Remove as_tensor argument of set_tensors_from_ndarray_1d (#2615)

Summary:
## Motivation

This PR removes the `as_tensor` argument of the `set_tensors_from_ndarray_1d` function in `numpy_utils.py`.

The reason for this change is that the previous implementation
- messed up the float precision: That is, even when trying to use `float32` precision, this always transformed floats into `float64` precision
- did only use the default function anyway.

This change was discussed previously with Balandat and esantorella in #2596 .

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

Yes, I have read it.

Pull Request resolved: #2615

Test Plan:
I checked the format using `ufmt format .` and verified that all tests are still running using `pytest -ra`.

## Related PRs

This PR is related to #2597 and #2596, fixing a similar issue (`float32` being broken by some internal calculations).

Reviewed By: Balandat

Differential Revision: D65537491

Pulled By: saitcakmak

fbshipit-source-id: f178a761691ce092a1b06d020158ea367fc6c99e

botorch/optim/closures/core.py

@@ -85,7 +85,6 @@ def __init__(
         closure: Callable[[], tuple[Tensor, Sequence[Tensor | None]]],
         parameters: dict[str, Tensor],
         as_array: Callable[[Tensor], npt.NDArray] = None,  # pyre-ignore [9]
-        as_tensor: Callable[[npt.NDArray], Tensor] = torch.as_tensor,
         get_state: Callable[[], npt.NDArray] = None,  # pyre-ignore [9]
         set_state: Callable[[npt.NDArray], None] = None,  # pyre-ignore [9]
         fill_value: float = 0.0,
@@ -99,14 +98,13 @@ def __init__(
                 Expected to correspond with the first `len(parameters)` optional
                 gradient tensors returned by `closure`.
             as_array: Callable used to convert tensors to ndarrays.
-            as_tensor: Callable used to convert ndarrays to tensors.
             get_state: Callable that returns the closure's state as an ndarray. When
                 passed as `None`, defaults to calling `get_tensors_as_ndarray_1d`
                 on `closure.parameters` while passing `as_array` (if given by the user).
             set_state: Callable that takes a 1-dimensional ndarray and sets the
                 closure's state. When passed as `None`, `set_state` defaults to
                 calling `set_tensors_from_ndarray_1d` with `closure.parameters` and
-                a given ndarray while passing `as_tensor`.
+                a given ndarray.
             fill_value: Fill value for parameters whose gradients are None. In most
                 cases, `fill_value` should either be zero or NaN.
             persistent: Boolean specifying whether an ndarray should be retained
@@ -128,15 +126,12 @@ def __init__(
             as_array = partial(as_ndarray, dtype=np_float64)
 
         if set_state is None:
-            set_state = partial(
-                set_tensors_from_ndarray_1d, parameters, as_tensor=as_tensor
-            )
+            set_state = partial(set_tensors_from_ndarray_1d, parameters)
 
         self.closure = closure
         self.parameters = parameters
 
         self.as_array = as_ndarray
-        self.as_tensor = as_tensor
         self._get_state = get_state
         self._set_state = set_state
 

botorch/optim/utils/numpy_utils.py

@@ -9,7 +9,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Iterator
-
 from itertools import tee
 
 import numpy as np
@@ -113,7 +112,6 @@ def get_tensors_as_ndarray_1d(
 def set_tensors_from_ndarray_1d(
     tensors: Iterator[Tensor] | dict[str, Tensor],
     array: npt.NDArray,
-    as_tensor: Callable[[npt.NDArray], Tensor] = torch.as_tensor,
 ) -> None:
     r"""Sets the values of one more tensors based off of a vector of assignments."""
     named_tensors_iter = (
@@ -125,7 +123,11 @@ def set_tensors_from_ndarray_1d(
             try:
                 size = tnsr.numel()
                 vals = array[index : index + size] if tnsr.ndim else array[index]
-                tnsr.copy_(as_tensor(vals).to(tnsr).view(tnsr.shape).to(tnsr))
+                tnsr.copy_(
+                    torch.as_tensor(vals, device=tnsr.device, dtype=tnsr.dtype).view(
+                        tnsr.shape
+                    )
+                )
                 index += size
             except Exception as e:
                 raise RuntimeError(