Some const declarations changed to constexpr
PiperOrigin-RevId: 307629520 Change-Id: I22dcc35701417a184b77791ec3efe4a2957251bc
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@ -38,7 +38,7 @@ namespace benchmark {
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struct Options {
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// kDefaultMicros specifies the default time to run the benchmark, and is used
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// if neither max_iters nor max_micros is set.
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static const int64 kDefaultMicros = 3000000;
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static constexpr int64 kDefaultMicros = 3000000;
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int64 max_iters = 0; // Maximum iterations to run, ignored if <= 0.
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int64 max_micros = 0; // Maximum microseconds to run, ignored if <= 0.
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@ -191,7 +191,7 @@ class EMBenchmarkHelper {
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public:
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// Length of tensors. TODO(tucker): make this a variable parameter.
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static const int kTDim = 1024;
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static constexpr int kTDim = 1024;
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int num_ops() const { return add_kernels_.size(); }
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size_t tensor_size() const {
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@ -694,8 +694,8 @@ class OpKernelContext {
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checkpoint::TensorSliceReaderCacheWrapper* slice_reader_cache = nullptr;
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// Support for forwarding reservations (used by ScopedAllocator).
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static const int kNeverForward = -2;
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static const int kNoReservation = -1;
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static constexpr int kNeverForward = -2;
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static constexpr int kNoReservation = -1;
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// Values in [0,...) represent reservations for the indexed output.
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const int* forward_from_array = nullptr;
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@ -74,7 +74,7 @@ class ShapeInferenceTest : public ::testing::Test {
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void TestMergeHandles(bool input_not_output);
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void TestRelaxHandles(bool input_not_output);
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static const int kVersion = 0; // used for graph-def version.
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static constexpr int kVersion = 0; // used for graph-def version.
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};
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TEST_F(ShapeInferenceTest, InputOutputByName) {
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@ -103,10 +103,10 @@ class TensorShapeRep {
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// We use the max value of uint16 or uint32 to represent unknown shapes, so
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// the maximum representable valid shape in these representations is one less.
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static const int64 kMaxRep16 = std::numeric_limits<uint16>::max() - 1;
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static const int64 kMaxRep32 = std::numeric_limits<uint32>::max() - 1;
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static const uint16 kUnknownRep16 = std::numeric_limits<uint16>::max();
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static const uint32 kUnknownRep32 = std::numeric_limits<uint32>::max();
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static constexpr int64 kMaxRep16 = std::numeric_limits<uint16>::max() - 1;
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static constexpr int64 kMaxRep32 = std::numeric_limits<uint32>::max() - 1;
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static constexpr uint16 kUnknownRep16 = std::numeric_limits<uint16>::max();
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static constexpr uint32 kUnknownRep32 = std::numeric_limits<uint32>::max();
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Rep16* as16() { return reinterpret_cast<Rep16*>(buf()); }
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Rep32* as32() { return reinterpret_cast<Rep32*>(buf()); }
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@ -134,7 +134,7 @@ class TensorShapeRep {
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// We store the number of dimensions in byte 14, and the RepTag in byte 15.
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// Bytes [0..13] vary depending on the representation.
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// A value of 255 indicates unknown rank in the PartialTensorShape case.
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static const uint8 kUnknownRank = 255;
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static constexpr uint8 kUnknownRank = 255;
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uint8 ndims_byte() const { return buf()[14]; }
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void set_ndims_byte(uint8 nd) { buf()[14] = nd; }
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@ -116,7 +116,7 @@ namespace internal {
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template <typename T>
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struct is_floating_point_type {
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static const bool value = std::is_same<T, Eigen::half>::value ||
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static constexpr bool value = std::is_same<T, Eigen::half>::value ||
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std::is_same<T, float>::value ||
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std::is_same<T, double>::value ||
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std::is_same<T, std::complex<float>>::value ||
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@ -26,10 +26,10 @@ namespace tensorflow {
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// Functions to define quantization attribute of types.
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struct true_type {
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static const bool value = true;
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static constexpr bool value = true;
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};
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struct false_type {
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static const bool value = false;
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static constexpr bool value = false;
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};
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// Default is_quantized is false.
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@ -42,7 +42,7 @@ struct PartitionOptions {
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// A function that returns the incarnation of a device given the
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// device's fullname. If not found, GetIncarnationFunc should return
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// kIllegalIncarnation.
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static const uint64 kIllegalIncarnation = 0;
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static constexpr uint64 kIllegalIncarnation = 0;
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typedef std::function<uint64(const string&)> GetIncarnationFunc;
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GetIncarnationFunc get_incarnation = nullptr;
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@ -101,7 +101,7 @@ class SubProcess {
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string* stderr_output);
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private:
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static const int kNFds = 3;
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static constexpr int kNFds = 3;
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static bool chan_valid(int chan) { return ((chan >= 0) && (chan < kNFds)); }
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static bool retry(int e) {
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return ((e == EINTR) || (e == EAGAIN) || (e == EWOULDBLOCK));
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@ -522,7 +522,7 @@ class FileStream : public ::tensorflow::protobuf::io::ZeroCopyInputStream {
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}
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private:
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static const int kBufSize = 512 << 10;
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static constexpr int kBufSize = 512 << 10;
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RandomAccessFile* file_;
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int64 pos_;
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@ -114,7 +114,7 @@ class TStringOutputStream : public protobuf::io::ZeroCopyOutputStream {
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int64_t ByteCount() const override;
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private:
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static const int kMinimumSize = 16;
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static constexpr int kMinimumSize = 16;
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tstring* target_;
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};
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@ -61,7 +61,7 @@ class TensorSliceReader {
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};
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typedef std::function<Status(const string&, Table**)> OpenTableFunction;
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static const int kLoadAllShards = -1;
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static constexpr int kLoadAllShards = -1;
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TensorSliceReader(const string& filepattern);
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TensorSliceReader(const string& filepattern, OpenTableFunction open_function);
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TensorSliceReader(const string& filepattern, OpenTableFunction open_function,
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@ -68,7 +68,7 @@ class TensorSliceWriter {
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static size_t MaxBytesPerElement(DataType dt);
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private:
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static const size_t kMaxMessageBytes = 1LL << 31;
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static constexpr size_t kMaxMessageBytes = 1LL << 31;
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// Filling in the TensorProto in a SavedSlice will add the following
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// header bytes, in addition to the data:
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// - 1 byte: TensorProto tag and wire format
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@ -77,7 +77,7 @@ class TensorSliceWriter {
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// - <= 5 bytes: *_val length
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// However, we add 1KB of slack, to be conservative and guard
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// against other additions to the TensorProto.
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static const size_t kTensorProtoHeaderBytes = 1 << 10;
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static constexpr size_t kTensorProtoHeaderBytes = 1 << 10;
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const string filename_;
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const CreateBuilderFunction create_builder_;
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@ -26,11 +26,11 @@ struct ModelHints {
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using ModelHint = uint64_t;
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// By default we want the fastest inference
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static const ModelHint kFastestInference = 0x00000000;
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static constexpr ModelHint kFastestInference = 0x00000000;
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// Can improve compilation time, but inference can be slower
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static const ModelHint kReduceKernelsCount = 0x00000001;
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static constexpr ModelHint kReduceKernelsCount = 0x00000001;
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// Can improve tuning time, but inference can be slower
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static const ModelHint kFastTuning = 0x00000002;
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static constexpr ModelHint kFastTuning = 0x00000002;
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void Add(ModelHint hint) {
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if (hint == kFastestInference) {
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@ -1094,7 +1094,7 @@ TEST(InterpreterTensorsCapacityTest, TestExceedHeadroom) {
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}
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struct TestExternalContext : public TfLiteExternalContext {
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static const TfLiteExternalContextType kType = kTfLiteGemmLowpContext;
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static constexpr TfLiteExternalContextType kType = kTfLiteGemmLowpContext;
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static TestExternalContext* Get(TfLiteContext* context) {
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return reinterpret_cast<TestExternalContext*>(
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@ -816,40 +816,40 @@ struct TypeUnion;
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template <>
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struct TypeUnion<float> {
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public:
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static const TensorType tensor_type = TensorType::TensorType_FLOAT32;
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static const TfLiteType tflite_type = TfLiteType::kTfLiteFloat32;
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static constexpr TensorType tensor_type = TensorType::TensorType_FLOAT32;
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static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteFloat32;
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typedef float ScalarType;
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};
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template <>
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struct TypeUnion<int32_t> {
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public:
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static const TensorType tensor_type = TensorType::TensorType_INT32;
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static const TfLiteType tflite_type = TfLiteType::kTfLiteInt32;
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static constexpr TensorType tensor_type = TensorType::TensorType_INT32;
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static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteInt32;
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typedef int32_t ScalarType;
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};
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template <>
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struct TypeUnion<int16_t> {
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public:
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static const TensorType tensor_type = TensorType::TensorType_INT16;
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static const TfLiteType tflite_type = TfLiteType::kTfLiteInt16;
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static constexpr TensorType tensor_type = TensorType::TensorType_INT16;
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static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteInt16;
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typedef int16_t ScalarType;
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};
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template <>
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struct TypeUnion<int8_t> {
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public:
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static const TensorType tensor_type = TensorType::TensorType_INT8;
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static const TfLiteType tflite_type = TfLiteType::kTfLiteInt8;
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static constexpr TensorType tensor_type = TensorType::TensorType_INT8;
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static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteInt8;
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typedef int8_t ScalarType;
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};
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template <>
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struct TypeUnion<uint8_t> {
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public:
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static const TensorType tensor_type = TensorType::TensorType_UINT8;
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static const TfLiteType tflite_type = TfLiteType::kTfLiteUInt8;
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static constexpr TensorType tensor_type = TensorType::TensorType_UINT8;
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static constexpr TfLiteType tflite_type = TfLiteType::kTfLiteUInt8;
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typedef uint8_t ScalarType;
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};
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@ -39,19 +39,19 @@ struct DeviceOptions {
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// this flag prevents it from ever being deallocated. Potentially saves
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// thrashing the thread stack memory allocation, but at the potential cost of
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// some memory space.
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static const unsigned kDoNotReclaimStackAllocation = 0x1;
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static constexpr unsigned kDoNotReclaimStackAllocation = 0x1;
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// The following options refer to synchronization options when
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// using SynchronizeStream or SynchronizeContext.
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// Synchronize with spinlocks.
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static const unsigned kScheduleSpin = 0x02;
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static constexpr unsigned kScheduleSpin = 0x02;
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// Synchronize with spinlocks that also call CPU yield instructions.
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static const unsigned kScheduleYield = 0x04;
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static constexpr unsigned kScheduleYield = 0x04;
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// Synchronize with a "synchronization primitive" (e.g. mutex).
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static const unsigned kScheduleBlockingSync = 0x08;
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static constexpr unsigned kScheduleBlockingSync = 0x08;
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static const unsigned kMask = 0xf; // Mask of all available flags.
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static constexpr unsigned kMask = 0xf; // Mask of all available flags.
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// Constructs an or-d together set of device options.
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explicit DeviceOptions(unsigned flags) : flags_(flags) {
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@ -39,10 +39,10 @@ namespace stream_executor {
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// memory for cudnn convolutions.
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class RedzoneAllocator : public ScratchAllocator {
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public:
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static const int64 kDefaultMemoryLimit = 1LL << 32; // 4GB
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static const int64 kDefaultRedzoneSize =
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static constexpr int64 kDefaultMemoryLimit = 1LL << 32; // 4GB
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static constexpr int64 kDefaultRedzoneSize =
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1LL << 23; // 8MiB per side, 16MiB total.
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static const uint8 kDefaultRedzonePattern = -1;
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static constexpr uint8 kDefaultRedzonePattern = -1;
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RedzoneAllocator(Stream* stream, DeviceMemoryAllocator* memory_allocator,
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GpuAsmOpts gpu_compilation_opts_,
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int64 memory_limit = kDefaultMemoryLimit,
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@ -40,8 +40,8 @@ namespace rng {
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// thread-hostility.
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class RngSupport {
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public:
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static const int kMinSeedBytes = 16;
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static const int kMaxSeedBytes = INT_MAX;
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static constexpr int kMinSeedBytes = 16;
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static constexpr int kMaxSeedBytes = INT_MAX;
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// Releases any random-number-generation resources associated with this
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// support object in the underlying platform implementation.
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@ -723,7 +723,7 @@ class StreamExecutor {
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// Only one worker thread is needed; little work will be done by the
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// executor.
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static const int kNumBackgroundThreads = 1;
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static constexpr int kNumBackgroundThreads = 1;
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// Indicates if StreamExecutor operation tracing should be performed.
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bool tracing_enabled_;
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