Tensor parallelism for multi-step scheduling #457
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tzielinski-habana
requested review from
michalkuligowski,
kzawora-intel,
kdamaszk and
mswiniarsk
michalkuligowski
approved these changes
Nov 5, 2024
| @@ -97,7 +98,10 @@ def subtuple(obj: object, | |||
| if to_override is None: | |||
| to_override = {} | |||
| fields = set(to_copy) | set(to_override.keys()) | |||
| values = {f: to_override.get(f, getattr(obj, f)) for f in fields} | |||
| if type(obj) is dict: | |||
| values = {key: obj[key] for key in fields if key in obj} | |||
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Is this method called often or sizes of object fields huge?
keys_in_obj = [key for key in fields if key in obj]
values = {key: obj[key] for key in keys_in_obj}
I think (based on obj size 1000 and fields 100 in timeit) prefiltering may decrease number of total iterations depending on the size of the obj.
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This PR implements tensor parallelism for multi-step scheduling.