Crate tensorlogic_scirs_backend 

SciRS2-backed executor (CPU/SIMD/GPU via features).

Version: 0.1.0-beta.1 | Status: Production Ready

This crate provides a production-ready implementation of the TensorLogic execution traits using the SciRS2 scientific computing library.

Core Features

Execution Engine

• Forward pass: Tensor operations (einsum, element-wise, reductions)
• Backward pass: Automatic differentiation with stored intermediate values
• Gradient checking: Numeric verification for correctness
• Batch execution: Parallel processing support for multiple inputs

Performance

• Memory pooling: Efficient tensor allocation with shape-based reuse
• Operation fusion: Analysis and optimization opportunities
• SIMD support: Vectorized operations via feature flags
• Profiling: Detailed performance monitoring and tracing

Reliability

• Error handling: Comprehensive error types with detailed context
• Execution tracing: Multi-level debugging and operation tracking
• Numerical stability: Fallback mechanisms for NaN/Inf handling
• Shape validation: Runtime shape inference and verification

Testing

• 104 tests: Including unit, integration, and property-based tests
• Property tests: Mathematical properties verified with proptest
• Gradient tests: Numeric gradient checking for autodiff correctness

Module Organization

• executor: Core Scirs2Exec implementation
• autodiff: Backward pass and gradient computation
• gradient_ops: Advanced gradient operations (STE, Gumbel-Softmax, soft quantifiers)
• error: Comprehensive error types and validation
• fallback: Numerical stability and NaN/Inf handling
• tracing: Execution debugging and performance tracking
• memory_pool: Efficient tensor allocation
• fusion: Operation fusion analysis
• gradient_check: Numeric gradient verification
• shape_inference: Runtime shape validation
• batch_executor: Parallel batch processing
• profiled_executor: Performance profiling wrapper
• capabilities: Runtime capability detection
• dependency_analyzer: Graph dependency analysis for parallel execution
• parallel_executor: Multi-threaded parallel execution using Rayon
• device: Device management (CPU/GPU selection)
• execution_mode: Execution mode abstractions (Eager/Graph/JIT)
• precision: Precision control (f32/f64/mixed)

Re-exports

pub use batch_executor::ParallelBatchExecutor;

pub use checkpoint::Checkpoint;

pub use checkpoint::CheckpointConfig;

pub use checkpoint::CheckpointManager;

pub use checkpoint::CheckpointMetadata;

pub use cuda_detect::cuda_device_count;

pub use cuda_detect::cuda_devices_to_device_list;

pub use cuda_detect::detect_cuda_devices;

pub use cuda_detect::is_cuda_available;

pub use cuda_detect::CudaDeviceInfo;

pub use custom_ops::BinaryCustomOp;

pub use custom_ops::CustomOp;

pub use custom_ops::CustomOpContext;

pub use custom_ops::EluOp;

pub use custom_ops::GeluOp;

pub use custom_ops::HardSigmoidOp;

pub use custom_ops::HardSwishOp;

pub use custom_ops::LeakyReluOp;

pub use custom_ops::MishOp;

pub use custom_ops::OpRegistry;

pub use custom_ops::SoftplusOp;

pub use custom_ops::SwishOp;

pub use dependency_analyzer::DependencyAnalysis;

pub use dependency_analyzer::DependencyStats;

pub use dependency_analyzer::OperationDependency;

pub use device::Device;

pub use device::DeviceError;

pub use device::DeviceManager;

pub use device::DeviceType;

pub use error::NumericalError;

pub use error::NumericalErrorKind;

pub use error::ShapeMismatchError;

pub use error::TlBackendError;

pub use error::TlBackendResult;

pub use execution_mode::CompilationStats;

pub use execution_mode::CompiledGraph;

pub use execution_mode::ExecutionConfig;

pub use execution_mode::ExecutionMode;

pub use execution_mode::MemoryPlan;

pub use execution_mode::OptimizationConfig;

pub use fallback::is_valid;

pub use fallback::sanitize_tensor;

pub use fallback::FallbackConfig;

pub use gpu_readiness::assess_gpu_readiness;

pub use gpu_readiness::generate_recommendations;

pub use gpu_readiness::recommend_batch_size;

pub use gpu_readiness::GpuCapability;

pub use gpu_readiness::GpuReadinessReport;

pub use gpu_readiness::WorkloadProfile;

pub use gradient_ops::gumbel_softmax;

pub use gradient_ops::gumbel_softmax_backward;

pub use gradient_ops::soft_exists;

pub use gradient_ops::soft_exists_backward;

pub use gradient_ops::soft_forall;

pub use gradient_ops::soft_forall_backward;

pub use gradient_ops::ste_threshold;

pub use gradient_ops::ste_threshold_backward;

pub use gradient_ops::GumbelSoftmaxConfig;

pub use gradient_ops::QuantifierMode;

pub use gradient_ops::SteConfig;

pub use graph_optimizer::GraphOptimizer;

pub use graph_optimizer::GraphOptimizerBuilder;

pub use graph_optimizer::OptimizationPass;

pub use graph_optimizer::OptimizationStats;

pub use inplace_ops::can_execute_inplace;

pub use inplace_ops::is_shape_preserving;

pub use inplace_ops::InplaceExecutor;

pub use inplace_ops::InplaceStats;

pub use memory_profiler::AllocationRecord;

pub use memory_profiler::AtomicMemoryCounter;

pub use memory_profiler::MemoryProfiler;

pub use memory_profiler::MemoryStats as ProfilerMemoryStats;

pub use metrics::format_bytes;

pub use metrics::shared_metrics;

pub use metrics::AtomicMetrics;

pub use metrics::MemoryStats;

pub use metrics::MetricsCollector;

pub use metrics::MetricsConfig;

pub use metrics::MetricsSummary;

pub use metrics::OperationRecord;

pub use metrics::OperationStats;

pub use metrics::SharedMetrics;

pub use metrics::ThroughputStats;

pub use parallel_executor::ParallelConfig;

pub use parallel_executor::ParallelScirs2Exec;

pub use parallel_executor::ParallelStats;

pub use precision::ComputePrecision;

pub use precision::Precision;

pub use precision::PrecisionConfig;

pub use precision::Scalar;

pub use profiled_executor::ProfiledScirs2Exec;

pub use quantization::calibrate_quantization;

pub use quantization::QatConfig;

pub use quantization::QuantizationGranularity;

pub use quantization::QuantizationParams;

pub use quantization::QuantizationScheme;

pub use quantization::QuantizationStats;

pub use quantization::QuantizationType;

pub use quantization::QuantizedTensor;

pub use shape_inference::validate_tensor_shapes;

pub use shape_inference::Scirs2ShapeInference;

pub use tracing::ExecutionTracer;

pub use tracing::TraceEvent;

pub use tracing::TraceLevel;

Modules

batch_executor

Batch execution support for parallel processing.

capabilities

Backend capability detection and reporting.

checkpoint

Checkpoint and resume functionality for training workflows.

cuda_detect

CUDA device detection utilities.

custom_ops

Custom operations infrastructure with dynamic registration.

dependency_analyzer

Dependency analysis for parallel execution of EinsumGraph operations.

device

Device management for tensor computations.

error

Comprehensive error types for tensorlogic-scirs-backend.

execution_mode

Execution mode abstractions for different execution strategies.

fallback

Fallback mechanisms for numerical stability.

fusion

Operation fusion for improved performance.

gpu_readiness

GPU Readiness Framework

gradient_check

Numeric gradient checking utilities for verifying analytical gradients.

gradient_ops

Advanced gradient operations for non-differentiable logical operations.

graph_optimizer

Graph optimization passes for improved execution performance.

inplace_ops

In-place operations for memory optimization.

memory_pool

Memory pooling for efficient tensor allocation.

memory_profiler

Memory Profiling Utilities for TensorLogic

metrics

Comprehensive performance monitoring and metrics collection.

parallel_executor

Parallel executor implementation using Rayon for multi-threaded execution.

precision

Precision control for tensor computations.

profiled_executor

Performance profiling support for execution monitoring.

quantization

Quantization Infrastructure for TensorLogic

shape_inference

Shape inference and validation support.

tracing

Execution tracing and debugging support.

Structs

ForwardTape

Stores intermediate values from forward pass for gradient computation

Scirs2Exec

Type Aliases

Scirs2Tensor