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[runtime][pytorch-3dunet_vaiq]: element at index 0 (-0.712965) does not match the expected (-0.713635) for InstanceNormalization
What happened?
I am seeing runtime mismatch for pytorch-3dunet_vaiq with error element at index 0 (-0.712965) does not match the expected (-0.713635) ;
module {
func.func @torch_jit(%arg0: !torch.vtensor<[1,1,64,128,128],f32>) -> !torch.vtensor<[1,8,2097152],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} {
%0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_module_1.weight> : tensor<1xf32>} : () -> !torch.vtensor<[1],f32>
%1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_module_1.bias> : tensor<1xf32>} : () -> !torch.vtensor<[1],f32>
%2 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_module_2.weight> : tensor<16x1x3x3x3xf32>} : () -> !torch.vtensor<[16,1,3,3,3],f32>
%none = torch.constant.none
%3 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%4 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%5 = torch.operator "onnx.QuantizeLinear"(%arg0, %3, %4) : (!torch.vtensor<[1,1,64,128,128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1,64,128,128],si8>
%6 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%7 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%8 = torch.operator "onnx.DequantizeLinear"(%5, %6, %7) : (!torch.vtensor<[1,1,64,128,128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1,64,128,128],f32>
%9 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__4> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%10 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__5> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%11 = torch.operator "onnx.QuantizeLinear"(%0, %9, %10) : (!torch.vtensor<[1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1],si8>
%12 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__6> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%13 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__7> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%14 = torch.operator "onnx.DequantizeLinear"(%11, %12, %13) : (!torch.vtensor<[1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1],f32>
%15 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__8> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%16 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__9> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%17 = torch.operator "onnx.QuantizeLinear"(%1, %15, %16) : (!torch.vtensor<[1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1],si8>
%18 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__10> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%19 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__11> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%20 = torch.operator "onnx.DequantizeLinear"(%17, %18, %19) : (!torch.vtensor<[1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1],f32>
%21 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__12> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64>
%22 = torch.operator "onnx.Reshape"(%8, %21) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[1,1,64,128,128],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[1,1,1048576],f32>
%23 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__13> : tensor<1xf32>} : () -> !torch.vtensor<[1],f32>
%24 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__14> : tensor<1xf32>} : () -> !torch.vtensor<[1],f32>
%25 = torch.operator "onnx.InstanceNormalization"(%22, %23, %24) {torch.onnx.epsilon = 9.99999974E-6 : f32} : (!torch.vtensor<[1,1,1048576],f32>, !torch.vtensor<[1],f32>, !torch.vtensor<[1],f32>) -> !torch.vtensor<[1,1,1048576],f32>
%26 = torch.operator "onnx.Shape"(%8) : (!torch.vtensor<[1,1,64,128,128],f32>) -> !torch.vtensor<[5],si64>
%27 = torch.operator "onnx.Reshape"(%25, %26) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[1,1,1048576],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[1,1,64,128,128],f32>
%28 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__15> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64>
%29 = torch.operator "onnx.Unsqueeze"(%14, %28) : (!torch.vtensor<[1],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[1,1,1,1],f32>
%30 = torch.operator "onnx.Mul"(%27, %29) : (!torch.vtensor<[1,1,64,128,128],f32>, !torch.vtensor<[1,1,1,1],f32>) -> !torch.vtensor<[1,1,64,128,128],f32>
%31 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__16> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64>
%32 = torch.operator "onnx.Unsqueeze"(%20, %31) : (!torch.vtensor<[1],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[1,1,1,1],f32>
%33 = torch.operator "onnx.Add"(%30, %32) : (!torch.vtensor<[1,1,64,128,128],f32>, !torch.vtensor<[1,1,1,1],f32>) -> !torch.vtensor<[1,1,64,128,128],f32>
%34 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__17> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%35 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__18> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%36 = torch.operator "onnx.QuantizeLinear"(%33, %34, %35) : (!torch.vtensor<[1,1,64,128,128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1,64,128,128],si8>
%37 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__19> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%38 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__20> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%39 = torch.operator "onnx.DequantizeLinear"(%36, %37, %38) : (!torch.vtensor<[1,1,64,128,128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1,64,128,128],f32>
%40 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__21> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%41 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__22> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%42 = torch.operator "onnx.QuantizeLinear"(%2, %40, %41) : (!torch.vtensor<[16,1,3,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[16,1,3,3,3],si8>
%43 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__23> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%44 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__24> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%45 = torch.operator "onnx.DequantizeLinear"(%42, %43, %44) : (!torch.vtensor<[16,1,3,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[16,1,3,3,3],f32>
%46 = torch.operator "onnx.Conv"(%39, %45) {torch.onnx.dilations = [1 : si64, 1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [3 : si64, 3 : si64, 3 : si64], torch.onnx.pads = [1 : si64, 1 : si64, 1 : si64, 1 : si64, 1 : si64, 1 : si64], torch.onnx.strides = [1 : si64, 1 : si64, 1 : si64]} : (!torch.vtensor<[1,1,64,128,128],f32>, !torch.vtensor<[16,1,3,3,3],f32>) -> !torch.vtensor<[1,16,64,128,128],f32>
%47 = torch.operator "onnx.Relu"(%46) : (!torch.vtensor<[1,16,64,128,128],f32>) -> !torch.vtensor<[1,16,64,128,128],f32>
%48 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__25> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%49 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__26> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%50 = torch.operator "onnx.QuantizeLinear"(%47, %48, %49) : (!torch.vtensor<[1,16,64,128,128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,16,64,128,128],si8>
%51 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__27> : tensor<f32>} : () -> !torch.vtensor<[],f32>
%52 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__28> : tensor<si8>} : () -> !torch.vtensor<[],si8>
%53 = torch.operator "onnx.DequantizeLinear"(%50, %51, %52) : (!torch.vtensor<[1,16,64,128,128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,16,64,128,128],f32>
%54 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__29> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64>
%55 = torch.operator "onnx.Reshape"(%53, %54) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[1,16,64,128,128],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[1,8,2097152],f32>
%56 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__30> : tensor<8xf32>} : () -> !torch.vtensor<[8],f32>
%57 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__31> : tensor<8xf32>} : () -> !torch.vtensor<[8],f32>
%58 = torch.operator "onnx.InstanceNormalization"(%55, %56, %57) {torch.onnx.epsilon = 9.99999974E-6 : f32} : (!torch.vtensor<[1,8,2097152],f32>, !torch.vtensor<[8],f32>, !torch.vtensor<[8],f32>) -> !torch.vtensor<[1,8,2097152],f32>
return %58 : !torch.vtensor<[1,8,2097152],f32>
}
}
{-#
dialect_resources: {
builtin: {
_module_1.weight: "0x080000000000643F",
_module_1.bias: "0x080000000000AEBD",
_module_2.weight: "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",
_: "0x080000000000803D",
__1: "0x0800000000",
__2: "0x080000000000803D",
__3: "0x0800000000",
__4: "0x080000000000003C",
__5: "0x0800000000",
__6: "0x080000000000003C",
__7: "0x0800000000",
__8: "0x080000000000803A",
__9: "0x0800000000",
__10: "0x080000000000803A",
__11: "0x0800000000",
__12: "0x0800000000000000000000000100000000000000FFFFFFFFFFFFFFFF",
__13: "0x080000000000803F",
__14: "0x0800000000000000",
__15: "0x08000000010000000000000002000000000000000300000000000000",
__16: "0x08000000010000000000000002000000000000000300000000000000",
__17: "0x080000000000003D",
__18: "0x0800000000",
__19: "0x080000000000003D",
__20: "0x0800000000",
__21: "0x080000000000003B",
__22: "0x0800000000",
__23: "0x080000000000003B",
__24: "0x0800000000",
__25: "0x080000000000803C",
__26: "0x0800000000",
__27: "0x080000000000803C",
__28: "0x0800000000",
__29: "0x0800000000000000000000000800000000000000FFFFFFFFFFFFFFFF",
__30: "0x080000000000803F0000803F0000803F0000803F0000803F0000803F0000803F0000803F",
__31: "0x080000000000000000000000000000000000000000000000000000000000000000000000"
}
}
#-}
Uploading input.0.bin.txt…
Steps to reproduce your issue
steps to reproduce:
iree-compile model.torch_onnx.mlir --iree-hal-target-backends=llvm-cpu -o out.vmfb
iree-run-module --module=out.vmfb --device="local-task" --input="[email protected]" --expected_output="1x8x2097152xf32=@golden_output.0.bin"
IREE Version: IREE compiler version 20240819.990 @ aeda14995f16ed1302db616adf0c03acf80f27ee LLVM version 20.0.0git
Not able to upload golden_output.0.bin.txt file as size is 65M.
What component(s) does this issue relate to?
Runtime
Version information
No response
Additional context
No response
Duplicate of https://github.com/iree-org/iree/issues/18200#issuecomment-2286472770? Both might be a problem with DequantizeLinear
@IanWood1
This is an issue exclusively with InstanceNormalization when used on certain inputs. I'll work with @pdhirajkumarprasad on getting a smaller reproducer. It may be a front end (e.g. torch-mlir) issue.
closing this as issue not seen, we are working on analyzing numeric failure and will file/monitor separately