Also adds a test for unintended tensor aliasing in LR schedulers

Depends on pytorch#163098
Fixes pytorch#163105

…163098)

Prevents edge cases in SequentialLR and ReduceLROnPlateau which could corrupt learning rates or trigger recompilation.

Supersedes pytorch#162360
Fixes pytorch#162359
Fixes pytorch#163093

While putting pytorch#162360 together, I noticed the class of issue I was fixing (i.e. unintended aliasing in lr_schedulers when using Tensor lrs) appeared in several other places. @janeyx99 suggested I put together a follow-up PR.

There are several bugs resembling the one fixed in pytorch#162360. I added a helper to fix these:
```python
def _update_param_group_val(param_group: dict[str, Any], key: str, val: float | Tensor):
    """Set param_group[key] to val without aliasing or assignment when they're both tensors.
    Raises a KeyError if param_group[key] does not exist.
    """
    if isinstance(param_group[key], Tensor):
        param_group[key].fill_(_to_scalar(val))
    else:
        param_group[key] = val
```

And applied it to fix bugs in `SequentialLR.__init__` and `LRScheduler._update_lr`. I also added it to `CyclicLR.__init__` which was using an equivalent pattern, and `CosineAnnealingWarmRestarts.step` which *should* have had a similar issue:
```python
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
    param_group["lr"] = lr
```

But did not, because `get_lr()` actually returns tensors when using a tensor lr (despite its `list[float]` return type annotation). Relying on this propagation seems fragile, so I conservatively added the method here as well. I'll be fixing the type annotations and several related issues in followup PRs built off of this one.
Pull Request resolved: pytorch#163098
Approved by: https://github.com/janeyx99

…163098)

Prevents edge cases in SequentialLR and ReduceLROnPlateau which could corrupt learning rates or trigger recompilation.

Supersedes pytorch#162360
Fixes pytorch#162359
Fixes pytorch#163093

While putting pytorch#162360 together, I noticed the class of issue I was fixing (i.e. unintended aliasing in lr_schedulers when using Tensor lrs) appeared in several other places. @janeyx99 suggested I put together a follow-up PR.

There are several bugs resembling the one fixed in pytorch#162360. I added a helper to fix these:
```python
def _update_param_group_val(param_group: dict[str, Any], key: str, val: float | Tensor):
    """Set param_group[key] to val without aliasing or assignment when they're both tensors.
    Raises a KeyError if param_group[key] does not exist.
    """
    if isinstance(param_group[key], Tensor):
        param_group[key].fill_(_to_scalar(val))
    else:
        param_group[key] = val
```

And applied it to fix bugs in `SequentialLR.__init__` and `LRScheduler._update_lr`. I also added it to `CyclicLR.__init__` which was using an equivalent pattern, and `CosineAnnealingWarmRestarts.step` which *should* have had a similar issue:
```python
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
    param_group["lr"] = lr
```

But did not, because `get_lr()` actually returns tensors when using a tensor lr (despite its `list[float]` return type annotation). Relying on this propagation seems fragile, so I conservatively added the method here as well. I'll be fixing the type annotations and several related issues in followup PRs built off of this one.
Pull Request resolved: pytorch#163098
Approved by: https://github.com/janeyx99

…163098)

Prevents edge cases in SequentialLR and ReduceLROnPlateau which could corrupt learning rates or trigger recompilation.

Supersedes pytorch#162360
Fixes pytorch#162359
Fixes pytorch#163093

While putting pytorch#162360 together, I noticed the class of issue I was fixing (i.e. unintended aliasing in lr_schedulers when using Tensor lrs) appeared in several other places. @janeyx99 suggested I put together a follow-up PR.

There are several bugs resembling the one fixed in pytorch#162360. I added a helper to fix these:
```python
def _update_param_group_val(param_group: dict[str, Any], key: str, val: float | Tensor):
    """Set param_group[key] to val without aliasing or assignment when they're both tensors.
    Raises a KeyError if param_group[key] does not exist.
    """
    if isinstance(param_group[key], Tensor):
        param_group[key].fill_(_to_scalar(val))
    else:
        param_group[key] = val
```

And applied it to fix bugs in `SequentialLR.__init__` and `LRScheduler._update_lr`. I also added it to `CyclicLR.__init__` which was using an equivalent pattern, and `CosineAnnealingWarmRestarts.step` which *should* have had a similar issue:
```python
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
    param_group["lr"] = lr
```

But did not, because `get_lr()` actually returns tensors when using a tensor lr (despite its `list[float]` return type annotation). Relying on this propagation seems fragile, so I conservatively added the method here as well. I'll be fixing the type annotations and several related issues in followup PRs built off of this one.
Pull Request resolved: pytorch#163098
Approved by: https://github.com/janeyx99