[Mlir-commits] [mlir] fbcd0c6 - Updates to 'tosa.reshape' verifier (#87416)

[llvmlistbot at llvm.org]

Author: Rafael Ubal
Date: 2024-04-03T13:49:55-04:00
New Revision: fbcd0c65f7b2f65e0ee58e5448b88af39faf10f1

URL: https://github.com/llvm/llvm-project/commit/fbcd0c65f7b2f65e0ee58e5448b88af39faf10f1
DIFF: https://github.com/llvm/llvm-project/commit/fbcd0c65f7b2f65e0ee58e5448b88af39faf10f1.diff

LOG: Updates to 'tosa.reshape' verifier (#87416)

This addition catches common cases of malformed `tosa.reshape` ops. This
prevents the `--tosa-to-tensor` pass from asserting when fed invalid
operations, as these will be caught ahead of time by the verifier.

Closes #87396

Added: 
    

Modified: 
    mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
    mlir/test/Dialect/Tosa/invalid.mlir

Removed: 
    


################################################################################
diff  --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 6e6e8435073812..e06ac9a27ae4cc 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -955,25 +955,34 @@ LogicalResult tosa::ReshapeOp::inferReturnTypeComponents(
 }
 
 mlir::LogicalResult tosa::ReshapeOp::verify() {
-  ShapedType inputType = llvm::cast<ShapedType>(getInput1().getType());
-  ShapedType outputType = llvm::cast<ShapedType>(getType());
+  TensorType inputType = getInput1().getType();
+  RankedTensorType outputType = getType();
 
   if (hasZeroDimension(inputType) || hasZeroDimension(outputType))
     return emitOpError() << "tensor has a dimension with size zero. Each "
                             "dimension of a tensor must have size >= 1";
 
+  if ((int64_t) getNewShape().size() != outputType.getRank())
+    return emitOpError() << "new shape does not match result rank";
+
+  for (auto [newShapeDim, outputShapeDim] :
+       zip(getNewShape(), outputType.getShape()))
+    if (newShapeDim != -1 && outputShapeDim != ShapedType::kDynamic &&
+        newShapeDim != outputShapeDim)
+      return emitOpError() << "new shape is inconsistent with result shape";
+
   if (inputType.hasStaticShape() && outputType.hasStaticShape()) {
     int64_t inputElementsNum = inputType.getNumElements();
     int64_t outputElementsNum = outputType.getNumElements();
     if (inputElementsNum != outputElementsNum) {
-      return emitOpError() << "Cannot reshape " << inputElementsNum
+      return emitOpError() << "cannot reshape " << inputElementsNum
                            << " elements into " << outputElementsNum;
     }
   }
 
   int missingDims = llvm::count(getNewShape(), -1);
   if (missingDims > 1)
-    return emitOpError() << "At most one target dimension can be -1";
+    return emitOpError() << "expected at most one target dimension to be -1";
 
   return mlir::success();
 }

diff  --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index 38ba48f365eabe..730ac41dd7a8d3 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -243,38 +243,70 @@ func.func @test_reshape_type_mismatch(%arg0 : tensor<13x21x3xf32>) -> () {
 
 // -----
 
-func.func @test_reverse_axis_out_of_range(%arg0 : tensor<13x21x3xf32>) -> () {
-  // expected-error at +1 {{'tosa.reverse' op expect input tensor rank (3) to be larger than reverse axis (5)}}
-  %0 = tosa.reverse %arg0 {axis = 5 : i32} : (tensor<13x21x3xf32>) -> tensor<?x?x?xi32>
+func.func @test_reshape_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> () {
+  // expected-error at +1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
+  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<13x0x3xf32>) -> tensor<13x0x3xf32>
   return
 }
 
 // -----
 
-func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> {
-  // expected-error at +1 {{'tosa.const' op failed to verify that all of {value, output} have same shape}}
-  %0 = "tosa.const"() {value = dense<0.000000e+00> : tensor<1x1xf32>} : () -> tensor<100x100xf32>
-  return %0 : tensor<100x100xf32>
+func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
+  // expected-error at +1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
+  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<?x0x3xf32>) -> tensor<13x0x3xf32>
+  return
 }
 
 // -----
 
-func.func @test_reshape_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> () {
-  // expected-error at +1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
-  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<13x0x3xf32>) -> tensor<13x0x3xf32>
+func.func @test_reshape_rank_mismatch(%arg0 : tensor<?xf32>) -> () {
+  // expected-error at +1 {{'tosa.reshape' op new shape does not match result rank}}
+  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 4>} : (tensor<?xf32>) -> tensor<?xf32>
   return
 }
 
 // -----
 
-func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
-  // expected-error at +1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
-  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<?x0x3xf32>) -> tensor<13x0x3xf32>
+func.func @test_reshape_inconsistent_result_type(%arg0 : tensor<?xf32>) -> () {
+  // expected-error at +1 {{'tosa.reshape' op new shape is inconsistent with result shape}}
+  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, 4, -1>} : (tensor<?xf32>) -> tensor<?x3x5xf32>
+  return
+}
+
+// -----
+
+func.func @test_reshape_invalid_size(%arg0 : tensor<2x4xf32>) -> () {
+  // expected-error at +1 {{'tosa.reshape' op cannot reshape 8 elements into 15}}
+  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 3, 5>} : (tensor<2x4xf32>) -> tensor<3x5xf32>
+  return
+}
+
+// -----
+
+func.func @test_reshape_invalid_placeholders(%arg0 : tensor<?xf32>) -> () {
+  // expected-error at +1 {{'tosa.reshape' op expected at most one target dimension to be -1}}
+  %0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 2, -1, -1>} : (tensor<?xf32>) -> tensor<2x?x?xf32>
   return
 }
 
 // -----
 
+func.func @test_reverse_axis_out_of_range(%arg0 : tensor<13x21x3xf32>) -> () {
+  // expected-error at +1 {{'tosa.reverse' op expect input tensor rank (3) to be larger than reverse axis (5)}}
+  %0 = tosa.reverse %arg0 {axis = 5 : i32} : (tensor<13x21x3xf32>) -> tensor<?x?x?xi32>
+  return
+}
+
+// -----
+
+func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> {
+  // expected-error at +1 {{'tosa.const' op failed to verify that all of {value, output} have same shape}}
+  %0 = "tosa.const"() {value = dense<0.000000e+00> : tensor<1x1xf32>} : () -> tensor<100x100xf32>
+  return %0 : tensor<100x100xf32>
+}
+
+// -----
+
 func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
   // expected-error at +1 {{'tosa.conv2d' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
   %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}