[hf-model] error: expected sizes to be non-negative, but got -1

[pdhirajkumarprasad]

What happened?

For the given IR

module {
  func.func @main_graph(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[11,1,1,384],f32>, %arg2: !torch.vtensor<[?,?,?,?],f32>, %arg3:!torch.vtensor<[11,1,100,384],f32>, %arg4: !torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[11,1,?,384],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 = "2.6.0"} {
    %136 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<1.0> : tensor<11x1x1x384xf32>} : () -> !torch.vtensor<[11,1,1,384],f32> 
    %137 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<1.0> : tensor<11x1x100x384xf32>} : () -> !torch.vtensor<[11,1,100,384],f32> 
    %138 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<1.0> : tensor<11x384x32x54xf32>} : () -> !torch.vtensor<[11,384,32,54],f32> 
    %139 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<1> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %none = torch.constant.none
    %219 = torch.operator "onnx.Shape"(%arg0) : (!torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %220 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %221 = torch.operator "onnx.Gather"(%219, %220) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %223 = torch.operator "onnx.Shape"(%arg0) : (!torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %224 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %225 = torch.operator "onnx.Gather"(%223, %224) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %270 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__23> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %271 = torch.operator "onnx.Div"(%221, %270) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %274 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__24> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %275 = torch.operator "onnx.Div"(%225, %274) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %283 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__27> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %285 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__28> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %287 = torch.operator "onnx.Concat"(%283, %285) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %302 = torch.operator "onnx.Cast"(%287) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],si64> 
    %303 = torch.operator "onnx.Concat"(%139, %302) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4],si64> 
    %304 = torch.operator "onnx.Resize"(%138, %none, %none, %303) {torch.onnx.coordinate_transformation_mode = "half_pixel", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "cubic", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[11,384,32,54],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> 
    %305 = torch.operator "onnx.Shape"(%304) : (!torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %306 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__33> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %307 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__34> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %308 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__35> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %309 = torch.operator "onnx.Slice"(%305, %307, %308, %306) : (!torch.vtensor<[4],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %310 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__36> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %311 = torch.operator "onnx.Concat"(%309, %310) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %312 = torch.operator "onnx.Reshape"(%304, %311) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %313 = torch.operator "onnx.Transpose"(%312) {torch.onnx.perm = [0 : si64, 2 : si64, 1 : si64]} : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> 
    %314 = torch.operator "onnx.Mul"(%271, %275) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %315 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__37> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %316 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__38> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %317 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__39> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %318 = torch.operator "onnx.Unsqueeze"(%314, %317) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %319 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__40> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %320 = torch.operator "onnx.Concat"(%315, %316, %318, %319) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64> 
    %321 = torch.operator "onnx.Reshape"(%313, %320) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> 
    %322 = torch.operator "onnx.Concat"(%136, %321, %137) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[11,1,1,384],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[11,1,100,384],f32>) -> !torch.vtensor<[11,1,?,384],f32> 
    return %322: !torch.vtensor<[11,1,?,384],f32>
  }
}

{-#
  dialect_resources: {
    builtin: {
      __1: "0x080000000200000000000000",
      __3: "0x080000000300000000000000",
      __23: "0x080000001000000000000000",
      __24: "0x080000001000000000000000",
      __27: "0x080000000000000000000000",
      __28: "0x080000000000000000000000",
      __33: "0x080000000000000000000000",
      __34: "0x080000000000000000000000",
      __35: "0x080000000200000000000000",
      __36: "0x08000000FFFFFFFFFFFFFFFF",
      __37: "0x080000000B00000000000000",
      __38: "0x080000000100000000000000",
      __39: "0x080000000000000000000000",
      __40: "0x080000008001000000000000"
    }
  }
#-}

getting error as

error: expected sizes to be non-negative, but got -1
    %322 = torch.operator "onnx.Concat"(%136, %321, %137) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[11,1,1,384],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[11,1,100,384],f32>) -> !torch.vtensor<[11,1,?,384],f32>

during iree-flow-canonicalization post CSE.

command:

iree-compile tt.mlir --iree-hal-target-backends=llvm-cpu --iree-llvmcpu-target-cpu=host -o abc.vmfb

IREE version: IREE compiler version 3.1.0rc20241217 @ 362b554

model: From HF top 1000 most downloaded models(hf_yolos-small-finetuned-license-plate-detection)

dump with '--mlir-print-ir-after-all --mlir-print-ir-before-all --mlir-disable-threading --mlir-elide-elementsattrs-if-larger=4'

dump.log

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Compiler

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