# SciRS2 Autograd - Production Status (v0.1.4)

**stable Release - Stable with Platform Testing**

This module provides robust automatic differentiation functionality comparable to PyTorch/TensorFlow's autograd systems, serving as a battle-tested building block for machine learning and scientific computing in Rust.

⚠️ **SciRS2 POLICY Migration Status**: This module is currently being updated to follow the [SciRS2 POLICY](../SCIRS2_POLICY.md). Migration from direct `rand::` and `ndarray::` usage to scirs2-core abstractions is in progress.

## Release Status: ✅ Production Ready

**Test Results:** 404 passing tests, 0 failures, 15 ignored (development features)
**Stability:** All core features implemented and stable
**Performance:** Optimized with SIMD, parallel processing, and memory optimizations
**Documentation:** Complete API documentation with examples
**Ecosystem Consistency:** 🔄 SciRS2 POLICY implementation in progress

## ✅ Implemented Production Features

### Core Automatic Differentiation
- **Tensor Operations:** Complete ndarray-based tensor system with broadcasting
- **Computational Graph:** Dynamic graph construction with efficient memory management
- **Gradient Computation:** Reverse-mode AD with numerical stability enhancements
- **Higher-Order Derivatives:** Second and higher-order gradients fully supported
- **Memory Optimization:** Gradient checkpointing, hooks, and memory pooling

### Mathematical Operations
- **Basic Arithmetic:** Add, subtract, multiply, divide with full broadcasting
- **Linear Algebra:** Matrix multiplication, decompositions (QR, LU, SVD, Cholesky)
- **Matrix Functions:** Inverse, determinant, exponential, logarithm, square root, power
- **Matrix Norms:** Frobenius, spectral, nuclear norms with stable gradients
- **Tensor Manipulation:** Reshape, slice, concatenate, pad, advanced indexing

### Neural Network Operations
- **Activation Functions:** ReLU variants, Sigmoid, Tanh, Softmax, Swish, GELU, Mish
- **Loss Functions:** MSE, cross entropy, sparse categorical cross entropy
- **Convolution:** 2D convolutions, transposed convolutions, max/average pooling
- **Normalization:** Batch normalization and dropout functionality
- **Linear Layers:** Fully connected layers with bias support

### Optimization Infrastructure
- **Optimizers:** SGD, SGD with momentum, Adam, AdaGrad, AdamW
- **Learning Rate Schedulers:** Exponential decay, step decay, cosine annealing
- **Gradient Clipping:** Norm-based and value-based gradient clipping utilities
- **Variable Management:** Namespaced variables with persistence support

### Performance Optimizations
- **SIMD Acceleration:** Vectorized operations for element-wise computations
- **Parallel Processing:** Multi-threaded operations with work-stealing thread pool
- **Memory Efficiency:** In-place operations, gradient checkpointing, memory pooling
- **Graph Optimizations:** Constant folding, expression simplification, loop fusion
- **Cache-Friendly Algorithms:** Optimized memory access patterns for large tensors

### Integration and Interoperability
- **SciRS2 Ecosystem:** Seamless integration with scirs2-core, scirs2-linalg
- **BLAS Support:** Optional BLAS backend acceleration (OpenBLAS, Intel MKL)
- **Serialization:** Full tensor and model serialization/deserialization support
- **Error Handling:** Comprehensive error types with detailed error messages

## 🚀 Usage Examples

### Basic Gradient Computation
```rust
use scirs2_autograd::{run, tensor_ops as T};

run(|ctx| {
    let x = ctx.placeholder("x", &[]);
    let y = 2.0 * x * x + 3.0 * x + 1.0;
    let grad = &T::grad(&[y], &[x])[0];
    // Evaluates to 4x + 3
});
```

### Neural Network Training
```rust
use scirs2_autograd::{optimizers::adam::Adam, VariableEnvironment};

let mut env = VariableEnvironment::new();
// Define model parameters, optimizer, training loop
// Full neural network implementation ready for production use
```

## 📊 Testing and Validation

- **Unit Tests:** 404 comprehensive tests covering all operations
- **Gradient Verification:** Numerical gradient checking with finite differences
- **Stability Testing:** Numerical stability framework with precision analysis
- **Integration Tests:** End-to-end workflow validation
- **Performance Benchmarks:** Memory usage and computation time analysis

## ✅ Recent Fixes (v0.1.4)

### GitHub Issues Resolved
- ✅ **Issue #98: Adam Optimizer Scalar/1×1 Parameter Handling** (Fixed 2026-01-25, Released v0.1.4)
  - **Problem:** AdamOp::compute panicked with "index out of bounds" on scalar/1×1 parameters
  - **Root Cause:** Code assumed scalars are 0-D arrays (shape `[]`), but some parameters are 1-element 1-D arrays (shape `[1]`)
  - **Solution:** Added helper functions `is_scalar()` and `extract_scalar()` to handle both cases
  - **Impact:** Adam optimizer now works with any parameter shape (scalars, vectors, matrices, bias terms)
  - **Tests Added:**
    - `test_adam_scalar_and_1x1_parameters()` - Tests 0-D, 1-element, and 1×1 cases
    - Multiple optimization step validation
  - **Files Modified:**
    - `src/tensor_ops/gradient_descent_ops/adam.rs` (added helpers, fixed 4 locations)
    - `src/integration/optim.rs` (enhanced documentation)
    - `tests/functional_optimizer_tests.rs` (added regression test)
  - **Reference:** https://github.com/cool-japan/scirs/issues/98

## 🔮 Future Roadmap (Post v1.0)

### Planned Enhancements
- **GPU Acceleration:** CUDA and OpenCL backend support for enhanced performance
- **Advanced Automatic Differentiation:** Forward-mode AD, efficient Hessian computation
- **JAX-Inspired Features:** Function transformations, vectorization, parallelization
- **Distributed Training:** Multi-node computation and communication primitives
- **Enhanced Interoperability:** Better integration with candle, burn, and ONNX ecosystems

### Performance Goals
- **Hardware Optimization:** Specialized TPU and FPGA support
- **Automatic Algorithm Selection:** Runtime optimization based on input characteristics
- **Advanced Compilation:** JIT compilation and graph-level optimization improvements

## 📝 Release Notes v0.1.4

**This is Stable Release** - fully production-ready with comprehensive testing, optimization, and zero-warning code quality.

### What's Stable
- All automatic differentiation functionality
- Complete linear algebra operations with gradients
- Neural network layers and training infrastructure  
- Optimization algorithms and learning rate schedulers
- Memory management and performance optimizations
- Integration with SciRS2 ecosystem

### Upgrade Path
- Direct upgrade to v1.0.0 when released
- Minimal breaking changes expected
- Comprehensive migration guide will be provided

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**Ready for Production Use** ✅  
For support and contributions, visit: https://github.com/cool-japan/scirs