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This pull request adds support for triton kernels in `torch` and `torch/cuda`, and refactors and tests the existing triton kernel for BSR matrix multiplication. It also adds a test case to ensure that importing `torch` does not implicitly import `triton`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98403
Approved by: https://github.com/malfet, https://github.com/cpuhrsch
This PR enables sum tests for sparse sample inputs. Previously, the tests existed but were never run because the sum OpInfo instance was created without specifying `supports_sparse_*=True`. To avoid such mistakes in the future, the following PR https://github.com/pytorch/pytorch/pull/100392 enables the `supports_sparse_*` flags automatically when OpInfo creation specifies `sample_inputs_sparse_*_func`.
In addition, the PR applies several fixes to sum tests for sparse sample inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100391
Approved by: https://github.com/cpuhrsch
### Motivation of this PR
This patch is to migrate `spmm_reduce` from `torch-sparse` (a 3rd party dependency for PyG) to `torch`, which is a response to the initial proposal for fusion of **Gather, Apply Scatter** in Message Passing of GNN inference/training. https://github.com/pytorch/pytorch/issues/71300
**GAS** is the major step for Message Passing, the behavior of **GAS** can be classified into 2 kinds depending on the storage type of `EdgeIndex` which records the connections of nodes:
* COO: the hotspot is `scatter_reduce`
* CSR: the hotspot is `spmm_reduce`
The reduce type can be choose from: "max", "mean", "max", "min".
extend `torch.sparse.mm` with an `reduce` argument, maps to `torch.sparse_mm.reduce` internally.
`sparse_mm_reduce` is registered under the TensorTypeId of `SparseCsrCPU`, and this operator requires an internal interface `_sparse_mm_reduce_impl` which has dual outputs:
* `out` - the actual output
* `arg_out` - records output indices in the non zero elements if the reduce type is "max" or "min", this is only useful for training. So for inference, it will not be calculated.
### Performance
Benchmark on GCN for obgn-products on Xeon single socket, the workload is improved by `4.3x` with this patch.
Performance benefit for training will be bigger, the original backward impl for `sum|mean` is sequential; the original backward impl for `max|min` is not fused.
#### before:
```
----------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
----------------------------- ------------ ------------ ------------ ------------ ------------ ------------
torch_sparse::spmm_sum 97.09% 56.086s 97.09% 56.088s 6.232s 9
aten::linear 0.00% 85.000us 1.38% 795.485ms 88.387ms 9
aten::matmul 0.00% 57.000us 1.38% 795.260ms 88.362ms 9
aten::mm 1.38% 795.201ms 1.38% 795.203ms 88.356ms 9
aten::relu 0.00% 50.000us 0.76% 440.434ms 73.406ms 6
aten::clamp_min 0.76% 440.384ms 0.76% 440.384ms 73.397ms 6
aten::add_ 0.57% 327.801ms 0.57% 327.801ms 36.422ms 9
aten::log_softmax 0.00% 23.000us 0.10% 55.503ms 18.501ms 3
```
#### after
```
----------------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
----------------------------- ------------ ------------ ------------ ------------ ------------ ------------
aten::spmm_sum 87.35% 11.826s 87.36% 11.827s 1.314s 9
aten::linear 0.00% 92.000us 5.87% 794.451ms 88.272ms 9
aten::matmul 0.00% 62.000us 5.87% 794.208ms 88.245ms 9
aten::mm 5.87% 794.143ms 5.87% 794.146ms 88.238ms 9
aten::relu 0.00% 53.000us 3.35% 452.977ms 75.496ms 6
aten::clamp_min 3.35% 452.924ms 3.35% 452.924ms 75.487ms 6
aten::add_ 2.58% 348.663ms 2.58% 348.663ms 38.740ms 9
aten::argmax 0.42% 57.473ms 0.42% 57.475ms 14.369ms 4
aten::log_softmax 0.00% 22.000us 0.39% 52.605ms 17.535ms 3
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83727
Approved by: https://github.com/jgong5, https://github.com/cpuhrsch, https://github.com/rusty1s, https://github.com/pearu
As per title.
Additionally we also introduce support for:
- Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation).
- Batch support with broadcasting for either of the arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078
Approved by: https://github.com/cpuhrsch
Follow-up of #89582 to drop flags like `CUDA11OrLater` in tests. Note that in some places it appears that `TEST_WITH_ROCM` is _implicitly_ guarded against via the `CUDA11OrLater` version check, based on my best-guess of how `torch.version.cuda` would behave in ROCM builds, so I've added `not TEST_WITH_ROCM` in cases where ROCM wasn't previously explicitly allowed.
CC @ptrblck @malfet @ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92605
Approved by: https://github.com/ngimel
As per title.
Additionally we also introduce support for:
- Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation).
- Batch support with broadcasting for either of the arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078
Approved by: https://github.com/cpuhrsch
As per title.
Additionally we also introduce support for:
- Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation).
- Batch support with broadcasting for either of the arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078
Approved by: https://github.com/cpuhrsch
This PR is a copy of https://github.com/pytorch/pytorch/pull/90849 that merge was reverted.
The PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI:
`torch.sparse.check_sparse_tensor_invariants` class provides different ways to enable/disable the invariant checking.
`torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden.
The PR fixes https://github.com/pytorch/pytorch/issues/90833
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92094
Approved by: https://github.com/cpuhrsch
### Target and Background
This PR is improving the performance of `sampled_addmm` on CPU device. This is part of effort for improving PyG performance on CPU for GNN training/inference.
The current implementation is a reference design which converts `SparseCSR` tensor back to dense tensor and then do the addmm and convert back to `SparseCSR` again: this is going to be very slow and won't be able to run most of the datasets under https://github.com/snap-stanford/ogb (convert to dense would trigger `OOM`).
### Benchmarks
Right now we don't have any hands-on benchmark or workload to test this since this operator is not used in PyG yet. I fetched the dataset from `ogb-products` where:
* number of nodes: 2.4 * 10^6
* number of edges: 1.26 * 10^8
* number of features: 128
So if we store the **adjacency matrix** is dense, it is going to be 2.4 * 2.4 * 4 * 10^12 bytes, this will be OOB on current code. I abstract the first 1k rows to compare, **1100x** speedup:
CPU: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz, dual socket, 20 cores per socket.
```
### before: run 1000 rows from the whole dataset
sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1212.000 ms!
### after: run 1000 rows from the whole dataset
sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1.102 ms!
### after: run the whole dataset
sampled_addmm: running dataset ogb-products (the whole dataset) 2449029 rows: each iter takes 873.306 ms!
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90978
Approved by: https://github.com/pearu, https://github.com/cpuhrsch
This PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI:
- `torch.enable_check_sparse_tensor_invariants` and `torch.is_check_sparse_tensor_invariants_enabled` functions to globally enable/disable the invariant checks and to retrieve the state of the feature, respectively
- `torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden.
The PR also fixes https://github.com/pytorch/pytorch/issues/90833
# Main issue
*The following content is outdated after merging the PRs in this ghstack but kept for the record.*
The importance of this feature is that when enabling the invariants checks by default, say, via
<details>
```
$ git diff
diff --git a/torch/__init__.py b/torch/__init__.py
index c8543057c7..19a91d0482 100644
--- a/torch/__init__.py
+++ b/torch/__init__.py
@@ -1239,3 +1239,8 @@ if 'TORCH_CUDA_SANITIZER' in os.environ:
# Populate magic methods on SymInt and SymFloat
import torch.fx.experimental.symbolic_shapes
+
+# temporarily enable sparse tensor arguments validation in unsafe
+# constructors:
+
+torch._C._set_check_sparse_tensor_invariants(True)
```
</details>
a massive number of test failures/errors occur in test_sparse_csr.py tests:
```
$ pytest -sv test/test_sparse_csr.py
<snip>
==== 4293 failed, 1557 passed, 237 skipped, 2744 errors in 69.71s (0:01:09) ====
```
that means that we are silently constructing sparse compressed tensors that do not satisfy the sparse tensor invariants. In particular, the following errors are raised:
```
AssertionError: "resize_as_sparse_compressed_tensor_: self and src must have the same layout" does not match "expected values to be a strided and contiguous tensor"
RuntimeError: CUDA error: device-side assert triggered
RuntimeError: `col_indices[..., crow_indices[..., i - 1]:crow_indices[..., i]] for all i = 1, ..., nrows are sorted and distinct along the last dimension values` is not satisfied.
RuntimeError: expected col_indices to be a strided and contiguous tensor
RuntimeError: expected row_indices to be a strided and contiguous tensor
RuntimeError: expected values to be a strided and contiguous tensor
RuntimeError: for_each: failed to synchronize: cudaErrorAssert: device-side assert triggered
RuntimeError: tensor dimensionality must be sum of batch, base, and dense dimensionalities (=0 + 2 + 0) but got 3
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90849
Approved by: https://github.com/amjames, https://github.com/cpuhrsch
### Target and Background
This PR is improving the performance of `sampled_addmm` on CPU device. This is part of effort for improving PyG performance on CPU for GNN training/inference.
The current implementation is a reference design which converts `SparseCSR` tensor back to dense tensor and then do the addmm and convert back to `SparseCSR` again: this is going to be very slow and won't be able to run most of the datasets under https://github.com/snap-stanford/ogb (convert to dense would trigger `OOM`).
### Benchmarks
Right now we don't have any hands-on benchmark or workload to test this since this operator is not used in PyG yet. I fetched the dataset from `ogb-products` where:
* number of nodes: 2.4 * 10^6
* number of edges: 1.26 * 10^8
* number of features: 128
So if we store the **adjacency matrix** is dense, it is going to be 2.4 * 2.4 * 4 * 10^12 bytes, this will be OOB on current code. I abstract the first 1k rows to compare, **1100x** speedup:
CPU: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz, dual socket, 20 cores per socket.
```
### before: run 1000 rows from the whole dataset
sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1212.000 ms!
### after: run 1000 rows from the whole dataset
sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1.102 ms!
### after: run the whole dataset
sampled_addmm: running dataset ogb-products (the whole dataset) 2449029 rows: each iter takes 873.306 ms!
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90978
Approved by: https://github.com/pearu, https://github.com/cpuhrsch
This PR extends the `Tensor.to_sparse()` method to `Tensor.to_sparse(layout=None, blocksize=None)` in a BC manner (`layout=None` means `layout=torch.sparse_coo`).
In addition, the PR adds support for the following conversions:
- non-hybrid/hybrid COO tensor to CSR or CSC or a COO tensor
- short, bool, byte, char, bfloat16, int, long, half CSR tensor to a BSR tensor
and fixes the following conversions:
- hybrid COO to COO tensor
- non-batch/batch hybrid BSR to BSR or BSC tensor
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89502
Approved by: https://github.com/amjames, https://github.com/cpuhrsch
As per title. This implementation is not the most optimal and could be improved albeit with native kernels (i.e. block matching need not be materialized).
Compared to existing kernels it offers:
- Half float support (In fact, any dtype that supports `matmul` will work).
- Arbitrary block sizes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85551
Approved by: https://github.com/amjames, https://github.com/cpuhrsch
Fixes#84999
This PR
- uses device option to set sparse compressed tensor instance device
- enables shape and device inference tests that was disabled due to an oversight
- fixes a bug in shape inference of hybrid tensors
- fixes a bug in to_sparse_bsr of a cuda tensor
- updates tests that catch the above bugs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85240
Approved by: https://github.com/cpuhrsch
A longstanding confusion in the implementation of fake tensor and proxy tensor is what to do about torch.ops.aten.sym_sizes and related calls. In particular, when you have a tensor that (1) has symbolic shapes and (2) has a `__torch_dispatch__` call, previously, you would always get `__torch_dispatch__` calls for sizes/strides query, *even if you didn't request it* via the dispatch kwargs in `make_wrapper_subclass`.
The reason for this is because we were previously mixing several concepts: "I want to dispatch to Python", "I want to call a virtual method" and "I have dynamic shapes". A single boolean variable controlled all of these things, and so it was not possible to understand inside TensorImpl what the user had actually originally requested.
In this PR, we track each of these concepts individually so that we can preserve user intent. Then, we combine these into a single "policy" variable that controls whether or not we can use the fastpath or not. For the policy to trigger, we only need one of the exceptional cases to be true.
Billing of changes:
* Rename `set_sizes_strides_policy` to `set_custom_sizes_strides`; in general, you cannot DIRECTLY set policy; you have to indirectly set it by the public functions.
* Some helpers for sizes and strides, since it's more complicated (as it is an enum, rather than just bools as is the case for device and layout). `matches_python_custom` is used to test the Python dispatch user ask. `matches_policy` does the policy test (only used in the user facing functions.)
* I reorged the accessor methods so that they are more logical. This makes the diff bad, so I recommend reading the final code directly.
* The default custom implementations now more reliably call their default() implementations
* As bonus refactor, I devirtualized some functions that don't need to be virtual
* `set_sym_sizes_and_strides` is renamed to `set_sizes_and_strides` to make it easier to use in template contexts; it optionally takes a storage offset now so you can set all three values at the same time. If you use the SymInt overload but there are no symbolic integers, we give you a normal resize.
* This adds `sym_storage_offset` since we had that in the symbolic shapes branch and there's no reason not to put it in (and it reduces merge conflicts)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84641
Approved by: https://github.com/wconstab
Enables:
test_bmm_cuda_float64
test_bmm_deterministic_cuda_float64
test_csr_matvec_cuda_complex128
test_csr_matvec_cuda_complex64
test_csr_matvec_cuda_float32
test_csr_matvec_cuda_float64
To enable the above tests had to add some more hip mappings for the hipification process.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78939
Approved by: https://github.com/pruthvistony, https://github.com/malfet
A todo in the tests which should have been removed and addressed before the initial PR landed was left, and so left holes in testing BSR-> Dense. This addresses the underlying issue and removes the hole in test coverage. #8071 Introduces more comprehensive test coverage for sparse compressed <-> Dense conversion in general.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82120
Approved by: https://github.com/nikitaved, https://github.com/bhosmer
Thus avoiding `TypeError: 'float' object cannot be interpreted as an integer` when trying to create integer tensor from floating point values
Use `c10::checked_convert` to detect overflows during tensor construction from scalars. Modify sparse_csr test that violated this rule
Fixes#69319
Tested in #81233
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81372
Approved by: https://github.com/ezyang, https://github.com/ngimel
