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[Dy2St][NO.11] pir dy2st unittest fix test_save_inference_model - Part 3 #60211

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merged 4 commits into from
Dec 21, 2023
Merged

[Dy2St][NO.11] pir dy2st unittest fix test_save_inference_model - Part 3 #60211

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f0ef2d7
test
gouzil Dec 21, 2023
a1fa70b
Merge branch 'develop' of github.com:gouzil/Paddle into dy2st_pir_api…
gouzil Dec 21, 2023
da2e3ef
`paddle._pir_ops` -> `_C_ops.add`
gouzil Dec 21, 2023
757b7ae
fix TestPartialProgramRaiseError
gouzil Dec 21, 2023
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4 changes: 2 additions & 2 deletions python/paddle/nn/functional/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1960,9 +1960,9 @@ def linear(x, weight, bias=None, name=None):
return _C_ops.linear(x, weight, bias)

elif in_pir_mode():
out = paddle._pir_ops.matmul(x, weight, False, False)
out = _C_ops.matmul(x, weight, False, False)
if bias is not None:
return paddle._pir_ops.add(out, bias)
return _C_ops.add(out, bias)
else:
return out
else:
Expand Down
144 changes: 79 additions & 65 deletions test/dygraph_to_static/test_save_inference_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,28 +21,33 @@
Dy2StTestBase,
compare_legacy_with_pt,
test_ast_only,
test_legacy_and_pt_and_pir,
)

import paddle
from paddle import base
from paddle.autograd import PyLayer
from paddle.jit.api import to_static
from paddle.framework import use_pir_api
from paddle.jit.dy2static.partial_program import partial_program_from
from paddle.jit.dy2static.pir_partial_program import (
partial_program_from as pir_partial_program_from,
)
from paddle.jit.translated_layer import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX

SEED = 2020

np.random.seed(SEED)

place = base.CUDAPlace(0) if base.is_compiled_with_cuda() else base.CPUPlace()
place = (
paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace()
)


class SimpleFcLayer(paddle.nn.Layer):
def __init__(self, fc_size):
super().__init__()
self._linear = paddle.nn.Linear(fc_size, fc_size)

@to_static(full_graph=True)
def forward(self, x):
y = self._linear(x)
z = self._linear(y)
Expand All @@ -69,7 +74,6 @@ def __init__(self, fc_size):
super().__init__()
self._linear = paddle.nn.Linear(fc_size, fc_size)

@to_static(full_graph=True)
def forward(self, x):
y = self._linear(x)
out = cus_tanh.apply(y)
Expand All @@ -88,28 +92,29 @@ def tearDown(self):
def test_save_inference_model(self):
fc_size = 20
x_data = np.random.random((fc_size, fc_size)).astype('float32')
with base.dygraph.guard(place):
base.default_startup_program().random_seed = SEED
base.default_main_program().random_seed = SEED

x = base.dygraph.to_variable(x_data)
layer = SimpleFcLayer(fc_size)
adam = paddle.optimizer.SGD(
learning_rate=0.1, parameters=layer.parameters()
)
base.default_startup_program().random_seed = SEED
base.default_main_program().random_seed = SEED

for i in range(5):
loss, pred = layer(x)
loss.backward()
adam.minimize(loss)
layer.clear_gradients()
# test for saving model in dygraph.guard
infer_model_prefix = os.path.join(
self.temp_dir.name, "test_dy2stat_inference_in_guard/model"
)
infer_model_dir = os.path.join(
self.temp_dir.name, "test_dy2stat_inference_in_guard"
)
x = paddle.to_tensor(x_data)
layer = paddle.jit.to_static(SimpleFcLayer(fc_size))
adam = paddle.optimizer.SGD(
learning_rate=0.1, parameters=layer.parameters()
)

for i in range(5):
loss, pred = layer(x)
loss.backward()
adam.minimize(loss)
layer.clear_gradients()
# test for saving model in dygraph.guard
infer_model_prefix = os.path.join(
self.temp_dir.name, "test_dy2stat_inference_in_guard/model"
)
infer_model_dir = os.path.join(
self.temp_dir.name, "test_dy2stat_inference_in_guard"
)
# TODO(pir-save-load): Fix this after we support save/load in PIR
if not use_pir_api():
paddle.jit.save(
layer=layer,
path=infer_model_prefix,
Expand All @@ -118,25 +123,26 @@ def test_save_inference_model(self):
)
# Check the correctness of the inference
dygraph_out, _ = layer(x)
self.check_save_inference_model(layer, [x_data], dygraph_out.numpy())
self.check_save_inference_model(
layer, [x_data], dygraph_out.numpy(), fetch=[loss]
)
self.check_save_inference_model(
layer, [x_data], dygraph_out.numpy(), feed=[x]
)
self.check_save_inference_model(
layer, [x_data], dygraph_out.numpy()
)
self.check_save_inference_model(
layer, [x_data], dygraph_out.numpy(), fetch=[loss]
)
self.check_save_inference_model(
layer, [x_data], dygraph_out.numpy(), feed=[x]
)

@test_ast_only
def test_save_pylayer_model(self):
fc_size = 20
x_data = np.random.random((fc_size, fc_size)).astype('float32')
paddle.base.framework._set_expected_place(place)
paddle.framework._set_expected_place(place)

base.default_startup_program().random_seed = SEED
base.default_main_program().random_seed = SEED
paddle.disable_static()
x = base.dygraph.to_variable(x_data)
layer = SimplePyLayerNet(fc_size)
x = paddle.to_tensor(x_data)
layer = paddle.jit.to_static(SimplePyLayerNet(fc_size))
adam = paddle.optimizer.SGD(
learning_rate=0.1, parameters=layer.parameters()
)
Expand All @@ -150,25 +156,27 @@ def test_save_pylayer_model(self):
infer_model_prefix = os.path.join(
self.temp_dir.name, "test_dy2stat_inference_in_guard/model_pylayer"
)
paddle.jit.save(
layer=layer,
path=infer_model_prefix,
input_spec=[x],
output_spec=[pred],
)
# Check the correctness of the inference
loss_out, _ = layer(x)
# TODO(pir-save-load): Fix this after we support save/load in PIR
if not use_pir_api():
paddle.jit.save(
layer=layer,
path=infer_model_prefix,
input_spec=[x],
output_spec=[pred],
)
# Check the correctness of the inference
loss_out, _ = layer(x)

loss_out_numpy = float(loss_out)
self.check_save_inference_model(
layer, [x_data], loss_out_numpy, enable_pir=False
)
self.check_save_inference_model(
layer, [x_data], loss_out_numpy, fetch=[loss], enable_pir=False
)
self.check_save_inference_model(
layer, [x_data], loss_out_numpy, feed=[x], enable_pir=False
)
loss_out_numpy = float(loss_out)
self.check_save_inference_model(
layer, [x_data], loss_out_numpy, enable_pir=False
)
self.check_save_inference_model(
layer, [x_data], loss_out_numpy, fetch=[loss], enable_pir=False
)
self.check_save_inference_model(
layer, [x_data], loss_out_numpy, feed=[x], enable_pir=False
)

def check_save_inference_model(
self, model, inputs, gt_out, feed=None, fetch=None, enable_pir=True
Expand Down Expand Up @@ -224,30 +232,36 @@ def load_and_run_inference(
fetch_list=fetch_targets,
)

paddle.disable_static()
return np.array(results[0])


class TestPartialProgramRaiseError(Dy2StTestBase):
@test_ast_only
@test_legacy_and_pt_and_pir
def test_param_type(self):
x_data = np.random.random((20, 20)).astype('float32')

with base.dygraph.guard(base.CPUPlace()):
net = SimpleFcLayer(20)
x = base.dygraph.to_variable(x_data)
out = net(x)
net = paddle.jit.to_static(SimpleFcLayer(20))
x = paddle.to_tensor(x_data)
out = net(x)

program_cache = net.forward.program_cache
_, (concrete_program, _) = program_cache.last()
program_cache = net.forward.program_cache
_, (concrete_program, _) = program_cache.last()

params = concrete_program.parameters
params = concrete_program.parameters

concrete_program.parameters = params[0]
# TypeError: Type of self._params should be list or tuple,
# but received <class 'paddle.base.framework.EagerParamBase'>.
with self.assertRaises(TypeError):
concrete_program.parameters = params[0]
# TypeError: Type of self._params should be list or tuple,
# but received <class 'paddle.base.framework.EagerParamBase'>.
with self.assertRaises(TypeError):
if use_pir_api():
pir_partial_program_from(concrete_program)
else:
partial_program_from(concrete_program)

# Under PIR, params are tuples and cannot be modified
if not use_pir_api():
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这里也可以考虑把with self.assertRaises(TypeError):移上来，这样就不需要跳过pir_api了

params[0] = "linear.w.0"
concrete_program.parameters = params
# TypeError: Type of self._params[0] should be framework.EagerParamBase,
Expand Down