diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index a19ff5770f8..2f1bafef2aa 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -4019,6 +4019,29 @@ op { } } } +op { + name: "Cholesky" + input_arg { + name: "input" + type_attr: "T" + } + output_arg { + name: "output" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_DOUBLE + type: DT_FLOAT + type: DT_COMPLEX64 + type: DT_COMPLEX128 + } + } + } +} op { name: "CholeskyGrad" input_arg { @@ -11506,6 +11529,47 @@ op { } } } +op { + name: "MatrixTriangularSolve" + input_arg { + name: "matrix" + type_attr: "T" + } + input_arg { + name: "rhs" + type_attr: "T" + } + output_arg { + name: "output" + type_attr: "T" + } + attr { + name: "lower" + type: "bool" + default_value { + b: true + } + } + attr { + name: "adjoint" + type: "bool" + default_value { + b: false + } + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_DOUBLE + type: DT_FLOAT + type: DT_COMPLEX64 + type: DT_COMPLEX128 + } + } + } +} op { name: "Max" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index b520e3fa5ef..e3bb192a020 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -3985,11 +3985,13 @@ op { list { type: DT_DOUBLE type: DT_FLOAT + type: DT_COMPLEX64 + type: DT_COMPLEX128 } } } summary: "Computes the Cholesky decomposition of one or more square matrices." - 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 `[..., :, :]`." + 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." } op { name: "CholeskyGrad" @@ -11222,6 +11224,8 @@ op { list { type: DT_DOUBLE type: DT_FLOAT + type: DT_COMPLEX64 + type: DT_COMPLEX128 } } }