#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
A converter that simplifies using numpy-based optimizers with generic torch
`nn.Module` classes. This enables using a `scipy.optim.minimize` optimizer
for optimizing module parameters.
"""
from __future__ import annotations
from collections import OrderedDict
from math import inf
from numbers import Number
from typing import Dict, List, Optional, Set, Tuple
from warnings import warn
import numpy as np
import torch
from botorch.optim.utils import (
_get_extra_mll_args,
_handle_numerical_errors,
get_name_filter,
get_parameters_and_bounds,
TorchAttr,
)
from gpytorch.mlls import MarginalLogLikelihood
from torch.nn import Module
[docs]def module_to_array(
module: Module,
bounds: Optional[Dict[str, Tuple[Optional[float], Optional[float]]]] = None,
exclude: Optional[Set[str]] = None,
) -> Tuple[np.ndarray, Dict[str, TorchAttr], Optional[np.ndarray]]:
r"""Extract named parameters from a module into a numpy array.
Only extracts parameters with requires_grad, since it is meant for optimizing.
Args:
module: A module with parameters. May specify parameter constraints in
a `named_parameters_and_constraints` method.
bounds: A dictionary mapping parameter names t lower and upper bounds.
of lower and upper bounds. Bounds specified here take precedence
over bounds on the same parameters specified in the constraints
registered with the module.
exclude: A list of parameter names that are to be excluded from extraction.
Returns:
3-element tuple containing
- The parameter values as a numpy array.
- An ordered dictionary with the name and tensor attributes of each
parameter.
- A `2 x n_params` numpy array with lower and upper bounds if at least
one constraint is finite, and None otherwise.
Example:
>>> mll = ExactMarginalLogLikelihood(model.likelihood, model)
>>> parameter_array, property_dict, bounds_out = module_to_array(mll)
"""
warn(
"`module_to_array` is marked for deprecation, consider using "
"`get_parameters_and_bounds`, `get_parameters_as_ndarray_1d`, or "
"`get_bounds_as_ndarray` instead.",
DeprecationWarning,
)
param_dict, bounds_dict = get_parameters_and_bounds(
module=module,
name_filter=None if exclude is None else get_name_filter(exclude),
requires_grad=True,
)
if bounds is not None:
bounds_dict.update(bounds)
# Record tensor metadata and read parameter values to the tape
param_tape: List[Number] = []
property_dict = OrderedDict()
with torch.no_grad():
for name, param in param_dict.items():
property_dict[name] = TorchAttr(param.shape, param.dtype, param.device)
param_tape.extend(param.view(-1).cpu().double().tolist())
# Extract lower and upper bounds
start = 0
bounds_np = None
params_np = np.asarray(param_tape)
for name, param in param_dict.items():
numel = param.numel()
if name in bounds_dict:
for row, bound in enumerate(bounds_dict[name]):
if bound is None:
continue
if torch.is_tensor(bound):
if (bound == (2 * row - 1) * inf).all():
continue
bound = bound.detach().cpu()
elif bound == (2 * row - 1) * inf:
continue
if bounds_np is None:
bounds_np = np.full((2, len(params_np)), ((-inf,), (inf,)))
bounds_np[row, start : start + numel] = bound
start += numel
return params_np, property_dict, bounds_np
[docs]def set_params_with_array(
module: Module, x: np.ndarray, property_dict: Dict[str, TorchAttr]
) -> Module:
r"""Set module parameters with values from numpy array.
Args:
module: Module with parameters to be set
x: Numpy array with parameter values
property_dict: Dictionary of parameter names and torch attributes as
returned by module_to_array.
Returns:
Module: module with parameters updated in-place.
Example:
>>> mll = ExactMarginalLogLikelihood(model.likelihood, model)
>>> parameter_array, property_dict, bounds_out = module_to_array(mll)
>>> parameter_array += 0.1 # perturb parameters (for example only)
>>> mll = set_params_with_array(mll, parameter_array, property_dict)
"""
warn(
"`_set_params_with_array` is marked for deprecation, consider using "
"`set_parameters_from_ndarray_1d` instead.",
DeprecationWarning,
)
param_dict = OrderedDict(module.named_parameters())
start_idx = 0
for p_name, attrs in property_dict.items():
# Construct the new tensor
if len(attrs.shape) == 0: # deal with scalar tensors
end_idx = start_idx + 1
new_data = torch.tensor(
x[start_idx], dtype=attrs.dtype, device=attrs.device
)
else:
end_idx = start_idx + np.prod(attrs.shape)
new_data = torch.tensor(
x[start_idx:end_idx], dtype=attrs.dtype, device=attrs.device
).view(*attrs.shape)
start_idx = end_idx
# Update corresponding parameter in-place. Disable autograd to update.
param_dict[p_name].requires_grad_(False)
param_dict[p_name].copy_(new_data)
param_dict[p_name].requires_grad_(True)
return module
def _scipy_objective_and_grad(
x: np.ndarray, mll: MarginalLogLikelihood, property_dict: Dict[str, TorchAttr]
) -> Tuple[float, np.ndarray]:
r"""Get objective and gradient in format that scipy expects.
Args:
x: The (flattened) input parameters.
mll: The MarginalLogLikelihood module to evaluate.
property_dict: The property dictionary required to "unflatten" the input
parameter vector, as generated by `module_to_array`.
Returns:
2-element tuple containing
- The objective value.
- The gradient of the objective.
"""
warn("`_scipy_objective_and_grad` is marked for deprecation.", DeprecationWarning)
mll = set_params_with_array(mll, x, property_dict)
train_inputs, train_targets = mll.model.train_inputs, mll.model.train_targets
mll.zero_grad()
try: # catch linear algebra errors in gpytorch
output = mll.model(*train_inputs)
args = [output, train_targets] + _get_extra_mll_args(mll)
loss = -mll(*args).sum()
except RuntimeError as e:
return _handle_numerical_errors(error=e, x=x)
loss.backward()
i = 0
param_dict = OrderedDict(mll.named_parameters())
grad = np.zeros(sum([tattr.shape.numel() for tattr in property_dict.values()]))
for p_name in property_dict:
t = param_dict[p_name]
size = t.numel()
if t.requires_grad and t.grad is not None:
grad[i : i + size] = t.grad.detach().view(-1).cpu().double().clone().numpy()
i += size
mll.zero_grad()
return loss.item(), grad
