Files

T

Omar Sobh 16161bb9df deps: align all 56 per-crate Cargo.toml files to thiserror v2

The workspace root was upgraded to thiserror = "2" in an earlier commit,
but 56 per-crate Cargo.toml files still independently declared "1.0".
These crates do not use workspace.dependencies inheritance for thiserror.
All updated to thiserror = "2" for complete fleet alignment.

Includes: rtx-backend, rtx-tensor, rtx-losses, rtx-backend-cuda/rocm/metal,
all training crates (rtx-auto, rtx-rl, rtx-distributed, rtx-federated, etc.),
specialized crates (rtx-science, rtx-platform, rtx-nmf, rtx-neuro-*),
production crates (rtx-streaming, rtx-serving-api), and all demo crates.

cargo check --workspace: PASSES.

2026-04-26 11:45:14 -07:00

src

Initial commit

2026-03-04 00:08:42 +00:00

Cargo.toml

deps: align all 56 per-crate Cargo.toml files to thiserror v2

2026-04-26 11:45:14 -07:00

README.md

Initial commit

2026-03-04 00:08:42 +00:00

README.md

rtx-rustybooks

Integration bridge between RustyBooks GPU crates and RustyTorch ML framework.

Note

: The rustybooks project (rustybooks) has been merged into horizon. This integration crate is currently disabled pending refactoring to work with the new unified architecture.

Status

Backend	Status	Tests
Metal (Apple Silicon)	Disabled	Awaiting integration refactor
CUDA	Disabled	Awaiting integration refactor

Features

[dependencies]
rtx-rustybooks = { path = "crates/integration/rtx-rustybooks", features = ["metal"] }

Available Features

Feature	Description	Status
`metal`	Full Metal backend (macOS)	Working
`direct-storage-metal`	NVMe→GPU transfers	Working
`profiling-metal`	GPU profiling/counters	Working
`tma-metal`	Tensor Memory Accelerator	Working
`cuda`	CUDA backend	Needs fixes

Modules

memory - Direct Storage Integration

Wraps gpu-direct for zero-copy NVMe→GPU transfers.

use rtx_rustybooks::memory::{DirectStoragePool, DirectBuffer};

let pool = DirectStoragePool::new()?;
let mut buffer = pool.allocate(1024 * 1024)?; // 1MB

// Load directly from NVMe to GPU
pool.load_from_storage(Path::new("weights.bin"), &mut buffer).await?;

profiler - Extended Kernel Profiling

Wraps gpu-profiler for detailed GPU metrics.

use rtx_rustybooks::profiler::{ExtendedKernelProfiler, ReportFormat};

let profiler = ExtendedKernelProfiler::new()?;

let result = profiler.profile_launch("matmul", || {
    // GPU kernel execution
});

println!("Occupancy: {:.1}%", result.warp_occupancy() * 100.0);
println!("Bandwidth: {:.1} GB/s", result.achieved_bandwidth_gbps());

// Export report
let report = profiler.export_report(ReportFormat::Html);

tensor - TMA Operations

Wraps tensor-accelerator for optimized tensor operations.

use rtx_rustybooks::tensor::{TmaOps, TileConfig, TensorOp, Architecture};

// Generate optimized kernel for architecture
let kernel = generate_tiled_kernel(
    TensorOp::Gemm,
    &[1024, 1024],
    Architecture::AppleSilicon
);

// TMA-accelerated matmul
let c = a.matmul_tma(&b)?;

Architecture Support

Architecture	Tile Config	Shared Memory
Apple Silicon (M1-M4)	64x64x32	32 KB
NVIDIA Ampere (A100)	128x128x32	164 KB
NVIDIA Blackwell (B200)	128x128x64	228 KB

Known Issues

CUDA Backend

The CUDA backend has conditional compilation conflicts in gpu-direct:

GPUBuffer type defined in both memory.rs and fallback.rs
When CUDA feature is enabled but runtime is absent, type mismatches occur

Workaround: Use Metal backend on macOS, or ensure CUDA toolkit is installed for CUDA builds.

Testing

# Run all tests with Metal
cargo test -p rtx-rustybooks --features "metal"

# Run without GPU features (basic tests only)
cargo test -p rtx-rustybooks

Dependencies

This crate was designed to bridge:

gpu-direct → Direct storage I/O
gpu-profiler → GPU performance counters
tensor-accelerator → TMA primitives

These crates were originally in RustyBooks (rustybooks) and have been merged into horizon.