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October 23, 2025 03:18
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GPT-2 Medium modded-nanogpt single GPU Friendly
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| import os | |
| import sys | |
| with open(sys.argv[0]) as f: | |
| code = f.read() # read the code of this file ASAP, for logging | |
| import uuid | |
| import time | |
| import copy | |
| from dataclasses import dataclass | |
| from functools import lru_cache | |
| from pathlib import Path | |
| os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" | |
| import torch | |
| torch.empty(1, device="cuda", requires_grad=True).backward() # prevents a bug on some systems | |
| from torch import Tensor, nn | |
| import torch.nn.functional as F | |
| import torch.distributed as dist | |
| # use of FlexAttention contributed by @KoszarskyB | |
| from torch.nn.attention.flex_attention import BlockMask, flex_attention | |
| torch._inductor.config.coordinate_descent_tuning = True # we have banned this flag for new records because it causes compilation to take 30min | |
| torch._dynamo.config.error_on_nested_fx_trace = False | |
| torch._dynamo.config.compiled_autograd = True | |
| # ----------------------------------------------------------------------------- | |
| # 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, grad: Tensor, momentum: 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) | |
| 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, rank=0, world_size=1): | |
| self.rank = rank | |
| self.world_size = world_size | |
| defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum) | |
| 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"]) | |
| 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) | |
| update( | |
| p.view(torch.uint16), state["mantissa"], state["momentum_buffer"], | |
| p.grad, momentum, | |
| 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, x0: Tensor, block_mask: BlockMask, lambdas: Tensor, sa_lambdas: Tensor): | |
| x = lambdas[0] * x + lambdas[1] * x0 | |
| 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.embed = 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(3)]) | |
| 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]) for _ in range(num_layers)], # block lambdas | |
| *[torch.tensor([0.5, 0.5]) for _ in range(num_layers)], # SA lambdas | |
| ])) | |
| 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]] + [None] * (len(self.blocks) - 6) + [ve[0], ve[1], ve[2]] | |
| 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 = x0 = norm(self.embed(input_seq)[None]) # use of norm here by @Grad62304977 | |
| skip_connections = [] | |
| skip_map = { | |
| 9: 6, | |
| 10: 4, | |
| 11: 2, | |
| } | |
| skip_weights = self.scalars[:len(self.blocks)] | |
| lambdas = self.scalars[1 * len(self.blocks): 3 * len(self.blocks)].view(-1, 2) | |
| sa_lambdas = self.scalars[3 * len(self.blocks): 5 * 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], x0, block_masks[i], lambdas[i], sa_lambdas[i]) | |
| skip_connections.append(x) | |
| x = norm(x) | |
| 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 = 5960 # number of iterations to run | |
| cooldown_frac = 0.7 # fraction of training spent cooling down the learning rate | |
| # 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 | |
| grad_accum_steps = 8 | |
| 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()}" | |
| os.makedirs("logs", exist_ok=True) | |
| logfile = f"logs/{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) | |
| from torch._logging._internal import trace_structured # noqa: E402 | |
| import torch._inductor.codecache # noqa: E402 | |
| import torch._inductor.graph # noqa: E402 | |
| def _patched_trace_structured(name, metadata_fn, **kwargs): | |
| if name == "inductor_output_code": | |
| print0(f"inductor_output_code: {metadata_fn().get('filename', 'Unknown')}") | |
| trace_structured(name, metadata_fn, **kwargs) | |
| torch._inductor.codecache.trace_structured = _patched_trace_structured | |
| torch._inductor.graph.trace_structured = _patched_trace_structured | |
| # 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 | |
| return subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True).stdout | |
| 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.embed.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 | |
| optimizer1 = torch.optim.AdamW(adam_param_groups, betas=(0.8, 0.95), eps=1e-10, weight_decay=0.0, fused=True) | |
| optimizer2 = Muon(hidden_matrix_params, lr=0.025, momentum=0.95, rank=rank, world_size=world_size) | |
| optimizers: list[torch.optim.Optimizer] = [optimizer1, optimizer2] | |
| 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 optimizers} | |
| for opt in 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 | |
| # 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 | |
| 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 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 optimizers: | |
| opt.step() | |
| model.zero_grad(set_to_none=True) | |
| model.load_state_dict(initial_state["model"]) | |
| for opt, opt_state in zip(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 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 ----------------- | |
| # Single GPU Cope | |
| for micro_step in range(args.grad_accum_steps): | |
| inputs, targets = next(train_loader) | |
| loss = model(inputs, targets, get_window_size_blocks(step)) | |
| # Scale loss to account for accumulation | |
| (loss / args.grad_accum_steps).backward() | |
| # Now sync gradients across GPUs (no-op for single GPU, but keeps code compatible) | |
| 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 optimizers: | |
| for group in opt.param_groups: | |
| group["lr"] = group["initial_lr"] * get_lr(step) | |
| for group in optimizer2.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 optimizers: | |
| torch.futures.collect_all(opt2futures[opt]).wait() | |
| opt.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, May 27 2025, 17:12:29) [GCC 11.4.0] | |
| Running PyTorch 2.10.0.dev20251011+cu126 compiled for CUDA 12.6 | |
| Wed Oct 22 23:48:09 2025 | |
| +-----------------------------------------------------------------------------------------+ | |
| | NVIDIA-SMI 570.148.08 Driver Version: 570.148.08 CUDA Version: 12.8 | | |
| |-----------------------------------------+------------------------+----------------------+ | |
| | 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 GH200 480GB On | 00000000:DD:00.0 Off | 0 | | |
| | N/A 37C P0 132W / 700W | 1209MiB / 97871MiB | 1% Default | | |
| | | | Disabled | | |
| +-----------------------------------------+------------------------+----------------------+ | |
| +-----------------------------------------------------------------------------------------+ | |
| | Processes: | | |
| | GPU GI CI PID Type Process name GPU Memory | | |
| | ID ID Usage | | |
| |=========================================================================================| | |
| | 0 N/A N/A 1490932 C /usr/bin/python3 1198MiB | | |
| +-----------------------------------------------------------------------------------------+ | |
| ==================================================================================================== | |
| inductor_output_code: /tmp/torchinductor_ubuntu/73/c73ikm72p2quiai3sqdxwpo7rfm3lbhchfd4n7sajma6aygikqk5.py | |
| inductor_output_code: /tmp/torchinductor_ubuntu/kt/cktggbnutxfmhxvrczehwg5d4ldsradikagngi3razufjrte5bo7.py | |
| inductor_output_code: /tmp/torchinductor_ubuntu/iy/ciyn3qb4rvyclqgyihb2ftbnv7yifqds4wo2ca5vguuvrj4j6rlz.py | |
| inductor_output_code: /tmp/torchinductor_ubuntu/xm/cxmgthvbi2txk32dp4jeoaisiq26ei2jcs7tzjmiy7fix6zg4wqy.py | |
| inductor_output_code: /tmp/torchinductor_ubuntu/bt/cbti6cezfxhtkgxbqeqceao23cusyy34ifnwych7eoexkuta3lbu.py | |
| inductor_output_code: /tmp/torchinductor_ubuntu/wp/cwpvs74zbbce2lvk6d6tzylhydspjlflrwnlgxkeinarsqlvfx6k.py | |
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| step:5960/5960 val_loss:2.920684 train_time:11411326ms step_avg:1914.65ms | |
| peak memory allocated: 52705 MiB reserved: 55354 MiB |
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