Ifu 2023 03 31 #38
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Ifu 2023 03 31 #38
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Summary: Pull Request resolved: pytorch#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
Summary: This PR addresses the issue pytorch#1636 akin to https://github.com/pytorch/FBGEMM/blob/8616ed701015f8b9e4c2825ce592b204b4cfaf28/fbgemm_gpu/test/split_table_batched_embeddings_test.py#L1009 Pull Request resolved: pytorch#1635 Reviewed By: shintaro-iwasaki Differential Revision: D44033725 Pulled By: q10 fbshipit-source-id: 49f28fc2f1c20948a42728eebf3defc5195baa5d
… when using freq based methods (pytorch#1352) Summary: Pull Request resolved: pytorch#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
…ard (pytorch#1642) Summary: Pull Request resolved: pytorch#1642 Remove sync point in jagged_dense_elementwise_add_jagged_output backward Reviewed By: brad-mengchi Differential Revision: D44039901 fbshipit-source-id: 8e7e23e4d9e01359e67e5b166adc57f894a1224d
…ytorch#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: pytorch#1639 Reviewed By: shintaro-iwasaki Differential Revision: D44076312 Pulled By: q10 fbshipit-source-id: 6b2d083022feb7421b26da2d998678e00c11f283
Summary: fix build with gcc-13 Pull Request resolved: pytorch#1640 Reviewed By: shintaro-iwasaki Differential Revision: D44044422 Pulled By: q10 fbshipit-source-id: 692ec9c34f4aaf726294a2b643fbceabf8159033
Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
…ch#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: pytorch#1646 Reviewed By: shintaro-iwasaki Differential Revision: D44157228 Pulled By: q10 fbshipit-source-id: 6403ea9955856157785c50837b0b8e4c0cd26d53
Summary: Pull Request resolved: pytorch#1629 Replaces magic numbers with constexpr variables Reviewed By: sryap Differential Revision: D43776442 fbshipit-source-id: 5cef7566816f8730f5daa08948ee3260367787aa
Summary: Pull Request resolved: pytorch#1645 as in title Reviewed By: jianyuh Differential Revision: D44096435 fbshipit-source-id: a7a87a14ffecc2fb6e0be74d199d385357946672
Summary: Pull Request resolved: pytorch#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
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: ROCm/hipamd@86a1634 Pull Request resolved: pytorch#1655 Test Plan: GitHub Actions' AMD CI Reviewed By: q10, brad-mengchi Differential Revision: D44242622 Pulled By: shintaro-iwasaki fbshipit-source-id: c9b88155ebf1ed881b2d03e3be0e8991b4b30174
Summary: Pull Request resolved: pytorch#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
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: pytorch#1658 Reviewed By: shintaro-iwasaki Differential Revision: D44306708 Pulled By: q10 fbshipit-source-id: 5f0862f18eca7151759d9983aa97849222539d7d
Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
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: pytorch#1652 Reviewed By: shintaro-iwasaki Differential Revision: D44287321 Pulled By: q10 fbshipit-source-id: 4bf2bcf66d528939865d42b6deafc470bee55d17
Summary: Pull Request resolved: pytorch#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
Summary: - Rewrite the documentation builds job to use the build infrastructure tooling - Rename workflow files for consistency Pull Request resolved: pytorch#1673 Reviewed By: shintaro-iwasaki Differential Revision: D44472660 Pulled By: q10 fbshipit-source-id: 60434c1f7098b7efa8c750133bb22f14fc98d5dc
Summary: Pull Request resolved: pytorch#1675 Original commit changeset: 02413256b4a6 Original Phabricator Diff: D44326540 Reviewed By: q10, jianyuh Differential Revision: D44475251 fbshipit-source-id: 5be66944a833e03a2737fc6d1baaa5c351455b2c
Summary: Pull Request resolved: pytorch#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
pytorch#1667) Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
Summary: Pull Request resolved: pytorch#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
pytorch#1669) Summary: Pull Request resolved: pytorch#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
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 pytorch#1661 and pytorch#1662 that break its compilation under ROCm 5.3 Pull Request resolved: pytorch#1668 Reviewed By: shintaro-iwasaki Differential Revision: D44453594 Pulled By: q10 fbshipit-source-id: 2030cd0e00c6ff9694c2783dfd62c31cf5543da2
…ts loading (pytorch#1676) Summary: Pull Request resolved: pytorch#1676 Export a function to reset the embedding specs by target location Reviewed By: RoshanPAN, houseroad Differential Revision: D44338258 fbshipit-source-id: 502733e9f3a164450a02656d2822492fbf69f994
…rch#1670) Summary: Pull Request resolved: pytorch#1670 ATT Reviewed By: RoshanPAN, houseroad Differential Revision: D44338257 fbshipit-source-id: c091666c7a4d294c283f5e3774d0494089fc3478
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update hipify_torch and remove the manual mapping of the C10 macros.
all tests pass (batched_unary_embeddings_test skipped)
test_log.txt