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torch-mlir/lib/RefBackend/CMakeLists.txt

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[RefBackend] Move runtime related code under npcomp/RefBackend/ Other than the dialect definitions (which will live in standard Dialect/ subdirectory), the goal here is to keep RefBackend-related code nested in {include/npcomp,lib,test}/RefBackend.
2020-10-08 07:11:41 +08:00
add_subdirectory(Runtime)
add_subdirectory(JITHelpers)
[RefBackend] Rename "E2E" to RefBackend.
2020-10-07 07:14:37 +08:00
add_mlir_library(NPCOMPRefBackend
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
BypassShapes.cpp
[RefBackend] Rename "E2E" to RefBackend.
2020-10-07 07:14:37 +08:00
RefBackend.cpp
Lower to LLVM dialect. With this commit, we finish conversion to LLVM dialect, and should be ready for subsequent commits to convert to an LLVM module and let LLVM codegen to native machine code. This required a custom "lower to LLVM" pass to support lowering tcp.abort_if to a runtime call. In the future, this pass will grow to do type conversions for our own runtime types as we add those.
2020-05-21 09:48:53 +08:00
LowerToLLVM.cpp
Rework e2e flow to use new "npcomprt" This ~totally reworks the existing "runtime" stuff to be more principled and usable, such as from Python. It's still not fully production-quality, mainly in the department of memory management (e.g. it currently leaks memory; we need to figure out "who frees memrefs" + the analysis and transformation needed to do that (maybe use upstream buffer allocation pass?)). The user API is in include/npcomp/runtime/UserAPI.h, though include/npcomp/JITRuntime/JITModule.h is a friendlier wrapper. The stuff under {include,lib}/runtime is totally firewalled from the compiler and tiny (<6kB, though no attention has gone into optimizing that size). For example, we don't link in libSupport into the runtime, instead having our own bare bones replacements for basics like ArrayRef (the JITRuntime helps with bridging that gap, since it *can* depend on all common LLVM utilities). The overall features of npcomprt is that it exposes a module that with multiple function entry points. Each function has arguments and results that are tensor-valued, and npcomprt::Tensor is the runtime type that is used to interact with that (and a npcomprt::Ref<T> reference-counting wrapper is provided to wrap npcomprt::Tensor in the common case). From an implementation perspective, an npcomprt module at the LLVM/object/binary level exposes a single module descriptor struct that has pointers to other metadata (currently just a list of function metadata descriptors). All interactions with the npcomp runtime are keyed off of that module descriptor, including function lookups and dispatching. This is done to dodge platform ABI issues and also allow enough reflection to e.g. verify provided arguments. Most of the compiler-side work here was in LowerToNpcomprtABI and LowerToLLVM. Also, - Rename npcomp_rt/NpcompRt to npcomprt/Npcomprt; it was getting annoying to type the underscores/caps. - misc improvements to bash_helpers.sh
2020-07-09 08:15:40 +08:00
LowerToNpcomprtABI.cpp
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
TensorToMemref/LowerConstantTensorsToMemref.cpp
TensorToMemref/LowerShapedResultsToMemref.cpp
TensorToMemref/LowerStdToMemref.cpp
TensorToMemref/LowerStructuralToMemref.cpp
Initial TCF/TCP E2E seed. Very much WIP. This is enough to get tcf.add down to approximately the "linalg.generic on buffers" level of abstraction. (but there are nuances)
2020-05-07 09:41:54 +08:00
ADDITIONAL_HEADER_DIRS
[RefBackend] Rename "E2E" to RefBackend.
2020-10-07 07:14:37 +08:00
${PROJECT_SRC_DIR}/include/npcomp/RefBackend
Initial TCF/TCP E2E seed. Very much WIP. This is enough to get tcf.add down to approximately the "linalg.generic on buffers" level of abstraction. (but there are nuances)
2020-05-07 09:41:54 +08:00
DEPENDS
[RefBackend] Rename "E2E" to RefBackend.
2020-10-07 07:14:37 +08:00
NPCOMPRefBackendPassIncGen
Initial TCF/TCP E2E seed. Very much WIP. This is enough to get tcf.add down to approximately the "linalg.generic on buffers" level of abstraction. (but there are nuances)
2020-05-07 09:41:54 +08:00
LINK_COMPONENTS
Core
LINK_LIBS PUBLIC
MLIRIR
Bump llvm to ed46e84c7aaffd847656ac559acb06089096ec33. * Minor change of MLIRStandardOps -> MLIRStandard
2020-10-07 13:02:57 +08:00
MLIRLinalg
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
MLIRSCFToStandard
MLIRShapeToStandard
Bump llvm to ed46e84c7aaffd847656ac559acb06089096ec33. * Minor change of MLIRStandardOps -> MLIRStandard
2020-10-07 13:02:57 +08:00
MLIRStandard
[RefE2E] Add support for unary ops exp and tanh This is fairly mechanical.
2020-09-25 08:14:21 +08:00
MLIRStandardOpsTransforms
Lower to LLVM dialect. With this commit, we finish conversion to LLVM dialect, and should be ready for subsequent commits to convert to an LLVM module and let LLVM codegen to native machine code. This required a custom "lower to LLVM" pass to support lowering tcp.abort_if to a runtime call. In the future, this pass will grow to do type conversions for our own runtime types as we add those.
2020-05-21 09:48:53 +08:00
MLIRStandardToLLVM
Initial TCF/TCP E2E seed. Very much WIP. This is enough to get tcf.add down to approximately the "linalg.generic on buffers" level of abstraction. (but there are nuances)
2020-05-07 09:41:54 +08:00
)
[cmake] Updates for basic shared library support (#7) Mostly this is CMake cleanup. Several library dependencies are missing, which is often revealed with shared library builds. Also, it's generally bad to link directly against LLVM libraries because it fails when using LLVM_LINK_LLVM_DYLIB. MLIR will pull in libLLVM.so, and there will be duplicate linkage with the the explicit libraries. There may need to be more refactoring here.
2020-08-06 05:49:18 +08:00
[RefBackend] Rename "E2E" to RefBackend.
2020-10-07 07:14:37 +08:00
mlir_check_all_link_libraries(NPCOMPRefBackend)