Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fixes logging when setting weights #2465

Merged
merged 2 commits into from
Nov 25, 2024
+16 −17
Merged

Fixes logging when setting weights #2465

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
aa3ba41
Fixes logging breaking when setting weights
ibraheem-opentensor Nov 23, 2024
bf92d2f
Adds back kwargs
ibraheem-opentensor Nov 23, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
33 changes: 16 additions & 17 deletions bittensor/utils/weight_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@
"""Conversion for weight between chain representation and np.array or torch.Tensor"""

import hashlib
import logging
import typing
from typing import Union, Optional

Expand Down Expand Up @@ -256,10 +255,10 @@ def process_weights_for_netuid(
"""

logging.debug("process_weights_for_netuid()")
logging.debug("weights", *weights)
logging.debug("netuid", netuid)
logging.debug("subtensor", subtensor)
logging.debug("metagraph", metagraph)
logging.debug(f"weights: {weights}")
logging.debug(f"netuid {netuid}")
logging.debug(f"subtensor: {subtensor}")
logging.debug(f"metagraph: {metagraph}")

# Get latest metagraph from chain if metagraph is None.
if metagraph is None:
Expand All @@ -278,9 +277,9 @@ def process_weights_for_netuid(
quantile = exclude_quantile / U16_MAX
min_allowed_weights = subtensor.min_allowed_weights(netuid=netuid)
max_weight_limit = subtensor.max_weight_limit(netuid=netuid)
logging.debug("quantile", quantile)
logging.debug("min_allowed_weights", min_allowed_weights)
logging.debug("max_weight_limit", max_weight_limit)
logging.debug(f"quantile: {quantile}")
logging.debug(f"min_allowed_weights: {min_allowed_weights}")
logging.debug(f"max_weight_limit: {max_weight_limit}")

# Find all non zero weights.
non_zero_weight_idx = (
Expand All @@ -298,7 +297,7 @@ def process_weights_for_netuid(
if use_torch()
else np.ones((metagraph.n), dtype=np.int64) / metagraph.n
)
logging.debug("final_weights", *final_weights)
logging.debug(f"final_weights: {final_weights}")
final_weights_count = (
torch.tensor(list(range(len(final_weights))))
if use_torch()
Expand All @@ -321,7 +320,7 @@ def process_weights_for_netuid(
else np.ones((metagraph.n), dtype=np.int64) * 1e-5
) # creating minimum even non-zero weights
weights[non_zero_weight_idx] += non_zero_weights
logging.debug("final_weights", *weights)
logging.debug(f"final_weights: {weights}")
normalized_weights = normalize_max_weight(x=weights, limit=max_weight_limit)
nw_arange = (
torch.tensor(list(range(len(normalized_weights))))
Expand All @@ -330,7 +329,7 @@ def process_weights_for_netuid(
)
return nw_arange, normalized_weights

logging.debug("non_zero_weights", *non_zero_weights)
logging.debug(f"non_zero_weights: {non_zero_weights}")

# Compute the exclude quantile and find the weights in the lowest quantile
max_exclude = max(0, len(non_zero_weights) - min_allowed_weights) / len(
Expand All @@ -342,21 +341,21 @@ def process_weights_for_netuid(
if use_torch()
else np.quantile(non_zero_weights, exclude_quantile)
)
logging.debug("max_exclude", max_exclude)
logging.debug("exclude_quantile", exclude_quantile)
logging.debug("lowest_quantile", lowest_quantile)
logging.debug(f"max_exclude: {max_exclude}")
logging.debug(f"exclude_quantile: {exclude_quantile}")
logging.debug(f"lowest_quantile: {lowest_quantile}")

# Exclude all weights below the allowed quantile.
non_zero_weight_uids = non_zero_weight_uids[lowest_quantile <= non_zero_weights]
non_zero_weights = non_zero_weights[lowest_quantile <= non_zero_weights]
logging.debug("non_zero_weight_uids", *non_zero_weight_uids)
logging.debug("non_zero_weights", *non_zero_weights)
logging.debug(f"non_zero_weight_uids: {non_zero_weight_uids}")
logging.debug(f"non_zero_weights: {non_zero_weights}")

# Normalize weights and return.
normalized_weights = normalize_max_weight(
x=non_zero_weights, limit=max_weight_limit
)
logging.debug("final_weights", *normalized_weights)
logging.debug(f"final_weights: {normalized_weights}")

return non_zero_weight_uids, normalized_weights

Expand Down
Loading