[mlir][tosa] Support unranked input/weight tensors for convolution ops

This commit ensures that convolution operators including:
conv2d, depthwise_conv2d, transpose_conv2d and conv3d, can have
unranked input/weight operands.

In order to support operands with unranked tensors, the tablegen
definition was relaxed. The relaxation of tensor type will later
be checked by the validation pass, should the user wish to use it.

Change-Id: I33334909e0d4d0676daae81bfc4647e86abc063a
Signed-off-by: Luke Hutton <[email protected]>

Author: lhutton1

mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td

@@ -124,7 +124,7 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
 
   let arguments = (ins
     Tosa_Tensor4D:$input,
-    TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
+    Tosa_Tensor4D:$weight,
     Tosa_Tensor1D:$bias,
     Tosa_ScalarIntOrFloatTensor:$input_zp,
     Tosa_ScalarIntOrFloatTensor:$weight_zp,
@@ -169,7 +169,7 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
 
   let arguments = (ins
     Tosa_Tensor5D:$input,
-    TosaTensorRankOf<[Tosa_Weight], [5]>:$weight,
+    Tosa_Tensor5D:$weight,
     Tosa_Tensor1D:$bias,
     Tosa_ScalarIntOrFloatTensor:$input_zp,
     Tosa_ScalarIntOrFloatTensor:$weight_zp,
@@ -215,7 +215,7 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
 
   let arguments = (ins
     Tosa_Tensor4D:$input,
-    TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
+    Tosa_Tensor4D:$weight,
     Tosa_Tensor1D:$bias,
     Tosa_ScalarIntOrFloatTensor:$input_zp,
     Tosa_ScalarIntOrFloatTensor:$weight_zp,
@@ -429,7 +429,7 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
 
   let arguments = (ins
     Tosa_Tensor4D:$input,
-    TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
+    Tosa_Tensor4D:$weight,
     Tosa_Tensor1D:$bias,
     Tosa_ScalarIntOrFloatTensor:$input_zp,
     Tosa_ScalarIntOrFloatTensor:$weight_zp,

mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td

@@ -84,11 +84,6 @@ def Tosa_QuantizedInt : AnyTypeOf<[Tosa_QuantizedType<"uint8", [8], 0>,
 def Tosa_AnyNumber : AnyTypeOf<[Tosa_Int, Tosa_QuantizedInt, AnyFloat],
                                 "number">;
 
-// For weight tensors from tosa::Conv2DOp, tosa::Conv3DOp,
-// tosa::DepthwiseConv2DOp, tosa::TransposeConv2DOp
-def Tosa_Weight : AnyTypeOf<[Tosa_Int4, Tosa_Int8,
-                             Tosa_QuantizedInt, AnyFloat]>;
-
 //===----------------------------------------------------------------------===//
 // TOSA Tensor Conformance
 //===----------------------------------------------------------------------===//

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

@@ -282,14 +282,14 @@ static LogicalResult verifyConvOp(T op) {
   // tensors.
   auto inputType = llvm::dyn_cast<RankedTensorType>(op.getInput().getType());
   if (!inputType) {
-    op.emitOpError("expect a ranked tensor for input, got ") << op.getInput();
-    return failure();
+    // Skip following checks if input is not ranked
+    return success();
   }
 
   auto weightType = llvm::dyn_cast<RankedTensorType>(op.getWeight().getType());
   if (!weightType) {
-    op.emitOpError("expect a ranked tensor for weight, got ") << op.getWeight();
-    return failure();
+    // Skip following checks if weight is not ranked
+    return success();
   }
 
   auto inputEType = inputType.getElementType();
@@ -2899,14 +2899,6 @@ LogicalResult TransposeConv2DOp::verify() {
     return emitOpError("expect all stride values to be >= 1, got [")
            << strides << "]";
 
-  const auto inputType = llvm::dyn_cast<RankedTensorType>(getInput().getType());
-
-  const auto outputType =
-      llvm::dyn_cast<RankedTensorType>(getOutput().getType());
-
-  const auto weightType =
-      llvm::dyn_cast<RankedTensorType>(getWeight().getType());
-
   const auto checkPadAgainstKernelDim =
       [this](int64_t pad_value, int64_t kernel_dim_size,
              llvm::StringRef pad_name,
@@ -2920,69 +2912,77 @@ LogicalResult TransposeConv2DOp::verify() {
   };
 
   const llvm::ArrayRef<int64_t> padding = getOutPad();
-
   const int64_t outPadTop = padding[0];
   const int64_t outPadBottom = padding[1];
+  const int64_t outPadLeft = padding[2];
+  const int64_t outPadRight = padding[3];
 
-  const int64_t kernelHeight = weightType.getDimSize(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 auto weightType =
+      llvm::dyn_cast<RankedTensorType>(getWeight().getType());
 
-  const int64_t kernelWidth = weightType.getDimSize(2);
+  if (weightType) {
+    const int64_t kernelHeight = weightType.getDimSize(1);
+    if (!ShapedType::isDynamic(kernelHeight)) {
+      if (failed(checkPadAgainstKernelDim(outPadTop, kernelHeight,
+                                          "out_pad_top", "KH")))
+        return failure();
 
-  const int64_t outPadLeft = padding[2];
-  const int64_t outPadRight = padding[3];
+      if (failed(checkPadAgainstKernelDim(outPadBottom, kernelHeight,
+                                          "out_pad_bottom", "KH")))
+        return failure();
+    }
 
-  if (!ShapedType::isDynamic(kernelWidth)) {
-    if (failed(checkPadAgainstKernelDim(outPadLeft, kernelWidth, "out_pad_left",
-                                        "KW")))
-      return failure();
+    const int64_t kernelWidth = weightType.getDimSize(2);
+    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();
+      if (failed(checkPadAgainstKernelDim(outPadRight, kernelWidth,
+                                          "out_pad_right", "KW")))
+        return failure();
+    }
   }
 
   // Rest of the checks depend on the output type being a RankedTensorType
+  const auto outputType =
+      llvm::dyn_cast<RankedTensorType>(getOutput().getType());
   if (!outputType)
     return success();
 
-  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 auto inputType = llvm::dyn_cast<RankedTensorType>(getInput().getType());
+  if (inputType && weightType) {
+    const int64_t inputHeight = inputType.getDimSize(1);
+    const int64_t kernelHeight = weightType.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 inputWidth = inputType.getDimSize(2);
-  const int64_t outputWidth = outputType.getDimSize(2);
+    const int64_t inputWidth = inputType.getDimSize(2);
+    const int64_t kernelWidth = weightType.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;
+    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 auto biasType = llvm::dyn_cast<RankedTensorType>(getBias().getType());

mlir/test/Dialect/Tosa/invalid.mlir

@@ -33,19 +33,9 @@ func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>,
 
 // -----
 
-func.func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
-  %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
-  // expected-error@+1 {{'tosa.conv2d' op expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
-  %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
-           : (tensor<*xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
-  return %0 : tensor<1x27x27x16xi8>
-}
-
-// -----
-
 func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
   %zp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
-  // expected-error@+1 {{'tosa.conv2d' op operand #1 must be 4D tensor of 4-bit signless integer or 8-bit signless integer or Quint8 type or Qint4 type or Qint8 type or Qint16 type or Qint32 type or floating-point values, but got 'tensor<*xi8>'}}
+  // expected-error@+1 {{'tosa.conv2d' op illegal: operand/result data types not supported}}
   %0 = tosa.conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
            : (tensor<1x29x29x4xi8>, tensor<*xi8>, tensor<16xi8>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x27x27x16xi8>
   return %0 : tensor<1x27x27x16xi8>

mlir/test/Dialect/Tosa/ops.mlir

@@ -70,6 +70,13 @@ func.func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %
   return %0 : tensor<1x4x4x8xf32>
 }
 
+// -----
+// CHECK-LABEL: conv2d_unranked_input
+func.func @test_conv2d_unranked_input(%arg0: tensor<*xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x4x4x8xf32> {
+  %0 = tosa.conv2d %arg0, %arg1, %arg2, %arg3, %arg4 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, local_bound = true} : (tensor<*xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x4x4x8xf32>
+  return %0 : tensor<1x4x4x8xf32>
+}
+
 // -----
 // CHECK-LABEL: conv2d_quant_uniform
 func.func @test_conv2d_quant_uniform(%arg0: tensor<1x4x4x4x!quant.uniform<i8:f32, 0.01>>, %arg1: tensor<8x1x1x4x!quant.uniform<i8:f32, 0.01>>, %arg2: tensor<8x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x4x4x8x!quant.uniform<i32:f32, 0.01>> {
@@ -202,6 +209,20 @@ func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x
   return %0 : tensor<1x32x32x16xf32>
 }
 
+// -----
+// CHECK-LABEL: transpose_conv2d_unranked_input
+func.func @test_transpose_conv2d_unranked_input(%arg0: tensor<*xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  %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<*xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
+// -----
+// CHECK-LABEL: transpose_conv2d_unranked_weight
+func.func @test_transpose_conv2d_unranked_weight(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<*xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {
+  %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<*xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x32x32x16xf32>
+  return %0 : tensor<1x32x32x16xf32>
+}
+
 // -----
 // CHECK-LABEL: transpose_conv2d_with_local_bound
 func.func @test_transpose_conv2d_with_local_bound(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>, %arg3: tensor<1xf32>, %arg4: tensor<1xf32>) -> tensor<1x32x32x16xf32> {