#include <ATen/Config.h>

#include <ATen/Context.h>

#include <c10/core/CPUAllocator.h>
#include <c10/util/Logging.h>

#include <algorithm>
#include <array>
#include <cctype>
#include <stdexcept>
#include <string>

#include <ATen/cpu/FlushDenormal.h>

#ifdef USE_FBGEMM
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wextra-semi")
#include <fbgemm/Fbgemm.h>
C10_DIAGNOSTIC_POP()
#endif // USE_FBGEMM
#if defined(__aarch64__) && !defined(C10_MOBILE)
#include <cpuinfo.h>
#endif
namespace at {

namespace {

/*
  These const variables defined the fp32 precisions for different backend
  We have "generic", "cuda", "mkldnn" backend now and we can choose fp32
  prevision from "ieee", "tf32", "bf16" and "none". The "ieee" precision means
  IEEE standard floating point format, "tf32" and "bf16" means we are allowed to
  use "tf32" or "bf16" as internal computation data types for fp32 computations.
  And "none" means it is override-able by parent's node

  generic->mkldnn->matmul
                ->conv
                ->rnn
         ->cuda ->matmul
                ->conv
                ->rnn
*/
const std::map<std::string, std::vector<std::string>> _fp32_precisions = {
    {"generic", {{"ieee", "tf32", "bf16", "none"}}},
    {"mkldnn", {{"ieee", "tf32", "bf16", "none"}}},
    {"cuda", {{"ieee", "tf32", "none"}}}};

// Check whether the backend and op are legal
void check_fp32_prec_backend_and_op(
    const std::string& backend,
    const std::string& op) {
  static std::vector<std::string> backends = {"generic", "mkldnn", "cuda"};
  static std::vector<std::string> operators = {"conv", "matmul", "rnn", "all"};
  TORCH_CHECK(
      std::find(backends.begin(), backends.end(), backend) != backends.end(),
      "Invalid backend: ",
      backend);
  TORCH_CHECK(
      std::find(operators.begin(), operators.end(), op) != operators.end(),
      "Invalid operator: ",
      op);
  if (backend == "generic") {
    TORCH_CHECK(op == "all", "Invalid operation for generic backend: ", op);
  }
  }

  // Return whether the precision is supported by backends
  bool validate_fp32_prec(
      const std::string& backend,
      const std::string& precision) {
    auto iterp = _fp32_precisions.find(backend);
    TORCH_CHECK(iterp != _fp32_precisions.end());
    auto precisions = iterp->second;
    bool valid = std::find(precisions.begin(), precisions.end(), precision) !=
        precisions.end();
    return valid;
  }

  C10_ALWAYS_INLINE void warn_deprecated_fp32_precision_api(){
    TORCH_WARN_ONCE(
      "Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' "
      "or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, "
      "torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see "
      "https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices"
    );
  }
} // namespace

Context::Context() = default;

// TODO: This could be bad juju if someone calls globalContext() in the
// destructor of an object with static lifetime.
Context& globalContext() {
  static Context globalContext_;
  return globalContext_;
}

// NB: This method is *purely* whether or not a user requested
// that CuDNN was enabled, it doesn't actually say anything about
// whether or not CuDNN is actually usable.
bool Context::userEnabledCuDNN() const {
  return enabled_cudnn;
}

void Context::setUserEnabledCuDNN(bool e) {
  enabled_cudnn = e;
}

bool Context::userEnabledMkldnn() const {
  return enabled_mkldnn;
}

void Context::setUserEnabledMkldnn(bool e) {
  enabled_mkldnn = e;
}

bool Context::deterministicCuDNN() const {
  return deterministic_cudnn;
}

void Context::setDeterministicCuDNN(bool b) {
  deterministic_cudnn = b;
}

bool Context::deterministicMkldnn() const {
  return deterministic_mkldnn;
}

void Context::setDeterministicMkldnn(bool b) {
  deterministic_mkldnn = b;
}

bool Context::deterministicAlgorithms() const {
  return _deterministic_algorithms;
}

bool Context::deterministicAlgorithmsWarnOnly() const {
  return _deterministic_algorithms_warn_only;
}

void Context::setDeterministicAlgorithms(bool b, bool warn_only = false) {
  _deterministic_algorithms = b;
  _deterministic_algorithms_warn_only = warn_only;
}

bool Context::deterministicFillUninitializedMemory() const {
  return _deterministic_fill_uninitialized_memory;
}

void Context::setDeterministicFillUninitializedMemory(bool b) {
  _deterministic_fill_uninitialized_memory = b;
}

void Context::alertNotDeterministic(std::string_view const& caller) {
  if (globalContext().deterministicAlgorithms()) {
    if (globalContext().deterministicAlgorithmsWarnOnly()) {
      TORCH_WARN(
        caller, " does not have a deterministic implementation, but you set "
        "'torch.use_deterministic_algorithms(True, warn_only=True)'. "
        "You can file an issue at https://github.com/pytorch/pytorch/issues "
        "to help us prioritize adding deterministic support for this operation.");
    } else {
      TORCH_CHECK(false,
        caller, " does not have a deterministic implementation, but you set "
        "'torch.use_deterministic_algorithms(True)'. You can turn off "
        "determinism just for this operation, or you can use the "
        "'warn_only=True' option, if that's acceptable for your application. "
        "You can also file an issue at https://github.com/pytorch/pytorch/issues "
        "to help us prioritize adding deterministic support for this operation.");
    }
  }
}

bool Context::userEnabledNNPACK() const {
  return enabled_nnpack;
}

void Context::setUserEnabledNNPACK(bool e) {
  enabled_nnpack = e;
}

bool Context::allowTF32CuDNN(const std::string& op) const {
  if (op.empty()){
    bool allow_tf32_rnn = float32Precision("cuda", "rnn") == "tf32";
    bool allow_tf32_conv = float32Precision("cuda", "conv") == "tf32";
    TORCH_CHECK(
        allow_tf32_rnn == allow_tf32_conv && allow_tf32_rnn == allow_tf32_cudnn,
        "PyTorch is checking whether allow_tf32 is enabled for cuDNN without a specific operator name,",
        "but the current flag(s) indicate that cuDNN conv and cuDNN RNN have different TF32 flags.",
        "This combination indicates that you have used a mix of the legacy and new APIs to set the TF32 flags. ",
        "We suggest only using the new API to set the TF32 flag(s). See also: ",
        "https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices");
  } else {
    return float32Precision("cuda", op) == "tf32";
  }
  warn_deprecated_fp32_precision_api();
  return allow_tf32_cudnn;
}

void Context::setAllowTF32CuDNN(bool b) {
  setFloat32Precision("cuda", "rnn", b ? "tf32" : "none");
  setFloat32Precision("cuda", "conv", b ? "tf32" : "none");
  allow_tf32_cudnn = b;
  warn_deprecated_fp32_precision_api();
}

void Context::setSDPPriorityOrder(const std::vector<int64_t>& order) {
  // TODO*eqy): should it always be the number of backends - 1 (override backend excluded?)
  TORCH_CHECK(at::num_sdp_backends == sdp_priority_order.size(),
    "setSDPPriority order expected ", sdp_priority_order.size() - 1, " but got ",
    at::num_sdp_backends, " unique backends specified in priority order.");
  for (uint32_t i = 0; i < order.size(); i++) {
    sdp_priority_order[i] = (at::SDPBackend) order[i];
  }
}

std::array<at::SDPBackend, at::num_sdp_backends> Context::sDPPriorityOrder() {
  return sdp_priority_order;
}

bool Context::allowTF32OneDNN() const {
  return allow_tf32_onednn;
}

  // NOLINTNEXTLINE(clang-diagnostic-unused-parameter)
  void Context::setAllowTF32OneDNN(bool b){
  #ifdef USE_XPU
  allow_tf32_onednn = b;
  #else
  TORCH_WARN("TF32 acceleration on top of oneDNN is available for Intel GPUs. The current Torch version does not have Intel GPU Support.");
  #endif
}

bool Context::userEnabledFlashSDP() const {
  return enabled_flashSDP;
}

void Context::setSDPUseFlash(bool e) {
  enabled_flashSDP = e;
}

bool Context::userEnabledMemEfficientSDP() const {
  return enabled_mem_efficientSDP;
}

void Context::setSDPUseMemEfficient(bool e) {
  enabled_mem_efficientSDP = e;
}

bool Context::userEnabledMathSDP() const {
  return enabled_mathSDP;
}

void Context::setSDPUseMath(bool e) {
  enabled_mathSDP = e;
}

bool Context::allowFP16BF16ReductionMathSDP() const {
  return allow_fp16_bf16_reduction_mathSDP;
}

void Context::setAllowFP16BF16ReductionMathSDP(bool e) {
  allow_fp16_bf16_reduction_mathSDP = e;
}

bool Context::userEnabledCuDNNSDP() const {
  return enabled_cudnnSDP;
}

void Context::setSDPUseCuDNN(bool e) {
  enabled_cudnnSDP = e;
}

void Context::setSDPUseOverrideable(bool e) {
  enabled_overrideable = e;
}

bool Context::userEnabledOverrideableSDP() const {
  return enabled_overrideable;
}

static constexpr const auto cublas_config_var_name = "CUBLAS_WORKSPACE_CONFIG";
static constexpr const std::array<const char*, 2> cublas_deterministic_configs = {":4096:8", ":16:8"};

bool Context::checkCuBLASConfigDeterministic() {
  // If using CUDA 10.2 or greater, need to make sure CuBLAS workspace config
  // is set to deterministic setting
  if (hasCUDART()) {
    const auto workspace_config = c10::utils::get_env(cublas_config_var_name);
    return (workspace_config == cublas_deterministic_configs[0] || workspace_config == cublas_deterministic_configs[1]);
  }
  return true;
}

void Context::alertCuBLASConfigNotDeterministic() const {
  static const bool cublas_config_deterministic = checkCuBLASConfigDeterministic();
  if (C10_LIKELY(!deterministicAlgorithms() || cublas_config_deterministic)) {
    return;
  }

  auto msg = c10::str(
    "Deterministic behavior was enabled with either `torch.use_deterministic_algorithms(True)` or ",
    "`at::Context::setDeterministicAlgorithms(true)`, but this operation is not deterministic because ",
    "it uses CuBLAS and you have CUDA >= 10.2. To enable deterministic behavior in this ",
    "case, you must set an environment variable before running your PyTorch application: ",
    cublas_config_var_name, "=", cublas_deterministic_configs[0], " or ",
    cublas_config_var_name, "=", cublas_deterministic_configs[1], ". For more information, go to ",
    "https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility"
  );

  if (deterministicAlgorithmsWarnOnly()) {
    TORCH_WARN(msg);
  } else {
    TORCH_CHECK(false, msg);
  }
}

bool Context::benchmarkCuDNN() const {
  return benchmark_cudnn;
}

void Context::setBenchmarkCuDNN(bool b) {
  benchmark_cudnn = b;
}

int Context::benchmarkLimitCuDNN() const {
  return benchmark_limit_cudnn;
}

void Context::setBenchmarkLimitCuDNN(int b) {
  benchmark_limit_cudnn = b;
}

bool Context::immediateMiopen() const {
  return immediate_miopen;
}

void Context::setImmediateMiopen(bool b) {
  immediate_miopen = b;
}

bool Context::allowTF32CuBLAS() const {
  bool legacy_allow_tf32 = float32_matmul_precision != at::Float32MatmulPrecision::HIGHEST;
  bool allow_tf32_new = float32Precision("cuda", "matmul") == "tf32";
  TORCH_CHECK(
      legacy_allow_tf32 == allow_tf32_new,
      "PyTorch is checking whether allow_tf32_new is enabled for cuBlas matmul,",
      "Current status indicate that you have used mix of the legacy and new APIs to set the TF32 status for cublas matmul. ",
      "We suggest only using the new API to set the TF32 flag. See also: ",
      "https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices");
  warn_deprecated_fp32_precision_api();
  return allow_tf32_new;
}

void Context::setAllowTF32CuBLAS(bool b) {
  float32_matmul_precision = b ? at::Float32MatmulPrecision::HIGH : at::Float32MatmulPrecision::HIGHEST;
  setFloat32Precision("cuda", "matmul", b ? "tf32" : "ieee");
}

Float32MatmulPrecision Context::float32MatmulPrecision() const {
  bool invalid = float32Precision("cuda", "matmul") == "tf32" &&
      float32_matmul_precision == at::Float32MatmulPrecision::HIGHEST;
  invalid = invalid ||
      (float32Precision("mkldnn", "matmul") == "bf16" &&
       float32_matmul_precision != at::Float32MatmulPrecision::MEDIUM);
  invalid = invalid ||
      (float32Precision("mkldnn", "matmul") == "tf32" &&
       float32_matmul_precision != at::Float32MatmulPrecision::HIGH);
  TORCH_CHECK(
      !invalid,
      "PyTorch is checking the matmul precision without a specific backend name,",
      "Current status indicate that you have used mix of the legacy and new APIs to set the matmul precision. ",
      "We suggest only using the new API for matmul precision. See also: ",
      "https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices");
  warn_deprecated_fp32_precision_api();
  return float32_matmul_precision;
}

std::string Context::float32Precision(const std::string& backend, const std::string& op) const {
  check_fp32_prec_backend_and_op(backend, op);
  auto precision = fp32_precision.find(backend)->second.find(op)->second;
  if (precision == "none")
    precision = fp32_precision.find(backend)->second.find("all")->second;
  if (precision == "none")
    precision = fp32_precision.find("generic")->second.find("all")->second;
  bool valid_prec = validate_fp32_prec(backend, precision);
  return valid_prec ? precision : "none";
}

void Context::setFloat32MatmulPrecision(const std::string &s) {
  auto match = [this](const std::string & s_) {
    warn_deprecated_fp32_precision_api();
    // TODO: consider if CuDNN field needs to also be set for potential future CuDNN ops like multi-headed attention
    if (s_ == "highest") {
      float32_matmul_precision = at::Float32MatmulPrecision::HIGHEST;
      setFloat32Precision("cuda", "matmul", "ieee");
      setFloat32Precision("mkldnn", "matmul", "ieee");
      return true;
    } else if (s_ == "high") {
      float32_matmul_precision = at::Float32MatmulPrecision::HIGH;
      setFloat32Precision("cuda", "matmul", "tf32");
      setFloat32Precision("mkldnn", "matmul", "tf32");
      return true;
    } else if (s_ == "medium") {
      float32_matmul_precision = at::Float32MatmulPrecision::MEDIUM;
      setFloat32Precision("cuda", "matmul", "tf32");
      setFloat32Precision("mkldnn", "matmul", "bf16");
      return true;
    }
    return false;
  };
  if (match(s)) { return; }
  std::string sl;
  std::transform(s.begin(), s.end(), sl.begin(),
                 [](unsigned char c) -> unsigned char { return std::tolower(c); });
  if (match(sl)) { return; }
  TORCH_WARN(s, " is not one of 'highest', 'high', or 'medium'; the current"
    "setFloat32MatmulPrecision call has no effect.");
}

void Context::setFloat32Precision(const std::string& backend, const std::string& op, const std::string& p) {
  check_fp32_prec_backend_and_op(backend, op);
  if (validate_fp32_prec(backend, p)) {
    fp32_precision[backend][op] = p;
  } else {
    std::string msg;
    auto iterp = _fp32_precisions.find(backend);
    TORCH_CHECK(iterp != _fp32_precisions.end());
    for (const auto& p : iterp->second) {
      msg += p;
      msg += " ";
    }
    TORCH_WARN(
        "you have set wrong precision for backend:",
        backend,
        " setFloat32Precision call has no effect.",
        "Please choose precision from: ",
        msg);
  }
}

at::LinalgBackend Context::linalgPreferredBackend() const {
  return linalg_preferred_backend;
}

void Context::setLinalgPreferredBackend(at::LinalgBackend b) {
  linalg_preferred_backend = b;
  TORCH_CHECK((b != at::LinalgBackend::Cusolver) || hasCuSOLVER(),
      "Cannot set preferred backend to cuSOLVER if PyTorch has not been compiled with cuSOLVER.");
  TORCH_CHECK((b != at::LinalgBackend::Magma) || hasMAGMA(),
      "Cannot set preferred backend to MAGMA if PyTorch has not been compiled with MAGMA.");
  if (b != at::LinalgBackend::Default) {
    TORCH_WARN_ONCE(
      "torch.backends.cuda.preferred_linalg_library is an experimental feature. "
      "If you see any error or unexpected behavior when this flag is set "
      "please file an issue on GitHub."
    );
  }
}

at::BlasBackend Context::blasPreferredBackend() {
  // Rather than put logic for interpreting what Default means at every
  // call site for blasPreferredBackend(), we set it to an actual value.
  if (blas_preferred_backend == at::BlasBackend::Default) {
    blas_preferred_backend = at::BlasBackend::Cublas;
    // This logic sits in the getter because it needs to validate
    // values set via env vars such as TORCH_BLAS_PREFER_CUBLASLT
    // which initialize the backend without calling the setter
#ifdef USE_ROCM
    // AMD Instinct targets prefer hipblaslt
    static const bool hipblaslt_preferred = []() {
      static const std::vector<std::string> archs = {
          "gfx90a", "gfx942",
#if ROCM_VERSION >= 60400
          "gfx1200", "gfx1201",
#endif
#if ROCM_VERSION >= 60500
          "gfx950"
#endif
      };
      for (auto index: c10::irange(detail::getCUDAHooks().deviceCount())) {
        if (!detail::getCUDAHooks().isGPUArch(archs, index)) {
          return false;
        }
      }
      return true;
    }();
    if (hipblaslt_preferred) {
      blas_preferred_backend = at::BlasBackend::Cublaslt;
    }
#endif
  }

#ifdef USE_ROCM
  // hipblaslt support for all archs is not as complete as hipblas
  if (blas_preferred_backend == at::BlasBackend::Cublaslt) {
    static const bool hipblaslt_unsupported = []() {
      if(!hasCuBLASLt())
      {
          return true;
      }
      static const std::vector<std::string> archs = {
          "gfx90a", "gfx942",
#if ROCM_VERSION >= 60300
          "gfx1100", "gfx1101", "gfx1200", "gfx1201", "gfx908",
#endif
#if ROCM_VERSION >= 60500
          "gfx950"
#endif
      };
      for (auto index: c10::irange(detail::getCUDAHooks().deviceCount())) {
        if (!detail::getCUDAHooks().isGPUArch(archs, index)) {
          TORCH_WARN_ONCE(
            "Attempting to use hipBLASLt on an unsupported architecture! "
            "Overriding blas backend to hipblas");
          return true;
        }
      }
      return false;
    }();
    if (hipblaslt_unsupported) blas_preferred_backend = at::BlasBackend::Cublas;
  }
#endif
  return blas_preferred_backend;
}

bool Context::ckSupported() {
#ifdef USE_ROCM
  static const std::vector<std::string> supported_archs = {
    "gfx90a", "gfx942", "gfx950"
  };
  for (auto index : c10::irange(detail::getCUDAHooks().deviceCount())) {
    if(!detail::getCUDAHooks().isGPUArch(supported_archs, index)) {
      TORCH_WARN_ONCE(
        "Attempting to use CK on an unsupported architecture! Cannot set backend to CK");
      return false;
    }
  }
  return true;
#else
  return false;
#endif
}

void Context::setBlasPreferredBackend(at::BlasBackend b) {
#ifdef _MSC_VER
  TORCH_WARN_ONCE(
    "torch.backends.cuda.preferred_blas_library is an experimental feature. "
    "It is not supported on Windows."
  );
#else
  TORCH_CHECK((b != at::BlasBackend::Cublaslt) || hasCuBLASLt(),
      "Cannot set preferred backend to cuBLASLt if PyTorch has not been compiled with cuBLASLt.");
#ifdef USE_ROCM
  static const bool ckSupportedFlag = ckSupported();
  static const bool hasCKGEMMFlag = hasCKGEMM();
  TORCH_CHECK((b != at::BlasBackend::Ck) || (ckSupportedFlag && hasCKGEMMFlag),
      "Cannot set preferred blas backend to CK since following conditions are not true: ",
      "architecture supported for CK: ", ckSupportedFlag,
      ", PyTorch built with CK GEMM support: ", hasCKGEMMFlag);
#endif
  if (b != at::BlasBackend::Default && b != at::BlasBackend::Cublas) {
    TORCH_WARN_ONCE(
      "torch.backends.cuda.preferred_blas_library is an experimental feature. "
      "If you see any error or unexpected behavior when this flag is set "
      "please file an issue on GitHub."
    );
  }
  blas_preferred_backend = b;
#endif
}

at::ROCmFABackend Context::getROCmFAPreferredBackend() {
#ifdef USE_ROCM
  // Set potential "Default" value so we don't have to interpret at call sites.
  // We use aotriton backend as the default, for now.
  if(rocm_fa_preferred_backend == at::ROCmFABackend::Default) {
    rocm_fa_preferred_backend = at::ROCmFABackend::AOTriton;
  } else if (rocm_fa_preferred_backend == at::ROCmFABackend::Ck) {
    // This logic sits in the getter because it needs to validate
    // values set via env vars such as TORCH_ROCM_FA_PREFER_CK
    // which initialize the backend without calling the setter
    // Perform validity checking
    static const bool hasCKSDPAFlag = hasCKSDPA();
    static const bool ckSupportedFlag = ckSupported();
    if(!(hasCKSDPAFlag && ckSupportedFlag)){
      TORCH_WARN_ONCE(
        "Cannot set preferred SDPA backend to CK since following conditions are not true: ",
        "architecture supported for CK: ", ckSupportedFlag,
        ", PyTorch built with CK SDPA support: ", hasCKSDPAFlag);
      rocm_fa_preferred_backend = at::ROCmFABackend::AOTriton;
    }
  }
#endif

  return rocm_fa_preferred_backend;
}

void Context::setROCmFAPreferredBackend(at::ROCmFABackend b) {
#ifdef USE_ROCM
  static const bool hasCKSDPAFlag = hasCKSDPA();
  static const bool ckSupportedFlag = ckSupported();
  TORCH_CHECK((b != at::ROCmFABackend::Ck) || (hasCKSDPAFlag && ckSupportedFlag),
      "Cannot set preferred SDPA backend to CK since following conditions are not true: ",
      "architecture supported for CK: ", ckSupportedFlag,
      ", PyTorch built with CK SDPA support: ", hasCKSDPAFlag);
#endif
  rocm_fa_preferred_backend = b;
}

bool Context::allowFP16ReductionCuBLAS() const {
  return allow_fp16_reduction_cublas;
}

void Context::setAllowFP16ReductionCuBLAS(bool b) {
  allow_fp16_reduction_cublas = b;
}

bool Context::allowBF16ReductionCuBLAS() const {
  return allow_bf16_reduction_cublas;
}

void Context::setAllowBF16ReductionCuBLAS(bool b) {
  allow_bf16_reduction_cublas = b;
}

bool Context::allowFP16AccumulationCuBLAS() const {
  return allow_fp16_accumulation_cublas;
}

void Context::setAllowFP16AccumulationCuBLAS(bool b) {
  allow_fp16_accumulation_cublas = b;
}

std::optional<int32_t> Context::_SMCarveout_EXPERIMENTAL() const {
  return sm_carveout;
}

void Context::_setSMCarveout_EXPERIMENTAL(std::optional<int32_t> c) {
  if (c.has_value()) {
    TORCH_WARN_ONCE(
      "Setting the SM carveout for matmuls is a temporary experimental mitigation for performance issues, "
      "while more robust solutions are developed. It may be removed at any moment without notice.");
  }
  sm_carveout = c;
}

bool Context::hasMKL() {
#if AT_MKL_ENABLED()
  return true;
#else
  return false;
#endif
}

bool Context::hasMKLDNN() {
#if AT_MKLDNN_ENABLED()
  return true;
#else
  return false;
#endif
}

bool Context::hasKleidiAI() {
  return AT_KLEIDIAI_ENABLED();
}

bool Context::hasOpenMP() {
#ifdef _OPENMP
  return true;
#else
  return false;
#endif
}

bool Context::hasLAPACK() {
#if AT_BUILD_WITH_LAPACK()
  return true;
#else
  return false;
#endif
}

bool Context::hasEigenSparse() {
#if AT_USE_EIGEN_SPARSE()
  return true;
#else
  return false;
#endif
}

at::QEngine Context::qEngine() const {
  static auto _quantized_engine = []() {
    at::QEngine qengine = at::kNoQEngine;
#if defined(C10_MOBILE) && defined(USE_PYTORCH_QNNPACK)
    qengine = at::kQNNPACK;
#endif

#if AT_MKLDNN_ENABLED()
    qengine = at::kONEDNN;
#endif

#ifdef USE_FBGEMM
    if (fbgemm::fbgemmSupportedCPU()) {
      /* X86 is enabled if and only if fbgemm is available.
       * It combines goodness of fbgemm and onednn by dispatching.
       * If onednn not available, always dispatch to fbgemm.
       * Make it default qengine for X86 CPU platforms.
      */
      qengine = at::kX86;
    }
#endif
    return qengine;
  }();
  auto qt_engine = quantized_engine.load();
  return qt_engine == at::QEngine::NoQEngine ? _quantized_engine : qt_engine;
}

void Context::setQEngine(at::QEngine e) {
  const auto& qengines = supportedQEngines();
  if (std::find(qengines.begin(), qengines.end(), e) != qengines.end()) {
    quantized_engine.store(e);
    return;
  }
  TORCH_CHECK(false, "quantized engine ", toString(e), " is not supported");
}

const std::vector<at::QEngine>& Context::supportedQEngines() {
  static auto supported_qengines = []() {
    std::vector<at::QEngine> engines = {};
    // Engines are listed in priority order: later one wins
    // By default we prefer FBGEMM if we're running on server side
    // QNNPACK on server side has some issue, so we disable it by default.
#ifdef USE_PYTORCH_QNNPACK
    engines.push_back(at::kQNNPACK);
#endif

#if AT_MKLDNN_ENABLED()
    engines.push_back(at::kONEDNN);
#endif

#ifdef USE_FBGEMM
    if (fbgemm::fbgemmSupportedCPU()) {
      engines.push_back(at::kX86);
      // The X86 qengine is available if and only if FBGEMM is available
      engines.push_back(at::kFBGEMM);
    }
#endif

    return engines;
  }();
  return supported_qengines;
}

bool Context::isXNNPACKAvailable() {
#ifdef USE_XNNPACK
  return true;
#else
  return false;
#endif
}

void Context::setCheckSparseTensorInvariants(bool e) {
  enable_sparse_tensor_invariant_checks = e;
}

bool Context::checkSparseTensorInvariants() const {
  return enable_sparse_tensor_invariant_checks;
}

bool Context::releaseWeightsWhenPrepacking() const {
  return release_original_weights;
}

void Context::setReleaseWeightsWhenPrepacking(bool e) {
  release_original_weights = e;
}

bool Context::setFlushDenormal(bool on) {
  return at::cpu::set_flush_denormal(on);
}

Allocator* getCPUAllocator() {
  return c10::GetCPUAllocator();
}

// override_allow_tf32_flag = true
//    means the allow_tf32 flags are overridden and tf32 is force disabled
// override_allow_tf32_flag = false
//    means the original allow_tf32 flags are followed
thread_local static bool override_allow_tf32_flag = false;

NoTF32Guard::NoTF32Guard() {
  if (!override_allow_tf32_flag) {
    changed = true;
    override_allow_tf32_flag = true;
  }
}

NoTF32Guard::~NoTF32Guard() {
  if (changed) {
    override_allow_tf32_flag = false;
  }
}

bool NoTF32Guard::should_disable_tf32() {
  return override_allow_tf32_flag;
}

// Ops can query this flag to know they are in the backward pass.
// This information can be used, for example, to select implementations
// with different numerical or performance characteristics.
// See https://pytorch.org/docs/stable/notes/numerical_accuracy.html for details.
thread_local static bool rocm_is_backward_pass;

ROCmBackwardPassGuard::ROCmBackwardPassGuard() {
  rocm_is_backward_pass = true;
}

ROCmBackwardPassGuard::~ROCmBackwardPassGuard() {
  rocm_is_backward_pass = false;
}

bool ROCmBackwardPassGuard::is_backward_pass() {
  return rocm_is_backward_pass;
}

bool Context::areVmapFallbackWarningsEnabled() const {
  return display_vmap_fallback_warnings_;
}

void Context::setDisplayVmapFallbackWarnings(bool enabled) {
  display_vmap_fallback_warnings_ = enabled;
}

bool Context::isDefaultMobileCPUAllocatorSet() {
  return prev_allocator_ptr_ != nullptr;
}

void Context::setDefaultMobileCPUAllocator() {
  TORCH_CHECK(prev_allocator_ptr_ == nullptr,
      "Already within the scope of another non-default cpu allocator."
      "Cannot set another allocator.");
  // Setting the priority high to make sure no other allocator gets used instead of this.
  prev_allocator_ptr_ = c10::GetCPUAllocator();
  c10::SetCPUAllocator(c10::GetDefaultMobileCPUAllocator(), /*priority*/ 100);
}

void Context::unsetDefaultMobileCPUAllocator() {
  TORCH_CHECK(prev_allocator_ptr_ != nullptr,
      "setDefaultMobileCPUAllocator must have been called "
      "before unsetDefaultMobileCPUAllocator.");
  // Setting the priority high to make sure no other allocator gets used instead of this.
  c10::SetCPUAllocator(prev_allocator_ptr_ , /*priority*/ 100);
  prev_allocator_ptr_ = nullptr;
}

bool Context::allowFP16ReductionCPU() const {
  return allow_fp16_reduction_cpu;
}

void Context::setAllowFP16ReductionCPU(bool b) {
  if ( b && !allow_fp16_reduction_cpu) {
    // Check that CPU supports fp16 reductions
#if defined(__aarch64__) && !defined(C10_MOBILE)
    if (!cpuinfo_initialize() || !cpuinfo_has_arm_fp16_arith())
#else
    // NOLINTNEXTLINE(facebook-hte-MissingBraces)
    if (true)
#endif
      TORCH_CHECK(false, "Float16 arithmetic is not supported by the CPU!");
  }
  allow_fp16_reduction_cpu = b;
}
} // namespace at