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>

src/transformers/__init__.py

@@ -438,6 +438,7 @@ if is_torch_available():
 
     # Optimization
     from .optimization import (
+        Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,

src/transformers/optimization.py

@@ -316,3 +316,190 @@ class AdamW(Optimizer):
                     p.data.add_(p.data, alpha=-group["lr"] * group["weight_decay"])
 
         return loss
+
+
+class Adafactor(Optimizer):
+    """
+    AdaFactor pytorch implementation can be used as a drop in replacement for Adam
+    original fairseq code: https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
+
+    Paper: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` https://arxiv.org/abs/1804.04235
+    Note that this optimizer internally adjusts the learning rate depending on the *scale_parameter*, *relative_step* and
+    *warmup_init* options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and `relative_step=False`.
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining parameter groups
+        lr (float, optional): external learning rate (default: None)
+        eps (tuple[float, float]): regularization constants for square gradient
+            and parameter scale respectively (default: (1e-30, 1e-3))
+        clip_threshold (float, default 1.0): threshold of root mean square of final gradient update
+        decay_rate (float, default: -0.8): coefficient used to compute running averages of square
+        beta1 (float): coefficient used for computing running averages of gradient
+        weight_decay (float, default=0): weight decay (L2 penalty)
+        scale_parameter (bool, default: True): if True, learning rate is scaled by root mean square of
+        relative_step (bool, default: True): if True, time-dependent learning rate is computed instead of external learning rate
+        warmup_init (bool, default: False): time-dependent learning rate computation depends on whether warm-up initialization is being used
+
+    This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.
+
+    Recommended T5 finetuning settings:
+        scheduled LR warm-up to fixed LR, disable relative updates, use clip threshold: https://arxiv.org/abs/2004.14546
+        Adafactor(model.parameters(), lr=1e-3, relative_step=False, warmup_init=True)
+        Alternatively, relative_step with warmup_init can be used.
+        Training without LR warmup or clip threshold, is not recommended. Additional optimizer operations like gradient clipping, should not be used alongside Adafactor.
+
+    Usage::
+        # replace AdamW with Adafactor
+        optimizer = Adafactor(model.parameters(), lr=1e-3, eps=(1e-30, 1e-3), clip_threshold=1.0,
+            decay_rate=-0.8, beta1=None, weight_decay=0.0, relative_step=False,
+            scale_parameter=False, warmup_init=False,)
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=None,
+        eps=(1e-30, 1e-3),
+        clip_threshold=1.0,
+        decay_rate=-0.8,
+        beta1=None,
+        weight_decay=0.0,
+        scale_parameter=True,
+        relative_step=True,
+        warmup_init=False,
+    ):
+        if lr is not None and relative_step:
+            raise ValueError("Cannot combine manual lr and relative_step options")
+        if warmup_init and not relative_step:
+            raise ValueError("warmup_init requires relative_step=True")
+
+        defaults = dict(
+            lr=lr,
+            eps=eps,
+            clip_threshold=clip_threshold,
+            decay_rate=decay_rate,
+            beta1=beta1,
+            weight_decay=weight_decay,
+            scale_parameter=scale_parameter,
+            relative_step=relative_step,
+            warmup_init=warmup_init,
+        )
+        super().__init__(params, defaults)
+
+    @staticmethod
+    def _get_lr(param_group, param_state):
+        rel_step_sz = param_group["lr"]
+        if param_group["relative_step"]:
+            min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2
+            rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
+        param_scale = 1.0
+        if param_group["scale_parameter"]:
+            param_scale = max(param_group["eps"][1], param_state["RMS"])
+        return param_scale * rel_step_sz
+
+    @staticmethod
+    def _get_options(param_group, param_shape):
+        factored = len(param_shape) >= 2
+        use_first_moment = param_group["beta1"] is not None
+        return factored, use_first_moment
+
+    @staticmethod
+    def _rms(tensor):
+        return tensor.norm(2) / (tensor.numel() ** 0.5)
+
+    @staticmethod
+    def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col):
+        r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_()
+        c_factor = exp_avg_sq_col.rsqrt()
+        return torch.mm(r_factor.unsqueeze(-1), c_factor.unsqueeze(0))
+
+    def step(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.data
+                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.data
+                if p.data.dtype in {torch.float16, torch.bfloat16}:
+                    p_data_fp32 = p_data_fp32.float()
+
+                state["step"] += 1
+                state["RMS"] = self._rms(p_data_fp32)
+                group["lr"] = self._get_lr(group, state)
+
+                beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
+                update = (grad ** 2) + group["eps"][0]
+                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_(1.0 - beta2t, update.mean(dim=-1))
+                    exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2))
+
+                    # 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_(1.0 - beta2t, update)
+                    update = exp_avg_sq.rsqrt().mul_(grad)
+
+                update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
+                update.mul_(group["lr"])
+
+                if use_first_moment:
+                    exp_avg = state["exp_avg"]
+                    exp_avg.mul_(group["beta1"]).add_(1 - group["beta1"], update)
+                    update = exp_avg
+
+                if group["weight_decay"] != 0:
+                    p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
+
+                p_data_fp32.add_(-update)
+
+                if p.data.dtype in {torch.float16, torch.bfloat16}:
+                    p.data.copy_(p_data_fp32)
+
+        return loss

tests/test_optimization.py

@@ -26,6 +26,7 @@ if is_torch_available():
     import torch
 
     from transformers import (
+        Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
@@ -80,6 +81,31 @@ class OptimizationTest(unittest.TestCase):
             w.grad.zero_()
         self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
 
+    def test_adafactor(self):
+        w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
+        target = torch.tensor([0.4, 0.2, -0.5])
+        criterion = torch.nn.MSELoss()
+        # No warmup, constant schedule, no gradient clipping
+        optimizer = Adafactor(
+            params=[w],
+            lr=1e-2,
+            eps=(1e-30, 1e-3),
+            clip_threshold=1.0,
+            decay_rate=-0.8,
+            beta1=None,
+            weight_decay=0.0,
+            relative_step=False,
+            scale_parameter=False,
+            warmup_init=False,
+        )
+        for _ in range(1000):
+            loss = criterion(w, target)
+            loss.backward()
+            optimizer.step()
+            w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
+            w.grad.zero_()
+        self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
+
 
 @require_torch
 class ScheduleInitTest(unittest.TestCase):