Removes ArrayFingerprint op.

PiperOrigin-RevId: 258815969
Change-Id: Ibfaec0be0bbf92dce7a1e0e0c2b92f70813b549f

Author: Johannes Ball?

tensorflow_compression/cc/kernels/range_coding_helper_kernels.cc

@@ -31,7 +31,6 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/types.h"
-#include "external/farmhash_archive/src/farmhash.h"
 
 namespace tensorflow_compression {
 namespace {
@@ -207,30 +206,5 @@ class PmfToCdfOp : public OpKernel {
 REGISTER_KERNEL_BUILDER(Name("PmfToQuantizedCdf").Device(DEVICE_CPU),
                         PmfToCdfOp);
 
-class ArrayFingerprintOp : public tensorflow::OpKernel {
- public:
-  using OpKernel::OpKernel;
-
-  void Compute(tensorflow::OpKernelContext* context) override {
-    const Tensor& input = context->input(0);
-    OP_REQUIRES(context, tensorflow::DataTypeCanUseMemcpy(input.dtype()),
-                InvalidArgument("Data type not supported: ",
-                                tensorflow::DataTypeString(input.dtype())));
-
-    const int64 size =
-        input.shape().num_elements() * tensorflow::DataTypeSize(input.dtype());
-    auto data = input.bit_casted_shaped<char, 1>({size});
-
-    Tensor* output;
-    OP_REQUIRES_OK(context,
-                   context->allocate_output(0, TensorShape{}, &output));
-
-    output->scalar<int64>()() =
-        ::util::Fingerprint64(data.data(), static_cast<size_t>(data.size()));
-  }
-};
-
-REGISTER_KERNEL_BUILDER(Name("ArrayFingerprint").Device(tensorflow::DEVICE_CPU),
-                        ArrayFingerprintOp);
 }  // namespace
 }  // namespace tensorflow_compression

tensorflow_compression/cc/kernels/range_coding_helper_kernels_test.cc

@@ -150,62 +150,6 @@ TEST_F(PmfToQuantizedCdfOpTest, ShapeFn) {
   INFER_OK(op, "[3,4,5]", "[d0_0,d0_1,6]");
 }
 
-class FingerprintOpTest : public tensorflow::OpsTestBase {
- protected:
-  void MakeFingerprintOp(Tensor* tensor) {
-    TF_ASSERT_OK(tensorflow::NodeDefBuilder("fingerprint", "ArrayFingerprint")
-                     .Input(tensorflow::FakeInput(tensor->dtype()))
-                     .Finalize(node_def()));
-    TF_ASSERT_OK(InitOp());
-
-    inputs_.clear();
-    inputs_.emplace_back(tensor);
-  }
-};
-
-TEST_F(FingerprintOpTest, Verify) {
-  std::random_device rd;
-  random::PhiloxRandom gen(rd(), rd());
-  random::SimplePhilox rand(&gen);
-  for (tensorflow::DataType dtype : tensorflow::kRealNumberTypes) {
-    const int rank = rand.Uniform(4);
-
-    TensorShape shape;
-    for (int i = 0; i < rank; ++i) {
-      shape.AddDim(rand.Uniform(9) + 1);
-    }
-
-    Tensor tensor(dtype, shape);
-
-    const int64 length = shape.num_elements() * tensorflow::DataTypeSize(dtype);
-    auto buffer = tensor.bit_casted_shaped<char, 1>({length});
-    buffer.setRandom();
-
-    MakeFingerprintOp(&tensor);
-    TF_ASSERT_OK(RunOpKernel());
-    Tensor fingerprint0 = *GetOutput(0);
-
-    // Change one byte in the buffer.
-    const int64 pos = rand.Uniform(length);
-    buffer(pos) = ~buffer(pos);
-
-    MakeFingerprintOp(&tensor);
-    TF_ASSERT_OK(RunOpKernel());
-    Tensor fingerprint1 = *GetOutput(0);
-
-    EXPECT_NE(fingerprint0.tensor_data(), fingerprint1.tensor_data());
-  }
-}
-
-TEST_F(FingerprintOpTest, FingerprintShapeFn) {
-  tensorflow::ShapeInferenceTestOp op("ArrayFingerprint");
-
-  INFER_OK(op, "?", "[]");
-  INFER_OK(op, "[]", "[]");
-  INFER_OK(op, "[1]", "[]");
-  INFER_OK(op, "[1,2]", "[]");
-  INFER_OK(op, "[1,2,3]", "[]");
-}
 }  // namespace
 }  // namespace tensorflow_compression
 

tensorflow_compression/cc/ops/range_coding_ops.cc

@@ -270,16 +270,5 @@ by this op prior to quantization. Therefore the user is responsible for
 normalizing PMF if necessary.
 )doc");
 
-REGISTER_OP("ArrayFingerprint")
-    .Input("input: T")
-    .Output("fingerprint: int64")
-    .Attr("T: realnumbertype")
-    .SetShapeFn(shape_inference::ScalarShape)
-    .Doc(R"doc(
-Produces fingerprint of the input data.
-
-input: Tensor to be fingerprinted.
-fingerprint: Fingerprint value of input.
-)doc");
 }  // namespace
 }  // namespace tensorflow_compression