diff --git a/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_10_st.py b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_10_st.py new file mode 100644 index 0000000000000..1a46bae4fba36 --- /dev/null +++ b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_10_st.py @@ -0,0 +1,302 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# repo: diffusers_sub_grpah +# model: stable_diffusion +# api:paddle.nn.functional.conv.conv2d||method:transpose||method:flatten||api:paddle.nn.functional.norm.layer_norm||api:paddle.nn.functional.common.linear||api:paddle.nn.functional.common.linear||api:paddle.nn.functional.common.linear||method:reshape||method:transpose||method:reshape||method:transpose||method:reshape||method:transpose||api:paddle.tensor.linalg.matmul||method:__mul__||api:paddle.nn.functional.activation.softmax||api:paddle.tensor.linalg.matmul||method:transpose||method:reshape||api:paddle.nn.functional.common.linear||api:paddle.nn.functional.common.dropout||method:__truediv__||method:__add__||api:paddle.nn.functional.norm.layer_norm||api:paddle.nn.functional.common.linear||api:paddle.nn.functional.common.linear||api:paddle.nn.functional.common.linear||method:reshape||method:transpose||method:reshape||method:transpose||method:reshape||method:transpose||api:paddle.tensor.linalg.matmul||method:__mul__||api:paddle.nn.functional.activation.softmax||api:paddle.tensor.linalg.matmul||method:transpose||method:reshape||api:paddle.nn.functional.common.linear||api:paddle.nn.functional.common.dropout||method:__truediv__||method:__add__||api:paddle.nn.functional.norm.layer_norm||api:paddle.nn.functional.common.linear||method:chunk||api:paddle.nn.functional.activation.gelu||method:__mul__||api:paddle.nn.functional.common.dropout||api:paddle.nn.functional.common.linear||method:__add__||method:reshape||method:transpose||api:paddle.nn.functional.conv.conv2d||method:__add__ +import unittest + +import numpy as np + +import paddle + + +class LayerCase(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.parameter_0 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_1 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_2 = self.create_parameter( + shape=[320, 320], + dtype=paddle.float32, + ) + self.parameter_3 = self.create_parameter( + shape=[320, 320], + dtype=paddle.float32, + ) + self.parameter_4 = self.create_parameter( + shape=[768, 320], + dtype=paddle.float32, + ) + self.parameter_5 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_6 = self.create_parameter( + shape=[2560], + dtype=paddle.float32, + ) + self.parameter_7 = self.create_parameter( + shape=[320, 320], + dtype=paddle.float32, + ) + self.parameter_8 = self.create_parameter( + shape=[320, 2560], + dtype=paddle.float32, + ) + self.parameter_9 = self.create_parameter( + shape=[320, 320, 1, 1], + dtype=paddle.float32, + ) + self.parameter_10 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_11 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_12 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_13 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_14 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_15 = self.create_parameter( + shape=[1280, 320], + dtype=paddle.float32, + ) + self.parameter_16 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_17 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_18 = self.create_parameter( + shape=[768, 320], + dtype=paddle.float32, + ) + self.parameter_19 = self.create_parameter( + shape=[320, 320], + dtype=paddle.float32, + ) + self.parameter_20 = self.create_parameter( + shape=[320, 320], + dtype=paddle.float32, + ) + self.parameter_21 = self.create_parameter( + shape=[320, 320, 1, 1], + dtype=paddle.float32, + ) + self.parameter_22 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_23 = self.create_parameter( + shape=[320, 320], + dtype=paddle.float32, + ) + + def forward( + self, + var_0, # (shape: [1, 320, 1, 1], dtype: paddle.float32, stop_gradient: False) + var_1, # (shape: [], dtype: paddle.int32, stop_gradient: True) + var_2, # (shape: [], dtype: paddle.int32, stop_gradient: True) + var_3, # (shape: [1, 320, 1, 1], dtype: paddle.float32, stop_gradient: False) + var_4, # (shape: [1, 4, 768], dtype: paddle.float32, stop_gradient: True) + ): + var_5 = paddle.nn.functional.conv.conv2d( + var_0, self.parameter_21, self.parameter_17, [1, 1], 0, [1, 1], 1 + ) + var_6 = var_5.transpose([0, 2, 3, 1]) + var_7 = var_6.flatten(1, 2) + var_8 = paddle.nn.functional.norm.layer_norm( + var_7, + normalized_shape=[320], + weight=self.parameter_5, + bias=self.parameter_10, + epsilon=1e-05, + ) + var_9 = paddle.nn.functional.common.linear( + x=var_8, weight=self.parameter_7, bias=None, name=None + ) + var_10 = paddle.nn.functional.common.linear( + x=var_8, weight=self.parameter_3, bias=None, name=None + ) + var_11 = paddle.nn.functional.common.linear( + x=var_8, weight=self.parameter_19, bias=None, name=None + ) + var_12 = var_9.reshape([0, 0, 8, 40]) + var_13 = var_12.transpose([0, 2, 1, 3]) + var_14 = var_10.reshape([0, 0, 8, 40]) + var_15 = var_14.transpose([0, 2, 1, 3]) + var_16 = var_11.reshape([0, 0, 8, 40]) + var_17 = var_16.transpose([0, 2, 1, 3]) + var_18 = paddle.tensor.linalg.matmul(var_13, var_15, transpose_y=True) + var_19 = var_18 * 0.15811388300841897 + var_20 = paddle.nn.functional.activation.softmax(var_19, axis=-1) + var_21 = paddle.tensor.linalg.matmul(var_20, var_17) + var_22 = var_21.transpose([0, 2, 1, 3]) + var_23 = var_22.reshape([0, 0, 320]) + var_24 = paddle.nn.functional.common.linear( + x=var_23, + weight=self.parameter_20, + bias=self.parameter_14, + name=None, + ) + var_25 = paddle.nn.functional.common.dropout( + var_24, + p=0.0, + axis=None, + training=True, + mode='upscale_in_train', + name=None, + ) + var_26 = var_25 / 1.0 + var_27 = var_26 + var_7 + var_28 = paddle.nn.functional.norm.layer_norm( + var_27, + normalized_shape=[320], + weight=self.parameter_22, + bias=self.parameter_13, + epsilon=1e-05, + ) + var_29 = paddle.nn.functional.common.linear( + x=var_28, weight=self.parameter_23, bias=None, name=None + ) + var_30 = paddle.nn.functional.common.linear( + x=var_4, weight=self.parameter_4, bias=None, name=None + ) + var_31 = paddle.nn.functional.common.linear( + x=var_4, weight=self.parameter_18, bias=None, name=None + ) + var_32 = var_29.reshape([0, 0, 8, 40]) + var_33 = var_32.transpose([0, 2, 1, 3]) + var_34 = var_30.reshape([0, 0, 8, 40]) + var_35 = var_34.transpose([0, 2, 1, 3]) + var_36 = var_31.reshape([0, 0, 8, 40]) + var_37 = var_36.transpose([0, 2, 1, 3]) + var_38 = paddle.tensor.linalg.matmul(var_33, var_35, transpose_y=True) + var_39 = var_38 * 0.15811388300841897 + var_40 = paddle.nn.functional.activation.softmax(var_39, axis=-1) + var_41 = paddle.tensor.linalg.matmul(var_40, var_37) + var_42 = var_41.transpose([0, 2, 1, 3]) + var_43 = var_42.reshape([0, 0, 320]) + var_44 = paddle.nn.functional.common.linear( + x=var_43, weight=self.parameter_2, bias=self.parameter_0, name=None + ) + var_45 = paddle.nn.functional.common.dropout( + var_44, + p=0.0, + axis=None, + training=True, + mode='upscale_in_train', + name=None, + ) + var_46 = var_45 / 1.0 + var_47 = var_46 + var_27 + var_48 = paddle.nn.functional.norm.layer_norm( + var_47, + normalized_shape=[320], + weight=self.parameter_12, + bias=self.parameter_16, + epsilon=1e-05, + ) + var_49 = paddle.nn.functional.common.linear( + var_48, self.parameter_8, self.parameter_6 + ) + out = var_49.chunk(2, axis=-1) + var_50 = out[0] + var_51 = out[1] + var_52 = paddle.nn.functional.activation.gelu(var_51) + var_53 = var_50 * var_52 + var_54 = paddle.nn.functional.common.dropout( + var_53, + p=0.0, + axis=None, + training=True, + mode='upscale_in_train', + name=None, + ) + var_55 = paddle.nn.functional.common.linear( + var_54, self.parameter_15, self.parameter_1 + ) + var_56 = var_55 + var_47 + var_57 = var_56.reshape([-1, var_1, var_2, 320]) + var_58 = var_57.transpose([0, 3, 1, 2]) + var_59 = paddle.nn.functional.conv.conv2d( + var_58, self.parameter_9, self.parameter_11, [1, 1], 0, [1, 1], 1 + ) + var_60 = var_59 + var_3 + return var_60 + + +def create_paddle_inputs(): + inputs = ( + paddle.rand(shape=[1, 320, 1, 1], dtype=paddle.float32), + paddle.randint(low=1, high=2, shape=[1], dtype=paddle.int32), + paddle.randint(low=1, high=2, shape=[1], dtype=paddle.int32), + paddle.rand(shape=[1, 320, 1, 1], dtype=paddle.float32), + paddle.rand(shape=[1, 4, 768], dtype=paddle.float32), + ) + return inputs + + +class TestLayer(unittest.TestCase): + def setUp(self): + self.inputs = create_paddle_inputs() + self.net = LayerCase() + + def train(self, net, to_static, with_prim=False, with_cinn=False): + if to_static: + paddle.set_flags({'FLAGS_prim_all': with_prim}) + if with_cinn: + build_strategy = paddle.static.BuildStrategy() + build_strategy.build_cinn_pass = True + net = paddle.jit.to_static( + net, build_strategy=build_strategy, full_graph=True + ) + else: + net = paddle.jit.to_static(net, full_graph=True) + paddle.seed(123) + outs = net(*self.inputs) + return outs + + def test_ast_prim_cinn(self): + st_out = self.train(self.net, to_static=True) + cinn_out = self.train( + self.net, to_static=True, with_prim=False, with_cinn=False + ) + for st, cinn in zip( + paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out) + ): + np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8) + + +if __name__ == '__main__': + unittest.main() diff --git a/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_11_st.py b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_11_st.py new file mode 100644 index 0000000000000..88af233ed678a --- /dev/null +++ b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_11_st.py @@ -0,0 +1,110 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# repo: diffusers_sub_grpah +# model: stable_diffusion +# api:paddle.nn.functional.activation.silu||api:paddle.nn.functional.common.dropout||api:paddle.nn.functional.conv.conv2d||api:paddle.nn.functional.conv.conv2d||method:__add__||method:__truediv__ +import unittest + +import numpy as np + +import paddle + + +class LayerCase(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.parameter_0 = self.create_parameter( + shape=[640], + dtype=paddle.float32, + ) + self.parameter_1 = self.create_parameter( + shape=[640, 320, 1, 1], + dtype=paddle.float32, + ) + self.parameter_2 = self.create_parameter( + shape=[640, 640, 3, 3], + dtype=paddle.float32, + ) + self.parameter_3 = self.create_parameter( + shape=[640], + dtype=paddle.float32, + ) + + def forward( + self, + var_0, # (shape: [1, 640, 1, 1], dtype: paddle.float32, stop_gradient: False) + var_1, # (shape: [1, 320, 1, 1], dtype: paddle.float32, stop_gradient: False) + ): + var_2 = paddle.nn.functional.activation.silu(var_0, None) + var_3 = paddle.nn.functional.common.dropout( + var_2, + p=0.0, + axis=None, + training=True, + mode='upscale_in_train', + name=None, + ) + var_4 = paddle.nn.functional.conv.conv2d( + var_3, self.parameter_2, self.parameter_0, [1, 1], 1, [1, 1], 1 + ) + var_5 = paddle.nn.functional.conv.conv2d( + var_1, self.parameter_1, self.parameter_3, [1, 1], 0, [1, 1], 1 + ) + var_6 = var_5 + var_4 + var_7 = var_6 / 1.0 + return var_7 + + +def create_paddle_inputs(): + inputs = ( + paddle.rand(shape=[1, 640, 1, 1], dtype=paddle.float32), + paddle.rand(shape=[1, 320, 1, 1], dtype=paddle.float32), + ) + return inputs + + +class TestLayer(unittest.TestCase): + def setUp(self): + self.inputs = create_paddle_inputs() + self.net = LayerCase() + + def train(self, net, to_static, with_prim=False, with_cinn=False): + if to_static: + paddle.set_flags({'FLAGS_prim_all': with_prim}) + if with_cinn: + build_strategy = paddle.static.BuildStrategy() + build_strategy.build_cinn_pass = True + net = paddle.jit.to_static( + net, build_strategy=build_strategy, full_graph=True + ) + else: + net = paddle.jit.to_static(net, full_graph=True) + paddle.seed(123) + outs = net(*self.inputs) + return outs + + def test_ast_prim_cinn(self): + st_out = self.train(self.net, to_static=True) + cinn_out = self.train( + self.net, to_static=True, with_prim=True, with_cinn=False + ) + for st, cinn in zip( + paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out) + ): + np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8) + + +if __name__ == '__main__': + unittest.main() diff --git a/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_12_st.py b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_12_st.py new file mode 100644 index 0000000000000..c00bc83ec80af --- /dev/null +++ b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_12_st.py @@ -0,0 +1,79 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# repo: diffusers_sub_grpah +# model: stable_diffusion +# method:cast||api:paddle.tensor.attribute.shape||method:__getitem__||method:__getitem__||method:__getitem__||method:__getitem__ +import unittest + +import numpy as np + +import paddle + + +class LayerCase(paddle.nn.Layer): + def __init__(self): + super().__init__() + + def forward( + self, + var_0, # (shape: [1, 640, 1, 1], dtype: paddle.float32, stop_gradient: False) + ): + var_1 = var_0.cast('float32') + var_2 = paddle.tensor.attribute.shape(var_1) + var_3 = var_2[0] + var_4 = var_2[1] + var_5 = var_2[2] + var_6 = var_2[3] + return var_1, var_5, var_6 + + +def create_paddle_inputs(): + inputs = (paddle.rand(shape=[1, 640, 1, 1], dtype=paddle.float32),) + return inputs + + +class TestLayer(unittest.TestCase): + def setUp(self): + self.inputs = create_paddle_inputs() + self.net = LayerCase() + + def train(self, net, to_static, with_prim=False, with_cinn=False): + if to_static: + paddle.set_flags({'FLAGS_prim_all': with_prim}) + if with_cinn: + build_strategy = paddle.static.BuildStrategy() + build_strategy.build_cinn_pass = True + net = paddle.jit.to_static( + net, build_strategy=build_strategy, full_graph=True + ) + else: + net = paddle.jit.to_static(net, full_graph=True) + paddle.seed(123) + outs = net(*self.inputs) + return outs + + def test_ast_prim_cinn(self): + st_out = self.train(self.net, to_static=True) + cinn_out = self.train( + self.net, to_static=True, with_prim=True, with_cinn=False + ) + for st, cinn in zip( + paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out) + ): + np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8) + + +if __name__ == '__main__': + unittest.main() diff --git a/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_8_st.py b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_8_st.py new file mode 100644 index 0000000000000..5cef564d61a46 --- /dev/null +++ b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_8_st.py @@ -0,0 +1,99 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# repo: diffusers_sub_grpah +# model: stable_diffusion +# api:paddle.nn.functional.activation.silu||api:paddle.nn.functional.common.dropout||api:paddle.nn.functional.conv.conv2d||method:__add__||method:__truediv__ +import unittest + +import numpy as np + +import paddle + + +class LayerCase(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.parameter_0 = self.create_parameter( + shape=[320], + dtype=paddle.float32, + ) + self.parameter_1 = self.create_parameter( + shape=[320, 320, 3, 3], + dtype=paddle.float32, + ) + + def forward( + self, + var_0, # (shape: [1, 320, 1, 1], dtype: paddle.float32, stop_gradient: False) + var_1, # (shape: [1, 320, 1, 1], dtype: paddle.float32, stop_gradient: False) + ): + var_2 = paddle.nn.functional.activation.silu(var_0, None) + var_3 = paddle.nn.functional.common.dropout( + var_2, + p=0.0, + axis=None, + training=True, + mode='upscale_in_train', + name=None, + ) + var_4 = paddle.nn.functional.conv.conv2d( + var_3, self.parameter_1, self.parameter_0, [1, 1], 1, [1, 1], 1 + ) + var_5 = var_1 + var_4 + var_6 = var_5 / 1.0 + return var_6 + + +def create_paddle_inputs(): + inputs = ( + paddle.rand(shape=[1, 320, 1, 1], dtype=paddle.float32), + paddle.rand(shape=[1, 320, 1, 1], dtype=paddle.float32), + ) + return inputs + + +class TestLayer(unittest.TestCase): + def setUp(self): + self.inputs = create_paddle_inputs() + self.net = LayerCase() + + def train(self, net, to_static, with_prim=False, with_cinn=False): + if to_static: + paddle.set_flags({'FLAGS_prim_all': with_prim}) + if with_cinn: + build_strategy = paddle.static.BuildStrategy() + build_strategy.build_cinn_pass = True + net = paddle.jit.to_static( + net, build_strategy=build_strategy, full_graph=True + ) + else: + net = paddle.jit.to_static(net, full_graph=True) + paddle.seed(123) + outs = net(*self.inputs) + return outs + + def test_ast_prim_cinn(self): + st_out = self.train(self.net, to_static=True) + cinn_out = self.train( + self.net, to_static=True, with_prim=True, with_cinn=True + ) + for st, cinn in zip( + paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out) + ): + np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8) + + +if __name__ == '__main__': + unittest.main() diff --git a/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_9_st.py b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_9_st.py new file mode 100644 index 0000000000000..a03d352478fe1 --- /dev/null +++ b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_9_st.py @@ -0,0 +1,79 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# repo: diffusers_sub_grpah +# model: stable_diffusion +# method:cast||api:paddle.tensor.attribute.shape||method:__getitem__||method:__getitem__||method:__getitem__||method:__getitem__ +import unittest + +import numpy as np + +import paddle + + +class LayerCase(paddle.nn.Layer): + def __init__(self): + super().__init__() + + def forward( + self, + var_0, # (shape: [1, 320, 1, 1], dtype: paddle.float32, stop_gradient: False) + ): + var_1 = var_0.cast('float32') + var_2 = paddle.tensor.attribute.shape(var_1) + var_3 = var_2[0] + var_4 = var_2[1] + var_5 = var_2[2] + var_6 = var_2[3] + return var_1, var_5, var_6 + + +def create_paddle_inputs(): + inputs = (paddle.rand(shape=[1, 320, 1, 1], dtype=paddle.float32),) + return inputs + + +class TestLayer(unittest.TestCase): + def setUp(self): + self.inputs = create_paddle_inputs() + self.net = LayerCase() + + def train(self, net, to_static, with_prim=False, with_cinn=False): + if to_static: + paddle.set_flags({'FLAGS_prim_all': with_prim}) + if with_cinn: + build_strategy = paddle.static.BuildStrategy() + build_strategy.build_cinn_pass = True + net = paddle.jit.to_static( + net, build_strategy=build_strategy, full_graph=True + ) + else: + net = paddle.jit.to_static(net, full_graph=True) + paddle.seed(123) + outs = net(*self.inputs) + return outs + + def test_ast_prim_cinn(self): + st_out = self.train(self.net, to_static=True) + cinn_out = self.train( + self.net, to_static=True, with_prim=True, with_cinn=False + ) + for st, cinn in zip( + paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out) + ): + np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8) + + +if __name__ == '__main__': + unittest.main()