[mlir][Linalg] Rewrite PadTensorOp to enable its comprehensive bufferization.

[nicolasvasilache]

Summary

Add the rewrite of PadTensorOp to InitTensor + InsertSlice before the
bufferization analysis starts.

This is exercised via a more advanced integration test.

Since the new behavior triggers folding, 2 tests need to be updated.
One of those seems to exhibit a folding issue with switch and is modified.

[pifon2a]

mlir/lib/Dialect/Linalg/Transforms/ComprehensiveBufferize.cpp

2641

remove?

[nicolasvasilache]

mlir/lib/Dialect/Linalg/Transforms/ComprehensiveBufferize.cpp

2641

thanks!

[nicolasvasilache]

Drop spurious spew.

mlir/lib/Dialect/Linalg/Transforms/ComprehensiveBufferize.cpp

 //  Lastly, note that layout map chosen to bufferize is the most dynamic
 //  canonical strided layout of the proper rank. This ensures compatibility with
 //  expected layouts after transformations. Combinations of memref.cast +
 //  canonicalization are responsible for clean ups.
 
 #include "PassDetail.h"
 #include "mlir/Dialect/Linalg/IR/LinalgOps.h"
 #include "mlir/Dialect/Linalg/Passes.h"
+#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/SCF.h"
 #include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/Dialect/Vector/VectorOps.h"
 #include "mlir/IR/Operation.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Pass/PassManager.h"
 #include "mlir/Transforms/BufferUtils.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "mlir/Transforms/Passes.h"
 
 #include "llvm/ADT/DenseSet.h"
 #include "llvm/ADT/EquivalenceClasses.h"
 #include "llvm/ADT/ScopeExit.h"
 #include "llvm/ADT/SetOperations.h"
 #include "llvm/ADT/SetVector.h"
 #include "llvm/ADT/TypeSwitch.h"
bufferize(OpBuilder &b, CallOpInterface callOp, BlockAndValueMapping &bvm,
   SmallVector<Type> resultTypes;
   SmallVector<Value> hoistedArguments;
   if (funcOp.body().empty()) {
     if (llvm::any_of(funcOp.getType().getResults(), isaTensor))
       return callOp->emitError()
              << "cannot bufferize bodiless function that returns a tensor";
   } else {
     ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
-    if (!returnOp)
-      return funcOp->emitError() << "cannot bufferize a FuncOp with tensors "
-                                    "and without a unique ReturnOp";
+    assert(returnOp && "expected func with single return op");
 
     // For each FuncOp result, keep track of which inplace argument it reuses.
     for (OpOperand &returnOperand : returnOp->getOpOperands()) {
       Type returnType = returnOperand.get().getType();
       if (!isaTensor(returnType)) {
         resultTypes.push_back(returnType);
         continue;
       }
FunctionType bufferizedFuncType =
                                           TypeRange{}, bufferizedFunctionTypes);
     funcOp.setType(bufferizedFuncType);
     LLVM_DEBUG(DBGS() << "End bufferizeFuncOpBoundary no fun body: " << funcOp);
     return success();
   }
 
   // Support only single return-terminated block in the function.
   ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
-  if (!returnOp)
-    return funcOp->emitError() << "cannot bufferize a FuncOp with tensors and "
-                                  "without a unique ReturnOp";
+  assert(returnOp && "expected func with single return op");
 
   // 1. For each FuncOp result, keep track of which inplace argument it reuses.
   SmallVector<Value> returnValues;
   for (OpOperand &returnOperand : returnOp->getOpOperands()) {
     // If return operand is equivalent to some bbArg, no need to return it.
     Value returnVal = returnOperand.get();
     if (getEquivalentEnclosingFuncBBArg(returnVal, aliasInfo))
       continue;

 getFuncOpsOrderedByCalls(ModuleOp moduleOp,
                          SmallVectorImpl<FuncOp> &orderedFuncOps,
                          DenseMap<FuncOp, DenseSet<Operation *>> &callerMap) {
   // For each FuncOp, the set of functions called by it (i.e. the union of
   // symbols of all nested CallOpInterfaceOp).
   DenseMap<FuncOp, DenseSet<FuncOp>> calledBy;
   // For each FuncOp, the number of CallOpInterface it contains.
   DenseMap<FuncOp, unsigned> numberCallOpsContainedInFuncOp;
-  WalkResult res = moduleOp.walk([&](FuncOp funcOp) {
+  WalkResult res = moduleOp.walk([&](FuncOp funcOp) -> WalkResult {
+    if (!funcOp.body().empty()) {
+      ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
+      if (!returnOp)
+        return funcOp->emitError()
+               << "cannot bufferize a FuncOp with tensors and "
+                  "without a unique ReturnOp";
+    }
+
     numberCallOpsContainedInFuncOp[funcOp] = 0;
     return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
       // Only support CallOp for now.
       if (!isa<CallOp>(callOp.getOperation()))
         return callOp->emitError() << "expected a CallOp";
       FuncOp calledFunction = getCalledFunction(callOp);
       assert(calledFunction && "could not retrieved called FuncOp");
       auto it = callerMap.try_emplace(calledFunction, DenseSet<Operation *>{});
struct LinalgComprehensiveModuleBufferize
   void runOnOperation() override;
 
   void getDependentDialects(DialectRegistry &registry) const override {
     registry.insert<linalg::LinalgDialect, memref::MemRefDialect>();
   }
 };
 } // end namespace
 
+static void applyEnablingTransformations(ModuleOp moduleOp) {
+  RewritePatternSet patterns(moduleOp.getContext());
+  patterns.add<GeneralizePadTensorOpPattern>(moduleOp.getContext());
+  (void)applyPatternsAndFoldGreedily(moduleOp, std::move(patterns));
+}
+
 void LinalgComprehensiveModuleBufferize::runOnOperation() {
   ModuleOp moduleOp = getOperation();
+  applyEnablingTransformations(moduleOp);
+
+  moduleOp.dump();
 
   SmallVector<FuncOp> orderedFuncOps;
   DenseMap<FuncOp, DenseSet<Operation *>> callerMap;
   DenseMap<FuncOp, FunctionType> bufferizedFunctionTypes;
   if (failed(getFuncOpsOrderedByCalls(moduleOp, orderedFuncOps, callerMap)))
     return signalPassFailure();
 
   GlobalCreator globalCreator(moduleOp);

mlir/test/Dialect/Linalg/comprehensive-module-bufferize-analysis.mlir

 func @scf_for_deps(%A : tensor<?xf32> {linalg.inplaceable = true},
                    %B : tensor<?xf32> {linalg.inplaceable = true},
                    %lb : index, %ub : index, %step : index)
   -> (tensor<?xf32>, tensor<?xf32>)
 {
   // %r0 must be out of place because one use of %t in the subsequent production 
   // of %r1 is read.
   //      CHECK: scf.for
+  // CHECK-NEXT: call
   // CHECK-NEXT: scf.yield
   // CHECK-NEXT: {__inplace_results_attr__ = ["false"]}
   %r0 = scf.for %i = %lb to %ub step %step iter_args(%t = %A) -> (tensor<?xf32>) {
+    call @some_use(%t) : (tensor<?xf32>) -> ()
     scf.yield %t : tensor<?xf32>
   }
 
   // %r1 bufferizes inplace fine.
   //      CHECK: scf.for
   // CHECK-NEXT: call
   // CHECK-NEXT: scf.yield
   // CHECK-NEXT: {__inplace_results_attr__ = ["true"]}
   %r1 = scf.for %i = %lb to %ub step %step iter_args(%t = %A) -> (tensor<?xf32>) {
     call @some_use(%t) : (tensor<?xf32>) -> ()
     scf.yield %t : tensor<?xf32>
   }
 
   // %r2 must be out of place because one use of %t in the subsequent production 
   // of %r3 is read.
   //      CHECK: linalg.tiled_loop
+  // CHECK-NEXT: call
   // CHECK-NEXT: linalg.yield
   // CHECK-NEXT: {__inplace_results_attr__ = ["false"]}
   %r2 = linalg.tiled_loop (%i) = (%lb) to (%ub) step (%step)
         ins()
         outs(%t = %B: tensor<?xf32>) {
+    call @some_use(%t) : (tensor<?xf32>) -> ()
     linalg.yield %t : tensor<?xf32>
   }
 
   // %r3 bufferizes inplace fine.
   //      CHECK: linalg.tiled_loop
   // CHECK-NEXT: call
   // CHECK-NEXT: linalg.yield
   // CHECK-NEXT: {__inplace_results_attr__ = ["true"]}

mlir/test/Dialect/Linalg/comprehensive-module-bufferize-invalid.mlir

 // -----
 
 // expected-error @+1 {{cannot bufferize bodiless function that returns a tensor}}
 func private @foo() -> tensor<?xf32>
 
 // -----
 
 // expected-error @+1 {{cannot bufferize a FuncOp with tensors and without a unique ReturnOp}}
-func @switch(%flag : i32, %caseOperand : i32, %t1 : tensor<f32>, %t2 : tensor<f32>)
-    -> (tensor<f32>) 
+func @swappy(%cond1 : i1, %cond2 : i1, %t1 : tensor<f32>, %t2 : tensor<f32>)
+    -> (tensor<f32>, tensor<f32>) 
 {
-  switch %flag : i32, [
-    default: ^bb1(%caseOperand : i32),
-    42: ^bb2(%caseOperand : i32)
-  ]
-
-  ^bb1(%bb1arg : i32):
-    return %t1 : tensor<f32>
-  ^bb2(%bb2arg : i32):
-    return %t2 : tensor<f32>
+  cond_br %cond1, ^bb1, ^bb2
+
+  ^bb1:
+    %T:2 = scf.if %cond2 -> (tensor<f32>, tensor<f32>) {
+      scf.yield %t1, %t2 : tensor<f32>, tensor<f32>
+    } else {
+      scf.yield %t2, %t1 : tensor<f32>, tensor<f32>
+    }
+    return %T#0, %T#1 : tensor<f32>, tensor<f32>
+  ^bb2:
+    return %t2, %t1 : tensor<f32>, tensor<f32>
 }
 
 // -----
 
 // expected-error @-3 {{expected callgraph to be free of circular dependencies}}
 
 func @foo() {
   call @bar() : () -> ()

mlir/test/Integration/Dialect/Linalg/CPU/test-comprehensive-bufferize.mlir

 // RUN: mlir-opt %s -canonicalize -cse -linalg-comprehensive-module-bufferize |\
 // RUN: mlir-opt -convert-vector-to-scf -lower-affine -convert-linalg-to-loops |\
 // RUN: mlir-opt -canonicalize -convert-scf-to-std -convert-vector-to-llvm -convert-std-to-llvm | \
 
 // RUN: mlir-cpu-runner -O3 -e main -entry-point-result=void \
 // RUN:   -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext |\
 // RUN: FileCheck %s
 
-func @init_and_dot(%a: tensor<64xf32>, %b: tensor<64xf32>, %c: tensor<f32>) -> tensor<f32> {
-  %v0 = constant 0.0 : f32
+#map0 = affine_map<(d0, d1)[s0] -> ((d1 - d0) ceildiv s0)>
+#map1 = affine_map<(d0, d1)[s0] -> ((d0 - d1) ceildiv s0)>
+
+func @init_and_dot(%arg0: tensor<64xf32>, %arg1: tensor<64xf32>, %arg2: tensor<f32> {linalg.inplaceable = true}) -> tensor<f32> {
+  %c64 = constant 64 : index
+  %cst = constant 0.000000e+00 : f32
+  %c2 = constant 2 : index
+  %c0 = constant 0 : index
+  %0 = linalg.fill(%cst, %arg2) : f32, tensor<f32> -> tensor<f32>
+  %1 = affine.apply #map0(%c0, %c64)[%c2]
+  %2 = linalg.init_tensor [%1, 2] : tensor<?x2xf32>
+  %3 = scf.for %arg3 = %c0 to %c64 step %c2 iter_args(%arg4 = %2) -> (tensor<?x2xf32>) {
+    %8 = affine.apply #map1(%arg3, %c0)[%c2]
+    %9 = tensor.extract_slice %arg1[%arg3] [2] [1] : tensor<64xf32> to tensor<2xf32>
+    %10 = tensor.cast %9 : tensor<2xf32> to tensor<?xf32>
+    %11 = linalg.pad_tensor %10 low[%c0] high[%c0]  {
+    ^bb0(%arg5: index):  // no predecessors
+      linalg.yield %cst : f32
+    } : tensor<?xf32> to tensor<2xf32>
+    %12 = tensor.insert_slice %11 into %arg4[%8, 0] [1, 2] [1, 1] : tensor<2xf32> into tensor<?x2xf32>
+    scf.yield %12 : tensor<?x2xf32>
+  }
+
+  // %B = tensor.cast %3 : tensor<?x2xf32> to tensor<*xf32>
+  // call @print_memref_f32(%B) : (tensor<*xf32>) -> ()
 
-  %d = linalg.fill(%v0, %c) : f32, tensor<f32> -> tensor<f32>
+  %4 = affine.apply #map0(%c0, %c64)[%c2]
+  %5 = linalg.init_tensor [%4, 2] : tensor<?x2xf32>
+  %6 = scf.for %arg3 = %c0 to %c64 step %c2 iter_args(%arg4 = %5) -> (tensor<?x2xf32>) {
+    %8 = affine.apply #map1(%arg3, %c0)[%c2]
+    %9 = tensor.extract_slice %arg0[%arg3] [2] [1] : tensor<64xf32> to tensor<2xf32>
+    %10 = tensor.cast %9 : tensor<2xf32> to tensor<?xf32>
+    %11 = linalg.pad_tensor %10 low[%c0] high[%c0]  {
+    ^bb0(%arg5: index):  // no predecessors
+      linalg.yield %cst : f32
+    } : tensor<?xf32> to tensor<2xf32>
+    %12 = tensor.insert_slice %11 into %arg4[%8, 0] [1, 2] [1, 1] : tensor<2xf32> into tensor<?x2xf32>
+    scf.yield %12 : tensor<?x2xf32>
+  }
+
+  // %A = tensor.cast %6 : tensor<?x2xf32> to tensor<*xf32>
+  // call @print_memref_f32(%A) : (tensor<*xf32>) -> ()
 
-  %e = linalg.dot ins(%a, %b : tensor<64xf32>,tensor<64xf32>)
-    outs(%d: tensor<f32>) -> tensor<f32>
+  // %C = tensor.cast %0 : tensor<f32> to tensor<*xf32>
+  // call @print_memref_f32(%C) : (tensor<*xf32>) -> ()
 
-  return %e : tensor<f32>
+  %7 = scf.for %arg3 = %c0 to %c64 step %c2 iter_args(%arg4 = %0) -> (tensor<f32>) {
+    %8 = tensor.extract_slice %arg0[%arg3] [2] [1] : tensor<64xf32> to tensor<2xf32>
+    %9 = tensor.cast %8 : tensor<2xf32> to tensor<?xf32>
+    %10 = tensor.extract_slice %arg1[%arg3] [2] [1] : tensor<64xf32> to tensor<2xf32>
+    %11 = tensor.cast %10 : tensor<2xf32> to tensor<?xf32>
+    %12 = affine.apply #map1(%arg3, %c0)[%c2]
+    %13 = tensor.extract_slice %6[%12, 0] [1, 2] [1, 1] : tensor<?x2xf32> to tensor<2xf32>
+    %14 = affine.apply #map1(%arg3, %c0)[%c2]
+    %15 = tensor.extract_slice %3[%14, 0] [1, 2] [1, 1] : tensor<?x2xf32> to tensor<2xf32>
+    %16 = linalg.dot ins(%13, %15 : tensor<2xf32>, tensor<2xf32>) outs(%arg4 : tensor<f32>) -> tensor<f32>
+
+    // %AA = tensor.cast %13 : tensor<2xf32> to tensor<*xf32>
+    // call @print_memref_f32(%AA) : (tensor<*xf32>) -> ()
+    // %BB = tensor.cast %15 : tensor<2xf32> to tensor<*xf32>
+    // call @print_memref_f32(%BB) : (tensor<*xf32>) -> ()
+    // %CC = tensor.cast %16 : tensor<f32> to tensor<*xf32>
+    // call @print_memref_f32(%CC) : (tensor<*xf32>) -> ()
+
+    scf.yield %16 : tensor<f32>
+  }
+  return %7 : tensor<f32>
 }
 
 func @main() {
   %v0 = constant 0.0 : f32
   %v1 = constant 1.0 : f32
   %v2 = constant 2.0 : f32
 
   %A = linalg.init_tensor [64] : tensor<64xf32>