[CudaGraph][SOT] Add unit tests for splitting the static graph into piecewise graphs that support cuda_graph (#3590)

* add unitest

* change sot_warmup_sizes

* wtf; add missed commit

tests/graph_optimization/test_static_graph_cuda_graph_split.py

@@ -0,0 +1,121 @@
+"""
+# 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 os
+
+os.environ["FLAGS_cuda_graph_blacklist"] = "pd_op.matmul,pd_op.transpose"
+
+
+import unittest
+
+import paddle
+import paddle.nn as nn
+
+from fastdeploy.config import (
+    CacheConfig,
+    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 Attention(nn.Layer):
+    def __init__(self, fd_config: FDConfig) -> None:
+        super().__init__()
+        self.embed_tokens = nn.Embedding(num_embeddings=100, embedding_dim=32)
+        self.qkv_proj = nn.Linear(32, 64)
+        self.attn = nn.MultiHeadAttention(embed_dim=64, num_heads=1)
+        self.o_proj = nn.Linear(64, 32)
+
+    def forward(
+        self,
+        ids_remove_padding,
+        forward_meta: ForwardMeta,
+    ):
+        hidden_states = self.embed_tokens(forward_meta.ids_remove_padding)
+        qkv_out = self.qkv_proj(hidden_states)
+        attn_out = self.attn(qkv_out)
+        output = self.o_proj(attn_out)
+
+        return output
+
+    def forward_dynamic(
+        self,
+        ids_remove_padding,
+        forward_meta: ForwardMeta,
+    ):
+        hidden_states = self.embed_tokens(forward_meta.ids_remove_padding)
+        qkv_out = self.qkv_proj(hidden_states)
+        attn_out = self.attn(qkv_out)
+        output = self.o_proj(attn_out)
+
+        return output
+
+
+class TestModel(nn.Layer):
+    def __init__(self, fd_config: FDConfig, **kwargs):
+        super().__init__()
+        self.model = Attention(fd_config)
+
+    def forward(self, ids_remove_padding, forward_meta: ForwardMeta):
+        return self.model(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)
+
+    def forward_correct(self, ids_remove_padding, forward_meta: ForwardMeta):
+        return self.model.forward_dynamic(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)
+
+
+class TestStaticGraphCUDAGraphSplit(unittest.TestCase):
+
+    def test(self):
+        """Run test case"""
+        # Set FastDeploy config
+        graph_opt_config = GraphOptimizationConfig({"use_cudagraph": True, "graph_opt_level": 1})
+        parallel_config = ParallelConfig({"max_num_seqs": 1})
+        graph_opt_config._set_cudagraph_sizes(max_num_seqs=parallel_config.max_num_seqs)
+        graph_opt_config.init_with_cudagrpah_size(max_num_seqs=parallel_config.max_num_seqs)
+        cache_config = CacheConfig({})
+
+        fd_config = FDConfig(
+            graph_opt_config=graph_opt_config,
+            parallel_config=parallel_config,
+            cache_config=cache_config,
+            test_mode=True,
+        )
+
+        test_model1 = TestModel(fd_config=fd_config)
+        x = paddle.randint(32, shape=[1, 8])
+        forward_meta1 = ForwardMeta(input_ids=x, ids_remove_padding=x, step_use_cudagraph=True)
+
+        # Triger Capture
+        _ = test_model1(x, forward_meta=forward_meta1)
+
+        # Reaplay
+        _ = test_model1(x, forward_meta=forward_meta1)
+        output1 = test_model1(x, forward_meta=forward_meta1)
+
+        # Corrent output
+        output1_correct = test_model1.forward_correct(x, forward_meta=forward_meta1)
+
+        assert (output1 == output1_correct).all()
+
+
+if __name__ == "__main__":
+    unittest.main()