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Reformat Passes.cpp and remove torch-globalize-pipeline. 

The pipeline is subsumed by our lowering pipelines.
pull/213/head
Sean Silva 2021-04-29 15:13:21 -07:00
parent 4678a7fedd
commit fb5f149e04
2 changed files with 69 additions and 80 deletions
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4
include/npcomp/Dialect/Torch/Transforms/Passes.h
View File

@ -22,10 +22,6 @@ std::unique_ptr<OperationPass<ModuleOp>> createGlobalizeObjectGraphPass();
std::unique_ptr<OperationPass<ModuleOp>>
createPrepareForGlobalizeObjectGraphPass();
/// Creates a pipeline that "globalizes" the given program.
/// See the documentation on torch-globalize-object-graph for more details.
void createGlobalizePipeline(OpPassManager &pm);
/// Creates a pipeline that lowers the object graph IR that is produced by
/// TorchScript import into the form expected by npcomp-verify-backend-contract.
void createLowerObjectGraphPipeline(OpPassManager &pm);

145
lib/Dialect/Torch/Transforms/Passes.cpp
View File

@ -28,9 +28,6 @@ namespace {
void mlir::NPCOMP::registerTorchPasses() {
::registerPasses();
mlir::PassPipelineRegistration<>(
"torch-globalize-pipeline", "Globalization pipeline.",
mlir::NPCOMP::Torch::createGlobalizePipeline);
mlir::PassPipelineRegistration<>(
"torchscript-to-npcomp-backend-pipeline",
"Pipeline lowering torch object graph to npcomp backend format.",
@ -41,82 +38,78 @@ void mlir::NPCOMP::registerTorchPasses() {
mlir::NPCOMP::Torch::createLowerToNpcompBackendPipeline);
}
void mlir::NPCOMP::Torch::createGlobalizePipeline(OpPassManager &pm) {
void mlir::NPCOMP::Torch::createLowerObjectGraphPipeline(OpPassManager &pm) {
// When we import TorchScript IR, we import their entire "compilation unit",
// which can contain numerous functions unrelated to the current program,
// which breaks torch-globalization-pipeline; for example, there can be
// random functions referencing types that haven't been imported
// as part of the root `torch.nn.Module` we imported. Those will
// be unreferenced private functions which symbol-dce will clean up nicely.
pm.addPass(createSymbolDCEPass());
// Globalize the program. The rest of the compiler assumes a globalized
// program, which makes all analyses and transforms significantly easier
// to write.
pm.addPass(createPrepareForGlobalizeObjectGraphPass());
pm.addPass(createGlobalizeObjectGraphPass());
// "lower" `torch.global_slot` ops by deleting them if unused, which we
// currently require because we don't have a lowering path for backends to
// handle them.
// Torch usually inserts a few unused global slots so this ends up hitting
// every single module even if it doesn't have any explicit slots.
// TODO: Support global slots in backends.
pm.addPass(createSymbolDCEPass());
// Currently, our shape inference is not powerful enough to deal with
// calls, so inline everything.
// TODO: Improve shape inference.
pm.addPass(createInlinerPass());
// Incorporate user annotations and remove signature Python-isms.
pm.addPass(createAdjustCallingConventionsPass());
createLowerToNpcompBackendPipeline(pm);
}
void mlir::NPCOMP::Torch::createLowerObjectGraphPipeline(OpPassManager &pm) {
// When we import TorchScript IR, we import their entire "compilation unit",
// which can contain numerous functions unrelated to the current program,
// which breaks torch-globalization-pipeline; for example, there can be
// random functions referencing types that haven't been imported
// as part of the root `torch.nn.Module` we imported. Those will
// be unreferenced private functions which symbol-dce will clean up nicely.
pm.addPass(createSymbolDCEPass());
// Globalize the program. The rest of the compiler assumes a globalized
// program, which makes all analyses and transforms significantly easier
// to write.
pm.addPass(createPrepareForGlobalizeObjectGraphPass());
pm.addPass(createGlobalizeObjectGraphPass());
// "lower" `torch.global_slot` ops by deleting them if unused, which we
// currently require because we don't have a lowering path for backends to
// handle them.
// Torch usually inserts a few unused global slots so this ends up hitting
// every single module even if it doesn't have any explicit slots.
// TODO: Support global slots in backends.
pm.addPass(createSymbolDCEPass());
// Currently, our shape inference is not powerful enough to deal with
// calls, so inline everything.
// TODO: Improve shape inference.
pm.addPass(createInlinerPass());
// Incorporate user annotations and remove signature Python-isms.
pm.addPass(createAdjustCallingConventionsPass());
void mlir::NPCOMP::Torch::createLowerToNpcompBackendPipeline(
OpPassManager &pm) {
// Recognize ATen kernels.
pm.addNestedPass<FuncOp>(aten::createRecognizeKernelsPass());
createLowerToNpcompBackendPipeline(pm);
}
void mlir::NPCOMP::Torch::createLowerToNpcompBackendPipeline(OpPassManager &pm) {
// Recognize ATen kernels.
pm.addNestedPass<FuncOp>(aten::createRecognizeKernelsPass());
// Convert the bulk of the program to ranked tensors with known dtype.
// This is the input to the backend layer that we are aiming for.
// First, unilaterally convert public functions to tensor.
// The way this pass is currently written, this implies that
// as pipeline authors, we are restricting our users to not be able to see
// updates to "out params" on their public functions.
// This is deemed ok for now.
pm.addPass(Numpy::createPublicFunctionsToTensorPass());
// Inline global slots, which for most inference scenarios deletes them.
// This also exposes more information to intraprocedural transformations
// below like ArrayToTensor and RefineTypes.
// TODO: Don't rely on this pass to "lower" global slots by deleting.
// This pass should eventually be "just an optimization".
pm.addPass(createInlineGlobalSlotsPass());
// Convert the bulk of non-ABI-visible arrays to tensors.
pm.addNestedPass<FuncOp>(Numpy::createArrayToTensorPass());
// Do shape and dtype refinement.
// We could do it sooner, but the pass currently doesn't have transfer
// functions for array ops.
pm.addNestedPass<FuncOp>(Torch::createRefineTypesPass());
// Propagate to ABI return types the shape/dtype information discovered by
// the previous pass. Doing this is ABI-compatible for our backends.
pm.addPass(Numpy::createRefinePublicReturnPass());
// Clean up a few stray array/tensor conversion remnants.
pm.addNestedPass<FuncOp>(Numpy::createArrayToTensorPass());
// Lower to TCP (+ guards) which is the input to codegen backends.
// Most of this should be subsumed by aten->linalg+guards conversions.
// (the guard generation will be automated from the linalg Op DSL).
pm.addNestedPass<FuncOp>(createConvertATenToLinalgPass());
pm.addNestedPass<FuncOp>(createConvertATenToTCFPass());
pm.addNestedPass<FuncOp>(createConvertTCFToStdPass());
pm.addNestedPass<FuncOp>(createConvertElementwiseToLinalgPass());
// Verify that we have lowered to the form that backends expect.
// This fails compilation (signalPassFailure) if the IR is not in the
// correct form.
pm.addPass(CommonBackend::createVerifyBackendContractPass());
// Convert the bulk of the program to ranked tensors with known dtype.
// This is the input to the backend layer that we are aiming for.
// First, unilaterally convert public functions to tensor.
// The way this pass is currently written, this implies that
// as pipeline authors, we are restricting our users to not be able to see
// updates to "out params" on their public functions.
// This is deemed ok for now.
pm.addPass(Numpy::createPublicFunctionsToTensorPass());
// Inline global slots, which for most inference scenarios deletes them.
// This also exposes more information to intraprocedural transformations
// below like ArrayToTensor and RefineTypes.
// TODO: Don't rely on this pass to "lower" global slots by deleting.
// This pass should eventually be "just an optimization".
pm.addPass(createInlineGlobalSlotsPass());
// Convert the bulk of non-ABI-visible arrays to tensors.
pm.addNestedPass<FuncOp>(Numpy::createArrayToTensorPass());
// Do shape and dtype refinement.
// We could do it sooner, but the pass currently doesn't have transfer
// functions for array ops.
pm.addNestedPass<FuncOp>(Torch::createRefineTypesPass());
// Propagate to ABI return types the shape/dtype information discovered by
// the previous pass. Doing this is ABI-compatible for our backends.
pm.addPass(Numpy::createRefinePublicReturnPass());
// Clean up a few stray array/tensor conversion remnants.
pm.addNestedPass<FuncOp>(Numpy::createArrayToTensorPass());
// Lower to TCP (+ guards) which is the input to codegen backends.
// Most of this should be subsumed by aten->linalg+guards conversions.
// (the guard generation will be automated from the linalg Op DSL).
pm.addNestedPass<FuncOp>(createConvertATenToLinalgPass());
pm.addNestedPass<FuncOp>(createConvertATenToTCFPass());
pm.addNestedPass<FuncOp>(createConvertTCFToStdPass());
pm.addNestedPass<FuncOp>(createConvertElementwiseToLinalgPass());
// Verify that we have lowered to the form that backends expect.
// This fails compilation (signalPassFailure) if the IR is not in the
// correct form.
pm.addPass(CommonBackend::createVerifyBackendContractPass());
}