Fix TOSA verifier to emit verbose errors

Also as a test for invalid ops which was missing.

diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 2a9d5d4..78a6b1b 100644 (file)
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -527,8 +527,14 @@ template <typename T> static LogicalResult verifyConvOp(T op) {
   auto weightType = op.weight().getType().template dyn_cast<RankedTensorType>();
 
   // Must be ranked tensor types
-  if (!inputType || !weightType)
+  if (!inputType) {
+    op.emitOpError("expect a ranked tensor for input, got ") << op.input();
     return failure();
+  }
+  if (!weightType) {
+    op.emitOpError("expect a ranked tensor for weight, got ") << op.weight();
+    return failure();
+  }
 
   auto inputEType = inputType.getElementType();
   auto weightEType = weightType.getElementType();
@@ -537,14 +543,21 @@ template <typename T> static LogicalResult verifyConvOp(T op) {
   bool weightIsQuant = !weightEType.template isa<FloatType>();
 
   // Either both must be quantized or both unquantized.
-  if (inputIsQuant != weightIsQuant)
+  if (inputIsQuant != weightIsQuant) {
+    op.emitOpError(
+        "expect both input and weight to be float or not together, got ")
+        << inputEType << " and " << weightEType;
     return failure();
+  }
 
   // Quantized type must have constructed the quantizationattr, and unquantized
   // types should not have a quantizationattr.
   if ((inputIsQuant && !op.quantization_info()) ||
-      (!inputIsQuant && op.quantization_info()))
+      (!inputIsQuant && op.quantization_info())) {
+    op.emitOpError("quantizationattr is required for quantized type, and not "
+                   "allowed for float type");
     return failure();
+  }
 
   return success();
 }

diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
new file mode 100644 (file)
index 0000000..b5be2f3
--- /dev/null
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -0,0 +1,39 @@
+// RUN: mlir-opt %s -split-input-file -verify-diagnostics
+
+
+func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
+  // expected-error@+1 {{expect both input and weight to be float or not together, got 'f32' and 'i8'}}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+           : (tensor<1x29x29x4xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
+  return %0 : tensor<1x27x27x16xi8>
+}
+
+// -----
+
+func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
+  // expected-error@+1 {{expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+           : (tensor<*xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
+  return %0 : tensor<1x27x27x16xi8>
+}
+
+// -----
+
+func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
+  // expected-error@+1 {{expect a ranked tensor for weight, got <block argument> of type 'tensor<*xi8>' at index: 1}}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+           : (tensor<1x29x29x4xi8>, tensor<*xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
+  return %0 : tensor<1x27x27x16xi8>
+}
+
+
+// -----
+
+func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
+  // expected-error@+1 {{'tosa.conv2d' op quantizationattr is required for quantized type, and not allowed for float type}}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+           : (tensor<1x29x29x4xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
+  return %0 : tensor<1x27x27x16xi8>
+}
+
+