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80 lines
3.2 KiB
80 lines
3.2 KiB
5 months ago
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r"""Functional interface."""
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import math
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from torch import Tensor
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from typing import List
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from .adadelta import adadelta # type: ignore[attr-defined] # noqa: F401
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from .adagrad import adagrad, _make_sparse # type: ignore[attr-defined] # noqa: F401
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from .adam import adam # type: ignore[attr-defined] # noqa: F401
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from .adamw import adamw # type: ignore[attr-defined] # noqa: F401
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from .adamax import adamax # type: ignore[attr-defined] # noqa: F401
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from .asgd import asgd # type: ignore[attr-defined] # noqa: F401
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from .nadam import nadam # type: ignore[attr-defined] # noqa: F401
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from .radam import radam # type: ignore[attr-defined] # noqa: F401
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from .rmsprop import rmsprop # type: ignore[attr-defined] # noqa: F401
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from .rprop import rprop # type: ignore[attr-defined] # noqa: F401
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from .sgd import sgd # type: ignore[attr-defined] # noqa: F401
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# TODO: use foreach API in optim._functional to do all the computation
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def sparse_adam(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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state_steps: List[int],
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*,
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eps: float,
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beta1: float,
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beta2: float,
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lr: float,
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maximize: bool):
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r"""Functional API that performs Sparse Adam algorithm computation.
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See :class:`~torch.optim.SparseAdam` for details.
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"""
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for i, param in enumerate(params):
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grad = grads[i]
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grad = grad if not maximize else -grad
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grad = grad.coalesce() # the update is non-linear so indices must be unique
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grad_indices = grad._indices()
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grad_values = grad._values()
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if grad_values.numel() == 0:
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# Skip update for empty grad
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continue
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size = grad.size()
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exp_avg = exp_avgs[i]
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exp_avg_sq = exp_avg_sqs[i]
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step = state_steps[i]
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def make_sparse(values):
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constructor = grad.new
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if grad_indices.dim() == 0 or values.dim() == 0:
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return constructor().resize_as_(grad)
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return constructor(grad_indices, values, size)
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# Decay the first and second moment running average coefficient
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# old <- b * old + (1 - b) * new
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# <==> old += (1 - b) * (new - old)
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old_exp_avg_values = exp_avg.sparse_mask(grad)._values()
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exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1)
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exp_avg.add_(make_sparse(exp_avg_update_values))
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old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values()
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exp_avg_sq_update_values = grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2)
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exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values))
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# Dense addition again is intended, avoiding another sparse_mask
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numer = exp_avg_update_values.add_(old_exp_avg_values)
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exp_avg_sq_update_values.add_(old_exp_avg_sq_values)
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denom = exp_avg_sq_update_values.sqrt_().add_(eps)
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del exp_avg_update_values, exp_avg_sq_update_values
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bias_correction1 = 1 - beta1 ** step
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bias_correction2 = 1 - beta2 ** step
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step_size = lr * math.sqrt(bias_correction2) / bias_correction1
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param.add_(make_sparse(-step_size * numer.div_(denom)))
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