conv_debug.mlir

module {
  func.func @torch_jit(%arg0: !torch.vtensor<[1,3,240,240],f32>, %arg1: !torch.vtensor<[1],si64> , %arg2:!torch.vtensor<[32,3,3,3],f32>, %arg3: !torch.vtensor<[32],f32>) -> !torch.vtensor<[?,32,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 21 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.12.1"} {
    %233 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_onnx__Concat_1461> : tensor<4xsi64>} : () -> !torch.vtensor<[4],si64> 
    // %233 = [0, 1, 0, 1]
    %238 = torch.operator "onnx.ConstantOfShape"(%arg1) {torch.onnx.value = dense_resource<_> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64> 
    // // [0, 0, 0, 0]
    %239 = torch.operator "onnx.Concat"(%233, %238) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[8],si64> 
    // // [1, 0, 1, 0, 0, 0, 0, 0]
    %240 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    // %240 = [4, 2]
    %241 = torch.operator "onnx.Reshape"(%239, %240) : (!torch.vtensor<[8],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4,2],si64> 
    // [[1, 0], 
    //  [1, 0],
    //  [0, 0],
    //  [0, 0],
    // ]
    %242 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    // %242 = [0]
    %243 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    // %243 = [-1]
    %244 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__4> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    // %244 = [1]
    %245 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__5> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    // %245 = [-1]
    %246 = torch.operator "onnx.Slice"(%241, %243, %arg1, %242, %245) : (!torch.vtensor<[4,2],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,2],si64> 
    // Data, starts, ends, axes, steps
    // Data = 4x2xi64=[
      // [0, 1],
      // [0, 1],
      // [0, 0],
      // [0, 0],
    // ]
    // Starts = -1
    // ends = 4
    // axes = 0
    // steps = -1
    // %246 = Result = 4x2xi64=[
      // [0, 0],
      // [0, 0],
      // [0, 1],
      // [0, 1],
    // ]
    %247 = torch.operator "onnx.Transpose"(%246) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[4,2],si64>) -> !torch.vtensor<[2,4],si64> 
    // Onnx.Transpose(Result, 1, 0)
    // Input = 4x2xi64, Result = 2x4xi64
    // Transpose_Result = [
    //    [0, 0, 0, 0]
    //    [1, 1, 0, 0]
    // ]
    %248 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__6> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    // %248 = [-1]
    %249 = torch.operator "onnx.Reshape"(%247, %248) : (!torch.vtensor<[2,4],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[8],si64> 
    // Reshape(Transpose_result, %248)
    // Input = 2x4xi64, Result = 8xi64
    // %249 = Result_reshape = [0, 0, 0, 0, 1, 1, 0, 0]
    %250 = torch.operator "onnx.Cast"(%249) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[8],si64>) -> !torch.vtensor<[8],si64> 
    %251 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__7> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    // %251 = [0]
    %252 = torch.operator "onnx.Pad"(%arg0, %250, %251) {torch.onnx.mode = "constant"} : (!torch.vtensor<[1,3,240,240],f32>, !torch.vtensor<[8],si64>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?,?,?],f32> 
    // %252 = Input = [1x3x240x240xf32], Output = [2x4x240x240xf32]
    %253 = torch.operator "onnx.Conv"(%252, %arg2, %arg3) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[32,3,3,3],f32>, !torch.vtensor<[32],f32>) -> !torch.vtensor<[?,32,?,?],f32> 
    return %253 : !torch.vtensor<[?,32,?,?],f32>
  }
}

{-#
  dialect_resources: {
    builtin: {
      _onnx__Concat_1461: "0x080000000000000000000000010000000000000000000000000000000100000000000000",
      _: "0x080000000000000000000000",
      __1: "0x08000000FFFFFFFFFFFFFFFF0200000000000000",
      __2: "0x080000000000000000000000",
      __3: "0x08000000FFFFFFFFFFFFFFFF",
      __4: "0x080000000100000000000080",
      __5: "0x08000000FFFFFFFFFFFFFFFF",
      __6: "0x08000000FFFFFFFFFFFFFFFF",
      __7: "0x0800000000000000"
    }
  }
#-}