torch/distributed/optim/functional_adam.py

from typing import List, Dict, Optional, Tuple
import torch
import torch.optim._functional as F

from torch import Tensor

__all__ : List[str] = []

# Define a TorchScript compatible Functional Adam Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
# we explicitly allow the distributed optimizer pass gradients to
# the `step` function. In this way, 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 _FunctionalAdam(object):
    def __init__(
        self,
        params: List[Tensor],
        lr: float = 1e-3,
        betas: Tuple[float, float] = (0.9, 0.999),
        eps: float = 1e-8,
        weight_decay: float = 0.0,
        amsgrad: bool = False,
        maximize: bool = False,
        foreach: bool = False,
        fused: bool = False,
        _allow_empty_param_list: bool = False,
    ):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        if not 0.0 <= weight_decay:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))

        self.defaults = {
            "lr": lr,
            "eps": eps,
            "beta1": betas[0],
            "beta2": betas[1],
            "weight_decay": weight_decay,
        }
        self.amsgrad = amsgrad
        self.maximize = maximize
        self.foreach = foreach
        self.fused = fused
        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}

    def step_param(self, param: Tensor, grad: Optional[Tensor]):
        """
        Similar to step, but operates on a single parameter and optionally a
        gradient tensor.
        """
        params = [param]
        params_with_grad = []
        grads = []
        exp_avgs = []
        exp_avg_sqs = []
        max_exp_avg_sqs = []
        state_steps: List[Tensor] = []
        if grad is not None:
            params_with_grad.append(param)
            grads.append(grad)
        if param not in self.state:
            self.state[param] = {}
            state = self.state[param]
            state['step'] = torch.tensor(0.0)
            state['exp_avg'] = torch.zeros_like(param, memory_format=torch.preserve_format)
            state['exp_avg_sq'] = torch.zeros_like(param, memory_format=torch.preserve_format)
            if self.amsgrad:
                state['max_exp_avg_sq'] = torch.zeros_like(param, memory_format=torch.preserve_format)

        state = self.state[param]
        exp_avgs.append(state['exp_avg'])
        exp_avg_sqs.append(state['exp_avg_sq'])

        if self.amsgrad:
            max_exp_avg_sqs.append(state['max_exp_avg_sq'])

        state_steps.append(state['step'])
        with torch.no_grad():
            F.adam(params_with_grad,
                   grads,
                   exp_avgs,
                   exp_avg_sqs,
                   max_exp_avg_sqs,
                   state_steps,
                   amsgrad=self.amsgrad,
                   maximize=self.maximize,
                   beta1=self.defaults['beta1'],
                   beta2=self.defaults['beta2'],
                   lr=self.defaults['lr'],
                   weight_decay=self.defaults['weight_decay'],
                   eps=self.defaults['eps'],
                   foreach=self.foreach,
                   fused=self.fused,
                   grad_scale=None,
                   found_inf=None)

    def step(self, gradients: List[Optional[Tensor]]):
        params = self.param_group['params']
        params_with_grad = []
        grads = []
        exp_avgs = []
        exp_avg_sqs = []
        max_exp_avg_sqs = []
        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)}"
            )

        for param, gradient in zip(self.param_group['params'], gradients):
            if gradient is not None:
                params_with_grad.append(param)
                grads.append(gradient)
                # Lazy state initialization
                if param not in self.state:
                    self.state[param] = {}
                    state = self.state[param]
                    state['step'] = torch.tensor(0.0)
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(param, memory_format=torch.preserve_format)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(param, memory_format=torch.preserve_format)
                    if self.amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(param, memory_format=torch.preserve_format)

                state = self.state[param]

                exp_avgs.append(state['exp_avg'])
                exp_avg_sqs.append(state['exp_avg_sq'])

                if self.amsgrad:
                    max_exp_avg_sqs.append(state['max_exp_avg_sq'])

                state_steps.append(state['step'])

        with torch.no_grad():
            F.adam(params_with_grad,
                   grads,
                   exp_avgs,
                   exp_avg_sqs,
                   max_exp_avg_sqs,
                   state_steps,
                   amsgrad=self.amsgrad,
                   maximize=self.maximize,
                   beta1=self.defaults['beta1'],
                   beta2=self.defaults['beta2'],
                   lr=self.defaults['lr'],
                   weight_decay=self.defaults['weight_decay'],
                   eps=self.defaults['eps'],
                   foreach=self.foreach,
                   fused=self.fused,
                   grad_scale=None,
                   found_inf=None)