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[mlir][tosa] Fix quantized type for tosa.conv2d canonicalization 

Wrong type was used for the result type in the tosa.conv_2d canonicalization.
The type should match the result element type should match the result type
not the input element type.

Differential Revision: https://reviews.llvm.org/D115463
This commit is contained in:
Rob Suderman 2021-12-09 12:32:15 -08:00
parent 2717f62c97
commit 46c96fca0e
2 changed files with 20 additions and 1 deletions
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2
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
View File

@ -482,7 +482,7 @@ struct Conv2DFullyConnectedOptimization
inputShape[0] * inputShape[1] * inputShape[2], weightShape[0]}; inputShape[0] * inputShape[1] * inputShape[2], weightShape[0]};
auto fullyConnectedShapeType = RankedTensorType::get( auto fullyConnectedShapeType = RankedTensorType::get(
fullyConnectedShape, fullyConnectedShape,
weight.getType().dyn_cast<RankedTensorType>().getElementType()); resultType.dyn_cast<ShapedType>().getElementType());
Value fullyConnectedValue; Value fullyConnectedValue;
if (op.quantization_info()) { if (op.quantization_info()) {

19
mlir/test/Dialect/Tosa/canonicalize.mlir
View File

@ -86,6 +86,25 @@ func @conv2d_as_fully_connected(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<3x1x
// ----- // -----
// CHECK-LABEL: @conv2d_as_fully_connected_quant
func @conv2d_as_fully_connected_quant(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<3x1x1x2xi8>, %arg2: tensor<3xi32>) -> tensor<4x10x10x3xi32> {
// CHECK-NOT: "tosa.conv2d"
// CHECK: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [400, 2]}
// CHECK-SAME: -> tensor<400x2xi8>
// CHECK: %[[VAR1:.*]] = "tosa.reshape"(%arg1) {new_shape = [3, 2]}
// CHECK-SAME: -> tensor<3x2xi8>
// CHECK: %[[VAR2:.*]] = "tosa.fully_connected"(%[[VAR0]], %[[VAR1]], %arg2)
// CHECK-SAME: quantization_info = {input_zp = 42 : i32, weight_zp = 24 : i32}
// CHECK-SAME: -> tensor<400x3xi32>
// CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [4, 10, 10, 3]}
// CHECK-SAME: -> tensor<4x10x10x3xi32>
// CHECK: return %[[VAR3]]
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1], quantization_info = {input_zp = 42 : i32, weight_zp = 24 : i32}} : (tensor<4x10x10x2xi8>, tensor<3x1x1x2xi8>, tensor<3xi32>) -> tensor<4x10x10x3xi32>
return %0 : tensor<4x10x10x3xi32>
}
// -----
// CHECK-LABEL: @conv2d_stride_2 // CHECK-LABEL: @conv2d_stride_2
func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32> { func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32> {
// CHECK: "tosa.conv2d" // CHECK: "tosa.conv2d"