int64_t kernelWidth = weightTy.getDimSize(2);
llvm::SmallVector<int64_t> convPad(4, 0);
- convPad[0] = kernelHeight - 1 - pad[0];
- convPad[1] = kernelHeight - 1 - pad[1];
- convPad[2] = kernelWidth - 1 - pad[2];
- convPad[3] = kernelWidth - 1 - pad[3];
+ convPad[0] = kernelHeight - 1 + pad[0];
+ convPad[1] = kernelHeight - 1 + pad[1];
+ convPad[2] = kernelWidth - 1 + pad[2];
+ convPad[3] = kernelWidth - 1 + pad[3];
auto reverse1 = rewriter.create<tosa::ReverseOp>(
loc, weightTy, weight, rewriter.getI64IntegerAttr(1));
// If strides are all 1 we dont need to use this one.
if (llvm::all_of(stride, [](int64_t v) { return v == 1; }))
- return failure();
+ return rewriter.notifyMatchFailure(op, "non-one stride found.");
if (!inputTy.hasStaticShape() || !weightTy.hasStaticShape() ||
!biasTy.hasStaticShape() || !resultTy.hasStaticShape())
rewriter, loc, UnrankedTensorType::get(resultETy), conv2d,
rewriter.getI64ArrayAttr(convReshapeDims1));
- // Slice out the final result.
- llvm::SmallVector<int64_t, 4> sliceBegin = {0, 0, 0, 0};
- llvm::SmallVector<int64_t, 4> sliceSize(resultTy.getShape().begin(),
- resultTy.getShape().begin());
- sliceBegin[1] = pad[0];
- sliceBegin[2] = pad[2];
+ // Determine the amount to slice / pad from the result start.
+ int64_t resultSliceTop = std::max<int64_t>(0, -pad[0]);
+ int64_t resultSliceLeft = std::max<int64_t>(0, -pad[2]);
+ int64_t resultPadTop = std::max<int64_t>(0, pad[0]);
+ int64_t resultPadLeft = std::max<int64_t>(0, pad[2]);
+
+ // Try to slice the targetted result size, cap to the convolutions width.
+ int64_t resultSliceHeight =
+ std::min<int64_t>(convReshapeDims1[1] - resultSliceTop,
+ resultTy.getDimSize(1) - resultPadTop);
+ int64_t resultSliceWidth =
+ std::min<int64_t>(convReshapeDims1[2] - resultSliceLeft,
+ resultTy.getDimSize(2) - resultPadLeft);
+
+ llvm::SmallVector<int64_t, 4> sliceBegin = {0, resultSliceTop,
+ resultSliceLeft, 0};
+ llvm::SmallVector<int64_t, 4> sliceSize(convReshapeDims1.begin(),
+ convReshapeDims1.end());
+ sliceSize[1] = resultSliceHeight;
+ sliceSize[2] = resultSliceWidth;
auto slice = createOpAndInfer<tosa::SliceOp>(
rewriter, loc, UnrankedTensorType::get(resultETy), conv2d,
rewriter.getI64ArrayAttr(sliceBegin),
- rewriter.getI64ArrayAttr(resultTy.getShape()))
+ rewriter.getI64ArrayAttr(sliceSize))
.getResult();
- auto addBias =
- createOpAndInfer<tosa::AddOp>(rewriter, loc, op.getType(), slice, bias);
+ llvm::SmallVector<int32_t, 8> resultPadding = {0, 0, 0, 0, 0, 0, 0, 0};
+ resultPadding[2] = resultPadTop;
+ resultPadding[3] = resultTy.getDimSize(1) - resultPadTop - sliceSize[1];
+ resultPadding[4] = resultPadLeft;
+ resultPadding[5] = resultTy.getDimSize(2) - resultPadLeft - sliceSize[2];
+
+ DenseElementsAttr resultPaddingAttr = DenseIntElementsAttr::get(
+ RankedTensorType::get({4, 2}, rewriter.getI32Type()), resultPadding);
+
+ Value resultPaddingVal = createOpAndInfer<tosa::ConstOp>(
+ rewriter, loc, resultPaddingAttr.getType(), resultPaddingAttr);
- rewriter.replaceOp(op, addBias.getResult());
+ auto resultPad = createOpAndInfer<tosa::PadOp>(
+ rewriter, loc, UnrankedTensorType::get(resultETy), slice,
+ resultPaddingVal);
+ rewriter.replaceOpWithNewOp<tosa::AddOp>(op, op.getType(), resultPad, bias);
return success();
}
};
// RUN: mlir-opt --split-input-file --tosa-optional-decompositions %s | FileCheck %s
// CHECK-LABEL: @transpose_conv2d
-func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x?x?x5xf32> {
+func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x18x19x5xf32> {
// CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64}
// CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64}
- // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = [1, 1], pad = [2, 2, 5, 5], stride = [1, 1]}
+ // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2)
+ // CHECK-SAME: dilation = [1, 1], pad = [2, 2, 5, 5], stride = [1, 1]
%0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [0, 0, 0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x18x19x5xf32>
- %1 = tensor.cast %0 : tensor<2x18x19x5xf32> to tensor<2x?x?x5xf32>
- return %1 : tensor<2x?x?x5xf32>
+ return %0 : tensor<2x18x19x5xf32>
}
// -----
// CHECK-LABEL: @transpose_conv2d_quantized
+
func.func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x18x19x5xi32>) {
// CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64}
// CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64}
// -----
// CHECK-LABEL: @transpose_conv2d_quantized_padded
-func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x7x7x18xi8>, %arg1: tensor<12x3x5x18xi8>, %arg2: tensor<12xi32>) -> (tensor<2x7x7x12xi32>) {
- // CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64}
- // CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64}
- // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = [1, 1], pad = [1, 1, 2, 2], quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = [1, 1]}
- %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [1, 1, 2, 2], quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x7x7x18xi8>, tensor<12x3x5x18xi8>, tensor<12xi32>) -> tensor<2x7x7x12xi32>
- return %0 : tensor<2x7x7x12xi32>
+func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x21x26x5xi32>) {
+ // CHECK-DAG: %[[REV0:.+]] = "tosa.reverse"(%0) {axis = 2 : i64}
+ // CHECK-DAG: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64}
+ // CHECK: "tosa.conv2d"(%arg0, %1, %arg2)
+ // CHECK-SAME: dilation = [1, 1], pad = [3, 4, 8, 9],
+ // CHECK-SAME: quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, stride = [1, 1]}
+ %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {
+ out_pad = [1, 2, 3, 4],
+ quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>,
+ out_shape = [-1, -1, -1, -1],
+ stride = [1, 1]} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>) -> tensor<2x21x26x5xi32>
+ return %0 : tensor<2x21x26x5xi32>
}
// -----
%0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [0, 0, 0, 0], quantization_info = #tosa.conv_quant<input_zp = -22, weight_zp = 42>, out_shape = [-1, -1, -1, -1], stride = [2, 3]} : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>) -> tensor<2x35x47x5xi32>
return %0 : tensor<2x35x47x5xi32>
}
+
+// -----
+
+// CHECK-LABEL: @transpose_conv2d_strided_overpad
+func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 : tensor<1x2x1x1xi8>, %arg2 : tensor<1xi32>) -> (tensor<1x19x2x1xi32>) {
+ // CHECK: %[[WEIGHT_PAD:.+]] = "tosa.const"()
+ // CHECK-SAME{literal}: value = dense<[[0, 0], [0, 0], [0, 1], [0, 0]]> : tensor<4x2xi32>
+ // CHECK: %[[WEIGHT_PERMS:.+]] = "tosa.const"() {value = dense<[2, 4, 0, 1, 3, 5]> : tensor<6xi32>} : () -> tensor<6xi32>
+ // CHECK: %[[INPUT_PAD:.+]] = "tosa.const"()
+ // CHECK-SAME{literal}: value = dense<[[0, 0], [1, 1], [0, 0], [0, 0]]> : tensor<4x2xi32>}
+ // CHECK: %[[ZERO:.+]] = "tosa.const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>
+ // CHECK: %[[RESULT_PERMS:.+]] = "tosa.const"() {value = dense<[0, 1, 3, 2, 4, 5]> : tensor<6xi32>}
+ // CHECK: %[[RESULT_PAD:.+]] = "tosa.const"()
+ // CHECK-SAME{literal}: value = dense<[[0, 0], [2, 0], [0, 0], [0, 0]]> : tensor<4x2xi32>}
+ // CHECK: %[[PAD_WEIGHT:.+]] = "tosa.pad"(%arg1, %[[WEIGHT_PAD]]) {quantization_info = #tosa.pad_quant<input_zp = 93>}
+ // CHECK: %[[RESHAPE_WEIGHT_0:.+]] = "tosa.reshape"(%[[PAD_WEIGHT]]) {new_shape = [1, 2, 1, 1, 2, 1]}
+ // CHECK: %[[TRANSPOSE_WEIGHT:.+]] = "tosa.transpose"(%[[RESHAPE_WEIGHT_0]], %[[WEIGHT_PERMS]])
+ // CHECK: %[[RESHAPE_WEIGHT_1:.+]] = "tosa.reshape"(%[[TRANSPOSE_WEIGHT]]) {new_shape = [2, 2, 1, 1]}
+ // CHECK: %[[REVERSE:.+]] = "tosa.reverse"(%[[RESHAPE_WEIGHT_1]]) {axis = 1 : i64}
+ // CHECK: %[[PAD_INPUT:.+]] = "tosa.pad"(%arg0, %[[INPUT_PAD]]) {quantization_info = #tosa.pad_quant<input_zp = -103>}
+ // CHECK: %[[CONV:.+]] = "tosa.conv2d"(%[[PAD_INPUT]], %[[REVERSE]], %[[ZERO]])
+ // CHECK-SAME{literal}: dilation = [1, 1], pad = [0, 0, 0, 0], quantization_info = #tosa.conv_quant<input_zp = -103, weight_zp = 93>, stride = [1, 1]}
+ // CHECK: %[[RESHAPE_RESULT_0:.+]] = "tosa.reshape"(%[[CONV]]) {new_shape = [1, 17, 1, 1, 2, 1]}
+ // CHECK: %[[TRANSPOSE_RESULT:.+]] = "tosa.transpose"(%[[RESHAPE_RESULT_0]], %[[RESULT_PERMS]])
+ // CHECK: %[[RESHAPE_RESULT_1:.+]] = "tosa.reshape"(%[[TRANSPOSE_RESULT]]) {new_shape = [1, 17, 2, 1]}
+ // CHECK: %[[PAD_RESULT:.+]] = "tosa.pad"(%[[RESHAPE_RESULT_1]], %[[RESULT_PAD]])
+ // CHECK: %[[ADD:.+]] = "tosa.add"(%[[PAD_RESULT]], %arg2)
+ %2 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {
+ out_pad = [2, 0, 0, 1],
+ out_shape = [1, -1, -1, 1],
+ stride = [1, 2],
+ quantization_info = #tosa.conv_quant<input_zp = -103, weight_zp = 93>} :
+ (tensor<1x16x1x1xi8>, tensor<1x2x1x1xi8>, tensor<1xi32>) -> (tensor<1x19x2x1xi32>)
+ "func.return" (%2) : (tensor<1x19x2x1xi32>) -> ()
+}