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// See docs in ../ops/array_ops.cc.
#define EIGEN_USE_THREADS
#if GOOGLE_CUDA
#define EIGEN_USE_GPU
#endif // GOOGLE_CUDA
#include "tensorflow/core/kernels/reverse_sequence_op.h"
#include <memory>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/public/tensor_shape.h"
#include "tensorflow/core/public/tensor.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
template <typename Device>
void CheckErrors(OpKernelContext* context, int seq_dim) {
const Tensor& input = context->input(0);
const Tensor& seq_lens = context->input(1);
auto seq_lens_t = seq_lens.vec<int64>();
std::vector<int64> seq_lens_vec(seq_lens_t.size());
// Copy seq_len info down for validity checks
context->eigen_device<Device>().memcpyDeviceToHost(
seq_lens_vec.data(), seq_lens_t.data(),
sizeof(int64) * seq_lens_t.size());
OP_REQUIRES(context, 0 != seq_dim, errors::InvalidArgument("0 == seq_dim"));
OP_REQUIRES(context, seq_dim < input.dims(),
errors::InvalidArgument("seq_dim must be < input.dims()", "( ",
seq_dim, " vs. ", input.dims(), ")"));
OP_REQUIRES(context, seq_lens.NumElements() == input.dim_size(0),
errors::InvalidArgument("len(seq_lens) != input.dims(", 0, "), ",
"(", seq_lens.NumElements(), " vs. ",
input.dim_size(seq_dim)));
for (int d = 0; d < seq_lens_vec.size(); ++d) {
OP_REQUIRES(context, seq_lens_vec[d] >= 0,
errors::InvalidArgument("seq_lens(", d, ") < 0"));
OP_REQUIRES(context, seq_lens_vec[d] <= input.dim_size(seq_dim),
errors::InvalidArgument("seq_lens(", d, ") > input.dims(",
seq_dim, ")"));
}
}
template <>
void CheckErrors<GPUDevice>(OpKernelContext* context, int seq_dim) {
const Tensor& input = context->input(0);
const Tensor& seq_lens = context->input(1);
OP_REQUIRES(context, 0 != seq_dim, errors::InvalidArgument("0 == seq_dim"));
OP_REQUIRES(context, seq_dim < input.dims(),
errors::InvalidArgument("seq_dim must be < input.dims()", "( ",
seq_dim, " vs. ", input.dims(), ")"));
OP_REQUIRES(context, seq_lens.NumElements() == input.dim_size(0),
errors::InvalidArgument("len(seq_lens) != input.dims(", 0, "), ",
"(", seq_lens.NumElements(), " vs. ",
input.dim_size(seq_dim)));
}
template <typename Device, typename T>
class ReverseSequenceOp : public OpKernel {
public:
explicit ReverseSequenceOp(OpKernelConstruction* context)
: OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("seq_dim", &seq_dim_));
}
void Compute(OpKernelContext* context) override {
const Tensor& input = context->input(0);
const Tensor& seq_lens = context->input(1);
// Preliminary validation of sizes.
OP_REQUIRES(context, TensorShapeUtils::IsVector(seq_lens.shape()),
errors::InvalidArgument("seq_lens input must be 1-dim, not ",
seq_lens.dims()));
auto seq_lens_t = seq_lens.vec<int64>();
CheckErrors<Device>(context, seq_dim_);
const int input_dims = input.dims();
Tensor* output = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, input.shape(), &output));
#define HANDLE_DIM(NDIM) \
case NDIM: \
functor::ReverseSequence<Device, T, NDIM>::Compute( \
context->eigen_device<Device>(), input.tensor<T, NDIM>(), seq_dim_, \
seq_lens_t, output->tensor<T, NDIM>()); \
break;
switch (input_dims) {
HANDLE_DIM(2);
HANDLE_DIM(3);
HANDLE_DIM(4);
HANDLE_DIM(5);
default:
OP_REQUIRES(context, false,
errors::InvalidArgument(
"ReverseSequenceOp : Unhandled input dimensions: ",
input_dims));
}
}
private:
int32 seq_dim_;
TF_DISALLOW_COPY_AND_ASSIGN(ReverseSequenceOp);
};
#define REGISTER_REVERSE_SEQUENCE(type) \
REGISTER_KERNEL_BUILDER( \
Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
ReverseSequenceOp<CPUDevice, type>);
TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE);
#if GOOGLE_CUDA
// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC(T, Dims) \
template <> \
void ReverseSequence<GPUDevice, T, Dims>::Compute( \
const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input, \
int32 seq_dim, TTypes<int64>::ConstVec seq_lens, \
typename TTypes<T, Dims>::Tensor output); \
extern template struct ReverseSequence<GPUDevice, T, Dims>;
#define DECLARE_GPU_SPECS(T) \
DECLARE_GPU_SPEC(T, 2); \
DECLARE_GPU_SPEC(T, 3); \
DECLARE_GPU_SPEC(T, 4); \
DECLARE_GPU_SPEC(T, 5);
TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS);
} // namespace functor
// Registration of the GPU implementations.
#define REGISTER_REVERSE_SEQUENCE_GPU(type) \
REGISTER_KERNEL_BUILDER( \
Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
ReverseSequenceOp<GPUDevice, type>);
TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU);
#undef REGISTER_REVERSE_SEQUENCE_GPU
#endif // GOOGLE_CUDA
} // namespace tensorflow
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