From 08e3991f9a3125b1286e2958d395d84dc1f81785 Mon Sep 17 00:00:00 2001 From: Nick Wang Date: Mon, 6 Jan 2025 22:03:37 +0800 Subject: [PATCH] more dummy implemtention --- README.md | 7 + .../tensorflow/core/framework/tensor.h | 43 -- .../tensorflow/core/platform/status.h | 22 + .../tensorflow/core/public/session.h | 2 +- src/TensorflowCpy/source/tensorflow.cpp | 694 ++++++++++++++++-- 5 files changed, 655 insertions(+), 113 deletions(-) diff --git a/README.md b/README.md index 1ab8c95..ceb78c8 100644 --- a/README.md +++ b/README.md @@ -76,6 +76,13 @@ install-cmake: make install ``` +Use following commands to start build and test + +```sh +make distclean source-all example-tensorflow TYPE=D USE_LIBTENSORFLOW_CC=0 +gdb --tui -q --args ./build/sample/sample --name whole_flow -- ./sample/saved_model/ +``` + ## 🛡 License [![License](https://img.shields.io/github/license/chaoqing/PyCXpress)](https://github.com/chaoqing/PyCXpress/blob/master/LICENSE) diff --git a/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/framework/tensor.h b/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/framework/tensor.h index 6bc8f07..e89077a 100644 --- a/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/framework/tensor.h +++ b/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/framework/tensor.h @@ -271,44 +271,6 @@ class Tensor { return true; } - /// \brief Slice this tensor along the 1st dimension. - - /// I.e., the returned tensor satisfies - /// returned[i, ...] == this[dim0_start + i, ...]. - /// The returned tensor shares the underlying tensor buffer with this - /// tensor. - /// - /// NOTE: The returned tensor may not satisfy the same alignment - /// requirement as this tensor depending on the shape. The caller - /// must check the returned tensor's alignment before calling certain - /// methods that have alignment requirement (e.g., `flat()`, `tensor()`). - /// - /// NOTE: When fed with an N-dimensional tensor, this method returns a tensor - /// also with N dimensions. If you want to select a sub tensor, see SubSlice. - /// - /// REQUIRES: `dims()` >= 1 - /// REQUIRES: `0 <= dim0_start <= dim0_limit <= dim_size(0)` - Tensor Slice(int64_t dim0_start, int64_t dim0_limit) const; - - /// \brief Select a subslice from this tensor along the 1st dimension. - /// - /// When fed with an N-dimensional tensor, this method returns a tensor with - /// N-1 dimensions, where the returned tensor is a subslice of the input - /// tensor along the first dimension. The N-1 dimensions of the returned - /// tensor are the last N-1 dimensions of the input tensor. - /// - /// NOTE: The returned tensor may not satisfy the same alignment - /// requirement as this tensor depending on the shape. The caller - /// must check the returned tensor's alignment before calling certain - /// methods that have alignment requirement (e.g., `flat()`, `tensor()`). - /// - /// REQUIRES: `dims()` >= 1 - /// REQUIRES: `0 <= index < dim_size(0)` - Tensor SubSlice(int64_t index) const; - - /// Render the first `max_entries` values in `*this` into a string. - std::string SummarizeValue(int64_t max_entries, bool print_v2 = false) const; - /// A human-readable summary of the tensor suitable for debugging. // `num_values` is the number of actual data values in the tensor // included in the message. If the tensor might be resident in @@ -316,11 +278,6 @@ class Tensor { std::string DebugString(int num_values) const; std::string DebugString() const { return DebugString(3); } - // Variant of DebugString() that should be used for possibly non-CPU tensors. - // If the tensor is not resident on CPU, we can't read its values as - // DebugString() does. - std::string DeviceSafeDebugString() const; - /// \brief Returns a `StringPiece` mapping the current tensor's buffer. /// /// The returned `StringPiece` may point to memory location on devices diff --git a/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/platform/status.h b/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/platform/status.h index 2044201..28789d0 100644 --- a/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/platform/status.h +++ b/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/platform/status.h @@ -17,6 +17,20 @@ namespace errors { typedef ::tensorflow::error::Code Code; } // namespace errors + +struct SourceLocation { + uint32_t line; + const char* file_name; + + static SourceLocation current(uint32_t line = 0, + const char* file_name = nullptr) { + SourceLocation loc; + loc.line = line; + loc.file_name = file_name; + return loc; + } +}; + /// @ingroup core /// Denotes success or failure of a call in Tensorflow. class Status { @@ -24,6 +38,11 @@ class Status { /// Create a success status. Status() {} + /// \brief Create a status with the specified error code and msg as a + /// human-readable string containing more detailed information. + Status(error::Code code, std::string_view msg, + SourceLocation loc = SourceLocation::current()); + /// Copy the specified status. Status(const Status& s); Status& operator=(const Status& s); @@ -54,6 +73,9 @@ class Status { void IgnoreError() const; private: + error::Code m_code = error::Code::OK; + //std::string_view _msg; + //SourceLocation _loc; }; // OkStatus() diff --git a/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/public/session.h b/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/public/session.h index 69000b0..857cb80 100644 --- a/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/public/session.h +++ b/src/TensorflowCpy/include/tensorflow_cpy/tensorflow/core/public/session.h @@ -9,9 +9,9 @@ #include #include +#include "../../core/common_runtime/device_mgr.h" #include "../../core/framework/tensor.h" #include "../../core/platform/status.h" -#include "../../core/common_runtime/device_mgr.h" #include "../../core/public/session_options.h" // clang-format off diff --git a/src/TensorflowCpy/source/tensorflow.cpp b/src/TensorflowCpy/source/tensorflow.cpp index 8f6a07d..09aba37 100644 --- a/src/TensorflowCpy/source/tensorflow.cpp +++ b/src/TensorflowCpy/source/tensorflow.cpp @@ -4,101 +4,657 @@ namespace tensorflow_cpy { #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wunused-parameter" namespace stream_executor { - bool StreamExecutor::DeviceMemoryUsage(long*, long*) const { return true; } - const DeviceDescription& StreamExecutor::GetDeviceDescription() const { - static DeviceDescription dummy; - return dummy; + /***********************************************************/ + // tensorflow/stream_executor/stream_executor_pimpl.h // + /***************************************************/ + StreamExecutor::~StreamExecutor() {} + + port::Status StreamExecutor::Init() { return port::Status::OK(); } + + PlatformKind StreamExecutor::platform_kind() const { return PlatformKind::kCuda; } + + const Platform* StreamExecutor::platform() const { return platform_; } + + bool StreamExecutor::SynchronizeAllActivity() { + // Default implementation - derived classes should override + return true; } - DeviceDescription::DeviceDescription() {} - port::Status StreamExecutor::SynchronousMemcpyD2H(DeviceMemoryBase const&, long, void*) { - return port::Status::OK(); + + port::Status StreamExecutor::SynchronousMemZero(DeviceMemoryBase* location, uint64_t size) { + if (location == nullptr) { + return port::Status(error::INVALID_ARGUMENT, "location cannot be null"); + } + return SynchronousMemSet(location, 0, size); } - port::Status StreamExecutor::SynchronousMemcpyH2D(void const*, long, DeviceMemoryBase*) { - return port::Status::OK(); + + port::Status StreamExecutor::SynchronousMemSet(DeviceMemoryBase* location, int value, + uint64_t size) { + // Default implementation - derived classes should override + return port::Status(error::UNIMPLEMENTED, "SynchronousMemSet not implemented"); + } + + bool StreamExecutor::SynchronousMemcpy(DeviceMemoryBase* device_dst, const void* host_src, + uint64_t size) { + return SynchronousMemcpyH2D(host_src, size, device_dst).ok(); } - port::Status StreamExecutor::SynchronousMemSet(DeviceMemoryBase*, int, unsigned long) { - return port::Status::OK(); + + bool StreamExecutor::SynchronousMemcpy(void* host_dst, const DeviceMemoryBase& device_src, + uint64_t size) { + return SynchronousMemcpyD2H(device_src, size, host_dst).ok(); + } + + port::Status StreamExecutor::SynchronousMemcpyH2D(const void* host_src, int64_t size, + DeviceMemoryBase* device_dst) { + // Default implementation - derived classes should override + return port::Status(error::UNIMPLEMENTED, "SynchronousMemcpyH2D not implemented"); } + + port::Status StreamExecutor::SynchronousMemcpyD2H(const DeviceMemoryBase& device_src, + int64_t size, void* host_dst) { + // Default implementation - derived classes should override + return port::Status(error::UNIMPLEMENTED, "SynchronousMemcpyD2H not implemented"); + } + + bool StreamExecutor::SynchronousMemcpy(DeviceMemoryBase* device_dst, + const DeviceMemoryBase& device_src, uint64_t size) { + // Default implementation - derived classes should override + return false; + } + + const DeviceDescription& StreamExecutor::GetDeviceDescription() const { + static DeviceDescription description; // Default empty description + return description; + } + + int64_t StreamExecutor::GetDeviceLoad() const { + return 0; // Default implementation returns no load + } + + bool StreamExecutor::DeviceMemoryUsage(int64_t* free, int64_t* total) const { + // Default implementation - derived classes should override + if (free) *free = 0; + if (total) *total = 0; + return false; + } + + int StreamExecutor::PlatformDeviceCount() const { return 1; } + + /***********************************************************/ + // tensorflow/stream_executor/platform.h // + /***************************************************/ + Platform::~Platform() {} + + DeviceDescription::DeviceDescription() {} }; // namespace stream_executor - namespace tensorflow { - namespace se = stream_executor; - SessionOptions::SessionOptions() {} + + namespace tensorflow { + /***********************************************************/ + // tensorflow/core/platform/logging.h // + /***************************************************/ namespace internal { + + LogMessage::LogMessage(const char* fname, int line, int severity) + : fname_(fname), line_(line), severity_(severity) {} + + LogMessage::~LogMessage() { + // Write message to stderr + std::cerr << fname_ << ":" << line_ << " " << str() << std::endl; + } + + LogMessage& LogMessage::AtLocation(const char* fname, int line) { + fname_ = fname; + line_ = line; + return *this; + } + + int64_t LogMessage::MaxVLogLevel() { + return 0; // Default to no verbose logging + } + LogMessageFatal::LogMessageFatal(const char* file, int line) : LogMessage(file, line, FATAL) {} - LogMessageFatal::~LogMessageFatal() { exit(1); } - LogMessage::LogMessage(const char* file, int line, int severity) {} - LogMessage::~LogMessage() {} - }; // namespace internal + LogMessageFatal::~LogMessageFatal() { + std::cerr << str() << std::endl; + abort(); + } + + int64_t MinLogLevelFromEnv() { + return 0; // Default to INFO level + } + + int64_t MaxVLogLevelFromEnv() { + return 0; // Default to no verbose logging + } + void LogString(const char* fname, int line, int severity, const std::string& message) { + LogMessage(fname, line, severity) << message; + } + + } // namespace internal + + void UpdateLogVerbosityIfDefined(const char* env_var) { + // No-op for now + } + + + /***********************************************************/ + // tensorflow/core/platform/status.h // + /***************************************************/ + Status::Status(error::Code code, std::string_view msg, SourceLocation loc) : m_code(code) {} + Status::Status(const Status& s) = default; + Status& Status::operator=(const Status& s) = default; + + bool Status::ok() const { return true; } // Simplified implementation + + errors::Code Status::code() const { + return errors::Code::OK; // Simplified implementation + } + + const std::string& Status::error_message() const { + static const std::string empty; + return empty; // Simplified implementation + } + + std::string Status::ToString() const { + if (ok()) return "OK"; + std::stringstream ss; + ss << code() << ": " << error_message(); + return ss.str(); + } + + void Status::IgnoreError() const {} + + Status OkStatus() { return Status(); } + + std::ostream& operator<<(std::ostream& os, const Status& x) { + os << x.ToString(); + return os; + } + + /***********************************************************/ + // tensorflow/core/platform/refcount.h // + /***************************************************/ namespace core { - RefCounted::RefCounted() {} - bool RefCounted::Unref() const { return true; } + + RefCounted::RefCounted() : ref_(1) {} + + void RefCounted::Ref() const { ref_.fetch_add(1, std::memory_order_relaxed); } + + bool RefCounted::Unref() const { + if (ref_.fetch_sub(1, std::memory_order_acq_rel) == 1) { + delete this; + return true; + } + return false; + } + + int_fast32_t RefCounted::RefCount() const { + return ref_.load(std::memory_order_relaxed); + } + + bool RefCounted::RefCountIsOne() const { + return ref_.load(std::memory_order_acquire) == 1; + } + } // namespace core + /***********************************************************/ + // tensorflow/core/platform/env.h // + /***************************************************/ + Env::Env() {} + + Env* Env::Default() { + // This should return the default environment implementation + // For now, return nullptr as this needs platform-specific implementation + return nullptr; + } + + /***********************************************************/ + // tensorflow/core/public/session_options.h // + /***************************************************/ + + SessionOptions::SessionOptions() : env(nullptr) {} + + /***********************************************************/ + // tensorflow/core/public/session.h // + /***************************************************/ + Session::Session() {} + Session::~Session() {} + + Status Session::LocalDeviceManager(const DeviceMgr** output) { + return Status(); // Simplified implementation + } + + Status Session::MakeCallable(const CallableOptions& callable_options, + CallableHandle* out_handle) { + return Status(); // Simplified implementation + } + + Status Session::RunCallable(CallableHandle handle, const std::vector& feed_tensors, + std::vector* fetch_tensors, RunMetadata* run_metadata) { + return Status(); // Simplified implementation + } + + Status Session::ReleaseCallable(CallableHandle handle) { + return Status(); // Simplified implementation + } + + Status Session::Finalize() { + return Status(); // Simplified implementation + } + + Status NewSession(const SessionOptions& options, Session** out_session) { + *out_session = nullptr; + return Status(); // Simplified implementation + } + + Session* NewSession(const SessionOptions& options) { + return nullptr; // Simplified implementation + } + + + /***********************************************************/ + // tensorflow/core/common_runtime/device_mgr.h // + /***************************************************/ + + DeviceMgr::~DeviceMgr() = default; + + Device* DeviceMgr::HostCPU() const { + for (Device* d : ListDevices()) { + if (d->device_type() == "CPU") { + return d; + } + } + return nullptr; + } + + /***********************************************************/ + // tensorflow/core/common_runtime/gpu/gpu_init.h // + /***************************************************/ + + static stream_executor::Platform* gpu_platform = nullptr; + + stream_executor::Platform* GPUMachineManager() { + // Simplified implementation that returns the singleton + return gpu_platform; + } + + + /***********************************************************/ + // tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.h // + /***************************************************/ + GPUBFCAllocator::GPUBFCAllocator(std::unique_ptr sub_allocator, + size_t total_memory, const std::string& name, + const Options& opts) { + // Implementation will be added later + } + + std::string GPUBFCAllocator::Name() { + // Return the name provided during construction + return "GPUBFCAllocator"; // Placeholder implementation + } + + void* GPUBFCAllocator::AllocateRaw(size_t alignment, size_t num_bytes) { + // Placeholder implementation + return nullptr; + } + + void GPUBFCAllocator::DeallocateRaw(void* ptr) { + // Placeholder implementation + } + + + /***********************************************************/ + // tensorflow/core/framework/tensor_shape.h // + /***************************************************/ + TensorShape::TensorShape(std::initializer_list dim_sizes) { + // Implementation will be added + } + + TensorShape::TensorShape() { + // Initialize empty shape + } + + int64_t TensorShape::num_elements() const { + int64_t result = 1; + for (auto d = begin(); d != end(); ++d) { + result *= (*d).size; + } + return result; + } + + void TensorShape::AddDim(int64_t size) { + CHECK_GE(size, 0); + // Implementation will be added + } + + void TensorShape::AppendShape(const TensorShape& shape) { + for (auto d = shape.begin(); d != shape.end(); ++d) { + AddDim((*d).size); + } + } + + void TensorShape::InsertDim(int d, int64_t size) { + CHECK_GE(d, 0); + CHECK_GE(size, 0); + // Implementation will be added + } + + void TensorShape::set_dim(int d, int64_t size) { + CHECK_GE(d, 0); + CHECK_LT(d, dims()); + CHECK_GE(size, 0); + // Implementation will be added + } + + void TensorShape::RemoveDimRange(int begin, int end) { + // Implementation will be added + } + + int TensorShape::dims() const { + // Implementation will be added + return 0; + } + + int64_t TensorShape::dim_size(int d) const { + CHECK_GE(d, 0); + CHECK_LT(d, dims()); + // Implementation will be added + return 0; + } + + TensorShapeIter TensorShape::begin() const { + return TensorShapeIter(this, 0); + } + + TensorShapeIter TensorShape::end() const { + return TensorShapeIter(this, dims()); + } + + bool TensorShape::IsSameSize(const TensorShape& b) const { + if (dims() != b.dims()) return false; + for (int d = 0; d < dims(); d++) { + if (dim_size(d) != b.dim_size(d)) return false; + } + return true; + } - Env* Env::Default() { return nullptr; } - OpRegistry* OpRegistry::Global() { return nullptr; } - Status OpRegistry::ProcessRegistrations() const { return Status::OK(); } - void OpRegistry::DeferRegistrations() {} + // TensorShapeUtils implementation + Status TensorShapeUtils::MakeShape(const int32_t* dims, int64_t n, TensorShape* out) { + *out = TensorShape(); + for (int64_t i = 0; i < n; ++i) { + out->AddDim(dims[i]); + } + return Status::OK(); + } - std::string AllocatorStats::DebugString() const { return ""; } - int DataTypeSize(DataType) { return 0; } + Status TensorShapeUtils::MakeShape(const int64_t* dims, int64_t n, TensorShape* out) { + *out = TensorShape(); + for (int64_t i = 0; i < n; ++i) { + out->AddDim(dims[i]); + } + return Status::OK(); + } + bool TensorShapeUtils::StartsWith(const TensorShape& shape, const TensorShape& prefix) { + if (shape.dims() < prefix.dims()) return false; + for (int i = 0; i < prefix.dims(); ++i) { + if (shape.dim_size(i) != prefix.dim_size(i)) return false; + } + return true; + } - Allocator::~Allocator() {} - GPUBFCAllocator::GPUBFCAllocator( - std::unique_ptr >, unsigned long, - std::__cxx11::basic_string, std::allocator > const&, - GPUBFCAllocator::Options const&) {} + bool TensorShapeUtils::EndsWith(const TensorShape& shape, const TensorShape& suffix) { + if (shape.dims() < suffix.dims()) return false; + for (int i = 0; i < suffix.dims(); ++i) { + if (shape.dim_size(shape.dims() - suffix.dims() + i) != suffix.dim_size(i)) { + return false; + } + } + return true; + } + + + /***********************************************************/ + // tensorflow_cpy/tensorflow/core/framework/tensor.h // + /***************************************************/ - std::string GPUBFCAllocator::Name() { return ""; } - void* GPUBFCAllocator::AllocateRaw(size_t alignment, size_t num_bytes) { return nullptr; } - void GPUBFCAllocator::DeallocateRaw(void* ptr) {} + int DataTypeSize(DataType dt) { + switch (dt) { + case DataType::DT_BOOL: + return sizeof(EnumToDataType::Type); + default: + return 0; + } + } + + // Default constructor creates a 1-dimensional, 0-element float tensor + Tensor::Tensor() : shape_(TensorShape({0})), buf_(nullptr) {} + // Constructor with type and shape + Tensor::Tensor(DataType type, const TensorShape& shape) : shape_(shape), buf_(nullptr) { + // Allocate buffer using CPU allocator + size_t size = shape.num_elements() * DataTypeSize(type); + if (size > 0) { + // Use default CPU allocator + // Allocator* allocator = cpu_allocator(); + // void* data = allocator->AllocateRaw(alignof(std::max_align_t), size); + // buf_ = new TensorBuffer(data); + } + } - Status ValidateGPUMachineManager() { return Status::OK(); } - se::Platform* GPUMachineManager() { return nullptr; } - std::string GpuPlatformName() { return ""; } + // Constructor with allocator, type and shape + Tensor::Tensor(Allocator* a, DataType type, const TensorShape& shape) + : shape_(shape), buf_(nullptr) { + size_t size = shape.num_elements() * DataTypeSize(type); + if (size > 0) { + // void* data = a->AllocateRaw(alignof(std::max_align_t), size); + // buf_ = new TensorBuffer(data); + } + } + + // Constructor with type, shape and buffer + Tensor::Tensor(DataType type, const TensorShape& shape, TensorBuffer* buf) + : shape_(shape), buf_(buf) { + if (buf_) { + buf_->Ref(); + } + } + + // Constructor with type, shape and RefCountPtr buffer + Tensor::Tensor(DataType type, TensorShape shape, core::RefCountPtr buf) + : shape_(std::move(shape)), buf_(buf.release()) {} + + // Copy constructor + Tensor::Tensor(const Tensor& other) : shape_(other.shape_), buf_(other.buf_) { + if (buf_) { + buf_->Ref(); + } + } + + // Move constructor + Tensor::Tensor(Tensor&& other) : shape_(std::move(other.shape_)), buf_(other.buf_) { + other.buf_ = nullptr; + } + + // Destructor + Tensor::~Tensor() { + if (buf_) { + buf_->Unref(); + } + } + + // Move assignment operator + Tensor& Tensor::operator=(Tensor&& other) { + if (this != &other) { + if (buf_) { + buf_->Unref(); + } + shape_ = std::move(other.shape_); + buf_ = other.buf_; + other.buf_ = nullptr; + } + return *this; + } + + // Implementation of CopyFromInternal + void Tensor::CopyFromInternal(const Tensor& other, const TensorShape& shape) { + shape_ = shape; + if (buf_) { + buf_->Unref(); + } + buf_ = other.buf_; + if (buf_) { + buf_->Ref(); + } + } + + // Check if tensor is initialized + bool Tensor::IsInitialized() const { return buf_ != nullptr || NumElements() == 0; } + + // Get total bytes + size_t Tensor::TotalBytes() const { + if (shape_.num_elements() == 0 || buf_ == nullptr) { + return 0; + } + return buf_->size(); + } + + // Get allocated bytes + size_t Tensor::AllocatedBytes() const { + if (buf_ == nullptr) return 0; + return buf_->size(); + } + + // Check if tensors share buffer + bool Tensor::SharesBufferWith(const Tensor& b) const { + return buf_ != nullptr && buf_ == b.buf_; + } + + + std::string Tensor::DebugString(int num_values) const { return "Not implemented"; } + + // Static method to build tensor + Status Tensor::BuildTensor(DataType type, const TensorShape& shape, Tensor* out_tensor) { + if (out_tensor == nullptr) { + return Status(error::INVALID_ARGUMENT, "out_tensor cannot be nullptr"); + } + + *out_tensor = Tensor(type, shape); + return Status::OK(); + } + /***********************************************************/ + // tensorflow/core/framework/allocator.h // + /***************************************************/ + Allocator::~Allocator() = default; + + std::string AllocatorStats::DebugString() const { + return "AllocatorStats"; // Simplified implementation + } + + namespace { + class CPUAllocator : public Allocator { + public: + std::string Name() override { return "cpu"; } + + void* AllocateRaw(size_t alignment, size_t num_bytes) override { + void* ptr = nullptr; + // Simple aligned allocation + posix_memalign(&ptr, alignment, num_bytes); + return ptr; + } + + void DeallocateRaw(void* ptr) override { free(ptr); } + }; + + static CPUAllocator* cpu_alloc = nullptr; + } // namespace + + SubAllocator::SubAllocator(const std::vector& alloc_visitors, + const std::vector& free_visitors) + : alloc_visitors_(alloc_visitors), free_visitors_(free_visitors) {} + + void SubAllocator::VisitAlloc(void* ptr, int index, size_t num_bytes) { + for (const auto& v : alloc_visitors_) { + v(ptr, index, num_bytes); + } + } + + void SubAllocator::VisitFree(void* ptr, int index, size_t num_bytes) { + for (const auto& v : free_visitors_) { + v(ptr, index, num_bytes); + } + } + + Allocator* cpu_allocator_base() { + if (cpu_alloc == nullptr) { + cpu_alloc = new CPUAllocator; + } + return cpu_alloc; + } + + Allocator* cpu_allocator(int numa_node) { + // Simplified implementation that ignores numa_node + return cpu_allocator_base(); + } + + void EnableCPUAllocatorStats() {} + void DisableCPUAllocatorStats() {} + bool CPUAllocatorStatsEnabled() { return false; } + void EnableCPUAllocatorFullStats() {} + bool CPUAllocatorFullStatsEnabled() { return false; } + + + /***********************************************************/ + // tensorflow/core/framework/op.h // + /***************************************************/ + OpRegistry::OpRegistry() = default; + OpRegistry::~OpRegistry() = default; + + OpRegistry* OpRegistry::Global() { + static OpRegistry* global_op_registry = new OpRegistry; + return global_op_registry; + } + + Status OpRegistry::ProcessRegistrations() const { return Status::OK(); } + + void OpRegistry::DeferRegistrations() { + // Placeholder implementation + } + + /***********************************************************/ + // tensorflow/core/framework/device.h // + /***************************************************/ + + DeviceBase::~DeviceBase() = default; + + const DeviceAttributes& DeviceBase::attributes() const { + LOG(FATAL) << "Device does not implement attributes()"; + static DeviceAttributes empty_attributes; + return empty_attributes; + } + + const std::string& DeviceBase::name() const { + LOG(FATAL) << "Device does not implement name()"; + static std::string empty_name; + return empty_name; + } + + Device::Device(Env* env, const DeviceAttributes& device_attributes) + : DeviceBase(env), device_attributes_(device_attributes) {} + + Device::~Device() = default; + + /***********************************************************/ + // tensorflow/cc/saved_model/loader.h // + /***************************************************/ Status LoadSavedModel(const SessionOptions& session_options, const RunOptions& run_options, const std::string& export_dir, const std::unordered_set& tags, SavedModelBundle* const bundle) { return Status::OK(); } - errors::Code Status::code() const { return errors::Code::OK; } - - const std::string& Status::error_message() const { - static std::string null = ""; - return null; - } - - void Status::IgnoreError() const {} - bool Status::ok() const { return true; } - Status& Status::operator=(Status const&) { return *this; } - std::string Status::ToString() const { return ""; } - SubAllocator::SubAllocator( - std::vector, - std::allocator > > const&, - std::vector, - std::allocator > > const&) {} - void SubAllocator::VisitAlloc(void*, int, unsigned long) {} - void SubAllocator::VisitFree(void*, int, unsigned long) {} - - TensorShape::TensorShape() {} - void TensorShape::AddDim(long) {} - int TensorShape::dims() const { return 0; } - int64_t TensorShape::dim_size(int) const { return 0; } - int64_t TensorShape::num_elements() const { return 0; } - TensorShape::TensorShape(std::initializer_list) {} - - std::string Tensor::DebugString(int) const { return ""; } - Tensor::~Tensor() {} - Tensor::Tensor(Allocator*, tensorflow::DataType, TensorShape const&) {} - Tensor::Tensor(Tensor const&) {} - Tensor::Tensor(DataType, TensorShape const&, TensorBuffer*) {} - size_t Tensor::TotalBytes() const { return 0; } }; // namespace tensorflow #pragma GCC diagnostic pop }; // namespace tensorflow_cpy