Broadcast aten.maximum.default and aten.minimum.default inputs
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aten.maximum.default inputs
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aten.maximum.default inputsaten.maximum.default and aten.minimum.default inputs
| if len(args) > 1: | ||
| other_tensor = args[1] | ||
| else: | ||
| other_tensor = kwargs["other"] |
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| if len(args) > 1: | |
| other_tensor = args[1] | |
| else: | |
| other_tensor = kwargs["other"] | |
| other_tensor = None # Explicitly initialize to a default value. | |
| if len(args) > 1: | |
| other_tensor = args[1] | |
| else: | |
| other_tensor = kwargs["other"] |
| if new_shape is not None or new_dtype is not None: | ||
| shape = new_shape if new_shape is not None else new_node.meta["val"].size() | ||
| dtype = new_dtype if new_dtype is not None else new_node.meta["val"].dtype | ||
| fake_mode = FakeTensorMode() | ||
| fake_tensor = fake_mode.from_tensor(torch.zeros(shape, dtype=dtype)) | ||
| new_node.meta["val"] = fake_tensor |
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can you clarify the need for this?
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call_function is to create a new_node and is assigned meta from current_node which is being traversed, but new_node's shape & dtype may not same with cur_node (for example, new_node.target is aten.expand from current_node and then shape change), so there give the option for user to specify the correct shape & dtype
| if input_tensor_shape == torch.Size([]): | ||
| input_tensor_shape = torch.Size([1]) |
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can you clarify the need for this?
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below code cannot handle [], and the result of expand [] and [1] is the same, so I see [] as [1]
ok, then I cancel this PR and just wait tt-metal support tenstorrent/tt-metal#12852 |
Pull request was converted to draft
Ticket
#592
Problem description
aten.maximumhave some broadcasting issue of tenstorrent/tt-metal#12852 , I do the workaround by usingaten.expandto broadcast its inputs beforehand, andaten.expandmay lowered to ttnn laterWhat's changed