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[Dy2St][tests][46-49, 16] paddle.jit.enable_to_static->enable_to_static_guard #59730

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Merged
SigureMo merged 4 commits into from
Dec 15, 2023
Merged

[Dy2St][tests][46-49, 16] paddle.jit.enable_to_static->enable_to_static_guard #59730

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2e4c755
add dy2st guard 46-49
gouzil Dec 5, 2023
9cd89a5
clean
gouzil Dec 7, 2023
2b44323
Merge branch 'develop' of github.com:gouzil/Paddle into dy2st_with_en…
gouzil Dec 13, 2023
f1550b4
fix
gouzil Dec 13, 2023
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188 changes: 96 additions & 92 deletions test/dygraph_to_static/test_lac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@

os.environ["CUDA_VISIBLE_DEVICES"] = "2"

from dygraph_to_static_utils import Dy2StTestBase
from dygraph_to_static_utils import Dy2StTestBase, enable_to_static_guard

import paddle
from paddle import _legacy_C_ops, base
Expand Down Expand Up @@ -531,93 +531,95 @@ def setUp(self):
self.dy_param_path = os.path.join(self.temp_dir.name, 'lac_dy_param')

def train(self, args, to_static):
paddle.jit.enable_to_static(to_static)
place = (
base.CUDAPlace(0)
if base.is_compiled_with_cuda()
else base.CPUPlace()
)
with base.dygraph.guard(place):
paddle.seed(SEED)
paddle.framework.random._manual_program_seed(SEED)

reader = get_random_input_data(
args.batch_size, args.vocab_size, args.num_labels
with enable_to_static_guard(to_static):
place = (
base.CUDAPlace(0)
if base.is_compiled_with_cuda()
else base.CPUPlace()
)
train_loader = create_dataloader(reader, place)
with base.dygraph.guard(place):
paddle.seed(SEED)
paddle.framework.random._manual_program_seed(SEED)

model = LexNet(args)
optimizer = paddle.optimizer.Adam(
learning_rate=args.base_learning_rate,
parameters=model.parameters(),
)
chunk_eval = ChunkEval(
int(math.ceil((args.num_labels - 1) / 2.0)), "IOB"
)
reader = get_random_input_data(
args.batch_size, args.vocab_size, args.num_labels
)
train_loader = create_dataloader(reader, place)

step = 0

loss_data = []
for epoch_id in range(args.epoch):
for batch in train_loader():
words, targets, length = batch
start_time = time.time()
avg_cost, crf_decode = model(words, targets, length)
loss_data.append(float(avg_cost))

# backward and optimization
avg_cost.backward()
optimizer.minimize(avg_cost)
model.clear_gradients()
end_time = time.time()

if step % args.print_steps == 0:
(
precision,
recall,
f1_score,
num_infer_chunks,
num_label_chunks,
num_correct_chunks,
) = chunk_eval(
input=crf_decode, label=targets, seq_length=length
)
outputs = [avg_cost, precision, recall, f1_score]
avg_cost, precision, recall, f1_score = (
np.mean(x.numpy()) for x in outputs
)

print(
"[train] step = %d, loss = %f, P: %f, R: %f, F1: %f, elapsed time %f"
% (
step,
avg_cost,
model = LexNet(args)
optimizer = paddle.optimizer.Adam(
learning_rate=args.base_learning_rate,
parameters=model.parameters(),
)
chunk_eval = ChunkEval(
int(math.ceil((args.num_labels - 1) / 2.0)), "IOB"
)

step = 0

loss_data = []
for epoch_id in range(args.epoch):
for batch in train_loader():
words, targets, length = batch
start_time = time.time()
avg_cost, crf_decode = model(words, targets, length)
loss_data.append(float(avg_cost))

# backward and optimization
avg_cost.backward()
optimizer.minimize(avg_cost)
model.clear_gradients()
end_time = time.time()

if step % args.print_steps == 0:
(
precision,
recall,
f1_score,
end_time - start_time,
num_infer_chunks,
num_label_chunks,
num_correct_chunks,
) = chunk_eval(
input=crf_decode,
label=targets,
seq_length=length,
)
)

step += 1
# save inference model
if to_static:
paddle.jit.save(
layer=model,
path=self.model_save_prefix,
input_spec=input_specs,
output_spec=[crf_decode],
input_names_after_prune=[
input_specs[0].name,
input_specs[-1].name,
],
)
else:
paddle.save(
model.state_dict(), self.dy_param_path + '.pdparams'
)
outputs = [avg_cost, precision, recall, f1_score]
avg_cost, precision, recall, f1_score = (
np.mean(x.numpy()) for x in outputs
)

print(
"[train] step = %d, loss = %f, P: %f, R: %f, F1: %f, elapsed time %f"
% (
step,
avg_cost,
precision,
recall,
f1_score,
end_time - start_time,
)
)

step += 1
# save inference model
if to_static:
paddle.jit.save(
layer=model,
path=self.model_save_prefix,
input_spec=input_specs,
output_spec=[crf_decode],
input_names_after_prune=[
input_specs[0].name,
input_specs[-1].name,
],
)
else:
paddle.save(
model.state_dict(), self.dy_param_path + '.pdparams'
)

return np.array(loss_data)
return np.array(loss_data)

def test_train(self):
st_out = self.train(self.args, to_static=True)
Expand Down Expand Up @@ -645,19 +647,21 @@ def verify_predict(self):

def predict_dygraph(self, batch):
words, targets, length = batch
paddle.jit.enable_to_static(False)
with base.dygraph.guard(self.place):
model = LexNet(self.args)
# load dygraph trained parameters
model_dict = paddle.load(self.dy_param_path + ".pdparams")
model.set_dict(model_dict)
model.eval()

_, pred_res = model(
to_variable(words), to_variable(targets), to_variable(length)
)
with enable_to_static_guard(False):
with base.dygraph.guard(self.place):
model = LexNet(self.args)
# load dygraph trained parameters
model_dict = paddle.load(self.dy_param_path + ".pdparams")
model.set_dict(model_dict)
model.eval()

_, pred_res = model(
to_variable(words),
to_variable(targets),
to_variable(length),
)

return pred_res.numpy()
return pred_res.numpy()

def predict_static(self, batch):
"""
Expand Down
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