You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
661 lines
29 KiB
661 lines
29 KiB
from typing import List, Optional, Union, Tuple
|
|
|
|
import torch
|
|
from torch import Tensor
|
|
from .optimizer import (Optimizer, ParamsT, _use_grad_for_differentiable, _get_value,
|
|
_stack_if_compiling, _dispatch_sqrt, _default_to_fused_or_foreach,
|
|
_get_scalar_dtype, _capturable_doc, _differentiable_doc, _foreach_doc,
|
|
_fused_doc, _maximize_doc, _view_as_real)
|
|
from torch.utils._foreach_utils import _get_fused_kernels_supported_devices
|
|
|
|
__all__ = ['Adam', 'adam']
|
|
|
|
|
|
class Adam(Optimizer):
|
|
def __init__(self,
|
|
params: ParamsT,
|
|
lr: Union[float, Tensor] = 1e-3,
|
|
betas: Tuple[float, float] = (0.9, 0.999),
|
|
eps: float = 1e-8,
|
|
weight_decay: float = 0,
|
|
amsgrad: bool = False,
|
|
*,
|
|
foreach: Optional[bool] = None,
|
|
maximize: bool = False,
|
|
capturable: bool = False,
|
|
differentiable: bool = False,
|
|
fused: Optional[bool] = None):
|
|
if not 0.0 <= lr:
|
|
raise ValueError(f"Invalid learning rate: {lr}")
|
|
if isinstance(lr, Tensor) and foreach and not capturable:
|
|
raise ValueError("lr as a Tensor is not supported for capturable=False and foreach=True")
|
|
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, amsgrad=amsgrad,
|
|
maximize=maximize, foreach=foreach, capturable=capturable,
|
|
differentiable=differentiable, fused=fused)
|
|
super().__init__(params, defaults)
|
|
|
|
if fused:
|
|
if differentiable:
|
|
raise RuntimeError("`fused` does not support `differentiable`")
|
|
self._step_supports_amp_scaling = True
|
|
# TODO(crcrpar): [low prec params & their higher prec copy]
|
|
# Support AMP with FP16/BF16 model params which would need
|
|
# higher prec copy of params to do update math in higher prec to
|
|
# alleviate the loss of information.
|
|
fused_supported_devices = _get_fused_kernels_supported_devices()
|
|
if not all(
|
|
p.device.type in fused_supported_devices and
|
|
torch.is_floating_point(p) for pg in self.param_groups for p in pg['params']
|
|
):
|
|
raise RuntimeError("`fused=True` requires all the params to be floating point Tensors of "
|
|
f"supported devices: {fused_supported_devices}.")
|
|
if foreach:
|
|
raise RuntimeError("`fused` and `foreach` cannot be `True` together.")
|
|
|
|
def __setstate__(self, state):
|
|
super().__setstate__(state)
|
|
for group in self.param_groups:
|
|
group.setdefault('amsgrad', False)
|
|
group.setdefault('maximize', False)
|
|
group.setdefault('foreach', None)
|
|
group.setdefault('capturable', False)
|
|
group.setdefault('differentiable', False)
|
|
fused = group.setdefault('fused', None)
|
|
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(is_fused=fused), device=p.device)
|
|
if group['capturable'] or group['fused']
|
|
else torch.tensor(step_val, dtype=_get_scalar_dtype()))
|
|
|
|
def _init_group(
|
|
self,
|
|
group,
|
|
params_with_grad,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
max_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('Adam does not support sparse gradients, please consider SparseAdam instead')
|
|
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(is_fused=group['fused']), device=p.device)
|
|
if group['capturable'] or group['fused']
|
|
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)
|
|
if group['amsgrad']:
|
|
# Maintains max of all exp. moving avg. of sq. grad. values
|
|
state['max_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['amsgrad']:
|
|
max_exp_avg_sqs.append(state['max_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
|
|
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 = []
|
|
grads = []
|
|
exp_avgs = []
|
|
exp_avg_sqs = []
|
|
max_exp_avg_sqs = []
|
|
state_steps = []
|
|
beta1, beta2 = group['betas']
|
|
|
|
has_complex = self._init_group(
|
|
group,
|
|
params_with_grad,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
max_exp_avg_sqs,
|
|
state_steps)
|
|
|
|
adam(
|
|
params_with_grad,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
max_exp_avg_sqs,
|
|
state_steps,
|
|
amsgrad=group['amsgrad'],
|
|
has_complex=has_complex,
|
|
beta1=beta1,
|
|
beta2=beta2,
|
|
lr=group['lr'],
|
|
weight_decay=group['weight_decay'],
|
|
eps=group['eps'],
|
|
maximize=group['maximize'],
|
|
foreach=group['foreach'],
|
|
capturable=group['capturable'],
|
|
differentiable=group['differentiable'],
|
|
fused=group['fused'],
|
|
grad_scale=getattr(self, "grad_scale", None),
|
|
found_inf=getattr(self, "found_inf", None),
|
|
)
|
|
|
|
return loss
|
|
|
|
|
|
Adam.__doc__ = r"""Implements Adam algorithm.
|
|
|
|
.. math::
|
|
\begin{aligned}
|
|
&\rule{110mm}{0.4pt} \\
|
|
&\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2
|
|
\text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)} \\
|
|
&\hspace{13mm} \lambda \text{ (weight decay)}, \: \textit{amsgrad},
|
|
\:\textit{maximize} \\
|
|
&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
|
|
v_0\leftarrow 0 \text{ (second moment)},\: \widehat{v_0}^{max}\leftarrow 0\\[-1.ex]
|
|
&\rule{110mm}{0.4pt} \\
|
|
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
|
|
|
|
&\hspace{5mm}\textbf{if} \: \textit{maximize}: \\
|
|
&\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\
|
|
&\hspace{5mm}\textbf{else} \\
|
|
&\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
|
|
&\hspace{5mm}\textbf{if} \: \lambda \neq 0 \\
|
|
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
|
|
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
|
|
&\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
|
|
&\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
|
|
&\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\
|
|
&\hspace{5mm}\textbf{if} \: amsgrad \\
|
|
&\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max},
|
|
\widehat{v_t}) \\
|
|
&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/
|
|
\big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\
|
|
&\hspace{5mm}\textbf{else} \\
|
|
&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/
|
|
\big(\sqrt{\widehat{v_t}} + \epsilon \big) \\
|
|
&\rule{110mm}{0.4pt} \\[-1.ex]
|
|
&\bf{return} \: \theta_t \\[-1.ex]
|
|
&\rule{110mm}{0.4pt} \\[-1.ex]
|
|
\end{aligned}
|
|
|
|
For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_.
|
|
""" + fr"""
|
|
Args:
|
|
params (iterable): iterable of parameters to optimize or dicts defining
|
|
parameter groups
|
|
lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR
|
|
is not yet supported for all our implementations. Please use a float
|
|
LR if you are not also specifying fused=True or capturable=True.
|
|
betas (Tuple[float, float], optional): coefficients used for computing
|
|
running averages of gradient and its square (default: (0.9, 0.999))
|
|
eps (float, optional): term added to the denominator to improve
|
|
numerical stability (default: 1e-8)
|
|
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
|
|
amsgrad (bool, optional): whether to use the AMSGrad variant of this
|
|
algorithm from the paper `On the Convergence of Adam and Beyond`_
|
|
(default: False)
|
|
{_foreach_doc}
|
|
{_maximize_doc}
|
|
{_capturable_doc}
|
|
{_differentiable_doc}
|
|
{_fused_doc}
|
|
.. _Adam\: A Method for Stochastic Optimization:
|
|
https://arxiv.org/abs/1412.6980
|
|
.. _On the Convergence of Adam and Beyond:
|
|
https://openreview.net/forum?id=ryQu7f-RZ
|
|
|
|
"""
|
|
|
|
|
|
def adam(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],
|
|
# 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,
|
|
fused: Optional[bool] = None,
|
|
grad_scale: Optional[Tensor] = None,
|
|
found_inf: Optional[Tensor] = None,
|
|
has_complex: bool = False,
|
|
*,
|
|
amsgrad: bool,
|
|
beta1: float,
|
|
beta2: float,
|
|
lr: Union[float, Tensor],
|
|
weight_decay: float,
|
|
eps: float,
|
|
maximize: bool):
|
|
r"""Functional API that performs Adam algorithm computation.
|
|
|
|
See :class:`~torch.optim.Adam` for details.
|
|
"""
|
|
# Respect when the user inputs False/True for foreach or fused. We only want to change
|
|
# the default when neither have been user-specified. Note that we default to foreach
|
|
# and pass False to use_fused. This is not a mistake--we want to give the fused impl
|
|
# bake-in time before making it the default, even if it is typically faster.
|
|
if fused is None and foreach is None:
|
|
_, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False)
|
|
# Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False.
|
|
if foreach and isinstance(lr, Tensor) and not capturable:
|
|
foreach = False
|
|
if fused is None:
|
|
fused = False
|
|
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 fused and torch.jit.is_scripting():
|
|
raise RuntimeError("torch.jit.script not supported with fused optimizers")
|
|
|
|
if fused and not torch.jit.is_scripting():
|
|
func = _fused_adam
|
|
elif foreach and not torch.jit.is_scripting():
|
|
func = _multi_tensor_adam
|
|
else:
|
|
func = _single_tensor_adam
|
|
|
|
func(params,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
max_exp_avg_sqs,
|
|
state_steps,
|
|
amsgrad=amsgrad,
|
|
has_complex=has_complex,
|
|
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)
|
|
|
|
|
|
def _single_tensor_adam(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,
|
|
has_complex: bool,
|
|
beta1: float,
|
|
beta2: float,
|
|
lr: Union[float, Tensor],
|
|
weight_decay: float,
|
|
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:
|
|
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."
|
|
|
|
# update step
|
|
step_t += 1
|
|
|
|
if weight_decay != 0:
|
|
grad = grad.add(param, alpha=weight_decay)
|
|
|
|
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)
|
|
|
|
# Decay the first and second moment running average coefficient
|
|
exp_avg.lerp_(grad, 1 - beta1)
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), 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_adam(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,
|
|
has_complex: bool,
|
|
beta1: float,
|
|
beta2: float,
|
|
lr: Union[float, Tensor],
|
|
weight_decay: float,
|
|
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:
|
|
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 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, 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():
|
|
|
|
# Handle complex parameters
|
|
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)
|
|
|
|
if weight_decay != 0:
|
|
# 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(device_grads, device_params, alpha=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) # type: ignore[assignment]
|
|
|
|
# Set intermediate to the max. for normalizing running avg. of gradient when amsgrad
|
|
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_adam(
|
|
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,
|
|
has_complex: bool, # Needed for consistency.
|
|
beta1: float,
|
|
beta2: float,
|
|
lr: Union[float, Tensor],
|
|
weight_decay: float,
|
|
eps: float,
|
|
maximize: bool,
|
|
capturable: bool, # Needed for consistency.
|
|
differentiable: 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_adam_(
|
|
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))
|