Add AdaFactor optimizer from fairseq (#6722)
* AdaFactor optimizer ported from fairseq. Tested for T5 finetuning and MLM -- reduced memory consumption compared to ADAM. * update PR fixes, add basic test * bug -- incorrect params in test * bugfix -- import Adafactor into test * bugfix -- removed accidental T5 include * resetting T5 to master * bugfix -- include Adafactor in __init__ * longer loop for adafactor test * remove double error class declare * lint * black * isort * Update src/transformers/optimization.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * single docstring * Cleanup docstring Co-authored-by: Nikolai Y <nikolai.yakovenko@point72.com> Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
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Nikolai Yakovenko
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@@ -438,6 +438,7 @@ if is_torch_available():
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# Optimization
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from .optimization import (
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Adafactor,
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AdamW,
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get_constant_schedule,
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get_constant_schedule_with_warmup,
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@@ -316,3 +316,190 @@ class AdamW(Optimizer):
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p.data.add_(p.data, alpha=-group["lr"] * group["weight_decay"])
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return loss
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class Adafactor(Optimizer):
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"""
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AdaFactor pytorch implementation can be used as a drop in replacement for Adam
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original fairseq code: https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
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Paper: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` https://arxiv.org/abs/1804.04235
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Note that this optimizer internally adjusts the learning rate depending on the *scale_parameter*, *relative_step* and
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*warmup_init* options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and `relative_step=False`.
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining parameter groups
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lr (float, optional): external learning rate (default: None)
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eps (tuple[float, float]): regularization constants for square gradient
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and parameter scale respectively (default: (1e-30, 1e-3))
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clip_threshold (float, default 1.0): threshold of root mean square of final gradient update
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decay_rate (float, default: -0.8): coefficient used to compute running averages of square
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beta1 (float): coefficient used for computing running averages of gradient
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weight_decay (float, default=0): weight decay (L2 penalty)
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scale_parameter (bool, default: True): if True, learning rate is scaled by root mean square of
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relative_step (bool, default: True): if True, time-dependent learning rate is computed instead of external learning rate
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warmup_init (bool, default: False): time-dependent learning rate computation depends on whether warm-up initialization is being used
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This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.
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Recommended T5 finetuning settings:
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scheduled LR warm-up to fixed LR, disable relative updates, use clip threshold: https://arxiv.org/abs/2004.14546
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Adafactor(model.parameters(), lr=1e-3, relative_step=False, warmup_init=True)
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Alternatively, relative_step with warmup_init can be used.
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Training without LR warmup or clip threshold, is not recommended. Additional optimizer operations like gradient clipping, should not be used alongside Adafactor.
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Usage::
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# replace AdamW with Adafactor
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optimizer = Adafactor(model.parameters(), lr=1e-3, eps=(1e-30, 1e-3), clip_threshold=1.0,
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decay_rate=-0.8, beta1=None, weight_decay=0.0, relative_step=False,
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scale_parameter=False, warmup_init=False,)
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"""
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def __init__(
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self,
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params,
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lr=None,
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eps=(1e-30, 1e-3),
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clip_threshold=1.0,
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decay_rate=-0.8,
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beta1=None,
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weight_decay=0.0,
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scale_parameter=True,
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relative_step=True,
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warmup_init=False,
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):
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if lr is not None and relative_step:
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raise ValueError("Cannot combine manual lr and relative_step options")
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if warmup_init and not relative_step:
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raise ValueError("warmup_init requires relative_step=True")
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defaults = dict(
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lr=lr,
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eps=eps,
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clip_threshold=clip_threshold,
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decay_rate=decay_rate,
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beta1=beta1,
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weight_decay=weight_decay,
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scale_parameter=scale_parameter,
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relative_step=relative_step,
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warmup_init=warmup_init,
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)
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super().__init__(params, defaults)
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@staticmethod
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def _get_lr(param_group, param_state):
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rel_step_sz = param_group["lr"]
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if param_group["relative_step"]:
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min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2
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rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
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param_scale = 1.0
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if param_group["scale_parameter"]:
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param_scale = max(param_group["eps"][1], param_state["RMS"])
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return param_scale * rel_step_sz
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@staticmethod
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def _get_options(param_group, param_shape):
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factored = len(param_shape) >= 2
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use_first_moment = param_group["beta1"] is not None
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return factored, use_first_moment
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@staticmethod
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def _rms(tensor):
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return tensor.norm(2) / (tensor.numel() ** 0.5)
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@staticmethod
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def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col):
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r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_()
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c_factor = exp_avg_sq_col.rsqrt()
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return torch.mm(r_factor.unsqueeze(-1), c_factor.unsqueeze(0))
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def step(self, closure=None):
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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.data
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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.data
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if p.data.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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group["lr"] = self._get_lr(group, state)
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beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
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update = (grad ** 2) + group["eps"][0]
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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_(1.0 - beta2t, update.mean(dim=-1))
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exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2))
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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_(1.0 - beta2t, update)
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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_(group["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_(1 - group["beta1"], update)
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update = exp_avg
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if group["weight_decay"] != 0:
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p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
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p_data_fp32.add_(-update)
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if p.data.dtype in {torch.float16, torch.bfloat16}:
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p.data.copy_(p_data_fp32)
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return loss
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@@ -26,6 +26,7 @@ if is_torch_available():
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import torch
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from transformers import (
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Adafactor,
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AdamW,
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get_constant_schedule,
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get_constant_schedule_with_warmup,
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@@ -80,6 +81,31 @@ class OptimizationTest(unittest.TestCase):
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w.grad.zero_()
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self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
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def test_adafactor(self):
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w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
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target = torch.tensor([0.4, 0.2, -0.5])
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criterion = torch.nn.MSELoss()
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# No warmup, constant schedule, no gradient clipping
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optimizer = Adafactor(
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params=[w],
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lr=1e-2,
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eps=(1e-30, 1e-3),
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clip_threshold=1.0,
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decay_rate=-0.8,
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beta1=None,
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weight_decay=0.0,
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relative_step=False,
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scale_parameter=False,
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warmup_init=False,
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)
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for _ in range(1000):
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loss = criterion(w, target)
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loss.backward()
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optimizer.step()
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w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
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w.grad.zero_()
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self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
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@require_torch
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class ScheduleInitTest(unittest.TestCase):
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