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train_05_muon.py
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import os
import sys
import wandb
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
import glob
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
# -----------------------------------------------------------------------------
# Custom operators: FP8 matmul 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.div(x_s).to(torch.float8_e4m3fn)
w_f8 = w.div(w_s).to(torch.float8_e4m3fn)
out = torch._scaled_mm(
x_f8,
w_f8.T,
out_dtype=torch.bfloat16,
scale_a=x.new_tensor(x_s, dtype=torch.float32),
scale_b=x.new_tensor(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(x_s, dtype=torch.float32)
w_inv_s = grad.new_tensor(w_s, dtype=torch.float32)
grad_inv_s = grad.new_tensor(grad_s, dtype=torch.float32)
grad_f8 = grad.div(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.T.contiguous().T.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)
# -----------------------------------------------------------------------------
# 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
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.
Some warnings:
- 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).
- To use it with 4D convolutional filters, it works well to just flatten their last 3 dimensions.
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]
param_groups = []
for size in {p.numel() for p in params}:
b = torch.empty(world_size, size, dtype=torch.bfloat16, device="cuda")
group = dict(params=[p for p in params if p.numel() == size],
update_buffer=b, update_buffer_views=[b[i] for i in range(world_size)])
param_groups.append(group)
super().__init__(param_groups, defaults)
@torch.no_grad()
def step(self):
for group in self.param_groups:
update_buffer: Tensor = group["update_buffer"]
update_buffer_views: list[Tensor] = group["update_buffer_views"]
params: list[Tensor] = group["params"]
prev_handle = None
prev_params_world = None
def update_prev(p_world_list):
for p_world, g_world in zip(p_world_list, update_buffer_views):
scale = -group["lr"] * max(1, p_world.size(-2)/p_world.size(-1))**0.5
p_world.add_(g_world.view_as(p_world), alpha=scale)
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
if g is None: continue
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 - group["momentum"])
g = g.lerp_(buf, group["momentum"]) if group["nesterov"] else buf
g = zeropower_via_newtonschulz5(g, steps=group["ns_steps"]).flatten()
else:
g = update_buffer_views[self.rank]
# Overlap current compute with previous communication
if prev_handle:
prev_handle.wait()
update_prev(prev_params_world)
# Start new communication immediately after compute
handle = dist.all_gather_into_tensor(update_buffer, g, async_op=True)
prev_handle = handle
prev_params_world = params[base_i : base_i + self.world_size]
if prev_handle:
prev_handle.wait()
update_prev(prev_params_world)
# -----------------------------------------------------------------------------
# PyTorch nn.Module definitions for the model
def norm(x: Tensor):
return F.rms_norm(x, (x.size(-1),))
class CastedLinear(nn.Linear):
def __init__(self, in_features: int, out_features: int, use_fp8=False, x_s=1.0, w_s=1.0, grad_s=1.0):
super().__init__(in_features, out_features, bias=False)
self.use_fp8 = use_fp8
self.x_s = x_s
self.w_s = w_s
self.grad_s = grad_s
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: Tensor):
if self.use_fp8 and self.training:
_x = x.flatten(0, -2)
out: Tensor = torch.ops.nanogpt.mm(_x, self.weight, x_s=self.x_s, w_s=self.w_s, grad_s=self.grad
341A
_s)[0]
return out.reshape(*x.shape[:-1], -1)
else:
return F.linear(x, self.weight.type_as(x))
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
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, hdim, dim).uniform_(-bound, bound))
self.lambdas = nn.Parameter(torch.tensor([0.5, 0.5]))
self.rotary = Rotary(head_dim, max_seq_len)
self.c_proj = CastedLinear(hdim, dim)
self.c_proj.weight.detach().zero_() # zero init suggested by @Grad62304977
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, self.head_dim).chunk(3, dim=-2)
q, k = norm(q), norm(k) # QK norm @Grad62304977
q, k = self.rotary(q), self.rotary(k)
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
# 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
y = flex_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), block_mask=block_mask, scale=0.12).transpose(1, 2)
y = y.contiguous().view(B, T, self.num_heads * self.head_dim) # re-assemble all head outputs side by side
y = self.c_proj(y)
return y
class MLP(nn.Module):
def __init__(self, dim: int):
super().__init__()
hdim = 4 * dim
self.c_fc = CastedLinear(dim, hdim)
self.c_proj = CastedLinear(hdim, dim)
self.c_proj.weight.detach().zero_() # zero init suggested by @Grad62304977
def forward(self, x: Tensor):
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, 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)
self.lambdas = nn.Parameter(torch.tensor([1., 0.]))
def forward(self, x: Tensor, ve: Tensor | None, x0: Tensor, block_mask: BlockMask):
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
# -----------------------------------------------------------------------------
# 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 = CastedLinear(model_dim, next_multiple_of_n(vocab_size, n=128),
use_fp8=True, x_s=(model_dim**0.5)/448, w_s=24/448, grad_s=1/448)
self.lm_head.weight.detach().zero_() # @Grad62304977
# Add learnable skip connection weights for decoder layers
assert num_layers % 2 == 0
self.skip_weights = nn.Parameter(torch.ones(num_layers//2))
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, 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
# U-net design by @brendanh0gan
skip_connections = []
n = len(self.skip_weights)
for i in range(len(self.blocks)):
if i >= n:
x = x + self.skip_weights[i - n] * skip_connections.pop()
x = self.blocks[i](x, ve[i], x0, block_masks[i])
if i < n:
skip_connections.append(x)
x = norm(x)
logits = self.lm_head(x).float()
# @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 / (7.5 * x.size(-1)**0.5))
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), target_seq, reduction='sum' if self.training else 'mean')
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 = [Path(file) for file in sorted(glob.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 = 48*1024 # FlexAttention sequence length
val_seq_len = 4*64*1024 # FlexAttention sequence length for validation
# optimization
num_iterations = 1770 # number of iterations to run
cooldown_frac = 0.4 # 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
args = Hyperparameters()
# 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
logfile = None
if master_process:
run_id = uuid.uuid4()
os.makedirs("logs", exist_ok=True)
logfile = f"logs/{run_id}.txt"
print(logfile)
wandb.init(project="nanogpt-base", config=args, name="05_muon")
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)
########################################
# Construct model and optimizer #
########################################
model: nn.Module = GPT(vocab_size=args.vocab_size, num_layers=12, num_heads=6, model_dim=768,
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 = [p for n, p in model.blocks.named_parameters() if p.ndim >= 2 and "embed" not in n]
embed_params = [p for n, p in model.named_parameters() if "embed" in n]
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=0.22), dict(params=embed_params, lr=0.6), dict(params=scalar_params, lr=0.04)]
# 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), eps=1e-10, fused=True)
optimizer2 = Muon(hidden_matrix_params, lr=0.05, momentum=0.95, rank=rank, world_size=world_size)
optimizers = [optimizer1, optimizer2]
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:
w = (1 - x) / args.cooldown_frac
return w * 1.0 + (1 - w) * 0.1
# 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
window_size = next_multiple_of_n(1728 * x, 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 = dict(model=copy.deepcopy(model.state_dict()),
optimizers=[copy.deepcopy(opt.state_dict()) for opt in optimizers]) # save the initial state
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 #
########################################
train_loader = distributed_data_generator(args.train_files, world_size * args.train_seq_len, rank, world_size)
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)
# --------------- 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_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:.4f} 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
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 -----------------
inputs, targets = next(train_loader)
loss = model(inputs, targets, get_window_size_blocks(step))
loss.backward()
if master_process:
wandb.log({
"loss": loss.item(),
"train_time": training_time_ms,
"step_avg": training_time_ms / max(step, 1)
})
for param in model.parameters():
dist.all_reduce(param.grad, op=dist.ReduceOp.AVG)
# 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:
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()