normalization_generated.h

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// automatically generated by the FlatBuffers compiler, do not modify
 
 
#ifndef FLATBUFFERS_GENERATED_NORMALIZATION_TT_TARGET_TTNN_H_
#define FLATBUFFERS_GENERATED_NORMALIZATION_TT_TARGET_TTNN_H_
 
#include "flatbuffers/flatbuffers.h"
 
// Ensure the included flatbuffers.h is the same version as when this file was
// generated, otherwise it may not be compatible.
static_assert(FLATBUFFERS_VERSION_MAJOR == 24 &&
              FLATBUFFERS_VERSION_MINOR == 3 &&
              FLATBUFFERS_VERSION_REVISION == 25,
             "Non-compatible flatbuffers version included");
 
#include "ttmlir/Target/Common/types_generated.h"
#include "ttmlir/Target/TTNN/types_generated.h"
 
namespace tt {
namespace target {
namespace ttnn {
 
struct SoftmaxOp;
struct SoftmaxOpBuilder;
 
struct BatchNormOp;
struct BatchNormOpBuilder;
 
struct SoftmaxOp FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
  typedef SoftmaxOpBuilder Builder;
  struct Traits;
  static FLATBUFFERS_CONSTEXPR_CPP11 const char *GetFullyQualifiedName() {
    return "tt.target.ttnn.SoftmaxOp";
  }
  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
    VT_IN = 4,
    VT_OUT = 6,
    VT_DIMENSION = 8
  };
  const tt::target::ttnn::TensorRef *in() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_IN);
  }
  const tt::target::ttnn::TensorRef *out() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_OUT);
  }
  int32_t dimension() const {
    return GetField<int32_t>(VT_DIMENSION, 0);
  }
  bool Verify(::flatbuffers::Verifier &verifier) const {
    return VerifyTableStart(verifier) &&
           VerifyOffset(verifier, VT_IN) &&
           verifier.VerifyTable(in()) &&
           VerifyOffset(verifier, VT_OUT) &&
           verifier.VerifyTable(out()) &&
           VerifyField<int32_t>(verifier, VT_DIMENSION, 4) &&
           verifier.EndTable();
  }
};
 
struct SoftmaxOpBuilder {
  typedef SoftmaxOp Table;
  ::flatbuffers::FlatBufferBuilder &fbb_;
  ::flatbuffers::uoffset_t start_;
  void add_in(::flatbuffers::Offset<tt::target::ttnn::TensorRef> in) {
    fbb_.AddOffset(SoftmaxOp::VT_IN, in);
  }
  void add_out(::flatbuffers::Offset<tt::target::ttnn::TensorRef> out) {
    fbb_.AddOffset(SoftmaxOp::VT_OUT, out);
  }
  void add_dimension(int32_t dimension) {
    fbb_.AddElement<int32_t>(SoftmaxOp::VT_DIMENSION, dimension, 0);
  }
  explicit SoftmaxOpBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
        : fbb_(_fbb) {
    start_ = fbb_.StartTable();
  }
  ::flatbuffers::Offset<SoftmaxOp> Finish() {
    const auto end = fbb_.EndTable(start_);
    auto o = ::flatbuffers::Offset<SoftmaxOp>(end);
    return o;
  }
};
 
inline ::flatbuffers::Offset<SoftmaxOp> CreateSoftmaxOp(
    ::flatbuffers::FlatBufferBuilder &_fbb,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> in = 0,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> out = 0,
    int32_t dimension = 0) {
  SoftmaxOpBuilder builder_(_fbb);
  builder_.add_dimension(dimension);
  builder_.add_out(out);
  builder_.add_in(in);
  return builder_.Finish();
}
 
struct SoftmaxOp::Traits {
  using type = SoftmaxOp;
  static auto constexpr Create = CreateSoftmaxOp;
};
 
struct BatchNormOp FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table {
  typedef BatchNormOpBuilder Builder;
  struct Traits;
  static FLATBUFFERS_CONSTEXPR_CPP11 const char *GetFullyQualifiedName() {
    return "tt.target.ttnn.BatchNormOp";
  }
  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
    VT_INPUT = 4,
    VT_RUNNING_MEAN = 6,
    VT_RUNNING_VAR = 8,
    VT_TRAINING = 10,
    VT_EPSILON = 12,
    VT_MOMENTUM = 14,
    VT_WEIGHT = 16,
    VT_BIAS = 18,
    VT_MEMORY_CONFIG = 20,
    VT_OUT = 22
  };
  const tt::target::ttnn::TensorRef *input() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_INPUT);
  }
  const tt::target::ttnn::TensorRef *running_mean() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_RUNNING_MEAN);
  }
  const tt::target::ttnn::TensorRef *running_var() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_RUNNING_VAR);
  }
  bool training() const {
    return GetField<uint8_t>(VT_TRAINING, 0) != 0;
  }
  float epsilon() const {
    return GetField<float>(VT_EPSILON, 0.0f);
  }
  float momentum() const {
    return GetField<float>(VT_MOMENTUM, 0.0f);
  }
  const tt::target::ttnn::TensorRef *weight() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_WEIGHT);
  }
  const tt::target::ttnn::TensorRef *bias() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_BIAS);
  }
  const tt::target::ttnn::MemoryConfig *memory_config() const {
    return GetPointer<const tt::target::ttnn::MemoryConfig *>(VT_MEMORY_CONFIG);
  }
  const tt::target::ttnn::TensorRef *out() const {
    return GetPointer<const tt::target::ttnn::TensorRef *>(VT_OUT);
  }
  bool Verify(::flatbuffers::Verifier &verifier) const {
    return VerifyTableStart(verifier) &&
           VerifyOffset(verifier, VT_INPUT) &&
           verifier.VerifyTable(input()) &&
           VerifyOffset(verifier, VT_RUNNING_MEAN) &&
           verifier.VerifyTable(running_mean()) &&
           VerifyOffset(verifier, VT_RUNNING_VAR) &&
           verifier.VerifyTable(running_var()) &&
           VerifyField<uint8_t>(verifier, VT_TRAINING, 1) &&
           VerifyField<float>(verifier, VT_EPSILON, 4) &&
           VerifyField<float>(verifier, VT_MOMENTUM, 4) &&
           VerifyOffset(verifier, VT_WEIGHT) &&
           verifier.VerifyTable(weight()) &&
           VerifyOffset(verifier, VT_BIAS) &&
           verifier.VerifyTable(bias()) &&
           VerifyOffset(verifier, VT_MEMORY_CONFIG) &&
           verifier.VerifyTable(memory_config()) &&
           VerifyOffset(verifier, VT_OUT) &&
           verifier.VerifyTable(out()) &&
           verifier.EndTable();
  }
};
 
struct BatchNormOpBuilder {
  typedef BatchNormOp Table;
  ::flatbuffers::FlatBufferBuilder &fbb_;
  ::flatbuffers::uoffset_t start_;
  void add_input(::flatbuffers::Offset<tt::target::ttnn::TensorRef> input) {
    fbb_.AddOffset(BatchNormOp::VT_INPUT, input);
  }
  void add_running_mean(::flatbuffers::Offset<tt::target::ttnn::TensorRef> running_mean) {
    fbb_.AddOffset(BatchNormOp::VT_RUNNING_MEAN, running_mean);
  }
  void add_running_var(::flatbuffers::Offset<tt::target::ttnn::TensorRef> running_var) {
    fbb_.AddOffset(BatchNormOp::VT_RUNNING_VAR, running_var);
  }
  void add_training(bool training) {
    fbb_.AddElement<uint8_t>(BatchNormOp::VT_TRAINING, static_cast<uint8_t>(training), 0);
  }
  void add_epsilon(float epsilon) {
    fbb_.AddElement<float>(BatchNormOp::VT_EPSILON, epsilon, 0.0f);
  }
  void add_momentum(float momentum) {
    fbb_.AddElement<float>(BatchNormOp::VT_MOMENTUM, momentum, 0.0f);
  }
  void add_weight(::flatbuffers::Offset<tt::target::ttnn::TensorRef> weight) {
    fbb_.AddOffset(BatchNormOp::VT_WEIGHT, weight);
  }
  void add_bias(::flatbuffers::Offset<tt::target::ttnn::TensorRef> bias) {
    fbb_.AddOffset(BatchNormOp::VT_BIAS, bias);
  }
  void add_memory_config(::flatbuffers::Offset<tt::target::ttnn::MemoryConfig> memory_config) {
    fbb_.AddOffset(BatchNormOp::VT_MEMORY_CONFIG, memory_config);
  }
  void add_out(::flatbuffers::Offset<tt::target::ttnn::TensorRef> out) {
    fbb_.AddOffset(BatchNormOp::VT_OUT, out);
  }
  explicit BatchNormOpBuilder(::flatbuffers::FlatBufferBuilder &_fbb)
        : fbb_(_fbb) {
    start_ = fbb_.StartTable();
  }
  ::flatbuffers::Offset<BatchNormOp> Finish() {
    const auto end = fbb_.EndTable(start_);
    auto o = ::flatbuffers::Offset<BatchNormOp>(end);
    return o;
  }
};
 
inline ::flatbuffers::Offset<BatchNormOp> CreateBatchNormOp(
    ::flatbuffers::FlatBufferBuilder &_fbb,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> input = 0,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> running_mean = 0,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> running_var = 0,
    bool training = false,
    float epsilon = 0.0f,
    float momentum = 0.0f,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> weight = 0,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> bias = 0,
    ::flatbuffers::Offset<tt::target::ttnn::MemoryConfig> memory_config = 0,
    ::flatbuffers::Offset<tt::target::ttnn::TensorRef> out = 0) {
  BatchNormOpBuilder builder_(_fbb);
  builder_.add_out(out);
  builder_.add_memory_config(memory_config);
  builder_.add_bias(bias);
  builder_.add_weight(weight);
  builder_.add_momentum(momentum);
  builder_.add_epsilon(epsilon);
  builder_.add_running_var(running_var);
  builder_.add_running_mean(running_mean);
  builder_.add_input(input);
  builder_.add_training(training);
  return builder_.Finish();
}
 
struct BatchNormOp::Traits {
  using type = BatchNormOp;
  static auto constexpr Create = CreateBatchNormOp;
};
 
}  // namespace ttnn
}  // namespace target
}  // namespace tt
 
#endif  // FLATBUFFERS_GENERATED_NORMALIZATION_TT_TARGET_TTNN_H_