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 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 # turn this off for a faster compile time (but slightly slower run) # ----------------------------------------------------------------------------- # Custom operators : FP8 matmul for lm_head by @YouJiacheng @torch.library.custom_op("nanogpt::mm", mutates_args=()) def mm_op(x: Tensor, w: Tensor, x_s: float, w_s: float, grad_s: float) -> tuple[Tensor, Tensor, Tensor]: @torch.compile def impl(x: Tensor, w: Tensor): assert x.is_contiguous() and w.is_contiguous() x_f8 = x.mul(x_s).to(torch.float8_e4m3fn) w_f8 = w.mul(w_s).to(torch.float8_e4m3fn) out = torch._scaled_mm( x_f8, w_f8.t(), out_dtype=torch.bfloat16, scale_a=x.new_tensor(1 / x_s, dtype=torch.float32), scale_b=x.new_tensor(1 / w_s, dtype=torch.float32), use_fast_accum=True, ) return out, x_f8, w_f8 return impl(x, w) @mm_op.register_fake def _(x: Tensor, w: Tensor, *_): assert x.ndim == w.ndim == 2 assert x.shape[1] == w.shape[1] assert x.device == w.device assert x.is_contiguous() and w.is_contiguous() return x @ w.t(), x.to(torch.float8_e4m3fn), w.to(torch.float8_e4m3fn) @torch.library.custom_op("nanogpt::mm_backward", mutates_args=()) def mm_backward_op(g: Tensor, x_f8: Tensor, w_f8: Tensor, x_s: float, w_s: float, grad_s: float) -> tuple[Tensor, Tensor]: @torch.compile def impl(grad: Tensor, x_f8: Tensor, w_f8: Tensor): assert grad.is_contiguous() x_inv_s = grad.new_tensor(1 / x_s, dtype=torch.float32) w_inv_s = grad.new_tensor(1 / w_s, dtype=torch.float32) grad_inv_s = grad.new_tensor(1 / grad_s, dtype=torch.float32) grad_f8 = grad.mul(grad_s).to(torch.float8_e5m2) grad_x = torch._scaled_mm( grad_f8, w_f8.t().contiguous().t(), out_dtype=torch.bfloat16, scale_a=grad_inv_s, scale_b=w_inv_s, use_fast_accum=False, ) # faster than grad_f8_t @ x_f8, for (d_out, d_in) == (50304, 768) grad_w = torch._scaled_mm( x_f8.t().contiguous(), grad_f8.t().contiguous().t(), out_dtype=torch.float32, scale_a=x_inv_s, scale_b=grad_inv_s, use_fast_accum=False, ).t() return grad_x, grad_w return impl(g, x_f8, w_f8) @mm_backward_op.register_fake def _(g: Tensor, x_f8: Tensor, w_f8: Tensor, *_): return x_f8.to(torch.bfloat16), w_f8.to(torch.float32) def backward(ctx, grad_out: Tensor, *_): x_f8, w_f8 = ctx.saved_tensors x_s, w_s, grad_s = ctx.scales grad_x, grad_w = torch.ops.nanogpt.mm_backward( grad_out, x_f8, w_f8, x_s, w_s, grad_s ) return grad_x, grad_w, None, None, None def setup_context(ctx: torch.autograd.function.FunctionCtx, inputs, output): *_, x_s, w_s, grad_s = inputs _, x_f8, w_f8 = output ctx.save_for_backward(x_f8, w_f8) ctx.scales = x_s, w_s, grad_s ctx.set_materialize_grads(False) mm_op.register_autograd(backward, setup_context=setup_context) def lm_head_fp8(x: Tensor, w: Tensor) -> Tensor: _x = x.flatten(0, -2) out: Tensor = torch.ops.nanogpt.mm(_x, w, x_s=2.0, w_s=32.0, grad_s=2.0**29)[0] return out.reshape(*x.shape[:-1], -1) # ----------------------------------------------------------------------------- # Muon optimizer @torch.compile def zeropower_via_newtonschulz5(G: Tensor, steps: int) -> 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}' ~ Uniform(0.5, 1.5), 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 a, b, c = (3.4445, -4.7750, 2.0315) 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 _ in range(steps): 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 class Muon(torch.optim.Optimizer): """ Muon - MomentUm Orthogonalized by Newton-schulz 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. Some warnings: - This optimizer assumes that all parameters passed in are 2D. - It 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). - To use it with 4D convolutional filters, it works well to just flatten their last 3 dimensions. - We believe it is unlikely to work well for training with small batch size. - We believe it may not work well for finetuning pretrained models, but we haven"t tested this. - We have not yet tried this optimizer for training scenarios larger than NanoGPT (124M). Arguments: lr: The learning rate used by the internal SGD. momentum: The momentum used by the internal SGD. nesterov: Whether to use Nesterov-style momentum in the internal SGD. (recommended) ns_steps: The number of Newton-Schulz iteration steps to use. """ def __init__(self, params, lr=0.02, momentum=0.95, nesterov=True, ns_steps=5, rank=0, world_size=1): self.rank = rank self.world_size = world_size defaults = dict(lr=lr, momentum=momentum, nesterov=nesterov, ns_steps=ns_steps) params: list[Tensor] = [*params] assert all(isinstance(p, Tensor) for p in params) sizes = {p.numel() for p in params} def create_update_buffer(size: int): b = torch.empty(self.world_size, size, dtype=torch.bfloat16, device="cuda") return dict(update_buffer=b, update_buffer_views=[b[i] for i in range(self.world_size)]) param_groups = [ dict(params=[p for p in params if p.numel() == size], **create_update_buffer(size)) for size in sizes] super().__init__(param_groups, defaults) @torch.no_grad() def step(self): for group in self.param_groups: lr = group["lr"] momentum = group["momentum"] nesterov = group["nesterov"] ns_steps = group["ns_steps"] update_buffer = group["update_buffer"] update_buffer_views: list[Tensor] = group["update_buffer_views"] # generate weight updates in distributed fashion params: list[Tensor] = group["params"] handle = None params_world = None def update_prev(): # optimized Muon implementation contributed by @YouJiacheng if params_world is None: return assert handle is not None handle.wait() for p_world, g_world in zip(params_world, update_buffer_views): p_world.add_( g_world.view_as(p_world), alpha=-lr * max(1, p_world.size(-2) / p_world.size(-1)) ** 0.5, ) for base_i in range(len(params))[::self.world_size]: if base_i + self.rank < len(params): p = params[base_i + self.rank] g = p.grad assert g is not None state = self.state[p] if "momentum_buffer" not in state: state["momentum_buffer"] = torch.zeros_like(g) buf: Tensor = state["momentum_buffer"] buf.lerp_(g, 1 - momentum) g = g.lerp_(buf, momentum) if nesterov else buf g = zeropower_via_newtonschulz5(g, steps=ns_steps).flatten() else: g = update_buffer_views[self.rank] update_prev() # async all_gather instead of sync all_reduce by @YouJiacheng handle = dist.all_gather_into_tensor(update_buffer, g, async_op=True) params_world = params[base_i : base_i + self.world_size] update_prev() # ----------------------------------------------------------------------------- # PyTorch nn.Module definitions for the model def norm(x): return F.rms_norm(x, (x.size(-1),)) class CastedLinear(nn.Linear): def __init__(self, in_features: int, out_features: int): super().__init__(in_features, out_features, bias=False) def reset_parameters(self) -> None: std = 0.5 * (self.in_features ** -0.5) # 0.5 is a bit better than the default 1/sqrt(3) bound = (3 ** 0.5) * std with torch.no_grad(): self.weight.uniform_(-bound, bound) def forward(self, x): return F.linear(x, self.weight.type_as(x)) class Rotary(nn.Module): def __init__(self, dim: int, max_seq_len=65536): 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, layer_idx: int): super().__init__() assert dim % num_heads == 0 self.num_heads = num_heads std = 0.5 * (dim ** -0.5) bound = (3 ** 0.5) * std # improved init scale by @YouJiacheng # merged QKV weights: suggested by many, implemented by @fernbear.bsky.social, and further improved by @YouJiacheng # https://x.com/hi_tysam/status/1879699187107033311 self.qkv_w = nn.Parameter(torch.empty(3, dim, dim).uniform_(-bound, bound)) self.lambdas = nn.Parameter(torch.tensor([0.5, 0.5])) self.rotary = Rotary(dim // num_heads) # dim // num_heads = head_dim self.c_proj = CastedLinear(dim, dim) self.c_proj.weight.detach().zero_() # zero init suggested by @Grad62304977 # 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): 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.qkv_w.flatten(end_dim=1).type_as(x)).view(B, T, 3*self.num_heads, -1).chunk(3, dim=-2) if ve is not None: v = self.lambdas[0] * v + self.lambdas[1] * ve.view_as(v) # @KoszarskyB & @Grad62304977 else: # skip mid-layers token value embeddings by @YouJiacheng v = self.lambdas[0] * v q, k = norm(q), norm(k) # QK norm @Grad62304977 q, k = self.rotary(q), self.rotary(k) y = flex_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), block_mask=block_mask, scale=self.attn_scale) y = y.transpose(1, 2).contiguous().view_as(x) # re-assemble all head outputs side by side y = self.c_proj(y) return y class MLP(nn.Module): def __init__(self, dim): super().__init__() self.c_fc = CastedLinear(dim, 4 * dim) self.c_proj = CastedLinear(4 * dim, dim) self.c_proj.weight.detach().zero_() # zero init suggested by @Grad62304977 def forward(self, x): x = self.c_fc(x) x = F.relu(x).square() # https://arxiv.org/abs/2109.08668v2; ~1-2% better than GELU; suggested by @SKYLINEZ007 and @Grad62304977 x = self.c_proj(x) return x class Block(nn.Module): def __init__(self, model_dim: int, num_heads: int, layer_idx: int): super().__init__() # skip attention of blocks.7 (the 8th layer) by @YouJiacheng self.attn = CausalSelfAttention(model_dim, num_heads, layer_idx) if layer_idx != 7 else None self.mlp = MLP(model_dim) self.lambdas = nn.Parameter(torch.tensor([1., 0.])) def forward(self, x, ve, x0, block_mask): x = self.lambdas[0] * x + self.lambdas[1] * x0 if self.attn is not None: x = x + self.attn(norm(x), ve, block_mask) x = x + self.mlp(norm(x)) return x class ValueEmbedding(nn.Module): def __init__(self, num_embeddings: int, embedding_dim: int): super().__init__() self.embed = nn.ModuleList([nn.Embedding(num_embeddings, embedding_dim) for _ in range(3)]) def forward(self, input_seq) -> list[Tensor | None]: ve = [emb(input_seq) for emb in self.embed] # 012 ... 012 structure on token value embeddings by @YouJiacheng, improved on @leloykun's U-net structure ve = [ve[0], ve[1], ve[2], None, None, None, None, None, None, None, None, None, None, ve[0], ve[1], ve[2]] return ve # ----------------------------------------------------------------------------- # 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): 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 self.value_embeds = ValueEmbedding(vocab_size, model_dim) self.blocks = nn.ModuleList([Block(model_dim, num_heads, layer_idx) for layer_idx in range(num_layers)]) # U-net design by @brendanh0gan self.num_encoder_layers = num_layers // 2 # Half of the layers for encoder self.num_decoder_layers = num_layers - self.num_encoder_layers # Remaining for decoder # Add learnable skip connection weights for decoder layers self.skip_weights = nn.Parameter(torch.ones(self.num_decoder_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 = CastedLinear(model_dim, next_multiple_of_n(vocab_size, n=128)) self.lm_head.weight.detach().zero_() # @Grad62304977 def forward(self, input_seq: Tensor, target_seq: Tensor, sliding_window_num_blocks: Tensor): BLOCK_SIZE = 128 assert input_seq.ndim == 1 assert len(input_seq) % BLOCK_SIZE == 0 NUM_BLOCKS = len(input_seq) // BLOCK_SIZE docs = (input_seq == 50256).cumsum(0) docs_low = docs.view(-1, BLOCK_SIZE)[:, 0].contiguous() docs_high = docs.view(-1, BLOCK_SIZE)[:, -1].contiguous() 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_mask: Tensor): num_blocks = dense_mask.sum(dim=-1, dtype=torch.int32) indices = dense_mask.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 def create_doc_swc_block_masks(sliding_window_num_blocks: Tensor): kv_idx = block_idx = torch.arange(NUM_BLOCKS, dtype=torch.int32, device="cuda") q_idx = block_idx[:, None] causal_bm = q_idx >= kv_idx causal_full_bm = q_idx > kv_idx document_bm = (docs_low[:, None] <= docs_high) & (docs_low <= docs_high[:, None]) document_full_bm = (docs_low[:, None] == docs_high) & (docs_low == docs_high[:, None]) nonzero_bm = causal_bm & document_bm full_bm = causal_full_bm & document_full_bm kv_num_blocks, kv_indices = dense_to_ordered(nonzero_bm & ~full_bm) full_kv_num_blocks, full_kv_indices = dense_to_ordered(full_bm) def build_bm(sw_num_blocks: Tensor) -> BlockMask: return BlockMask.from_kv_blocks( torch.clamp_max(kv_num_blocks, torch.clamp_min(sw_num_blocks - full_kv_num_blocks, 1)), kv_indices, torch.clamp_max(full_kv_num_blocks, sw_num_blocks - 1), full_kv_indices, BLOCK_SIZE=BLOCK_SIZE, mask_mod=document_causal, ) return build_bm(sliding_window_num_blocks), build_bm(sliding_window_num_blocks // 2) # Long-short SWA block masks by @leloykun & @YouJiacheng, adapated from suggestion by @Grad62304977, following Gemma 2 paper long_bm, short_bm = create_doc_swc_block_masks(sliding_window_num_blocks) x = x0 = norm(self.embed(input_seq)[None]) # use of norm here by @Grad62304977 ve = self.value_embeds(input_seq) assert len(ve) == len(self.blocks) ve_enc, ve_dec = ve[:self.num_encoder_layers], ve[self.num_encoder_layers:] assert len(ve_enc) == self.num_encoder_layers and len(ve_dec) == self.num_decoder_layers # Store outputs for U-Net skip connections skip_connections = [] # Encoder pass - process only the first half of the blocks block_masks = [long_bm, short_bm, short_bm, short_bm, long_bm, short_bm, short_bm, short_bm] for i in range(self.num_encoder_layers): x = self.blocks[i](x, ve_enc[i], x0, block_masks[i]) skip_connections.append(x) # Decoder pass - process the remaining blocks with weighted skip connections block_masks.reverse() for i in range(self.num_decoder_layers): x = x + self.skip_weights[i] * skip_connections.pop() x = self.blocks[self.num_encoder_layers + i](x, ve_dec[i], x0, block_masks[i]) x = norm(x) logits = lm_head_fp8(x, self.lm_head.weight) if self.training else self.lm_head(x) # @Grad62304977 added tanh softcapping following Gemma 2 paper, @KoszarskyB reduced it from 30 to 15, @YouJiacheng shifted it by +15 (2*sigmoid(2*x)=tanh(x)+1) logits = 30 * torch.sigmoid(logits.float() / 7.5) loss = F.cross_entropy(logits.view(-1, logits.size(-1)), target_seq) return loss # ----------------------------------------------------------------------------- # Our own simple Distributed Data Loader def _load_data_shard(file: Path): header = torch.from_file(f"{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 # optimization batch_size = 8*64*1024 # batch size in tokens num_iterations = 7500 # number of iterations to run cooldown_frac = 0.4 # fraction of training spent cooling down the learning rate # evaluation and logging val_loss_every = 125 # every how many steps to evaluate val loss? 0 for only at the end # implementation seq_len = 64*1024 # FlexAttention sequence length save_checkpoint = False args = Hyperparameters() # torchrun sets these env variables rank = int(os.environ["RANK"]) world_size = int(os.environ["WORLD_SIZE"]) 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 logfile = None if master_process: run_id = uuid.uuid4() os.makedirs("logs", exist_ok=True) logfile = f"logs/{run_id}.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 return subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True).stdout print0(nvidia_smi()) print0("="*100) # load data train_loader = distributed_data_generator(args.train_files, args.batch_size, rank, world_size) model = GPT(vocab_size=50257, num_layers=16, num_heads=8, model_dim=1024).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 = [p for p in model.blocks.parameters() if p.ndim >= 2] embed_params = [model.embed.weight, *model.value_embeds.parameters()] scalar_params = [p for p in model.parameters() if p.ndim < 2] head_params = [model.lm_head.weight] # init the optimizer(s) adam_params = [dict(params=head_params, lr=0.003), 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.Adam(adam_params, betas=(0.8, 0.95), fused=True, eps=1e-10) optimizer2 = Muon(hidden_matrix_params, lr=0.025, momentum=0.95, rank=rank, world_size=world_size) optimizers = [optimizer1, optimizer2] # learning rate schedule: stable then decay def get_lr(it: int): t = 1 - it / args.num_iterations # time remaining in training assert 1 >= t >= 0 w = min(t / args.cooldown_frac, 1.0) # 1 -> 0 return w * 1.0 + (1 - w) * 0.1 schedulers = [torch.optim.lr_scheduler.LambdaLR(opt, get_lr) for opt in optimizers] @lru_cache(1) def sw_num_blks(window_size: int): return torch.tensor(window_size // 128, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True) model: nn.Module = torch.compile(model) training_time_ms = 0 # start the clock torch.cuda.synchronize() t0 = time.perf_counter() # begin training train_steps = args.num_iterations for step in range(train_steps + 1): last_step = (step == train_steps) # This effectively ignores timing first 10 steps, which are slower for weird reasons. # Alternately, and slightly more correctly in terms of benchmarking, we could do 10 # steps with dummy data first, and then re-initialize the model and reset the loader. if step == 10: training_time_ms = 0 t0 = time.perf_counter() timed_steps = float("nan") if step <= 11 else (step - 10) + 1 # <= 11 to avoid bug in val # Linearly increase the block-wise sliding window size over training 128 -> 1792: # increase by @fernbear.bsky.social; block-wise by @YouJiacheng window_size = next_multiple_of_n(1728 * step / train_steps, n=128) # --------------- VALIDATION SECTION ----------------- if last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0): # stop the clock torch.cuda.synchronize() training_time_ms += 1000 * (time.perf_counter() - t0) model.eval() val_bs = world_size * args.seq_len assert args.val_tokens % val_bs == 0 val_steps = args.val_tokens // val_bs val_loader = distributed_data_generator(args.val_files, val_bs, rank, world_size) val_loss = 0 with torch.no_grad(): for _ in range(val_steps): x, y = next(val_loader) val_loss += model(x, y, sw_num_blks(window_size)) 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:.4f} train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms/(timed_steps-1):.2f}ms", console=True) model.train() # start the clock again torch.cuda.synchronize() 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}", exist_ok=True) torch.save(log, f"logs/{run_id}/state_step{step:06d}.pt") # the last step only has the validation loop, so break to avoid training break # --------------- TRAINING SECTION BEGIN ----------------- inputs, targets = next(train_loader) for input_seq, target_seq in zip(inputs.split(args.seq_len), targets.split(args.seq_len)): model(input_seq, target_seq, sw_num_blks(window_size)).backward() for param in model.parameters(): dist.all_reduce(param.grad, op=dist.ReduceOp.AVG) # momentum warmup for Muon frac = min(step / 300, 1) for group in optimizer2.param_groups: group["momentum"] = (1 - frac) * 0.85 + frac * 0.95 # step the optimizers and schedulers for opt, sched in zip(optimizers, schedulers): opt.step() sched.step() # null the gradients model.zero_grad(set_to_none=True) # logging approx_time = training_time_ms + 1000 * (time.perf_counter() - t0) print0(f"step:{step+1}/{train_steps} train_time:{approx_time:.0f}ms step_avg:{approx_time/timed_steps:.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" ) dist.destroy_process_group() ==================================================================================================== Running Python 3.12.7 (main, Jan 19 2025, 05:38:41) [GCC 13.2.0] Running PyTorch 2.7.0.dev20250110+cu126 compiled for CUDA 12.6 Sun Jan 19 05:45:49 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 550.127.05 Driver Version: 550.127.05 CUDA Version: 12.6 | |-----------------------------------------+------------------------+----------------------+ | 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 | 00000000:61:00.0 Off | 0 | | N/A 25C P0 117W / 700W | 7746MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 1 NVIDIA H100 80GB HBM3 On | 00000000:62:00.0 Off | 0 | | N/A 29C P0 120W / 700W | 3456MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 2 NVIDIA H100 80GB HBM3 On | 00000000:63:00.0 Off | 0 | | N/A 30C P0 115W / 700W | 3456MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 3 NVIDIA H100 80GB HBM3 On | 00000000:64:00.0 Off | 0 | | N/A 28C P0 117W / 700W | 3456MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 4 NVIDIA H100 80GB HBM3 On | 00000000:6A:00.0 Off | 0 | | N/A 26C P0 119W / 700W | 3456MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 5 NVIDIA H100 80GB HBM3 On | 00000000:6B:00.0 Off | 0 | | N/A 29C P0 116W / 700W | 3456MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 6 NVIDIA H100 80GB HBM3 On | 00000000:6C:00.0 Off | 0 | | N/A 48C P0 130W / 700W | 3456MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ | 7 NVIDIA H100 80GB HBM3 On | 00000000:6D:00.0 Off | 0 | | N/A 27C P0 120W / 700W | 3216MiB / 81559MiB | 0% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| +-----------------------------------------------------------------------------------------+ ==================================================================================================== step:0/7500 val_loss:10.8258 train_time:0ms step_avg:nanms step:1/7500 train_time:960506ms step_avg:nanms step:2/7500 train_time:963371ms step_avg:nanms step:3/7500 train_time:963579ms step_avg:nanms step:4/7500 train_time:963798ms step_avg:nanms step:5/7500 train_time:964016ms step_avg:nanms step:6/7500 train_time:964233ms step_avg:nanms step:7/7500 train_time:964456ms step_avg:nanms step:8/7500 train_time:964681ms step_avg:nanms step:9/7500 train_time:964902ms step_avg:nanms step:10/7500 train_time:965122ms step_avg:nanms step:11/7500 train_time:221ms step_avg:nanms step:12/7500 train_time:442ms step_avg:nanms step:13/7500 train_time:666ms step_avg:221.89ms step:14/7500 train_time:887ms step_avg:221.76ms step:15/7500 train_time:1109ms step_avg:221.79ms step:16/7500 train_time:1331ms step_avg:221.76ms step:17/7500 train_time:1554ms step_avg:221.97ms step:18/7500 train_time:1775ms step_avg:221.93ms step:19/7500 train_time:1997ms step_avg:221.84ms step:20/7500 train_time:2219ms step_avg:221.85ms step:21/7500 train_time:2438ms step_avg:221.62ms step:22/7500 train_time:2660ms step_avg:221.69ms step:23/7500 train_time:2882ms step_avg:221.70ms step:24/7500 train_time:3104ms step_avg:221.69ms step:25/7500 train_time:3326ms step_avg:221.73ms step:26/7500 train_time:3547ms step_avg:221.69ms step:27/7500 train_time:3769ms step_avg:221.70ms step:28/7500 train_time:3991ms step_avg:221.73ms step:29/7500 train_time:4212ms 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step_avg:234.51ms step:7484/7500 train_time:1752741ms step_avg:234.51ms step:7485/7500 train_time:1752982ms step_avg:234.51ms step:7486/7500 train_time:1753228ms step_avg:234.51ms step:7487/7500 train_time:1753483ms step_avg:234.52ms step:7488/7500 train_time:1753728ms step_avg:234.52ms step:7489/7500 train_time:1753976ms step_avg:234.52ms step:7490/7500 train_time:1754221ms step_avg:234.52ms step:7491/7500 train_time:1754471ms step_avg:234.52ms step:7492/7500 train_time:1754719ms step_avg:234.53ms step:7493/7500 train_time:1754964ms step_avg:234.53ms step:7494/7500 train_time:1755212ms step_avg:234.53ms step:7495/7500 train_time:1755459ms step_avg:234.53ms step:7496/7500 train_time:1755703ms step_avg:234.53ms step:7497/7500 train_time:1755946ms step_avg:234.53ms step:7498/7500 train_time:1756192ms step_avg:234.53ms step:7499/7500 train_time:1756438ms step_avg:234.54ms step:7500/7500 train_time:1756682ms step_avg:234.54ms step:7500/7500 val_loss:2.9166 train_time:1756892ms step_avg:234.57ms peak memory allocated: 56828 MiB reserved: 61840 MiB