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from typing import Dict, List, Optional
import torch
import torch.optim._functional as F
from torch import Tensor
__all__: List[str] = []
# Define a TorchScript compatible Functional Adagrad Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
# we explicitly let the user pass gradients to the `step` function
# this is so that we could separate the gradients and parameters
# and allow multithreaded trainer to update the parameters
# without data traces on accumulating to the same .grad.
# NOTE: This should be only used by distributed optimizer internals
# and not meant to expose to the user.
@torch.jit.script
class _FunctionalAdagrad:
def __init__(
self,
params: List[Tensor],
lr: float = 1e-2,
lr_decay: float = 0.0,
weight_decay: float = 0.0,
initial_accumulator_value: float = 0.0,
warmup_lr_multiplier: float = 1.0,
warmup_num_iters: float = 0.0,
eps: float = 1e-10,
coalesce_grad: bool = True,
foreach: bool = False,
maximize: bool = False,
_allow_empty_param_list: bool = False,
):
self.defaults = {
"lr": lr,
"lr_decay": lr_decay,
"eps": eps,
"weight_decay": weight_decay,
"initial_accumulator_value": initial_accumulator_value,
"warmup_lr_multiplier": warmup_lr_multiplier,
"warmup_num_iters": warmup_num_iters,
}
self.coalesce_grad = coalesce_grad
self.foreach = foreach
self.maximize = maximize
self.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {})
if len(params) == 0 and not _allow_empty_param_list:
raise ValueError("optimizer got an empty parameter list")
# NOTE: we only have one param_group and don't allow user to add additional
# param group as it's not a common use case.
self.param_group = {"params": params}
# TODO: no union or any types in TorchScript, make step a scalar tensor instead
# This is also needed by if we want to share_memory on the step across processes
for p in self.param_group["params"]:
self.state[p] = {
"sum": torch.full_like(p.data, initial_accumulator_value),
"step": torch.tensor(0.0),
}
def step(self, gradients: List[Optional[Tensor]]):
params = self.param_group["params"]
params_with_grad = []
grads = []
state_sums = []
state_steps: List[Tensor] = []
if len(params) != len(gradients):
raise ValueError(
"the gradients passed in does not equal to the size of the parameters!"
+ f"Params length: {len(params)}. "
+ f"Gradients length: {len(gradients)}"
)
has_sparse_grad, has_complex = False, False
for param, gradient in zip(self.param_group["params"], gradients):
if gradient is not None:
has_sparse_grad |= gradient.is_sparse
has_complex |= torch.is_complex(param)
params_with_grad.append(param)
grads.append(gradient)
state = self.state[param]
state_sums.append(state["sum"])
state_steps.append(state["step"])
with torch.no_grad():
F.adagrad(
params,
grads,
state_sums,
state_steps,
lr=self.defaults["lr"],
weight_decay=self.defaults["weight_decay"],
lr_decay=self.defaults["lr_decay"],
eps=self.defaults["eps"],
has_sparse_grad=has_sparse_grad,
foreach=self.foreach,
maximize=self.maximize,
has_complex=has_complex,
)
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