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1// RUN: mlir-opt %s \2// RUN: --pass-pipeline="builtin.module(transform-interpreter{ \3// RUN: debug-bind-trailing-args=linalg.matmul,linalg.elementwise},\4// RUN: canonicalize,cse,symbol-dce)" \5// RUN: --split-input-file --verify-diagnostics6 7// ****************************** IMPORTANT NOTE ******************************8//9// If you are changing this file, you may also need to change10// mlir/docs/Tutorials/Transform accordingly.11//12// ****************************************************************************13 14module attributes {transform.with_named_sequence} {15 transform.named_sequence @__transform_main(16 %arg0: !transform.any_op,17 // expected-note @below {{handle to invalidated ops}}18 %arg1: !transform.op<"linalg.matmul">,19 %arg2: !transform.op<"linalg.elementwise">) {20 // The actual tiling transformation takes tile sizes as attributes.21 // expected-note @below {{invalidated by this transform op that consumes its operand #0 and invalidates all handles to payload IR entities associated with this operand and entities nested in them}}22 %tiled, %loop = transform.structured.tile_using_forall %arg1 tile_sizes [4, 32]23 : (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op)24 25 // This is trying to use an invalidated handle leading to undefined behavior.26 // expected-error @below {{uses a handle invalidated by a previously executed transform op}}27 transform.debug.emit_remark_at %arg1, "remark" : !transform.op<"linalg.matmul">28 transform.yield29 }30}31 32// Original function to optimize.33func.func @fc_relu(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,34 %bias: tensor<512x512xf32>, %output: tensor<512x512xf32>)35 -> tensor<512x512xf32> {36 // Matrix-matrix multiplication.37 // expected-note @below {{payload op}}38 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)39 outs(%output: tensor<512x512xf32>) -> tensor<512x512xf32>40 41 // Elementwise addition.42 %biased = linalg.elementwise kind=#linalg.elementwise_kind<add>43 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>)44 outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>45 46 // Elementwise max with 0 (ReLU).47 %c0f = arith.constant dense<0.0> : tensor<512x512xf32>48 %relued = linalg.elementwise kind=#linalg.elementwise_kind<max_signed>49 ins(%biased, %c0f : tensor<512x512xf32>, tensor<512x512xf32>)50 outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>51 func.return %relued : tensor<512x512xf32>52}53 54// -----55 56module attributes {transform.with_named_sequence} {57 transform.named_sequence @__transform_main(58 %arg0: !transform.any_op,59 %arg1: !transform.op<"linalg.matmul">,60 %arg2: !transform.op<"linalg.elementwise">) {61 // We can cast one type to another as long as operations are compatible62 // with both types. This creates "aliasing" handles.63 // expected-note @below {{handle to invalidated ops}}64 %casted = transform.cast %arg1 : !transform.op<"linalg.matmul"> to65 !transform.any_op66 67 // The actual tiling transformation takes tile sizes as attributes.68 // expected-note @below {{invalidated by this transform op that consumes its operand #0 and invalidates all handles to payload IR entities associated with this operand and entities nested in them}}69 %tiled, %loop = transform.structured.tile_using_forall %arg1 tile_sizes [4, 32]70 : (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op)71 72 // Consuming an operand invalidates the consumed handle and any other handle that is73 // associated with the same payload operations, or payload operations nested in them.74 // expected-error @below {{uses a handle invalidated by a previously executed transform op}}75 transform.debug.emit_remark_at %casted, "remark"76 : !transform.any_op77 transform.yield78 }79}80 81// Original function to optimize.82func.func @fc_relu(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,83 %bias: tensor<512x512xf32>, %output: tensor<512x512xf32>)84 -> tensor<512x512xf32> {85 // Matrix-matrix multiplication.86 // expected-note @below {{payload op}}87 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)88 outs(%output: tensor<512x512xf32>) -> tensor<512x512xf32>89 90 // Elementwise addition.91 %biased = linalg.elementwise kind=#linalg.elementwise_kind<add>92 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>)93 outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>94 95 // Elementwise max with 0 (ReLU).96 %c0f = arith.constant dense<0.0> : tensor<512x512xf32>97 %relued = linalg.elementwise kind=#linalg.elementwise_kind<max_signed>98 ins(%biased, %c0f : tensor<512x512xf32>, tensor<512x512xf32>)99 outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>100 func.return %relued : tensor<512x512xf32>101}102