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1945 lines (1538 loc) · 60.2 KB
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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/util/mirror_pad_mode.h"
#include "tensorflow/core/util/padding.h"
namespace tensorflow {
REGISTER_OP("Pack")
.Input("values: N * T")
.Output("output: T")
.Attr("N: int >= 1")
.Attr("T: type")
.Doc(R"doc(
Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Packs the `N` tensors in `values` into a tensor with rank one higher than each
tensor in `values` and shape `[N] + values[0].shape`. The output satisfies
`output[i, ...] = values[i][...]`.
This is the opposite of `unpack`.
values: Must be of same shape and type.
output: The packed tensor.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Unpack")
.Input("value: T")
.Output("output: num * T")
.Attr("num: int >= 0")
.Attr("T: type")
.Doc(R"doc(
Unpacks the outer dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` by chipping it along the first dimension.
The i'th tensor in `output` is the slice `value[i, ...]`. Each tensor in
`output` has shape `value.shape[1:]`.
This is the opposite of `pack`.
value: 1-D or higher, with first dimension `num`.
output: The list of tensors unpacked from `value`.
)doc");
// --------------------------------------------------------------------------
// TODO(josh11b): Remove the >= 2 constraint, once we can rewrite the graph
// in the N == 1 case to remove the node.
REGISTER_OP("Concat")
.Input("concat_dim: int32")
.Input("values: N * T")
.Output("output: T")
.Attr("N: int >= 2")
.Attr("T: type")
.Doc(R"doc(
Concatenates tensors along one dimension.
concat_dim: 0-D. The dimension along which to concatenate. Must be in the
range [0, rank(values)).
values: The `N` Tensors to concatenate. Their ranks and types must match,
and their sizes must match in all dimensions except `concat_dim`.
output: A `Tensor` with the concatenation of values stacked along the
`concat_dim` dimension. This tensor's shape matches that of `values` except
in `concat_dim` where it has the sum of the sizes.
)doc");
REGISTER_OP("ConcatOffset")
.Input("concat_dim: int32")
.Input("shape: N * int32")
.Output("offset: N * int32")
.Attr("N: int >= 2")
.Doc(R"doc(
Computes offsets of concat inputs within its output.
For example:
```prettyprint
# 'x' is [2, 2, 7]
# 'y' is [2, 3, 7]
# 'z' is [2, 5, 7]
concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
```
concat_dim: The dimension along which to concatenate.
shape: The `N` int32 vectors representing shape of tensors being concatenated.
output: The `N` int32 vectors representing the starting offset
of input tensors within the concatenated output.
This is typically used by gradient computations for a concat operation.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Split")
.Input("split_dim: int32")
.Input("value: T")
.Output("output: num_split * T")
.Attr("num_split: int >= 1")
.Attr("T: type")
.Doc(R"doc(
Splits a tensor into `num_split` tensors along one dimension.
split_dim: 0-D. The dimension along which to split. Must be in the range
`[0, rank(value))`.
num_split: The number of ways to split. Must evenly divide
`value.shape[split_dim]`.
value: The tensor to split.
output: They are identically shaped tensors, whose shape matches that of `value`
except along `split_dim`, where their sizes are
`values.shape[split_dim] / num_split`.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Const")
.Output("output: dtype")
.Attr("value: tensor")
.Attr("dtype: type")
.Doc(R"doc(
Returns a constant tensor.
value: Attr `value` is the tensor to return.
)doc");
// --------------------------------------------------------------------------
// TODO(mgubin): Update the doc when the freeze_graph script supports converting
// into memmapped format.
REGISTER_OP("ImmutableConst")
.Attr("dtype: type")
.Attr("shape: shape")
.Attr("memory_region_name: string")
.Output("tensor: dtype")
.Doc(R"doc(
Returns immutable tensor from memory region.
The current implementation memmaps the tensor from a file.
dtype: Type of the returned tensor.
shape: Shape of the returned tensor.
memory_region_name: Name of readonly memory region used by the tensor, see
NewReadOnlyMemoryRegionFromFile in tensorflow::Env.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("ZerosLike").Input("x: T").Output("y: T").Attr("T: type").Doc(R"doc(
Returns a tensor of zeros with the same shape and type as x.
x: a tensor of type T.
y: a tensor of the same shape and type as x but filled with zeros.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Diag")
.Input("diagonal: T")
.Output("output: T")
.Attr("T: {float, double, int32, int64, complex64}")
.Doc(R"doc(
Returns a diagonal tensor with a given diagonal values.
Given a `diagonal`, this operation returns a tensor with the `diagonal` and
everything else padded with zeros. The diagonal is computed as follows:
Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of
rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:
`output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else.
For example:
```prettyprint
# 'diagonal' is [1, 2, 3, 4]
tf.diag(diagonal) ==> [[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]]
```
diagonal: Rank k tensor where k is at most 3.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("DiagPart")
.Input("input: T")
.Output("diagonal: T")
.Attr("T: {float, double, int32, int64, complex64}")
.Doc(R"doc(
Returns the diagonal part of the tensor.
This operation returns a tensor with the `diagonal` part
of the `input`. The `diagonal` part is computed as follows:
Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
tensor of rank `k` with dimensions `[D1,..., Dk]` where:
`diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
For example:
```prettyprint
# 'input' is [[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]]
tf.diag_part(input) ==> [1, 2, 3, 4]
```
input: Rank k tensor where k is 2, 4, or 6.
diagonal: The extracted diagonal.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("BatchMatrixDiag")
.Input("diagonal: T")
.Output("output: T")
.Attr("T: type")
.Doc(R"doc(
Returns a batched diagonal tensor with a given batched diagonal values.
Given a `diagonal`, this operation returns a tensor with the `diagonal` and
everything else padded with zeros. The diagonal is computed as follows:
Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a
tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:
`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
For example:
```prettyprint
# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
and diagonal.shape = (2, 4)
tf.batch_matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]],
[[5, 0, 0, 0]
[0, 6, 0, 0]
[0, 0, 7, 0]
[0, 0, 0, 8]]]
which has shape (2, 4, 4)
```
diagonal: Rank `k`, where `k >= 1`.
output: Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("BatchMatrixDiagPart")
.Input("input: T")
.Output("diagonal: T")
.Attr("T: type")
.Doc(R"doc(
Returns the batched diagonal part of a batched tensor.
This operation returns a tensor with the `diagonal` part
of the batched `input`. The `diagonal` part is computed as follows:
Assume `input` has `k` dimensions `[I, J, K, ..., N, N]`, then the output is a
tensor of rank `k - 1` with dimensions `[I, J, K, ..., N]` where:
`diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.
The input must be at least a matrix.
For example:
```prettyprint
# 'input' is [[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]],
[[5, 0, 0, 0]
[0, 6, 0, 0]
[0, 0, 7, 0]
[0, 0, 0, 8]]]
and input.shape = (2, 4, 4)
tf.batch_matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
which has shape (2, 4)
```
input: Rank `k` tensor where `k >= 2` and the last two dimensions are equal.
diagonal: The extracted diagonal(s) having shape
`diagonal.shape = input.shape[:-1]`.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("BatchMatrixBandPart")
.Input("input: T")
.Input("num_lower: int64")
.Input("num_upper: int64")
.Output("band: T")
.Attr("T: type")
.Doc(R"doc(
Copy a tensor setting everything outside a central band in each innermost matrix
to zero.
The `band` part is computed as follows:
Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
tensor with the same shape where
`band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
The indicator function 'in_band(m, n)` is one if
`(num_lower < 0 || (m-n) <= num_lower)) &&
(num_upper < 0 || (n-m) <= num_upper)`, and zero otherwise.
For example:
```prettyprint
# if 'input' is [[ 0, 1, 2, 3]
[-1, 0, 1, 2]
[-2, -1, 0, 1]
[-3, -2, -1, 0]],
tf.batch_matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3]
[-1, 0, 1, 2]
[ 0, -1, 0, 1]
[ 0, 0, -1, 0]],
tf.batch_matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0]
[-1, 0, 1, 0]
[-2, -1, 0, 1]
[ 0, -2, -1, 0]]
```
Useful special cases:
```prettyprint
tf.batch_matrix_band_part(input, 0, -1) ==> Upper triangular part.
tf.batch_matrix_band_part(input, -1, 0) ==> Lower triangular part.
tf.batch_matrix_band_part(input, 0, 0) ==> Diagonal.
```
input: Rank `k` tensor.
num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
lower triangle.
num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep
entire upper triangle.
band: Rank `k` tensor of the same shape as input. The extracted banded tensor.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Reverse")
.Input("tensor: T")
.Input("dims: bool")
.Output("output: T")
.Attr("T: {uint8, int8, int32, bool, half, float, double}")
.Doc(R"Doc(
Reverses specific dimensions of a tensor.
Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
of `tensor`, this operation reverses each dimension i of `tensor` where
`dims[i]` is `True`.
`tensor` can have up to 8 dimensions. The number of dimensions
of `tensor` must equal the number of elements in `dims`. In other words:
`rank(tensor) = size(dims)`
For example:
```prettyprint
# tensor 't' is [[[[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]],
# [[12, 13, 14, 15],
# [16, 17, 18, 19],
# [20, 21, 22, 23]]]]
# tensor 't' shape is [1, 2, 3, 4]
# 'dims' is [False, False, False, True]
reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
[ 7, 6, 5, 4],
[ 11, 10, 9, 8]],
[[15, 14, 13, 12],
[19, 18, 17, 16],
[23, 22, 21, 20]]]]
# 'dims' is [False, True, False, False]
reverse(t, dims) ==> [[[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]]]]
# 'dims' is [False, False, True, False]
reverse(t, dims) ==> [[[[8, 9, 10, 11],
[4, 5, 6, 7],
[0, 1, 2, 3]]
[[20, 21, 22, 23],
[16, 17, 18, 19],
[12, 13, 14, 15]]]]
```
tensor: Up to 8-D.
dims: 1-D. The dimensions to reverse.
output: The same shape as `tensor`.
)Doc");
// --------------------------------------------------------------------------
REGISTER_OP("EditDistance")
.Input("hypothesis_indices: int64")
.Input("hypothesis_values: T")
.Input("hypothesis_shape: int64")
.Input("truth_indices: int64")
.Input("truth_values: T")
.Input("truth_shape: int64")
.Attr("normalize: bool = True")
.Attr("T: type")
.Output("output: float")
.Doc(R"doc(
Computes the (possibly normalized) Levenshtein Edit Distance.
The inputs are variable-length sequences provided by SparseTensors
(hypothesis_indices, hypothesis_values, hypothesis_shape)
and
(truth_indices, truth_values, truth_shape).
The inputs are:
hypothesis_indices: The indices of the hypothesis list SparseTensor.
This is an N x R int64 matrix.
hypothesis_values: The values of the hypothesis list SparseTensor.
This is an N-length vector.
hypothesis_shape: The shape of the hypothesis list SparseTensor.
This is an R-length vector.
truth_indices: The indices of the truth list SparseTensor.
This is an M x R int64 matrix.
truth_values: The values of the truth list SparseTensor.
This is an M-length vector.
truth_shape: The shape of the truth list SparseTensor.
This is an R-length vector.
truth_shape: truth indices, vector.
normalize: boolean (if true, edit distances are normalized by length of truth).
The output is:
output: A dense float tensor with rank R - 1.
For the example input:
// hypothesis represents a 2x1 matrix with variable-length values:
// (0,0) = ["a"]
// (1,0) = ["b"]
hypothesis_indices = [[0, 0, 0],
[1, 0, 0]]
hypothesis_values = ["a", "b"]
hypothesis_shape = [2, 1, 1]
// truth represents a 2x2 matrix with variable-length values:
// (0,0) = []
// (0,1) = ["a"]
// (1,0) = ["b", "c"]
// (1,1) = ["a"]
truth_indices = [[0, 1, 0],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0]]
truth_values = ["a", "b", "c", "a"]
truth_shape = [2, 2, 2]
normalize = true
The output will be:
// output is a 2x2 matrix with edit distances normalized by truth lengths.
output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis
[0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Fill")
.Input("dims: int32")
.Input("value: T")
.Output("output: T")
.Attr("T: type")
.Doc(R"doc(
Creates a tensor filled with a scalar value.
This operation creates a tensor of shape `dims` and fills it with `value`.
For example:
```prettyprint
# Output tensor has shape [2, 3].
fill([2, 3], 9) ==> [[9, 9, 9]
[9, 9, 9]]
```
dims: 1-D. Represents the shape of the output tensor.
value: 0-D (scalar). Value to fill the returned tensor.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Gather")
.Input("params: Tparams")
.Input("indices: Tindices")
.Attr("validate_indices: bool = true")
.Output("output: Tparams")
.Attr("Tparams: type")
.Attr("Tindices: {int32,int64}")
.Doc(R"doc(
Gather slices from `params` according to `indices`.
`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
# Scalar indices
output[:, ..., :] = params[indices, :, ... :]
# Vector indices
output[i, :, ..., :] = params[indices[i], :, ... :]
# Higher rank indices
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
If `indices` is a permutation and `len(indices) == params.shape[0]` then
this operation will permute `params` accordingly.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../images/Gather.png" alt>
</div>
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("GatherNd")
.Input("params: Tparams")
.Input("indices: Tindices")
.Output("output: Tparams")
.Attr("Tparams: type")
.Attr("Tindices: {int32,int64}")
.Doc(R"doc(
Gather values from `params` according to `indices`.
`indices` must be integer tensor, containing indices into `params`.
It must be shape `[d_0, ..., d_N, R]` where `R` is the rank of `params`.
The innermost dimension of `indices` (with length `R`) corresponds to the
indices of `params`.
Produces an output tensor with shape `[d_0, ..., d_{n-1}]` where:
output[i, j, k, ...] = params[indices[i, j, k, ..., :]]
e.g. for `indices` a matrix:
output[i] = params[indices[i, :]]
params: R-D. The tensor from which to gather values.
indices: (N+1)-D. Index tensor having shape `[d_0, ..., d_N, R]`.
output: N-D. Values from `params` gathered from indices given by `indices`.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Identity")
.Input("input: T")
.Output("output: T")
.Attr("T: type")
.Doc(R"Doc(
Return a tensor with the same shape and contents as the input tensor or value.
)Doc");
// --------------------------------------------------------------------------
REGISTER_OP("RefIdentity")
.Input("input: Ref(T)")
.Output("output: Ref(T)")
.Attr("T: type")
.Doc(R"Doc(
Return the same ref tensor as the input ref tensor.
)Doc");
// --------------------------------------------------------------------------
REGISTER_OP("StopGradient")
.Input("input: T")
.Output("output: T")
.Attr("T: type")
.Doc(R"Doc(
Stops gradient computation.
When executed in a graph, this op outputs its input tensor as-is.
When building ops to compute gradients, this op prevents the contribution of
its inputs to be taken into account. Normally, the gradient generator adds ops
to a graph to compute the derivatives of a specified 'loss' by recursively
finding out inputs that contributed to its computation. If you insert this op
in the graph it inputs are masked from the gradient generator. They are not
taken into account for computing gradients.
This is useful any time you want to compute a value with TensorFlow but need
to pretend that the value was a constant. Some examples include:
* The *EM* algorithm where the *M-step* should not involve backpropagation
through the output of the *E-step*.
* Contrastive divergence training of Boltzmann machines where, when
differentiating the energy function, the training must not backpropagate
through the graph that generated the samples from the model.
* Adversarial training, where no backprop should happen through the adversarial
example generation process.
)Doc");
// --------------------------------------------------------------------------
REGISTER_OP("CheckNumerics")
.Input("tensor: T")
.Output("output: T")
.Attr("T: {half, float, double}")
.Attr("message: string")
.Doc(R"doc(
Checks a tensor for NaN and Inf values.
When run, reports an `InvalidArgument` error if `tensor` has any values
that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is.
message: Prefix of the error message.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Reshape")
.Input("tensor: T")
.Input("shape: int32")
.Output("output: T")
.Attr("T: type")
.Doc(R"Doc(
Reshapes a tensor.
Given `tensor`, this operation returns a tensor that has the same values
as `tensor` with shape `shape`.
If one component of `shape` is the special value -1, the size of that dimension
is computed so that the total size remains constant. In particular, a `shape`
of `[-1]` flattens into 1-D. At most one component of `shape` can be -1.
If `shape` is 1-D or higher, then the operation returns a tensor with shape
`shape` filled with the values of `tensor`. In this case, the number of elements
implied by `shape` must be the same as the number of elements in `tensor`.
For example:
```prettyprint
# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor 't' has shape [9]
reshape(t, [3, 3]) ==> [[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
# tensor 't' is [[[1, 1], [2, 2]],
# [[3, 3], [4, 4]]]
# tensor 't' has shape [2, 2, 2]
reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
[3, 3, 4, 4]]
# tensor 't' is [[[1, 1, 1],
# [2, 2, 2]],
# [[3, 3, 3],
# [4, 4, 4]],
# [[5, 5, 5],
# [6, 6, 6]]]
# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
# -1 can also be used to infer the shape
# -1 is inferred to be 9:
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 2:
reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 3:
reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
[2, 2, 2],
[3, 3, 3]],
[[4, 4, 4],
[5, 5, 5],
[6, 6, 6]]]
# tensor 't' is [7]
# shape `[]` reshapes to a scalar
reshape(t, []) ==> 7
```
shape: Defines the shape of the output tensor.
)Doc");
// --------------------------------------------------------------------------
REGISTER_OP("InvertPermutation").Input("x: int32").Output("y: int32").Doc(R"doc(
Computes the inverse permutation of a tensor.
This operation computes the inverse of an index permutation. It takes a 1-D
integer tensor `x`, which represents the indices of a zero-based array, and
swaps each value with its index position. In other words, for an output tensor
`y` and an input tensor `x`, this operation computes the following:
`y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
The values must include 0. There can be no duplicate values or negative values.
For example:
```prettyprint
# tensor `x` is [3, 4, 0, 2, 1]
invert_permutation(x) ==> [2, 4, 3, 0, 1]
```
x: 1-D.
y: 1-D.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Transpose")
.Input("x: T")
.Input("perm: int32")
.Output("y: T")
.Attr("T: type")
.Doc(R"doc(
Shuffle dimensions of x according to a permutation.
The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
`y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Unique")
.Input("x: T")
.Output("y: T")
.Output("idx: int32")
.Attr("T: type")
.Doc(R"doc(
Finds unique elements in a 1-D tensor.
This operation returns a tensor `y` containing all of the unique elements of `x`
sorted in the same order that they occur in `x`. This operation also returns a
tensor `idx` the same size as `x` that contains the index of each value of `x`
in the unique output `y`. In other words:
`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
For example:
```prettyprint
# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx = unique(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
```
x: 1-D.
y: 1-D.
idx: 1-D.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("UniqueWithCounts")
.Input("x: T")
.Output("y: T")
.Output("idx: int32")
.Output("count: int32")
.Attr("T: type")
.Doc(R"doc(
Finds unique elements in a 1-D tensor.
This operation returns a tensor `y` containing all of the unique elements of `x`
sorted in the same order that they occur in `x`. This operation also returns a
tensor `idx` the same size as `x` that contains the index of each value of `x`
in the unique output `y`. Finally, it returns a third tensor `count` that
contains the count of each element of `y` in `x`. In other words:
`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
For example:
```prettyprint
# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]
```
x: 1-D.
y: 1-D.
idx: 1-D.
count: 1-D.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Shape")
.Input("input: T")
.Output("output: int32")
.Attr("T: type")
.Doc(R"doc(
Returns the shape of a tensor.
This operation returns a 1-D integer tensor representing the shape of `input`.
For example:
```prettyprint
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]
```
)doc");
REGISTER_OP("ShapeN")
.Input("input: N * T")
.Output("output: N * int32")
.Attr("N: int32")
.Attr("T: type")
.Doc(R"doc(
Returns shape of tensors.
This operation returns N 1-D integer tensors representing shape of `input[i]s`.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("ReverseSequence")
.Input("input: T")
.Input("seq_lengths: int64")
.Output("output: T")
.Attr("seq_dim: int")
.Attr("batch_dim: int = 0")
.Attr("T: type")
.Doc(R"doc(
Reverses variable length slices.
This op first slices `input` along the dimension `batch_dim`, and for each
slice `i`, reverses the first `seq_lengths[i]` elements along
the dimension `seq_dim`.
The elements of `seq_lengths` must obey `seq_lengths[i] < input.dims[seq_dim]`,
and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.
The output slice `i` along dimension `batch_dim` is then given by input
slice `i`, with the first `seq_lengths[i]` slices along dimension
`seq_dim` reversed.
For example:
```prettyprint
# Given this:
batch_dim = 0
seq_dim = 1
input.dims = (4, 8, ...)
seq_lengths = [7, 2, 3, 5]
# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
# while entries past seq_lens are copied through:
output[0, 7:, :, ...] = input[0, 7:, :, ...]
output[1, 2:, :, ...] = input[1, 2:, :, ...]
output[2, 3:, :, ...] = input[2, 3:, :, ...]
output[3, 2:, :, ...] = input[3, 2:, :, ...]
```
In contrast, if:
```prettyprint
# Given this:
batch_dim = 2
seq_dim = 0
input.dims = (8, ?, 4, ...)
seq_lengths = [7, 2, 3, 5]
# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
# while entries past seq_lens are copied through:
output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
```
input: The input to reverse.
seq_lengths: 1-D with length `input.dims(batch_dim)` and
`max(seq_lengths) < input.dims(seq_dim)`
seq_dim: The dimension which is partially reversed.
batch_dim: The dimension along which reversal is performed.
output: The partially reversed input. It has the same shape as `input`.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Rank")
.Input("input: T")
.Output("output: int32")
.Attr("T: type")
.Doc(R"doc(
Returns the rank of a tensor.
This operation returns an integer representing the rank of `input`.
For example:
```prettyprint
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
# shape of tensor 't' is [2, 2, 3]
rank(t) ==> 3
```
**Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
of a tensor is the number of indices required to uniquely select each element
of the tensor. Rank is also known as "order", "degree", or "ndims."
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Size")
.Input("input: T")
.Output("output: int32")
.Attr("T: type")
.Doc(R"doc(
Returns the size of a tensor.
This operation returns an integer representing the number of elements in
`input`.
For example:
```prettyprint
# 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
size(t) ==> 12
```
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Slice")
.Input("input: T")
.Input("begin: Index")
.Input("size: Index")
.Output("output: T")
.Attr("T: type")
.Attr("Index: {int32,int64}")
.Doc(R"doc(
Return a slice from 'input'.
The output tensor is a tensor with dimensions described by 'size'
whose values are extracted from 'input' starting at the offsets in
'begin'.
*Requirements*:
0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)
begin: begin[i] specifies the offset into the 'i'th dimension of
'input' to slice from.
size: size[i] specifies the number of elements of the 'i'th dimension
of 'input' to slice. If size[i] is -1, all remaining elements in dimension
i are included in the slice (i.e. this is equivalent to setting
size[i] = input.dim_size(i) - begin[i]).
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("Tile")
.Input("input: T")
.Input("multiples: int32")
.Output("output: T")
.Attr("T: type")
.Doc(R"doc(
Constructs a tensor by tiling a given tensor.
This operation creates a new tensor by replicating `input` `multiples` times.
The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
and the values of `input` are replicated `multiples[i]` times along the 'i'th
dimension. For example, tiling `[a b c d]` by `[2]` produces
`[a b c d a b c d]`.
input: 1-D or higher.
multiples: 1-D. Length must be the same as the number of dimensions in `input`
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("TileGrad")
.Input("input: T")
.Input("multiples: int32")