Correctness has a reference.
CPU execution is a required part of the runtime. Accelerator operations are checked against it with explicit numerical tolerances.
INDEPENDENT · APACHE 2.0 · PRE-ALPHA
Tensor compute.
From Python
to silicon.
A compact tensor runtime for people who want to understand what runs underneath. Python up front. C++20 at the core. CPU, Metal, and CUDA behind one interface.
Experimental by design. Explicit about its limits.
first_tensor.pyPYTHON
import tensorcx as cx
device = cx.best_device()
x = cx.ones((1_000_000,), dtype=cx.float32, device=device)
y = cx.ones((1_000_000,), dtype=cx.float32, device=device)
z = x + y
print(z.cpu().numpy()[:5])
# [2. 2. 2. 2. 2.]OUTPUT
[2. 2. 2. 2. 2.]One million elements. One familiar API.Python APIC++20 core backend-neutral
CPUMetalCUDA
INTERFACEPython 3.11+FOUNDATIONC++20 / nanobindEXECUTIONSynchronous & explicitLICENSEApache 2.0
01 / THE APPROACH
Small enough to follow.
Built to be understood.
Explore tensor execution without losing sight of the runtime, the memory, or the hardware.
The core stays neutral.
Device discovery, buffers, and operation dispatch share a contract. Platform-specific details stay inside each backend.
Experiments stay explicit.
Custom kernels live under cx.experimental. Metal code generation and optional MLIR compilation have defined, narrow scopes.
02 / HARDWARE, HONESTLY
Know what runs. And where.
CPUREFERENCE
The required baseline. Tensor operations, matmul, reductions, and normalization.
MetalAPPLE SILICONStatic Metal kernels, an optional optimized matmul path, and experimental generated kernels.
CUDAOPT-INFloat32 primitives validated on sm_52. Broader GPU and toolchain coverage remains open.
Current scope: contiguous tensors, broadcasting, explicit devices and dtype conversion. Autograd and distributed training are outside the current runtime.
03 / OPEN THE WORKBENCH