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// See docs in ../ops/data_flow_ops.cc.
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/public/tensor.h"
namespace tensorflow {
// Shared code that is not dependent on the type of T. We do this to reduce
// code size by not duplicating all this for all T (float, double, int32, etc.)
class DynamicPartitionOp_Shared : public OpKernel {
public:
explicit DynamicPartitionOp_Shared(OpKernelConstruction* c) : OpKernel(c) {
OP_REQUIRES_OK(c, c->GetAttr("num_partitions", &num_partitions_));
// QUESTION: It'd be nice to support DT_INT16, DT_UINT8, etc.
// to input[1]. Should we have the framework do some sort of
// integer promotion automatically, or should that be something
// that users have to do explicitly with a conversion operator
// in the graph?
}
void ValidateAndAllocateOutputs(OpKernelContext* c, const Tensor** data,
const Tensor** partitions,
OpOutputList* Tout) {
OP_REQUIRES_OK(c, c->input("data", data));
OP_REQUIRES_OK(c, c->input("partitions", partitions));
OP_REQUIRES(c, TensorShapeUtils::StartsWith((*data)->shape(),
(*partitions)->shape()),
errors::InvalidArgument(
"data.shape must start with partitions.shape, ",
"got data.shape = ", (*data)->shape().ShortDebugString(),
", partitions.shape = ",
(*partitions)->shape().ShortDebugString()));
// Count how many occurrences of each partition id we have in partitions
gtl::InlinedVector<int, 32> partition_count(num_partitions_);
auto e_partitions = (*partitions)->flat<int32>();
const int64 N = e_partitions.dimension(0);
for (int64 i = 0; i < N; i++) {
const int32 p = e_partitions(i);
OP_REQUIRES(c, p >= 0 && p < num_partitions_,
errors::InvalidArgument(
"partitions", SliceString((*partitions)->shape(), i),
" = ", p, " is not in [0, ", num_partitions_, ")"));
partition_count[p]++;
}
// Allocate output tensors of the right size
OP_REQUIRES_OK(c, c->output_list("outputs", Tout));
for (int p = 0; p < num_partitions_; p++) {
TensorShape shape;
shape.AddDim(partition_count[p]);
for (int i = (*partitions)->dims(); i < (*data)->dims(); i++) {
shape.AddDim((*data)->dim_size(i));
}
Tensor* out;
OP_REQUIRES_OK(c, Tout->allocate(p, shape, &out));
}
}
protected:
int num_partitions_;
static string SliceString(const TensorShape& shape, const int64 flat) {
// Special case rank 0 and 1
const int dims = shape.dims();
if (dims == 0) return "";
if (dims == 1) return strings::StrCat("[", flat, "]");
// Compute strides
gtl::InlinedVector<int64, 32> strides(dims);
strides.back() = 1;
for (int i = dims - 2; i >= 0; i--) {
strides[i] = strides[i + 1] * shape.dim_size(i + 1);
}
// Unflatten index
int64 left = flat;
string result;
for (int i = 0; i < dims; i++) {
strings::StrAppend(&result, i ? "," : "[", left / strides[i]);
left %= strides[i];
}
strings::StrAppend(&result, "]");
return result;
}
};
template <class T>
class DynamicPartitionOp : public DynamicPartitionOp_Shared {
public:
explicit DynamicPartitionOp(OpKernelConstruction* c)
: DynamicPartitionOp_Shared(c) {}
void Compute(OpKernelContext* c) override {
const Tensor* data;
const Tensor* partitions;
OpOutputList outputs;
ValidateAndAllocateOutputs(c, &data, &partitions, &outputs);
if (!c->status().ok()) return;
if (num_partitions_ == 0 || data->NumElements() == 0) return;
auto e_partitions = partitions->flat<int32>();
const int64 N = e_partitions.dimension(0);
gtl::InlinedVector<int, 32> output_index(num_partitions_);
if (partitions->dims() == data->dims()) {
// Walk through data and copy the data to the appropriate output tensor
const auto data_flat = data->flat<T>();
std::vector<Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>,
Eigen::Aligned> > out_vec;
for (int p = 0; p < num_partitions_; p++) {
out_vec.push_back(outputs[p]->vec<T>());
}
for (int64 i = 0; i < N; i++) {
const int32 p = e_partitions(i);
out_vec[p](output_index[p]) = data_flat(i);
output_index[p]++;
}
} else {
// If data has extra dimensions, use Eigen slices
std::vector<Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor>,
Eigen::Aligned> > out_flat;
for (int p = 0; p < num_partitions_; p++) {
out_flat.push_back(outputs[p]->flat_outer_dims<T>());
}
// Walk through data and copy the data to the appropriate output tensor
const int64 slice_size = data->NumElements() / N;
const auto data_flat = data->shaped<T, 2>({N, slice_size});
Eigen::DSizes<Eigen::DenseIndex, 2> sizes(1, slice_size);
for (int64 i = 0; i < N; i++) {
const int32 p = e_partitions(i);
// outputs[p][output_index[p]++] = data[i]
Eigen::DSizes<Eigen::DenseIndex, 2> out_indices(output_index[p], 0);
Eigen::DSizes<Eigen::DenseIndex, 2> data_indices(i, 0);
out_flat[p].slice(out_indices, sizes) =
data_flat.slice(data_indices, sizes);
output_index[p]++;
}
}
}
};
#define REGISTER_DYNAMIC_PARTITION(T) \
REGISTER_KERNEL_BUILDER( \
Name("DynamicPartition").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
DynamicPartitionOp<T>)
TF_CALL_ALL_TYPES(REGISTER_DYNAMIC_PARTITION);
#undef REGISTER_DYNAMIC_PARTITION
} // namespace tensorflow
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