Add CLI support (M,N,K) to GEMMsBenchmark #1721
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Apr 25, 2023
| {M?M:16, N?N:256, K?K:512}, | ||
| {M?M:128, N?N:128, K?K:128}, | ||
| {M?M:256, N?N:512, K?K:256}, | ||
| {M?M:1024, N?N:1024, K?K:1024}, |
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We should parse argc and argv before calling performance_test, and just pass M, N, K to performance_test instead
Add CLI support (M,N,K) to GEMMsBenchmark
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liligwu
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May 5, 2023
* using different mechanism for host mapped pinned memory (#1638)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1638
This diff adds another mechanism for allocating the host mapped pinned memory to reduce adverse affect on other processes running on the same host when one process is doing some large allocations.
Reviewed By: zyan0, jianyuh
Differential Revision: D43950253
fbshipit-source-id: 41a434cb63354509d32e00c851c5f3a2d68be686
* disable use_cpu test (#1635)
Summary:
This PR addresses the issue https://github.com/pytorch/FBGEMM/issues/1636
akin to https://github.com/pytorch/FBGEMM/blob/8616ed701015f8b9e4c2825ce592b204b4cfaf28/fbgemm_gpu/test/split_table_batched_embeddings_test.py#L1009
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1635
Reviewed By: shintaro-iwasaki
Differential Revision: D44033725
Pulled By: q10
fbshipit-source-id: 49f28fc2f1c20948a42728eebf3defc5195baa5d
* Update API interface and reroute backend for exact_rowwise_adagrad FE when using freq based methods (#1352)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1352
1. Update interface to accomadate rowwise_adagrad_with_counter.
2. Route backend for rowwise_adagrad to the new rowwise_adagrad_with_counter when freq based methods (e.g. freq sgd, counter adjusted regularization) are used.
Reviewed By: csmiler
Differential Revision: D36788395
fbshipit-source-id: 8eb5da8a5c8b52bc1e237af1054aac9f7245c443
* Remove sync point in jagged_dense_elementwise_add_jagged_output backward (#1642)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1642
Remove sync point in jagged_dense_elementwise_add_jagged_output backward
Reviewed By: brad-mengchi
Differential Revision: D44039901
fbshipit-source-id: 8e7e23e4d9e01359e67e5b166adc57f894a1224d
* Add Comprehensive Build Instructions and Isolate CPU and ROCm Builds (#1639)
Summary:
- Remove `.post0` suffix from the autogenerated package version
- Document the full FBGEMM_GPU OSS build process in a separate Markdown file
- Remove installation of packages not needed for ROCm builds
- Migrate CPU and ROCm jobs to run on top of Docker containers instead of bare metal instances
- Update GitHub workflow configuration to cancel previous jobs for a PR if a new commit is pushed to the PR
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1639
Reviewed By: shintaro-iwasaki
Differential Revision: D44076312
Pulled By: q10
fbshipit-source-id: 6b2d083022feb7421b26da2d998678e00c11f283
* include cstdint (#1640)
Summary:
fix build with gcc-13
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1640
Reviewed By: shintaro-iwasaki
Differential Revision: D44044422
Pulled By: q10
fbshipit-source-id: 692ec9c34f4aaf726294a2b643fbceabf8159033
* Add support for group size > 54 in group_index_select (#1611)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1611
If group size is larger than 54, internally breaks the group down into
smaller groups (each subgroup size is less than or equal to 54).
Reviewed By: jianyuh
Differential Revision: D43585937
fbshipit-source-id: bf14eeb79881a5737dcf7660e3e0f56d21f7b326
* Implement cache miss emulation in UVM_CACHING (#1637)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1637
Enforce cache misses (even if trace-driven testing doesn't experience cache miss due to limited trace size) so that we can evaluate performance under cache misses.
Note that it's not exactly cache misses; enforce access to UVM by overriding lxu_cache_locations -- N / 256 requests.
Reviewed By: YuzeDaiMeta
Differential Revision: D42194019
fbshipit-source-id: ab04c1cc7a749e84d605cfe4f1687489ceab5725
* Add TensorAccessor with memcheck (#1602)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1602
Illegal memory access is a common problem during GPU kernel execution.
The FBGEMM GPU relies on PyTorch's `C10_CUDA_KERNEL_LAUNCH_CHECK()` and
the CUDA runtime to detect such problems and throw an error. However,
there are a few known issues with this approach.
(1) `C10_CUDA_KERNEL_LAUNCH_CHECK()` detects errors on the host.
However, due to the non-blocking, asynchronous nature of GPU kernel
execution, the error is caught on the host at a later point than where
the problematic kernel was launched. This can cause the stack trace
to be inaccurate and make debugging more difficult. Although the
issue can be fixed by running the code with `CUDA_LAUNCH_BLOCKING=1`,
this can change the state of the execution and cause Heisenbugs.
(2) Not all illegal memory accesses are caught by the runtime. This
means that the system may not always throw an error when illegal
memory access occurs.
(3) Although the runtime throws an error for illegal memory access, it
is difficult to pinpoint the specific kernel and memory buffer/address
that is causing the problem.
For all the aforementioned reasons, we attempt to catch and throw an
error as soon as possible in the kernel when illegal memory accesses
occur in FBGEMM GPU. We introduce the `FBGEMM_GPU_MEMCHECK` flag
to enable memory checking during compile time. We copy PyTorch's
`TensorAccessor.h` into the FBGEMM GPU and extend it to check every
memory access through the `PackedTensorAccessor`. If an invalid memory
access occurs, we throw an error using `CUDA_KERNEL_ASSERT`. The error
message includes the name of the tensor and the kernel that caused the
problem.
If `FBGEMM_GPU_MEMCHECK` is enabled, FBGEMM operators will use
`fbgemm::PackedTensorAccessor`. Otherwise, they will use
`at::PackedTensorAccessor`
`FBGEMM_GPU_MEMCHECK` integration in FBGEMM ops will be done in
subsequent diffs
Reviewed By: r-barnes
Differential Revision: D43421838
fbshipit-source-id: c8ef04970d94bb097cb5f09b42f994db72845167
* Fix compiling with Xcode 14.3 (#1648)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1648
This hack is not needed in Xcode 14.3 anymore, where the clang version is 14.0.3. So change the workaround to only include up to 14.0.2.
Reviewed By: MatzeB
Differential Revision: D44130421
fbshipit-source-id: 1fb2948567941bdf6ee9487ccfaa9dfb2caf92dd
* Add support for building FBGEMM_GPU against Python 3.11 in OSS (#1646)
Summary:
- Parallelize the FBGEMM CI builds to build and test static and shared libraries independently instead of in serial
- Move the FBGEMM CI builds to run inside Docker containers
- Add support for building FBGEMM_GPU against Python 3.11 in OSS
- Move all FBGEMM_GPU nightly and release build jobs to run inside `amazonlinux:2023` Docker container
- Assuming no build errors or resource starvation, the full OSS build process now runs under 30 minutes.
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1646
Reviewed By: shintaro-iwasaki
Differential Revision: D44157228
Pulled By: q10
fbshipit-source-id: 6403ea9955856157785c50837b0b8e4c0cd26d53
* Remove magic numbers from fbgemm/Types.h (#1629)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1629
Replaces magic numbers with constexpr variables
Reviewed By: sryap
Differential Revision: D43776442
fbshipit-source-id: 5cef7566816f8730f5daa08948ee3260367787aa
* added check to avoid div 0 errors in cache report (#1645)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1645
as in title
Reviewed By: jianyuh
Differential Revision: D44096435
fbshipit-source-id: a7a87a14ffecc2fb6e0be74d199d385357946672
* jagged_dense_bmm operator optimization (#1643)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1643
This diff optimizes the jagged_dense_bmm operator with the following optimizations:
* tiling across thread blocks, and use GPU shared memory for thread block
* tiling across threads within a thread block, and use registers for each thread
Reviewed By: brad-mengchi
Differential Revision: D43674845
fbshipit-source-id: 85f0abf89fa958f79636ef59c3070a1c569b73c2
* jagged_dense_bmm: fix ROCm test failures (#1655)
Summary:
This patch fixes test failures on AMD GPUs.
1. Remove `__restrict__ `. I don't think it is needed even for CUDA, but it confuses HIPCC.
2. Use `uint32_t` instead of `auto`: old ROCm (including ROCm <= 5.3) does not have `+=` operator for the type of `blockIdx.z`, causing a compilation error. We observed that this issue is fixed in ROCm 5.4.3, but let's use `uint32_t` for now. We should revisit and use `auto` later. See this for details: https://github.com/ROCm-Developer-Tools/hipamd/commit/86a1634c642daeda1e984d4124bcc2aeba5c4e19
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1655
Test Plan: GitHub Actions' AMD CI
Reviewed By: q10, brad-mengchi
Differential Revision: D44242622
Pulled By: shintaro-iwasaki
fbshipit-source-id: c9b88155ebf1ed881b2d03e3be0e8991b4b30174
* Support embedding dim 1024 ~ 2048 (#1656)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1656
wushirong reported the failure on https://fburl.com/code/hae91ra7 .
- The embedding config is from f418615450 .
- `max_int8_128b_rows` is 10 --> D = 1280
Our embedding dim has grown to 1024 + ?
Note that the static shared memory can only go up to 48 KB:
> Kernels relying on shared memory allocations over 48 KB per block are architecture-specific, as such they must use dynamic shared memory (rather than statically sized arrays)
in https://docs.nvidia.com/cuda/cuda-c-programming-guide/
for ptx shared mem error:
```
[2023-03-21T22:04:33.899-07:00] ptxas error : Entry function '_ZN4nbit60INT8_split_embedding_codegen_forward_weighted_kernel_small_LIiN3c104HalfELm2ELm4ELm4E
Lm8ELm16ELb1EEEvN2at27GenericPackedTensorAccessorIhLm1ENS3_17RestrictPtrTraitsElEES6_NS4_IiLm1ES5_iEENS4_IlLm1ES5_iEENS4_IhLm1ES5_iEES7_N10fbgemm_gpu12FixedDiv
isorENS4_IT_Lm1ES5_iEESD_llNS4_IfLm1ES5_iEENS4_IT0_Lm2ES5_iEENS4_IhLm2ES5_lEES7_' uses too much shared data (0x10080 bytes, 0xc000 max)
```
Currently we reduce `InputRowsInFlight` to bypass the issue (the static shared memory used in the kernel is
```
typedef uint4 AllBuffers[WarpsPerBlock][OutputRowsPerThread][InputRowsInFlight][NumUint4LoadsPerRow];
__shared__ AllBuffers buffers;
```
Long term, we can change the static shared memory to dynamic shared memory, and increase the shared memory size to be 64 KB +.
Reviewed By: wushirong
Differential Revision: D44270081
fbshipit-source-id: 367ae838ea073dfe58d859ea3c0e6c7190beca6a
* Containerize the remaining FBGEMM_GPU CI jobs (#1658)
Summary:
- Containerize the remaining FBGEMM_GPU CI jobs
- Add Conda cleanups to make PyTorch and CUDA installs more reliable
- Update post-install checks for PyTorch to work with ROCm
- Update the CI to continue running on jobs that fail on just a few variants
- Use PIP to install PyTorch GPU nightly as the nightly packages show up in PIP more reliably than in Conda
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1658
Reviewed By: shintaro-iwasaki
Differential Revision: D44306708
Pulled By: q10
fbshipit-source-id: 5f0862f18eca7151759d9983aa97849222539d7d
* Add tbe_input_combine_with_length for GPU (#1647)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1647
Implement `tbe_input_combine_with_length` for GPU. The operator takes
3 lists of tensors (`indices`, `lengths`, and `per_sample_weights`)
and concatenates each one into a single tensor. Implicit type casting
is also performed if the input types are different from the output
types. `indices` and `lengths` tensors can be of type `int32_t` or
`int64_t`. The outputs for `indices` concatenation and `lengths`
concatenation are fixed to `int32_t`. `per_sample_weights` must be
`float`.
Reviewed By: bangshengtang
Differential Revision: D44076452
fbshipit-source-id: f6ce8628e7345093bb55835f9523870c2914516f
* jagged_jagged_bmm operator optimization (#1644)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1644
This diff optimizes the jagged_jagged_bmm operator using tiling across thread blocks and GPU shared memory.
Reviewed By: brad-mengchi
Differential Revision: D44029528
fbshipit-source-id: fa5cd5a26893f935427bce5efb7dfcc731c3f47d
* Specify device to emulate_cache_miss kernel (#1660)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1660
When enabled emulate cache miss, it caused illegal memory access, if we're using more than one GPU. It turns out that previous diff didn't specify device within emulate_cache_miss kernel.
This diff fixes it. In addition, cleaned up a bit (e.g., no need to used index_t based kernel launch for emulate_cache_miss kernel, as lxu_cache_locations is always with int32_t.
Reviewed By: sryap, YuzeDaiMeta
Differential Revision: D44340131
fbshipit-source-id: d99ba2364e9030cbca6c1166e578d24d99646bb1
* Add C++17 Support to FBGEMM and FBGEMM_GPU OSS builds (#1652)
Summary:
- Add C++17 support for the entire FBGEMM_GPU build
- Add C++17 support for the entire FBGEMM build
- Update FBGEMM tests and benchmarks to be C++17-compatible
- Make FBGEMM builds output more logging
- Cherry-pick code changes from D43776442 v4 now that C++17 is fully supported
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1652
Reviewed By: shintaro-iwasaki
Differential Revision: D44287321
Pulled By: q10
fbshipit-source-id: 4bf2bcf66d528939865d42b6deafc470bee55d17
* Prune CPU/GPU TBE optimizer codegen (#1659)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1659
This diff aims to reduce the build time and libary size of
`//deeplearning/fbgemm/fbgemm_gpu/codegen:embedding_ops`.
The diff modifies the build target to generate and compile only the
necessary files. This is based on the fact that CPU and GPU do not
support all optimizers in `SplitTBE`. (Before this diff, all optimizers
were generated and compiled for both CPU and GPU.)
The following is the list of supported optimizers
|OptimType|Generated optimizer|Supported on CPU|Supported on GPU|
|EXACT_ADAGRAD|adagrad|x|x|
|EXACT_ROWWISE_ADAGRAD|rowwise_adagrad_with_counter|x|x|
||rowwise_adagrad|x|x|
|EXACT_ROWWISE_WEIGHTED_ADAGRAD|rowwise_weighted_adagrad|x|x|
|EXACT_SGD|sgd|x|x|
|SGD|approx_sgd|x|x|
|ROWWISE_ADAGRAD|approx_rowwise_adagrad_with_counter|x||
||approx_rowwise_adagrad|x||
|ADAM|adam||x|
|LAMB|lamb||x|
|LARS_SGD|lars_sgd||x|
|PARTIAL_ROWWISE_ADAM|partial_rowwise_adam||x|
|PARTIAL_ROWWISE_LAMB|partial_rowwise_lamb||x|
|-|rowwise_adagrad_with_weight_decay|||
|-|approx_rowwise_adagrad_with_weight_decay|||
Note: x = supported
Reviewed By: jianyuh
Differential Revision: D44326540
fbshipit-source-id: 02413256b4a675f13ada8e8820820cb5112cb405
* Fix the Documentation Build Job (#1673)
Summary:
- Rewrite the documentation builds job to use the build infrastructure tooling
- Rename workflow files for consistency
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1673
Reviewed By: shintaro-iwasaki
Differential Revision: D44472660
Pulled By: q10
fbshipit-source-id: 60434c1f7098b7efa8c750133bb22f14fc98d5dc
* Back out "Prune CPU/GPU TBE optimizer codegen" (#1675)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1675
Original commit changeset: 02413256b4a6
Original Phabricator Diff: D44326540
Reviewed By: q10, jianyuh
Differential Revision: D44475251
fbshipit-source-id: 5be66944a833e03a2737fc6d1baaa5c351455b2c
* Prepare bounds_check_indices for VBE (#1633)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1633
Prepare `bounds_check_indices` for variable batch size TBE (VBE).
- Update the frontend API to accept VBE args
- Update the backend logic to process VBE data
Reviewed By: jianyuh
Differential Revision: D43253703
fbshipit-source-id: 2870f0c41a96265650281a9b6362d4e6dc48009b
* Move pruning/index_remapping support to embedding inplace update files (#1667)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1667
As title. This diff moves pruning/index_remapping support to embedding inplace update files.
Reviewed By: jianyuh
Differential Revision: D44409419
fbshipit-source-id: 93fc91d83502eb95cb0feca2a8a03b003c336078
* jagged_softmax forward optimization (#1661)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1661
This diff optimizes jagged_softmax forward with more efficient reduction from cub library.
Reviewed By: brad-mengchi
Differential Revision: D44161021
fbshipit-source-id: bf2e059d14ef4d7ad311edac65155a463ba653ff
* jagged_softmax backward optimization (#1662)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1662
This diff optimizes jagged_softmax backward with more efficient reduction from cub library
Reviewed By: brad-mengchi
Differential Revision: D44205819
fbshipit-source-id: cd1d7a886d6ba68201dc1ad782c2e8cde7ff706b
* multi-gpu all_to_one improvements (#1674)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1674
improved multi-gpu all_to_one with:
1. new intermediate hop selection taking advantage of distinct NVLinks
2. overlapping of intermediate hop transfers with each-other and with direct-peer transfers
Reviewed By: doehyun
Differential Revision: D44285941
fbshipit-source-id: 0202083f04388b5ba60b8155809433f334993ef4
* Extract and export weights offsets/placements initialization functions (#1669)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1669
Extract portions initializing the weights_placements/offsets tensors into separate functions and jit.export them.
SplitState is converted to a NamedTuple since we can't jit.script a dataclass that also holds an enum.
Reviewed By: houseroad
Differential Revision: D44338256
fbshipit-source-id: e1c12e5956f7217d51cd190958c3764d220e521d
* Fix the ROCm Test Job (#1668)
Summary:
- Clean up the ROCm test job and re-enable ROCm testing on the rocm instances.
- Update the build scripts framework to build FBGEMM_GPU against the correct hardware target that it is intended to be tested on. One thing that was discovered was that if FBGEMM_GPU was built with `PYTORCH_ROCM_ARCH=gfx90a` but run on `gfx908` target, the tests will fail with a segfault. While the failure is expected, the segfault can be unfriendly and confusing for users.
- Enable correct compilation of `merge_pooled_embeddings` operator under ROCm
- Fix existing code in `jagged_tensor_ops` from PR https://github.com/pytorch/FBGEMM/issues/1661 and https://github.com/pytorch/FBGEMM/issues/1662 that break its compilation under ROCm 5.3
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1668
Reviewed By: shintaro-iwasaki
Differential Revision: D44453594
Pulled By: q10
fbshipit-source-id: 2030cd0e00c6ff9694c2783dfd62c31cf5543da2
* Use exported functions instead of calling initialize_weights in weights loading (#1676)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1676
Export a function to reset the embedding specs by target location
Reviewed By: RoshanPAN, houseroad
Differential Revision: D44338258
fbshipit-source-id: 502733e9f3a164450a02656d2822492fbf69f994
* Extract index remappings array initialization and jit.export it (#1670)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1670
ATT
Reviewed By: RoshanPAN, houseroad
Differential Revision: D44338257
fbshipit-source-id: c091666c7a4d294c283f5e3774d0494089fc3478
* Disable COUNTER in FBGEMM test (#1683)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1683
Disable FBGEMM test on COUNTER mode temporarily.
Reviewed By: sryap
Differential Revision: D44589052
fbshipit-source-id: f2af6f9e3cce75d4c599c4708055e5f52ac705e2
* update hipify_torch and remove manual mapping of C10 macros (#1682)
Summary: Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1682
Reviewed By: shintaro-iwasaki
Differential Revision: D44599348
Pulled By: q10
fbshipit-source-id: 8f968a7c21b09358eac070a35ee15d5b767ea94c
* Install NVIDIA Drivers on Instances Missing the Drivers (#1684)
Summary:
- Use the pytorch/test-infra action ot install NVIDIA drivers properly if the instance is missing the drivers
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1684
Reviewed By: shintaro-iwasaki
Differential Revision: D44603925
Pulled By: q10
fbshipit-source-id: 712bdf5c2af67c5a6f540567abcc47ed892912c1
* Clean up the linting job (#1686)
Summary:
Sumary:
- Clean up the linting job to use the build scripts infrastructure
- Delete the Conda prefix directory before creating a new environment, if it exists
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1686
Reviewed By: shintaro-iwasaki
Differential Revision: D44646234
Pulled By: q10
fbshipit-source-id: d754efeadffb265c9e55bc302606fc1e60ef8b51
* reduce_to_one (#1571)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1571
reduce_to_one for row-wise sharding in inference
Similar approach to all_to_one but without having the source waiting for target to be ready for potential WAR and WAW dependency violation because in this reduce_to_one implementation we create a new destination tensor.
Reviewed By: xing-liu, jianyuh
Differential Revision: D34263436
fbshipit-source-id: 7b1630b395311cfd6fef124113436f87f51a6fba
* Reorganize the build scripts (#1685)
Summary: Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1685
Reviewed By: r-barnes, shintaro-iwasaki
Differential Revision: D44654808
Pulled By: q10
fbshipit-source-id: a58987b4a3970139bba72db8cecc89c0256fba76
* Prune CPU/GPU TBE optimizer codegen (#1678)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1678
This diff aims to reduce the build time and libary size of
`//deeplearning/fbgemm/fbgemm_gpu/codegen:embedding_ops`.
[1/2] Update `lookup_invoker` to enable the function invoker based on
`has_cpu_support` and `has_gpu_support`
[2/2] Update the code generation part
The diff modifies the build target to generate and compile only the
necessary files. This is based on the fact that CPU and GPU do not
support all optimizers in `SplitTBE`. (Before this diff, all optimizers
were generated and compiled for both CPU and GPU.)
The following is the list of supported optimizers
|OptimType|Generated optimizer|Supported on CPU|Supported on GPU|
|EXACT_ADAGRAD|adagrad|x|x|
|EXACT_ROWWISE_ADAGRAD|rowwise_adagrad_with_counter|x|x|
||rowwise_adagrad|x|x|
|EXACT_ROWWISE_WEIGHTED_ADAGRAD|rowwise_weighted_adagrad|x|x|
|EXACT_SGD|sgd|x|x|
|SGD|approx_sgd|x|x|
|ROWWISE_ADAGRAD|approx_rowwise_adagrad_with_counter|x||
||approx_rowwise_adagrad|x||
|ADAM|adam||x|
|LAMB|lamb||x|
|LARS_SGD|lars_sgd||x|
|PARTIAL_ROWWISE_ADAM|partial_rowwise_adam||x|
|PARTIAL_ROWWISE_LAMB|partial_rowwise_lamb||x|
|-|rowwise_adagrad_with_weight_decay|||
|-|approx_rowwise_adagrad_with_weight_decay|||
Reviewed By: q10
Differential Revision: D44484764
fbshipit-source-id: f04710e66498bdcbdad619d48411c2403316901c
* thread tiling for jagged_jagged_bmm (#1691)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1691
This diff adds thread tiling optimization in jagged_jagged_bmm operator, where each thread will process a tile of elements instead of one. The implementation is similar to the one applied to jagged_dense_bmm: D43674845.
Reviewed By: brad-mengchi
Differential Revision: D44764339
fbshipit-source-id: ca4cf257bac755ab97754fdc6605072cfbfb1c4d
* tune the tile sizes for jagged_dense_bmm (#1692)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1692
Tune the tile sizes based on the input tensor size. If M > N, then use larger tile size in M dimension, otherwise use larger tile size in N dimension.
Reviewed By: brad-mengchi
Differential Revision: D44791699
fbshipit-source-id: 348a66089d781e9fef141b63d7a56e6dfa5da905
* Populate supported optims to match OSS Pytorch state dict (#1632)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1632
ATT.
Reviewed By: jianyuh
Differential Revision: D43887969
fbshipit-source-id: 048ff61a925113b29c547abf20d7acdc4a50b8d7
* Build Scripts and README Improvements (#1695)
Summary:
- Update build scripts to print out cc, c++, and nvcc preprocessor defines
- Print out all undefined symbols in the output library after build to inspect whether or not templates have been un-instantiated
- Handle the case where `TORCH_CUDA_ARCH_LIST` is pre-defined in the environment
- Clean up the FBGEMM_GPU READMEs to consolidate all FBGEMM_GPU build instructions into `docs/BuildInstructions.md`
- Fix the build badges for FBGEMM and FBGEMM_GPU
- Add Slack contact information to the READMEs
- Remove deprecated GitHub workflows and build scripts in favor of the new scripts, which cover all the functionality of the old scripts
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1695
Reviewed By: shintaro-iwasaki
Differential Revision: D44901368
Pulled By: q10
fbshipit-source-id: bef6045347c905a051970e4e5f8630175e0f5ef6
* Add Documentation to Work Around GCC 12 Regressions (#1697)
Summary: Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1697
Reviewed By: shintaro-iwasaki
Differential Revision: D44935915
Pulled By: q10
fbshipit-source-id: e1bdd4ebff18bd9708208a5b659ef9a93ebc866a
* Fix build instructions (#1701)
Summary:
This change fixes a missing step (cd) in the build instructions.
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1701
Reviewed By: sryap
Differential Revision: D45011147
Pulled By: q10
fbshipit-source-id: 704ce5bd3cfbd62c31f434c830a7300e5d645024
* Fix a build error from -Wno-unused-but-set-variable (#1702)
Summary:
This project is compiled with -Wall and -Werror (see https://github.com/pytorch/FBGEMM/pull/868) and is throwing an error for the unused variable here. This code appears to be debugging code that was used to verify that the function it's contained in was originally implemented properly so the most straightforward solution is to just remove it.
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1702
Reviewed By: sryap
Differential Revision: D45011174
Pulled By: q10
fbshipit-source-id: 2c252cfa6063789371f5fba5f642c2f4fb72455f
* Fix exception in QuantUtilsTest (#1703)
Summary:
This test mistakenly calls reserve() to set a vector's length instead of resize(). reserve() allocates memory for the specified number of elements, but does not actually increase the number of elements that can legally be stored in the vector. This test runs with ASAN enabled which is catching this illegal access and causing the test to fail.
This change fixes the code to instead call resize(); the test now passes.
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1703
Reviewed By: sryap
Differential Revision: D45011317
Pulled By: q10
fbshipit-source-id: 2840d7bfcfb46ca1523f55e77a3834a1d561c045
* Support EXACT_ADAGRAD in `get_optimizer_state` (#1700)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1700
This diff support `get_optimizer_state` for exact_adagrad.
Exact_adagrad is not supported in `get_optimizer_state`. However, this is needed for creating fused optimizer in torchrec.
Reviewed By: r-barnes
Differential Revision: D44963975
fbshipit-source-id: e2f523dfc1e1d17a4925e7ce4a9e65829f1cf1b0
* Split the Rendering of `embedding_forward_quantized_split_template.cu` into Smaller Files (#1694)
Summary:
`embedding_forward_quantized_split_template.cu` is a very large jinja template that renders 30+ C++ templates, which are then instantiated to over 600+ kernel functions. There are three sets of jinja templates in `embedding_forward_quantized_split_template.cu`: those related to `int_nbit_split_embedding_*`, `pruned_hashmap_lookup_*` and `pruned_array_lookup_*`..
Currently, the rendering produces a single file, which takes a large amount of time to compile. This PR does two things at a high level. First, it breaks up the jinja template into multiple jinja templates. Then, it forces each of these smaller jinja templates to render multiple source files instead of a single source file. This change will enable build parallelization and overall build time savings.
Details:
- Port improvements to `embedding_forward_quantized_split_template.cu` from D44707812
- Move the non-jinja-template code inside `embedding_forward_quantized_split_template.cu` over to `embedding_forward_template_helpers.cuh`
- Move `pruned_hashmap_lookup_*` and `pruned_array_lookup_*` sets of jinja templates out to non-jinja-template `embedding_forward_quantized_split_lookup.cu`, since the template generated functions are redundant.
- Break the `int_nbit_split_embedding_*` set of jinja templates into two files, one for rendering kernel-side code (`embedding_forward_quantized_split_nbit_kernel_template.cu`) and the other for rendering host-side code (`embedding_forward_quantized_split_nbit_host_template.cu`)
- For the `int_nbit_split_embedding_*` host-side jinja template, make it render `weighted`, `unweighted`, and `unweighted nobag` variants into separate source files
- For the `int_nbit_split_embedding_*` kernel-side jinja template, make it render into N = [`weighted`, `unweighted`, and `unweighted nobag` variants ] x [ 6 embedding types ] separate source files, each containing a single C++ template kernel function. Also generate the code to explicitly instantiate the kernel templates. For each of the C++ templates being generated, there will be 2 {device-only bool} x [3-4] (output types) x [3-5] (cases) = 18-40 actual template instantiations
- To help with debugging missing template instantiations, print out all undefined symbols in the output library after build to inspect whether or not templates have been un-instantiated
- Update build scripts to print out `cc`, `c++`, and `nvcc` preprocessor defines
- Handle the case where `TORCH_CUDA_ARCH_LIST` is pre-defined in the environment
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1694
Reviewed By: sryap, r-barnes
Differential Revision: D44842524
Pulled By: q10
fbshipit-source-id: 96f92e40ab2fec598aeb8c483e94997ac050aae7
* Back out "Prune CPU/GPU TBE optimizer codegen" (#1706)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1706
Original commit changeset: f04710e66498
Original Phabricator Diff: D44484764
Reviewed By: q10, brad-mengchi, jianyuh, shintaro-iwasaki
Differential Revision: D45054051
fbshipit-source-id: 9d14504c76eb93b2f1b14f4c2ec4c5b807c7fc4a
* Use CUB kernel for 2D asynchronous_complete_cumsum (#1707)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1707
Temporarily use the CUB kernel instead of the custom kernel for 2D
`asynchronous_complete_cumsum`
Reviewed By: q10, brad-mengchi, jianyuh
Differential Revision: D45062784
fbshipit-source-id: cebe3992ff8ebec9c0f554e729b8d79a1eced1de
* Split the Code Generation for `embedding_backward_split_template.cu` into Smaller Files (#1705)
Summary:
`embedding_backward_split_template.cu` contains both jinja-template and non-jinja-template code, and some of the templating is unneccessary. Furthermore, the template generates both the vanilla and `nobag` variants of unweighted into the same source file. This PR moves the non-jinja-template code out of the template, de-duplicates code are unneccessarily templated, and splits the generation of the code to three files per optimizer, one for `weighted`, `unweighted nobag`, and `unweighted`.
Details:
- Migrate non-jinja-templated code out of `embedding_backward_split_template.cu` and into `embedding_backward_template_helpers.cuh`
- De-templatize `split_embedding_backward_codegen_{{ optimizer }}_{{ wdesc }}_find_long_segments` into `split_embedding_backward_codegen_find_long_segments` since there is no implementation difference between the optimizers and weighted vs unweighted
- Migrate `grad_mean_kernel` and `split_embedding_backward_codegen_find_long_segments` into a separate non-template source file to de-duplicate code generation and compilation
- Split the code generation of `embedding_backward_split_template.cu` into 3 files per optimizer, according to weighted, unweighted_nobag, and unweighted
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1705
Reviewed By: sryap
Differential Revision: D45073273
Pulled By: q10
fbshipit-source-id: e82ea643f8e67ad5aa0b3de03562532c5735453d
* Add jagged slice op for cpu (#1690)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1690
The context why this is needed is as follows
1) For really long sparse features we want to split them into multiple chunks that can be fed into the model
2) Slicing requires users to require per row start point & a maximum L.
Based on these requirements, a custom op mimicing the slice semantics of a normal tensor works best.
An example usage using pseudo code
```
input_jagged_tensor = [[1, 2, 3, 4], [1, 2, 3], [1, 2, 3, 4, 5, 6], [1], [1, 2]]
start = [0, 0, 0, 0, 0]
slice_length = 3
>> jagged_slice(input_jagged_tensor, start, slice_length)
output_jagged_tensor = [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1], [1, 2]]
```
A corresponding operation for dense tensor would look like
```
dense_tensor = torch.randn((8, 10))
slice_dense_tensor = dense_tensor[:, 1:3]
```
Reviewed By: sryap
Differential Revision: D44299744
fbshipit-source-id: 44996f2f2ec5fc5f31dda4cb3bd8f0241497df66
* Move radix sort to common utilities and add the possibility to handle negative integers (#1672)
Summary:
Move the `radix_sort` implementation to common utilities, so it can be used in PyTorch in case it was not built with FBGEMM GPU.
Add the possibility to handle negative integers, which is crucial for reusing `radix_sort` in PyTorch's `sort` operation.
Details:
This PR addresses two issues:
1. `radix_sort` is currently used in [scatter_reduce](https://github.com/dszwicht/pytorch/blob/master/aten/src/ATen/native/cpu/ScatterGatherKernel.cpp#L630) (please view this [comment](https://github.com/pytorch/pytorch/pull/82703/files#r1045360609) for more information). Till now `radix_sort` was under `fbgemm_gpu` subproject. It means that implementation was not available in PyTorch in case it was built for CPU - that's why `radix_sort` was copy pasted under aten directory in PyTorch. This PR moves `radix_sort` implementation to common utilities.
2. In GNN workloads we often sort 1D integer data with non-negative values, for example, when converting CSR to CSC format. Unfortunately, `torch.sort` for 1D data works sequentially. `radix_sort` seems to be a perfect match to accelerate described case. However, suppose we want to do that on the PyTorch site. In that case, we have to either fallback to a regular path after detecting negative numbers in the tensor or perform post-processing, by swapping positive and negative blocks of data (data like `[2, -1, -2, 1]` after sorting will be in the following form `[1, 2, -2, -1]`, due to the fact of how numbers are stored). Both these solutions are not elegant. As an alternative, I propose the extension of `radix_sort` algorithm, by giving it capability to work with negative numbers. This can be enabled by passing an optional parameter, `maybe_with_neg_vals`. If set to `true`, we will perform all passes (up to the most significant sign bit) and apply a special prefix sum combination in the last pass. An example of how we can reuse fbgemm in PyTorch can be found in my private fork, [here](https://github.com/dszwicht/pytorch/pull/2) (I also provide speedup data).
The above changes have several consequences:
1. `TORCH_CHECK` was replaced with `assert` as fbgemm CPU does not have PyTorch in its dependencies.
2. `__builtin_clz` was replaced with manual implementation as `__builtin_clz` is not portable.
Additional information for reviewers:
I did perform benchmarks of `radix_sort` before and after my code modification. I didn't observe any performance drop.
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1672
Reviewed By: sryap
Differential Revision: D44616959
Pulled By: q10
fbshipit-source-id: f34594478c94ec6610c05545feb2044b58d79d66
* Daily `arc lint --take CLANGFORMAT`
Reviewed By: bigfootjon
Differential Revision: D45141964
fbshipit-source-id: 58308a31522a3b1446835e358a93483b611c4b15
* `CMakeLists.txt` Cleanups (#1712)
Summary:
- Re-organize and comment the `CMakeLists.txt` for FBGEMM_GPU for better clarity
- Disable verbose HIPCC warnings that are non-actionable when building the ROCm variant of FBGEMM_GPU
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1712
Reviewed By: shintaro-iwasaki
Differential Revision: D45189904
Pulled By: q10
fbshipit-source-id: 3df6ff3b957886c64bc13fc6bc7a0147b74ee783
* support indices broadcast for reorder_batched_ad_indices (#1711)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1711
this is to support the case for request-only combined input sparse feature broadcast
when `broadcast_indices` is enabled, the assumption for the inputs:
- `cat_ad_offsets` and `cat_ad_indices` only contain the offsets and indices for the combined batches, where each batch only contain one instance (potentially multiple tables)
- `reordered_cat_ad_offsets` needs to be after broadcasted, and contains `num_ads_in_batch * num_tables + 1` elements
- `batch_offsets` is also after broadcasted
- `num_indices_after_broadcast` is required to allocate the output buffer
added coverage for the newly added branch
Reviewed By: r-barnes
Differential Revision: D45155887
fbshipit-source-id: 67f96d60168aa83cf24fef459addee89f06e1c6b
* Add a check that get_filelist python exec process worked (#1715)
Summary:
Add a check that get_filelist python exec worked.
If bad params (python, args, ...), get_filelist() was continuing without noticing/warning/erroring out,
making cmake failing later for weird reasons ("no sources").
Adds a safety check on the RESULT_VARIABLE of cmake execute_process().
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1715
Reviewed By: shintaro-iwasaki
Differential Revision: D45235231
Pulled By: q10
fbshipit-source-id: 049eae1fc5d7f42d73048e81c02c2f282d8859b0
* Fix compilation error under ROCm 5.3 (#1719)
Summary:
- Fix bug introduced by PR 1711 (D45155887), which broke compilation of FBGEMM_GPU under ROCm 5.3
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1719
Reviewed By: sryap
Differential Revision: D45238536
Pulled By: q10
fbshipit-source-id: de9d2aa01ced0a37be1ea7903a361e3a24beed8d
* Backward Split, pt. 2: Migrate `*_warp_per_row` and `*_cta_per_row` kernel templates out of `embedding_backward_split_template.cu` (#1710)
Summary:
- Migrate the definition of `split_embedding_*_backward_codegen_*_*_kernel_warp_per_row_1` from `embedding_backward_split_template.cu` over to `embedding_backward_split_kernel_warp_template.cu` and explicitly instantiate the templates separately
- Migrate the definition of `split_embedding_*_backward_codegen_*_*_kernel_cta_per_row_1` from `embedding_backward_split_template.cu` over to `embedding_backward_split_kernel_cta_template.cu` and explicitly instantiate the templates separately
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1710
Reviewed By: sryap
Differential Revision: D45205217
Pulled By: q10
fbshipit-source-id: 96b34e9389e70b64d8391f2c9d39f4009f3d65ce
* Add CLI support (M,N,K) to GEMMsBenchmark (#1721)
Summary:
Add CLI support (M,N,K) to GEMMsBenchmark
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1721
Reviewed By: sryap
Differential Revision: D45281533
Pulled By: q10
fbshipit-source-id: 0ce5b38f54877acb26421dead1d2dc63cd11a2a1
* Fix data conversion in radix sort that can cause data loss (#1718)
Summary:
Fix data conversion in `radix_sort` that can cause data loss.
Details:
When `elements_count` is passed to the internal kernel implementation it is implicitly converted from `int64_t` to `int`. It can cause data loss, resulting in a partially sorted array. This PR fixes this issue. As a result of changing the `elements_count` type in internal functions to `int64_t`, `histogram` and `histogram_ps` types also were updated (to not generate further conversions).
This is a follow-up for https://github.com/pytorch/FBGEMM/issues/1672.
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1718
Reviewed By: sryap
Differential Revision: D45253811
Pulled By: q10
fbshipit-source-id: a5368a4401f05ebc471cb17107297a48f43a75c0
* support lengths broadcast for reorder_batched_ad_lengths (#1716)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1716
similar to D45155887
when `broadcast_lengths` is enabled, the lengths are copied from the only instance of each batch, this is also to facilitate request-only broadcast
Reviewed By: r-barnes
Differential Revision: D45208736
fbshipit-source-id: 2c06cd4e9aae0c9c4e0668098de7db6f6da8c06b
* remove optional for two ops (#1722)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1722
remove unnecessary optional decorators for the two newly added sparse ops
Reviewed By: r-barnes
Differential Revision: D45286152
fbshipit-source-id: 26109548db1acbc8fdf1a5183977eb8c64b45d41
* Prepare bounds_check_indices for VBE (#1713)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1713
Prepare bounds_check_indices for variable batch size TBE (VBE).
- Update arg names
Reviewed By: jspark1105, r-barnes
Differential Revision: D45203680
fbshipit-source-id: 396c4122058db8dd1fc9eb5f0d620e8179c3e7a9
* Add check on configs and logging (#1728)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1728
Freq-SGD requires to set both `weight_decay_mode=WeightDecayMode.COUNTER` and `counter_based_regularization` to kick in. Previously we checked when `weight_decay_mode` is set but no config provided. There's another missing case when the config is provided but users forget to set `weight_decay_mode`. We add the check in this diff.
In addition, added logging to print out whether **internally** counter is used or not to make debugging easier.
Reviewed By: dvksabin
Differential Revision: D45329516
fbshipit-source-id: 30389671c34a17d4baf48726f28096a670ede0b6
* Prepare transpose_embedding_input for VBE (#1717)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1717
Prepare `transpose_embedding_input` for variable batch size TBE (VBE).
- Update the frontend API with new args
Reviewed By: yuguo68
Differential Revision: D45212897
fbshipit-source-id: 5ad11a737130777fbe119aed6c7086e892752f4a
* Convert GEMMsBench timebreakdown to a runtime cli option (#1725)
Summary:
Convert timebreakdown to a runtime cli option.
Note: there is no code to measure packing, compute, kernel time ...
so these are (atm) reported as 0, only total time is measured.
```
M, N, K, Type, Packing (us), Kernel(us), Postproc (us), Total (us), GOPs
64, 800, 320, FBGEMM_i8_acc32, 0, 0, 0, 218.593, 149.9
64, 800, 320, FBGEMM_i8_acc16, 0.0, 0.0, 0.0, 187.6, 174.7
```
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1725
Reviewed By: sryap
Differential Revision: D45361847
Pulled By: q10
fbshipit-source-id: 4f2991a6208f0a5ae780729ce19bee611720953b
* Fix error with empty row_counter_dev (#1730)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1730
In some cases, `torch.max(row_counter_dev)` causes failure because `row_counter_dev` is an empty tensor, example flow (f431977946).
Here we guard the op by first checking if `row_counter_dev` is empty.
Reviewed By: sryap
Differential Revision: D45342010
fbshipit-source-id: 756a481c1098095f71dbb278ea84a01e89783790
* padding for fp8-rowwise quantization for varying length of 1D Tensor (#1729)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1729
As all gather becomes expensive for tensor/sequential parallel training, we create padded rowwise quantization/dequantization kernels for flattened tensor to convert between fp8 (stored as uint8 for gpu <= A100) and fp32 formats.
Since the activations/grads will be concat into 1d tensor for all gather, the scaling to fit into fp8 format's range might be tricky as small elements will be quantized to zero if the scale is chosen to accommodate the largest element in the model.
Thus, we continue to use row-wise quantization used in the previous all2all kernel. Every block with the size of "row_dim" will be quantized with the scale choose to accommodate the largest value in the block.
Since the total length of the flattened tensor will not always be divisible by row_dim, we'll pad the 1D tensor to multiple of row_dim. As such, the padding/unpadding is handled by quantize/dequantize kernels and will be invisible to API calling them.
Reviewed By: rohan-varma
Differential Revision:
D42721325
Privacy Context Container: L1138451
fbshipit-source-id: 33c712ba2fae709d29babee5ee4a8af6c7637b68
* Improve `TORCH_CHECK` diagnostics in files including deeplearning/fbgemm/fbgemm_gpu/codegen/embedding_forward_split_cpu.cpp (#1732)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1732
`TORCH_CHECK` produces pretty generic error messages. Using, eg, `TORCH_CHECK_GE` produces a message that shows the names of the variables being compared as well as their values at the time of comparison. This makes debugging easier.
- If you approve of this diff, please use the "Accept & Ship" button :-)
(7 files modified.)
Reviewed By: bangshengtang
Differential Revision: D45402701
fbshipit-source-id: 42501350543e31455e430b240e53f8e1883eb1ba
* Improve `TORCH_CHECK` diagnostics in files including deeplearning/fbgemm/fbgemm_gpu/codegen/embedding_backward_dense_host.cpp (#1733)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1733
`TORCH_CHECK` produces pretty generic error messages. Using, eg, `TORCH_CHECK_GE` produces a message that shows the names of the variables being compared as well as their values at the time of comparison. This makes debugging easier.
- If you approve of this diff, please use the "Accept & Ship" button :-)
(7 files modified.)
Reviewed By: bangshengtang
Differential Revision: D45402700
fbshipit-source-id: 275bf837341a00d1cd4642b31bf9168455fa6c77
* Build cleanups (#1731)
Summary:
- Further break up `setup_env.bash` into separate domain scripts for easier maintenance
- Update FBGEMM `CMakeLists.txt` to remove warning (https://github.com/pytorch/FBGEMM/issues/1714)
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1731
Reviewed By: sryap
Differential Revision: D45406676
Pulled By: q10
fbshipit-source-id: 3ff5a7e2486b6898cb450d268a092371da5c2717
* Improve `TORCH_CHECK` diagnostics in files including deeplearning/fbgemm/fbgemm_gpu/fb/src/split_embeddings_utils.cu (#1735)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1735
`TORCH_CHECK` produces pretty generic error messages. Using, eg, `TORCH_CHECK_GE` produces a message that shows the names of the variables being compared as well as their values at the time of comparison. This makes debugging easier.
- If you approve of this diff, please use the "Accept & Ship" button :-)
(7 files modified.)
Reviewed By: bangshengtang
Differential Revision: D45402704
fbshipit-source-id: 9e9b1c1f526a398bbe50c99055187195ab751fa2
* Improve `TORCH_CHECK` diagnostics in files including deeplearning/fbgemm/fbgemm_gpu/src/split_embeddings_utils.cu (#1737)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1737
`TORCH_CHECK` produces pretty generic error messages. Using, eg, `TORCH_CHECK_GE` produces a message that shows the names of the variables being compared as well as their values at the time of comparison. This makes debugging easier.
- If you approve of this diff, please use the "Accept & Ship" button :-)
(3 files modified.)
Reviewed By: bangshengtang
Differential Revision: D45402697
fbshipit-source-id: c490d39bc826eab44ec16cbcc86273f8d7258fd9
* Use volatile pointer in inclusive_sum_scan_kernel (#1739)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1739
In the multi-block cumsum case, the `inclusive_sum_scan_kernel`
implements the stream-scan technique in which each thread block has to
consume the preceding sum result from the previous block. The sum
result is passed via the `block_sums` buffer (global memory). To ensure
that the sum results are visible for inter-thread-block consumption,
the buffer has to be declared as `volatile` to prevent the compiler from
caching the results in registers. This diff adds the `volatile` keyword
to `block_sums`.
Reviewed By: q10
Differential Revision: D45435897
fbshipit-source-id: f81a25b43eda18ae1eb18bed33f595fc27ef2707
* BF16 support for HBC ops. (#1744)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1744
Adding BF16 support for HBC ops, and updates on tests.
Reviewed By: q10, sryap
Differential Revision: D45449360
fbshipit-source-id: 8321155b426143d80064f12a910c0626bdfafbba
* Use designated initializers & kernel launch checks in deeplearning/fbgemm/include/fbgemm/Utils.h (#1746)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1746
Designated initializers can make the code cleaner
- If you approve of this diff, please use the "Accept & Ship" button :-)
(1 files modified.)
Reviewed By: sryap
Differential Revision: D45464948
fbshipit-source-id: 28e38dc60b893fe7c91db0d791e069a6de87b420
* Dynamically determine platform name in FBGEMM scripts (#1742)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1742
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1738
Instead of hardcoding x86_64 when installing dependencies, let's now dynamically determine the platform name
Reviewed By: excelle08
Differential Revision: D45246996
fbshipit-source-id: d9031e76a915c2362be62c85a3c1f0786828ca8b
* Split the Rendering of `embedding_forward_split_template.cu` into Smaller Files (#1723)
Summary:
- Migrate `*_embedding_*_codegen_forward_*_kernel` out of `embedding_forward_split_template.cu` and into `embedding_forward_split_kernel_template.cu`
- Migrate `*_embedding_nobag_codegen_forward_unweighted_small_kernel` out of `embedding_forward_split_template.cu` and into `embedding_forward_split_kernel_small_template.cu`
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1723
Reviewed By: sryap
Differential Revision: D45363388
Pulled By: q10
fbshipit-source-id: 563ca610b15830aca854bc00d6a31fd6e8cb8a53
* Installation instructions for OSS (#1750)
Summary:
- Add installation instructions for OSS
- Migrate Installation, Test, and Documentation information out of the README
- Add link to GitHub Discussions in the README
- Migrate the Netlify configuration from website to TOML file in the repo so that build jobs are configurable by developers
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/1750
Reviewed By: sryap, shintaro-iwasaki
Differential Revision: D45540724
Pulled By: q10
fbshipit-source-id: beaab824cc5d441b96b89daea2a71f541e21f2ec
---------
Co-authored-by: Banit Agrawal <bagrawal@meta.com>
Co-authored-by: Sabin Devkota <devkotasabin@meta.com>
Co-authored-by: Junjie Yang <junjieyang@meta.com>
Co-authored-by: Benson Ma <bensonma415@meta.com>
Co-authored-by: Alfredo Tupone <tupone@gentoo.org>
Co-authored-by: Sarunya Pumma <sarunya@meta.com>
Co-authored-by: Doe Hyun Yoon <dhyoon@meta.com>
Co-authored-by: Matt Galloway <mattjgalloway@meta.com>
Co-authored-by: Richard Barnes <rbarnes@meta.com>
Co-authored-by: Xiao Sun <sunx@meta.com>
Co-authored-by: Rengan Xu <renganxu@meta.com>
Co-authored-by: siwasaki <siwasaki@fb.com>
Co-authored-by: Jianyu Huang <jianyuhuang@meta.com>
Co-authored-by: Yue Dong <yoyoyod@meta.com>
Co-authored-by: Geet Sethi <gsethi@meta.com>
Co-authored-by: Janet Yang <qxy11@meta.com>
Co-authored-by: Wang Zhou <wangzhou@meta.com>
Co-authored-by: Jongsoo Park <jongsoo@meta.com>
Co-authored-by: Tran Le <quytranle@meta.com>
Co-authored-by: Ryan Landay <rlanday@gmail.com>
Co-authored-by: Devashish Tyagi <devashisht@meta.com>
Co-authored-by: Szwichtenberg, Damian <damian.szwichtenberg@intel.com>
Co-authored-by: generatedunixname89002005325676 <generatedunixname89002005325676@fb.com>
Co-authored-by: Bangsheng Tang <bangsheng@meta.com>
Co-authored-by: William Tambellini <wtambellini@sdl.com>
Co-authored-by: Jason Park <jasonjk@meta.com>
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Add CLI support (M,N,K) to GEMMsBenchmark