93 lines
3.2 KiB
C++
93 lines
3.2 KiB
C++
/* Copyright 2017 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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// XLA Pack operator.
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#include <limits>
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#include <vector>
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#include "tensorflow/compiler/tf2xla/type_util.h"
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#include "tensorflow/compiler/tf2xla/xla_helpers.h"
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#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
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#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
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#include "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/compiler/xla/literal_util.h"
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#include "tensorflow/core/framework/bounds_check.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/framework/register_types.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/tensor_types.h"
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#include "tensorflow/core/framework/types.h"
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#include "tensorflow/core/lib/core/status.h"
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#include "tensorflow/core/platform/types.h"
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namespace tensorflow {
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namespace {
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class PackOp : public XlaOpKernel {
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public:
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explicit PackOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
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OP_REQUIRES_OK(ctx, ctx->GetAttr("axis", &axis_));
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}
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void Compile(XlaOpKernelContext* ctx) override {
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std::vector<xla::XlaOp> values;
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std::vector<TensorShape> shapes;
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OP_REQUIRES_OK(ctx, ctx->InputList("values", &values, &shapes));
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const int num = values.size();
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OP_REQUIRES(ctx, num >= 0,
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errors::InvalidArgument("Pack requires >= 1 arguments"));
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// Verify that all input shapes match
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for (int i = 1; i < num; i++) {
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OP_REQUIRES(ctx, shapes[0].IsSameSize(shapes[i]),
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errors::InvalidArgument(
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"Shapes of all inputs must match: values[0].shape = ",
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shapes[0].DebugString(), " != values[", i, "].shape = ",
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shapes[i].DebugString()));
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}
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int expanded_num_dims = shapes[0].dims() + 1;
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int axis = axis_;
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if (axis < 0) axis += expanded_num_dims;
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OP_REQUIRES(ctx, 0 <= axis && axis < expanded_num_dims,
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errors::InvalidArgument("axis = ", axis_, " not in [",
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-expanded_num_dims, ", ",
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expanded_num_dims, ")"));
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std::vector<xla::XlaOp> reshaped_inputs(num);
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TensorShape child_shape(shapes[0]);
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child_shape.InsertDim(axis, 1);
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for (int i = 0; i < num; ++i) {
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// Reshape the inputs to have an extra dimension of size 1.
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reshaped_inputs[i] = xla::Reshape(values[i], child_shape.dim_sizes());
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}
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ctx->SetOutput(0, xla::ConcatInDim(ctx->builder(), reshaped_inputs, axis));
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}
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private:
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int axis_;
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};
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REGISTER_XLA_OP(Name("Pack"), PackOp);
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} // namespace
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} // namespace tensorflow
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