Skip to content
Projects
Groups
Snippets
Help
This project
Loading...
Sign in / Register
Toggle navigation
T
timing
Overview
Overview
Details
Activity
Cycle Analytics
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Charts
Issues
0
Issues
0
List
Board
Labels
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Charts
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Charts
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
lvzhengyang
timing
Commits
d00bbe81
Commit
d00bbe81
authored
Jan 25, 2024
by
lvzhengyang
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
test model
parent
f4bb5ddb
Show whitespace changes
Inline
Side-by-side
Showing
10 changed files
with
144 additions
and
0 deletions
+144
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.EF.dist.png
+0
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.EF.png
+0
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.ER.dist.png
+0
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.ER.png
+0
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LF.dist.png
+0
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LF.png
+0
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LR.dist.png
+0
-0
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LR.png
+0
-0
cell_delay/self_att.py
+51
-0
cell_delay/test_model.py
+93
-0
No files found.
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.EF.dist.png
0 → 100644
View file @
d00bbe81
20.5 KB
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.EF.png
0 → 100644
View file @
d00bbe81
24.3 KB
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.ER.dist.png
0 → 100644
View file @
d00bbe81
22.1 KB
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.ER.png
0 → 100644
View file @
d00bbe81
24.8 KB
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LF.dist.png
0 → 100644
View file @
d00bbe81
21.4 KB
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LF.png
0 → 100644
View file @
d00bbe81
28.7 KB
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LR.dist.png
0 → 100644
View file @
d00bbe81
22 KB
cell_delay/figures/INVx1_ASAP7_75t_R/INVx1_ASAP7_75t_R.LR.png
0 → 100644
View file @
d00bbe81
28.2 KB
cell_delay/self_att.py
View file @
d00bbe81
...
...
@@ -7,6 +7,8 @@ import pickle
import
math
import
pdb
from
torch.utils.data
import
Dataset
,
DataLoader
class
MLP
(
torch
.
nn
.
Module
):
def
__init__
(
self
,
*
sizes
,
batchnorm
=
False
,
dropout
=
False
):
super
()
.
__init__
()
...
...
@@ -100,6 +102,25 @@ def load_cell_delay_data():
f
.
close
()
return
data
class
myDataset
(
Dataset
):
def
__init__
(
self
,
data
,
libcell
,
key
):
self
.
delays
=
data
[
'delays'
][
libcell
][
key
]
self
.
topos
=
data
[
'topos'
][
libcell
][
key
]
def
__len__
(
self
):
return
len
(
self
.
topos
)
def
__getitem__
(
self
,
idx
):
return
self
.
topos
[
idx
][
0
][
0
],
self
.
topos
[
idx
][
0
][
1
],
self
.
topos
[
idx
][
1
][
0
],
self
.
topos
[
idx
][
1
][
1
],
self
.
delays
[
idx
]
def
collate_fn
(
data
):
input_data
=
[]
labels
=
[]
for
dd
in
data
:
input_data
.
append
((
dd
[
0
],
dd
[
1
],
dd
[
2
],
dd
[
3
]))
labels
.
append
(
dd
[
4
])
return
input_data
,
labels
def
preprocess
(
data
):
libcell
=
'INVx1_ASAP7_75t_R'
key
=
'A-Y'
...
...
@@ -153,8 +174,38 @@ def preprocess(data):
os
.
makedirs
(
save_dir
,
exist_ok
=
True
)
torch
.
save
(
model
.
state_dict
(),
save_path
)
def
train
(
loader
):
model
=
CellDelayPred
(
7
,
4
,
32
)
model
.
cuda
()
model
=
torch
.
nn
.
DataParallel
(
model
)
optimizer
=
torch
.
optim
.
Adam
(
model
.
parameters
(),
lr
=
0.0005
)
for
batch_idx
,
(
topo
,
delay
)
in
enumerate
(
loader
):
num_data
=
len
(
topo
)
batch_loss
=
torch
.
zeros
(
num_data
,
device
=
'cuda'
)
for
di
in
range
(
num_data
):
fanin_topo
=
torch
.
from_numpy
(
topo
[
di
][
0
])
.
cuda
()
.
float
()
.
unsqueeze_
(
0
)
fanin_id
=
topo
[
di
][
1
]
fanout_topo
=
torch
.
from_numpy
(
topo
[
di
][
2
])
.
cuda
()
.
float
()
.
unsqueeze_
(
0
)
fanout_id
=
topo
[
di
][
3
]
pred
=
model
(
fanin_topo
,
fanout_topo
,
fanin_id
,
fanout_id
)
truth
=
torch
.
from_numpy
(
delay
[
di
])
.
cuda
()
.
float
()
.
unsqueeze_
(
0
)
batch_loss
[
di
]
=
F
.
mse_loss
(
pred
,
truth
)
pdb
.
set_trace
()
if
__name__
==
'__main__'
:
data
=
load_cell_delay_data
()
# naive training
preprocess
(
data
)
"""
# use DataLoader
libcell = 'INVx1_ASAP7_75t_R'
key = 'A-Y'
dataset = myDataset(data, libcell, key)
loader = DataLoader(dataset, batch_size=4, collate_fn=collate_fn)
train(loader)
"""
cell_delay/test_model.py
0 → 100644
View file @
d00bbe81
import
torch
import
numpy
as
np
import
dgl
import
torch.nn.functional
as
F
import
random
import
pdb
import
time
import
argparse
import
os
from
sklearn.metrics
import
r2_score
import
matplotlib.pyplot
as
plt
from
self_att
import
*
def
load_model
(
model_path
):
model
=
CellDelayPred
(
7
,
4
,
32
)
model
.
load_state_dict
(
torch
.
load
(
model_path
))
model
.
cuda
()
return
model
def
test
(
data
,
model
):
libcell
=
'INVx1_ASAP7_75t_R'
key
=
'A-Y'
delays
=
np
.
stack
(
data
[
'delays'
][
libcell
][
key
])
delays_log
=
np
.
log
(
delays
)
topos
=
data
[
'topos'
][
libcell
][
key
]
fanin_topos
,
fanin_ids
,
fanout_topos
,
fanout_ids
=
[],
[],
[],
[]
for
topo
in
topos
:
fanin_topos
.
append
(
torch
.
tensor
(
topo
[
0
][
0
])
.
float
())
fanin_ids
.
append
(
topo
[
0
][
1
])
fanout_topos
.
append
(
torch
.
tensor
(
topo
[
1
][
0
])
.
float
())
fanout_ids
.
append
(
topo
[
1
][
1
])
num_data
=
len
(
topos
)
pred_delays_log
=
np
.
zeros
((
num_data
,
4
))
model
.
eval
()
with
torch
.
no_grad
():
for
di
in
range
(
num_data
):
fanin
=
fanin_topos
[
di
]
fanin
=
fanin
.
cuda
()
fanin
.
unsqueeze_
(
0
)
fanout
=
fanout_topos
[
di
]
fanout
=
fanout
.
cuda
()
fanout
.
unsqueeze_
(
0
)
pred
=
model
(
fanin
,
fanout
,
fanin_ids
[
di
],
fanout_ids
[
di
])
pred_delays_log
[
di
]
=
pred
.
cpu
()
.
numpy
()
corner_names
=
{
0
:
'ER'
,
1
:
'EF'
,
2
:
'LR'
,
3
:
'LF'
}
pred_delays
=
np
.
exp
(
pred_delays_log
)
for
corner
in
range
(
4
):
tt
=
delays
[:,
corner
]
pp
=
pred_delays
[:,
corner
]
minv
=
min
(
tt
.
min
(),
pp
.
min
())
-
0.2
maxv
=
max
(
tt
.
max
(),
pp
.
max
())
+
0.2
maxv
=
min
(
2000
,
maxv
)
plt
.
axis
(
"square"
)
plt
.
title
(
f
'cell delay prediction ({corner_names[corner]}) of libcell {libcell}'
)
plt
.
xlabel
(
'Truth/ns'
)
plt
.
ylabel
(
'Predicted/ns'
)
plt
.
xlim
(
minv
-
10
,
maxv
+
10
)
plt
.
ylim
(
minv
-
10
,
maxv
+
10
)
plt
.
axline
((
minv
,
minv
),
(
maxv
,
maxv
),
color
=
'r'
)
# plt.axline((500, minv), (500, maxv), color='black', linestyle='-.')
plt
.
scatter
(
tt
,
pp
,
s
=
10
,
c
=
'b'
)
save_dir
=
os
.
path
.
join
(
'figures'
,
libcell
)
os
.
makedirs
(
save_dir
,
exist_ok
=
True
)
plt
.
savefig
(
os
.
path
.
join
(
save_dir
,
f
'{libcell}.{corner_names[corner]}.png'
))
plt
.
clf
()
# draw delay distribution
diff
=
pp
-
tt
# filter diff
diff
=
diff
[
np
.
abs
(
diff
)
<=
200
]
print
(
f
"{corner_names[corner]} remove {pp.size - diff.size} / {pp.size} large diff points"
)
plt
.
hist
(
diff
,
bins
=
100
)
plt
.
xlabel
(
'Diff'
)
plt
.
ylabel
(
'Frequency'
)
plt
.
xlim
(
-
200
,
200
)
plt
.
title
(
f
'cell delay pred err distr ({corner_names[corner]}) of libcell {libcell}'
)
plt
.
savefig
(
os
.
path
.
join
(
save_dir
,
f
'{libcell}.{corner_names[corner]}.dist.png'
))
plt
.
clf
()
if
__name__
==
'__main__'
:
data
=
load_cell_delay_data
()
model_path
=
'./weights/e-109-loss-1622.447.pt'
model
=
load_model
(
model_path
)
test
(
data
,
model
)
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment