toolkit/util/adafactor_stochastic_rounding.py

# ref https://github.com/Nerogar/OneTrainer/compare/master...stochastic_rounding
import math
import torch
from torch import Tensor


def copy_stochastic_(target: Tensor, source: Tensor):
    # create a random 16 bit integer
    result = torch.randint_like(
        source,
        dtype=torch.int32,
        low=0,
        high=(1 << 16),
    )

    # add the random number to the lower 16 bit of the mantissa
    result.add_(source.view(dtype=torch.int32))

    # mask off the lower 16 bit of the mantissa
    result.bitwise_and_(-65536)  # -65536 = FFFF0000 as a signed int32

    # copy the higher 16 bit into the target tensor
    target.copy_(result.view(dtype=torch.float32))


@torch.no_grad()
def step_adafactor(self, closure=None):
    """
    Performs a single optimization step
    Arguments:
        closure (callable, optional): A closure that reevaluates the model
            and returns the loss.
    """
    loss = None
    if closure is not None:
        loss = closure()

    for group in self.param_groups:
        for p in group["params"]:
            if p.grad is None:
                continue
            grad = p.grad
            if grad.dtype in {torch.float16, torch.bfloat16}:
                grad = grad.float()
            if grad.is_sparse:
                raise RuntimeError("Adafactor does not support sparse gradients.")

            state = self.state[p]
            grad_shape = grad.shape

            factored, use_first_moment = self._get_options(group, grad_shape)
            # State Initialization
            if len(state) == 0:
                state["step"] = 0

                if use_first_moment:
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(grad)
                if factored:
                    state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
                    state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
                else:
                    state["exp_avg_sq"] = torch.zeros_like(grad)

                state["RMS"] = 0
            else:
                if use_first_moment:
                    state["exp_avg"] = state["exp_avg"].to(grad)
                if factored:
                    state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
                    state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
                else:
                    state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)

            p_data_fp32 = p
            if p.dtype in {torch.float16, torch.bfloat16}:
                p_data_fp32 = p_data_fp32.float()

            state["step"] += 1
            state["RMS"] = self._rms(p_data_fp32)
            lr = self._get_lr(group, state)

            beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
            eps = group["eps"][0] if isinstance(group["eps"], list) else group["eps"]
            update = (grad ** 2) + eps
            if factored:
                exp_avg_sq_row = state["exp_avg_sq_row"]
                exp_avg_sq_col = state["exp_avg_sq_col"]

                exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t))
                exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t))

                # Approximation of exponential moving average of square of gradient
                update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
                update.mul_(grad)
            else:
                exp_avg_sq = state["exp_avg_sq"]

                exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t))
                update = exp_avg_sq.rsqrt().mul_(grad)

            update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
            update.mul_(lr)

            if use_first_moment:
                exp_avg = state["exp_avg"]
                exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"]))
                update = exp_avg

            if group["weight_decay"] != 0:
                p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr))

            p_data_fp32.add_(-update)

            if p.dtype == torch.bfloat16:
                copy_stochastic_(p, p_data_fp32)
            elif p.dtype == torch.float16:
                p.copy_(p_data_fp32)

    return loss