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lvzhengyang
timing
Commits
1bbeaf49
Commit
1bbeaf49
authored
Jan 22, 2024
by
lvzhengyang
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implement a train logic
parent
f81e4bdf
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3 changed files
with
48 additions
and
13 deletions
+48
-13
.gitignore
+2
-0
build_graph/create_graph.py
+2
-0
build_model/load_graph.py
+44
-13
No files found.
.gitignore
View file @
1bbeaf49
...
@@ -2,3 +2,5 @@ place_parser
...
@@ -2,3 +2,5 @@ place_parser
data/asap7
data/asap7
*/__pycache__
*/__pycache__
*.log
*.log
build_model/runs
build_model/weights
build_graph/create_graph.py
View file @
1bbeaf49
...
@@ -153,6 +153,7 @@ def check_legal(data_dir, parsed_libs_dir, parsed_sdf_dir, save_dir, block_name)
...
@@ -153,6 +153,7 @@ def check_legal(data_dir, parsed_libs_dir, parsed_sdf_dir, save_dir, block_name)
pickle
.
dump
(
illegal_leaves
,
f
)
pickle
.
dump
(
illegal_leaves
,
f
)
f
.
close
()
f
.
close
()
# DEPRECATED!
def
create_graph5
(
data_dir
,
parsed_libs_dir
,
parsed_sdf_dir
,
save_dir
,
block_name
):
def
create_graph5
(
data_dir
,
parsed_libs_dir
,
parsed_sdf_dir
,
save_dir
,
block_name
):
node_names
=
np
.
load
(
os
.
path
.
join
(
data_dir
,
'node_names.npy'
))
node_names
=
np
.
load
(
os
.
path
.
join
(
data_dir
,
'node_names.npy'
))
node_x
=
np
.
load
(
os
.
path
.
join
(
data_dir
,
'node_x.npy'
))
node_x
=
np
.
load
(
os
.
path
.
join
(
data_dir
,
'node_x.npy'
))
...
@@ -1123,6 +1124,7 @@ def create_graph6(data_dir, parsed_libs_dir, parsed_sdf_dir, save_dir, block_nam
...
@@ -1123,6 +1124,7 @@ def create_graph6(data_dir, parsed_libs_dir, parsed_sdf_dir, save_dir, block_nam
g
.
ndata
[
'n_slews'
]
=
ndata
[
'n_slews'
]
.
float
()
g
.
ndata
[
'n_slews'
]
=
ndata
[
'n_slews'
]
.
float
()
g
.
ndata
[
'nf'
]
=
ndata
[
'nf'
]
.
float
()
g
.
ndata
[
'nf'
]
=
ndata
[
'nf'
]
.
float
()
g
.
ndata
[
'n_is_timing_endpt'
]
=
ndata
[
'n_is_timing_endpt'
]
.
float
()
g
.
ndata
[
'n_is_timing_endpt'
]
=
ndata
[
'n_is_timing_endpt'
]
.
float
()
g
.
ndata
[
'n_net_delays'
]
=
ndata
[
'n_net_delays'
]
.
float
()
g
.
edges
[
'cell_out'
]
.
data
[
'e_delay'
]
=
torch
.
tensor
(
edges_delay
[
'cell_out'
])
.
float
()
g
.
edges
[
'cell_out'
]
.
data
[
'e_delay'
]
=
torch
.
tensor
(
edges_delay
[
'cell_out'
])
.
float
()
g
.
edges
[
'cell_out'
]
.
data
[
'ef'
]
=
torch
.
tensor
(
edges_features
[
'cell_out'
])
.
float
()
g
.
edges
[
'cell_out'
]
.
data
[
'ef'
]
=
torch
.
tensor
(
edges_features
[
'cell_out'
])
.
float
()
...
...
build_model/load_graph.py
View file @
1bbeaf49
...
@@ -8,6 +8,8 @@ import time
...
@@ -8,6 +8,8 @@ import time
import
argparse
import
argparse
import
os
import
os
from
sklearn.metrics
import
r2_score
from
sklearn.metrics
import
r2_score
from
torch.utils.tensorboard
import
SummaryWriter
# import tee
# import tee
from
model
import
PredModel
from
model
import
PredModel
...
@@ -49,6 +51,8 @@ def load_data():
...
@@ -49,6 +51,8 @@ def load_data():
for
block
in
blocks
:
for
block
in
blocks
:
graph_path
=
os
.
path
.
join
(
dir_prefix
,
block
,
"parsed"
,
f
"{block}.graph.bin"
)
graph_path
=
os
.
path
.
join
(
dir_prefix
,
block
,
"parsed"
,
f
"{block}.graph.bin"
)
g
=
dgl
.
load_graphs
(
graph_path
)[
0
][
0
]
.
to
(
'cuda'
)
g
=
dgl
.
load_graphs
(
graph_path
)[
0
][
0
]
.
to
(
'cuda'
)
g
.
ndata
[
'n_net_delays_log'
]
=
torch
.
log
(
0.0001
+
g
.
ndata
[
'n_net_delays'
])
+
7.6
g
.
edges
[
'cell_out'
]
.
data
[
'e_cell_delays_log'
]
=
torch
.
log
(
0.0001
+
g
.
edges
[
'cell_out'
]
.
data
[
'e_delay'
])
topo
,
topo_time
=
gen_topo
(
g
)
topo
,
topo_time
=
gen_topo
(
g
)
ts
=
{
ts
=
{
'input_nodes'
:
(
g
.
ndata
[
'nf'
][:,
1
]
<
0.5
)
.
nonzero
()
.
flatten
()
.
type
(
torch
.
int64
),
'input_nodes'
:
(
g
.
ndata
[
'nf'
][:,
1
]
<
0.5
)
.
nonzero
()
.
flatten
()
.
type
(
torch
.
int64
),
...
@@ -60,31 +64,58 @@ def load_data():
...
@@ -60,31 +64,58 @@ def load_data():
'endpoints'
:
(
g
.
ndata
[
'n_is_timing_endpt'
]
>
0.5
)
.
nonzero
()
.
flatten
()
.
type
(
torch
.
long
),
'endpoints'
:
(
g
.
ndata
[
'n_is_timing_endpt'
]
>
0.5
)
.
nonzero
()
.
flatten
()
.
type
(
torch
.
long
),
'topo'
:
topo
,
'topo'
:
topo
,
}
}
mask
=
torch
.
zeros
(
g
.
nodes
()
.
size
(
0
),
dtype
=
torch
.
bool
,
device
=
'cuda'
)
mask
[
ts
[
'input_nodes'
]]
=
1
ts
[
'mask'
]
=
mask
g
.
ndata
[
'n_net_delays_log'
][
mask
]
=
0
data
[
block
]
=
g
,
ts
data
[
block
]
=
g
,
ts
return
data
return
data
def
train
(
model
,
data_train
):
def
train
(
model
,
data_train
):
writer
=
SummaryWriter
()
optimizer
=
torch
.
optim
.
Adam
(
model
.
parameters
(),
lr
=
0.0005
)
optimizer
=
torch
.
optim
.
Adam
(
model
.
parameters
(),
lr
=
0.0005
)
# debug: load graph one by one
batch_size
=
5
for
k
,
(
g
,
ts
)
in
data_train
.
items
():
print
(
f
'-------- {k} --------'
)
try
:
pred_net_delays
,
pred_cell_delays
,
pred_atslew
=
model
(
g
,
ts
)
except
:
print
(
'error'
)
pdb
.
set_trace
()
# batch_size = 5
batch_size
=
1
for
e
in
range
(
100000
):
for
e
in
range
(
100000
):
model
.
train
()
model
.
train
()
train_loss_tot_net_delays
,
train_loss_tot_cell_delays
,
train_loss_tot_ats
=
0
,
0
,
0
train_loss_tot_net_delays
,
train_loss_tot_cell_delays
,
train_loss_tot_ats
=
0
,
0
,
0
train_loss_epoch_net_delays
,
train_loss_epoch_cell_delays
=
0
,
0
optimizer
.
zero_grad
()
for
k
,
(
g
,
ts
)
in
random
.
sample
(
data_train
.
items
(),
batch_size
):
for
k
,
(
g
,
ts
)
in
random
.
sample
(
data_train
.
items
(),
batch_size
):
pred_net_delays
,
pred_cell_delays
,
pred_atslew
=
model
(
g
,
ts
)
pred_net_delays
,
pred_cell_delays
,
pred_atslew
=
model
(
g
,
ts
)
pdb
.
set_trace
()
# concern about delays only
# net delays are defined on pins
# add mask to select fanin pins only
pred_net_delays
[
ts
[
'mask'
]]
=
0
loss_net_delays
=
F
.
mse_loss
(
pred_net_delays
,
g
.
ndata
[
'n_net_delays_log'
])
train_loss_tot_net_delays
+=
loss_net_delays
.
item
()
train_loss_epoch_net_delays
+=
loss_net_delays
.
item
()
loss_cell_delays
=
F
.
mse_loss
(
pred_cell_delays
,
g
.
edges
[
'cell_out'
]
.
data
[
'e_cell_delays_log'
])
train_loss_tot_cell_delays
+=
loss_cell_delays
.
item
()
train_loss_epoch_cell_delays
=
loss_cell_delays
.
item
()
# loss_cell_delays = 0
(
loss_net_delays
+
loss_cell_delays
)
.
backward
()
optimizer
.
step
()
writer
.
add_scalar
(
'net_delays_loss/train: '
,
train_loss_epoch_net_delays
,
e
)
writer
.
add_scalar
(
'cell_delays_loss/train: '
,
train_loss_epoch_cell_delays
,
e
)
train_loss_epoch_net_delays
,
train_loss_epoch_cell_delays
=
0
,
0
# log
if
(
e
+
1
)
%
10
==
0
:
print
(
'epoch: {}, net_delay_loss (train): {:.6f}, cell_delay_loss (train): {:.6f}'
.
format
(
e
+
1
,
train_loss_tot_net_delays
,
train_loss_tot_cell_delays
))
# save model
if
(
e
+
1
)
%
100
==
0
:
print
(
'-------- Save Model --------'
)
save_path
=
os
.
path
.
join
(
'weights'
,
f
'{e}.pt'
)
torch
.
save
(
model
.
state_dict
(),
save_path
)
writer
.
close
()
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
data
=
load_data
()
data
=
load_data
()
...
...
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