import os
import sys

with open(sys.argv[0]) as f:
    code = f.read()  # read the code of this file ASAP, for logging
import copy
import time
import uuid
from dataclasses import dataclass
from functools import lru_cache
from pathlib import Path

os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
import torch

torch.empty(
    1, device="cuda", requires_grad=True
).backward()  # prevents a bug on some systems
import torch.distributed as dist
import torch.nn.functional as F
from torch import nn, Tensor

# use of FlexAttention contributed by @KoszarskyB
from torch.nn.attention.flex_attention import BlockMask, flex_attention


torch._inductor.config.coordinate_descent_tuning = (
    True  # we allow this flag for medium track
)
# torch._dynamo.config.compiled_autograd = True


class Snoo:
    """
    @DominikKallusky, @vishal9-team, @vinaysrao

    Sparse Nesterov Outer Optimizer (Snoo) is a momentum-based wrapper to any optimizer that can
    improve the stability and smoothness of the optimization process and thus the quality
    of large language models (LLM) and other models. Snoo implicitly adds temporal regularization
    to the parameters, thus smoothing the training trajectory and instilling a bias towards flatter
    minima and lower parameter norms. Snoo is computationally efficient, incurring minimal overhead
    in compute and moderate memory usage.
    """

    @torch.no_grad()
    def __init__(self, model: nn.Module, lr: float, momentum: float, k: int) -> None:
        self.model = model
        self.lr = lr
        self.momentum = momentum
        self.k = k
        self.current_step = 0
        self.outer_buf = [p.clone() for p in model.parameters()]
        self.model_params = list(self.model.parameters())
        self.optimizer = torch.optim.SGD(
            self.model.parameters(),
            lr=lr,
            momentum=momentum,
            nesterov=True,
            fused=True,
        )

    @torch.no_grad()
    def step(
        self,
    ) -> None:
        if self.current_step % self.k == 0:
            for p_new, p_old in zip(self.model_params, self.outer_buf):
                p_new.grad = p_old.data - p_new.data
                p_new.copy_(p_old, non_blocking=True)

            self.optimizer.step()

            for p_new, p_old in zip(self.model_params, self.outer_buf):
                p_old.copy_(p_new, non_blocking=True)
        self.current_step += 1

    def state_dict(self):
        state_dict = {
            "current_step": self.current_step,
            "lr": self.lr,
            "momentum": self.momentum,
            "k": self.k,
            "outer_buf": [p.clone() for p in self.outer_buf],
            "optimizer_state_dict": self.optimizer.state_dict(),
        }
        return state_dict

    def load_state_dict(self, state_dict):
        self.current_step = state_dict["current_step"]
        self.lr = state_dict["lr"]
        self.momentum = state_dict["momentum"]
        self.k = state_dict["k"]
        for p_src, p_dst in zip(state_dict["outer_buf"], self.outer_buf):
            p_dst.copy_(p_src)
        self.optimizer.load_state_dict(state_dict["optimizer_state_dict"])


# -----------------------------------------------------------------------------
# Muon optimizer


def zeropower_via_newtonschulz5(G: Tensor) -> Tensor:
    """
    Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
    quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
    of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
    zero even beyond the point where the iteration no longer converges all the way to one everywhere
    on the interval. This iteration therefore does not produce UV^T but rather something like US'V^T
    where S' is diagonal with S_{ii}' ∈ [1 - l, 1 + r], which turns out not to hurt model
    performance at all relative to UV^T, where USV^T = G is the SVD.
    """
    assert (
        G.ndim >= 2
    )  # batched Muon implementation by @scottjmaddox, and put into practice in the record by @YouJiacheng
    X = G.bfloat16()
    if G.size(-2) > G.size(-1):
        X = X.mT

    # Ensure spectral norm is at most 1
    X = X / (X.norm(dim=(-2, -1), keepdim=True) + 1e-7)
    # Perform the NS iterations
    for a, b, c in [
        (4.0848, -6.8946, 2.9270),
        (3.9505, -6.3029, 2.6377),
        (3.7418, -5.5913, 2.3037),
        (2.8769, -3.1427, 1.2046),
        (2.8366, -3.0525, 1.2012),
    ]:
        A = X @ X.mT
        B = (
            b * A + c * A @ A
        )  # quintic computation strategy adapted from suggestion by @jxbz, @leloykun, and @YouJiacheng
        X = a * X + B @ X

    if G.size(-2) > G.size(-1):
        X = X.mT
    return X


@torch.compile
def update(
    acc_bf16_view_u16: Tensor,
    mantissa: Tensor,
    momentum_buffer: Tensor,
    update_smoothing_buffer: Tensor,
    grad: Tensor,
    momentum: Tensor,
    update_smoothing: Tensor,
    eff_lr: Tensor,
    eff_weight_decay: Tensor,
):
    assert acc_bf16_view_u16.dtype == mantissa.dtype == torch.uint16
    grad = grad.float()
    momentum_buffer.copy_(momentum * momentum_buffer + (1 - momentum) * grad)
    v = zeropower_via_newtonschulz5(momentum * momentum_buffer + (1 - momentum) * grad)

    update_smoothing_buffer.copy_(update_smoothing * update_smoothing_buffer + (1 - update_smoothing) * v)
    v = update_smoothing_buffer

    acc_m_u32 = (acc_bf16_view_u16.to(torch.uint32) << 16) | mantissa.to(torch.uint32)
    acc_m_u32.view(torch.float32).mul_(1 - eff_weight_decay)
    acc_m_u32.view(torch.float32).add_(other=v, alpha=-eff_lr)
    acc_bf16_view_u16.copy_((acc_m_u32 >> 16).to(torch.uint16))
    mantissa.copy_(acc_m_u32.to(torch.uint16))


class Muon(torch.optim.Optimizer):
    """
    Muon - MomentUm Orthogonalized by Newton-schulz

    https://kellerjordan.github.io/posts/muon/

    Muon internally runs standard SGD-momentum, and then performs an orthogonalization post-
    processing step, in which each 2D parameter's update is replaced with the nearest orthogonal
    matrix. To efficiently orthogonalize each update, we use a Newton-Schulz iteration, which has
    the advantage that it can be stably run in bfloat16 on the GPU.

    Warning: This optimizer should not be used for the embedding layer, the final fully connected layer,
    or any {0,1}-D parameters; those should all be optimized by a standard method (e.g., AdamW).
    """

    def __init__(
        self, params, lr=0.02, weight_decay=0.01, momentum=0.95, update_smoothing=0.0, rank=0, world_size=1
    ):
        self.rank = rank
        self.world_size = world_size
        defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, update_smoothing=update_smoothing)
        super().__init__(params, defaults)
        assert all(
            p.dtype == torch.bfloat16
            for group in self.param_groups
            for p in group["params"]
        )

    @torch.no_grad()
    def step(self):
        futures: list[torch.Future] = []
        for group in self.param_groups:
            params: list[Tensor] = group["params"]
            params_pad = params + [torch.empty_like(params[-1])] * self.world_size
            momentum = torch._as_tensor_fullprec(group["momentum"])
            update_smoothing = torch._as_tensor_fullprec(group["update_smoothing"])
            for base_i in range(len(params))[:: self.world_size]:
                if base_i + self.rank < len(params):
                    p = params[base_i + self.rank]
                    state = self.state[p]
                    if len(state) == 0:
                        state["mantissa"] = torch.zeros_like(p, dtype=torch.uint16)
                        state["momentum_buffer"] = torch.zeros_like(
                            p, dtype=torch.float32
                        )
                        state["update_smoothing_buffer"] = torch.zeros_like(
                            p, dtype=torch.bfloat16
                        )
                    update(
                        p.view(torch.uint16),
                        state["mantissa"],
                        state["momentum_buffer"],
                        state["update_smoothing_buffer"],
                        p.grad,
                        momentum,
                        update_smoothing,
                        eff_lr=torch._as_tensor_fullprec(
                            group["lr"] * max(1, p.size(-2) / p.size(-1)) ** 0.5
                        ),
                        eff_weight_decay=torch._as_tensor_fullprec(
                            group["lr"]
                            * group["weight_decay"]
                            * getattr(p, "wd_mul", 1.0)
                        ),
                    )
                futures.append(
                    dist.all_gather(
                        params_pad[base_i : base_i + self.world_size],
                        params_pad[base_i + self.rank],
                        async_op=True,
                    ).get_future()
                )
        torch.futures.collect_all(futures).wait()


# -----------------------------------------------------------------------------
# PyTorch nn.Module definitions for the model


def norm(x: Tensor):
    return F.rms_norm(x, (x.size(-1),))


@torch.no_grad()
def init_linear(w: Tensor):
    std = 0.5 * (w.size(-1) ** -0.5)  # 0.5 is a bit better than the default 1/sqrt(3)
    bound = (3**0.5) * std
    return w.uniform_(-bound, bound)


class Rotary(nn.Module):
    def __init__(self, dim: int, max_seq_len: int):
        super().__init__()
        # half-truncate RoPE by @YouJiacheng (w/ base freq tuning)
        angular_freq = (1 / 1024) ** torch.linspace(
            0, 1, steps=dim // 4, dtype=torch.float32
        )
        angular_freq = torch.cat([angular_freq, angular_freq.new_zeros(dim // 4)])
        t = torch.arange(max_seq_len, dtype=torch.float32)
        theta = torch.einsum("i,j -> ij", t, angular_freq)
        self.cos = nn.Buffer(theta.cos(), persistent=False)
        self.sin = nn.Buffer(theta.sin(), persistent=False)

    def forward(self, x_BTHD: Tensor):
        assert self.cos.size(0) >= x_BTHD.size(-3)
        cos, sin = (
            self.cos[None, : x_BTHD.size(-3), None, :],
            self.sin[None, : x_BTHD.size(-3), None, :],
        )
        x1, x2 = x_BTHD.to(dtype=torch.float32).chunk(2, dim=-1)
        y1 = x1 * cos + x2 * sin
        y2 = x1 * (-sin) + x2 * cos
        return torch.cat((y1, y2), 3).type_as(x_BTHD)


class CausalSelfAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int, max_seq_len: int, head_dim=128):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim
        hdim = num_heads * head_dim
        # merged QKV weights: suggested by many, implemented by @fernbear.bsky.social, and further improved by @YouJiacheng
        # https://x.com/hi_tysam/status/1879699187107033311
        self.qkvo_w = nn.Parameter(init_linear(torch.empty(4, hdim, dim)).bfloat16())
        self.qkvo_w.detach()[3].zero_()  # out zero init suggested by @Grad62304977
        self.rotary = Rotary(head_dim, max_seq_len)
        # scale the attention logits by given constant, instead of the default head_dim**-0.5, by @leloykun
        # inspired by learnable scalars used by @brendanh0gan https://x.com/hi_tysam/status/1879693583898591283
        self.attn_scale = 0.12

    def forward(
        self, x: Tensor, ve: Tensor | None, block_mask: BlockMask, lambdas: Tensor
    ):
        B, T = x.size(0), x.size(1)  # batch size, sequence length
        assert B == 1, "Must use batch size = 1 for FlexAttention"
        q, k, v = (
            F.linear(x, self.qkvo_w[:3].flatten(end_dim=1))
            .view(B, T, 3 * self.num_heads, self.head_dim)
            .chunk(3, dim=-2)
        )
        q, k = norm(q), norm(k)  # QK norm @Grad62304977
        q, k = self.rotary(q), self.rotary(k)
        v = norm(v)
        if ve is not None:
            v = lambdas[0] * v + lambdas[1] * ve.view_as(
                v
            )  # @KoszarskyB & @Grad62304977
        else:  # skip mid-layers token value embeddings by @YouJiacheng
            v = lambdas[0] * v
        y = flex_attention(
            q.transpose(1, 2),
            k.transpose(1, 2),
            v.transpose(1, 2),
            block_mask=block_mask,
            scale=self.attn_scale,
        ).transpose(1, 2)
        y = y.contiguous().view(
            B, T, self.num_heads * self.head_dim
        )  # re-assemble all head outputs side by side
        y = F.linear(y, self.qkvo_w[3])
        return y


class MLP(nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        hdim = 4 * dim
        self.fc_w = nn.Parameter(init_linear(torch.empty(hdim, dim)).bfloat16())
        self.proj_w = nn.Parameter(torch.zeros(dim, hdim).bfloat16())
        self.fc_w.wd_mul = 2.0
        self.proj_w.wd_mul = 2.0

    def forward(self, x: Tensor):
        x = F.linear(x, self.fc_w)
        x = F.relu(
            x
        ).square()  # https://arxiv.org/abs/2109.08668v2; ~1-2% better than GELU; suggested by @SKYLINEZ007 and @Grad62304977
        x = F.linear(x, self.proj_w)
        return x


class Block(nn.Module):
    def __init__(self, dim: int, num_heads: int, max_seq_len: int, layer_idx: int):
        super().__init__()
        # skip attention of blocks.7 (the 8th layer) by @YouJiacheng
        self.attn = (
            CausalSelfAttention(dim, num_heads, max_seq_len) if layer_idx != 7 else None
        )
        self.mlp = MLP(dim)

    def forward(
        self,
        x: Tensor,
        ve: Tensor | None,
        x00: Tensor,
        x01: Tensor,
        block_mask: BlockMask,
        lambdas: Tensor,
        sa_lambdas: Tensor,
    ):
        x = lambdas[0] * x + lambdas[1] * x00 + lambdas[2] * x01
        if self.attn is not None:
            x = x + self.attn(x, ve, block_mask, sa_lambdas)
        x = x + self.mlp(norm(x))
        return x


# -----------------------------------------------------------------------------
# The main model


def next_multiple_of_n(v: float | int, *, n: int):
    return next(x for x in range(n, int(v) + 1 + n, n) if x >= v)


class GPT(nn.Module):
    def __init__(
        self,
        vocab_size: int,
        num_layers: int,
        num_heads: int,
        model_dim: int,
        max_seq_len: int,
    ):
        super().__init__()
        self.embed1 = nn.Embedding(vocab_size, model_dim)
        self.embed2 = nn.Embedding(vocab_size, model_dim)
        # token value embeddings by @KoszarskyB - inspired by @Grad62304977's value residual implementation following https://arxiv.org/abs/2410.17897
        # value embedding code simplification inspired by @ragulpr https://github.com/KellerJordan/modded-nanogpt/pull/78
        self.value_embeds = nn.ModuleList(
            [nn.Embedding(vocab_size, model_dim) for _ in range(5)]
        )
        self.blocks = nn.ModuleList(
            [Block(model_dim, num_heads, max_seq_len, i) for i in range(num_layers)]
        )
        # there are only 50257 unique GPT-2 tokens; we extend to nearest multiple of 128 for efficiency.
        # suggested to me by @Grad62304977. this originates from Karpathy's experiments.
        self.lm_head_w = nn.Parameter(
            torch.zeros(next_multiple_of_n(vocab_size, n=128), model_dim)
        )
        # Add learnable skip connection weights for decoder layers
        assert num_layers % 2 == 0
        self.scalars = nn.Parameter(
            torch.cat(
                [
                    torch.ones(num_layers),  # skip_weights
                    *[
                        torch.tensor([1.0, 0.0, 0.0]) for _ in range(num_layers)
                    ],  # block lambdas
                    *[
                        torch.tensor([0.5, 0.5]) for _ in range(num_layers)
                    ],  # SA lambdas
                    torch.zeros(1),  # Smear-MTP lambda
                    torch.tensor([1.0, 0.0]),  # backout lambdas
                ]
            )
        )
        for m in self.modules():
            if isinstance(m, nn.Embedding):
                m.bfloat16()

    def create_blockmasks(self, input_seq: Tensor, sliding_window_num_blocks: Tensor):
        BLOCK_SIZE = 128
        docs = (input_seq == 50256).cumsum(0)

        def document_causal(b, h, q_idx, kv_idx):
            causal_mask = q_idx >= kv_idx
            document_mask = docs[q_idx] == docs[kv_idx]
            return causal_mask & document_mask

        def dense_to_ordered(dense_blockmask: Tensor):
            num_blocks = dense_blockmask.sum(dim=-1, dtype=torch.int32)
            indices = (
                dense_blockmask.argsort(dim=-1, descending=False, stable=True)
                .flip(-1)
                .to(torch.int32)
            )
            return num_blocks[None, None].contiguous(), indices[None, None].contiguous()

        # manual block mask creation by @YouJiacheng
        assert len(input_seq) % BLOCK_SIZE == 0
        NUM_BLOCKS = len(input_seq) // BLOCK_SIZE
        block_idx = torch.arange(NUM_BLOCKS, dtype=torch.int32, device="cuda")
        causal_blockmask_any = block_idx[:, None] >= block_idx
        causal_blockmask_all = block_idx[:, None] > block_idx
        docs_low = docs.view(-1, BLOCK_SIZE)[:, 0].contiguous()
        docs_high = docs.view(-1, BLOCK_SIZE)[:, -1].contiguous()
        document_blockmask_any = (docs_low[:, None] <= docs_high) & (
            docs_high[:, None] >= docs_low
        )
        document_blockmask_all = (docs_low[:, None] == docs_high) & (
            docs_high[:, None] == docs_low
        )
        blockmask_any = causal_blockmask_any & document_blockmask_any
        blockmask_all = causal_blockmask_all & document_blockmask_all
        partial_kv_num_blocks, partial_kv_indices = dense_to_ordered(
            blockmask_any & ~blockmask_all
        )
        full_kv_num_blocks, full_kv_indices = dense_to_ordered(blockmask_all)

        def build_bm(window_size_blocks: Tensor) -> BlockMask:
            return BlockMask.from_kv_blocks(
                torch.clamp_max(
                    partial_kv_num_blocks,
                    torch.clamp_min(window_size_blocks - full_kv_num_blocks, 1),
                ),
                partial_kv_indices,
                torch.clamp_max(full_kv_num_blocks, window_size_blocks - 1),
                full_kv_indices,
                BLOCK_SIZE=BLOCK_SIZE,
                mask_mod=document_causal,
            )

        # Long-short SWA block masks by @leloykun & @YouJiacheng, adapated from suggestion by @Grad62304977, following Gemma 2 paper
        return build_bm(sliding_window_num_blocks), build_bm(
            sliding_window_num_blocks // 2
        )

    def forward(
        self, input_seq: Tensor, target_seq: Tensor, sliding_window_num_blocks: Tensor
    ):
        assert input_seq.ndim == 1

        ve = [value_embed(input_seq) for value_embed in self.value_embeds]
        # 012 ... 012 structure on token value embeddings by @YouJiacheng, improved on @leloykun's U-net structure
        ve = (
            [ve[0], ve[1], ve[2], ve[3], ve[4]]
            + [None] * (len(self.blocks) - 10)
            + [ve[0], ve[1], ve[2], ve[3], ve[4]]
        )
        assert len(ve) == len(self.blocks)

        long_bm, short_bm = self.create_blockmasks(input_seq, sliding_window_num_blocks)
        block_masks = [
            long_bm,
            short_bm,
            short_bm,
            short_bm,
            long_bm,
            short_bm,
            short_bm,
            short_bm,
            short_bm,
            short_bm,
            short_bm,
            long_bm,
            short_bm,
            short_bm,
            short_bm,
            long_bm,
        ]
        assert len(block_masks) == len(self.blocks)

        x = x00 = norm(
            self.embed1(input_seq)[None]
        )  # use of norm here by @Grad62304977
        x01 = norm(self.embed2(input_seq)[None])

        skip_connections = []
        skip_map = {
            9: 6,
            10: 4,
            11: 2,
        }
        skip_weights = self.scalars[: len(self.blocks)]
        lambdas = self.scalars[1 * len(self.blocks) : 4 * len(self.blocks)].view(-1, 3)
        sa_lambdas = self.scalars[4 * len(self.blocks) : 6 * len(self.blocks)].view(
            -1, 2
        )
        for i in range(len(self.blocks)):
            if i in skip_map:
                x = x + skip_weights[skip_map[i]] * skip_connections[skip_map[i]]
            x = self.blocks[i](
                x, ve[i], x00, x01, block_masks[i], lambdas[i], sa_lambdas[i]
            )
            skip_connections.append(x)

        skip_lambdas = self.scalars[-2:]
        x = norm(x) * skip_lambdas[0] + norm(skip_connections[11]) * skip_lambdas[1]
        # Smear-MTP by snimu
        x = torch.cat(
            [x[:, :1], x[:, 1:] + self.scalars[-3] * x[:, :-1]],
            dim=1,
        )

        if self.training:
            logits: Tensor = F.linear(
                x.flatten(end_dim=1), self.lm_head_w.bfloat16()
            ).float()
            loss = F.cross_entropy(
                15 * logits * torch.rsqrt(logits.square() + 225), target_seq
            )
            return loss

        loss = 0
        for i in range(4):
            logits: Tensor = F.linear(
                x.flatten(end_dim=1).chunk(4)[i], self.lm_head_w.bfloat16()
            ).float()
            loss += (
                F.cross_entropy(
                    15 * logits * torch.rsqrt(logits.square() + 225),
                    target_seq.chunk(4)[i],
                )
                / 4
            )
        return loss


# -----------------------------------------------------------------------------
# Our own simple Distributed Data Loader


def _load_data_shard(file: Path):
    header = torch.from_file(
        str(file), False, 256, dtype=torch.int32
    )  # header is 256 int32
    assert header[0] == 20240520, "magic number mismatch in the data .bin file"
    assert header[1] == 1, "unsupported version"
    num_tokens = int(header[2])  # number of tokens (claimed)
    with file.open("rb", buffering=0) as f:
        tokens = torch.empty(
            num_tokens, dtype=torch.uint16, pin_memory=True
        )  # avoid pin_memory copy by @YouJiacheng
        f.seek(256 * 4)
        nbytes = f.readinto(tokens.numpy())  # avoid bytes->array copy by @YouJiacheng
        assert nbytes == 2 * num_tokens, "number of tokens read does not match header"
    return tokens


def distributed_data_generator(
    filename_pattern: str, batch_size: int, rank: int, world_size: int
):
    files = sorted(Path.cwd().glob(filename_pattern))
    assert batch_size % world_size == 0
    local_batch_size = batch_size // world_size
    file_iter = iter(
        files
    )  # use itertools.cycle(files) instead if you want to do multi-epoch training
    tokens, pos = _load_data_shard(next(file_iter)), 0
    while True:
        if pos + batch_size + 1 >= len(tokens):
            tokens, pos = _load_data_shard(next(file_iter)), 0
        buf = tokens[pos + rank * local_batch_size :][: local_batch_size + 1]
        inputs = buf[:-1].to(
            device="cuda", dtype=torch.int32, non_blocking=True
        )  # no sync on host side;
        targets = buf[1:].to(
            device="cuda", dtype=torch.int64, non_blocking=True
        )  # H2D in another stream isn't helpful.
        pos += batch_size
        yield inputs, targets


# -----------------------------------------------------------------------------
# int main


@dataclass
class Hyperparameters:
    # data
    train_files = "data/fineweb10B/fineweb_train_*.bin"  # input .bin to train on
    val_files = (
        "data/fineweb10B/fineweb_val_*.bin"  # input .bin to eval validation loss on
    )
    val_tokens = 10485760  # how many tokens of validation data? it's important to keep this fixed for consistent comparisons
    train_seq_len = 64 * 1024  # FlexAttention sequence length
    val_seq_len = 4 * 64 * 1024  # FlexAttention sequence length for validation
    # optimization
    num_iterations = 5535  # number of iterations to run
    cooldown_frac = 0.7  # fraction of training spent cooling down the learning rate
    final_lr_scale = 0.01
    # architecture
    vocab_size = 50257
    # evaluation and logging
    val_loss_every = (
        125  # every how many steps to evaluate val loss? 0 for only at the end
    )
    save_checkpoint = False


args = Hyperparameters()

run_id = int(os.environ.get("RUN_ID", 0))
# torchrun sets these env variables
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
assert world_size == 8  # this code is designed for 8xH100
assert torch.cuda.is_available()
device = torch.device("cuda", int(os.environ["LOCAL_RANK"]))
torch.cuda.set_device(device)
dist.init_process_group(backend="nccl", device_id=device)
dist.barrier()
master_process = rank == 0  # this process will do logging, checkpointing etc.

# begin logging
if master_process:
    run_id_full = f"{run_id:03d}_{uuid.uuid4()}"
    path = f"logs"
    os.makedirs(path, exist_ok=True)
    logfile = f"{path}/{run_id_full}.txt"
    print(logfile)


def print0(s, console=False):
    if master_process:
        with open(logfile, "a") as f:
            if console:
                print(s)
            print(s, file=f)


# begin by printing this file (the Python code)
print0(code)
print0("=" * 100)
# log information about the hardware/software environment this is running on
print0(f"Running Python {sys.version}")
print0(
    f"Running PyTorch {torch.version.__version__} compiled for CUDA {torch.version.cuda}"
)


def nvidia_smi():
    import subprocess  # avoid top level import

    try:
        return subprocess.run(
            ["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
        ).stdout
    except FileNotFoundError:
        print0("nvidia-smi not found. Using library functions instead.", console=True)
    import pynvml

    pynvml.nvmlInit()
    device_count = pynvml.nvmlDeviceGetCount()
    print0(
        f"{'GPU':<3} {'Name':<25} {'Temp':<5} {'Power':<10} {'Mem Used':<15} {'Mem Total':<15} {'Util':<6}",
        console=True,
    )
    for i in range(device_count):
        handle = pynvml.nvmlDeviceGetHandleByIndex(i)
        name = pynvml.nvmlDeviceGetName(handle)
        temp = pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU)
        power = pynvml.nvmlDeviceGetPowerUsage(handle) // 1000  # mW to W
        mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
        mem_used = mem_info.used // (1024 * 1024)
        mem_total = mem_info.total // (1024 * 1024)
        util = pynvml.nvmlDeviceGetUtilizationRates(handle).gpu
        print0(
            f"{i:<3} {name:<25} {temp:<5} {power:<10} {mem_used:<15} {mem_total:<15} {util:<6}%",
            console=True,
        )
    pynvml.nvmlShutdown()


print0(nvidia_smi())
print0("=" * 100)

########################################
#    Construct model and optimizer     #
########################################

model: nn.Module = GPT(
    vocab_size=args.vocab_size,
    num_layers=16,
    num_heads=8,
    model_dim=1024,
    max_seq_len=max(args.train_seq_len, args.val_seq_len),
).cuda()
for m in model.modules():
    if isinstance(m, nn.Embedding):
        m.bfloat16()
for param in model.parameters():
    dist.broadcast(param.detach(), 0)

# collect the parameters to optimize
hidden_matrix_params = sorted(
    (p for p in model.blocks.parameters() if p.ndim >= 2),
    key=lambda x: x.size(),
    reverse=True,
)
embed_params = [
    *model.embed1.parameters(),
    *model.embed2.parameters(),
    *model.value_embeds.parameters(),
]
scalar_params = [model.scalars]
head_params: list[nn.Parameter] = [model.lm_head_w]
# sanity check
params_collections = [hidden_matrix_params, embed_params, scalar_params, head_params]
optimized_parameters_set = {p for params in params_collections for p in params}
assert optimized_parameters_set == {*model.parameters()}
assert len(optimized_parameters_set) == sum(len(lst) for lst in params_collections)

# init the optimizer(s)
adam_param_groups = [
    dict(params=head_params, lr=1 / 320),
    dict(params=embed_params, lr=0.3),
    dict(params=scalar_params, lr=0.015),
]
# small adam epsilon by @YouJiacheng. this is an alternate method of fixing the world_size dependence
# discovered by @fernbear.bsky.social https://x.com/hi_tysam/status/1879692937589875094
inner_optimizers = [
    torch.optim.AdamW(
        adam_param_groups, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0, fused=True
    )
]
inner_hidden_optim = Muon(
    hidden_matrix_params, lr=0.03, momentum=0.95, update_smoothing=0.2, rank=rank, world_size=world_size
)
inner_optimizers += [inner_hidden_optim]
outer_optim = Snoo(model, lr=0.68, momentum=0.37, k=28)
all_optimizers: list[torch.optim.Optimizer] = [outer_optim] + inner_optimizers


def opt_params(opt: torch.optim.Optimizer) -> list[nn.Parameter]:
    return [p for group in opt.param_groups for p in group["params"]]


opt2params = {opt: opt_params(opt) for opt in inner_optimizers}
for opt in inner_optimizers:
    for group in opt.param_groups:
        group["initial_lr"] = group["lr"]


# learning rate schedule: stable then decay
def get_lr(step: int):
    x = step / args.num_iterations  # progress in training
    assert 0 <= x < 1
    if x < 1 - args.cooldown_frac:
        return 1.0
    else:
        return (1 - x) / args.cooldown_frac * (1 - args.final_lr_scale) + args.final_lr_scale

# attention window size schedule: linearly increase
@lru_cache(1)
def get_window_size_blocks_helper(window_size: int):
    return torch.tensor(window_size // 128, dtype=torch.int32, pin_memory=True).cuda(
        non_blocking=True
    )


def get_window_size_blocks(step: int):
    x = step / args.num_iterations  # progress in training
    assert 0 <= x <= 1
    # Linearly increase the block-wise sliding window size over training 128 -> 1792
    # increase by @fernbear.bsky.social; block-wise by @YouJiacheng
    factor = 4 * x**3 - 6 * x**2 + 3 * x  # cubic schedule by @jadenj3o
    window_size = next_multiple_of_n(3456 * factor, n=128)
    return get_window_size_blocks_helper(window_size)


model: nn.Module = torch.compile(model, dynamic=False)

########################################
#            Warmup kernels            #
########################################

# Warmup the training kernels, then re-initialize the state so we aren't cheating
warmup_steps = 10
initial_state = copy.deepcopy(
    dict(
        model=model.state_dict(),
        optimizers=[opt.state_dict() for opt in all_optimizers],
    )
)
for _ in range(warmup_steps):
    inputs = targets = torch.randint(
        0, args.vocab_size, size=(args.train_seq_len,), device="cuda"
    )
    model(inputs.to(torch.int32), targets, get_window_size_blocks(0)).backward()
    for param in model.parameters():
        dist.all_reduce(param.grad, op=dist.ReduceOp.AVG)
    for opt in inner_optimizers:
        opt.step()
    outer_optim.step()
    model.zero_grad(set_to_none=True)
model.load_state_dict(initial_state["model"])
for opt, opt_state in zip(all_optimizers, initial_state["optimizers"]):
    opt.load_state_dict(opt_state)
del initial_state

########################################
#        Training and validation       #
########################################

torch.cuda.reset_peak_memory_stats()
train_loader = distributed_data_generator(
    args.train_files, world_size * args.train_seq_len, rank, world_size
)
training_time_ms = 0
# start the clock
dist.barrier()
t0 = time.perf_counter()
# begin training
train_steps = args.num_iterations
for step in range(train_steps + 1):
    last_step = step == train_steps

    # --------------- VALIDATION SECTION -----------------
    if last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0):
        # stop the clock
        dist.barrier()
        training_time_ms += 1000 * (time.perf_counter() - t0)
        model.eval()
        val_batch_size = world_size * args.val_seq_len
        assert args.val_tokens % val_batch_size == 0
        val_steps = args.val_tokens // val_batch_size
        val_loader = distributed_data_generator(
            args.val_files, val_batch_size, rank, world_size
        )
        val_loss = 0
        with torch.no_grad():
            for _ in range(val_steps):
                inputs, targets = next(val_loader)
                val_loss += model(inputs, targets, get_window_size_blocks(step))
        val_loss /= val_steps
        del val_loader
        dist.all_reduce(val_loss, op=dist.ReduceOp.AVG)
        print0(
            f"step:{step}/{train_steps} val_loss:{val_loss:.6f} train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms/max(step, 1):.2f}ms",
            console=True,
        )
        model.train()
        # start the clock again
        dist.barrier()
        t0 = time.perf_counter()

    if last_step:
        if master_process and args.save_checkpoint:
            log = dict(
                step=step,
                code=code,
                model=model.state_dict(),
                optimizers=[opt.state_dict() for opt in all_optimizers],
            )
            os.makedirs(f"logs/{run_id_full}", exist_ok=True)
            torch.save(log, f"logs/{run_id_full}/state_step{step:06d}.pt")
        # the last step only has the validation loop, so break to avoid training
        break

    # --------------- TRAINING SECTION -----------------
    inputs, targets = next(train_loader)
    model(inputs, targets, get_window_size_blocks(step)).backward()
    opt2futures = {
        opt: [
            dist.all_reduce(p.grad, op=dist.ReduceOp.AVG, async_op=True).get_future()
            for p in params
        ]
        for opt, params in opt2params.items()
    }
    # set optimization hyperparameters
    for opt in inner_optimizers:
        for group in opt.param_groups:
            group["lr"] = group["initial_lr"] * get_lr(step)
    for group in inner_hidden_optim.param_groups:
        frac = min(step / 300, 1)  # momentum warmup for muon
        group["momentum"] = (1 - frac) * 0.85 + frac * 0.95

    # step the optimizers
    for opt in inner_optimizers:
        torch.futures.collect_all(opt2futures[opt]).wait()
        opt.step()
    outer_optim.step()
    # null the gradients
    model.zero_grad(set_to_none=True)
    # logging
    approx_training_time_ms = training_time_ms + 1000 * (time.perf_counter() - t0)
    print0(
        f"step:{step+1}/{train_steps} train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms/(step + 1):.2f}ms",
        console=True,
    )

print0(
    f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
    f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB",
    console=True,
)
dist.destroy_process_group()

====================================================================================================
Running Python 3.10.12 (main, Aug 15 2025, 14:32:43) [GCC 11.4.0]
Running PyTorch 2.10.0.dev20251103+cu126 compiled for CUDA 12.6
Mon Nov  3 12:53:02 2025       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.06             Driver Version: 535.183.06   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA H100 80GB HBM3          On  | 00000000:00:0B.0 Off |                    0 |
| N/A   27C    P0             111W / 700W |   5825MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA H100 80GB HBM3          On  | 00000000:00:0C.0 Off |                    0 |
| N/A   30C    P0             112W / 700W |   1515MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   2  NVIDIA H100 80GB HBM3          On  | 00000000:00:0D.0 Off |                    0 |
| N/A   29C    P0             108W / 700W |   1515MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   3  NVIDIA H100 80GB HBM3          On  | 00000000:00:0E.0 Off |                    0 |
| N/A   28C    P0             110W / 700W |   1515MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   4  NVIDIA H100 80GB HBM3          On  | 00000000:00:0F.0 Off |                    0 |
| N/A   27C    P0             109W / 700W |   1515MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   5  NVIDIA H100 80GB HBM3          On  | 00000000:00:10.0 Off |                    0 |
| N/A   29C    P0             109W / 700W |   1515MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   6  NVIDIA H100 80GB HBM3          On  | 00000000:00:11.0 Off |                    0 |
| N/A   30C    P0             108W / 700W |   1515MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   7  NVIDIA H100 80GB HBM3          On  | 00000000:00:12.0 Off |                    0 |
| N/A   28C    P0             109W / 700W |   1515MiB / 81559MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A    313827      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
|    0   N/A  N/A    313828      C   ...tu/modded-nanogpt/.venv/bin/python3      610MiB |
|    0   N/A  N/A    313829      C   ...tu/modded-nanogpt/.venv/bin/python3      610MiB |
|    0   N/A  N/A    313830      C   ...tu/modded-nanogpt/.venv/bin/python3      610MiB |
|    0   N/A  N/A    313831      C   ...tu/modded-nanogpt/.venv/bin/python3      610MiB |
|    0   N/A  N/A    313832      C   ...tu/modded-nanogpt/.venv/bin/python3      610MiB |
|    0   N/A  N/A    313833      C   ...tu/modded-nanogpt/.venv/bin/python3      610MiB |
|    0   N/A  N/A    313834      C   ...tu/modded-nanogpt/.venv/bin/python3      610MiB |
|    1   N/A  N/A    313828      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
|    2   N/A  N/A    313829      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
|    3   N/A  N/A    313830      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
|    4   N/A  N/A    313831      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
|    5   N/A  N/A    313832      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
|    6   N/A  N/A    313833      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
|    7   N/A  N/A    313834      C   ...tu/modded-nanogpt/.venv/bin/python3     1506MiB |
+---------------------------------------------------------------------------------------+

====================================================================================================
step:0/5535 val_loss:10.825840 train_time:0ms step_avg:0.13ms
step:1/5535 train_time:90ms step_avg:89.75ms
step:2/5535 train_time:268ms step_avg:134.08ms
step:3/5535 train_time:478ms step_avg:159.45ms
step:4/5535 train_time:700ms step_avg:174.89ms
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step:6/5535 train_time:1150ms step_avg:191.62ms
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step:8/5535 train_time:1612ms step_avg:201.46ms
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step:10/5535 train_time:2061ms step_avg:206.15ms
step:11/5535 train_time:2291ms step_avg:208.28ms
step:12/5535 train_time:2522ms step_avg:210.16ms
step:13/5535 train_time:2749ms step_avg:211.49ms
step:14/5535 train_time:2976ms step_avg:212.56ms
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peak memory allocated: 56810 MiB reserved: 63776 MiB