[ONNX] Use torch.aten.quantize_per_channel + torch.aten.dequantize.self for QLinearConv weights

[anuragsingh-tt]

The ONNX QLinearConv to Torch conversion subtracts the zero point and multiplies the scale manually:

// -----
module {
  func.func @test_qlinearconv_weight_per_channel_quantization(%arg0: !torch.vtensor<[?,3,224,224],ui8>, %arg1: !torch.vtensor<[],f32>, %arg2: !torch.vtensor<[],ui8>, %arg3: !torch.vtensor<[64,3,7,7],si8>, %arg4: !torch.vtensor<[64],f32>, %arg5: !torch.vtensor<[64],si8>, %arg6: !torch.vtensor<[],f32>, %arg7: !torch.vtensor<[],ui8>, %arg8: !torch.vtensor<[64],si32>) -> !torch.vtensor<[?,64,112,112],ui8> attributes {torch.onnx_meta.ir_version = 4 : si64, torch.onnx_meta.opset_version = 21 : si64, torch.onnx_meta.opset_versions = {ai.onnx.ml = 2 : si64, ai.onnx.preview.training = 1 : si64, ai.onnx.training = 1 : si64, com.microsoft = 1 : si64, com.microsoft.experimental = 1 : si64, com.microsoft.mlfeaturizers = 1 : si64, com.microsoft.nchwc = 1 : si64}, torch.onnx_meta.producer_name = "onnx.quantize", torch.onnx_meta.producer_version = "0.1.0"} {
    %0 = torch.aten.item %arg2 : !torch.vtensor<[],ui8> -> !torch.int
    %1 = torch.aten.item %arg7 : !torch.vtensor<[],ui8> -> !torch.int
    %2 = torch.aten.item %arg1 : !torch.vtensor<[],f32> -> !torch.float
    %3 = torch.aten.item %arg6 : !torch.vtensor<[],f32> -> !torch.float
    %none = torch.constant.none
    %false = torch.constant.bool false
    %int6 = torch.constant.int 6
    %4 = torch.aten.to.dtype %arg0, %int6, %false, %false, %none : !torch.vtensor<[?,3,224,224],ui8>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[?,3,224,224],f32>
    %float1.000000e00 = torch.constant.float 1.000000e+00
    %5 = torch.aten.sub.Scalar %4, %0, %float1.000000e00 : !torch.vtensor<[?,3,224,224],f32>, !torch.int, !torch.float -> !torch.vtensor<[?,3,224,224],f32>
    %6 = torch.aten.mul.Scalar %5, %2 : !torch.vtensor<[?,3,224,224],f32>, !torch.float -> !torch.vtensor<[?,3,224,224],f32>
    %int1 = torch.constant.int 1
    %7 = torch.aten.unsqueeze %arg4, %int1 : !torch.vtensor<[64],f32>, !torch.int -> !torch.vtensor<[64,1],f32>
    %8 = torch.aten.unsqueeze %arg5, %int1 : !torch.vtensor<[64],si8>, !torch.int -> !torch.vtensor<[64,1],si8>
    %int2 = torch.constant.int 2
    %9 = torch.aten.unsqueeze %7, %int2 : !torch.vtensor<[64,1],f32>, !torch.int -> !torch.vtensor<[64,1,1],f32>
    %10 = torch.aten.unsqueeze %8, %int2 : !torch.vtensor<[64,1],si8>, !torch.int -> !torch.vtensor<[64,1,1],si8>
    %int3 = torch.constant.int 3
    %11 = torch.aten.unsqueeze %9, %int3 : !torch.vtensor<[64,1,1],f32>, !torch.int -> !torch.vtensor<[64,1,1,1],f32>
    %12 = torch.aten.unsqueeze %10, %int3 : !torch.vtensor<[64,1,1],si8>, !torch.int -> !torch.vtensor<[64,1,1,1],si8>
    %13 = torch.aten.to.dtype %arg3, %int6, %false, %false, %none : !torch.vtensor<[64,3,7,7],si8>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[64,3,7,7],f32>
    %14 = torch.aten.sub.Tensor %13, %12, %float1.000000e00 : !torch.vtensor<[64,3,7,7],f32>, !torch.vtensor<[64,1,1,1],si8>, !torch.float -> !torch.vtensor<[64,3,7,7],f32>
    %15 = torch.aten.mul.Tensor %14, %11 : !torch.vtensor<[64,3,7,7],f32>, !torch.vtensor<[64,1,1,1],f32> -> !torch.vtensor<[64,3,7,7],f32>
    %16 = torch.aten.to.dtype %arg8, %int6, %false, %false, %none : !torch.vtensor<[64],si32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[64],f32>
    %int3_0 = torch.constant.int 3
    %int3_1 = torch.constant.int 3
    %int3_2 = torch.constant.int 3
    %int3_3 = torch.constant.int 3
    %int0 = torch.constant.int 0
    %17 = torch.prim.ListConstruct %int3_0, %int3_1 : (!torch.int, !torch.int) -> !torch.list<int>
    %int1_4 = torch.constant.int 1
    %int1_5 = torch.constant.int 1
    %int2_6 = torch.constant.int 2
    %int2_7 = torch.constant.int 2
    %18 = torch.prim.ListConstruct %int1_4, %int1_5 : (!torch.int, !torch.int) -> !torch.list<int>
    %19 = torch.prim.ListConstruct %int2_6, %int2_7 : (!torch.int, !torch.int) -> !torch.list<int>
    %20 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
    %false_8 = torch.constant.bool false
    %int1_9 = torch.constant.int 1
    %21 = torch.aten.convolution %6, %15, %16, %19, %17, %18, %false_8, %20, %int1_9 : !torch.vtensor<[?,3,224,224],f32>, !torch.vtensor<[64,3,7,7],f32>, !torch.vtensor<[64],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,64,112,112],f32>
    %int13 = torch.constant.int 13
    %22 = torch.aten.quantize_per_tensor %21, %3, %1, %int13 : !torch.vtensor<[?,64,112,112],f32>, !torch.float, !torch.int, !torch.int -> !torch.vtensor<[?,64,112,112],!torch.quint8>
    %23 = torch.aten.int_repr %22 : !torch.vtensor<[?,64,112,112],!torch.quint8> -> !torch.vtensor<[?,64,112,112],ui8>
    return %23 : !torch.vtensor<[?,64,112,112],ui8>
  }
}

but a Quantize -> Dequantize -> Convolution sequence in ONNX instead uses torch.aten.quantize_per_tensor -> torch.aten.int_repr -> torch.aten._make_per_tensor_quantized_tensor -> torch.aten.dequantize.self which would be the more appropriate sequence.