vall-e/vall_e/utils/ext/muon.py

# From https://github.com/MoonshotAI/Moonlight/blob/master/examples/toy_train.py
# because it combines both param types and makes life easier with DeepSpeed

import os
import math
import torch
import torch.distributed as dist

@torch.compile
def zeropower_via_newtonschulz5(G, steps):
	"""
	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 len(G.shape) == 2
	a, b, c = (3.4445, -4.7750, 2.0315)
	X = G.bfloat16()
	if G.size(0) > G.size(1):
		X = X.T
	# Ensure spectral norm is at most 1
	X = X / (X.norm() + 1e-7)
	# Perform the NS iterations
	for _ in range(steps):
		A = X @ X.T
		B = (
			b * A + c * A @ A
		)  # adapted from suggestion by @jxbz, @leloykun, and @YouJiacheng
		X = a * X + B @ X

	if G.size(0) > G.size(1):
		X = X.T
	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:
	- We believe this optimizer 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.

	Arguments:
		muon_params: The parameters to be optimized by Muon.
		lr: The learning rate. The updates will have spectral norm of `lr`. (0.02 is a good default)
		momentum: The momentum used by the internal SGD. (0.95 is a good default)
		nesterov: Whether to use Nesterov-style momentum in the internal SGD. (recommended)
		ns_steps: The number of Newton-Schulz iterations to run. (6 is probably always enough)
		adamw_params: The parameters to be optimized by AdamW. Any parameters in `muon_params` which are
		{0, 1}-D or are detected as being the embed or lm_head will be optimized by AdamW as well.
		adamw_lr: The learning rate for the internal AdamW.
		adamw_betas: The betas for the internal AdamW.
		adamw_eps: The epsilon for the internal AdamW.
		adamw_wd: The weight decay for the internal AdamW.
	"""

	def __init__(
		self,
		params=None,
		lr=1e-3,
		wd=0.1,
		momentum=0.95,
		nesterov=True,
		ns_steps=5,
		betas=(0.95, 0.95),
		eps=1e-8,
	):

		defaults = dict(
			lr=lr,
			wd=wd,
			momentum=momentum,
			nesterov=nesterov,
			ns_steps=ns_steps,
			betas=betas,
			eps=eps,
			muon=False,
		)

		super().__init__(params, defaults)

	def adjust_lr_for_muon(self, lr, param_shape):
		A, B = param_shape[:2]
		# We adjust the learning rate and weight decay based on the size of the parameter matrix
		# as describted in the paper
		adjusted_ratio = 0.2 * math.sqrt(max(A, B))
		adjusted_lr = lr * adjusted_ratio
		return adjusted_lr

	def step(self, closure=None):
		"""Perform a single optimization step.

		Args:
			closure (Callable, optional): A closure that reevaluates the model
				and returns the loss.
		"""
		loss = None
		if closure is not None:
			with torch.enable_grad():
				loss = closure()

		for group in self.param_groups:

			############################
			#		   Muon		   #
			############################
			if group["muon"]:
				# import pdb; pdb.set_trace()
				lr = group["lr"]
				wd = group["wd"]
				momentum = group["momentum"]

				# generate weight updates in distributed fashion
				for p in group["params"]:
					# sanity check
					g = p.grad
					if g is None:
						continue
					if g.ndim > 2:
						g = g.view(g.size(0), -1)
					assert g is not None

					# calc update
					state = self.state[p]
					if "momentum_buffer" not in state:
						state["momentum_buffer"] = torch.zeros_like(g)
					buf = state["momentum_buffer"]
					buf.mul_(momentum).add_(g)
					if group["nesterov"]:
						g = g.add(buf, alpha=momentum)
					else:
						g = buf
					u = zeropower_via_newtonschulz5(g, steps=group["ns_steps"])

					# scale update
					adjusted_lr = self.adjust_lr_for_muon(lr, p.shape)

					# apply weight decay
					p.data.mul_(1 - lr * wd)

					# apply update
					p.data.add_(u, alpha=-adjusted_lr)

			############################
			#	   AdamW backup	   #
			############################
			else:
				lr = group['lr']
				beta1, beta2 = group["betas"]
				eps = group["eps"]
				weight_decay = group["wd"]

				for p in group["params"]:
					g = p.grad
					if g is None:
						continue
					state = self.state[p]
					if "step" not in state:
						state["step"] = 0
						state["moment1"] = torch.zeros_like(g)
						state["moment2"] = torch.zeros_like(g)
					state["step"] += 1
					step = state["step"]
					buf1 = state["moment1"]
					buf2 = state["moment2"]
					buf1.lerp_(g, 1 - beta1)
					buf2.lerp_(g.square(), 1 - beta2)

					g = buf1 / (eps + buf2.sqrt())

					bias_correction1 = 1 - beta1**step
					bias_correction2 = 1 - beta2**step
					scale = bias_correction1 / bias_correction2**0.5
					p.data.mul_(1 - lr * weight_decay)
					p.data.add_(g, alpha=-lr / scale)

		return loss