"] = (step - step_last) * cfg.tokens_per_step / (now - t_last)
	rec["tflops"] = rec["tok_per_s"] * flops_per_token / 1e12 # achieved model TFLOPS
	rec["mfu"] = rec["tflops"] / cfg.peak_tflops
	t_last, step_last = now, step
	history.append(rec)
	log(f"step {step:5d} \| loss {rec['loss']:.4f} \| lr {lr:.2e} \| "
	f"grad {rec['grad_norm']:.3f} \| {rec['tok_per_s'] / 1e3:.1f}K tok/s \| "
	f"{rec['tflops']:.1f} TFLOPS \| MFU {rec['mfu']:.1%}")
	logger.log({"train/loss": rec["loss"], "train/lr": lr, "train/grad_norm": rec["grad_norm"],
	"train/tok_per_s": rec["tok_per_s"], "train/tflops": rec["tflops"],
	"train/mfu": rec["mfu"],
	"train/tokens": (step + 1) * cfg.tokens_per_step}, step=step)

	if step % cfg.eval_every == 0 or step == end - 1:
	val = evaluate(model, val_tokens, cfg)
	history.append({"step": step, "val_loss": val})
	logger.log({"val/loss": val, "val/best": min(val, best_val)}, step=step)
	saved = "ckpt.pt"
	if val < best_val:
	best_val = val
	# Weights only, for sampling / export later.
	save_checkpoint(run_dir / "best.pt", {"model": model.state_dict(),
	"model_cfg": model_cfg.to_dict(), "step": step, "val_loss": val})
	saved += " + best.pt"
	save_checkpoint(run_dir / "ckpt.pt", {
	"model": model.state_dict(), "optimizer": opt.state_dict(), "rng": rng.bit_generator.state,
	"step": step, "best_val": best_val, "model_cfg": model_cfg.to_dict(), "train_cfg": asdict(cfg),
	"wandb_id": logger.run_id,
	})
	log(f"eval {step:5d} \| val {val:.4f} \| best {best_val:.4f} \| saved {saved}")
	if tok is not None:
	gen = torch.Generator(cfg.device).manual_seed(cfg.seed) # same sampling noise each eval
	prompt = torch.tensor([tok.encode(SAMPLE_PROMPT).ids], device=cfg.device)
	out = sample(model, prompt, cfg.sample_tokens, temperature=0.8, top_k=50,
	eos_id=tok.token_to_id(EOS), generator=gen)
	text = tok.decode(out[0].tolist())
	log("sample \| " + text.replace("\n", " "))
	logger.log_sample(text, step=step)
	t_last, step_last = time.perf_counter(), step # eval time doesn't count toward tok/s
	return history


	def main() -> None:
	p = argparse.ArgumentParser(description="Train a tinyllm model")
	for f in fields(TrainConfig):
	if isinstance(f.default, bool):
	p.add_argument(f"--{f.name}", action=argparse.BooleanOptionalAction, default=f.default)
	else:
	p.add_argument(f"--{f.name}", type=type(f.default), default=f.default)
	train(TrainConfig(**vars(p.parse_args())))


	if __name__ == "__main__":
	main()