73 lines
3.0 KiB
C++
73 lines
3.0 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
|
|
// XLA-specific Fill Op.
|
|
|
|
#include "tensorflow/compiler/tf2xla/type_util.h"
|
|
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
|
|
#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
|
|
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
|
|
#include "tensorflow/compiler/xla/client/xla_builder.h"
|
|
#include "tensorflow/core/framework/kernel_def_builder.h"
|
|
#include "tensorflow/core/framework/register_types.h"
|
|
#include "tensorflow/core/framework/tensor_shape.h"
|
|
|
|
namespace tensorflow {
|
|
namespace {
|
|
|
|
class FillOp : public XlaOpKernel {
|
|
public:
|
|
explicit FillOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
|
|
|
|
void Compile(XlaOpKernelContext* ctx) override {
|
|
// The output of this Op is a tensor of shape 'dims_shape' with each
|
|
// element set to the scalar 'dims_literal'.
|
|
const TensorShape dims_shape = ctx->InputShape("dims");
|
|
const TensorShape value_shape = ctx->InputShape("value");
|
|
OP_REQUIRES(
|
|
ctx, TensorShapeUtils::IsVector(dims_shape),
|
|
errors::InvalidArgument("dims must be a vector of int32, got shape ",
|
|
dims_shape.DebugString()));
|
|
OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(value_shape),
|
|
errors::InvalidArgument("value must be a scalar, got shape ",
|
|
value_shape.DebugString()));
|
|
|
|
std::vector<int64> dims;
|
|
OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector("dims", &dims));
|
|
// Set dynamic dimension value to -1 so that we know which dimension is
|
|
// dynamic.
|
|
ctx->set_dynamic_dimension_is_minus_one(true);
|
|
std::vector<int64> dynamic_dims;
|
|
OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector("dims", &dynamic_dims));
|
|
|
|
auto output = xla::Broadcast(ctx->Input("value"), dims);
|
|
for (int64 i = 0; i < dims.size(); ++i) {
|
|
// If a dimension is dynamic, call set-dimension-size on the output.
|
|
if (dynamic_dims[i] == -1) {
|
|
auto dynamic_dim_size = xla::Slice(ctx->Input(0), {i}, {i + 1}, {1});
|
|
dynamic_dim_size = xla::Reshape(dynamic_dim_size, {});
|
|
dynamic_dim_size = xla::ConvertElementType(dynamic_dim_size, xla::S32);
|
|
output = xla::SetDimensionSize(output, dynamic_dim_size, i);
|
|
}
|
|
}
|
|
ctx->SetOutput(0, output);
|
|
}
|
|
};
|
|
|
|
REGISTER_XLA_OP(Name("Fill").CompileTimeConstantInput("dims"), FillOp);
|
|
|
|
} // namespace
|
|
} // namespace tensorflow
|