wehub-resource-sync
81 lines
3.1 KiB
C++
81 lines
3.1 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-specific Fill Op.
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#include <cstdint>
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#include <vector>
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#include "absl/status/status.h"
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#include "absl/strings/str_cat.h"
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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 "xla/hlo/builder/value_inference.h"
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#include "xla/hlo/builder/xla_builder.h"
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#include "xla/xla_data.pb.h"
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#include "tensorflow/core/framework/kernel_def_builder.h"
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#include "tensorflow/core/framework/register_types.h"
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#include "tensorflow/core/framework/tensor_shape.h"
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namespace tensorflow {
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namespace {
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class FillOp : public XlaOpKernel {
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public:
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explicit FillOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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// The output of this Op is a tensor of shape 'dims_shape' with each
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// element set to the scalar 'dims_literal'.
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const TensorShape dims_shape = ctx->InputShape("dims");
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const TensorShape value_shape = ctx->InputShape("value");
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OP_REQUIRES(ctx, TensorShapeUtils::IsVector(dims_shape),
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absl::InvalidArgumentError(
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absl::StrCat("dims must be a vector of int32, got shape ",
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dims_shape.DebugString())));
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OP_REQUIRES(
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ctx, TensorShapeUtils::IsScalar(value_shape),
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absl::InvalidArgumentError(absl::StrCat(
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"value must be a scalar, got shape ", value_shape.DebugString())));
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std::vector<int64_t> dims;
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OP_REQUIRES_OK(ctx,
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ctx->ConstantInputAsIntVector(
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"dims", &dims, xla::ValueInferenceMode::kUpperBound));
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std::vector<bool> dynamic_dims;
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OP_REQUIRES_OK(
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ctx, ctx->ResolveInputDynamismIntoPredVector("dims", &dynamic_dims));
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auto output = xla::Broadcast(ctx->Input("value"), dims);
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for (int64_t i = 0; i < dims.size(); ++i) {
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// If a dimension is dynamic, call set-dimension-size on the output.
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if (dynamic_dims[i]) {
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auto dynamic_dim_size = xla::Slice(ctx->Input(0), {i}, {i + 1}, {1});
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dynamic_dim_size = xla::Reshape(dynamic_dim_size, {});
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dynamic_dim_size = xla::ConvertElementType(dynamic_dim_size, xla::S32);
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output = xla::SetDimensionSize(output, dynamic_dim_size, i);
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}
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}
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ctx->SetOutput(0, output);
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}
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};
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REGISTER_XLA_OP(Name("Fill").CompileTimeConstantInput("dims"), FillOp);
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} // namespace
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} // namespace tensorflow
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