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torch-mlir/lib/Conversion/TorchToLinalg/CMakeLists.txt

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Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
add_mlir_conversion_library(TorchMLIRTorchToLinalg
Split up TorchToLinalg.cpp This helps keep things organized and also exposes more parallelism to the build system. It seems though that most of the compile time is actually spent in the headers though, so the wall time doesn't decrease as much as I had hoped (and now that the headers are being included multiple times, the cpu time actually increases a lot, sadly -- will try to dig into this).
2022-03-11 01:54:13 +08:00
DataMovement.cpp
IndirectDataMovement.cpp
Linear.cpp
Pooling.cpp
Random.cpp
Reduction.cpp
TensorConstructors.cpp
TensorScalarInterop.cpp
Significantly restructure torch/aten import design. This is a really major and invasive restructuring of the way we get torch operators (`torch::jit::Operator` / `c10::OperatorHandle`) into MLIR. Please forgive the challenging review, but due to the sheer invasiveness, it wasn't really practical do do it in sane smaller pieces. This fully replaces everything that was already working on the TorchScript path (actually, more -- we added tanh support to TorchToLinalg in order to delete the older code paths). Additionally, I've kept the lights on for the acap path too, including what little e2e stuff was working before (for expediency I made a few tiny compromises along the way that will be easy to undo when we give that path proper attention). Overview of the new design: - The torch operator `somens::someunqualname.someoverloadname` is imported as `torch.somens.someunqualname.someoverloadname` (skip the last dotted part if the overload name is empty), OR, if we don't have such an op registered, it is imported as `torch.operator "somens.someunqualname.someoverloadname" (...) : ...`. - The addition of the "overload name" is a critical element here, as the `(ns,unqual,overload)` triple is unique, which solves a lot of problems we were having. - This involves having separate MLIR ops for the `trailing_` and `.out` variants and all the different overloads. This seemed necessary, because the set of overloads is so wild and varied and unstructured. The previous design was leaning into some underlying structure that just isn't there -- the default situation is the "random overload that we want to manage on the MLIR side", rather than that being an exception. E.g. `aten::ne` (not-equal) has 21 overloads, only 4 of which are c10 dispatcher ops see [gist](https://gist.github.com/silvasean/190ba918c550c956260e21254e1b8aa1), and the "out" variant is really called `.Tensor_out` instead of `.out` as it frequently is for other ops. - Rationale for all being in `torch` namespace: the set of operators are so varied and unstructured that "dialect per namespace" doesn't result in anything resembling the typical MLIR dialect boundary expectations. We could maybe draw the boundary at dispatcher ops vs non-dispatcher ops, but that doesn't seem to really result in very much useful structure at this point in time. - Note: within the torch operator registry, we effectively have a mini-basicpy subdialect (already type-resolved), which is reasonably structured. - The existing Torch op interfaces are also removed -- now that we track the overload name, we can losslessly find the original operator. - Instead of `ATenRecognizeKernelsPass`, we now have a `ReduceOpVariantsPass` that keys off certain traits (and perhaps eventually interfaces) to reduce variants of ops to a smaller set, ideally operating on immutable tensors and using surrounding ops to model the mutability/aliasing aspects. - Note: `torch.ns.unqual.overload` ops allow both immutable and mutable tensors (unlike the previous hard distinction in the common case). This is a premonition for a future change that will introduce a bona fide `!torch.tensor` type that will clean up a bunch of stuff. - `TorchToLinalg` / `TorchToStd` supercede the existing "ATen->TCF->TCP->Linalg" path. - The new `torch_ods_gen.py` supercedes `torch_signature_ods_gen.py`. It should look somewhat familiar, but the benefit of hindsight has allowed a lot of simplifications. The overall trend seems to be to make the `torch` dialect a nice layer independent of anything else. It feels like as a natural result of various future changes we will be removing the reliance on basicpy+numpy dialects and have a nice self-contained type system too that properly models the TorchScript type system (including proper subtyping, mutable/immutable tensors, optional dtype, etc.). Recommended review order: - Start at some of the new import IR, e.g. in `frontends/pytorch/test/node_import/prim.py`, `frontends/pytorch/test/acap_export/test_export_add3.py`, and other tests. - `frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py` and associated generated files: - `include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td` - `include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td` - Inspect `ReduceOpVariants.cpp` / `reduce-op-variants.mlir` and the new traits in `include/npcomp/Dialect/Torch/IR/TorchTraits.h` - Various code changes in the import path in `frontends/pytorch/csrc/builder`. Probably most interesting is the new code in `torch_to_mlir_utils.cpp` that has the logic to create the `torch.operator` ops or `torch.ns.unqual.overload` ops. This is the [new ResNet IR](https://gist.github.com/silvasean/5407aafb710d07612b7b5b92eabecebe), just to be able to look at a substantial sample of IR in the new style.
2021-05-05 05:42:50 +08:00
TorchToLinalg.cpp
Split up TorchToLinalg.cpp This helps keep things organized and also exposes more parallelism to the build system. It seems though that most of the compile time is actually spent in the headers though, so the wall time doesn't decrease as much as I had hoped (and now that the headers are being included multiple times, the cpu time actually increases a lot, sadly -- will try to dig into this).
2022-03-11 01:54:13 +08:00
Uncategorized.cpp
Utils.cpp
Significantly restructure torch/aten import design. This is a really major and invasive restructuring of the way we get torch operators (`torch::jit::Operator` / `c10::OperatorHandle`) into MLIR. Please forgive the challenging review, but due to the sheer invasiveness, it wasn't really practical do do it in sane smaller pieces. This fully replaces everything that was already working on the TorchScript path (actually, more -- we added tanh support to TorchToLinalg in order to delete the older code paths). Additionally, I've kept the lights on for the acap path too, including what little e2e stuff was working before (for expediency I made a few tiny compromises along the way that will be easy to undo when we give that path proper attention). Overview of the new design: - The torch operator `somens::someunqualname.someoverloadname` is imported as `torch.somens.someunqualname.someoverloadname` (skip the last dotted part if the overload name is empty), OR, if we don't have such an op registered, it is imported as `torch.operator "somens.someunqualname.someoverloadname" (...) : ...`. - The addition of the "overload name" is a critical element here, as the `(ns,unqual,overload)` triple is unique, which solves a lot of problems we were having. - This involves having separate MLIR ops for the `trailing_` and `.out` variants and all the different overloads. This seemed necessary, because the set of overloads is so wild and varied and unstructured. The previous design was leaning into some underlying structure that just isn't there -- the default situation is the "random overload that we want to manage on the MLIR side", rather than that being an exception. E.g. `aten::ne` (not-equal) has 21 overloads, only 4 of which are c10 dispatcher ops see [gist](https://gist.github.com/silvasean/190ba918c550c956260e21254e1b8aa1), and the "out" variant is really called `.Tensor_out` instead of `.out` as it frequently is for other ops. - Rationale for all being in `torch` namespace: the set of operators are so varied and unstructured that "dialect per namespace" doesn't result in anything resembling the typical MLIR dialect boundary expectations. We could maybe draw the boundary at dispatcher ops vs non-dispatcher ops, but that doesn't seem to really result in very much useful structure at this point in time. - Note: within the torch operator registry, we effectively have a mini-basicpy subdialect (already type-resolved), which is reasonably structured. - The existing Torch op interfaces are also removed -- now that we track the overload name, we can losslessly find the original operator. - Instead of `ATenRecognizeKernelsPass`, we now have a `ReduceOpVariantsPass` that keys off certain traits (and perhaps eventually interfaces) to reduce variants of ops to a smaller set, ideally operating on immutable tensors and using surrounding ops to model the mutability/aliasing aspects. - Note: `torch.ns.unqual.overload` ops allow both immutable and mutable tensors (unlike the previous hard distinction in the common case). This is a premonition for a future change that will introduce a bona fide `!torch.tensor` type that will clean up a bunch of stuff. - `TorchToLinalg` / `TorchToStd` supercede the existing "ATen->TCF->TCP->Linalg" path. - The new `torch_ods_gen.py` supercedes `torch_signature_ods_gen.py`. It should look somewhat familiar, but the benefit of hindsight has allowed a lot of simplifications. The overall trend seems to be to make the `torch` dialect a nice layer independent of anything else. It feels like as a natural result of various future changes we will be removing the reliance on basicpy+numpy dialects and have a nice self-contained type system too that properly models the TorchScript type system (including proper subtyping, mutable/immutable tensors, optional dtype, etc.). Recommended review order: - Start at some of the new import IR, e.g. in `frontends/pytorch/test/node_import/prim.py`, `frontends/pytorch/test/acap_export/test_export_add3.py`, and other tests. - `frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py` and associated generated files: - `include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td` - `include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td` - Inspect `ReduceOpVariants.cpp` / `reduce-op-variants.mlir` and the new traits in `include/npcomp/Dialect/Torch/IR/TorchTraits.h` - Various code changes in the import path in `frontends/pytorch/csrc/builder`. Probably most interesting is the new code in `torch_to_mlir_utils.cpp` that has the logic to create the `torch.operator` ops or `torch.ns.unqual.overload` ops. This is the [new ResNet IR](https://gist.github.com/silvasean/5407aafb710d07612b7b5b92eabecebe), just to be able to look at a substantial sample of IR in the new style.
2021-05-05 05:42:50 +08:00
ADDITIONAL_HEADER_DIRS
Port the bulk of the remaining code to torch-mlir This leaves no real code outside torch-mlir. This also renames the "npcomp backend contract" to "linalg on tensors backend contract" as the name of the abstraction layer that RefBackend (IREE too) accepts.
2021-09-28 02:36:44 +08:00
${PROJECT_SOURCE_DIR}/include/torch-mlir/Conversion/TorchToLinalg
Significantly restructure torch/aten import design. This is a really major and invasive restructuring of the way we get torch operators (`torch::jit::Operator` / `c10::OperatorHandle`) into MLIR. Please forgive the challenging review, but due to the sheer invasiveness, it wasn't really practical do do it in sane smaller pieces. This fully replaces everything that was already working on the TorchScript path (actually, more -- we added tanh support to TorchToLinalg in order to delete the older code paths). Additionally, I've kept the lights on for the acap path too, including what little e2e stuff was working before (for expediency I made a few tiny compromises along the way that will be easy to undo when we give that path proper attention). Overview of the new design: - The torch operator `somens::someunqualname.someoverloadname` is imported as `torch.somens.someunqualname.someoverloadname` (skip the last dotted part if the overload name is empty), OR, if we don't have such an op registered, it is imported as `torch.operator "somens.someunqualname.someoverloadname" (...) : ...`. - The addition of the "overload name" is a critical element here, as the `(ns,unqual,overload)` triple is unique, which solves a lot of problems we were having. - This involves having separate MLIR ops for the `trailing_` and `.out` variants and all the different overloads. This seemed necessary, because the set of overloads is so wild and varied and unstructured. The previous design was leaning into some underlying structure that just isn't there -- the default situation is the "random overload that we want to manage on the MLIR side", rather than that being an exception. E.g. `aten::ne` (not-equal) has 21 overloads, only 4 of which are c10 dispatcher ops see [gist](https://gist.github.com/silvasean/190ba918c550c956260e21254e1b8aa1), and the "out" variant is really called `.Tensor_out` instead of `.out` as it frequently is for other ops. - Rationale for all being in `torch` namespace: the set of operators are so varied and unstructured that "dialect per namespace" doesn't result in anything resembling the typical MLIR dialect boundary expectations. We could maybe draw the boundary at dispatcher ops vs non-dispatcher ops, but that doesn't seem to really result in very much useful structure at this point in time. - Note: within the torch operator registry, we effectively have a mini-basicpy subdialect (already type-resolved), which is reasonably structured. - The existing Torch op interfaces are also removed -- now that we track the overload name, we can losslessly find the original operator. - Instead of `ATenRecognizeKernelsPass`, we now have a `ReduceOpVariantsPass` that keys off certain traits (and perhaps eventually interfaces) to reduce variants of ops to a smaller set, ideally operating on immutable tensors and using surrounding ops to model the mutability/aliasing aspects. - Note: `torch.ns.unqual.overload` ops allow both immutable and mutable tensors (unlike the previous hard distinction in the common case). This is a premonition for a future change that will introduce a bona fide `!torch.tensor` type that will clean up a bunch of stuff. - `TorchToLinalg` / `TorchToStd` supercede the existing "ATen->TCF->TCP->Linalg" path. - The new `torch_ods_gen.py` supercedes `torch_signature_ods_gen.py`. It should look somewhat familiar, but the benefit of hindsight has allowed a lot of simplifications. The overall trend seems to be to make the `torch` dialect a nice layer independent of anything else. It feels like as a natural result of various future changes we will be removing the reliance on basicpy+numpy dialects and have a nice self-contained type system too that properly models the TorchScript type system (including proper subtyping, mutable/immutable tensors, optional dtype, etc.). Recommended review order: - Start at some of the new import IR, e.g. in `frontends/pytorch/test/node_import/prim.py`, `frontends/pytorch/test/acap_export/test_export_add3.py`, and other tests. - `frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py` and associated generated files: - `include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td` - `include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td` - Inspect `ReduceOpVariants.cpp` / `reduce-op-variants.mlir` and the new traits in `include/npcomp/Dialect/Torch/IR/TorchTraits.h` - Various code changes in the import path in `frontends/pytorch/csrc/builder`. Probably most interesting is the new code in `torch_to_mlir_utils.cpp` that has the logic to create the `torch.operator` ops or `torch.ns.unqual.overload` ops. This is the [new ResNet IR](https://gist.github.com/silvasean/5407aafb710d07612b7b5b92eabecebe), just to be able to look at a substantial sample of IR in the new style.
2021-05-05 05:42:50 +08:00
DEPENDS
Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
TorchMLIRConversionPassIncGen
Significantly restructure torch/aten import design. This is a really major and invasive restructuring of the way we get torch operators (`torch::jit::Operator` / `c10::OperatorHandle`) into MLIR. Please forgive the challenging review, but due to the sheer invasiveness, it wasn't really practical do do it in sane smaller pieces. This fully replaces everything that was already working on the TorchScript path (actually, more -- we added tanh support to TorchToLinalg in order to delete the older code paths). Additionally, I've kept the lights on for the acap path too, including what little e2e stuff was working before (for expediency I made a few tiny compromises along the way that will be easy to undo when we give that path proper attention). Overview of the new design: - The torch operator `somens::someunqualname.someoverloadname` is imported as `torch.somens.someunqualname.someoverloadname` (skip the last dotted part if the overload name is empty), OR, if we don't have such an op registered, it is imported as `torch.operator "somens.someunqualname.someoverloadname" (...) : ...`. - The addition of the "overload name" is a critical element here, as the `(ns,unqual,overload)` triple is unique, which solves a lot of problems we were having. - This involves having separate MLIR ops for the `trailing_` and `.out` variants and all the different overloads. This seemed necessary, because the set of overloads is so wild and varied and unstructured. The previous design was leaning into some underlying structure that just isn't there -- the default situation is the "random overload that we want to manage on the MLIR side", rather than that being an exception. E.g. `aten::ne` (not-equal) has 21 overloads, only 4 of which are c10 dispatcher ops see [gist](https://gist.github.com/silvasean/190ba918c550c956260e21254e1b8aa1), and the "out" variant is really called `.Tensor_out` instead of `.out` as it frequently is for other ops. - Rationale for all being in `torch` namespace: the set of operators are so varied and unstructured that "dialect per namespace" doesn't result in anything resembling the typical MLIR dialect boundary expectations. We could maybe draw the boundary at dispatcher ops vs non-dispatcher ops, but that doesn't seem to really result in very much useful structure at this point in time. - Note: within the torch operator registry, we effectively have a mini-basicpy subdialect (already type-resolved), which is reasonably structured. - The existing Torch op interfaces are also removed -- now that we track the overload name, we can losslessly find the original operator. - Instead of `ATenRecognizeKernelsPass`, we now have a `ReduceOpVariantsPass` that keys off certain traits (and perhaps eventually interfaces) to reduce variants of ops to a smaller set, ideally operating on immutable tensors and using surrounding ops to model the mutability/aliasing aspects. - Note: `torch.ns.unqual.overload` ops allow both immutable and mutable tensors (unlike the previous hard distinction in the common case). This is a premonition for a future change that will introduce a bona fide `!torch.tensor` type that will clean up a bunch of stuff. - `TorchToLinalg` / `TorchToStd` supercede the existing "ATen->TCF->TCP->Linalg" path. - The new `torch_ods_gen.py` supercedes `torch_signature_ods_gen.py`. It should look somewhat familiar, but the benefit of hindsight has allowed a lot of simplifications. The overall trend seems to be to make the `torch` dialect a nice layer independent of anything else. It feels like as a natural result of various future changes we will be removing the reliance on basicpy+numpy dialects and have a nice self-contained type system too that properly models the TorchScript type system (including proper subtyping, mutable/immutable tensors, optional dtype, etc.). Recommended review order: - Start at some of the new import IR, e.g. in `frontends/pytorch/test/node_import/prim.py`, `frontends/pytorch/test/acap_export/test_export_add3.py`, and other tests. - `frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py` and associated generated files: - `include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td` - `include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td` - Inspect `ReduceOpVariants.cpp` / `reduce-op-variants.mlir` and the new traits in `include/npcomp/Dialect/Torch/IR/TorchTraits.h` - Various code changes in the import path in `frontends/pytorch/csrc/builder`. Probably most interesting is the new code in `torch_to_mlir_utils.cpp` that has the logic to create the `torch.operator` ops or `torch.ns.unqual.overload` ops. This is the [new ResNet IR](https://gist.github.com/silvasean/5407aafb710d07612b7b5b92eabecebe), just to be able to look at a substantial sample of IR in the new style.
2021-05-05 05:42:50 +08:00
LINK_LIBS PUBLIC
MLIRIR
MLIRPass
Bump LLVM (#958)
2022-06-23 11:23:46 +08:00
MLIRLinalgDialect
MLIRMathDialect
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
TorchMLIRTorchDialect
Significantly restructure torch/aten import design. This is a really major and invasive restructuring of the way we get torch operators (`torch::jit::Operator` / `c10::OperatorHandle`) into MLIR. Please forgive the challenging review, but due to the sheer invasiveness, it wasn't really practical do do it in sane smaller pieces. This fully replaces everything that was already working on the TorchScript path (actually, more -- we added tanh support to TorchToLinalg in order to delete the older code paths). Additionally, I've kept the lights on for the acap path too, including what little e2e stuff was working before (for expediency I made a few tiny compromises along the way that will be easy to undo when we give that path proper attention). Overview of the new design: - The torch operator `somens::someunqualname.someoverloadname` is imported as `torch.somens.someunqualname.someoverloadname` (skip the last dotted part if the overload name is empty), OR, if we don't have such an op registered, it is imported as `torch.operator "somens.someunqualname.someoverloadname" (...) : ...`. - The addition of the "overload name" is a critical element here, as the `(ns,unqual,overload)` triple is unique, which solves a lot of problems we were having. - This involves having separate MLIR ops for the `trailing_` and `.out` variants and all the different overloads. This seemed necessary, because the set of overloads is so wild and varied and unstructured. The previous design was leaning into some underlying structure that just isn't there -- the default situation is the "random overload that we want to manage on the MLIR side", rather than that being an exception. E.g. `aten::ne` (not-equal) has 21 overloads, only 4 of which are c10 dispatcher ops see [gist](https://gist.github.com/silvasean/190ba918c550c956260e21254e1b8aa1), and the "out" variant is really called `.Tensor_out` instead of `.out` as it frequently is for other ops. - Rationale for all being in `torch` namespace: the set of operators are so varied and unstructured that "dialect per namespace" doesn't result in anything resembling the typical MLIR dialect boundary expectations. We could maybe draw the boundary at dispatcher ops vs non-dispatcher ops, but that doesn't seem to really result in very much useful structure at this point in time. - Note: within the torch operator registry, we effectively have a mini-basicpy subdialect (already type-resolved), which is reasonably structured. - The existing Torch op interfaces are also removed -- now that we track the overload name, we can losslessly find the original operator. - Instead of `ATenRecognizeKernelsPass`, we now have a `ReduceOpVariantsPass` that keys off certain traits (and perhaps eventually interfaces) to reduce variants of ops to a smaller set, ideally operating on immutable tensors and using surrounding ops to model the mutability/aliasing aspects. - Note: `torch.ns.unqual.overload` ops allow both immutable and mutable tensors (unlike the previous hard distinction in the common case). This is a premonition for a future change that will introduce a bona fide `!torch.tensor` type that will clean up a bunch of stuff. - `TorchToLinalg` / `TorchToStd` supercede the existing "ATen->TCF->TCP->Linalg" path. - The new `torch_ods_gen.py` supercedes `torch_signature_ods_gen.py`. It should look somewhat familiar, but the benefit of hindsight has allowed a lot of simplifications. The overall trend seems to be to make the `torch` dialect a nice layer independent of anything else. It feels like as a natural result of various future changes we will be removing the reliance on basicpy+numpy dialects and have a nice self-contained type system too that properly models the TorchScript type system (including proper subtyping, mutable/immutable tensors, optional dtype, etc.). Recommended review order: - Start at some of the new import IR, e.g. in `frontends/pytorch/test/node_import/prim.py`, `frontends/pytorch/test/acap_export/test_export_add3.py`, and other tests. - `frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py` and associated generated files: - `include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td` - `include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td` - Inspect `ReduceOpVariants.cpp` / `reduce-op-variants.mlir` and the new traits in `include/npcomp/Dialect/Torch/IR/TorchTraits.h` - Various code changes in the import path in `frontends/pytorch/csrc/builder`. Probably most interesting is the new code in `torch_to_mlir_utils.cpp` that has the logic to create the `torch.operator` ops or `torch.ns.unqual.overload` ops. This is the [new ResNet IR](https://gist.github.com/silvasean/5407aafb710d07612b7b5b92eabecebe), just to be able to look at a substantial sample of IR in the new style.
2021-05-05 05:42:50 +08:00
)
Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
torch_mlir_target_includes(TorchMLIRTorchToLinalg)