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Remove numpy_converter functions, fit_gpytorch_scipy, and fit_gpytorc…
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…h_torch from BoTorch (pytorch#1995)

Summary:
## Motivation

Removes most of what was deprecated in pytorch#1439, in version 0.8.0, except for `_get_extra_mll_args` and `fit_gpytorch_model`. Removal was straightforward since the functionality was not used and relevant unit tests were clearly labeled and self-contained.

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes

Pull Request resolved: pytorch#1995

Test Plan:
Existing units; make sure codecov has not regressed from deleting tests.

## Related PRs

pytorch#1439

Reviewed By: Balandat

Differential Revision: D48738275

Pulled By: esantorella

fbshipit-source-id: dae2be3547dec7406ad342198521897cf0afdca8
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@esantorella @facebook-github-bot
esantorella authored and facebook-github-bot committed Oct 13, 2023
1 parent 6d330eb commit 055e5a7
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Showing 5 changed files with 3 additions and 914 deletions.
3 changes: 0 additions & 3 deletions botorch/optim/__init__.py
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Expand Up @@ -23,7 +23,6 @@
LogLinearHomotopySchedule,
)
from botorch.optim.initializers import initialize_q_batch, initialize_q_batch_nonneg
from botorch.optim.numpy_converter import module_to_array, set_params_with_array
from botorch.optim.optimize import (
gen_batch_initial_conditions,
optimize_acqf,
Expand Down Expand Up @@ -51,9 +50,7 @@
"optimize_acqf_discrete_local_search",
"optimize_acqf_mixed",
"optimize_acqf_homotopy",
"module_to_array",
"scipy_minimize",
"set_params_with_array",
"torch_minimize",
"ExpMAStoppingCriterion",
"FixedHomotopySchedule",
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228 changes: 2 additions & 226 deletions botorch/optim/fit.py
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Expand Up @@ -9,21 +9,7 @@
from __future__ import annotations

from functools import partial
from itertools import filterfalse
from time import monotonic
from typing import (
Any,
Callable,
Dict,
Iterator,
List,
Optional,
Pattern,
Sequence,
Set,
Tuple,
Union,
)
from typing import Any, Callable, Dict, Optional, Sequence, Set, Tuple, Union
from warnings import warn

from botorch.exceptions.warnings import OptimizationWarning
Expand All @@ -34,25 +20,11 @@
scipy_minimize,
torch_minimize,
)
from botorch.optim.numpy_converter import (
_scipy_objective_and_grad,
module_to_array,
set_params_with_array,
)
from botorch.optim.stopping import ExpMAStoppingCriterion
from botorch.optim.utils import (
_filter_kwargs,
_get_extra_mll_args,
get_name_filter,
get_parameters_and_bounds,
TorchAttr,
)
from botorch.optim.utils.model_utils import get_parameters
from botorch.optim.utils import get_parameters_and_bounds, TorchAttr
from botorch.utils.types import DEFAULT
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
from gpytorch.settings import fast_computations
from numpy import ndarray
from scipy.optimize import Bounds, minimize
from torch import Tensor
from torch.nn import Module
from torch.optim.adam import Adam
Expand Down Expand Up @@ -200,199 +172,3 @@ def fit_gpytorch_mll_torch(
callback=callback,
timeout_sec=timeout_sec,
)


def fit_gpytorch_scipy(
mll: MarginalLogLikelihood,
bounds: Optional[Dict[str, Tuple[Optional[float], Optional[float]]]] = None,
method: str = "L-BFGS-B",
options: Optional[Dict[str, Any]] = None,
track_iterations: bool = False,
approx_mll: bool = False,
scipy_objective: TScipyObjective = _scipy_objective_and_grad,
module_to_array_func: TModToArray = module_to_array,
module_from_array_func: TArrayToMod = set_params_with_array,
**kwargs: Any,
) -> Tuple[MarginalLogLikelihood, Dict[str, Union[float, List[OptimizationResult]]]]:
r"""Legacy method for scipy-based fitting of gpytorch models.
The model and likelihood in mll must already be in train mode. This method requires
that the model has `train_inputs` and `train_targets`.
Args:
mll: MarginalLogLikelihood to be maximized.
bounds: A dictionary mapping parameter names to tuples of lower and upper
bounds.
method: Solver type, passed along to scipy.optimize.minimize.
options: Dictionary of solver options, passed along to scipy.optimize.minimize.
approx_mll: If True, use gpytorch's approximate MLL computation. This is
disabled by default since the stochasticity is an issue for
determistic optimizers). Enabling this is only recommended when
working with large training data sets (n>2000).
Returns:
2-element tuple containing
- MarginalLogLikelihood with parameters optimized in-place.
- Dictionary with the following key/values:
"fopt": Best mll value.
"wall_time": Wall time of fitting.
"iterations": List of OptimizationResult objects with information on each
iteration. If track_iterations is False, will be empty.
"OptimizeResult": The result returned by `scipy.optim.minimize`.
"""
warn(
"`fit_gpytorch_scipy` is marked for deprecation, consider using "
"`scipy_minimize` or its model fitting helper `fit_gpytorch_mll_scipy`.",
DeprecationWarning,
)
start_time = monotonic()
iterations: List[OptimizationResult] = []

options = {} if options is None else options.copy()
exclude: Iterator[Union[Pattern, str]] = options.pop("exclude", None)
if exclude:
exclude, _ = zip( # get the qualified names of excluded parameters
*filterfalse(get_name_filter(exclude), mll.named_parameters())
)

x0, property_dict, bounds = module_to_array_func(
module=mll, exclude=exclude, bounds=bounds
)
if bounds is not None:
bounds = Bounds(lb=bounds[0], ub=bounds[1], keep_feasible=True)

def wrapper(x: ndarray) -> Tuple[float, ndarray]:
with fast_computations(log_prob=approx_mll):
return scipy_objective(x=x, mll=mll, property_dict=property_dict)

def store_iteration(xk):
iterations.append(
OptimizationResult(
step=len(iterations),
fval=float(wrapper(xk)[0]),
status=OptimizationStatus.RUNNING,
runtime=monotonic() - start_time,
)
)

result = minimize(
wrapper,
x0,
bounds=bounds,
method=method,
jac=True,
options=options,
callback=store_iteration if track_iterations else None,
)

info_dict = {
"fopt": float(result.fun),
"wall_time": monotonic() - start_time,
"iterations": iterations,
"OptimizeResult": result,
}
if not result.success:
try:
# Some result.message are bytes
msg = result.message.decode("ascii")
except AttributeError:
# Others are str
msg = result.message
warn(
f"Fitting failed with the optimizer reporting '{msg}'", OptimizationWarning
)

# Set to optimum
mll = module_from_array_func(mll, result.x, property_dict)
return mll, info_dict


def fit_gpytorch_torch(
mll: MarginalLogLikelihood,
bounds: Optional[Dict[str, Tuple[Optional[float], Optional[float]]]] = None,
optimizer_cls: Optimizer = Adam,
options: Optional[Dict[str, Any]] = None,
track_iterations: bool = False,
approx_mll: bool = False,
) -> Tuple[MarginalLogLikelihood, Dict[str, Union[float, List[OptimizationResult]]]]:
r"""Legacy method for torch-based fitting of gpytorch models.
The model and likelihood in mll must already be in train mode.
Note: this method requires that the model has `train_inputs` and `train_targets`.
Args:
mll: MarginalLogLikelihood to be maximized.
bounds: An optional dictionary mapping parameter names to tuples
of lower and upper bounds. Bounds specified here take precedence
over bounds on the same parameters specified in the constraints
registered with the module.
optimizer_cls: Torch optimizer to use. Must not require a closure.
options: options for model fitting. Relevant options will be passed to
the `optimizer_cls`. Additionally, options can include: "disp"
to specify whether to display model fitting diagnostics and "maxiter"
to specify the maximum number of iterations.
Returns:
2-element tuple containing
- mll with parameters optimized in-place.
- Dictionary with the following key/values:
"fopt": Best mll value.
"wall_time": Wall time of fitting.
"iterations": List of OptimizationResult objects with information on each
iteration. If track_iterations is False, will be empty.
Example:
>>> gp = SingleTaskGP(train_X, train_Y)
>>> mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
>>> mll.train()
>>> fit_gpytorch_torch(mll)
>>> mll.eval()
"""
warn(
"`fit_gpytorch_torch` is marked for deprecation, consider using "
"`torch_minimize` or its model fitting helper `fit_gpytorch_mll_torch`.",
DeprecationWarning,
)
_options = {"maxiter": 100, "disp": True, "lr": 0.05}
_options.update(options or {})
exclude = _options.pop("exclude", None)
parameters = get_parameters(
mll,
requires_grad=True,
name_filter=None if exclude is None else get_name_filter(exclude),
)

optimizer = optimizer_cls(
params=list(parameters.values()), **_filter_kwargs(optimizer_cls, **_options)
)
iterations: List[OptimizationResult] = []
stopping_criterion = ExpMAStoppingCriterion(
**_filter_kwargs(ExpMAStoppingCriterion, **_options)
)

def closure() -> Tuple[Tensor, Tuple[Tensor, ...]]:
optimizer.zero_grad()
with fast_computations(log_prob=approx_mll):
out = mll.model(*mll.model.train_inputs)
loss = -mll(out, mll.model.train_targets, *_get_extra_mll_args(mll)).sum()
loss.backward()

return loss, tuple(param.grad for param in parameters.values())

def store_iteration(parameters: Dict[str, Tensor], result: OptimizationResult):
iterations.append(result)

result = fit_gpytorch_mll_torch(
mll=mll,
closure=closure,
bounds=bounds,
parameters=parameters,
optimizer=optimizer,
stopping_criterion=stopping_criterion,
callback=store_iteration if track_iterations else None,
)
return mll, {
"fopt": result.fval,
"wall_time": result.runtime,
"iterations": iterations,
}
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