llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
torch-mlir/.gitignore

40 lines
415 B
Plaintext
Raw Permalink Normal View History

Add vim swap file in gitignore Signed-off-by: Bairen Yi <yibairen.byron@bytedance.com>
2021-02-19 09:17:15 +08:00
*.swp
Add support for constant_pad_nd Note that to enable folding of the code coming from an example like the ConstantPad2dStaticModule e2e test, support for other operations had to be added/improved: - aten::neg.int - aten::eq.float - aten::eq.str - prim::Uninitialized
2022-01-11 15:42:53 +08:00
.cache/
Add stub numpy dialect.
2020-04-27 08:20:58 +08:00
.vscode
Dockerize CI + Release builds (#1234) Gets both CI and Release builds integrated in one workflow. Mount ccache and pip cache as required for fast iterative builds Current Release docker builds still run with root perms, fix it in the future to run as the same user. There may be some corner cases left especially when switching build types etc. Docker build TEST plan: tl;dr: Build everythin: Releases (Python 3.8, 3.9, 3.10) and CIs. TM_PACKAGES="torch-mlir out-of-tree in-tree" 2.57s user 2.49s system 0% cpu 30:33.11 total Out of Tree + PyTorch binaries: Fresh build (purged cache): TM_PACKAGES="out-of-tree" 0.47s user 0.51s system 0% cpu 5:24.99 total Incremental with ccache: TM_PACKAGES="out-of-tree" 0.09s user 0.08s system 0% cpu 34.817 total Out of Tree + PyTorch from source Incremental TM_PACKAGES="out-of-tree" TM_USE_PYTORCH_BINARY=OFF 1.58s user 1.81s system 2% cpu 1:59.61 total In-Tree + PyTorch binaries: Fresh build and tests: (purge ccache) TM_PACKAGES="in-tree" 0.53s user 0.49s system 0% cpu 6:23.35 total Fresh build/ but with prior ccache TM_PACKAGES="in-tree" 0.45s user 0.66s system 0% cpu 3:57.47 total Incremental in-tree with all tests and regression tests TM_PACKAGES="in-tree" 0.16s user 0.09s system 0% cpu 2:18.52 total In-Tree + PyTorch from source Fresh build and tests: (purge ccache) TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF 2.03s user 2.28s system 0% cpu 11:11.86 total Fresh build/ but with prior ccache TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF 1.58s user 1.88s system 1% cpu 4:53.15 total Incremental in-tree with all tests and regression tests TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF 1.09s user 1.10s system 1% cpu 3:29.84 total Incremental without tests TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF TM_SKIP_TESTS=ON 1.52s user 1.42s system 3% cpu 1:15.82 total In-tree+out-of-tree + Pytorch Binaries TM_PACKAGES="out-of-tree in-tree" 0.25s user 0.18s system 0% cpu 3:01.91 total To clear all artifacts: rm -rf build build_oot llvm-build libtorch docker_venv externals/pytorch/build
2022-08-31 02:07:25 +08:00
.ccache
Update all python imports to be absolute and use a .env file to set the path correctly. This makes things just work for debugging in VSCode.
2020-05-07 14:25:04 +08:00
.env
NFC: Add .code-workspace to gitignore.
2021-01-09 02:34:49 +08:00
*.code-workspace
Add resnet inference jupyter notebook. This takes the example from torchscript_resnet18_e2e.py and puts it into a slightly cleaned up notebook form. It's still a little rough around the edges. Areas for improvement: - Installation / setup. - API usability. Also, - Add `npcomp-backend-to-iree-frontend-pipeline` since we will be adding more stuff there. - Slight cleanups.
2021-08-10 04:55:20 +08:00
.ipynb_checkpoints
Bump llvm-project to 3d92722f74993969243d1400bc3257ca3d03902f. (#369) * Picks up Python configure changes (was pinned to a bad intermediate commit). * Uses the new mlir_configure_python_dev_packages() to ensure CMake python is found consistently. * Fixes the JIT importer to build as a MODULE vs SHARED (needed for linking to Python as a module, per config changes). * Adds some notes to the README to help folks build a smaller set focused just on this project.
2021-10-22 12:09:00 +08:00
*.venv/
Set some wheel building optimization options. * Also adds a requirements.txt and updates docs to reference it versus stringy pip install. * Adds doc with instructions on creating a wheel. Fixes #370
2021-10-22 12:18:08 +08:00
mlir_venv/
Link against libtorch (#955) This moves torch-mlir to link against libtorch on macOS and linux TESTS: Tests pass. Tested release builds on linux and macOS
2022-07-01 03:40:17 +08:00
externals/pytorch/
libtorch*
Add stub numpy dialect.
2020-04-27 08:20:58 +08:00
Build packages for npcomp-torch. * Adds a minimal setup.py for frontends/pytorch * Makes npcomp-core export its headers and libraries * Adds a script to build packages. * Adds CI step to package and smoke test. * Will need some more tweaks and coordination prior to deploying (version locking etc).
2021-07-30 01:43:18 +08:00
/build/
[Pipeline] Use dedicated simplification pipeline for TorchDynamo frontend (#3376) Discord Thread: https://discord.com/channels/636084430946959380/1238330633328005243 ## Context: [This](https://github.com/llvm/torch-mlir/blob/main/python/torch_mlir/fx.py#L61) was updated to support e2e tests for the TorchDynamo frontend in Torch-MLIR, where we run FX decompositions and import the FX IR to generate Torch dialect, followed by `torch-function-to-torch-backend-pipeline`, skipping only the shape/type refinement for now. However, we should be able to skip many of the torch simplification passes, as depicted in the [frontend roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/images/roadmap_frontend.png). Based on IREE's TorchDynamo [pipeline](https://github.com/iree-org/iree/blob/main/compiler/plugins/input/Torch/InputConversion/Passes.cpp#L29), the only two passes we seem to require are: `ReduceOpVariantsPass` and `DecomposeComplexOpsPass`. This is inline with our findings as well based on initial exploration. This PR creates a dedicated frontend simplification pipeline for TorchDynamo / FX Importer which calls only `ReduceOpVariantsPass` and `DecomposeComplexOpsPass`. We rely on the e2e fx_importer tests to ensure we're not regressing by removing many of the passes that were historically needed for TorchScript. One notable change here is that we do not call the `LowerToBackendContractPass` anymore, which used to call `TorchSimplificationPipeline` iteratively until VerifyBackendContract was clean. Some of this was required for the shape/type refinement to converge, which seems a non-issue for Dynamo frontend. Do we anticipate this (the iterative invocation of TorchSimplificationPipeline followed by VerifyBackendContract) to be worth retaining in the Dynamo frontend pipeline? If so, I can make those changes, PLMK.
2024-05-22 20:23:18 +08:00
.build-cache/
Setuptools uses a separate build directory (#3023) * setuptools not steal the build directory name https://github.com/llvm/torch-mlir/pull/3021#issuecomment-1994447855 * support pre-built LLVM * support CMAKE_BUILD_TYPE env
2024-03-14 11:41:48 +08:00
/setup_build/
Add MLIRContext.dense_elements_attr to create an attribute from a python buffer/array.
2020-05-09 08:36:07 +08:00
__pycache__
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
2022-06-08 02:38:50 +08:00
*.pyc
Add MLIRContext.dense_elements_attr to create an attribute from a python buffer/array.
2020-05-09 08:36:07 +08:00
Expand pytype coverage for torch_signature_ods_gen.py
2020-11-25 02:42:56 +08:00
.pytype
Add basic setup.py file for the npcomp-core package. (#256)
2021-07-29 06:58:31 +08:00
# Pip artifacts.
*.egg-info
*.whl
Bring back Python packaging. Will add a CI job that builds and uploads snapshot packages next.
2021-10-06 04:16:12 +08:00
/wheelhouse
Add bazel build support (1/N) (#706) This PR adds rules for building the compiler part with bazel, a followup PRs will build the python bindings.
2022-04-07 02:20:39 +08:00
# Bazel
bazel-*
Torch-MLIR LTC Backend Lowering Codegen (#621) * Codegen and build LTC lowering * Add LazyShapeInference header
2022-02-26 08:50:09 +08:00
Added JIT to MLIR lowering (#724) * Added JIT to MLIR lowering Lowering to JIT is performed in a way similar to how it's done in the TS LTC backend. After a jit::Graph is constructed, it gets converted to a jit::Function, which is fed into the existing utility to generate an MlirModule in torch-mlir. * Renamed `csrc/backend` to `csrc/base_lazy_backend`
2022-04-15 00:40:10 +08:00
# Autogenerated files
Breakup python pytorch deps (#2582) This lifts the core of the jit_ir_importer and ltc out of the pt1 project, making them peers to it. As a side-effect of this layering, now the "MLIR bits" (dialects, etc) are not commingled with the various parts of the pt1 project, allowing pt1 and ltc to overlay cleanly onto a more fundamental "just MLIR" Python core. Prior to this, the Python namespace was polluted to the point that this could not happen. That "just MLIR" Python core will be introduced in a followup, which will create the space to upstream the FX and ONNX pure Python importers. This primary non-NFC change to the API is: * `torch_mlir.dialects.torch.importer.jit_ir` -> `torch_mlir.jit_ir_importer`. The rest is source code layering so that we can make the pt1 project optional without losing the other features. Progress on #2546.
2023-11-20 04:10:19 +08:00
/projects/ltc/csrc/base_lazy_backend/generated
Dockerize CI + Release builds (#1234) Gets both CI and Release builds integrated in one workflow. Mount ccache and pip cache as required for fast iterative builds Current Release docker builds still run with root perms, fix it in the future to run as the same user. There may be some corner cases left especially when switching build types etc. Docker build TEST plan: tl;dr: Build everythin: Releases (Python 3.8, 3.9, 3.10) and CIs. TM_PACKAGES="torch-mlir out-of-tree in-tree" 2.57s user 2.49s system 0% cpu 30:33.11 total Out of Tree + PyTorch binaries: Fresh build (purged cache): TM_PACKAGES="out-of-tree" 0.47s user 0.51s system 0% cpu 5:24.99 total Incremental with ccache: TM_PACKAGES="out-of-tree" 0.09s user 0.08s system 0% cpu 34.817 total Out of Tree + PyTorch from source Incremental TM_PACKAGES="out-of-tree" TM_USE_PYTORCH_BINARY=OFF 1.58s user 1.81s system 2% cpu 1:59.61 total In-Tree + PyTorch binaries: Fresh build and tests: (purge ccache) TM_PACKAGES="in-tree" 0.53s user 0.49s system 0% cpu 6:23.35 total Fresh build/ but with prior ccache TM_PACKAGES="in-tree" 0.45s user 0.66s system 0% cpu 3:57.47 total Incremental in-tree with all tests and regression tests TM_PACKAGES="in-tree" 0.16s user 0.09s system 0% cpu 2:18.52 total In-Tree + PyTorch from source Fresh build and tests: (purge ccache) TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF 2.03s user 2.28s system 0% cpu 11:11.86 total Fresh build/ but with prior ccache TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF 1.58s user 1.88s system 1% cpu 4:53.15 total Incremental in-tree with all tests and regression tests TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF 1.09s user 1.10s system 1% cpu 3:29.84 total Incremental without tests TM_PACKAGES="in-tree" TM_USE_PYTORCH_BINARY=OFF TM_SKIP_TESTS=ON 1.52s user 1.42s system 3% cpu 1:15.82 total In-tree+out-of-tree + Pytorch Binaries TM_PACKAGES="out-of-tree in-tree" 0.25s user 0.18s system 0% cpu 3:01.91 total To clear all artifacts: rm -rf build build_oot llvm-build libtorch docker_venv externals/pytorch/build
2022-08-31 02:07:25 +08:00
#Docker builds
build_oot/
docker_venv/
llvm-build/
Add compile_commands.json to gitignore (#1867)
2023-02-10 22:26:49 +08:00
# C++ build artifacts
compile_commands.json