Upgrade to DLPack 1.0. #145000

ysiraichi · 2025-01-16T21:38:45Z

Stack from ghstack (oldest at bottom):

This PR makes the necessary changes in order to upgrade PyTorch DLPack
support to version 1.0. In summary, we add support for the following:

Support both DLManagedTensor and DLManagedTensorVersioned when
producing and consuming DLPack capsules
New parameter for __dlpack__ method: max_version
Version checks:
- Fallback to old implementation if no max_version or if version
  lower than 1.0
- Check that the to-be-consumed capsule is of version up to 1.X

In order to accommodate these new specifications, this PR adds the
following main changes:

torch._C._to_dlpack_versioned Python API (Module.cpp): new Python
API for creating a versioned DLPack capsule (called by __dlpack__
method)
DLPackTraits<T> class (DLConvertor.h): select the correct
traits (e.g. capsule name, conversion functions) depending on which
DLPack tensor class is being used
toDLPackImpl<T> function (DLConvertor.cpp): populates the
common fields of both classes
fromDLPackImpl<T> function (DLConvertor.cpp): constructs a tensor
from a DLPAck capsule
fillVersion<T> function (DLConvertor.cpp): populates the version
field for DLManagedTensorVersioned (no-op for DLManagedTensor)
tensor_fromDLPackImpl<T> function (tensor_new.cpp): outer function
for constructing a tensor out of a DLPack capsule that also marks the
capsule as used

cc @ezyang @gchanan

[ghstack-poisoned]

pytorch-bot · 2025-01-16T21:38:50Z

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This comment was automatically generated by Dr. CI and updates every 15 minutes.

This PR makes the necessary changes in order to upgrade PyTorch DLPack support to version 1.0. In summary, we add support for the following: - Support both `DLManagedTensor` and `DLManagedTensorVersioned` when producing and consuming DLPack capsules - New parameter for `__dlpack__` method: `max_version` - Version checks: - Fallback to old implementation if no `max_version` or if version lower than 1.0 - Check that the to-be-consumed capsule is of version up to 1.X In order to accommodate these new specifications, this PR adds the following main changes: - `torch._C._to_dlpack_versioned` Python API (Module.cpp): new Python API for creating a versioned DLPack capsule (called by `__dlpack__` method) - `DLPackTraits<T>` class (DLConvertor.h): select the correct capsule name depending on which DLPack tensor class is being used - `toDLPackImpl<T>` function (DLConvertor.cpp): populates the common fields of both classes - `fillVersion<T>` function (DLConvertor.cpp): populates the version field for `DLManagedTensorVersioned` (no-op for `DLManagedTensor`) - `fromDLPackImpl<T>` function (tensor_new.cpp): common function for creating an `at::Tensor` for both classes, leaving the possible version check for its caller ghstack-source-id: 3ca1169 Pull Request resolved: #145000

[ghstack-poisoned]

This PR makes the necessary changes in order to upgrade PyTorch DLPack support to version 1.0. In summary, we add support for the following: - Support both `DLManagedTensor` and `DLManagedTensorVersioned` when producing and consuming DLPack capsules - New parameter for `__dlpack__` method: `max_version` - Version checks: - Fallback to old implementation if no `max_version` or if version lower than 1.0 - Check that the to-be-consumed capsule is of version up to 1.X In order to accommodate these new specifications, this PR adds the following main changes: - `torch._C._to_dlpack_versioned` Python API (Module.cpp): new Python API for creating a versioned DLPack capsule (called by `__dlpack__` method) - `DLPackTraits<T>` class (DLConvertor.h): select the correct capsule name depending on which DLPack tensor class is being used - `toDLPackImpl<T>` function (DLConvertor.cpp): populates the common fields of both classes - `fromDLPackImpl<T>` function (DLConvertor.cpp): constructs a tensor from a DLPAck capsule - `fillVersion<T>` function (DLConvertor.cpp): populates the version field for `DLManagedTensorVersioned` (no-op for `DLManagedTensor`) - `tensor_fromDLPackImpl<T>` function (tensor_new.cpp): outer function for constructing a tensor out of a DLPack capsule that also marks the capsule as used ghstack-source-id: e58ba67 Pull Request resolved: #145000

[ghstack-poisoned]

This PR makes the necessary changes in order to upgrade PyTorch DLPack support to version 1.0. In summary, we add support for the following: - Support both `DLManagedTensor` and `DLManagedTensorVersioned` when producing and consuming DLPack capsules - New parameter for `__dlpack__` method: `max_version` - Version checks: - Fallback to old implementation if no `max_version` or if version lower than 1.0 - Check that the to-be-consumed capsule is of version up to 1.X In order to accommodate these new specifications, this PR adds the following main changes: - `torch._C._to_dlpack_versioned` Python API (Module.cpp): new Python API for creating a versioned DLPack capsule (called by `__dlpack__` method) - `DLPackTraits<T>` class (DLConvertor.h): select the correct capsule name depending on which DLPack tensor class is being used - `toDLPackImpl<T>` function (DLConvertor.cpp): populates the common fields of both classes - `fromDLPackImpl<T>` function (DLConvertor.cpp): constructs a tensor from a DLPAck capsule - `fillVersion<T>` function (DLConvertor.cpp): populates the version field for `DLManagedTensorVersioned` (no-op for `DLManagedTensor`) - `tensor_fromDLPackImpl<T>` function (tensor_new.cpp): outer function for constructing a tensor out of a DLPack capsule that also marks the capsule as used ghstack-source-id: 063107b Pull Request resolved: #145000

albanD

Change sounds ok but what is the expectations in terms of BC when interracting with libraries that didn't upgrade to latest dlpack yet? Is that ok for all Tensors to be of the new version?

albanD · 2025-02-03T19:32:11Z

aten/src/ATen/DLConvertor.h

-TORCH_API Tensor fromDLPack(DLManagedTensor* src);
-TORCH_API Tensor
-fromDLPack(DLManagedTensor* src, std::function<void(void*)> deleter);
+TORCH_API DLManagedTensorVersioned* toDLPack(const Tensor& src);


Is this API used by C++ libraries? This would be a BC-breaking change for these users right?

I think it's being used by PyTorch/XLA. Yes, it's definitely BC-breaking. I was thinking that, since DLPack 1.0 should be the new default, the old version should have the name with a suffix. However, now that you brought this up, not being BC-breaking sounds more important.

In summary, I will change the names so that we are not BC-breaking.

albanD · 2025-02-03T19:32:39Z

aten/src/ATen/DLConvertor.h

+TORCH_API DLManagedTensor* toDLPackUnversioned(const Tensor& src);
+TORCH_API Tensor fromDLPack(
+    DLManagedTensorVersioned* src,
+    std::optional<std::function<void(void*)>> deleter = std::nullopt);


std::function is optional by default and doesn't require std::optional right?

aten/src/ATen/dlpack.h

albanD · 2025-02-03T19:34:21Z

torch/__init__.py

    solve,
 )
-from torch.utils.dlpack import from_dlpack, to_dlpack
+from torch.utils.dlpack import from_dlpack, to_dlpack, to_dlpack_unversioned


While I understand this matches current code, I don't think we want to have this as a new top level API? Being part of torch.utils.dlpack is enough?

That comment sounds right - I don't think it should be needed. For the introduction strategy to a versioned protocol, see the explanation and prototype code (if max_version is None: ....) at https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__dlpack__.html

If it's useful/needed to have a Python function to use here so that can be called from within Tensor.__dlpack__, then making it a private function by prepending an underscore should be fine I think.

albanD · 2025-02-03T19:35:59Z

torch/csrc/Module.cpp

     METH_NOARGS,
     nullptr},
    {"_to_dlpack", THPModule_toDLPack, METH_O, nullptr},
+    {"_to_dlpack_unversioned", THPModule_toDLPackUnversioned, METH_O, nullptr},


Why do we still need this one?

This does look necessary to me. For DLPack to change the ABI once, there's a dance that needs doing to be not-super-disruptive: continue returning the old (0.8) version, unless the consumer indicates it can handle the new (1.X) version by passing in max_version=(1, 0) (or (1, x) in the future).

albanD · 2025-02-03T19:37:02Z

torch/utils/dlpack.py

+        try:
+            # Try running __dlpack__ while specifying `max_version` argument.
+            dlpack = ext_tensor.__dlpack__(**kwargs)
+        except TypeError:


Is it guaranteed that they will fail with TypeError?

That should be the case - it's the standard error in Python for using a non-existing keyword.

rgommers

Thanks for tackling this @ysiraichi! This looks like a good start. A few high-level comments:

As noted inline, __dlpack__ gained 3 new keywords, so far this adds only max_version. I suspect it's safer to add all at once, because other libraries are probably going to assume that if max_version is present, the 1.0 support is complete.
It would be good to have new Python-level tests in test/test_dlpack.py. That will also make the changes in logic easier to review.
- There's one testing TODO for a very old numpy version there that may be nice to take along:
  
  pytorch/test/test_dlpack.py
  
  Line 239 in e9f6e27
  
  # TODO: add interchange tests once NumPy 1.22 (dlpack support) is required
I think this is inactionable right now, but adding for completeness: DLPack gained a new DLPACK_FLAG_BITMASK_READ_ONLY field, which in the future can feed into PyTorch's copy-on-write (COW) feature.

aten/src/ATen/dlpack.h

rgommers · 2025-02-04T15:46:01Z

torch/utils/dlpack.py

+        try:
+            # Try running __dlpack__ while specifying `max_version` argument.
+            dlpack = ext_tensor.__dlpack__(**kwargs)
+        except TypeError:


That should be the case - it's the standard error in Python for using a non-existing keyword.

rgommers · 2025-02-04T15:50:51Z

torch/__init__.py

    solve,
 )
-from torch.utils.dlpack import from_dlpack, to_dlpack
+from torch.utils.dlpack import from_dlpack, to_dlpack, to_dlpack_unversioned


That comment sounds right - I don't think it should be needed. For the introduction strategy to a versioned protocol, see the explanation and prototype code (if max_version is None: ....) at https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__dlpack__.html

rgommers · 2025-02-04T15:52:01Z

torch/_tensor.py

+
+            max_version (tuple[int, int] or None): An optional Python tuple with
+            2 integers, representing the maximum version the caller supports. If
+            None is passed, then PyTorch will fallback to DLPack 0.X, where versions


I'd rephrase as "If None (default), PyTorch will use DLPack 0.8".

rgommers · 2025-02-04T15:55:19Z

torch/__init__.py

    solve,
 )
-from torch.utils.dlpack import from_dlpack, to_dlpack
+from torch.utils.dlpack import from_dlpack, to_dlpack, to_dlpack_unversioned


If it's useful/needed to have a Python function to use here so that can be called from within Tensor.__dlpack__, then making it a private function by prepending an underscore should be fine I think.

rgommers · 2025-02-04T16:01:30Z

torch/_tensor.py

    __torch_dispatch__ = _C._disabled_torch_dispatch_impl

-    def __dlpack__(self, stream=None):
+    def __dlpack__(self, stream=None, max_version=None):


Note that there are also new dl_device and copy keywords (API docs).

rgommers · 2025-02-04T16:05:23Z

torch/csrc/Module.cpp

     METH_NOARGS,
     nullptr},
    {"_to_dlpack", THPModule_toDLPack, METH_O, nullptr},
+    {"_to_dlpack_unversioned", THPModule_toDLPackUnversioned, METH_O, nullptr},


This does look necessary to me. For DLPack to change the ABI once, there's a dance that needs doing to be not-super-disruptive: continue returning the old (0.8) version, unless the consumer indicates it can handle the new (1.X) version by passing in max_version=(1, 0) (or (1, x) in the future).

ysiraichi · 2025-02-07T14:16:05Z

@albanD @rgommers Since you have already reviewed this PR, I was thinking about adding the new __dlpack__ keywords + tests in a new PR stacked on top of this one. There are 2 reasons for that:

It would be easier to review the new changes
We could merge the whole stack at once when that PR is ready

Let me know if that works for you.

rgommers · 2025-02-07T15:14:54Z

@ysiraichi a stack with two separate PRs, with the dl_device and copy keywords in the second PR, sounds perfectly fine to me. Merging the changes in one go will be nice, but functionally those two keywords don't interact with max_version, so it'll be a clean split.

albanD · 2025-02-07T15:46:12Z

Separate PR on the stack sounds ok.
Note that you won't be able to land both together though if the bottom PR's CI is red though, they must all pass CI

[ghstack-poisoned]

This PR makes the necessary changes in order to upgrade PyTorch DLPack support to version 1.0. In summary, we add support for the following: - Support both `DLManagedTensor` and `DLManagedTensorVersioned` when producing and consuming DLPack capsules - New parameter for `__dlpack__` method: `max_version` - Version checks: - Fallback to old implementation if no `max_version` or if version lower than 1.0 - Check that the to-be-consumed capsule is of version up to 1.X In order to accommodate these new specifications, this PR adds the following main changes: - `torch._C._to_dlpack_versioned` Python API (Module.cpp): new Python API for creating a versioned DLPack capsule (called by `__dlpack__` method) - `DLPackTraits<T>` class (DLConvertor.h): select the correct capsule name depending on which DLPack tensor class is being used - `toDLPackImpl<T>` function (DLConvertor.cpp): populates the common fields of both classes - `fromDLPackImpl<T>` function (DLConvertor.cpp): constructs a tensor from a DLPAck capsule - `fillVersion<T>` function (DLConvertor.cpp): populates the version field for `DLManagedTensorVersioned` (no-op for `DLManagedTensor`) - `tensor_fromDLPackImpl<T>` function (tensor_new.cpp): outer function for constructing a tensor out of a DLPack capsule that also marks the capsule as used ghstack-source-id: 2b774ce Pull Request resolved: #145000

leofang · 2025-03-11T16:50:00Z

Gentle ping @ysiraichi, any chance we can get this work wrapped up in the near future? Note that we just tagged DLPack v1.1: https://github.com/dmlc/dlpack/releases/tag/v1.1, which added a few more dtype enums.

ysiraichi · 2025-03-11T17:03:54Z

Yeah. I'm still working on this. Have 2 more PRs to be opened.
Haven't had the time to get back to them, though. Will do so soon.

[ghstack-poisoned]

pytorchmergebot · 2025-06-20T15:32:28Z

@pytorchbot successfully started a revert job. Check the current status here.
Questions? Feedback? Please reach out to the PyTorch DevX Team

This reverts commit 6e185c5. Reverted #145000 on behalf of https://github.com/atalman due to failing internal tests ([comment](#145000 (comment)))

pytorchmergebot · 2025-06-20T15:32:50Z

@ysiraichi your PR has been successfully reverted.

ysiraichi · 2025-06-20T16:24:06Z

@atalman Could you share the internal errors?

atalman · 2025-06-20T16:56:30Z

I believe this needs to be imported internally and landed internally since we are missing some internal changes. Error is during compilation, I believe there is a code thats using older constructs:

error: unknown type name 'DLManagedTensorVersioned'
   15 | TORCH_API DLManagedTensorVersioned* toDLPackVersioned(const Tensor& src);
...
: error: unknown type name 'DLManagedTensorVersioned'
   19 |     DLManagedTensorVersioned* src,
      |     ^
....
rror: use of undeclared identifier 'DLManagedTensorVersioned'
   52 | struct DLPackTraits<DLManagedTensorVersioned> {
      |

ysiraichi · 2025-06-20T18:33:50Z

I see. DLManagedTensorVersioned is actually a struct that was introduced in this PR, and lives inside dlpack.h. If there's anything I can do on my side, let me know.

albanD · 2025-06-20T21:10:16Z

This is an executorch build issue for some reason.
@mergennachin do you know what is causing this?

ezyang · 2025-06-23T03:04:35Z

LLM proposes

diff --git a/fbcode/caffe2/aten/src/ATen/dlpack.h b/fbcode/caffe2/aten/src/ATen/dlpack.h
--- a/fbcode/caffe2/aten/src/ATen/dlpack.h
+++ b/fbcode/caffe2/aten/src/ATen/dlpack.h
@@ -292,7 +292,7 @@
  *
  * \note This is the current standard DLPack exchange data structure.
  */
-struct DLManagedTensorVersioned {
+typedef struct DLManagedTensorVersioned {
   /*!
    * \brief The API and ABI version of the current managed Tensor
    */
@@ -326,7 +326,7 @@
   uint64_t flags;
   /*! \brief DLTensor which is being memory managed */
   DLTensor dl_tensor;
-};
+} DLManagedTensorVersioned;
 
 #ifdef __cplusplus
 }  // DLPACK_EXTERN_C

Which is kind of sus, not sure if this fixes the problem.

NARRATOR: It did not work.

larryliu0820 · 2025-06-24T00:08:14Z

aten/src/ATen/DLConvertor.h

 TORCH_API ScalarType toScalarType(const DLDataType& dtype);
 TORCH_API DLManagedTensor* toDLPack(const Tensor& src);
-TORCH_API Tensor fromDLPack(DLManagedTensor* src);
+TORCH_API DLManagedTensorVersioned* toDLPackVersioned(const Tensor& src);


This fixes internal failures:

Suggested change

TORCH_API DLManagedTensorVersioned* toDLPackVersioned(const Tensor& src);

TORCH_API struct DLManagedTensorVersioned* toDLPackVersioned(const Tensor& src);

cc @ysiraichi could you please try suggested change. I can help import this PR internally and make sure the signal is green before merging.

This PR makes the necessary changes in order to upgrade PyTorch DLPack support to version 1.0. In summary, we add support for the following: - Support both `DLManagedTensor` and `DLManagedTensorVersioned` when producing and consuming DLPack capsules - New parameter for `__dlpack__` method: `max_version` - Version checks: - Fallback to old implementation if no `max_version` or if version lower than 1.0 - Check that the to-be-consumed capsule is of version up to 1.X In order to accommodate these new specifications, this PR adds the following main changes: - `torch._C._to_dlpack_versioned` Python API (Module.cpp): new Python API for creating a versioned DLPack capsule (called by `__dlpack__` method) - `DLPackTraits<T>` class (DLConvertor.h): select the correct traits (e.g. capsule name, conversion functions) depending on which DLPack tensor class is being used - `toDLPackImpl<T>` function (DLConvertor.cpp): populates the common fields of both classes - `fromDLPackImpl<T>` function (DLConvertor.cpp): constructs a tensor from a DLPAck capsule - `fillVersion<T>` function (DLConvertor.cpp): populates the version field for `DLManagedTensorVersioned` (no-op for `DLManagedTensor`) - `tensor_fromDLPackImpl<T>` function (tensor_new.cpp): outer function for constructing a tensor out of a DLPack capsule that also marks the capsule as used cc ezyang gchanan [ghstack-poisoned]

albanD

Thanks!
I don't have a devserver on hand, so trying to go with the regular land!

albanD · 2025-06-30T16:46:46Z

@pytorchbot merge

pytorchmergebot · 2025-06-30T16:48:35Z

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pytorchmergebot · 2025-06-30T16:48:52Z

Merge failed

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albanD · 2025-06-30T16:50:40Z

@pytorchbot merge -i

pytorchmergebot · 2025-06-30T16:52:29Z

Merge started

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Learn more about merging in the wiki.

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albanD · 2025-07-03T08:58:30Z

This landed smoothly, thank @ysiraichi for the change. We can merge the rest of the stack now!

tqchen · 2025-08-29T14:47:45Z

Just want to followup with #145000 (comment) that we have 1.1 that comes with F8 and F4 data types, would be great to get help on landing support for them, hopefully as a matter of updating

pytorch/aten/src/ATen/DLConvertor.cpp

Lines 66 to 90 in 0e45023

    
           // TODO(#146647): use macro here instead of spelling out each shell dtype 
        
           case ScalarType::Float8_e5m2: 
        
           case ScalarType::Float8_e5m2fnuz: 
        
           case ScalarType::Float8_e4m3fn: 
        
           case ScalarType::Float8_e4m3fnuz: 
        
           case ScalarType::Float8_e8m0fnu: 
        
             TORCH_CHECK_BUFFER(false, "float8 types are not supported by dlpack"); 
        
             break; 
        
           case ScalarType::Float4_e2m1fn_x2: 
        
             TORCH_CHECK_BUFFER(false, "float4 types are not supported by dlpack"); 
        
             break; 
        
           case ScalarType::QInt8: 
        
           case ScalarType::QUInt8: 
        
           case ScalarType::QInt32: 
        
           case ScalarType::QUInt4x2: 
        
           case ScalarType::QUInt2x4: 
        
             TORCH_CHECK_BUFFER(false, "QUInt/QInt types are not supported by dlpack"); 
        
             break; 
        
           case ScalarType::Bits1x8: 
        
           case ScalarType::Bits2x4: 
        
           case ScalarType::Bits4x2: 
        
           case ScalarType::Bits8: 
        
           case ScalarType::Bits16: 
        
             TORCH_CHECK_BUFFER(false, "Bit types are not supported by dlpack"); 
        
             break;

to map to https://github.com/dmlc/dlpack/blob/main/include/dlpack/dlpack.h#L162

ysiraichi · 2025-08-29T21:01:08Z

I'm sorry, but I'm not able to work on this anymore. Feel free to pick this up.

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Tag an official v1.0 release dmlc/dlpack#149

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[DLPack] add NumPy exchange tests. #150216

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Revert "Upgrade to DLPack 1.0. (#145000)"

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This reverts commit 6e185c5. Reverted #145000 on behalf of https://github.com/atalman due to failing internal tests ([comment](#145000 (comment)))

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Automatically move y (and sample_weight) to the same device and namespace as X scikit-learn/scikit-learn#28668

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	TORCH_API DLManagedTensorVersioned* toDLPackVersioned(const Tensor& src);
	TORCH_API struct DLManagedTensorVersioned* toDLPackVersioned(const Tensor& src);

Upgrade to DLPack 1.0. #145000

Upgrade to DLPack 1.0. #145000

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ysiraichi commented Jan 16, 2025 •

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