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metal PyTorch interop (#9229)

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* add from_blob support to mps cuda

* objc_id

* metal pytorch interop

* fix comments

---------

Co-authored-by: George Hotz <geohot@gmail.com>
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nimlgen
2025-02-24 22:36:08 +03:00
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@@ -20,13 +20,19 @@ tinygrad provides interoperability with OpenCL and PyTorch, allowing efficient t
**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` PyTorch Interoperability
### `CUDA`/`METAL` PyTorch Interoperability
You can seamlessly work with CUDA tensors between PyTorch and tinygrad without data copying:
You can seamlessly work with CUDA/MPS tensors between PyTorch and tinygrad without data copying:
```python
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 Interoperability