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import torch
import os
from collections import defaultdict
def parse_proxy(fname, scale):
f = open(fname, 'r')
layer_dict = {}
for line in f:
if 'proxy error' in line:
line = line.rstrip()
line = line[line.find('layer'):]
proxy_error = float(line[line.find(':') + 1:])
layer_name = ' '.join(line.split(' ')[1:3])
layer_dict[layer_name] = {scale: proxy_error}
return layer_dict
total = None
files = ['075', '080', '085', '090', '095', '100', '103', '105']
for key in files:
res = parse_proxy(f'/work/albert/two_bit_quant/slurm_out/e8p_s{key}.log', key)
if total is None:
total = res
else:
for key in res:
total[key].update(res[key])
hist = defaultdict(int)
best_layer = {}
for layer in total:
best = float('inf')
best_scale = None
for scale in total[layer]:
if total[layer][scale] < best:
best = total[layer][scale]
best_scale = scale
best_layer[layer] = best_scale
hist[best_scale] += 1
print(hist)
exit()
ckpt_path = '/work/albert/two_bit_quant/checkpoints'
out_path = os.path.join(ckpt_path, 'e8p_best_scale')
os.system(f'rm -rf {out_path}')
os.system(f'mkdir {out_path}')
os.system('cp {} {}'.format(
os.path.join(ckpt_path, f'e8p_s{files[0]}', 'config.pt'),
out_path))
for layer in best_layer:
src = os.path.join(ckpt_path, f'e8p_s{best_layer[layer]}', '{}.pt'.format(layer.replace(' ', '_')))
tgt = out_path
os.system(f'cp {src} {tgt}')
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