Skip to content

Commit

Permalink
add utility function to test.py in MLopt
Browse files Browse the repository at this point in the history
  • Loading branch information
@chetanyagoyal
chetanyagoyal committed Oct 22, 2024
1 parent 74da340 commit dfe7a29
Showing 1 changed file with 89 additions and 17 deletions.
106 changes: 89 additions & 17 deletions openfasoc/MLoptimization/test.py
View file
Original file line number Diff line number Diff line change
@@ -1,31 +1,103 @@
import numpy as np
import glayout_import
import sys

from sky130_nist_tapeout import safe_single_build_and_simulation
from sky130_nist_tapeout import opamp_parameters_serializer

import yaml
from pathlib import Path
import numpy as np
from typing import Union

def get_indices_from_ranges(opamp_parameters: Union[dict, np.array]) -> np.array:
"""The RL framework works by doing design searches within a user defined range for each parameter
For example, the RL framework may be told to search diffpair widths between 1um and 8um in steps of 1um
In this case, the RL framework will only ever try to make opamps with diff pair width one of [1,2,3,4,5,6,7,8]
this is done to limit the search space
The RL result will then show an index of the result. For example, if the best opamp in a design iteration had a with of 3um,
the corresponding index for 3um is 2 (3um is at the 2nd index in the array [1,2,3,4,5,6,7,8])
The purpose of this function to convert a list of opamp parameter into a list of indices into these range arrays
returns a list of indicies
input can either be given in dictionary or np.array format
here is an example dictionary input:
{'half_diffpair_params': (-987.654321, -987.654321, -987.654321), 'diffpair_bias': (-987.654321, -987.654321, -987.654321), 'half_common_source_params': (-987.654321, -987.654321, -987.654321, -987.654321), 'half_common_source_bias': (-987.654321, -987.654321, -987.654321, -987.654321), 'output_stage_params': (-987.654321, -987.654321, -987), 'output_stage_bias': (-987, -987.654321, -987.654321), 'half_pload': (-987.654321, -987.654321, -987.654321), 'mim_cap_size': (-987.654321, -987.654321), 'mim_cap_rows': -987, 'rmult': -987}
here is an example np.array indices output:
np.array([6, 2, 7, 6, 0, 5, 7, 0, 10, 6, 5, 13, 0, 6, 4, 2])
"""
# convert to dictionary format first
if isinstance(opamp_parameters, dict):
opamp_parameter_dict = opamp_parameters
else:
opamp_parameter_dict = opamp_parameters_de_serializer(opamp_parameters)
# Function to find the closest index in the range
def closest_index(range_params, number):
start, end, step = range_params
values = np.arange(start, end, step)
idx = (np.abs(values - number)).argmin()
return idx
# loop through params and find closest indices
opamp_parameter_arr = list()
for key, val in params.items():
if key == "diffpair_params0":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_diffpair_params"][0]))
elif key == "diffpair_params1":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_diffpair_params"][1]))
elif key == "diffpair_params2":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_diffpair_params"][2]))
elif key == "Diffpair_bias0":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["diffpair_bias"][0]))
elif key == "Diffpair_bias1":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["diffpair_bias"][1]))
elif key == "Diffpair_bias2":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["diffpair_bias"][2]))
elif key == "pamp_hparams0":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_common_source_params"][0]))
elif key == "pamp_hparams1":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_common_source_params"][1]))
elif key == "pamp_hparams2":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_common_source_params"][2]))
elif key == "bias0":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_common_source_bias"][0]))
elif key == "bias1":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_common_source_bias"][1]))
elif key == "bias2":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_common_source_bias"][2]))
elif key == "bias3":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_common_source_bias"][3]))
elif key == "half_pload1":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_pload"][0]))
elif key == "half_pload3":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["half_pload"][2]))
elif key == "mim_cap_rows":
opamp_parameter_arr.append(closest_index(val, opamp_parameter_dict["mim_cap_rows"]))
else:
raise ValueError("invalid key")
# this relies on the fact that the params dict should be in order
return np.array(opamp_parameter_arr)

params = {
"diffpair_params0" : [1, 8, 1],
"diffpair_params1" : [0.5, 2.1, 0.1],
"diffpair_params2" : [1, 13, 1],
"Diffpair_bias0" : [1, 8, 1],
"Diffpair_bias1" : [1, 4.5, 0.5],
"Diffpair_bias2" : [3, 13, 1],
"pamp_hparams0" : [1, 9, 1],
"pamp_hparams1" : [0.5, 2.1, 0.1],
"pamp_hparams2" : [2, 14, 1],
"bias0" : [1, 8, 1],
"bias1" : [0.5, 2.1, 0.1],
"bias2" : [3, 18, 1],
"bias3" : [2, 4, 1],
"half_pload1": [3, 10, 1],
"half_pload3": [4, 9, 1],
"mim_cap_rows" : [1, 4, 1],
}
"diffpair_params0" : [1, 8, 1],
"diffpair_params1" : [0.5, 2.1, 0.1],
"diffpair_params2" : [1, 13, 1],
"Diffpair_bias0" : [1, 8, 1],
"Diffpair_bias1" : [1, 4.5, 0.5],
"Diffpair_bias2" : [3, 13, 1],
"pamp_hparams0" : [1, 9, 1],
"pamp_hparams1" : [0.5, 2.1, 0.1],
"pamp_hparams2" : [2, 14, 1],
"bias0" : [1, 8, 1],
"bias1" : [0.5, 2.1, 0.1],
"bias2" : [3, 18, 1],
"bias3" : [2, 4, 1],
"half_pload1": [3, 10, 1],
"half_pload3": [4, 9, 1],
"mim_cap_rows" : [1, 4, 1],
}

paramss = []
params_id = list(params.keys())

Expand Down

0 comments on commit dfe7a29

Please sign in to comment.