89 lines
3.8 KiB
C++
89 lines
3.8 KiB
C++
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "absl/types/span.h"
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#include "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/compiler/xla/types.h"
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#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SLICING_H_
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#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SLICING_H_
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namespace xla {
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// Slices input starting from the base_indices and within the window_sizes,
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// using the supplied strides. This is the equivalent of the Python slicing op
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// [base_indices : base_indices+window_sizes : stride].
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XlaOp DynamicStridedSlice(XlaOp input, absl::Span<const XlaOp> base_indices,
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absl::Span<const int64> window_sizes,
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absl::Span<const int64> strides);
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// Updates a slice of 'x', i.e.,
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// x[start[0], ..., start[n]] = update
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XlaOp UpdateSlice(XlaOp x, XlaOp update, absl::Span<const int64> start);
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// Performs a slice in the minor dimensions of a tensor.
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// x[..., start[0]:end[0], ..., start[n]:end[n]]
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XlaOp SliceInMinorDims(XlaOp x, absl::Span<const int64> start,
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absl::Span<const int64> end);
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// Updates a slice of 'x', where 'start' contains a list of minor dimensions:
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// x[..., start[0]:..., ..., start[n]:...] = update
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XlaOp UpdateSliceInMinorDims(XlaOp x, XlaOp update,
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absl::Span<const int64> start);
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// Performs a dynamic slice in the minor dimensions of a tensor.
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XlaOp DynamicSliceInMinorDims(XlaOp x, absl::Span<const XlaOp> starts,
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absl::Span<const int64> sizes);
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XlaOp DynamicUpdateSliceInMinorDims(XlaOp x, XlaOp update,
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absl::Span<const XlaOp> starts);
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// Gathers values along an axis specified by dim.
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//
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// For a 3-D tensor the output is specified by:
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//
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// out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0
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// out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
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// out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
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//
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// If `input` is an n-dimensional tensor with size
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// [X0,X1,X2,..XN] and dim = i `index` must be an n-dimensional tensor with size
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// [X0,X1,...Y,Xi+1,...,X[N] where y >= 1 and `out` will have the same sizes as
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// `index`.
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XlaOp TorchGather(XlaOp input, XlaOp index, int64 dim, bool sparse = true);
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// idx = index[i][j][k]
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// output[idx][j][k] = combiner(input[idx][j][k], src[i][j][k]) # if dim == 0
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// output[i][idx][k] = combiner(input[i][idx][k], src[i][j][k]) # if dim == 1
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// output[i][j][idx] = combiner(input[i][j][idx], src[i][j][k]) # if dim == 2
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XlaOp TorchScatterDense(XlaOp input, XlaOp index, XlaOp src, int64 dim,
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const std::function<XlaOp(XlaOp, XlaOp)>& combiner);
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// Returns a new tensor which indexes the input tensor along dimension dim using
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// the entries in index.
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//
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// The returned tensor has the same number of dimensions as the original tensor
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// (input). The dimth dimension has the same size as the length of index; other
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// dimensions have the same size as in the original tensor.
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//
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// This operation supports 0 or more major batch dimensions that act like a
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// multidimensional loop over both the input and the index.
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XlaOp TorchIndexSelect(XlaOp input, XlaOp index, int64 dim,
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int64 batch_dims = 0);
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SLICING_H_
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