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48 lines
2.0 KiB
48 lines
2.0 KiB
13 hours ago
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# NOTE: this is probably the wrong backend to work on, you want backend.py
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# discussion about this in #pytorch-backend on our Discord
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# this is using high level debug features from torch and we should integrate deeper
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from tinygrad import Tensor
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import torch, contextlib
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from torch.utils._python_dispatch import TorchDispatchMode
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from tinygrad.dtype import _from_torch_dtype
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def empty_memory_format(size, dtype=None, layout=None, device=None, pin_memory=False, memory_format=None):
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return TTensor(Tensor.empty(*size, dtype=_from_torch_dtype(dtype)))
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# NOTE: if we have a way to change wrap/unwrap, these can be the same methods from backend.py
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tiny_backend = {
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"aten.empty.memory_format": empty_memory_format,
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"aten.view.default": lambda x,sz: TTensor(x.tiny.reshape(sz)),
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"aten.abs.default": lambda x: TTensor(x.tiny.abs()),
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"aten.eq.Tensor": lambda x,y: TTensor(x.tiny == y.tiny),
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"aten.bitwise_and.Tensor": lambda x,y: TTensor(x.tiny & y.tiny),
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"aten.ne.Scalar": lambda x,y: TTensor(x.tiny != y),
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"aten.mul.Tensor": lambda x,y: TTensor(x.tiny * y.tiny),
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"aten.masked_select.default": lambda x,y: TTensor(Tensor(x.tiny.numpy()[y.tiny.numpy()])),
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}
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class TTensor(torch.Tensor):
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tiny: Tensor
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context = contextlib.nullcontext
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@staticmethod
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def __new__(cls, tiny, *args, **kwargs):
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out = torch.Tensor._make_wrapper_subclass(cls, tiny.shape)
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torch._C._set_throw_on_mutable_data_ptr(out)
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out.tiny = tiny
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return out
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def __repr__(self): return super().__repr__(tensor_contents=f"{self.tiny}")
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def __torch_dispatch__(cls, func, types, args, kwargs=None):
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print(f"Dispatch Log: {func}(*{[type(x) for x in args]}, **{kwargs.keys()})")
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#print(f"Dispatch Log: {func}(*{args}, **{kwargs})")
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new_func = tiny_backend.get(str(func), None)
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if new_func is None: raise NotImplementedError(f"add support for {func}")
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return new_func(*args, **(kwargs or {}))
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class Dispatcher(TorchDispatchMode): __torch_dispatch__ = TTensor.__torch_dispatch__
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Dispatcher().__enter__()
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if __name__ == "__main__":
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a = torch.empty((4,), dtype=torch.int)
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