98 lines
3.7 KiB
C++
98 lines
3.7 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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#include "tensorflow/compiler/tf2xla/kernels/tensor_list_utils.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/core/lib/core/errors.h"
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namespace tensorflow {
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namespace {
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class AddNOp : public XlaOpKernel {
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public:
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explicit AddNOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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if (!ctx->ValidateInputsAreSameShape(this)) return;
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OP_REQUIRES(ctx, ctx->num_inputs() >= 1,
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errors::InvalidArgument("AddN requires at least one argument"));
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XlaExpression::Kind kind = ctx->InputExpression(0).kind();
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xla::XlaOp sum;
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switch (kind) {
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case XlaExpression::Kind::kTensorList: {
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// Check that all TensorLists are initialized.
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for (int i = 1; i < ctx->num_inputs(); ++i) {
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xla::XlaOp list = ctx->Input(i);
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bool is_initialized;
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OP_REQUIRES_OK(ctx, IsTensorListInitialized(list, &is_initialized));
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OP_REQUIRES(
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ctx, is_initialized,
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errors::InvalidArgument("TensorList input #", i,
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" for AddN op is an uninitialized list"));
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}
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// Nested TensorList is not supported.
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bool is_nested_list;
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OP_REQUIRES_OK(ctx, IsNestedTensorList(ctx->Input(0), &is_nested_list));
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OP_REQUIRES(ctx, !is_nested_list,
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errors::Unimplemented(
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"Nested TensorList is not supported for AddN op"));
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OP_REQUIRES_OK(ctx, GetTensorListBuffer(ctx->Input(0), &sum));
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xla::Shape sum_shape;
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OP_REQUIRES_OK(ctx,
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GetTensorListBufferShape(ctx->Input(0), &sum_shape));
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for (int i = 1; i < ctx->num_inputs(); ++i) {
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xla::XlaOp operand;
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OP_REQUIRES_OK(ctx, GetTensorListBuffer(ctx->Input(i), &operand));
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// Check that the shapes match.
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xla::Shape operand_shape;
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OP_REQUIRES_OK(
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ctx, GetTensorListBufferShape(ctx->Input(i), &operand_shape));
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OP_REQUIRES(
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ctx, sum_shape.dimensions() == operand_shape.dimensions(),
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errors::InvalidArgument(
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"TensorList arguments to AddN must all have the same ",
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"shape.\n", "Expected: ", sum_shape.DebugString(), "\n",
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"Found: ", operand_shape.DebugString()));
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sum = xla::Add(sum, operand);
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}
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xla::XlaOp push_index;
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OP_REQUIRES_OK(ctx, GetTensorListPushIndex(ctx->Input(0), &push_index));
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OP_REQUIRES_OK(ctx, BuildNonNestedTensorList(sum, push_index, &sum));
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ctx->SetTensorListOutput(0, sum);
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break;
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}
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default:
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sum = ctx->Input(0);
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for (int i = 1; i < ctx->num_inputs(); ++i) {
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sum = xla::Add(sum, ctx->Input(i));
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}
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ctx->SetOutput(0, sum);
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}
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}
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private:
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TF_DISALLOW_COPY_AND_ASSIGN(AddNOp);
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};
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REGISTER_XLA_OP(Name("AddN").AllowVariantTypes(), AddNOp);
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} // namespace
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} // namespace tensorflow
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