Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 332898101 Change-Id: Ia3d69f38a7d24771f23d5ef1942b6b80e950af85
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
a09165ad84
commit
14696f7e09
@ -44523,165 +44523,6 @@ func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes [
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// Reverses specific dimensions of a tensor.
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//
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// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
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// of `tensor`, this operation reverses each dimension i of `tensor` where
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// `dims[i]` is `True`.
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//
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// `tensor` can have up to 8 dimensions. The number of dimensions
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// of `tensor` must equal the number of elements in `dims`. In other words:
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//
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// `rank(tensor) = size(dims)`
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//
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// For example:
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//
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// ```
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// # tensor 't' is [[[[ 0, 1, 2, 3],
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// # [ 4, 5, 6, 7],
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// # [ 8, 9, 10, 11]],
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// # [[12, 13, 14, 15],
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// # [16, 17, 18, 19],
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// # [20, 21, 22, 23]]]]
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// # tensor 't' shape is [1, 2, 3, 4]
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//
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// # 'dims' is [False, False, False, True]
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// reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
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// [ 7, 6, 5, 4],
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// [ 11, 10, 9, 8]],
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// [[15, 14, 13, 12],
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// [19, 18, 17, 16],
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// [23, 22, 21, 20]]]]
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//
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// # 'dims' is [False, True, False, False]
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// reverse(t, dims) ==> [[[[12, 13, 14, 15],
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// [16, 17, 18, 19],
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// [20, 21, 22, 23]
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// [[ 0, 1, 2, 3],
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// [ 4, 5, 6, 7],
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// [ 8, 9, 10, 11]]]]
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//
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// # 'dims' is [False, False, True, False]
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// reverse(t, dims) ==> [[[[8, 9, 10, 11],
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// [4, 5, 6, 7],
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// [0, 1, 2, 3]]
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// [[20, 21, 22, 23],
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// [16, 17, 18, 19],
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// [12, 13, 14, 15]]]]
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// ```
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//
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// Arguments:
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// tensor: Up to 8-D.
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// dims: 1-D. The dimensions to reverse.
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//
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// Returns The same shape as `tensor`.
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func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "Reverse",
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Input: []tf.Input{
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tensor, dims,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// StringLowerAttr is an optional argument to StringLower.
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type StringLowerAttr func(optionalAttr)
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// StringLowerEncoding sets the optional encoding attribute to value.
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// If not specified, defaults to ""
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func StringLowerEncoding(value string) StringLowerAttr {
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return func(m optionalAttr) {
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m["encoding"] = value
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}
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}
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// Converts all uppercase characters into their respective lowercase replacements.
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//
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// Example:
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//
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// >>> tf.strings.lower("CamelCase string and ALL CAPS")
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// <tf.Tensor: shape=(), dtype=string, numpy=b'camelcase string and all caps'>
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//
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func StringLower(scope *Scope, input tf.Output, optional ...StringLowerAttr) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "StringLower",
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Input: []tf.Input{
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input,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Wraps an arbitrary MLIR computation expressed as a module with a main() function.
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//
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// This operation does not have an associated kernel and is not intended to be
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// executed in a regular TensorFlow session. Instead it is intended to be used for
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// testing or for special case where a user intends to pass custom MLIR computation
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// through a TensorFlow graph with the intent of having custom tooling processing
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// it downstream (when targeting a different environment, like TensorFlow lite for
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// example).
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// The MLIR module is expected to have a main() function that will be used as an
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// entry point. The inputs to the operations will be passed as argument to the
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// main() function and the returned values of the main function mapped to the
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// outputs.
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// Example usage:
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//
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// ```
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// import tensorflow as tf
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// from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op
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//
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// mlir_module = '''python
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// func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
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// %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
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// return %ret : tensor<10x10xf32>
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// }
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// '''
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//
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// @tf.function
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// def foo(x, y):
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// return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])
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//
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// graph_def = foo.get_concrete_function(tf.TensorSpec([10], tf.float32), tf.TensorSpec([10], tf.float32)).graph.as_graph_def()
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// ```
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func MlirPassthroughOp(scope *Scope, inputs []tf.Output, mlir_module string, Toutputs []tf.DataType) (outputs []tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"mlir_module": mlir_module, "Toutputs": Toutputs}
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opspec := tf.OpSpec{
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Type: "MlirPassthroughOp",
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Input: []tf.Input{
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tf.OutputList(inputs),
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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if scope.Err() != nil {
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return
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}
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var idx int
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var err error
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if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
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scope.UpdateErr("MlirPassthroughOp", err)
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return
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}
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return outputs
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}
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// Converts each string in the input Tensor to its hash mod by a number of buckets.
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//
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// The hash function is deterministic on the content of the string within the
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@ -47981,6 +47822,165 @@ func ParseSequenceExampleV2(scope *Scope, serialized tf.Output, debug_name tf.Ou
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return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, context_ragged_values, context_ragged_row_splits, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values, feature_list_dense_lengths, feature_list_ragged_values, feature_list_ragged_outer_splits, feature_list_ragged_inner_splits
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}
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// Reverses specific dimensions of a tensor.
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//
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// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
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// of `tensor`, this operation reverses each dimension i of `tensor` where
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// `dims[i]` is `True`.
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//
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// `tensor` can have up to 8 dimensions. The number of dimensions
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// of `tensor` must equal the number of elements in `dims`. In other words:
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//
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// `rank(tensor) = size(dims)`
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//
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// For example:
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//
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// ```
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// # tensor 't' is [[[[ 0, 1, 2, 3],
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// # [ 4, 5, 6, 7],
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// # [ 8, 9, 10, 11]],
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// # [[12, 13, 14, 15],
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// # [16, 17, 18, 19],
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// # [20, 21, 22, 23]]]]
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// # tensor 't' shape is [1, 2, 3, 4]
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//
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// # 'dims' is [False, False, False, True]
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// reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
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// [ 7, 6, 5, 4],
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// [ 11, 10, 9, 8]],
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// [[15, 14, 13, 12],
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// [19, 18, 17, 16],
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// [23, 22, 21, 20]]]]
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//
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// # 'dims' is [False, True, False, False]
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// reverse(t, dims) ==> [[[[12, 13, 14, 15],
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// [16, 17, 18, 19],
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// [20, 21, 22, 23]
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// [[ 0, 1, 2, 3],
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// [ 4, 5, 6, 7],
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// [ 8, 9, 10, 11]]]]
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//
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// # 'dims' is [False, False, True, False]
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// reverse(t, dims) ==> [[[[8, 9, 10, 11],
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// [4, 5, 6, 7],
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// [0, 1, 2, 3]]
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// [[20, 21, 22, 23],
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// [16, 17, 18, 19],
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// [12, 13, 14, 15]]]]
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// ```
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//
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// Arguments:
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// tensor: Up to 8-D.
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// dims: 1-D. The dimensions to reverse.
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//
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// Returns The same shape as `tensor`.
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func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "Reverse",
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Input: []tf.Input{
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tensor, dims,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Wraps an arbitrary MLIR computation expressed as a module with a main() function.
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//
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// This operation does not have an associated kernel and is not intended to be
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// executed in a regular TensorFlow session. Instead it is intended to be used for
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// testing or for special case where a user intends to pass custom MLIR computation
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// through a TensorFlow graph with the intent of having custom tooling processing
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// it downstream (when targeting a different environment, like TensorFlow lite for
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// example).
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// The MLIR module is expected to have a main() function that will be used as an
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// entry point. The inputs to the operations will be passed as argument to the
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// main() function and the returned values of the main function mapped to the
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// outputs.
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// Example usage:
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//
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// ```
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// import tensorflow as tf
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// from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op
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//
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// mlir_module = '''python
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// func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
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// %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
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// return %ret : tensor<10x10xf32>
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// }
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// '''
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//
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// @tf.function
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// def foo(x, y):
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// return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])
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//
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// graph_def = foo.get_concrete_function(tf.TensorSpec([10], tf.float32), tf.TensorSpec([10], tf.float32)).graph.as_graph_def()
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// ```
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func MlirPassthroughOp(scope *Scope, inputs []tf.Output, mlir_module string, Toutputs []tf.DataType) (outputs []tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"mlir_module": mlir_module, "Toutputs": Toutputs}
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opspec := tf.OpSpec{
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Type: "MlirPassthroughOp",
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Input: []tf.Input{
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tf.OutputList(inputs),
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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if scope.Err() != nil {
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return
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}
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var idx int
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var err error
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if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
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scope.UpdateErr("MlirPassthroughOp", err)
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return
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}
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return outputs
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}
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// StringLowerAttr is an optional argument to StringLower.
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type StringLowerAttr func(optionalAttr)
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// StringLowerEncoding sets the optional encoding attribute to value.
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// If not specified, defaults to ""
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func StringLowerEncoding(value string) StringLowerAttr {
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return func(m optionalAttr) {
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m["encoding"] = value
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}
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}
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// Converts all uppercase characters into their respective lowercase replacements.
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//
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// Example:
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//
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// >>> tf.strings.lower("CamelCase string and ALL CAPS")
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// <tf.Tensor: shape=(), dtype=string, numpy=b'camelcase string and all caps'>
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//
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func StringLower(scope *Scope, input tf.Output, optional ...StringLowerAttr) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "StringLower",
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Input: []tf.Input{
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input,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// CudnnRNNAttr is an optional argument to CudnnRNN.
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type CudnnRNNAttr func(optionalAttr)
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