mirror of https://github.com/hpcaitech/ColossalAI
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import operator
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import torch
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from ..registry import meta_patched_function
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from colossalai.fx.proxy import ColoProxy
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@meta_patched_function.register(operator.getitem)
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def operator_getitem(a, b):
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# copied from huggingface.utils.fx
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def to_concrete(t):
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if isinstance(t, torch.Tensor):
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concrete = torch.ones_like(t, device="cpu")
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if concrete.dtype in [torch.float16, torch.float32, torch.float64, torch.int32]:
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concrete = concrete.to(torch.int64)
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return concrete
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return t
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def _slice_convert(slice_obj):
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attrs = {'start': slice_obj.start, 'stop': slice_obj.stop, 'step': slice_obj.step}
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new_attrs = _slice_attr_convert(attrs)
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attr_dict_to_tuple = (new_attrs['start'], new_attrs['stop'], new_attrs['step'])
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return slice(*attr_dict_to_tuple)
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def _slice_attr_convert(attrs):
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new_attrs = {}
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for key, value in attrs.items():
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if isinstance(value, ColoProxy):
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new_attrs[key] = value.meta_data
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else:
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new_attrs[key] = value
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return new_attrs
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if isinstance(b, tuple):
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b = list(b)
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for index, element in enumerate(b):
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if isinstance(element, slice):
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b[index] = _slice_convert(element)
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b = tuple(b)
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elif isinstance(b, slice):
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b = _slice_convert(b)
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if isinstance(a, torch.Tensor):
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# TODO: infer shape without performing the computation.
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if isinstance(b, tuple):
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b = tuple(map(to_concrete, b))
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else:
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b = to_concrete(b)
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return operator.getitem(torch.empty_like(a, device="cpu"), b).to("meta")
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if isinstance(a, ColoProxy):
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# TODO: infer shape without performing the computation.
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if isinstance(b, tuple):
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b = tuple(map(to_concrete, b))
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else:
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b = to_concrete(b)
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return operator.getitem(torch.empty_like(a.meta_data, device="cpu"), b).to("meta")
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return operator.getitem(a, b)
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