Update ops-related pbtxt files.
PiperOrigin-RevId: 157573723
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@ -4019,6 +4019,29 @@ op {
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}
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}
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}
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}
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}
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}
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op {
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name: "Cholesky"
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input_arg {
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name: "input"
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type_attr: "T"
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}
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output_arg {
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name: "output"
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type_attr: "T"
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}
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attr {
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name: "T"
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type: "type"
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allowed_values {
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list {
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type: DT_DOUBLE
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type: DT_FLOAT
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type: DT_COMPLEX64
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type: DT_COMPLEX128
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}
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}
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}
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}
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op {
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op {
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name: "CholeskyGrad"
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name: "CholeskyGrad"
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input_arg {
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input_arg {
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@ -11506,6 +11529,47 @@ op {
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}
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}
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}
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}
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}
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}
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op {
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name: "MatrixTriangularSolve"
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input_arg {
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name: "matrix"
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type_attr: "T"
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}
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input_arg {
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name: "rhs"
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type_attr: "T"
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}
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output_arg {
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name: "output"
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type_attr: "T"
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}
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attr {
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name: "lower"
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type: "bool"
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default_value {
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b: true
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}
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}
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attr {
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name: "adjoint"
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type: "bool"
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default_value {
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b: false
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}
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}
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attr {
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name: "T"
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type: "type"
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allowed_values {
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list {
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type: DT_DOUBLE
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type: DT_FLOAT
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type: DT_COMPLEX64
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type: DT_COMPLEX128
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}
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}
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}
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}
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op {
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op {
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name: "Max"
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name: "Max"
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input_arg {
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input_arg {
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@ -3985,11 +3985,13 @@ op {
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list {
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list {
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type: DT_DOUBLE
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type: DT_DOUBLE
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type: DT_FLOAT
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type: DT_FLOAT
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type: DT_COMPLEX64
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type: DT_COMPLEX128
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}
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}
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}
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}
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}
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}
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summary: "Computes the Cholesky decomposition of one or more square matrices."
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summary: "Computes the Cholesky decomposition of one or more square matrices."
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description: "The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions\nform square matrices, with the same constraints as the single matrix Cholesky\ndecomposition above. The output is a tensor of the same shape as the input\ncontaining the Cholesky decompositions for all input submatrices `[..., :, :]`."
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description: "The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions\nform square matrices.\n\nThe input has to be symmetric and positive definite. Only the lower-triangular\npart of the input will be used for this operation. The upper-triangular part\nwill not be read.\n\nThe output is a tensor of the same shape as the input\ncontaining the Cholesky decompositions for all input submatrices `[..., :, :]`.\n\n**Note**: The gradient computation on GPU is faster for large matrices but\nnot for large batch dimensions when the submatrices are small. In this\ncase it might be faster to use the CPU."
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}
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}
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op {
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op {
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name: "CholeskyGrad"
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name: "CholeskyGrad"
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@ -11222,6 +11224,8 @@ op {
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list {
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list {
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type: DT_DOUBLE
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type: DT_DOUBLE
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type: DT_FLOAT
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type: DT_FLOAT
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type: DT_COMPLEX64
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type: DT_COMPLEX128
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}
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}
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}
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}
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}
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}
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