From 532322dfa2dfc5d2bd2f69343ba7b3e108b2d7be Mon Sep 17 00:00:00 2001 From: Siddharth Bhat Date: Fri, 4 Feb 2022 07:56:52 +0000 Subject: [PATCH] PDL crashes on my simple file MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ╭─siddu_druid@siddharth-lean ~/phd/mlir-hoopl-rete/test ‹master●› ╰─$ mlir-opt pdl-simple.mlir -allow-unregistered-dialect -test-pdl-bytecode-pass 148 ↵ PLEASE submit a bug report to https://github.com/llvm/llvm-project/issues/ and include the crash backtrace. Stack dump: 0. Program arguments: mlir-opt pdl-simple.mlir -allow-unregistered-dialect -test-pdl-bytecode-pass #0 0x00000000008be623 llvm::sys::PrintStackTrace(llvm::raw_ostream&, int) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x8be623) #1 0x00000000008bc2de llvm::sys::RunSignalHandlers() (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x8bc2de) #2 0x00000000008bec16 SignalHandler(int) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x8bec16) #3 0x00007fad2398d730 __restore_rt (/lib/x86_64-linux-gnu/libpthread.so.0+0x12730) #4 0x0000000000faa046 std::enable_if::value), void>::type mlir::ResultRange::replaceAllUsesWith(mlir::ValueRange&) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-pr oject/build/bin/mlir-opt+0xfaa046) #5 0x0000000001862fac mlir::RewriterBase::replaceOp(mlir::Operation*, mlir::ValueRange) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x1862fac) #6 0x00000000018d6ad5 (anonymous namespace)::ByteCodeExecutor::execute(mlir::PatternRewriter&, llvm::SmallVectorImpl*, llvm::Optional) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-proj ect/build/bin/mlir-opt+0x18d6ad5) #7 0x00000000018d8ec1 mlir::detail::PDLByteCode::rewrite(mlir::PatternRewriter&, mlir::detail::PDLByteCode::MatchResult const&, mlir::detail::PDLByteCodeMutableState&) const (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mli r-opt+0x18d8ec1) #8 0x00000000018f15b6 mlir::PatternApplicator::matchAndRewrite(mlir::Operation*, mlir::PatternRewriter&, llvm::function_ref, llvm::function_ref, llvm::function_ref) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x18f15b6) #9 0x00000000017aab4c mlir::applyPatternsAndFoldGreedily(llvm::MutableArrayRef, mlir::FrozenRewritePatternSet const&, mlir::GreedyRewriteConfig) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x17aab4c ) signed int, mlir::PassInstrumentor*, mlir::PassInstrumentation::PipelineParentInfo const*) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x174f404) +0x1725bf0) try&, llvm::ThreadPool*) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x1723dfa) ol, bool, bool) (/home/siddu_druid/phd/mlir-hoopl-rete/llvm-project/build/bin/mlir-opt+0x1723aaa) [2] 27957 segmentation fault mlir-opt pdl-simple.mlir -allow-unregistered-dialect -test-pdl-bytecode-pass ╭─siddu_druid@siddharth-lean ~/phd/mlir-hoopl-rete/test ‹master●› --- test/pdl-simple.mlir | 115 +++++++------------------------------------ 1 file changed, 18 insertions(+), 97 deletions(-) diff --git a/test/pdl-simple.mlir b/test/pdl-simple.mlir index baa4c7c..cf6f00b 100644 --- a/test/pdl-simple.mlir +++ b/test/pdl-simple.mlir @@ -3,115 +3,36 @@ // ----- //===----------------------------------------------------------------------===// -// 1-layer perceptron with split fwd/bwd operations +// Asm add rewrite to write add (int 0) a -> a //===----------------------------------------------------------------------===// module @patterns { // fc_fwd pdl.pattern : benefit(1) { - %in_type = pdl.type - %out_type = pdl.type - %weight_type = pdl.type - %rxact = pdl.operand : %in_type - %weight = pdl.operand : %weight_type - - %attr0 = pdl.attribute false - %op0 = pdl.operation "tf.MatMul" (%rxact, %weight : !pdl.value, !pdl.value) {"transpose_a" = %attr0, "transpose_b" = %attr0} -> (%out_type : !pdl.type) - - pdl.rewrite %op0 { - %op1 = pdl.operation "kernel.FcFwd" (%rxact, %weight : !pdl.value, !pdl.value) -> (%out_type : !pdl.type) - %val1 = pdl.result 0 of %op1 // txact - pdl.replace %op0 with (%val1 : !pdl.value) // tf.MatMul - } - } - - // fc_bwd - pdl.pattern : benefit(4) { - %in_type = pdl.type - %out_type = pdl.type - %weight_type = pdl.type - %const_type = pdl.type - %rxact = pdl.operand : %in_type - %rxdelta = pdl.operand : %out_type - %weight = pdl.operand : %weight_type - - %attr0 = pdl.attribute true - %attr1 = pdl.attribute false - %op0 = pdl.operation "tf.MatMul" (%rxact, %rxdelta : !pdl.value, !pdl.value) {"transpose_a" = %attr0, "transpose_b" = %attr1} -> (%weight_type : !pdl.type) + %c0_type = pdl.type + %cr_type = pdl.type + %cr = pdl.operand : %cr_type + + %c0_attr = pdl.attribute 0 : i32 + // TODO: is pdl.operation allowed to have empty arg list? + // %op0 = pdl.operation "asm.int" () {"value" = %c0_attr} -> (%c0_type : !pdl.type) + %op0 = pdl.operation "asm.int" {"value" = %c0_attr} -> (%c0_type : !pdl.type) %val0 = pdl.result 0 of %op0 - %op1 = pdl.operation "tf.Const" -> (%const_type : !pdl.type) - %val1 = pdl.result 0 of %op1 - %op2 = pdl.operation "tf.Mul" (%val0, %val1 : !pdl.value, !pdl.value) -> (%weight_type : !pdl.type) - %val2 = pdl.result 0 of %op2 - %op3 = pdl.operation "tf.Sub" (%weight, %val2 : !pdl.value, !pdl.value) -> (%weight_type : !pdl.type) - - pdl.rewrite %op3 { - %op4 = pdl.operation "kernel.FcBwd" (%rxact, %rxdelta, %weight : !pdl.value, !pdl.value, !pdl.value) -> (%weight_type : !pdl.type) - %val4 = pdl.result 0 of %op4 // weight_out - pdl.replace %op3 with (%val4 : !pdl.value) // tf.Sub - pdl.erase %op2 // tf.Mul - pdl.erase %op1 // tf.Const - pdl.erase %op0 // tf.MatMul - } - } - - // softmax_cross_entropy - pdl.pattern : benefit(6) { - %in_type = pdl.type - %label_type = pdl.type - %loss_type = pdl.type - %mean_loss_type = pdl.type - %mean_const_type = pdl.type - %mul_const_type = pdl.type - %rxact = pdl.operand : %in_type - %rxlabel = pdl.operand : %label_type - - %op0 = pdl.operation "tf.SparseSoftmaxCrossEntropyWithLogits" (%rxact, %rxlabel : !pdl.value, !pdl.value) -> (%loss_type, %in_type : !pdl.type, !pdl.type) - %val0_0 = pdl.result 0 of %op0 // loss - %val0_1 = pdl.result 1 of %op0 // gradient - %op1 = pdl.operation "tf.Const" -> (%mean_const_type : !pdl.type) - %val1 = pdl.result 0 of %op1 - %op2 = pdl.operation "tf.Mean" (%val0_0, %val1 : !pdl.value, !pdl.value) -> (%mean_loss_type : !pdl.type) - %val2 = pdl.result 0 of %op2 - %op3 = pdl.operation "tf.PreventGradient" (%val0_1 : !pdl.value) -> (%in_type : !pdl.type) - %val3 = pdl.result 0 of %op3 - %op4 = pdl.operation "tf.Const" -> (%mul_const_type : !pdl.type) - %val4 = pdl.result 0 of %op4 - %op5 = pdl.operation "tf.Mul" (%val3, %val4 : !pdl.value, !pdl.value) -> (%in_type : !pdl.type) + %opadd = pdl.operation "asm.add" (%val0, %cr : !pdl.value, !pdl.value) -> (%c0_type : !pdl.type) - pdl.rewrite { // roots: %op2, %op5 - %op6 = pdl.operation "kernel.SoftmaxCrossEntropy" (%rxact, %rxlabel : !pdl.value, !pdl.value) -> (%mean_loss_type, %in_type : !pdl.type, !pdl.type) - %val6_0 = pdl.result 0 of %op6 // txloss - %val6_1 = pdl.result 1 of %op6 // txdelta - pdl.replace %op5 with (%val6_1 : !pdl.value) // tf.Mul - pdl.erase %op4 // tf.Const - pdl.erase %op3 // tf.PreventGradient - pdl.replace %op2 with (%val6_0 : !pdl.value) // tf.Mean - pdl.erase %op1 // tf.Const - pdl.erase %op0 // tf.SparseSoftmaxCrossEntropyWithLogits + pdl.rewrite %opadd { + // %op1 = pdl.operation "kernel.FcFwd" (%rxact, %weight : !pdl.value, !pdl.value) -> (%out_type : !pdl.type) + %val1 = pdl.result 1 of %opadd + pdl.replace %op0 with (%val1 : !pdl.value) } } } -// CHECK-LABEL: test.mlp_split -// CHECK: %[[FWD:.*]] = "kernel.FcFwd"(%arg0, %arg2) : (tensor<2x20xf32>, tensor<20x10xf32>) -> tensor<2x10xf32> -// CHECK: %[[SM:.*]]:2 = "kernel.SoftmaxCrossEntropy"(%[[FWD]], %arg1) : (tensor<2x10xf32>, tensor<2xi32>) -> (tensor, tensor<2x10xf32>) -// CHECK: %[[BWD:.*]] = "kernel.FcBwd"(%arg0, %[[SM]]#1, %arg2) : (tensor<2x20xf32>, tensor<2x10xf32>, tensor<20x10xf32>) -> tensor<20x10xf32> -// CHECK: return %[[SM:.*]]#0, %[[BWD]] : tensor, tensor<20x10xf32> module @ir attributes { test.mlp_split } { - func @main(%arg0: tensor<2x20xf32>, %arg1: tensor<2xi32>, %arg2: tensor<20x10xf32>) -> (tensor, tensor<20x10xf32>) { - %0 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> - %1 = "tf.Const"() {value = dense<1.000000e-01> : tensor} : () -> tensor - %2 = "tf.Const"() {value = dense<5.000000e-01> : tensor<2x1xf32>} : () -> tensor<2x1xf32> - %3 = "tf.MatMul"(%arg0, %arg2) {transpose_a = false, transpose_b = false} : (tensor<2x20xf32>, tensor<20x10xf32>) -> tensor<2x10xf32> - %loss, %backprop = "tf.SparseSoftmaxCrossEntropyWithLogits"(%3, %arg1) : (tensor<2x10xf32>, tensor<2xi32>) -> (tensor<2xf32>, tensor<2x10xf32>) - %4 = "tf.Mean"(%loss, %0) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor - %5 = "tf.PreventGradient"(%backprop) : (tensor<2x10xf32>) -> tensor<2x10xf32> - %6 = "tf.Mul"(%5, %2) : (tensor<2x10xf32>, tensor<2x1xf32>) -> tensor<2x10xf32> - %7 = "tf.MatMul"(%arg0, %6) {transpose_a = true, transpose_b = false} : (tensor<2x20xf32>, tensor<2x10xf32>) -> tensor<20x10xf32> - %8 = "tf.Mul"(%7, %1) : (tensor<20x10xf32>, tensor) -> tensor<20x10xf32> - %9 = "tf.Sub"(%arg2, %8) : (tensor<20x10xf32>, tensor<20x10xf32>) -> tensor<20x10xf32> - return %4, %9 : tensor, tensor<20x10xf32> + func @main(%r: i32) -> (i32) { + %c0 = "asm.int"() { value = 0 : i32} : () -> (i32) + %add = "asm.add"(%c0, %r) : (i32, i32) -> (i32) + return %add : i32 } }