Add ADOPT optimizer

timm/optim/__init__.py

@@ -1,12 +1,14 @@
 from .adabelief import AdaBelief
 from .adafactor import Adafactor
+from .adafactor_bv import AdafactorBigVision
 from .adahessian import Adahessian
 from .adamp import AdamP
 from .adamw import AdamW
 from .adan import Adan
-from .adafactor_bv import AdafactorBigVision
+from .adopt import Adopt
 from .lamb import Lamb
 from .lars import Lars
+from .lion import Lion
 from .lookahead import Lookahead
 from .madgrad import MADGRAD
 from .nadam import Nadam
@@ -14,5 +16,5 @@ from .nvnovograd import NvNovoGrad
 from .radam import RAdam
 from .rmsprop_tf import RMSpropTF
 from .sgdp import SGDP
-from .lion import Lion
+
 from .optim_factory import create_optimizer, create_optimizer_v2, optimizer_kwargs

timm/optim/adopt.py

@@ -0,0 +1,493 @@
+""" ADOPT PyTorch Optimizer
+
+ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate: https://arxiv.org/abs/2411.02853
+
+Modified for reduced dependencies on PyTorch internals from original at: https://github.com/iShohei220/adopt
+
+@inproceedings{taniguchi2024adopt,
+ author={Taniguchi, Shohei and Harada, Keno and Minegishi, Gouki and Oshima, Yuta and Jeong, Seong Cheol and Nagahara, Go and Iiyama, Tomoshi and Suzuki, Masahiro and Iwasawa, Yusuke and Matsuo, Yutaka},
+ booktitle = {Advances in Neural Information Processing Systems},
+ title = {ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate},
+ year = {2024}
+}
+
+"""
+
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+
+from torch.optim.optimizer import Optimizer
+
+__all__ = ["Adopt", "adopt"]
+
+def _view_as_real(params, *state_and_grads):
+    for i, p in enumerate(params):
+        if torch.is_complex(p):
+            params[i] = torch.view_as_real(params[i])
+            for s in state_and_grads:
+                s[i] = torch.view_as_real(s[i])
+
+
+def _get_scalar_dtype(is_fused=None):
+    if is_fused:
+        return torch.float32
+    return (
+        torch.float64 if torch.get_default_dtype() == torch.float64 else torch.float32
+    )
+
+
+def _get_value(x):
+    # item is significantly faster than a cpu tensor in eager mode
+    if not torch.jit.is_scripting() and torch.compiler.is_compiling():
+        return x
+    else:
+        return x.item() if isinstance(x, torch.Tensor) else x
+
+
+class Adopt(Optimizer):
+    def __init__(
+            self,
+            params,
+            lr: Union[float, Tensor] = 1e-3,
+            betas: Tuple[float, float] = (0.9, 0.9999),
+            eps: float = 1e-6,
+            weight_decay: float = 0.0,
+            decoupled: bool = False,
+            *,
+            foreach: Optional[bool] = None,
+            maximize: bool = False,
+            capturable: bool = False,
+            differentiable: bool = False,
+    ):
+        if isinstance(lr, Tensor):
+            if foreach and not capturable:
+                raise ValueError(
+                    "lr as a Tensor is not supported for capturable=False and foreach=True"
+                )
+            if lr.numel() != 1:
+                raise ValueError("Tensor lr must be 1-element")
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            decoupled=decoupled,
+            maximize=maximize,
+            foreach=foreach,
+            capturable=capturable,
+            differentiable=differentiable,
+        )
+        super().__init__(params, defaults)
+
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("maximize", False)
+            group.setdefault("foreach", None)
+            group.setdefault("capturable", False)
+            group.setdefault("differentiable", False)
+            for p in group["params"]:
+                p_state = self.state.get(p, [])
+                if len(p_state) != 0 and not torch.is_tensor(p_state["step"]):
+                    step_val = float(p_state["step"])
+                    p_state["step"] = (
+                        torch.tensor(
+                            step_val,
+                            dtype=_get_scalar_dtype(),
+                            device=p.device,
+                        )
+                        if group["capturable"]
+                        else torch.tensor(step_val, dtype=_get_scalar_dtype())
+                    )
+
+    def _init_group(
+            self,
+            group,
+            params_with_grad,
+            grads,
+            exp_avgs,
+            exp_avg_sqs,
+            state_steps,
+    ):
+        has_complex = False
+        for p in group["params"]:
+            if p.grad is not None:
+                has_complex |= torch.is_complex(p)
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError(
+                        "ADOPT does not support sparse gradients"
+                    )
+                grads.append(p.grad)
+
+                state = self.state[p]
+                # Lazy state initialization
+                if len(state) == 0:
+                    # note(crcrpar): [special device hosting for step]
+                    # Deliberately host `step` on CPU if both capturable and fused are off.
+                    # This is because kernel launches are costly on CUDA and XLA.
+                    state["step"] = (
+                        torch.zeros(
+                            (),
+                            dtype=_get_scalar_dtype(),
+                            device=p.device,
+                        )
+                        if group["capturable"]
+                        else torch.tensor(0.0, dtype=_get_scalar_dtype())
+                    )
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if group["differentiable"] and state["step"].requires_grad:
+                    raise RuntimeError(
+                        "`requires_grad` is not supported for `step` in differentiable mode"
+                    )
+
+                # Foreach without capturable does not support a tensor lr
+                if (
+                        group["foreach"]
+                        and torch.is_tensor(group["lr"])
+                        and not group["capturable"]
+                ):
+                    raise RuntimeError(
+                        "lr as a Tensor is not supported for capturable=False and foreach=True"
+                    )
+
+                state_steps.append(state["step"])
+        return has_complex
+
+    #@_use_grad_for_differentiable  # FIXME internal context mgr, can't use
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Perform a single optimization step.
+
+        Args:
+            closure (Callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad: List[Tensor] = []
+            grads: List[Tensor] = []
+            exp_avgs: List[Tensor] = []
+            exp_avg_sqs: List[Tensor] = []
+            state_steps: List[Tensor] = []
+            beta1, beta2 = group["betas"]
+
+            has_complex = self._init_group(
+                group,
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+            )
+
+            adopt(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+                has_complex=has_complex,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group["lr"],
+                weight_decay=group["weight_decay"],
+                decoupled=group["decoupled"],
+                eps=group["eps"],
+                maximize=group["maximize"],
+                foreach=group["foreach"],
+                capturable=group["capturable"],
+                differentiable=group["differentiable"],
+                grad_scale=getattr(self, "grad_scale", None),
+                found_inf=getattr(self, "found_inf", None),
+            )
+
+        return loss
+
+
+def _single_tensor_adopt(
+        params: List[Tensor],
+        grads: List[Tensor],
+        exp_avgs: List[Tensor],
+        exp_avg_sqs: List[Tensor],
+        state_steps: List[Tensor],
+        grad_scale: Optional[Tensor],
+        found_inf: Optional[Tensor],
+        *,
+        has_complex: bool,
+        beta1: float,
+        beta2: float,
+        lr: Union[float, Tensor],
+        weight_decay: float,
+        decoupled: bool,
+        eps: float,
+        maximize: bool,
+        capturable: bool,
+        differentiable: bool,
+):
+    assert grad_scale is None and found_inf is None
+
+    if torch.jit.is_scripting():
+        # this assert is due to JIT being dumb and not realizing that the ops below
+        # have overloads to handle both float and Tensor lrs, so we just assert it's
+        # a float since most people using JIT are using floats
+        assert isinstance(lr, float)
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step_t = state_steps[i]
+
+        # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+        if not torch._utils.is_compiling() and capturable:
+            from torch.optim.optimizer import _get_capturable_supported_devices
+            capturable_supported_devices = _get_capturable_supported_devices()
+            assert (
+                    param.device.type == step_t.device.type
+                    and param.device.type in capturable_supported_devices
+            ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+        # update step
+        step_t += 1
+
+        if weight_decay != 0:
+            if decoupled:
+                param.add_(param, alpha=-lr * weight_decay)
+            else:
+                grad = grad.add(param, alpha=weight_decay)
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            if exp_avg is not None:
+                exp_avg = torch.view_as_real(exp_avg)
+            if exp_avg_sq is not None:
+                exp_avg_sq = torch.view_as_real(exp_avg_sq)
+            param = torch.view_as_real(param)
+
+        step = step_t if capturable or differentiable else _get_value(step_t)
+        if step == 1:
+            exp_avg_sq.addcmul_(grad, grad.conj())
+            continue
+
+        denom = torch.clamp(exp_avg_sq.sqrt(), eps)
+        if step == 2:
+            exp_avg.addcdiv_(grad, denom)
+        else:
+            exp_avg.mul_(beta1).addcdiv_(grad, denom, value=1 - beta1)
+
+        param.add_(exp_avg, alpha=-lr)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
+
+
+def _multi_tensor_adopt(
+        params: List[Tensor],
+        grads: List[Tensor],
+        exp_avgs: List[Tensor],
+        exp_avg_sqs: List[Tensor],
+        state_steps: List[Tensor],
+        grad_scale: Optional[Tensor],
+        found_inf: Optional[Tensor],
+        *,
+        has_complex: bool,
+        beta1: float,
+        beta2: float,
+        lr: Union[float, Tensor],
+        weight_decay: float,
+        decoupled: bool,
+        eps: float,
+        maximize: bool,
+        capturable: bool,
+        differentiable: bool,
+):
+    if len(params) == 0:
+        return
+
+    if isinstance(lr, Tensor) and not capturable:
+        raise RuntimeError(
+            "lr as a Tensor is not supported for capturable=False and foreach=True"
+        )
+
+    # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
+    if not torch._utils.is_compiling() and capturable:
+        from torch.optim.optimizer import _get_capturable_supported_devices
+        capturable_supported_devices = _get_capturable_supported_devices(
+            supports_xla=False
+        )
+        assert all(
+            p.device.type == step.device.type
+            and p.device.type in capturable_supported_devices
+            for p, step in zip(params, state_steps)
+        ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}."
+
+    assert grad_scale is None and found_inf is None
+
+    assert not differentiable, "_foreach ops don't support autograd"
+
+    grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
+        [params, grads, exp_avgs, exp_avg_sqs, state_steps]  # type: ignore[list-item]
+    )
+    for (
+            device_params_,
+            device_grads_,
+            device_exp_avgs_,
+            device_exp_avg_sqs_,
+            device_state_steps_,
+    ), _ in grouped_tensors.values():
+        device_params = cast(List[Tensor], device_params_)
+        device_grads = cast(List[Tensor], device_grads_)
+        device_exp_avgs = cast(List[Tensor], device_exp_avgs_)
+        device_exp_avg_sqs = cast(List[Tensor], device_exp_avg_sqs_)
+        device_state_steps = cast(List[Tensor], device_state_steps_)
+
+        # Handle complex parameters
+        if has_complex:
+            _view_as_real(
+                device_params, device_grads, device_exp_avgs, device_exp_avg_sqs
+            )
+
+        if maximize:
+            device_grads = torch._foreach_neg(device_grads)  # type: ignore[assignment]
+
+        # Update steps
+        # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
+        # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
+        # wrapped it once now. The alpha is required to assure we go to the right overload.
+        if not torch._utils.is_compiling() and device_state_steps[0].is_cpu:
+            torch._foreach_add_(
+                device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
+            )
+        else:
+            torch._foreach_add_(device_state_steps, 1)
+
+        if weight_decay != 0:
+            if decoupled:
+                torch._foreach_add_(device_params, device_params, alpha=-lr * weight_decay)
+            else:
+                # Re-use the intermediate memory (device_grads) already allocated for maximize
+                if maximize:
+                    torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
+                else:
+                    device_grads = torch._foreach_add(  # type: ignore[assignment]
+                        device_grads, device_params, alpha=weight_decay
+                    )
+
+        if device_state_steps[0] == 1:
+            torch._foreach_addcmul_(device_exp_avg_sqs, device_grads, device_grads)
+            continue
+
+        exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
+        exp_avg_sq_sqrt = torch._foreach_maximum(exp_avg_sq_sqrt, eps)
+
+        if device_state_steps[0] == 2:
+            torch._foreach_addcdiv_(device_exp_avgs, device_grads, exp_avg_sq_sqrt)
+        else:
+            torch._foreach_mul_(device_exp_avgs, beta1)
+            torch._foreach_addcdiv_(
+                device_exp_avgs, device_grads, exp_avg_sq_sqrt, value=1 - beta1
+            )
+
+        torch._foreach_add_(device_params, device_exp_avgs, alpha=-lr)
+        torch._foreach_mul_(device_exp_avg_sqs, beta2)
+        torch._foreach_addcmul_(
+            device_exp_avg_sqs, device_grads, device_grads, value=1 - beta2
+        )
+
+
+#@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adopt)  # FIXME internal context mgr, can't use
+def adopt(
+        params: List[Tensor],
+        grads: List[Tensor],
+        exp_avgs: List[Tensor],
+        exp_avg_sqs: List[Tensor],
+        state_steps: List[Tensor],
+        # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+        # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+        foreach: Optional[bool] = None,
+        capturable: bool = False,
+        differentiable: bool = False,
+        grad_scale: Optional[Tensor] = None,
+        found_inf: Optional[Tensor] = None,
+        has_complex: bool = False,
+        *,
+        beta1: float,
+        beta2: float,
+        lr: Union[float, Tensor],
+        weight_decay: float,
+        decoupled: bool,
+        eps: float,
+        maximize: bool,
+):
+    r"""Functional API that performs ADOPT algorithm computation.
+
+    """
+    if foreach is None:
+        foreach = False
+
+    # this check is slow during compilation, so we skip it
+    # if it's strictly needed we can add this check back in dynamo
+    if not torch._utils.is_compiling() and not all(
+            isinstance(t, torch.Tensor) for t in state_steps
+    ):
+        raise RuntimeError(
+            "API has changed, `state_steps` argument must contain a list of singleton tensors"
+        )
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError("torch.jit.script not supported with foreach optimizers")
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_adopt
+    else:
+        func = _single_tensor_adopt
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        state_steps,
+        has_complex=has_complex,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        decoupled=decoupled,
+        eps=eps,
+        maximize=maximize,
+        capturable=capturable,
+        differentiable=differentiable,
+        grad_scale=grad_scale,
+        found_inf=found_inf,
+    )

timm/optim/optim_factory.py

@@ -17,6 +17,7 @@ from .adafactor import Adafactor
 from .adahessian import Adahessian
 from .adamp import AdamP
 from .adan import Adan
+from .adopt import Adopt
 from .lamb import Lamb
 from .lars import Lars
 from .lion import Lion
@@ -359,6 +360,10 @@ def create_optimizer_v2(
         optimizer = Lion(parameters, **opt_args)
     elif opt_lower == 'adafactorbv':
         optimizer = AdafactorBigVision(parameters, **opt_args)
+    elif opt_lower == 'adopt':
+        optimizer = Adopt(parameters, **opt_args)
+    elif opt_lower == 'adoptw':
+        optimizer = Adopt(parameters, decoupled=True, **opt_args)
 
     # second order
     elif opt_lower == 'adahessian':