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82 lines
2.7 KiB
C++
82 lines
2.7 KiB
C++
#include "tensorflow/core/framework/op.h"
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#include "tensorflow/core/framework/shape_inference.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include <twml.h>
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#include "tensorflow_utils.h"
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using namespace tensorflow;
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REGISTER_OP("BatchPredictionTensorResponseWriter")
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.Attr("T: list({string, int32, int64, float, double})")
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.Input("keys: int64")
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.Input("values: T")
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.Output("result: uint8")
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.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
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return Status::OK();
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}).Doc(R"doc(
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A tensorflow OP that packages keys and dense tensors into a BatchPredictionResponse.
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values: list of tensors
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keys: feature ids from the original BatchPredictionRequest. (int64)
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Outputs
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bytes: output BatchPredictionRequest serialized using Thrift into a uint8 tensor.
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)doc");
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class BatchPredictionTensorResponseWriter : public OpKernel {
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public:
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explicit BatchPredictionTensorResponseWriter(OpKernelConstruction* context)
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: OpKernel(context) {}
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void Compute(OpKernelContext* context) override {
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const Tensor& keys = context->input(0);
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try {
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// set keys as twml::Tensor
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const twml::Tensor in_keys_ = TFTensor_to_twml_tensor(keys);
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// check sizes
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uint64_t num_keys = in_keys_.getNumElements();
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uint64_t num_values = context->num_inputs() - 1;
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OP_REQUIRES(context, num_values % num_keys == 0,
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errors::InvalidArgument("Number of dense tensors not multiple of dense keys"));
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// set dense tensor values
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std::vector<twml::RawTensor> in_values_;
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for (int i = 1; i < context->num_inputs(); i++) {
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in_values_.push_back(TFTensor_to_twml_raw_tensor(context->input(i)));
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}
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// no continuous predictions in this op, only tensors
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const twml::Tensor dummy_cont_keys_;
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const twml::Tensor dummy_cont_values_;
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// call constructor BatchPredictionResponse
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twml::BatchPredictionResponse tempResult(
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dummy_cont_keys_, dummy_cont_values_, in_keys_, in_values_);
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// determine the length of the result
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int len = tempResult.encodedSize();
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TensorShape result_shape = {1, len};
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// Create an output tensor, the size is determined by the content of input.
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Tensor* result = NULL;
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OP_REQUIRES_OK(context, context->allocate_output(0, result_shape,
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&result));
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twml::Tensor out_result = TFTensor_to_twml_tensor(*result);
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// Call writer of BatchPredictionResponse
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tempResult.write(out_result);
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} catch(const std::exception &e) {
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context->CtxFailureWithWarning(errors::InvalidArgument(e.what()));
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}
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}
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};
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REGISTER_KERNEL_BUILDER(
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Name("BatchPredictionTensorResponseWriter").Device(DEVICE_CPU),
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BatchPredictionTensorResponseWriter);
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