diff --git a/src/dialect/onnx/onnx_ops.cpp b/src/dialect/onnx/onnx_ops.cpp
index 8ffee1d..f7e3623 100644
--- a/src/dialect/onnx/onnx_ops.cpp
+++ b/src/dialect/onnx/onnx_ops.cpp
@@ -730,9 +730,9 @@ void ONNXConvNoBiasOp::inferShapes() {
   auto dataShape = dataTy.getShape();
   auto weightShape = weightTy.getShape();
 
-  // Lowest ranked input supported is of shape (N x C x H x W).
-  if (dataShape.size() < 4)
-     emitError("Data input shape must be at least (NxCxHxW).");
+  // Lowest supported convolution is a one dimensional convolution.
+  if (dataShape.size() < 3)
+    emitError("Data input shape must be at least (NxCxD1).");
 
   // Check that shape of weight and data have same length.
   if (dataShape.size() != weightShape.size())
diff --git a/test/mlir/onnx/onnx_shape_inference.mlir b/test/mlir/onnx/onnx_shape_inference.mlir
index 14c575d..fbd718c 100644
--- a/test/mlir/onnx/onnx_shape_inference.mlir
+++ b/test/mlir/onnx/onnx_shape_inference.mlir
@@ -143,27 +143,38 @@ func @test_matmul_10(%arg0 : tensor<?x42x32xf32>, %arg1 : tensor<32xf32>) -> ten
 /// Test shape inference for ConvNoBias operation and all its attributes.
 //===----------------------------------------------------------------------===//
 
+/// Default and required attributes for 1-D convolution.
+
+func @test_conv_no_bias_0(%arg0 : tensor<1x2x32xf32>, %arg1 : tensor<5x2x6xf32>) -> tensor<*xf32> {
+  %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_conv_no_bias_0
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<1x5x27xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x27xf32>
+}
+
 /// Default and required attributes.
 
 func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_1
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
+  // CHECK-LABEL: test_conv_no_bias_1
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
+}
 
 /// kernel_shape attribute.
 
 func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_2
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
+  // CHECK-LABEL: test_conv_no_bias_2
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
+}
 
 /// pads attribute.
 /// Use pads to make output size equal to input size by adding K - 1 to the result.
@@ -171,53 +182,53 @@ func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
 func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_3
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+  // CHECK-LABEL: test_conv_no_bias_3
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+}
 
 /// auto_pad set to SAME_UPPER and SAME_LOWER.
 
 func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_4
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+  // CHECK-LABEL: test_conv_no_bias_4
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+}
 
 func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_5
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+  // CHECK-LABEL: test_conv_no_bias_5
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+}
 
 /// auto_pad set to VALID.
 
 func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_6
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
+  // CHECK-LABEL: test_conv_no_bias_6
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
+}
 
 /// With strides attribute.
 
 func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_7
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
+  // CHECK-LABEL: test_conv_no_bias_7
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
+}
 
 /// auto_pad set to SAME_UPPER with strides attribute.
 /// The auto_pad will pas as if stride is equal to 1.
@@ -225,41 +236,41 @@ func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
 func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_8
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
+  // CHECK-LABEL: test_conv_no_bias_8
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
+}
 
 /// dilations attribute.
 
 func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_9
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
+  // CHECK-LABEL: test_conv_no_bias_9
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
+}
 
 /// dilations attribute with stride.
 
 func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_10
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
+  // CHECK-LABEL: test_conv_no_bias_10
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
+}
 
 /// dilations attribute with auto_pad set to SAME_UPPER.
 
 func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
   %0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
-}
 
-// CHECK-LABEL: test_conv_no_bias_11
-// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
-// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+  // CHECK-LABEL: test_conv_no_bias_11
+  // CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
+  // CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
+}