classifier.mlir

module  {
  torch.global_slot "private" @intent_mlp.layer0.weight : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<768x768xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<768x768xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  torch.global_slot "private" @intent_mlp.layer0.bias : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<768xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<768xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  torch.global_slot "private" @intent_mlp.layer2.weight : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<64x768xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<64x768xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  torch.global_slot "private" @intent_mlp.layer2.bias : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<64xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<64xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  torch.global_slot "private" @slot_mlp.layer0.weight : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<768x768xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<768x768xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  torch.global_slot "private" @slot_mlp.layer0.bias : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<768xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<768xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  torch.global_slot "private" @slot_mlp.layer2.weight : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<55x768xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<55x768xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  torch.global_slot "private" @slot_mlp.layer2.bias : !numpy.ndarray<*:!numpy.any_dtype>  {
    %cst = constant opaque<"", "0xDEADBEEF"> : tensor<55xf32>
    %0 = numpy.create_array_from_tensor %cst : (tensor<55xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
    torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
  }
  func @forward(%arg0: !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.TupleType {
    %bool_false = basicpy.bool_constant false
    %cst = constant 1.000000e-01 : f64
    %c0_i64 = constant 0 : i64
    %c9223372036854775807_i64 = constant 9223372036854775807 : i64
    %c1_i64 = constant 1 : i64
    %0 = torch.kernel_call "aten::dropout" %arg0, %cst, %bool_false : (!numpy.ndarray<*:!numpy.any_dtype>, f64, !basicpy.BoolType) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "float", "bool"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %1 = torch.kernel_call "aten::slice" %0, %c0_i64, %c0_i64, %c9223372036854775807_i64, %c1_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, i64, i64, i64, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "int", "int?", "int?", "int"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %2 = torch.kernel_call "aten::select" %1, %c1_i64, %c0_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, i64, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "int", "int"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %3 = torch.kernel_call "aten::slice" %2, %c0_i64, %c0_i64, %c9223372036854775807_i64, %c1_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, i64, i64, i64, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "int", "int?", "int?", "int"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %4 = torch.global_slot.get @intent_mlp.layer0.bias : !numpy.ndarray<*:!numpy.any_dtype>
    %5 = torch.global_slot.get @intent_mlp.layer0.weight : !numpy.ndarray<*:!numpy.any_dtype>
    %6 = torch.kernel_call "aten::t" %5 : (!numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %7 = torch.kernel_call "aten::addmm" %4, %3, %6, %c1_i64, %c1_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, i64, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor", "Tensor", "Scalar", "Scalar"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %8 = torch.kernel_call "aten::relu" %7 : (!numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %9 = torch.global_slot.get @intent_mlp.layer2.bias : !numpy.ndarray<*:!numpy.any_dtype>
    %10 = torch.global_slot.get @intent_mlp.layer2.weight : !numpy.ndarray<*:!numpy.any_dtype>
    %11 = torch.kernel_call "aten::t" %10 : (!numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %12 = torch.kernel_call "aten::addmm" %9, %8, %11, %c1_i64, %c1_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, i64, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor", "Tensor", "Scalar", "Scalar"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %13 = torch.kernel_call "aten::slice" %0, %c0_i64, %c0_i64, %c9223372036854775807_i64, %c1_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, i64, i64, i64, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "int", "int?", "int?", "int"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %14 = torch.global_slot.get @slot_mlp.layer0.bias : !numpy.ndarray<*:!numpy.any_dtype>
    %15 = torch.global_slot.get @slot_mlp.layer0.weight : !numpy.ndarray<*:!numpy.any_dtype>
    %16 = torch.kernel_call "aten::t" %15 : (!numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %17 = torch.kernel_call "aten::matmul" %13, %16 : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %18 = torch.kernel_call "aten::add_" %17, %14, %c1_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor", "Scalar"], sigIsMutable = true, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %19 = torch.kernel_call "aten::relu" %18 : (!numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %20 = torch.global_slot.get @slot_mlp.layer2.bias : !numpy.ndarray<*:!numpy.any_dtype>
    %21 = torch.global_slot.get @slot_mlp.layer2.weight : !numpy.ndarray<*:!numpy.any_dtype>
    %22 = torch.kernel_call "aten::t" %21 : (!numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %23 = torch.kernel_call "aten::matmul" %19, %22 : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %24 = torch.kernel_call "aten::add_" %23, %20, %c1_i64 : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor", "Scalar"], sigIsMutable = true, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
    %25 = basicpy.build_tuple %12, %24 : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.TupleType
    return %25 : !basicpy.TupleType
  }
}