File size: 2,423 Bytes
fb11af9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch.nn as nn
from . import logging
logger = logging.get_logger(__name__)
def pretty_print_trainable_parameters(model: nn.Module):
trainable_parameters = []
for n, p in model.named_parameters():
if p.requires_grad:
trainable_parameters.append(n)
printable_results = {}
for p in trainable_parameters:
param_split = p.split(".")
param_name = ""
digit_index = 0
layer_index_list = []
for split_item in param_split:
if split_item.isdigit():
param_name += f"<{digit_index}>."
layer_index_list.append(int(split_item))
digit_index += 1
else:
param_name += f"{split_item}."
param_name = param_name[:-1]
if param_name not in printable_results:
printable_results[param_name] = []
printable_results[param_name].append(layer_index_list)
train_param_info = "\n**** trainable parameters ****"
for param_key in printable_results.keys():
layer_idxs = np.array(printable_results[param_key])
if layer_idxs.shape[-1] == 0:
train_param_info += "\n" + param_key
continue
layer_min = layer_idxs.min(axis=0)
layer_max = layer_idxs.max(axis=0)
print_pattern = param_key
for index in range(len(layer_min)):
if layer_min[index] == layer_max[index]:
print_pattern = print_pattern.replace(f"<{index}>", f"[{layer_min[index]}]")
else:
print_pattern = print_pattern.replace(f"<{index}>", f"[{layer_min[index]}-{layer_max[index]}]")
train_param_info += "\n" + print_pattern
train_param_info += "\n**** trainable parameters ****"
logger.info_rank0(train_param_info)
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