# Copyright 2022-2023 MetaOPT Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The base class for zero-order gradient models."""
# pylint: disable=redefined-builtin
from __future__ import annotations
import abc
import functools
from typing import Any, Sequence
import torch
import torch.nn as nn
from torchopt.diff.zero_order.decorator import Method, Samplable, zero_order
from torchopt.nn.stateless import reparametrize
from torchopt.typing import Numeric, TupleOfTensors
__all__ = ['ZeroOrderGradientModule']
def enable_zero_order_gradients(
cls: type[ZeroOrderGradientModule],
method: Method = 'naive',
num_samples: int = 1,
sigma: float = 1.0,
) -> type[ZeroOrderGradientModule]:
"""Enable zero-order gradient estimation for the :func:`forward` method."""
cls_forward = cls.forward
if getattr(cls_forward, '__zero_order_gradients_enabled__', False):
raise TypeError(
'Zero-order gradient estimation is already enabled for the `forward` method.',
)
@functools.wraps(cls_forward)
def wrapped(self: ZeroOrderGradientModule, *input: Any, **kwargs: Any) -> torch.Tensor:
"""Do the forward pass calculation."""
named_params = tuple(self.named_parameters())
if len(named_params) == 0:
raise RuntimeError('The module has no parameters.')
params_names, flat_params = tuple(zip(*named_params))
@zero_order(self.sample, argnums=0, method=method, num_samples=num_samples, sigma=sigma)
def forward_fn(
__flat_params: TupleOfTensors,
*input: Any,
**kwargs: Any,
) -> torch.Tensor:
with reparametrize(self, zip(params_names, __flat_params)):
return cls_forward(self, *input, **kwargs)
return forward_fn(flat_params, *input, **kwargs)
wrapped.__zero_order_gradients_enabled__ = True # type: ignore[attr-defined]
cls.forward = wrapped # type: ignore[method-assign]
return cls
[docs]class ZeroOrderGradientModule(nn.Module, Samplable):
"""The base class for zero-order gradient models."""
[docs] def __init_subclass__( # pylint: disable=arguments-differ
cls,
method: Method = 'naive',
num_samples: int = 1,
sigma: float = 1.0,
) -> None:
"""Validate and initialize the subclass."""
super().__init_subclass__()
enable_zero_order_gradients(
cls,
method=method,
num_samples=num_samples,
sigma=sigma,
)
[docs] @abc.abstractmethod
def forward(self, *args: Any, **kwargs: Any) -> torch.Tensor:
"""Do the forward pass of the model."""
raise NotImplementedError
[docs] @abc.abstractmethod
def sample(
self,
sample_shape: torch.Size = torch.Size(), # noqa: B008 # pylint: disable=unused-argument
) -> torch.Tensor | Sequence[Numeric]:
# pylint: disable-next=line-too-long
"""Generate a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched."""
raise NotImplementedError