This PR updates OpInfo-based tests for NJTs:
* Adds extensive coverage across non-contiguous NJTs (both non-contiguous transposed and non-contiguous with holes)
    * The `_sample_njts()` helper that `sample_input_func`s utilize now produces non-contig NJTs as well
* Utilizes a `SampleInput`-based xfail system for granular classification of bugs. For example, it's possible to indicate that a class of ops is expected to fail only on non-contig with holes NJT inputs.
    * I decided on adding `SampleInput`s and utilizing this system over using test parametrization for two reasons:
        * Test perf - adding `SampleInput`s is faster than generating entire new tests
        * Avoiding the possibility of `sample_input_func`s not respecting the non-contig test parameter - this would result in silently incorrect passing of these tests. Keeping the responsibility for `SampleInput` generation firmly within each `OpInfo`'s `sample_input_func` means weirdness like this isn't possible
* Improves `SampleInput` naming for a bunch of `sample_input_func`s. This makes it easier to xfail them as needed. For example, binary / unary / other ops now use the new `_describe_njt()` helper to get a string repr that uniquely defines the type of NJT being passed to the op
* Adds appropriate `XFailRule`s to get tests passing for forward / backward / forward compile / backward compile. In general, each xfail corresponds to some bug that needs to be fixed

```python
# Represents a rule indicating how to xfail a particular test. It allows granularity
# at the device, dtype, op, and individual sample levels. This flexibility allows entire
# bugs to be represented by a single rule, even if this corresponds with multiple conceptual
# test cases across multiple ops.
@DataClass
class XFailRule:
    # expected error type
    error_type: TypeVar = Exception
    # expected error message
    error_msg: str = ".*"
    # function to indicate whether the rule applies; return True if so
    match_fn: Callable[[torch.device, torch.dtype, OpInfo, SampleInput], bool] = None
    # optional name for identifying the rule
    name: str = ""

    def match(self, device, dtype, op, sample) -> bool:
        return self.match_fn(device, dtype, op, sample)
```

Example:
```python
    # Bug when broadcasting a binary op with non-contiguous with holes NJT + dense
    # tensor with 1 in ragged dim.
    XFailRule(
        error_type=RuntimeError,
        error_msg="cannot call binary pointwise function .* with inputs of shapes",
        match_fn=lambda device, dtype, op, sample: (
            isinstance(op, BinaryUfuncInfo)
            and "noncontig_holes" in sample.name
            and "broadcasting 1 over ragged" in sample.name
        ),
        name="binary_noncontig_holes_broadcasting_1_over_ragged",
    ),
```
Pull Request resolved: #138370
Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer
ghstack dependencies: #140160

### Background
This PR adds the functionality to xfail / skip on a per-`SampleInput` basis for `OpInfo` tests. See #89354 and #82669 for some requests asking for this type of functionality.

This was originally landed for NJT in #138370 and is generalized and slightly tweaked here.

### Design
#### Principles
* Clean separation among `SampleInput` generation logic, test logic that uses the `SampleInput`s, and xfail / skip logic (which will change as bugs are addressed).
* Flexibility in xfail / skip predicate specification - ideally each bug can be handled by a single skip / xfail, even if it surfaces across a specific class of ops.
    * This is important in practice for NJT, where it's common to have a bug that affects all binary ops, for example.
* Opt-in with minimal test logic changes + no substantial impact on other tests.

#### Details
The core new concept is a `SampleRule`, which can be either an `XFailRule` or `SkipRule`.

```python
@DataClass
class SampleRule(ABC):
    # function to indicate whether the rule applies to this op; return True if so
    # NB: str arg of callable is device_type
    op_match_fn: Callable[[str, OpInfo], bool] = None
    # function to indicate whether the rule applies to this sample; return True if so
    sample_match_fn: Callable[[torch.device, SampleInput], bool] = None
    # optional name for identifying the rule
    name: str = ""

@DataClass
class XFailRule(SampleRule):
    # expected error type
    error_type: TypeVar = Exception
    # expected error message
    error_msg: str = ".*"

@DataClass
class SkipRule(SampleRule):
    ...
```

* See below for example usage details, but at a high level: each test should have a corresponding list of `sample_skips_and_xfails`.
    * The list of `sample_skips_and_xfails` is traversed in order, and the first rule that matches (if any) is applied, so order can matter.
    * The PR includes a logging mechanism for matched rules accessible by setting the loglevel to `DEBUG`.
* The split between `op_match_fn` and `sample_match_fn` is made to allow pre-filtering of the list of rules to get only those that apply to the op under test.
* Each `SampleInput` is run within a subtest context so they can be individually skipped / xfailed as needed. This also means that a test will no longer stop after the first erroring `SampleInput`; all samples will be run through test logic.

### Example Usage
Consider the following OpInfo test:
```python
class MyTestCase(TestCase):
    @ops(op_db)
    def test_foo(self, device, dtype, op):
        for sample in op.sample_inputs(device, dtype, requires_grad=False):
            # do some SampleInput-based test logic
            output = op.op(sample.input, *sample.args, **sample.kwargs)
            ...
```

This is a common pattern for such tests; simply generate a list of `SampleInputs` and run them through the op. Now say you want to xfail one of these `SampleInput`s for a given op. Today, you have to xfail the entire test or hack around this in the test logic.

This PR lets you do this to get very flexible xfail / skips based on op / sample input properties:
```python
# NB: Define rules for per-SampleInput xfails / skips. These can also be defined in-line in the @ops decorator, but
# it can be more readable to maintain these somewhere else. These are attempted to be matched in order and
# the first one that matches applies, so order can matter.
FOO_SKIPS_AND_XFAILS = [
    XFailRule(
        error_type=ValueError,
        error_mg="2D inputs not supported",
        op_match_fn=lambda device, op: (
            # NB: logic for which ops this rule applies to goes here
            op.full_name == "add"
        ),
        sample_match_fn=lambda device, sample: (
            # NB: logic which samples this rule applies to goes here
            sample.input.dim() == 2
        ),
        # NB: optional rule identifier can help with debugging matched rules
        name="add_with_2D_inputs_not_supported",
    ),
    # NB: This follows a similar structure as XFailRule but without error_type / error_msg. Obviously
    # this skips a particular SampleInput instead of xfailing :)
    SkipRule(...),
    ...
]

class MyTestCase(TestCase):
    @ops(op_db)
    @sample_skips_and_xfails(FOO_SKIPS_AND_XFAILS)
    # NB: the @ops decorator automatically filters out any rules that don't apply to this op
    def test_foo(self, device, dtype, op):
        for sample, subtest_ctx in op.sample_inputs(
            # NB: use_subtests=True is required for skips / xfails to work. If skips / xfails are defined and use_subtests != True,
            # an informative error will be thrown.
            device, dtype, requires_grad=False, use_subtests=True
        ):
            # NB: this subtest context manager runs each sample input as a "subtest" and handles skips / xfails appropriately
            with subtest_ctx(self):
                # do some SampleInput-based test logic
                output = op.op(sample.input, *sample.args, **sample.kwargs)
                ...
```

More examples can be seen in `test/test_nestedtensor.py`, where this system is used in practice.

I also demonstrate usage of syntactic sugar over this system in `test/functorch/test_vmap.py`. Here, a skip for the `to()` operator is replaced with a granular xfail for `test_vmap_exhaustive()`:
```python
...
# pre-existing xfail
xfail("item"),
# new granular xfail using syntactic sugar over the general system
xfailIf(
    "to",
    lambda sample: (
        sample.kwargs["memory_format"] == torch.channels_last
    ),
),
...
```
Pull Request resolved: #140443
Approved by: https://github.com/janeyx99, https://github.com/zou3519
ghstack dependencies: #140160, #138370

### Background
This PR adds the functionality to xfail / skip on a per-`SampleInput` basis for `OpInfo` tests. See pytorch#89354 and pytorch#82669 for some requests asking for this type of functionality.

This was originally landed for NJT in pytorch#138370 and is generalized and slightly tweaked here.

### Design
#### Principles
* Clean separation among `SampleInput` generation logic, test logic that uses the `SampleInput`s, and xfail / skip logic (which will change as bugs are addressed).
* Flexibility in xfail / skip predicate specification - ideally each bug can be handled by a single skip / xfail, even if it surfaces across a specific class of ops.
    * This is important in practice for NJT, where it's common to have a bug that affects all binary ops, for example.
* Opt-in with minimal test logic changes + no substantial impact on other tests.

#### Details
The core new concept is a `SampleRule`, which can be either an `XFailRule` or `SkipRule`.

```python
@DataClass
class SampleRule(ABC):
    # function to indicate whether the rule applies to this op; return True if so
    # NB: str arg of callable is device_type
    op_match_fn: Callable[[str, OpInfo], bool] = None
    # function to indicate whether the rule applies to this sample; return True if so
    sample_match_fn: Callable[[torch.device, SampleInput], bool] = None
    # optional name for identifying the rule
    name: str = ""

@DataClass
class XFailRule(SampleRule):
    # expected error type
    error_type: TypeVar = Exception
    # expected error message
    error_msg: str = ".*"

@DataClass
class SkipRule(SampleRule):
    ...
```

* See below for example usage details, but at a high level: each test should have a corresponding list of `sample_skips_and_xfails`.
    * The list of `sample_skips_and_xfails` is traversed in order, and the first rule that matches (if any) is applied, so order can matter.
    * The PR includes a logging mechanism for matched rules accessible by setting the loglevel to `DEBUG`.
* The split between `op_match_fn` and `sample_match_fn` is made to allow pre-filtering of the list of rules to get only those that apply to the op under test.
* Each `SampleInput` is run within a subtest context so they can be individually skipped / xfailed as needed. This also means that a test will no longer stop after the first erroring `SampleInput`; all samples will be run through test logic.

### Example Usage
Consider the following OpInfo test:
```python
class MyTestCase(TestCase):
    @ops(op_db)
    def test_foo(self, device, dtype, op):
        for sample in op.sample_inputs(device, dtype, requires_grad=False):
            # do some SampleInput-based test logic
            output = op.op(sample.input, *sample.args, **sample.kwargs)
            ...
```

This is a common pattern for such tests; simply generate a list of `SampleInputs` and run them through the op. Now say you want to xfail one of these `SampleInput`s for a given op. Today, you have to xfail the entire test or hack around this in the test logic.

This PR lets you do this to get very flexible xfail / skips based on op / sample input properties:
```python
# NB: Define rules for per-SampleInput xfails / skips. These can also be defined in-line in the @ops decorator, but
# it can be more readable to maintain these somewhere else. These are attempted to be matched in order and
# the first one that matches applies, so order can matter.
FOO_SKIPS_AND_XFAILS = [
    XFailRule(
        error_type=ValueError,
        error_mg="2D inputs not supported",
        op_match_fn=lambda device, op: (
            # NB: logic for which ops this rule applies to goes here
            op.full_name == "add"
        ),
        sample_match_fn=lambda device, sample: (
            # NB: logic which samples this rule applies to goes here
            sample.input.dim() == 2
        ),
        # NB: optional rule identifier can help with debugging matched rules
        name="add_with_2D_inputs_not_supported",
    ),
    # NB: This follows a similar structure as XFailRule but without error_type / error_msg. Obviously
    # this skips a particular SampleInput instead of xfailing :)
    SkipRule(...),
    ...
]

class MyTestCase(TestCase):
    @ops(op_db)
    @sample_skips_and_xfails(FOO_SKIPS_AND_XFAILS)
    # NB: the @ops decorator automatically filters out any rules that don't apply to this op
    def test_foo(self, device, dtype, op):
        for sample, subtest_ctx in op.sample_inputs(
            # NB: use_subtests=True is required for skips / xfails to work. If skips / xfails are defined and use_subtests != True,
            # an informative error will be thrown.
            device, dtype, requires_grad=False, use_subtests=True
        ):
            # NB: this subtest context manager runs each sample input as a "subtest" and handles skips / xfails appropriately
            with subtest_ctx(self):
                # do some SampleInput-based test logic
                output = op.op(sample.input, *sample.args, **sample.kwargs)
                ...
```

More examples can be seen in `test/test_nestedtensor.py`, where this system is used in practice.

I also demonstrate usage of syntactic sugar over this system in `test/functorch/test_vmap.py`. Here, a skip for the `to()` operator is replaced with a granular xfail for `test_vmap_exhaustive()`:
```python
...
# pre-existing xfail
xfail("item"),
# new granular xfail using syntactic sugar over the general system
xfailIf(
    "to",
    lambda sample: (
        sample.kwargs["memory_format"] == torch.channels_last
    ),
),
...
```
Pull Request resolved: pytorch#140443
Approved by: https://github.com/janeyx99, https://github.com/zou3519
ghstack dependencies: pytorch#140160, pytorch#138370

This PR contains several fixes related to non-contiguous NJTs:
1. Propagates `lengths` through op calls appropriately (see desc of pytorch#138098)
    * SDPA now calls `nested_view_from_values_offsets_lengths()` instead of `nested_view_from_values_offsets()`
2. Allows non-contig NJTs in unsqueeze / transpose / select
3. Expands padded dense -> NJT conversion to support non-contig NJTs
4. (unrelated sorry) Updates `split` / `split_with_sizes` to allow for optional `dim`, matching the ATen signature
Pull Request resolved: pytorch#140160
Approved by: https://github.com/cpuhrsch

This PR updates OpInfo-based tests for NJTs:
* Adds extensive coverage across non-contiguous NJTs (both non-contiguous transposed and non-contiguous with holes)
    * The `_sample_njts()` helper that `sample_input_func`s utilize now produces non-contig NJTs as well
* Utilizes a `SampleInput`-based xfail system for granular classification of bugs. For example, it's possible to indicate that a class of ops is expected to fail only on non-contig with holes NJT inputs.
    * I decided on adding `SampleInput`s and utilizing this system over using test parametrization for two reasons:
        * Test perf - adding `SampleInput`s is faster than generating entire new tests
        * Avoiding the possibility of `sample_input_func`s not respecting the non-contig test parameter - this would result in silently incorrect passing of these tests. Keeping the responsibility for `SampleInput` generation firmly within each `OpInfo`'s `sample_input_func` means weirdness like this isn't possible
* Improves `SampleInput` naming for a bunch of `sample_input_func`s. This makes it easier to xfail them as needed. For example, binary / unary / other ops now use the new `_describe_njt()` helper to get a string repr that uniquely defines the type of NJT being passed to the op
* Adds appropriate `XFailRule`s to get tests passing for forward / backward / forward compile / backward compile. In general, each xfail corresponds to some bug that needs to be fixed

```python
# Represents a rule indicating how to xfail a particular test. It allows granularity
# at the device, dtype, op, and individual sample levels. This flexibility allows entire
# bugs to be represented by a single rule, even if this corresponds with multiple conceptual
# test cases across multiple ops.
@DataClass
class XFailRule:
    # expected error type
    error_type: TypeVar = Exception
    # expected error message
    error_msg: str = ".*"
    # function to indicate whether the rule applies; return True if so
    match_fn: Callable[[torch.device, torch.dtype, OpInfo, SampleInput], bool] = None
    # optional name for identifying the rule
    name: str = ""

    def match(self, device, dtype, op, sample) -> bool:
        return self.match_fn(device, dtype, op, sample)
```

Example:
```python
    # Bug when broadcasting a binary op with non-contiguous with holes NJT + dense
    # tensor with 1 in ragged dim.
    XFailRule(
        error_type=RuntimeError,
        error_msg="cannot call binary pointwise function .* with inputs of shapes",
        match_fn=lambda device, dtype, op, sample: (
            isinstance(op, BinaryUfuncInfo)
            and "noncontig_holes" in sample.name
            and "broadcasting 1 over ragged" in sample.name
        ),
        name="binary_noncontig_holes_broadcasting_1_over_ragged",
    ),
```
Pull Request resolved: pytorch#138370
Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer
ghstack dependencies: pytorch#140160

### Background
This PR adds the functionality to xfail / skip on a per-`SampleInput` basis for `OpInfo` tests. See pytorch#89354 and pytorch#82669 for some requests asking for this type of functionality.

This was originally landed for NJT in pytorch#138370 and is generalized and slightly tweaked here.

### Design
#### Principles
* Clean separation among `SampleInput` generation logic, test logic that uses the `SampleInput`s, and xfail / skip logic (which will change as bugs are addressed).
* Flexibility in xfail / skip predicate specification - ideally each bug can be handled by a single skip / xfail, even if it surfaces across a specific class of ops.
    * This is important in practice for NJT, where it's common to have a bug that affects all binary ops, for example.
* Opt-in with minimal test logic changes + no substantial impact on other tests.

#### Details
The core new concept is a `SampleRule`, which can be either an `XFailRule` or `SkipRule`.

```python
@DataClass
class SampleRule(ABC):
    # function to indicate whether the rule applies to this op; return True if so
    # NB: str arg of callable is device_type
    op_match_fn: Callable[[str, OpInfo], bool] = None
    # function to indicate whether the rule applies to this sample; return True if so
    sample_match_fn: Callable[[torch.device, SampleInput], bool] = None
    # optional name for identifying the rule
    name: str = ""

@DataClass
class XFailRule(SampleRule):
    # expected error type
    error_type: TypeVar = Exception
    # expected error message
    error_msg: str = ".*"

@DataClass
class SkipRule(SampleRule):
    ...
```

* See below for example usage details, but at a high level: each test should have a corresponding list of `sample_skips_and_xfails`.
    * The list of `sample_skips_and_xfails` is traversed in order, and the first rule that matches (if any) is applied, so order can matter.
    * The PR includes a logging mechanism for matched rules accessible by setting the loglevel to `DEBUG`.
* The split between `op_match_fn` and `sample_match_fn` is made to allow pre-filtering of the list of rules to get only those that apply to the op under test.
* Each `SampleInput` is run within a subtest context so they can be individually skipped / xfailed as needed. This also means that a test will no longer stop after the first erroring `SampleInput`; all samples will be run through test logic.

### Example Usage
Consider the following OpInfo test:
```python
class MyTestCase(TestCase):
    @ops(op_db)
    def test_foo(self, device, dtype, op):
        for sample in op.sample_inputs(device, dtype, requires_grad=False):
            # do some SampleInput-based test logic
            output = op.op(sample.input, *sample.args, **sample.kwargs)
            ...
```

This is a common pattern for such tests; simply generate a list of `SampleInputs` and run them through the op. Now say you want to xfail one of these `SampleInput`s for a given op. Today, you have to xfail the entire test or hack around this in the test logic.

This PR lets you do this to get very flexible xfail / skips based on op / sample input properties:
```python
# NB: Define rules for per-SampleInput xfails / skips. These can also be defined in-line in the @ops decorator, but
# it can be more readable to maintain these somewhere else. These are attempted to be matched in order and
# the first one that matches applies, so order can matter.
FOO_SKIPS_AND_XFAILS = [
    XFailRule(
        error_type=ValueError,
        error_mg="2D inputs not supported",
        op_match_fn=lambda device, op: (
            # NB: logic for which ops this rule applies to goes here
            op.full_name == "add"
        ),
        sample_match_fn=lambda device, sample: (
            # NB: logic which samples this rule applies to goes here
            sample.input.dim() == 2
        ),
        # NB: optional rule identifier can help with debugging matched rules
        name="add_with_2D_inputs_not_supported",
    ),
    # NB: This follows a similar structure as XFailRule but without error_type / error_msg. Obviously
    # this skips a particular SampleInput instead of xfailing :)
    SkipRule(...),
    ...
]

class MyTestCase(TestCase):
    @ops(op_db)
    @sample_skips_and_xfails(FOO_SKIPS_AND_XFAILS)
    # NB: the @ops decorator automatically filters out any rules that don't apply to this op
    def test_foo(self, device, dtype, op):
        for sample, subtest_ctx in op.sample_inputs(
            # NB: use_subtests=True is required for skips / xfails to work. If skips / xfails are defined and use_subtests != True,
            # an informative error will be thrown.
            device, dtype, requires_grad=False, use_subtests=True
        ):
            # NB: this subtest context manager runs each sample input as a "subtest" and handles skips / xfails appropriately
            with subtest_ctx(self):
                # do some SampleInput-based test logic
                output = op.op(sample.input, *sample.args, **sample.kwargs)
                ...
```

More examples can be seen in `test/test_nestedtensor.py`, where this system is used in practice.

I also demonstrate usage of syntactic sugar over this system in `test/functorch/test_vmap.py`. Here, a skip for the `to()` operator is replaced with a granular xfail for `test_vmap_exhaustive()`:
```python
...
# pre-existing xfail
xfail("item"),
# new granular xfail using syntactic sugar over the general system
xfailIf(
    "to",
    lambda sample: (
        sample.kwargs["memory_format"] == torch.channels_last
    ),
),
...
```
Pull Request resolved: pytorch#140443
Approved by: https://github.com/janeyx99, https://github.com/zou3519
ghstack dependencies: pytorch#140160, pytorch#138370

ghstack-source-id: 05da0de
Pull Request resolved: pytorch/pytorch#140160