mirror of https://github.com/llvm/torch-mlir
Remove VerifyInvariantsBeforeBackendLowering
LowerToBackendContract now checks all this consistently.pull/1292/head
Sean Silva
parent
b1fa7a2b9d
commit
0e3ddbac91
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@ -41,9 +41,6 @@ void createTorchBackendToMhloBackendPipeline(
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const torch::Torch::TorchLoweringPipelineOptions &options);
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#endif
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std::unique_ptr<OperationPass<ModuleOp>>
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createVerifyInvariantsBeforeBackendLoweringPass();
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std::unique_ptr<OperationPass<ModuleOp>> createFuncBackendTypeConversionPass();
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std::unique_ptr<OperationPass<func::FuncOp>>
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@ -12,27 +12,6 @@
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include "mlir/Pass/PassBase.td"
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def VerifyInvariantsBeforeBackendLowering
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: Pass<"torch-verify-invariants-before-backend-lowering", "ModuleOp"> {
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let summary = "Verify invariants required by backend lowering";
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let constructor =
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"mlir::torch::TorchConversion::createVerifyInvariantsBeforeBackendLoweringPass()";
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let description = [{
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This pass checks any invariants needed by the process of lowering the
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`torch` dialect to the linalg-on-tensors backend contract.
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The most important invariant is that all tensors should be ranked and have
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a known dtype. It is useful to catch this early because it usually
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represents a simple bug in RefineTypes, but can manifest as many different
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kinds of obscure symptoms during lowering.
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TODO: This pass should probably be phrased as checking the
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"torch backend contract" and moved to that dialect once we have more
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substantial definition definition around what that layer is from an
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"allowlist" perspective.
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}];
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}
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def FuncBackendTypeConversion : Pass<"torch-func-backend-type-conversion", "ModuleOp"> {
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let summary = "Convert functions to operate on builtin tensors";
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let constructor = "mlir::torch::TorchConversion::createFuncBackendTypeConversionPass()";
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@ -18,7 +18,6 @@ add_mlir_library(TorchMLIRTorchConversionPasses
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BackendTypeConversion.cpp
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BackendTypeConversionPasses.cpp
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Passes.cpp
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VerifyInvariantsBeforeBackendLowering.cpp
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VerifyLinalgOnTensorsBackendContract.cpp
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VerifyTosaBackendContract.cpp
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@ -63,10 +63,6 @@ void mlir::torch::registerTorchConversionPasses() {
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void TorchConversion::createTorchBackendToLinalgOnTensorsBackendPipeline(
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OpPassManager &pm, const Torch::TorchLoweringPipelineOptions &options) {
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// Check some invariants to catch errors in a clear way.
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pm.addPass(
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TorchConversion::createVerifyInvariantsBeforeBackendLoweringPass());
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// Lower to linalg + guards which is the input to codegen backends.
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// We do this first as it tends to involve pattern-matching against constants,
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// (e.g. dimensions which must be constant in a ranked programming model)
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@ -101,10 +97,6 @@ void TorchConversion::createTorchBackendToLinalgOnTensorsBackendPipeline(
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void TorchConversion::createTorchBackendToTosaBackendPipeline(
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OpPassManager &pm, const Torch::TorchLoweringPipelineOptions &options) {
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// Check some invariants to catch errors in a clear way.
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pm.addPass(
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TorchConversion::createVerifyInvariantsBeforeBackendLoweringPass());
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pm.addNestedPass<func::FuncOp>(createConvertTorchToTosaPass());
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// Perform rank broadcasting so TosaToLinalg pass works
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pm.addNestedPass<func::FuncOp>(createTosaMakeBroadcastablePass());
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@ -130,10 +122,6 @@ void TorchConversion::createTorchBackendToTosaBackendPipeline(
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#ifdef TORCH_MLIR_ENABLE_MHLO
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void TorchConversion::createTorchBackendToMhloBackendPipeline(
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OpPassManager &pm, const Torch::TorchLoweringPipelineOptions &options) {
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// Check some invariants to catch errors in a clear way.
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pm.addPass(
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TorchConversion::createVerifyInvariantsBeforeBackendLoweringPass());
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pm.addNestedPass<func::FuncOp>(createConvertTorchToMhloPass());
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// Clean up any non-canonical code introduced above..
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@ -1,86 +0,0 @@
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//===- VerifyInvariantsBeforeBackendLowering.cpp -----------------*- C++-*-===//
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//
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// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "PassDetail.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
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#include "torch-mlir/Dialect/TorchConversion/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::TorchConversion;
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using namespace mlir::torch;
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static LogicalResult checkValueInvariants(Operation *errorReportOp, Value v) {
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// TODO: Make this an allowlist instead of a denylist.
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// TODO: Make this stricter.
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auto type = v.getType();
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if (auto valueTensorType = type.dyn_cast<Torch::ValueTensorType>()) {
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if (!valueTensorType.hasDtype() || !valueTensorType.hasSizes())
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return errorReportOp->emitError()
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.append("unsupported by backend lowering: tensor with unknown rank "
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"or dtype")
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.attachNote()
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.append("this is likely due to a missing shape transfer function in "
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"shape_lib_gen.py or missing case in RefineTypes");
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}
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return success();
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}
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namespace {
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class VerifyInvariantsBeforeBackendLoweringPass
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: public VerifyInvariantsBeforeBackendLoweringBase<
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VerifyInvariantsBeforeBackendLoweringPass> {
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void runOnOperation() override {
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if (getOperation()
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.walk([](Torch::GlobalSlotModuleInitializerOp op) {
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op.emitError()
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<< "unsupported by backend lowering: module initializers";
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return WalkResult::interrupt();
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})
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.wasInterrupted())
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return signalPassFailure();
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auto walkResult = getOperation().walk([&](Block *block) {
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// Check invariants on all the Value's in the program.
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// That is, check all BlockArgument's and OpResult's.
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for (BlockArgument arg : block->getArguments())
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if (failed(checkValueInvariants(block->getParentOp(), arg)))
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return WalkResult::interrupt();
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for (Operation &op : *block) {
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if (isa<Torch::OperatorOp>(op)) {
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op.emitError()
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.append("unsupported by backend lowering: `torch.operator` op")
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.attachNote()
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.append("this is likely due to a missing op that needs to be "
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"generated by torch_ods_gen.py");
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return WalkResult::interrupt();
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}
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for (OpResult result : op.getResults())
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if (failed(checkValueInvariants(&op, result)))
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return WalkResult::interrupt();
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}
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return WalkResult::advance();
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});
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if (walkResult.wasInterrupted())
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return signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<ModuleOp>> mlir::torch::TorchConversion::
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createVerifyInvariantsBeforeBackendLoweringPass() {
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return std::make_unique<VerifyInvariantsBeforeBackendLoweringPass>();
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}
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@ -1,36 +0,0 @@
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// RUN: torch-mlir-opt -split-input-file -verify-diagnostics %s -torch-verify-invariants-before-backend-lowering
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// -----
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func.func @unknown_rank(%arg0: !torch.vtensor<[],f32>) {
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// expected-error@+2 {{unsupported by backend lowering: tensor with unknown rank or dtype}}
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// expected-note@+1 {{this is likely due to a missing shape transfer function in shape_lib_gen.py}}
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%0 = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[],f32>, !torch.vtensor<[],f32> -> !torch.vtensor<*,f32>
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return
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}
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// -----
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func.func @unknown_dtype(%arg0: !torch.vtensor<[],f32>) {
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// expected-error@+2 {{unsupported by backend lowering: tensor with unknown rank or dtype}}
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// expected-note@+1 {{this is likely due to a missing shape transfer function in shape_lib_gen.py}}
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%0 = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[],f32>, !torch.vtensor<[],f32> -> !torch.vtensor<[],unk>
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return
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}
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// -----
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func.func @unresolved_operator(%arg0: !torch.vtensor<[],f32>, %arg1: !torch.int) {
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// expected-error@+2 {{unsupported by backend lowering: `torch.operator` op}}
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// expected-note@+1 {{this is likely due to a missing op that needs to be generated by torch_ods_gen.py}}
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torch.operator "aten.mul.Scalar"(%arg0, %arg1) : (!torch.vtensor<[],f32>, !torch.int) -> !torch.vtensor<[],f32>
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return
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}
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// -----
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// expected-error@+1 {{unsupported by backend lowering: module initializers}}
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torch.global_slot.module_initializer {
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torch.initialize.global_slots [
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]
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}
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@ -500,7 +500,6 @@ cc_library(
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"lib/Dialect/TorchConversion/Transforms/BackendTypeConversionPasses.cpp",
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"lib/Dialect/TorchConversion/Transforms/PassDetail.h",
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"lib/Dialect/TorchConversion/Transforms/Passes.cpp",
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"lib/Dialect/TorchConversion/Transforms/VerifyInvariantsBeforeBackendLowering.cpp",
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"lib/Dialect/TorchConversion/Transforms/VerifyLinalgOnTensorsBackendContract.cpp",
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"lib/Dialect/TorchConversion/Transforms/VerifyTosaBackendContract.cpp",
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],
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