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121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
# ref https://github.com/Nerogar/OneTrainer/compare/master...stochastic_rounding
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import math
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import torch
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from torch import Tensor
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def copy_stochastic_(target: Tensor, source: Tensor):
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# create a random 16 bit integer
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result = torch.randint_like(
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source,
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dtype=torch.int32,
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low=0,
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high=(1 << 16),
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)
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# add the random number to the lower 16 bit of the mantissa
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result.add_(source.view(dtype=torch.int32))
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# mask off the lower 16 bit of the mantissa
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result.bitwise_and_(-65536) # -65536 = FFFF0000 as a signed int32
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# copy the higher 16 bit into the target tensor
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target.copy_(result.view(dtype=torch.float32))
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@torch.no_grad()
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def step_adafactor(self, closure=None):
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"""
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Performs a single optimization step
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group["params"]:
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if p.grad is None:
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continue
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grad = p.grad
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if grad.dtype in {torch.float16, torch.bfloat16}:
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grad = grad.float()
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if grad.is_sparse:
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raise RuntimeError("Adafactor does not support sparse gradients.")
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state = self.state[p]
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grad_shape = grad.shape
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factored, use_first_moment = self._get_options(group, grad_shape)
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# State Initialization
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if len(state) == 0:
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state["step"] = 0
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if use_first_moment:
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# Exponential moving average of gradient values
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state["exp_avg"] = torch.zeros_like(grad)
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if factored:
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state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
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state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
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else:
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state["exp_avg_sq"] = torch.zeros_like(grad)
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state["RMS"] = 0
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else:
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if use_first_moment:
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state["exp_avg"] = state["exp_avg"].to(grad)
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if factored:
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state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
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state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
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else:
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state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
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p_data_fp32 = p
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if p.dtype in {torch.float16, torch.bfloat16}:
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p_data_fp32 = p_data_fp32.float()
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state["step"] += 1
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state["RMS"] = self._rms(p_data_fp32)
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lr = self._get_lr(group, state)
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beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
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eps = group["eps"][0] if isinstance(group["eps"], list) else group["eps"]
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update = (grad ** 2) + eps
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if factored:
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exp_avg_sq_row = state["exp_avg_sq_row"]
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exp_avg_sq_col = state["exp_avg_sq_col"]
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exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t))
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exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t))
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# Approximation of exponential moving average of square of gradient
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update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
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update.mul_(grad)
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else:
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exp_avg_sq = state["exp_avg_sq"]
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exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t))
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update = exp_avg_sq.rsqrt().mul_(grad)
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update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
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update.mul_(lr)
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if use_first_moment:
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exp_avg = state["exp_avg"]
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exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"]))
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update = exp_avg
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if group["weight_decay"] != 0:
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p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr))
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p_data_fp32.add_(-update)
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if p.dtype == torch.bfloat16:
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copy_stochastic_(p, p_data_fp32)
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elif p.dtype == torch.float16:
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p.copy_(p_data_fp32)
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return loss
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