docs/runtime.md
tinygrad supports various runtimes, enabling your code to scale across a wide range of devices. The default runtime can be automatically selected based on the available hardware, or you can force a specific runtime to be default using environment variables (e.g., CPU=1).
| Runtime | Description | Compiler Options | Requirements |
|---|---|---|---|
| NV | Provides acceleration for NVIDIA GPUs | nvrtc (default) | |
PTX (NV_PTX=1) | Ampere/Ada/Blackwell series GPUs. | ||
You can select an interface via NV_IFACE=(NVK|PCI). See NV interfaces for details. | |||
| AMD | Provides acceleration for AMD GPUs | LLVM (AMD_LLVM=1) | |
HIP/COMGR (AMD_HIP=1) | RDNA2 or newer GPUs. | ||
You can select an interface via AMD_IFACE=(KFD|PCI|USB). See AMD interfaces for details. | |||
| QCOM | Provides acceleration for QCOM GPUs | - | 6xx series GPUs |
| METAL | Utilizes Metal for acceleration on Apple devices | - | M1+ Macs; Metal 3.0+ for bfloat support |
| CUDA | Utilizes CUDA for acceleration on NVIDIA GPUs | nvrtc (default) | |
PTX (CUDA_PTX=1) | NVIDIA GPU with CUDA support | ||
| CL | Accelerates computations using OpenCL on GPUs | - | OpenCL 2.0 compatible device |
| CPU | Runs on CPU using the clang or llvm compiler | Clang JIT (default) | |
LLVM IR (CPU_LLVM=1) | clang compiler in system PATH | ||
| WEBGPU | Runs on GPU using the Dawn WebGPU engine (used in Google Chrome) | - | Dawn library installed and discoverable. Binaries: pydawn v0.3.0 |
tinygrad provides interoperability with OpenCL and PyTorch, allowing efficient tensor data sharing between frameworks through the Tensor.from_blob API. This enables zero-copy operations by working directly with external memory pointers.
Important: When using external memory pointers with tinygrad tensors, you must ensure these pointers remain valid throughout the entire lifetime of the tinygrad tensor to prevent memory corruption.
CUDA/METAL PyTorch InteroperabilityYou can seamlessly work with CUDA/MPS tensors between PyTorch and tinygrad without data copying:
from tinygrad.dtype import _from_torch_dtype
tensor1 = torch.tensor([1.0, 2.0, 3.0], device=torch.device("cuda"))
tiny_tensor1 = Tensor.from_blob(tensor1.data_ptr(), tensor1.shape, dtype=_from_torch_dtype(tensor1.dtype), device='CUDA')
# Before tinygrad calculations, mps needs to be synchronized to make sure data is valid.
if data.device.type == "mps": torch.mps.synchronize()
else: torch.cuda.synchronize()
x = (tiny_tensor1 + 1).realize()
QCOM OpenCL Interoperabilitytinygrad supports OpenCL interoperability on QCOM backend.
Buffer interop allows direct access to OpenCL memory buffers:
# create raw opencl buffer.
cl_buf = cl.clCreateBuffer(cl_context, cl.CL_MEM_READ_WRITE, 0x100, None, status := ctypes.c_int32())
# extract pointers
cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_buf), 8).cast('Q')[0]
rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
# create tiny tensor
tiny = Tensor.from_blob(rawbuf_ptr, (8, 8), dtype=dtypes.int, device='QCOM')
And the same for the images:
# create cl image.
cl_img = cl.clCreateImage2D(cl_context, cl.CL_MEM_READ_WRITE, cl.cl_image_format(cl.CL_RGBA, cl.CL_FLOAT), w, h, 0, None, status := ctypes.c_int32())
# extract pointers
cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_img), 8).cast('Q')[0]
rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
# create tiny tensor
tiny = Tensor.from_blob(rawbuf_ptr, (h*w*4,), dtype=dtypes.imagef((h,w)), device='QCOM')
AMD backend supports several interfaces for communicating with devices:
KFD: uses the amdgpu driverPCI: uses the AM driverUSB: USB3 interface for asm24xx chips.You can force an interface by setting AMD_IFACE to one of these values. In the case of AMD_IFACE=PCI, this may unbind your GPU from the amdgpu driver.
NV backend supports several interfaces for communicating with devices:
NVK: uses the nvidia driverPCI: uses the NV driver