[TOSA] Add ERROR_IF checks to TRANSPOSE_CONV2D verifer

This patch extends the verifier with following checks:

ERROR_IF(out_pad_top <= -KH || out_pad_bottom <= -KH);
ERROR_IF(out_pad_left <= -KW || out_pad_right <= -KW);
ERROR_IF(stride_y < 1 || stride_x < 1);
ERROR_IF(OH != (IH - 1) * stride_y + out_pad_top + out_pad_bottom + KH);
ERROR_IF(OW != (IW - 1) * stride_x + out_pad_left + out_pad_right + KW);
ERROR_IF(BC != OC && BC != 1);

Signed-off-by: Elen Kalda <[email protected]>

Author: ekalda

mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

@@ -2896,6 +2896,114 @@ LogicalResult TransposeConv2DOp::inferReturnTypeComponents(
 LogicalResult TransposeConv2DOp::verify() {
   if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
     return failure();
+
+  llvm::ArrayRef<int64_t> strides = getStride();
+  const int64_t strideY = strides[0];
+  const int64_t strideX = strides[1];
+
+  if (strideY < 1 || strideX < 1)
+    return emitOpError("expect all stride values to be >= 1, got [")
+           << strides << "]";
+
+  const RankedTensorType inputType =
+      llvm::dyn_cast<RankedTensorType>(getInput().getType());
+
+  const RankedTensorType outputType =
+      llvm::dyn_cast<RankedTensorType>(getOutput().getType());
+
+  const RankedTensorType weightType =
+      llvm::dyn_cast<RankedTensorType>(getWeight().getType());
+
+  llvm::ArrayRef<int64_t> padding = getOutPad();
+
+  const auto checkPadAgainstKernelDim =
+      [this](const int64_t pad_value, const int64_t kernel_dim_size,
+             const llvm::StringRef pad_name,
+             llvm::StringRef kernel_dim_name) -> LogicalResult {
+    if (pad_value <= -kernel_dim_size)
+      return emitOpError("expected ")
+             << pad_name << " > -" << kernel_dim_name
+             << ", but got: " << pad_name << "=" << pad_value << " and "
+             << kernel_dim_name << "=" << kernel_dim_size;
+    return success();
+  };
+
+  if (outputType) {
+    const int64_t kernelHeight = weightType.getDimSize(1);
+    const int64_t outPadTop = padding[0];
+    const int64_t outPadBottom = padding[1];
+
+    if (!ShapedType::isDynamic(kernelHeight)) {
+      if (failed(checkPadAgainstKernelDim(outPadTop, kernelHeight,
+                                          "out_pad_top", "KH")))
+        return failure();
+      if (failed(checkPadAgainstKernelDim(outPadBottom, kernelHeight,
+                                          "out_pad_bottom", "KH")))
+        return failure();
+
+      const int64_t inputHeight = inputType.getDimSize(1);
+      const int64_t outputHeight = outputType.getDimSize(1);
+
+      if (!ShapedType::isDynamic(inputHeight) &&
+          !ShapedType::isDynamic(outputHeight)) {
+        if (outputHeight != (inputHeight - 1) * strideY + outPadTop +
+                                outPadBottom + kernelHeight)
+          return emitOpError(
+                     "dimension mismatch: expected OH == (IH - 1) * stride_y "
+                     "+ out_pad_top + out_pad_bottom + KH, but got ")
+                 << outputHeight << " != (" << inputHeight << " - 1) * "
+                 << strideY << " + " << outPadTop << " + " << outPadBottom
+                 << " + " << kernelHeight;
+      }
+    }
+
+    const int64_t kernelWidth = weightType.getDimSize(2);
+    const int64_t outPadLeft = padding[2];
+    const int64_t outPadRight = padding[3];
+
+    if (!ShapedType::isDynamic(kernelWidth)) {
+      if (failed(checkPadAgainstKernelDim(outPadLeft, kernelWidth,
+                                          "out_pad_left", "KW")))
+        return failure();
+      if (failed(checkPadAgainstKernelDim(outPadRight, kernelWidth,
+                                          "out_pad_right", "KW")))
+        return failure();
+
+      const int64_t inputWidth = inputType.getDimSize(2);
+      const int64_t outputWidth = outputType.getDimSize(2);
+
+      if (!ShapedType::isDynamic(inputWidth) &&
+          !ShapedType::isDynamic(outputWidth)) {
+        if (outputWidth !=
+            (inputWidth - 1) * strideX + outPadLeft + outPadRight + kernelWidth)
+          return emitOpError(
+                     "dimension mismatch: expected OW == (IW - 1) * stride_x "
+                     "+ out_pad_left + out_pad_right + KW, but got ")
+                 << outputWidth << " != (" << inputWidth << " - 1) * "
+                 << strideX << " + " << outPadLeft << " + " << outPadRight
+                 << " + " << kernelWidth;
+      }
+    }
+
+    const RankedTensorType biasType =
+        llvm::dyn_cast<RankedTensorType>(getBias().getType());
+
+    if (!biasType)
+      return success();
+
+    const int64_t biasChannels = biasType.getDimSize(0);
+
+    // Skip further checks if bias is dynamic
+    if (biasChannels == ShapedType::kDynamic)
+      return success();
+
+    const int64_t outputChannels = outputType.getDimSize(3);
+    if (biasChannels != outputChannels && biasChannels != 1)
+      return emitOpError(
+                 "bias channels expected to be equal to output channels (")
+             << outputChannels << ") or 1, got " << biasChannels;
+  }
+
   return success();
 }
 

mlir/test/Dialect/Tosa/invalid.mlir

@@ -172,6 +172,78 @@ func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xi8>, %arg1: tensor<16x1
   return %0 : tensor<1x32x32x16xi8>
 }
 
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_top(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op expected out_pad_top > -KH, but got: out_pad_top=-3 and KH=1}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: -3, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_bottom(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op expected out_pad_bottom > -KH, but got: out_pad_bottom=-1 and KH=1}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, -1, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_left(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op expected out_pad_left > -KW, but got: out_pad_left=-8 and KW=1}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, -8, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_padding_right(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op expected out_pad_right > -KW, but got: out_pad_right=-9 and KW=1}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, -9>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_stride_y(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op expect all stride values to be >= 1, got [0, 1]}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 0, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_stride_x(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op expect all stride values to be >= 1, got [1, 0]}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 0>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_output_height(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x33x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op dimension mismatch: expected OH == (IH - 1) * stride_y + out_pad_top + out_pad_bottom + KH, but got 33 != (32 - 1) * 1 + 0 + 0 + 1}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 33, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x33x32x16xf32>
+  return %0 : tensor<1x33x32x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_output_width(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x40x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op dimension mismatch: expected OW == (IW - 1) * stride_x + out_pad_left + out_pad_right + KW, but got 40 != (32 - 1) * 1 + 0 + 0 + 1}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 40, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x40x16xf32>
+  return %0 : tensor<1x32x40x16xf32>
+}
+
+// -----
+
+func.func @test_transpose_conv2d_invalid_bias(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<5xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  // expected-error@+1 {{'tosa.transpose_conv2d' op bias channels expected to be equal to output channels (16) or 1, got 5}}
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
 // -----
 // CHECK-LABEL: conv2d_quant_any_acc
 func.func @test_conv2d_quant_any_acc(%arg0: tensor<1x4x4x4x!quant.any<i8<-8:7>>>, %arg1: tensor<8x1x1x4x!quant.any<i8<-8:7>>>, %arg2: tensor<8x!quant.any<i8<-8:7>>>) -> tensor<1x4x4x8x!quant.any<i8<-8:7>>> {

mlir/test/Dialect/Tosa/invalid_extension.mlir

@@ -165,11 +165,11 @@ func.func @test_depthwise_conv2d_non_const_input_zp(%arg0: tensor<1x4x4x4xi8>, %
 
 // -----
 
-func.func @test_transpose_conv2d_non_const_weight_zp(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x4x8xi32> {
+func.func @test_transpose_conv2d_non_const_weight_zp(%arg0: tensor<1x4x4x4xi8>, %arg1: tensor<1x1x4x2xi8>, %arg2: tensor<8xi32>, %arg3: tensor<1xi8>) -> tensor<1x4x7x8xi32> {
   %input_zp = "tosa.const"() {values = dense<0> : tensor<1xi8> } : () -> tensor<1xi8>
   // expected-error@+1 {{'tosa.transpose_conv2d' op expected compile time resolvable constant, but got variable value for operand #4}}
-  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %arg3 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x4x8xi32>
-  return %0 : tensor<1x4x4x8xi32>
+  %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %arg3 {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x4x4x4xi8>, tensor<1x1x4x2xi8>, tensor<8xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x4x7x8xi32>
+  return %0 : tensor<1x4x7x8xi32>
 }
 
 // -----