100 lines
4.0 KiB
C++
100 lines
4.0 KiB
C++
/* Copyright 2019 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 "tensorflow/compiler/tf2xla/xla_op_kernel.h"
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#include "tensorflow/compiler/xla/client/lib/constants.h"
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#include "tensorflow/compiler/xla/client/lib/slicing.h"
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#include "tensorflow/core/lib/core/errors.h"
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namespace tensorflow {
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namespace {
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class RollOp : public XlaOpKernel {
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public:
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explicit RollOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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const TensorShape input_shape = ctx->InputShape(0);
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xla::XlaOp shift = ctx->Input(1);
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const TensorShape shift_shape = ctx->InputShape(1);
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const TensorShape axis_shape = ctx->InputShape(2);
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int64 input_dims = input_shape.dims();
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OP_REQUIRES(ctx, input_dims >= 1,
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errors::InvalidArgument("input must be 1-D or higher"));
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OP_REQUIRES(ctx, shift_shape.dims() <= 1,
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errors::InvalidArgument(
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"shift must be a scalar or a 1-D vector. Found: ",
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shift_shape.DebugString()));
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OP_REQUIRES(ctx, axis_shape.dims() <= 1,
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errors::InvalidArgument(
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"axis must be a scalar or a 1-D vector. Found: ",
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shift_shape.DebugString()));
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OP_REQUIRES(
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ctx, shift_shape == axis_shape,
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errors::InvalidArgument("shift and axis must have the same size"));
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xla::Literal axis;
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OP_REQUIRES_OK(ctx, ctx->ConstantInput(2, &axis));
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xla::XlaOp output = ctx->Input(0);
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xla::PrimitiveType shift_type = ctx->input_xla_type(1);
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int64 num_axes = axis_shape.dims() == 0 ? 1 : axis_shape.dim_size(0);
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for (int64 i = 0; i != num_axes; ++i) {
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int64 cur_axis = axis_shape.dims() == 0 ? *axis.GetIntegralAsS64({})
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: *axis.GetIntegralAsS64({i});
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OP_REQUIRES(ctx, cur_axis >= -input_dims && cur_axis < input_dims,
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errors::InvalidArgument(
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absl::StrCat("axis ", cur_axis, " is out of range [-",
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input_dims, ", ", input_dims, ").")));
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if (cur_axis < 0) {
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cur_axis += input_dims;
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}
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xla::XlaOp offset =
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shift_shape.dims() == 0
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? shift
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: xla::Reshape(xla::SliceInDim(shift, /*start_index=*/i,
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/*limit_index=*/i + 1,
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/*stride=*/1, /*dimno=*/0),
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{});
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xla::XlaOp axis_size = xla::ConstantR0WithType(
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ctx->builder(), shift_type, input_shape.dim_size(cur_axis));
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// Adjust large offsets into [0, axis_size). This also makes negative
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// offsets positive.
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offset = ((offset % axis_size) + axis_size) % axis_size;
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// Stack two copies of the dimension, then slice from the calculated
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// offset.
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xla::XlaOp concat =
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xla::ConcatInDim(ctx->builder(), {output, output}, cur_axis);
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std::vector<xla::XlaOp> start_indices(
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input_shape.dims(), xla::Zero(ctx->builder(), shift_type));
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start_indices[cur_axis] = axis_size - offset;
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output =
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xla::DynamicSlice(concat, start_indices, input_shape.dim_sizes());
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}
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ctx->SetOutput(0, output);
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}
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private:
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TF_DISALLOW_COPY_AND_ASSIGN(RollOp);
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};
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REGISTER_XLA_OP(Name("Roll").CompileTimeConstantInput("axis"), RollOp);
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} // namespace
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} // namespace tensorflow
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