Update the signature and test of torch.hamming_window() #152682

ILCSFNO · 2025-05-02T11:38:16Z

Fixes #146590

pytorch-bot · 2025-05-02T11:38:20Z

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github-actions · 2025-05-02T11:41:57Z

Attention! native_functions.yaml was changed

If you are adding a new function or defaulted argument to native_functions.yaml, you cannot use it from pre-existing Python frontend code until our FC window passes (two weeks). Split your PR into two PRs, one which adds the new C++ functionality, and one that makes use of it from Python, and land them two weeks apart. See https://github.com/pytorch/pytorch/wiki/PyTorch's-Python-Frontend-Backward-and-Forward-Compatibility-Policy#forwards-compatibility-fc for more info.

Caused by:

aten/src/ATen/native/native_functions.yaml

ILCSFNO · 2025-05-02T11:46:06Z

@pytorchbot label "release notes: python_frontend"

ILCSFNO · 2025-05-02T12:11:00Z

@mikaylagawarecki Could you please have a review? Thks!
Wondering the warning of native_functions.yaml and confusing about whether and how to change here:

pytorch/aten/src/ATen/native/TensorFactories.cpp

Lines 1850 to 1930 in 50d4698

    
           // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ hamming_window ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
        
           Tensor hamming_window( 
        
               int64_t window_length, 
        
               std::optional<ScalarType> dtype, 
        
               std::optional<Layout> layout, 
        
               std::optional<Device> device, 
        
               std::optional<bool> pin_memory) { 
        
             return native::hamming_window( 
        
                 window_length, /*periodic=*/true, dtype, layout, device, pin_memory); 
        
           } 
        
           Tensor hamming_window( 
        
               int64_t window_length, 
        
               bool periodic, 
        
               std::optional<ScalarType> dtype, 
        
               std::optional<Layout> layout, 
        
               std::optional<Device> device, 
        
               std::optional<bool> pin_memory) { 
        
             return native::hamming_window( 
        
                 window_length, 
        
                 periodic, 
        
                 /*alpha=*/0.54, 
        
                 dtype, 
        
                 layout, 
        
                 device, 
        
                 pin_memory); 
        
           } 
        
           Tensor hamming_window( 
        
               int64_t window_length, 
        
               bool periodic, 
        
               double alpha, 
        
               std::optional<ScalarType> dtype, 
        
               std::optional<Layout> layout, 
        
               std::optional<Device> device, 
        
               std::optional<bool> pin_memory) { 
        
             return native::hamming_window( 
        
                 window_length, 
        
                 periodic, 
        
                 alpha, 
        
                 /*beta=*/0.46, 
        
                 dtype, 
        
                 layout, 
        
                 device, 
        
                 pin_memory); 
        
           } 
        
           Tensor hamming_window( 
        
               int64_t window_length, 
        
               bool periodic, 
        
               double alpha, 
        
               double beta, 
        
               std::optional<ScalarType> dtype_opt, 
        
               std::optional<Layout> layout, 
        
               std::optional<Device> device, 
        
               std::optional<bool> pin_memory) { 
        
             // See [Note: hacky wrapper removal for TensorOptions] 
        
             ScalarType dtype = c10::dtype_or_default(dtype_opt); 
        
             TensorOptions options = 
        
                 TensorOptions().dtype(dtype).layout(layout).device(device).pinned_memory( 
        
                     pin_memory); 
        
             window_function_checks("hamming_window", options, window_length); 
        
             if (window_length == 0) { 
        
               return at::empty({0}, options); 
        
             } 
        
             if (window_length == 1) { 
        
               return native::ones({1}, dtype, layout, device, pin_memory); 
        
             } 
        
             if (periodic) { 
        
               window_length += 1; 
        
             } 
        
             auto window = 
        
                 native::arange(window_length, dtype, layout, device, pin_memory); 
        
             window.mul_(c10::pi<double> * 2. / static_cast<double>(window_length - 1)) 
        
                 .cos_() 
        
                 .mul_(-beta) 
        
                 .add_(alpha); 
        
             return periodic ? window.narrow(0, 0, window_length - 1) : std::move(window); 
        
           }

for that some of the optional flag especially of the exact num, thanks!

mikaylagawarecki

Changing native_functions.yaml does not seem like the right change to me

I think it's possible that we might need a python wrapper to work around the arg parsing issues

cc @albanD

albanD

This is most likely BC-breaking indeed.
Should we just update the doc to fix the issue?

ILCSFNO · 2025-05-03T02:34:52Z

This is most likely BC-breaking indeed. Should we just update the doc to fix the issue?

Thanks! I searched just now and find that maybe the doc is alright, for that it only shows one usage of hamming_window with all params, no expanded usages are shown below. The only one usage meet with the change I made in native_functions.yaml.

pytorch/torch/_torch_docs.py

Lines 12424 to 12425 in 84aa098

    
           hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, *, dtype=None, \ 
        
           layout=torch.strided, device=None, requires_grad=False) -> Tensor

ILCSFNO · 2025-05-23T11:52:03Z

@albanD Could you please have a review? Fixed the doc to meet the signature shown in codes. Thanks.

ILCSFNO · 2025-07-02T04:56:26Z

@albanD Could you please have a review? Fixed the doc to meet the signature shown in codes. Thanks.

cc @albanD

albanD · 2025-07-02T16:38:47Z

The docs build is failing, could you fix the errors it reported:

/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/__init__.py:docstring of torch._VariableFunctionsClass.hamming_window:44: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/__init__.py:docstring of torch._VariableFunctionsClass.hamming_window:77: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/__init__.py:docstring of torch._VariableFunctionsClass.hamming_window:112: WARNING: Explicit markup ends without a blank line; unexpected unindent.
=========================

ILCSFNO · 2025-07-02T19:01:52Z

The docs build is failing, could you fix the errors it reported:

/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/__init__.py:docstring of torch._VariableFunctionsClass.hamming_window:44: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/__init__.py:docstring of torch._VariableFunctionsClass.hamming_window:77: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/__init__.py:docstring of torch._VariableFunctionsClass.hamming_window:112: WARNING: Explicit markup ends without a blank line; unexpected unindent.
=========================

@albanD Actually I understand what the warning means, but indeed no findings when I search for this mismatch. Could you please point it out if possible? Thks a lot!

albanD · 2025-07-02T19:39:31Z

I didn't look into it. The line number shared here is usually from the beginning of the docstring.
In this case, I guess a missing empty line?

ILCSFNO · 2025-07-03T01:34:33Z

@albanD Yes, I accept with you! This is caused by the lack of a blank line between an indented explicit markup block and more unindented text, e.g.

.. testcode:: ExHist

   print "This is a test"
 Output:                         <------------- There should be a blank line above this

 .. testoutput:: ExHist

Something shown here.

And I build locally trying to find the mismatch, but I succeed when I run USE_CUDA=0 python setup.py develop.
Furthermore, I wandering Line 44, 77, 112 is referred to which line but can't.

My building result of Line 15058-15202 of /torch/_C/_VariableFunctions.pyi is shown below:

def hamming_window(
    window_length: _int,
    periodic: _bool | None = True,
    alpha: _float | None = 0.54,
    beta: _float | None = 0.46,
    *,
    dtype: _dtype | None = None,
    layout: _layout | None = None,
    device: DeviceLikeType | None = None,
    pin_memory: _bool | None = False,
    requires_grad: _bool | None = False,
) -> Tensor: 
    r"""
    hamming_window(window_length, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor
    
    Hamming window function.
    
    .. math::
        w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
    
    where :math:`N` is the full window size.
    
    The input :attr:`window_length` is a positive integer controlling the
    returned window size. :attr:`periodic` flag determines whether the returned
    window trims off the last duplicate value from the symmetric window and is
    ready to be used as a periodic window with functions like
    :meth:`torch.stft`. Therefore, if :attr:`periodic` is true, the :math:`N` in
    above formula is in fact :math:`\text{window\_length} + 1`. Also, we always have
    ``torch.hamming_window(L, periodic=True)`` equal to
    ``torch.hamming_window(L + 1, periodic=False)[:-1])``.
    
    .. note::
        If :attr:`window_length` :math:`=1`, the returned window contains a single value 1.
    
    .. note::
        This is a generalized version of :meth:`torch.hann_window`.
    
    Arguments:
        window_length (int): the size of returned window
    
    Keyword args:
        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported.
        layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
              ``torch.strided`` (dense layout) is supported.
        device (:class:`torch.device`, optional): the desired device of returned tensor.
            Default: if ``None``, uses the current device for the default tensor type
            (see :func:`torch.set_default_device`). :attr:`device` will be the CPU
            for CPU tensor types and the current CUDA device for CUDA tensor types.
        pin_memory (bool, optional): If set, returned tensor would be allocated in
            the pinned memory. Works only for CPU tensors. Default: ``False``.
        requires_grad (bool, optional): If autograd should record operations on the
            returned tensor. Default: ``False``.
    
    Returns:
        Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window.
    
    .. function:: hamming_window(window_length, periodic, *, dtype=None, \
    layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor
       :noindex:
    
    Hamming window function with periodic specified.
    
    Arguments:
        window_length (int): the size of returned window
        periodic (bool): If True, returns a window to be used as periodic
            function. If False, return a symmetric window.
    
    Keyword args:
        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported.
        layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
              ``torch.strided`` (dense layout) is supported.
        device (:class:`torch.device`, optional): the desired device of returned tensor.
            Default: if ``None``, uses the current device for the default tensor type
            (see :func:`torch.set_default_device`). :attr:`device` will be the CPU
            for CPU tensor types and the current CUDA device for CUDA tensor types.
        pin_memory (bool, optional): If set, returned tensor would be allocated in
            the pinned memory. Works only for CPU tensors. Default: ``False``.
        requires_grad (bool, optional): If autograd should record operations on the
            returned tensor. Default: ``False``.
    
    Returns:
        Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window.
    
    .. function:: hamming_window(window_length, periodic, float alpha, *, dtype=None, \
    layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor
       :noindex:
    
    Hamming window function with periodic and alpha specified.
    
    Arguments:
        window_length (int): the size of returned window
        periodic (bool): If True, returns a window to be used as periodic
            function. If False, return a symmetric window.
        alpha (float): The coefficient :math:`\alpha` in the equation above
    
    Keyword args:
        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported.
        layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
              ``torch.strided`` (dense layout) is supported.
        device (:class:`torch.device`, optional): the desired device of returned tensor.
            Default: if ``None``, uses the current device for the default tensor type
            (see :func:`torch.set_default_device`). :attr:`device` will be the CPU
            for CPU tensor types and the current CUDA device for CUDA tensor types.
        pin_memory (bool, optional): If set, returned tensor would be allocated in
            the pinned memory. Works only for CPU tensors. Default: ``False``.
        requires_grad (bool, optional): If autograd should record operations on the
            returned tensor. Default: ``False``.
    
    Returns:
        Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window.
    
    .. function:: hamming_window(window_length, periodic, float alpha, float beta, *, dtype=None, \
    layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor
       :noindex:
    
    Hamming window function with periodic, alpha and beta specified.
    
    Arguments:
        window_length (int): the size of returned window
        periodic (bool): If True, returns a window to be used as periodic
            function. If False, return a symmetric window.
        alpha (float): The coefficient :math:`\alpha` in the equation above
        beta (float): The coefficient :math:`\beta` in the equation above
    
    Keyword args:
        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`). Only floating point types are supported.
        layout (:class:`torch.layout`, optional): the desired layout of returned window tensor. Only
              ``torch.strided`` (dense layout) is supported.
        device (:class:`torch.device`, optional): the desired device of returned tensor.
            Default: if ``None``, uses the current device for the default tensor type
            (see :func:`torch.set_default_device`). :attr:`device` will be the CPU
            for CPU tensor types and the current CUDA device for CUDA tensor types.
        pin_memory (bool, optional): If set, returned tensor would be allocated in
            the pinned memory. Works only for CPU tensors. Default: ``False``.
        requires_grad (bool, optional): If autograd should record operations on the
            returned tensor. Default: ``False``.
    
    Returns:
        Tensor: A 1-D tensor of size :math:`(\text{window\_length},)` containing the window.
    """
    ...

albanD · 2025-07-09T15:37:45Z

torch/_torch_docs.py

+.. function:: hamming_window(window_length, periodic, float alpha, *, dtype=None, \
+layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor
+   :noindex:


My guess is that these lines are the problematic ones. Either the line continuation or noindex position.

Let me see, what if break it into one line and noindex with 4 spaces? Though I notice that some other funcs remains 3 spaces.

.. function:: hamming_window(window_length, periodic, float alpha, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor :noindex:

I'll have a try and see what changes will happen on the error reported.

Changes seen, you're right! Actually one of them:

Pre-version: https://github.com/pytorch/pytorch/actions/runs/16016452841/job/45224096063 (Fail)

New-Version: https://github.com/pytorch/pytorch/actions/runs/16173999664/job/45657856489 (Succeed)

Changed to fix the line length to see what will happen then.

Guess that maybe .. function is seen as indent? No other similar references for that not found a sentence in other funcs as long as the description:

.. function:: hamming_window(window_length, periodic, float alpha, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor

@svekars any hint on this one? :)

Do you have to use .. function::?

Think that from above, decision is to just update the doc to fix the issue instead of fix the code. And from here, there're 4 types of usage in hamming_window:

pytorch/aten/src/ATen/native/TensorFactories.cpp

Lines 1872 to 1952 in 1d0f45d

// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ hamming_window ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Tensor hamming_window(

int64_t window_length,

std::optional<ScalarType> dtype,

std::optional<Layout> layout,

std::optional<Device> device,

std::optional<bool> pin_memory) {

return native::hamming_window(

window_length, /*periodic=*/true, dtype, layout, device, pin_memory);

}

Tensor hamming_window(

int64_t window_length,

bool periodic,

std::optional<ScalarType> dtype,

std::optional<Layout> layout,

std::optional<Device> device,

std::optional<bool> pin_memory) {

return native::hamming_window(

window_length,

periodic,

/*alpha=*/0.54,

dtype,

layout,

device,

pin_memory);

}

Tensor hamming_window(

int64_t window_length,

bool periodic,

double alpha,

std::optional<ScalarType> dtype,

std::optional<Layout> layout,

std::optional<Device> device,

std::optional<bool> pin_memory) {

return native::hamming_window(

window_length,

periodic,

alpha,

/*beta=*/0.46,

dtype,

layout,

device,

pin_memory);

}

Tensor hamming_window(

int64_t window_length,

bool periodic,

double alpha,

double beta,

std::optional<ScalarType> dtype_opt,

std::optional<Layout> layout,

std::optional<Device> device,

std::optional<bool> pin_memory) {

// See [Note: hacky wrapper removal for TensorOptions]

ScalarType dtype = c10::dtype_or_default(dtype_opt);

TensorOptions options =

TensorOptions().dtype(dtype).layout(layout).device(device).pinned_memory(

pin_memory);

window_function_checks("hamming_window", options, window_length);

if (window_length == 0) {

return at::empty({0}, options);

}

if (window_length == 1) {

return native::ones({1}, dtype, layout, device, pin_memory);

}

if (periodic) {

window_length += 1;

}

auto window =

native::arange(window_length, dtype, layout, device, pin_memory);

window.mul_(c10::pi<double> * 2. / static_cast<double>(window_length - 1))

.cos_()

.mul_(-beta)

.add_(alpha);

return periodic ? window.narrow(0, 0, window_length - 1) : std::move(window);

}

ILCSFNO · 2025-07-18T02:39:05Z

@albanD @svekars How about this one version?

albanD

Looks great!
What did you change

ILCSFNO · 2025-07-18T14:42:06Z

Guess that maybe .. function is seen as indent? No other similar references for that not found a sentence in other funcs as long as the description:
.. function:: hamming_window(window_length, periodic, float alpha, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) -> Tensor

Just like this, from:

.. function:: hamming_window(window_length, periodic, float alpha, *, dtype=None, layout=None, device=None, \
pin_memory=False, requires_grad=False) -> Tensor
   :noindex:

to:

.. function:: hamming_window(window_length, periodic, float alpha, *, dtype=None, layout=None, device=None, \
    pin_memory=False, requires_grad=False) -> Tensor
   :noindex:

A try which makes sense.

ILCSFNO · 2025-07-21T09:35:42Z

cc @albanD So shell we merge? Or just wait for other checks? Thanks a lot!

albanD · 2025-07-21T18:17:46Z

@pytorchbot merge

Merging!
FYI once the PR is approved, you can ask the bot to merge by yourself !

pytorchmergebot · 2025-07-21T18:19:59Z

Merge started

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ILCSFNO · 2025-07-21T18:21:02Z

Alright, thanks! For that I note:

Merging is blocked
You're not authorized to push to this branch. Visit https://docs.github.com/repositories/configuring-branches-and-merges-in-your-repository/managing-protected-branches/about-protected-branches for more information.

Thinking that it should be done by Collaborator, I'll try it if met next time. Thanks a lot!

pytorchmergebot · 2025-08-01T20:46:05Z

Successfully rebased patch-8 onto refs/remotes/origin/viable/strict, please pull locally before adding more changes (for example, via git checkout patch-8 && git pull --rebase)

pytorchmergebot · 2025-08-01T20:47:21Z

Merge started

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pytorchmergebot · 2025-08-01T20:47:31Z

Merge failed

Reason: 3 mandatory check(s) failed. The first few are:

Dig deeper by viewing the failures on hud

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Raised by workflow job

Failing merge rule: Core Maintainers

albanD · 2025-08-01T20:47:47Z

@pytorchbot merge

pytorch-bot · 2025-08-01T20:47:51Z

Pull workflow has not been scheduled for the PR yet. It could be because author doesn't have permissions to run those or skip-checks keywords were added to PR/commits, aborting merge. Please get/give approval for the workflows and/or remove skip ci decorators before next merge attempt. If you think this is a mistake, please contact PyTorch Dev Infra.

albanD · 2025-08-04T17:42:20Z

@pytorchbot merge

pytorchmergebot · 2025-08-04T17:44:23Z

Merge started

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Learn more about merging in the wiki.

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pytorchmergebot · 2025-08-04T17:44:35Z

Merge failed

Reason: 1 mandatory check(s) failed. The first few are:

pull / linux-jammy-py3.13-clang12 / test (default, 5, 5, lf.linux.4xlarge)

Dig deeper by viewing the failures on hud

Details for Dev Infra team

Raised by workflow job

Failing merge rule: Core Maintainers

albanD · 2025-08-04T17:48:12Z

@pytorchbot merge -h

pytorch-bot · 2025-08-04T17:48:15Z

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albanD · 2025-08-04T17:48:51Z

@pytorchbot merge -f "Doc build is good and this doesn't touch anything else"

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ILCSFNO · 2025-08-06T06:23:54Z

Thanks a lot!

Fixes pytorch#146590 Pull Request resolved: pytorch#152682 Approved by: https://github.com/albanD

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Signature should be extended for torch.hamming_window() #146590

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Update _torch_docs.py

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Update _torch_docs.py

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Update _torch_docs.py

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Update the signature and test of torch.hamming_window() (pytorch#152682)

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Fixes pytorch#146590 Pull Request resolved: pytorch#152682 Approved by: https://github.com/albanD

	// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ hamming_window ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

	Tensor hamming_window(
	int64_t window_length,
	std::optional<ScalarType> dtype,
	std::optional<Layout> layout,
	std::optional<Device> device,
	std::optional<bool> pin_memory) {
	return native::hamming_window(
	window_length, /periodic=/true, dtype, layout, device, pin_memory);
	}

	Tensor hamming_window(
	int64_t window_length,
	bool periodic,
	std::optional<ScalarType> dtype,
	std::optional<Layout> layout,
	std::optional<Device> device,
	std::optional<bool> pin_memory) {
	return native::hamming_window(
	window_length,
	periodic,
	/alpha=/0.54,
	dtype,
	layout,
	device,
	pin_memory);
	}

	Tensor hamming_window(
	int64_t window_length,
	bool periodic,
	double alpha,
	std::optional<ScalarType> dtype,
	std::optional<Layout> layout,
	std::optional<Device> device,
	std::optional<bool> pin_memory) {
	return native::hamming_window(
	window_length,
	periodic,
	alpha,
	/beta=/0.46,
	dtype,
	layout,
	device,
	pin_memory);
	}

	Tensor hamming_window(
	int64_t window_length,
	bool periodic,
	double alpha,
	double beta,
	std::optional<ScalarType> dtype_opt,
	std::optional<Layout> layout,
	std::optional<Device> device,
	std::optional<bool> pin_memory) {
	// See [Note: hacky wrapper removal for TensorOptions]
	ScalarType dtype = c10::dtype_or_default(dtype_opt);
	TensorOptions options =
	TensorOptions().dtype(dtype).layout(layout).device(device).pinned_memory(
	pin_memory);

	window_function_checks("hamming_window", options, window_length);
	if (window_length == 0) {
	return at::empty({0}, options);
	}
	if (window_length == 1) {
	return native::ones({1}, dtype, layout, device, pin_memory);
	}
	if (periodic) {
	window_length += 1;
	}
	auto window =
	native::arange(window_length, dtype, layout, device, pin_memory);
	window.mul_(c10::pi<double> * 2. / static_cast<double>(window_length - 1))
	.cos_()
	.mul_(-beta)
	.add_(alpha);
	return periodic ? window.narrow(0, 0, window_length - 1) : std::move(window);
	}

Update the signature and test of torch.hamming_window() #152682

Update the signature and test of torch.hamming_window() #152682

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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/152682

❌ 1 New Failure, 12 Pending, 1 Unrelated Failure

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Attention! native_functions.yaml was changed

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