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torch-mlir/lib/InitAll.cpp

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Make a real library for InitAll and extend it to conditionally initialize dependencies. * Conditioned on the top level CMake option to enable IREE. * There is still some warning flags and such that need triage, but it does build/work.
2020-06-12 08:47:14 +08:00
//===----------------------------------------------------------------------===//
//
// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "npcomp/InitAll.h"
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
#include "iree-dialects/Dialect/IREE/IREEDialect.h"
Bump llvm-project to c68d2895a1f4019b387c69d1e5eec31b0eb5e7b0 - dialect registration - StringAttr::get: order of context arg - math dialect - LogicalResult nodiscard - error message for invalid broadcast
2021-02-23 04:08:17 +08:00
#include "mlir/IR/Dialect.h"
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
2021-04-13 09:39:53 +08:00
#include "npcomp/Backend/Common/Passes.h"
Add support for compiling through IREE. Recommended review order: - Changes in frontends/pytorch/examples/ - Changes in python/npcomp/compiler/pytorch/backend/ - Boilerplate for the `npcomp-iree-backend-lower-linkage` pass. This change separates out a `npcomp.compiler.pytorch.backend.frontend_lowering` module that does the common lowering for all backends. The individual compiler backends `npcomp.compiler.pytorch.backend.{refjit,iree}` now accept a loosely defined "TCP + scalar code" IR mix that will be formalized in the future as the interface to codegen backends. This also required adding a small pass `npcomp-iree-backend-lower-linkage` which adds `iree.module.export` onto functions, and layering that into the frontend flow. The pass doesn't require a C++-level dependency on IREE, which is nice for now. TBD how we are going to handle lists (we hope we can get away with sneakerneting some td files and relying on loose IR compatibility). Running through IREE requires the ability to import `iree.compiler` and `iree.runtime`, which can be obtained as follows: ``` python3 -m pip install iree-compiler-snapshot iree-runtime-snapshot -f https://github.com/google/iree/releases/tag/snapshot-20210406.200 PYTHONPATH="${PYTHONPATH}:${MY_IREE_BUILD}/bindings/python/" ``` This patch makes it painfully clear that we don't have any e2e testing harness to really plug into, and also don't have a usable Python API to our compiler stack (something usable in a jupyter notebook). That will be addressed in subsequent commits. We've been flying by the seat of our pants with this `examples` directory that isn't subject to any kind of testing or real usability concerns.
2021-04-09 04:05:16 +08:00
#include "npcomp/Backend/IREE/Passes.h"
Bump llvm-project to c68d2895a1f4019b387c69d1e5eec31b0eb5e7b0 - dialect registration - StringAttr::get: order of context arg - math dialect - LogicalResult nodiscard - error message for invalid broadcast
2021-02-23 04:08:17 +08:00
#include "npcomp/Conversion/Passes.h"
[RefBackend] Rename RefBackend dialect to Refback I now realize that VerboseCamelCase is not the best choice for dialect directory/file names and C++ identifiers (take e.g. "Linalg", "Basicpy", etc. as prior art here; not LinearAlgebra or BasicPython). If I had to name the convention it seems to be "Shortword" (or of course just acronym dialects like LLVM, SCF, etc.). This rename also has the side benefit of differentiating RefBackend directories, which now refer to the actual backend itself, from Refback/Refbackrt, which are the dialects which happen to be used by that backend.
2020-10-08 08:30:10 +08:00
#include "npcomp/Dialect/Refback/IR/RefbackDialect.h"
#include "npcomp/Dialect/Refbackrt/IR/RefbackrtDialect.h"
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
#include "npcomp/Dialect/TorchConversion/IR/TorchConversionDialect.h"
#include "npcomp/Dialect/TorchConversion/Transforms/Passes.h"
[RefBackend] Rename "E2E" to RefBackend.
2020-10-07 07:14:37 +08:00
#include "npcomp/RefBackend/RefBackend.h"
Make a real library for InitAll and extend it to conditionally initialize dependencies. * Conditioned on the top level CMake option to enable IREE. * There is still some warning flags and such that need triage, but it does build/work.
2020-06-12 08:47:14 +08:00
Bump submodule versions. * llvm-project: b5924a8e27536d19dd5c4d302db29fb6163d5faa * mhlo: 848ca244d20f045b7921da55a98a04d95ef94f0e * Multiple breakages that need to be fixed. Fixes: * Refactor dialect registration * Remove all kindof methods (Casting functionality has been added upstream and is implicitly available, see https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types/1547.) * Update dialect registration to comply with https://reviews.llvm.org/D85495. * Remove type kinds and update some changed dialect signatures. * Upgrade ATen dialect to match upstream needs. * Move dialect registration to tablegen. * Register the ListType in tablegen. * Change dialect initialization signature. * Use TypeSwitch in MlirIr location printer. * Remove global registry depends from npcomp-opt. * Change LowerToLLVM to pass an MLIRContext vs an LLVMDialect for type creation. * Remove dep on MLIREDSCInterface that is removed upstream. * Thread through the DialectRegistry for opt and python-like tools. * Modernize pass registration (This was forced because the GEN_PASS_REGISTRATION code now generates inline functions vs literal pass registration statements) Co-authored-by: Marius Brehler <marius.brehler@iml.fraunhofer.de>
2020-08-28 06:09:10 +08:00
void mlir::NPCOMP::registerAllDialects(mlir::DialectRegistry &registry) {
// clang-format off
[torch-mlir earthmoving (2/N)] Python code movement. This moves the bulk of the Python code (including the Torch interop) from `frontends/pytorch` into `torch-mlir/TorchPlugin`. This also required reconciling a bunch of other Python-related stuff, like the `torch` dialects. As I did this, it was simpler to just remove all the old numpy/basicpy stuff because we were going to delete it anyway and it was faster than debugging an intermediate state that would only last O(days) anyway. torch-mlir has two top-level python packages (built into the `python_packages` directory): - `torch_mlir_dialects`: `torch` dialect Python bindings (does not depend on PyTorch). This also involves building the aggregate CAPI for `torch-mlir`. - `torch_mlir`: bindings to the part of the code that links against PyTorch (or C++ code that transitively does). Additionally, there remain two more Python packages in npcomp (but outside `torch-mlir`): - `npcomp_torch`: Contains the e2e test framework and testing configs that plug into RefBackend and IREE. - `npcomp_core`: Contains the low-level interfaces to RefBackend and IREE that `npcomp_torch` uses, along with its own `MLIR_PYTHON_PACKAGE_PREFIX=npcomp.` aggregation of the core MLIR python bindings. (all other functionality has been stripped out) After all the basicpy/numpy deletions, the `npcomp` C++ code is now very tiny. It basically just contains RefBackend and the `TorchConversion` dialect/passes (e.g. `TorchToLinalg.cpp`). Correspondingly, there are now 4 main testing targets paralleling the Python layering (which is reflective of the deeper underlying dependency structure) - `check-torch-mlir`: checks the `torch-mlir` pure MLIR C++ code. - `check-torch-mlir-plugin`: checks the code in `TorchPlugin` (e.g. TorchScript import) - `check-frontends-pytorch`: Checks the little code we have in `frontends/pytorch` -- mainly things related to the e2e framework itself. - `check-npcomp`: Checks the pure MLIR C++ code inside npcomp. There is a target `check-npcomp-all` that runs all of them. The `torch-mlir/build_standalone.sh` script does a standalone build of `torch-mlir`. The e2e tests (`tools/torchscript_e2e_test.sh`) are working too. The update_torch_ods script now lives in `torch-mlir/build_tools/update_torch_ods.sh` and expects a standalone build. This change also required a fix upstream related to cross-shlib Python dependencies, so we also update llvm-project to 8dca953dd39c0cd8c80decbeb38753f58a4de580 to get https://reviews.llvm.org/D109776 (no other fixes were needed for the integrate, thankfully). This completes most of the large source code changes. Next will be bringing the CI/packaging/examples back to life.
2021-09-11 02:44:38 +08:00
registry.insert<refbackrt::RefbackrtDialect,
[RefBackend] Rename RefBackend dialect to Refback I now realize that VerboseCamelCase is not the best choice for dialect directory/file names and C++ identifiers (take e.g. "Linalg", "Basicpy", etc. as prior art here; not LinearAlgebra or BasicPython). If I had to name the convention it seems to be "Shortword" (or of course just acronym dialects like LLVM, SCF, etc.). This rename also has the side benefit of differentiating RefBackend directories, which now refer to the actual backend itself, from Refback/Refbackrt, which are the dialects which happen to be used by that backend.
2020-10-08 08:30:10 +08:00
refback::RefbackDialect,
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
mlir::NPCOMP::TorchConversion::TorchConversionDialect,
iree::IREEDialect>();
Bump submodule versions. * llvm-project: b5924a8e27536d19dd5c4d302db29fb6163d5faa * mhlo: 848ca244d20f045b7921da55a98a04d95ef94f0e * Multiple breakages that need to be fixed. Fixes: * Refactor dialect registration * Remove all kindof methods (Casting functionality has been added upstream and is implicitly available, see https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types/1547.) * Update dialect registration to comply with https://reviews.llvm.org/D85495. * Remove type kinds and update some changed dialect signatures. * Upgrade ATen dialect to match upstream needs. * Move dialect registration to tablegen. * Register the ListType in tablegen. * Change dialect initialization signature. * Use TypeSwitch in MlirIr location printer. * Remove global registry depends from npcomp-opt. * Change LowerToLLVM to pass an MLIRContext vs an LLVMDialect for type creation. * Remove dep on MLIREDSCInterface that is removed upstream. * Thread through the DialectRegistry for opt and python-like tools. * Modernize pass registration (This was forced because the GEN_PASS_REGISTRATION code now generates inline functions vs literal pass registration statements) Co-authored-by: Marius Brehler <marius.brehler@iml.fraunhofer.de>
2020-08-28 06:09:10 +08:00
// clang-format on
Make a real library for InitAll and extend it to conditionally initialize dependencies. * Conditioned on the top level CMake option to enable IREE. * There is still some warning flags and such that need triage, but it does build/work.
2020-06-12 08:47:14 +08:00
}
void mlir::NPCOMP::registerAllPasses() {
[RefBackend] Rename "E2E" to RefBackend.
2020-10-07 07:14:37 +08:00
mlir::NPCOMP::registerRefBackendPasses();
Bump submodule versions. * llvm-project: b5924a8e27536d19dd5c4d302db29fb6163d5faa * mhlo: 848ca244d20f045b7921da55a98a04d95ef94f0e * Multiple breakages that need to be fixed. Fixes: * Refactor dialect registration * Remove all kindof methods (Casting functionality has been added upstream and is implicitly available, see https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types/1547.) * Update dialect registration to comply with https://reviews.llvm.org/D85495. * Remove type kinds and update some changed dialect signatures. * Upgrade ATen dialect to match upstream needs. * Move dialect registration to tablegen. * Register the ListType in tablegen. * Change dialect initialization signature. * Use TypeSwitch in MlirIr location printer. * Remove global registry depends from npcomp-opt. * Change LowerToLLVM to pass an MLIRContext vs an LLVMDialect for type creation. * Remove dep on MLIREDSCInterface that is removed upstream. * Thread through the DialectRegistry for opt and python-like tools. * Modernize pass registration (This was forced because the GEN_PASS_REGISTRATION code now generates inline functions vs literal pass registration statements) Co-authored-by: Marius Brehler <marius.brehler@iml.fraunhofer.de>
2020-08-28 06:09:10 +08:00
mlir::NPCOMP::registerConversionPasses();
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
mlir::NPCOMP::registerTorchConversionPasses();
Add support for compiling through IREE. Recommended review order: - Changes in frontends/pytorch/examples/ - Changes in python/npcomp/compiler/pytorch/backend/ - Boilerplate for the `npcomp-iree-backend-lower-linkage` pass. This change separates out a `npcomp.compiler.pytorch.backend.frontend_lowering` module that does the common lowering for all backends. The individual compiler backends `npcomp.compiler.pytorch.backend.{refjit,iree}` now accept a loosely defined "TCP + scalar code" IR mix that will be formalized in the future as the interface to codegen backends. This also required adding a small pass `npcomp-iree-backend-lower-linkage` which adds `iree.module.export` onto functions, and layering that into the frontend flow. The pass doesn't require a C++-level dependency on IREE, which is nice for now. TBD how we are going to handle lists (we hope we can get away with sneakerneting some td files and relying on loose IR compatibility). Running through IREE requires the ability to import `iree.compiler` and `iree.runtime`, which can be obtained as follows: ``` python3 -m pip install iree-compiler-snapshot iree-runtime-snapshot -f https://github.com/google/iree/releases/tag/snapshot-20210406.200 PYTHONPATH="${PYTHONPATH}:${MY_IREE_BUILD}/bindings/python/" ``` This patch makes it painfully clear that we don't have any e2e testing harness to really plug into, and also don't have a usable Python API to our compiler stack (something usable in a jupyter notebook). That will be addressed in subsequent commits. We've been flying by the seat of our pants with this `examples` directory that isn't subject to any kind of testing or real usability concerns.
2021-04-09 04:05:16 +08:00
mlir::NPCOMP::IREEBackend::registerIREEBackendPasses();
Add npcomp-verify-backend-contract pass. This pass verifies that a given module satisfies the contract that we have for backends. This is phrased as an "allowlist", because we want to keep this interface tight. Also, this gives much better diagnostics than a backend randomly crashing or failing to compile would (though they could still be improved). This was especially painful because if we had `tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend would convert it to a memref type and trip the "verify type invariants" assertion which gives no location or anything and crashed the process, which was very unpleasant. We implement this with the dialect conversion framework, which works reasonably well and was quick to put together and familiar, but is still very "op oriented". We probably want to make this hand-rolled eventually, especially the error reporting (the most useful kind of error for a dialect conversion user is not necessarily the best for this use case). Also, in production, these error will go to users, and need to be surfaced carefully such as "the compiler needs a type annotation on this function parameter" which in general requires some special analysis, wordsmithing, and overall awareness of the e2e use case (such as how much we can lean into certain source locations) to provide a meaningful user-level diagnostic. Also, add `inline` to the current frontend lowering pass pipeline to allow slightly more complicated programs that otherwise would fail on shape inference.
2021-04-13 09:39:53 +08:00
mlir::NPCOMP::CommonBackend::registerCommonBackendPasses();
Make a real library for InitAll and extend it to conditionally initialize dependencies. * Conditioned on the top level CMake option to enable IREE. * There is still some warning flags and such that need triage, but it does build/work.
2020-06-12 08:47:14 +08:00
}