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[Ospp] VisualDL Support Paddle PIR (#1279)
* VisualDL Adaptation for Paddle PIR * [Doc] Add document for VisualDL Adaptation for Paddle PIR * Dynamic graph changemodel interfaces support *.json file * [Doc] Add document for VisualDL Adaptation for Paddle PIR * [Doc] Add document for VisualDL Adaptation for Paddle PIR * Update Paddle PIR Visualization.md * Fix code style * Fix paddle dependency
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16 files changed

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‎demo/components/cond_inside_cond_test.py‎

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# Copyright (c) 2024 VisualDL Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =======================================================================
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import paddle
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from visualdl import LogWriter
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"""
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pseudocode:
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for i in range(1, 10):
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a = 2 * i
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if i < 5:
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if i >= 3:
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return a + a
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else:
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return a - a
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else:
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if i < 8:
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return a * a
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else:
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return a / a
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"""
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paddle.enable_static()
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def less_than_branch(i, a):
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return paddle.static.nn.cond(
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i >= 3.0,
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lambda: paddle.add(a, a),
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lambda: paddle.subtract(a, a),
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)
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def greater_equal_branch(i, a):
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return paddle.static.nn.cond(
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i < 8.0,
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lambda: paddle.multiply(a, a),
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lambda: paddle.divide(a, a),
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)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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i = paddle.static.data(name="i", shape=[1], dtype='float32')
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i.stop_gradient = False
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a = 2.0 * i
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out = paddle.static.nn.cond(
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i < 5.0,
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lambda: less_than_branch(i, a),
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lambda: greater_equal_branch(i, a),
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)
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mean = paddle.mean(out)
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with LogWriter(logdir="./log/cond_inside_cond_test/") as writer:
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writer.add_graph(
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model=main_program,
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input_spec=[paddle.static.InputSpec([1], dtype='float32')],
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verbose=True,
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is_pir=True)

‎demo/components/cond_test.py‎

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# Copyright (c) 2024 VisualDL Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =======================================================================
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import paddle
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from visualdl import LogWriter
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paddle.enable_static()
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"""
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pseudocode:
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for i in range(1, 10):
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a = 2 * i
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if i < 5:
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return a + a
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else:
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return a - a
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"""
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class ConditionalLayer(paddle.nn.Layer):
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def __init__(self):
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super(ConditionalLayer, self).__init__()
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def forward(self, i):
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a = 2.0 * i
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out = paddle.static.nn.cond(
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i < 5.0,
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lambda: paddle.add(a, a),
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lambda: paddle.subtract(a, a),
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)
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return out
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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i = paddle.static.data(name="i", shape=[1], dtype='float32')
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i.stop_gradient = False
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a = 2.0 * i
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out = paddle.static.nn.cond(
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i < 5.0,
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lambda: paddle.add(a, a),
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lambda: paddle.subtract(a, a),
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)
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mean = paddle.mean(out)
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with LogWriter(logdir="./log/cond_test/") as writer:
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writer.add_graph(
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model=main_program,
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input_spec=[paddle.static.InputSpec([1], 'float32')],
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verbose=True,
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is_pir=True)

‎demo/components/pir_graph_test.py‎

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# Copyright (c) 2024 VisualDL Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =======================================================================
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from visualdl import LogWriter
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class MyNet(nn.Layer):
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def __init__(self):
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super(MyNet, self).__init__()
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self.conv1 = nn.Conv2D(
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in_channels=1, out_channels=20, kernel_size=5, stride=1, padding=2)
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self.max_pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
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self.conv2 = nn.Conv2D(
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in_channels=20,
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out_channels=20,
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kernel_size=5,
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stride=1,
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padding=2)
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self.max_pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
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self.fc = nn.Linear(in_features=980, out_features=10)
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def forward(self, inputs):
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x = self.conv1(inputs)
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x = F.relu(x)
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x = self.max_pool1(x)
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x = self.conv2(x)
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x = F.relu(x)
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x = self.max_pool2(x)
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x = paddle.reshape(x, [x.shape[0], -1])
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x = self.fc(x)
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return x
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net = MyNet()
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with LogWriter(logdir="./log/pir_graph_test/") as writer:
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writer.add_graph(
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model=net,
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input_spec=[paddle.static.InputSpec([-1, 1, 28, 28], 'float32')],
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verbose=True,
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is_pir=True)

demo/components/pir_translate.py renamed to demo/components/pir_program_test.py

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# Copyright (c) 2024 VisualDL Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =======================================================================
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import paddle
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from paddle import ir
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from visualdl import LogWriter
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paddle.enable_static()
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batch_norm = paddle.nn.BatchNorm(32, act='relu', data_layout='NHWC')
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out = batch_norm(conv2d(tanh_out))
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newir_program = ir.translate_to_new_ir(main_program.desc)
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with LogWriter(logdir="./log/program_test/") as writer:
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writer.add_graph(
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model=newir_program,
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model=main_program,
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input_spec=[paddle.static.InputSpec([-1, 1, 28, 28], 'float32')],
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verbose=True,
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is_pir=True)

‎demo/components/while_test.py‎

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# Copyright (c) 2024 VisualDL Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =======================================================================
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import paddle
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from visualdl import LogWriter
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paddle.enable_static()
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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linear = paddle.nn.Linear(16, 10)
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def cond(i, loop_len, x, result):
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return i < loop_len
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def body(i, loop_len, x, result):
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result = linear(x)
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paddle.increment(i)
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return [i, loop_len, x, result]
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x = paddle.static.data(name='x', shape=[32, 16], dtype='float32')
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i = paddle.zeros(shape=[1], dtype='int64')
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loop_len = paddle.ones(shape=[1], dtype='int64')
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result = paddle.zeros(
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shape=x.shape[:-1] + linear.weight.shape[-1:], dtype="float32"
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)
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result.stop_gradient = False
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_, _, _, results = paddle.static.nn.while_loop(
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cond, body, [i, loop_len, x, result]
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)
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loss = paddle.mean(results)
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with LogWriter(logdir="./log/while_test/") as writer:
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writer.add_graph(
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model=main_program,
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input_spec=[paddle.static.InputSpec([1], 'float32')],
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verbose=True,
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is_pir=True)

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