[mlir][tosa][linalg] Apply direct tosa -> linalg Conv2D lowering

mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml

@@ -2575,6 +2575,143 @@ structured_op: !LinalgStructuredOpConfig
                     - !ScalarExpression
                       scalar_arg: KZp
 --- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+  name: conv_2d_nhwc_fhwc_q
+  cpp_class_name: Conv2DNhwcFhwcQOp
+  doc: |-
+    Performs 2-D convolution with zero point offsets.
+
+    Layout:
+      * Input: NHWC.
+      * Kernel: FHWC.
+
+    Numeric casting is performed on the operands to the inner multiply, promoting
+    them to the same data type as the accumulator/output. This includes the zero
+    point offsets common to quantized operations.
+  implements:
+  - LinalgConvolutionOpInterface
+structured_op: !LinalgStructuredOpConfig
+  args:
+  - !LinalgOperandDefConfig
+    name: I
+    kind: input_tensor
+    type_var: T1
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s0,
+      s1 * s2 + s3 * s4, s5 * s6 + s7 * s8, s9)>
+  - !LinalgOperandDefConfig
+    name: K
+    kind: input_tensor
+    type_var: T2
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s10,
+      s3, s7, s9)>
+  - !LinalgOperandDefConfig
+    name: IZp
+    kind: scalar
+    type_var: I32
+  - !LinalgOperandDefConfig
+    name: KZp
+    kind: scalar
+    type_var: I32
+  - !LinalgOperandDefConfig
+    name: O
+    kind: output_tensor
+    type_var: U
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s0,
+      s1, s5, s10)>
+  - !LinalgOperandDefConfig
+    name: strides
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] ->
+      (s2, s6)>
+    default_indices:
+    - 1
+    - 1
+  - !LinalgOperandDefConfig
+    name: dilations
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] ->
+      (s4, s8)>
+    default_indices:
+    - 1
+    - 1
+  indexing_maps: !LinalgIndexingMapsConfig
+    static_indexing_maps:
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d0, d1 * s2 + d4 * s4, d2 * s6 + d5 * s8, d6)>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d3, d4, d5, d6)>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> ()>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> ()>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d0, d1, d2, d3)>
+  iterator_types:
+  - parallel
+  - parallel
+  - parallel
+  - parallel
+  - reduction
+  - reduction
+  - reduction
+  assignments:
+  - !ScalarAssign
+    arg: O
+    value: !ScalarExpression
+      scalar_fn:
+        kind: binary
+        fn_name: add
+        operands:
+        - !ScalarExpression
+          scalar_arg: O
+        - !ScalarExpression
+          scalar_fn:
+            kind: binary
+            fn_name: mul
+            operands:
+            - !ScalarExpression
+              scalar_fn:
+                kind: binary
+                fn_name: sub
+                operands:
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: I
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: IZp
+            - !ScalarExpression
+              scalar_fn:
+                kind: binary
+                fn_name: sub
+                operands:
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: K
+                - !ScalarExpression
+                  scalar_fn:
+                    kind: type
+                    fn_name: cast_signed
+                    type_var: U
+                    operands:
+                    - !ScalarExpression
+                      scalar_arg: KZp
+--- !LinalgOpConfig
 metadata: !LinalgOpMetadata
   name: conv_2d_nchw_fchw
   cpp_class_name: Conv2DNchwFchwOp

mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp

@@ -248,25 +248,28 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
     pad.resize(pad.size() + 2, 0);
     input = applyPad(loc, input, pad, zeroAttr, rewriter);
 
-    // Transpose the kernel to match dimension ordering of the linalg
-    // convolution operation.
-    // TODO(suderman): See if this can be efficiently folded - check whether
-    // the input is used anywhere else, if not fold the constant.
-    SmallVector<int64_t> weightPerm;
-    for (int i = 1; i < resultTy.getRank(); i++)
-      weightPerm.push_back(i);
-    weightPerm.push_back(0);
-
-    SmallVector<int64_t> newWeightShape;
-    for (auto dim : weightPerm)
-      newWeightShape.push_back(weightShape[dim]);
-    auto weightPermAttr = rewriter.getI64TensorAttr(weightPerm);
-    Value weightPermValue =
-        rewriter.create<arith::ConstantOp>(loc, weightPermAttr);
-    Type newWeightTy =
-        RankedTensorType::get(newWeightShape, weightTy.getElementType());
-    weight = rewriter.create<tosa::TransposeOp>(loc, newWeightTy, weight,
-                                                weightPermValue);
+    // For Conv3D transpose the kernel to match dimension ordering of the linalg
+    // convolution operation. Conv2D has a 1-1 mapping in linalg so better to
+    // map directly and then transpose later if desired.
+    if (5 == inputTy.getRank()) {
+      // TODO(suderman): See if this can be efficiently folded - check whether
+      // the input is used anywhere else, if not fold the constant.
+      SmallVector<int64_t> weightPerm;
+      for (int i = 1; i < resultTy.getRank(); i++)
+        weightPerm.push_back(i);
+      weightPerm.push_back(0);
+
+      SmallVector<int64_t> newWeightShape;
+      for (auto dim : weightPerm)
+        newWeightShape.push_back(weightShape[dim]);
+      auto weightPermAttr = rewriter.getI64TensorAttr(weightPerm);
+      Value weightPermValue =
+          rewriter.create<arith::ConstantOp>(loc, weightPermAttr);
+      Type newWeightTy =
+          RankedTensorType::get(newWeightShape, weightTy.getElementType());
+      weight = rewriter.create<tosa::TransposeOp>(loc, newWeightTy, weight,
+                                                  weightPermValue);
+    }
 
     auto resultZeroAttr = rewriter.getZeroAttr(resultETy);
     Value emptyTensor = rewriter.create<tensor::EmptyOp>(
@@ -977,7 +980,7 @@ void mlir::tosa::populateTosaToLinalgNamedConversionPatterns(
     RewritePatternSet *patterns) {
   patterns->add<
       // clang-format off
-      ConvConverter<tosa::Conv2DOp, linalg::Conv2DNhwcHwcfOp, linalg::Conv2DNhwcHwcfQOp>,
+      ConvConverter<tosa::Conv2DOp, linalg::Conv2DNhwcFhwcOp, linalg::Conv2DNhwcFhwcQOp>,
       ConvConverter<tosa::Conv3DOp, linalg::Conv3DNdhwcDhwcfOp, linalg::Conv3DNdhwcDhwcfQOp>,
       DepthwiseConvConverter,
       MatMulConverter,

mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py

@@ -693,6 +693,36 @@ def conv_2d_nhwc_hwcf_q(
     ) * (TypeFn.cast_signed(U, K[D.kh, D.kw, D.c, D.f]) - TypeFn.cast_signed(U, KZp))
 
 
+@linalg_structured_op
+def conv_2d_nhwc_fhwc_q(
+    I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW, S.C),
+    K=TensorDef(T2, S.F, S.KH, S.KW, S.C),
+    IZp=ScalarDef(I32),
+    KZp=ScalarDef(I32),
+    O=TensorDef(U, S.N, S.OH, S.OW, S.F, output=True),
+    strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]),
+    dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1]),
+):
+    """Performs 2-D convolution with zero point offsets.
+
+    Layout:
+      * Input: NHWC.
+      * Kernel: FHWC.
+
+    Numeric casting is performed on the operands to the inner multiply, promoting
+    them to the same data type as the accumulator/output. This includes the zero
+    point offsets common to quantized operations.
+    """
+    implements(ConvolutionOpInterface)
+    domain(D.n, D.oh, D.ow, D.f, D.kh, D.kw, D.c)
+    O[D.n, D.oh, D.ow, D.f] += (
+        TypeFn.cast_signed(
+            U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c]
+        )
+        - TypeFn.cast_signed(U, IZp)
+    ) * (TypeFn.cast_signed(U, K[D.f, D.kh, D.kw, D.c]) - TypeFn.cast_signed(U, KZp))
+
+
 @linalg_structured_op
 def conv_2d_nchw_fchw(
     I=TensorDef(T1, S.N, S.C, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW),

mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir

@@ -363,13 +363,11 @@ func.func @avg_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> (tensor<?x5x33x62xf32>)
 
 // CHECK-LABEL: @conv2d_i8
 func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi8>, %bias: tensor<28xi8>) -> () {
-  // CHECK: %[[PERM:.+]] = arith.constant dense<[1, 2, 3, 0]>
-  // CHECK: %[[W:.+]] = tosa.transpose %arg1, %[[PERM]]
   // CHECK: %[[M_IN:.+]] = tensor.empty()
   // CHECK: %[[CST:.+]] = arith.constant 0
   // CHECK: %[[FILL:.+]] = linalg.fill
   // CHECK: %[[B_IN:.+]] = tensor.empty()
-  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[W]], %c0_i32_0, %c0_i32_1 : tensor<1x49x42x27xi8>, tensor<1x1x27x28xi8>, i32, i32) outs(%[[FILL]] : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
+  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_fhwc_q {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, %c0_i32_0, %c0_i32_1 : tensor<1x49x42x27xi8>, tensor<28x1x1x27xi8>, i32, i32) outs(%[[FILL]] : tensor<1x45x40x28xi32>) -> tensor<1x45x40x28xi32>
   // CHECK: %[[B:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[CONV]] : tensor<28xi8>, tensor<1x45x40x28xi32>) outs(%[[B_IN]] : tensor<1x45x40x28xi32>)
   // CHECK:   arith.extsi
   // CHECK:   arith.addi
@@ -385,13 +383,11 @@ func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi
 
 // CHECK-LABEL: @conv2d_f32
 func.func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<28xf32>) -> () {
-  // CHECK: %[[PERM:.+]] = arith.constant dense<[1, 2, 3, 0]>
-  // CHECK: %[[W:.+]] = tosa.transpose %arg1, %[[PERM]]
   // CHECK: %[[M_IN:.+]] = tensor.empty()
   // CHECK: %[[CST:.+]] = arith.constant 0
   // CHECK: %[[FILL:.+]] = linalg.fill
   // CHECK: %[[B_IN:.+]] = tensor.empty()
-  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[W]] : tensor<1x49x42x27xf32>, tensor<3x3x27x28xf32>) outs(%[[FILL]] : tensor<1x45x40x28xf32>)
+  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[FILL]] : tensor<1x45x40x28xf32>)
   // CHECK: %[[B:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[CONV]] : tensor<28xf32>, tensor<1x45x40x28xf32>) outs(%[[B_IN]] : tensor<1x45x40x28xf32>)
   // CHECK:   arith.addf
   // CHECK:   linalg.yield
@@ -408,13 +404,11 @@ func.func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27
 func.func @conv2d_dyn(%input: tensor<?x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<28xf32>) -> () {
   // CHECK: %[[C0:.+]] = arith.constant 0
   // CHECK: %[[BATCH:.+]] = tensor.dim %arg0, %[[C0]]
-  // CHECK: %[[PERM:.+]] = arith.constant dense<[1, 2, 3, 0]>
-  // CHECK: %[[W:.+]] = tosa.transpose %arg1, %[[PERM]]
   // CHECK: %[[M_IN:.+]] = tensor.empty(%[[BATCH]])
   // CHECK: %[[CST:.+]] = arith.constant 0
   // CHECK: %[[FILL:.+]] = linalg.fill
   // CHECK: %[[B_IN:.+]] = tensor.empty(%[[BATCH]])
-  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[W]] : tensor<?x49x42x27xf32>, tensor<3x3x27x28xf32>) outs(%[[FILL]] : tensor<?x45x40x28xf32>)
+  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x49x42x27xf32>, tensor<28x3x3x27xf32>) outs(%[[FILL]] : tensor<?x45x40x28xf32>)
   // CHECK: %[[B:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[CONV]] : tensor<28xf32>, tensor<?x45x40x28xf32>) outs(%[[B_IN]] : tensor<?x45x40x28xf32>)
   // CHECK:   %[[ADD:.+]] = arith.addf
   // CHECK:   linalg.yield %[[ADD]] : f32
@@ -468,13 +462,11 @@ func.func @conv2d_dyn_w_h(%input: tensor<1x?x?x27xf32>, %weights: tensor<28x3x3x
   // CHECK: %[[W_OUT:.+]] = arith.addi %[[DIVIDED_0]], %[[ONE_0]] : index
 
   // Running convolution
-  // CHECK: %[[PERM:.+]] = arith.constant dense<[1, 2, 3, 0]>
-  // CHECK: %[[WEIGHT:.+]] = tosa.transpose %arg1, %[[PERM]]
   // CHECK: %[[M_IN:.+]] = tensor.empty(%[[H_OUT]], %[[W_OUT]])
   // CHECK: %[[CST:.+]] = arith.constant 0
   // CHECK: %[[FILL:.+]] = linalg.fill
   // CHECK: %[[B_IN:.+]] = tensor.empty(%[[H_OUT]], %[[W_OUT]])
-  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_hwcf {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %[[WEIGHT]] : tensor<1x?x?x27xf32>, tensor<3x3x27x28xf32>) outs(%[[FILL]] : tensor<1x?x?x28xf32>)
+  // CHECK: %[[CONV:.+]] = linalg.conv_2d_nhwc_fhwc {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x?x?x27xf32>, tensor<28x3x3x27xf32>) outs(%[[FILL]] : tensor<1x?x?x28xf32>)
   // CHECK: %[[B:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[CONV]] : tensor<28xf32>, tensor<1x?x?x28xf32>) outs(%[[B_IN]] : tensor<1x?x?x28xf32>)
   // CHECK:   %[[ADD:.+]] = arith.addf
   // CHECK:   linalg.yield %[[ADD]] : f32
@@ -489,7 +481,7 @@ func.func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28
   // CHECK: %[[C0:.+]] = arith.constant 0
   // CHECK: tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
   // CHECK:   tensor.yield %[[C0]]
-  // CHECK: linalg.conv_2d_nhwc_hwcf
+  // CHECK: linalg.conv_2d_nhwc_fhwc
   %0 = tosa.conv2d %input, %weights, %bias {pad = array<i64: 1, 1, 1, 1>, stride = array<i64: 1, 1>, dilation = array<i64: 2, 1>} : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>) -> tensor<1x45x40x28xf32>
   return
 }
@@ -501,7 +493,7 @@ func.func @conv2d_quant(%arg0 : tensor<1x12x12x1xi8>, %arg1 : tensor<1024x3x3x1x
   // CHECK:   %[[C22:.+]] = arith.constant -22
   // CHECK: tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
   // CHECK:   tensor.yield %[[C22]]
-  // CHECK: linalg.conv_2d_nhwc_hwcf_q
+  // CHECK: linalg.conv_2d_nhwc_fhwc_q
   %0 = tosa.conv2d %arg0, %arg1, %arg2 {dilation = array<i64: 1, 1>, pad = array<i64: 1, 1, 1, 1>, quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = array<i64: 1, 1>} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x12x12x1024xi32>
   return
 }