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[BIT-636] Change u16 weight normalization to max-upscaling #1241

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merged 5 commits into from
Apr 14, 2023
Merged

[BIT-636] Change u16 weight normalization to max-upscaling #1241

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6cafb4b
Change u16 weight normalization to max-upscaling
opentaco Mar 29, 2023
fc0dabb
Skip zero sum in weight conversion
opentaco Mar 29, 2023
820c6f7
Round u16 weights
opentaco Apr 5, 2023
41a4e65
Merge branch 'staging' into feature/BIT-636/max-upscale-neuron-info
opentaco Apr 6, 2023
cf89a30
Merge branch 'staging' into feature/BIT-636/max-upscale-neuron-info
opentaco Apr 14, 2023
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10 changes: 7 additions & 3 deletions bittensor/utils/weight_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,10 @@ def convert_weight_uids_and_vals_to_tensor( n: int, uids: List[int], weights: Li
"""
row_weights = torch.zeros( [ n ], dtype=torch.float32 )
for uid_j, wij in list(zip( uids, weights )):
row_weights[ uid_j ] = float( wij ) / float(U16_MAX)
row_weights[ uid_j ] = float( wij ) # assumes max-upscaled values (w_max = U16_MAX).
row_sum = row_weights.sum()
if row_sum > 0:
row_weights /= row_sum # normalize
return row_weights

def convert_bond_uids_and_vals_to_tensor( n: int, uids: List[int], bonds: List[int] ) -> 'torch.LongTensor':
Expand Down Expand Up @@ -126,12 +129,13 @@ def convert_weights_and_uids_for_emit( uids: torch.LongTensor, weights: torch.Fl
if sum(weights) == 0:
return [],[] # Nothing to set on chain.
else:
weights = [ float(value) / sum(weights) for value in weights] # Initial normalization.
max_weight = float(max(weights))
weights = [float(value) / max_weight for value in weights] # max-upscale values (max_weight = 1).

weight_vals = []
weight_uids = []
for i, (weight_i, uid_i) in enumerate(list(zip(weights, uids))):
uint16_val = int(float(weight_i) * int(U16_MAX)) # convert to int representation.
uint16_val = round(float(weight_i) * int(U16_MAX)) # convert to int representation.

# Filter zeros
if uint16_val != 0: # Filter zeros
Expand Down