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689 lines
28 KiB
689 lines
28 KiB
5 months ago
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import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _dispatch_sqrt,
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_stack_if_compiling, _get_scalar_dtype, _capturable_doc, _differentiable_doc,
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_foreach_doc, _fused_doc, _maximize_doc, _default_to_fused_or_foreach,
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ParamsT, _view_as_real)
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from typing import List, Optional, Tuple, Union
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from torch.utils._foreach_utils import _get_fused_kernels_supported_devices
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__all__ = ["AdamW", "adamw"]
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class AdamW(Optimizer):
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def __init__(
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self,
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params: ParamsT,
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lr: Union[float, Tensor] = 1e-3,
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betas: Tuple[float, float] = (0.9, 0.999),
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eps: float = 1e-8,
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weight_decay: float = 1e-2,
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amsgrad: bool = False,
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*,
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maximize: bool = False,
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foreach: Optional[bool] = None,
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capturable: bool = False,
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differentiable: bool = False,
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fused: Optional[bool] = None,
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):
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if not 0.0 <= lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if isinstance(lr, Tensor) and foreach and not capturable:
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raise ValueError("lr as a Tensor is not supported for capturable=False and foreach=True")
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
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if not 0.0 <= weight_decay:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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defaults = dict(
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lr=lr,
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betas=betas,
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eps=eps,
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weight_decay=weight_decay,
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amsgrad=amsgrad,
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foreach=foreach,
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maximize=maximize,
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capturable=capturable,
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differentiable=differentiable,
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fused=fused,
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)
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super().__init__(params, defaults)
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if fused:
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if differentiable:
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raise RuntimeError("`fused` does not support `differentiable`")
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self._step_supports_amp_scaling = True
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# TODO(crcrpar): [low prec params & their higher prec copy]
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# Suppor AMP with FP16/BF16 model params which would need
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# higher prec copy of params to do update math in higher prec to
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# alleviate the loss of information.
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fused_supported_devices = _get_fused_kernels_supported_devices()
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if not all(
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p.device.type in fused_supported_devices and
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torch.is_floating_point(p)
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for pg in self.param_groups for p in pg['params']
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):
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raise RuntimeError("`fused=True` requires all the params to be floating point Tensors of "
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f"supported devices: {fused_supported_devices}.")
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if foreach:
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raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group.setdefault("amsgrad", False)
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group.setdefault("maximize", False)
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group.setdefault("foreach", None)
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group.setdefault("capturable", False)
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group.setdefault("differentiable", False)
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fused = group.setdefault("fused", None)
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for p in group["params"]:
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p_state = self.state.get(p, [])
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if len(p_state) != 0 and not torch.is_tensor(p_state['step']):
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step_val = float(p_state["step"])
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p_state["step"] = (torch.tensor(step_val, dtype=_get_scalar_dtype(is_fused=fused), device=p.device)
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if group['capturable'] or group['fused']
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else torch.tensor(step_val, dtype=_get_scalar_dtype()))
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def _init_group(
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self,
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group,
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params_with_grad,
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grads,
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amsgrad,
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exp_avgs,
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exp_avg_sqs,
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max_exp_avg_sqs,
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state_steps,
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):
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has_complex = False
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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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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError("AdamW does not support sparse gradients")
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grads.append(p.grad)
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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# note(crcrpar): Deliberately host `step` on CPU if both capturable and fused are off.
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# This is because kernel launches are costly on CUDA and XLA.
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state["step"] = (
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torch.zeros((), dtype=_get_scalar_dtype(is_fused=group["fused"]), device=p.device)
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if group["capturable"] or group["fused"]
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else torch.tensor(0.0, dtype=_get_scalar_dtype())
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)
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# Exponential moving average of gradient values
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state["exp_avg"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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# Exponential moving average of squared gradient values
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state["exp_avg_sq"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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if amsgrad:
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# Maintains max of all exp. moving avg. of sq. grad. values
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state["max_exp_avg_sq"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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exp_avgs.append(state["exp_avg"])
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exp_avg_sqs.append(state["exp_avg_sq"])
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if group['amsgrad']:
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max_exp_avg_sqs.append(state["max_exp_avg_sq"])
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if group['differentiable'] and state['step'].requires_grad:
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raise RuntimeError('`requires_grad` is not supported for `step` in differentiable mode')
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# Foreach without capturable does not support a tensor lr
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if group['foreach'] and isinstance(group['lr'], Tensor) and not group['capturable']:
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raise RuntimeError('lr as a Tensor is not supported for capturable=False and foreach=True')
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state_steps.append(state["step"])
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return has_complex
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@_use_grad_for_differentiable
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def step(self, closure=None):
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"""Perform a single optimization step.
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Args:
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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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self._cuda_graph_capture_health_check()
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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exp_avgs = []
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exp_avg_sqs = []
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max_exp_avg_sqs = []
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state_steps = []
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amsgrad = group["amsgrad"]
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beta1, beta2 = group["betas"]
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has_complex = self._init_group(
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group,
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params_with_grad,
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grads,
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amsgrad,
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exp_avgs,
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exp_avg_sqs,
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max_exp_avg_sqs,
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state_steps,
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)
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adamw(
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params_with_grad,
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grads,
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exp_avgs,
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exp_avg_sqs,
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max_exp_avg_sqs,
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state_steps,
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amsgrad=amsgrad,
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beta1=beta1,
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beta2=beta2,
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lr=group["lr"],
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weight_decay=group["weight_decay"],
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eps=group["eps"],
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maximize=group["maximize"],
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foreach=group["foreach"],
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capturable=group["capturable"],
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differentiable=group["differentiable"],
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fused=group["fused"],
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grad_scale=getattr(self, "grad_scale", None),
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found_inf=getattr(self, "found_inf", None),
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has_complex=has_complex,
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)
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return loss
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AdamW.__doc__ = r"""Implements AdamW algorithm.
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.. math::
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\begin{aligned}
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&\rule{110mm}{0.4pt} \\
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&\textbf{input} : \gamma \text{(lr)}, \: \beta_1, \beta_2
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\text{(betas)}, \: \theta_0 \text{(params)}, \: f(\theta) \text{(objective)},
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\: \epsilon \text{ (epsilon)} \\
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&\hspace{13mm} \lambda \text{(weight decay)}, \: \textit{amsgrad},
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\: \textit{maximize} \\
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&\textbf{initialize} : m_0 \leftarrow 0 \text{ (first moment)}, v_0 \leftarrow 0
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\text{ ( second moment)}, \: \widehat{v_0}^{max}\leftarrow 0 \\[-1.ex]
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&\rule{110mm}{0.4pt} \\
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&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
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&\hspace{5mm}\textbf{if} \: \textit{maximize}: \\
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&\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm}\textbf{else} \\
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&\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\
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&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
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&\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
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&\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
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&\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\
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&\hspace{5mm}\textbf{if} \: amsgrad \\
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&\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max},
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\widehat{v_t}) \\
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&\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
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\big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\
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&\hspace{5mm}\textbf{else} \\
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&\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
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\big(\sqrt{\widehat{v_t}} + \epsilon \big) \\
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&\rule{110mm}{0.4pt} \\[-1.ex]
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&\bf{return} \: \theta_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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\end{aligned}
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For further details regarding the algorithm we refer to `Decoupled Weight Decay Regularization`_.
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""" + fr"""
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR
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is not yet supported for all our implementations. Please use a float
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LR if you are not also specifying fused=True or capturable=True.
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay coefficient (default: 1e-2)
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amsgrad (bool, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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(default: False)
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{_maximize_doc}
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{_foreach_doc}
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{_capturable_doc}
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{_differentiable_doc}
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{_fused_doc}
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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def adamw(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: List[Tensor],
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max_exp_avg_sqs: List[Tensor],
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state_steps: List[Tensor],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
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foreach: Optional[bool] = None,
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capturable: bool = False,
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differentiable: bool = False,
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fused: Optional[bool] = None,
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grad_scale: Optional[Tensor] = None,
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found_inf: Optional[Tensor] = None,
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has_complex: bool = False,
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*,
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amsgrad: bool,
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beta1: float,
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beta2: float,
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lr: Union[float, Tensor],
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weight_decay: float,
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eps: float,
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maximize: bool,
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):
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r"""Functional API that performs AdamW algorithm computation.
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See :class:`~torch.optim.AdamW` for details.
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"""
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if not torch._utils.is_compiling() and not all(isinstance(t, torch.Tensor) for t in state_steps):
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raise RuntimeError(
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"API has changed, `state_steps` argument must contain a list of singleton tensors"
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)
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# Respect when the user inputs False/True for foreach or fused. We only want to change
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# the default when neither have been user-specified. Note that we default to foreach
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# and pass False to use_fused. This is not a mistake--we want to give the fused impl
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# bake-in time before making it the default, even if it is typically faster.
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if fused is None and foreach is None:
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_, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False)
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# Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
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if foreach and isinstance(lr, Tensor) and not capturable:
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foreach = False
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if fused is None:
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fused = False
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if foreach is None:
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foreach = False
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if foreach and torch.jit.is_scripting():
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raise RuntimeError("torch.jit.script not supported with foreach optimizers")
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if fused and torch.jit.is_scripting():
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raise RuntimeError("torch.jit.script not supported with fused optimizers")
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if fused and not torch.jit.is_scripting():
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func = _fused_adamw
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elif foreach and not torch.jit.is_scripting():
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func = _multi_tensor_adamw
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else:
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func = _single_tensor_adamw
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func(
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params,
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grads,
|
||
|
|
exp_avgs,
|
||
|
|
exp_avg_sqs,
|
||
|
|
max_exp_avg_sqs,
|
||
|
|
state_steps,
|
||
|
|
amsgrad=amsgrad,
|
||
|
|
beta1=beta1,
|
||
|
|
beta2=beta2,
|
||
|
|
lr=lr,
|
||
|
|
weight_decay=weight_decay,
|
||
|
|
eps=eps,
|
||
|
|
maximize=maximize,
|
||
|
|
capturable=capturable,
|
||
|
|
differentiable=differentiable,
|
||
|
|
grad_scale=grad_scale,
|
||
|
|
found_inf=found_inf,
|
||
|
|
has_complex=has_complex,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _single_tensor_adamw(
|
||
|
|
params: List[Tensor],
|
||
|
|
grads: List[Tensor],
|
||
|
|
exp_avgs: List[Tensor],
|
||
|
|
exp_avg_sqs: List[Tensor],
|
||
|
|
max_exp_avg_sqs: List[Tensor],
|
||
|
|
state_steps: List[Tensor],
|
||
|
|
grad_scale: Optional[Tensor],
|
||
|
|
found_inf: Optional[Tensor],
|
||
|
|
*,
|
||
|
|
amsgrad: bool,
|
||
|
|
beta1: float,
|
||
|
|
beta2: float,
|
||
|
|
lr: Union[Tensor, float],
|
||
|
|
weight_decay: float,
|
||
|
|
eps: float,
|
||
|
|
maximize: bool,
|
||
|
|
capturable: bool,
|
||
|
|
differentiable: bool,
|
||
|
|
has_complex: 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:
|
||
|
|
assert (
|
||
|
|
(param.is_cuda and step_t.is_cuda) or (param.is_xla and step_t.is_xla)
|
||
|
|
), "If capturable=True, params and state_steps must be CUDA or XLA tensors."
|
||
|
|
|
||
|
|
if torch.is_complex(param):
|
||
|
|
grad = torch.view_as_real(grad)
|
||
|
|
exp_avg = torch.view_as_real(exp_avg)
|
||
|
|
exp_avg_sq = torch.view_as_real(exp_avg_sq)
|
||
|
|
if amsgrad:
|
||
|
|
max_exp_avg_sqs[i] = torch.view_as_real(max_exp_avg_sqs[i])
|
||
|
|
param = torch.view_as_real(param)
|
||
|
|
|
||
|
|
# update step
|
||
|
|
step_t += 1
|
||
|
|
|
||
|
|
# Perform stepweight decay
|
||
|
|
param.mul_(1 - lr * weight_decay)
|
||
|
|
|
||
|
|
# Decay the first and second moment running average coefficient
|
||
|
|
exp_avg.lerp_(grad, 1 - beta1)
|
||
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
|
||
|
|
|
||
|
|
if capturable or differentiable:
|
||
|
|
step = step_t
|
||
|
|
|
||
|
|
bias_correction1 = 1 - beta1 ** step
|
||
|
|
bias_correction2 = 1 - beta2 ** step
|
||
|
|
|
||
|
|
step_size = lr / bias_correction1
|
||
|
|
step_size_neg = step_size.neg()
|
||
|
|
|
||
|
|
bias_correction2_sqrt = bias_correction2.sqrt()
|
||
|
|
|
||
|
|
if amsgrad:
|
||
|
|
# Maintains the maximum of all 2nd moment running avg. till now
|
||
|
|
if differentiable:
|
||
|
|
max_exp_avg_sq = max_exp_avg_sqs[i].clone()
|
||
|
|
else:
|
||
|
|
max_exp_avg_sq = max_exp_avg_sqs[i]
|
||
|
|
|
||
|
|
max_exp_avg_sqs[i].copy_(torch.maximum(max_exp_avg_sq, exp_avg_sq))
|
||
|
|
|
||
|
|
# Uses the max. for normalizing running avg. of gradient
|
||
|
|
# Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write
|
||
|
|
# (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor)
|
||
|
|
denom = (
|
||
|
|
max_exp_avg_sqs[i].sqrt() / (bias_correction2_sqrt * step_size_neg)
|
||
|
|
).add_(eps / step_size_neg)
|
||
|
|
else:
|
||
|
|
denom = (
|
||
|
|
exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)
|
||
|
|
).add_(eps / step_size_neg)
|
||
|
|
|
||
|
|
param.addcdiv_(exp_avg, denom)
|
||
|
|
else:
|
||
|
|
step = _get_value(step_t)
|
||
|
|
|
||
|
|
bias_correction1 = 1 - beta1 ** step
|
||
|
|
bias_correction2 = 1 - beta2 ** step
|
||
|
|
|
||
|
|
step_size = lr / bias_correction1
|
||
|
|
|
||
|
|
bias_correction2_sqrt = _dispatch_sqrt(bias_correction2)
|
||
|
|
|
||
|
|
if amsgrad:
|
||
|
|
# Maintains the maximum of all 2nd moment running avg. till now
|
||
|
|
torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])
|
||
|
|
|
||
|
|
# Use the max. for normalizing running avg. of gradient
|
||
|
|
denom = (max_exp_avg_sqs[i].sqrt() / bias_correction2_sqrt).add_(eps)
|
||
|
|
else:
|
||
|
|
denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)
|
||
|
|
|
||
|
|
param.addcdiv_(exp_avg, denom, value=-step_size)
|
||
|
|
|
||
|
|
# Lastly, switch back to complex view
|
||
|
|
if amsgrad and torch.is_complex(params[i]):
|
||
|
|
max_exp_avg_sqs[i] = torch.view_as_complex(max_exp_avg_sqs[i])
|
||
|
|
|
||
|
|
|
||
|
|
def _multi_tensor_adamw(
|
||
|
|
params: List[Tensor],
|
||
|
|
grads: List[Tensor],
|
||
|
|
exp_avgs: List[Tensor],
|
||
|
|
exp_avg_sqs: List[Tensor],
|
||
|
|
max_exp_avg_sqs: List[Tensor],
|
||
|
|
state_steps: List[Tensor],
|
||
|
|
grad_scale: Optional[Tensor],
|
||
|
|
found_inf: Optional[Tensor],
|
||
|
|
*,
|
||
|
|
amsgrad: bool,
|
||
|
|
beta1: float,
|
||
|
|
beta2: float,
|
||
|
|
lr: Union[Tensor, float],
|
||
|
|
weight_decay: float,
|
||
|
|
eps: float,
|
||
|
|
maximize: bool,
|
||
|
|
capturable: bool,
|
||
|
|
differentiable: bool,
|
||
|
|
has_complex: 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:
|
||
|
|
assert all(
|
||
|
|
p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)
|
||
|
|
), "If capturable=True, params and state_steps must be CUDA tensors."
|
||
|
|
|
||
|
|
assert not differentiable, "_foreach ops don't support autograd"
|
||
|
|
|
||
|
|
assert grad_scale is None and found_inf is None
|
||
|
|
|
||
|
|
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([
|
||
|
|
params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps])
|
||
|
|
for ((
|
||
|
|
device_params,
|
||
|
|
device_grads,
|
||
|
|
device_exp_avgs,
|
||
|
|
device_exp_avg_sqs,
|
||
|
|
device_max_exp_avg_sqs,
|
||
|
|
device_state_steps,
|
||
|
|
), _) in grouped_tensors.values():
|
||
|
|
if has_complex:
|
||
|
|
if amsgrad:
|
||
|
|
_view_as_real(device_params, device_grads, device_exp_avgs, device_exp_avg_sqs, device_max_exp_avg_sqs)
|
||
|
|
else:
|
||
|
|
_view_as_real(device_params, device_grads, device_exp_avgs, device_exp_avg_sqs)
|
||
|
|
|
||
|
|
if maximize:
|
||
|
|
device_grads = torch._foreach_neg(device_grads)
|
||
|
|
|
||
|
|
# 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 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)
|
||
|
|
|
||
|
|
# Perform stepweight decay
|
||
|
|
if weight_decay != 0:
|
||
|
|
torch._foreach_mul_(device_params, 1 - lr * weight_decay)
|
||
|
|
|
||
|
|
# Decay the first and second moment running average coefficient
|
||
|
|
torch._foreach_lerp_(device_exp_avgs, device_grads, 1 - beta1)
|
||
|
|
|
||
|
|
torch._foreach_mul_(device_exp_avg_sqs, beta2)
|
||
|
|
torch._foreach_addcmul_(device_exp_avg_sqs, device_grads, device_grads, 1 - beta2)
|
||
|
|
|
||
|
|
# Delete the local intermediate since it won't be used anymore to save on peak memory
|
||
|
|
del device_grads
|
||
|
|
|
||
|
|
if capturable:
|
||
|
|
bias_correction1 = torch._foreach_pow(beta1, device_state_steps)
|
||
|
|
bias_correction2 = torch._foreach_pow(beta2, device_state_steps)
|
||
|
|
# foreach_sub doesn't allow a scalar as the first arg
|
||
|
|
torch._foreach_sub_(bias_correction1, 1)
|
||
|
|
torch._foreach_sub_(bias_correction2, 1)
|
||
|
|
# we do not negate bias_correction1 as it'll need to be negated later anyway
|
||
|
|
torch._foreach_neg_(bias_correction2)
|
||
|
|
|
||
|
|
# foreach_div doesn't allow a scalar as the first arg
|
||
|
|
torch._foreach_div_(bias_correction1, lr)
|
||
|
|
torch._foreach_reciprocal_(bias_correction1)
|
||
|
|
|
||
|
|
torch._foreach_sqrt_(bias_correction2)
|
||
|
|
|
||
|
|
# Re-assign for clarity as we maintain minimal intermediates: we'll have
|
||
|
|
# step_size = - lr / (1 - beta1 ^ t) where t = num_steps
|
||
|
|
# bias_correction2_sqrt = sqrt(1 - beta2 ^ t)
|
||
|
|
step_size = bias_correction1
|
||
|
|
bias_correction2_sqrt = bias_correction2
|
||
|
|
|
||
|
|
if amsgrad:
|
||
|
|
# Maintains the maximum of all 2nd moment running avg. till now
|
||
|
|
torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)
|
||
|
|
|
||
|
|
# Use the max. for normalizing running avg. of gradient
|
||
|
|
exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
|
||
|
|
else:
|
||
|
|
exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
|
||
|
|
|
||
|
|
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
|
||
|
|
torch._foreach_add_(exp_avg_sq_sqrt, eps)
|
||
|
|
torch._foreach_div_(exp_avg_sq_sqrt, step_size)
|
||
|
|
|
||
|
|
# at this point, exp_avg_sq_sqrt = - (1 - beta^t) * [sqrt(exp_avg_sq / (1 - beta2^t)) + eps] / lr
|
||
|
|
torch._foreach_addcdiv_(device_params, device_exp_avgs, exp_avg_sq_sqrt)
|
||
|
|
else:
|
||
|
|
bias_correction1 = [1 - beta1 ** _get_value(step) for step in device_state_steps]
|
||
|
|
bias_correction2 = [1 - beta2 ** _get_value(step) for step in device_state_steps]
|
||
|
|
|
||
|
|
step_size = _stack_if_compiling([(lr / bc) * -1 for bc in bias_correction1])
|
||
|
|
|
||
|
|
bias_correction2_sqrt = [_dispatch_sqrt(bc) for bc in bias_correction2]
|
||
|
|
|
||
|
|
if amsgrad:
|
||
|
|
# Maintains the maximum of all 2nd moment running avg. till now
|
||
|
|
torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs)
|
||
|
|
|
||
|
|
# Use the max. for normalizing running avg. of gradient
|
||
|
|
exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs)
|
||
|
|
else:
|
||
|
|
exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs)
|
||
|
|
|
||
|
|
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
|
||
|
|
torch._foreach_add_(exp_avg_sq_sqrt, eps)
|
||
|
|
torch._foreach_addcdiv_(device_params, device_exp_avgs, exp_avg_sq_sqrt, step_size)
|
||
|
|
|
||
|
|
|
||
|
|
def _fused_adamw(
|
||
|
|
params: List[Tensor],
|
||
|
|
grads: List[Tensor],
|
||
|
|
exp_avgs: List[Tensor],
|
||
|
|
exp_avg_sqs: List[Tensor],
|
||
|
|
max_exp_avg_sqs: List[Tensor],
|
||
|
|
state_steps: List[Tensor],
|
||
|
|
grad_scale: Optional[Tensor],
|
||
|
|
found_inf: Optional[Tensor],
|
||
|
|
*,
|
||
|
|
amsgrad: bool,
|
||
|
|
beta1: float,
|
||
|
|
beta2: float,
|
||
|
|
lr: Union[float, Tensor],
|
||
|
|
weight_decay: float,
|
||
|
|
eps: float,
|
||
|
|
maximize: bool,
|
||
|
|
capturable: bool, # Needed for consistency.
|
||
|
|
differentiable: bool,
|
||
|
|
has_complex: bool,
|
||
|
|
) -> None:
|
||
|
|
if not params:
|
||
|
|
return
|
||
|
|
if differentiable:
|
||
|
|
raise RuntimeError("Adam with fused=True does not support differentiable=True")
|
||
|
|
|
||
|
|
grad_scale_dict = {grad_scale.device: grad_scale} if grad_scale is not None else None
|
||
|
|
found_inf_dict = {found_inf.device: found_inf} if found_inf is not None else None
|
||
|
|
|
||
|
|
# We only shuffle around the lr when it is a Tensor and on CUDA, otherwise, we prefer
|
||
|
|
# treating it as a scalar.
|
||
|
|
lr_dict = {lr.device: lr} if isinstance(lr, Tensor) and str(lr.device) != "cpu" else None
|
||
|
|
|
||
|
|
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
|
||
|
|
[params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps])
|
||
|
|
for (device, _), ((device_params,
|
||
|
|
device_grads,
|
||
|
|
device_exp_avgs,
|
||
|
|
device_exp_avg_sqs,
|
||
|
|
device_max_exp_avg_sqs,
|
||
|
|
device_state_steps,), _) in grouped_tensors.items():
|
||
|
|
device_grad_scale, device_found_inf = None, None
|
||
|
|
if grad_scale is not None:
|
||
|
|
if device not in grad_scale_dict:
|
||
|
|
grad_scale_dict[device] = grad_scale.to(device, non_blocking=True)
|
||
|
|
device_grad_scale = grad_scale_dict[device]
|
||
|
|
if found_inf is not None:
|
||
|
|
if found_inf not in found_inf_dict:
|
||
|
|
found_inf_dict[device] = found_inf.to(device, non_blocking=True)
|
||
|
|
device_found_inf = found_inf_dict[device]
|
||
|
|
if lr_dict is not None and device not in lr_dict:
|
||
|
|
lr_dict[device] = lr.to(device=device, non_blocking=True)
|
||
|
|
lr = lr_dict[device]
|
||
|
|
torch._foreach_add_(device_state_steps, 1)
|
||
|
|
torch._fused_adamw_(
|
||
|
|
device_params,
|
||
|
|
device_grads,
|
||
|
|
device_exp_avgs,
|
||
|
|
device_exp_avg_sqs,
|
||
|
|
device_max_exp_avg_sqs,
|
||
|
|
device_state_steps,
|
||
|
|
amsgrad=amsgrad,
|
||
|
|
lr=lr,
|
||
|
|
beta1=beta1,
|
||
|
|
beta2=beta2,
|
||
|
|
weight_decay=weight_decay,
|
||
|
|
eps=eps,
|
||
|
|
maximize=maximize,
|
||
|
|
grad_scale=device_grad_scale,
|
||
|
|
found_inf=device_found_inf,
|
||
|
|
)
|
||
|
|
if device_found_inf is not None:
|
||
|
|
torch._foreach_sub_(device_state_steps, [device_found_inf] * len(device_state_steps))
|