[Executor]Update graph test case and delete test_attention (#3257)

* 1.update graph test case 2.delete test_attention

* code style

* delete print

Author: gongshaotian

test/graph_optimization/test_cuda_graph.py

@@ -0,0 +1,168 @@
+"""
+# Copyright (c) 2025 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.
+"""
+
+import paddle
+
+from fastdeploy.config import FDConfig, GraphOptimizationConfig, ParallelConfig
+from fastdeploy.model_executor.forward_meta import ForwardMeta
+from fastdeploy.model_executor.graph_optimization.decorator import (
+    support_graph_optimization,
+)
+
+
+@support_graph_optimization
+class TestCase1SubLayer1(paddle.nn.Layer):
+    """Sub layer 1 of test case 1"""
+
+    def __init__(self, fd_config: FDConfig, **kwargs):
+        super().__init__()
+
+    def forward(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Sub layer1 forward pass"""
+
+        output = paddle.add(forward_meta.input_ids, forward_meta.input_ids)
+        return output
+
+    def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Sub layer1 Correct forward pass"""
+        output = paddle.add(forward_meta.input_ids, forward_meta.input_ids)
+        return output
+
+
+class TestCase1SubLayer2(paddle.nn.Layer):
+    """ """
+
+    def __init__(self, fd_config: FDConfig, **kwargs):
+        super().__init__()
+
+    def forward(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Sub layer2 forward pass"""
+        x = forward_meta.input_ids
+        y = forward_meta.input_ids
+        output = x + y
+        return output
+
+    def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Sub layer2 Correct forward pass"""
+        x = forward_meta.input_ids
+        y = forward_meta.input_ids
+        output = x + y
+        return output
+
+
+@support_graph_optimization
+class TestCase1SubLayer3(paddle.nn.Layer):
+    """ """
+
+    def __init__(self, fd_config: FDConfig, **kwargs):
+        super().__init__()
+
+    def forward(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Sub layer3 forward pass"""
+        output = paddle.matmul(forward_meta.input_ids, forward_meta.input_ids)
+        return output
+
+    def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Sub layer3 Correct forward pass"""
+        output = paddle.matmul(forward_meta.input_ids, forward_meta.input_ids)
+        return output
+
+
+class TestModel1(paddle.nn.Layer):
+    """Tast Model"""
+
+    def __init__(self, fd_config: FDConfig, **kwargs):
+        super().__init__()
+        self.fd_config = fd_config
+
+        self.sublayer1 = TestCase1SubLayer1(self.fd_config)
+        self.sublayer2 = TestCase1SubLayer2(self.fd_config)
+        self.sublayer3 = TestCase1SubLayer3(self.fd_config)
+
+        self.sublayer2_output_buffer = paddle.zeros([1])
+
+    def forward(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Test model for ward pass"""
+        # sublayer1 use cuda graph
+        sub_meta1 = forward_meta
+        sublayer1_output = self.sublayer1(ids_remove_padding=ids_remove_padding, forward_meta=sub_meta1)
+
+        # sublayer2 not use cuda garph
+        sub_meta2 = ForwardMeta(
+            input_ids=sublayer1_output, ids_remove_padding=sublayer1_output, step_use_cudagraph=False
+        )
+        sublayer2_output = self.sublayer2(ids_remove_padding=sublayer1_output, forward_meta=sub_meta2)
+        self.sublayer2_output_buffer.copy_(sublayer2_output, False)
+
+        # sublayer3 use cuda graph
+        sub_meta3 = ForwardMeta(
+            input_ids=self.sublayer2_output_buffer,
+            ids_remove_padding=self.sublayer2_output_buffer,
+            step_use_cudagraph=True,
+        )
+        sublayer3_output = self.sublayer3(ids_remove_padding=self.sublayer2_output_buffer, forward_meta=sub_meta3)
+
+        return sublayer3_output
+
+    def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
+        """Test model for ward pass"""
+
+        # sublayer1 not use cuda graph
+        sub_meta1 = forward_meta
+        sublayer1_output = self.sublayer1.forward_correct(
+            ids_remove_padding=ids_remove_padding, forward_meta=sub_meta1
+        )
+
+        # sublayer2 not use cuda garph
+        sub_meta2 = ForwardMeta(input_ids=sublayer1_output, ids_remove_padding=sublayer1_output)
+        sublayer2_output = self.sublayer2.forward_correct(ids_remove_padding=sublayer1_output, forward_meta=sub_meta2)
+
+        # sublayer3 not use cuda graph
+        sub_meta3 = ForwardMeta(input_ids=sublayer2_output, ids_remove_padding=sublayer2_output)
+        sublayer3_output = self.sublayer3.forward_correct(ids_remove_padding=sublayer2_output, forward_meta=sub_meta3)
+
+        return sublayer3_output
+
+
+def run_test_case():
+    """Run test case"""
+    # Set FastDeploy config
+    graph_opt_config = GraphOptimizationConfig(args={})
+    graph_opt_config.use_cudagraph = True
+    parallel_config = ParallelConfig(args={})
+    parallel_config.max_num_seqs = 1
+    fd_config = FDConfig(graph_opt_config=graph_opt_config, parallel_config=parallel_config)
+
+    # Run Test Case1
+    test_model1 = TestModel1(fd_config=fd_config)
+    input_tensor1 = paddle.ones([1])
+    forward_meta1 = ForwardMeta(input_ids=input_tensor1, ids_remove_padding=input_tensor1, step_use_cudagraph=True)
+
+    # Triger Capture
+    _ = test_model1(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
+
+    # Reaplay
+    _ = test_model1(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
+    output1 = test_model1(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
+
+    # Corrent output
+    output1_correct = test_model1.forward_correct(ids_remove_padding=input_tensor1, forward_meta=forward_meta1)
+
+    assert output1 == output1_correct
+
+
+if __name__ == "__main__":
+    run_test_case()