Summary:
Splitting this PR into two, one for the cuSPARSELt improvements, and one
for the inductor lowering.
This PR adds in the additional cuSPARSELt bindings into pytorch.
* `torch._cslt_sparse_mm_search` will be deprecated in a future PR,
so a warning has been added
* Added a header file for cuSPARSELtOps.cpp
* max_id is now available in `torch.backends.cusparselt` via
`torch.backends.cusparselt.get_max_alg_id()`
* fixed meta registrations for float8
Test Plan:
python test/test_sparse_semi_structured.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137427
Approved by: https://github.com/cpuhrsch, https://github.com/eqy
Summary:
Splitting this PR into two, one for the cuSPARSELt improvements, and one
for the inductor lowering.
This PR adds in the additional cuSPARSELt bindings into pytorch.
* `torch._cslt_sparse_mm_search` will be deprecated in a future PR,
so a warning has been added
* Added a header file for cuSPARSELtOps.cpp
* max_id is now available in `torch.backends.cusparselt` via
`torch.backends.cusparselt.get_max_alg_id()`
* fixed meta registrations for float8
Test Plan:
python test/test_sparse_semi_structured.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137427
Approved by: https://github.com/cpuhrsch, https://github.com/eqy
Summary:
Splitting this PR into two, one for the cuSPARSELt improvements, and one
for the inductor lowering.
This PR adds in the additional cuSPARSELt bindings into pytorch.
* `torch._cslt_sparse_mm_search` will be deprecated in a future PR,
so a warning has been added
* Added a header file for cuSPARSELtOps.cpp
* max_id is now available in `torch.backends.cusparselt` via
`torch.backends.cusparselt.get_max_alg_id()`
* fixed meta registrations for float8
Test Plan:
python test/test_sparse_semi_structured.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137427
Approved by: https://github.com/cpuhrsch, https://github.com/eqy
Summary:
This PR adds a lowering for `torch._cslt_sparse_mm` to find the optimal
alg_id and cache it when running with `torch.compile`
Seeing speedups on both bfloat16 and float8 dtypes:
<img width="641" alt="Screenshot 2024-10-17 at 2 10 38 PM" src="https://github.com/user-attachments/assets/b928cd11-32a3-43e5-b209-8e4028896f0b">
<img width="1274" alt="Screenshot 2024-10-17 at 1 39 03 PM" src="https://github.com/user-attachments/assets/d9edd684-a8ec-46fd-b3da-2e76dbcb7bb6">
* `torch._cslt_sparse_mm_search` has been modified to return optimal
split-k parameters as well as max alg_id.
* max_id is now available in `torch.backends.cusparselt` via
`torch.backends.cusparselt.get_max_alg_id()`
* fixed meta registrations for float8
Test Plan:
python test/test_sparse_semi_structured.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137427
Approved by: https://github.com/cpuhrsch
Summary:
This PR adds `torch.float8e4m3fn` support to cuSPARSELt and `to_sparse_semi_structured`.
This will let users to run fp8 + 2:4 sparse matmuls on Hopper GPUs with
cusparselt >= 0.6.2, via to `scaled_mm` API.
```
A = rand_sparse_semi_structured_mask(256, 128, dtype=torch.float16)
B = torch.rand(dense_input_shape, device=device).to(torch.float16).t()
A_fp8, A_scale = to_float8(A)
B_fp8, B_scale = to_float8(B)
dense_result = torch._scaled_mm(
A_fp8, B_fp8,
scale_a=A_scale, scale_b=B_scale,
out_dtype=out_dtype
)
A_fp8_sparse = to_sparse_semi_structured(A_fp8)
sparse_result = torch._scaled_mm(
A_fp8_sparse, B_fp8,
scale_a=A_scale, scale_b=B_scale,
out_dtype=out_dtype
)
```
Note that to keep this consistent with normal torch behavior, calling
`torch.mm(A_fp8_sparse, B_fp8)` will raise a NotImplementedError.
I also turned on cuSPARSELt by default and added CUSPARSELT_MAX_ID to the
backend to make the tests a bit cleaner
Test Plan:
```
python test/test_sparse_semi_structured -k scaled_mm
python test/test_sparse_semi_structured -k fp8
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136397
Approved by: https://github.com/drisspg
This PR adds in fast semi-structured sparsification kernels to PyTorch.
These kernels allow for accelerated semi-structured sparsification
kernels in PyTorch.
The kernels have been added as aten native functions
In particular, three new functions have been added:
* `torch._sparse_semi_structured_tile`
This function will return the packed representation and metadata for
both X and X', as well as the thread masks. Note that this applies 2:4
sparsity in a 4x4 tile instead of a 1x4 strip as usual.
* `torch._sparse_semi_structured_apply`
This function takes in an input tensor and thread masks from the above
function and returns a packed representation and metadata from applying
thread masks to the input tensor.
* `torch._sparse_semi_structured_apply_dense`
This function does the same thing as above but instead of returning the
tensor in the sparse representation it returns it in the dense
representation
The subclasses have also been updated to add a new
`prune_dense_static_sort`
classmethod to create sparse tensors with this format. I've added some
additional documentatino on how to calculate the compressed tensors
needed to create a SparseSemiStructuredTensor oneself.
To this end, there are two new helper functions added:
`sparse_semi_structured_tile`
`compute_compressed_swizzled_bitmask`
Differential Revision: [D56190801](https://our.internmc.facebook.com/intern/diff/D56190801)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122350
Approved by: https://github.com/cpuhrsch
This PR adds in fast semi-structured sparsification kernels to PyTorch.
These kernels allow for accelerated semi-structured sparsification
kernels in PyTorch.
The kernels have been added as aten native functions
In particular, three new functions have been added:
* `torch._sparse_semi_structured_tile`
This function will return the packed representation and metadata for
both X and X', as well as the thread masks. Note that this applies 2:4
sparsity in a 4x4 tile instead of a 1x4 strip as usual.
* `torch._sparse_semi_structured_apply`
This function takes in an input tensor and thread masks from the above
function and returns a packed representation and metadata from applying
thread masks to the input tensor.
* `torch._sparse_semi_structured_apply_dense`
This function does the same thing as above but instead of returning the
tensor in the sparse representation it returns it in the dense
representation
The subclasses have also been updated to add a new
`prune_dense_static_sort`
classmethod to create sparse tensors with this format. I've added some
additional documentatino on how to calculate the compressed tensors
needed to create a SparseSemiStructuredTensor oneself.
To this end, there are two new helper functions added:
`sparse_semi_structured_tile`
`compute_compressed_swizzled_bitmask`
Differential Revision: [D56190801](https://our.internmc.facebook.com/intern/diff/D56190801)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122350
Approved by: https://github.com/cpuhrsch
This PR adds in fast semi-structured sparsification kernels to PyTorch.
These kernels allow for accelerated semi-structured sparsification
kernels in PyTorch.
The kernels have been added as aten native functions
In particular, three new functions have been added:
* `torch._sparse_semi_structured_tile`
This function will return the packed representation and metadata for
both X and X', as well as the thread masks. Note that this applies 2:4
sparsity in a 4x4 tile instead of a 1x4 strip as usual.
* `torch._sparse_semi_structured_apply`
This function takes in an input tensor and thread masks from the above
function and returns a packed representation and metadata from applying
thread masks to the input tensor.
* `torch._sparse_semi_structured_apply_dense`
This function does the same thing as above but instead of returning the
tensor in the sparse representation it returns it in the dense
representation
The subclasses have also been updated to add a new
`prune_dense_static_sort`
classmethod to create sparse tensors with this format. I've added some
additional documentatino on how to calculate the compressed tensors
needed to create a SparseSemiStructuredTensor oneself.
To this end, there are two new helper functions added:
`sparse_semi_structured_tile`
`compute_compressed_swizzled_bitmask`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122350
Approved by: https://github.com/cpuhrsch
This PR adds in fast semi-structured sparsification kernels to PyTorch.
These kernels allow for accelerated semi-structured sparsification
kernels in PyTorch.
The kernels have been added as aten native functions
In particular, three new functions have been added:
* `torch._sparse_semi_structured_tile`
This function will return the packed representation and metadata for
both X and X', as well as the thread masks. Note that this applies 2:4
sparsity in a 4x4 tile instead of a 1x4 strip as usual.
* `torch._sparse_semi_structured_apply`
This function takes in an input tensor and thread masks from the above
function and returns a packed representation and metadata from applying
thread masks to the input tensor.
* `torch._sparse_semi_structured_apply_dense`
This function does the same thing as above but instead of returning the
tensor in the sparse representation it returns it in the dense
representation
The subclasses have also been updated to add a new
`prune_dense_static_sort`
classmethod to create sparse tensors with this format. I've added some
additional documentatino on how to calculate the compressed tensors
needed to create a SparseSemiStructuredTensor oneself.
To this end, there are two new helper functions added:
`sparse_semi_structured_tile`
`compute_compressed_swizzled_bitmask`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122350
Approved by: https://github.com/cpuhrsch
Summary:
This PR is a refactor of semi-structured sparsity support.
**deprecation**:
Before `torch.sparse.to_sparse_semi_structured` had a kwarg param
`transposed=False`, which has been removed. This kwarg was unused and
now thros a deprecation warning.
Namely, I've taken the subclassing implementation that xFormers has
created and brought it over to PyTorch, as part of our plan to upstream
runtime 2:4 sparsity.
I've also copied over all the op support that Daniel implemenented that
did not depend on the fast sparsification routines, into
`_sparse_semi_structured_ops.py`
With this subclass, all of our internal tests pass, as well as those in
xFormers.
The main change is that we now define a base subclass,
`SparseSemiStructuredTensor` that is inherited from for each of the
specific backends.
We also now can arbitrarily override the sparse dispatch table with
`_load_dispatch_table()`, idea being this is still general enough
where users don't need to modify pytorch source code to get their model
working.
This also adds in padding support and stores alg_id and fuse_transpose
as flags on the tensor, instead of hardcoding them.
There still remains two components in xFormers that will need to be
ported over eventually:
- the autograd functions (`Sparsify24`, `Sparsify24_like`)
- fast sparsification routines that they rely on
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117302
Approved by: https://github.com/alexsamardzic, https://github.com/HDCharles
