from typing import List, Dict, Optional
import torch
import torch.optim._functional as F
from torch import Tensor
# 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(object):
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,
):
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.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {})
if len(params) == 0:
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[int] = []
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)}"
)
for param, gradient in zip(self.param_group['params'], gradients):
if gradient is not None:
params_with_grad.append(param)
grads.append(gradient)
state = self.state[param]
state_sums.append(state['sum'])
# update the steps for each param group update
state['step'] += 1
# record the step after step update
state_steps.append(state['step'].item())
with torch.no_grad():
F.adagrad(params,
grads,
state_sums,
state_steps,
self.defaults['lr'],
self.defaults['weight_decay'],
self.defaults['lr_decay'],
self.defaults['eps'])