[Fix] Fix fuse derivative corner case and add UT

ppsci/autodiff/ad.py

@@ -134,12 +134,17 @@ def __call__(
                 self.Js[key] = _Jacobian(ys, xs)
             return self.Js[key](i, j, retain_graph, create_graph)
         else:
-            grads = paddle.grad(
+            xs_require: List["paddle.Tensor"] = [
+                xs[i] for i in range(len(xs)) if (ys, xs[i]) not in self.Js
+            ]
+            grads_require = paddle.grad(
                 ys,
-                xs,
+                xs_require,
                 create_graph=create_graph,
                 retain_graph=retain_graph,
             )
+
+            idx = 0
             Js_list = []
             for k, xs_ in enumerate(xs):
                 key = (ys, xs_)
@@ -148,7 +153,8 @@ def __call__(
                     f"{xs_.shape}"
                 )
                 if key not in self.Js:
-                    self.Js[key] = _Jacobian(ys, xs_, {0: grads[k]})
+                    self.Js[key] = _Jacobian(ys, xs_, {0: grads_require[idx]})
+                    idx += 1
                 Js_list.append(self.Js[key](i, j, retain_graph, create_graph))
             return Js_list
 

ppsci/equation/pde/base.py

@@ -39,8 +39,9 @@ def __init__(self):
 
         self.detach_keys: Optional[Tuple[str, ...]] = None
 
+    @staticmethod
     def create_symbols(
-        self, symbol_str: str
+        symbol_str: str,
     ) -> Union[sympy.Symbol, Tuple[sympy.Symbol, ...]]:
         """Create symbols
 

ppsci/utils/symbolic.py

@@ -305,6 +305,7 @@ def __init__(
         self.create_graph = create_graph
         self.retain_graph = retain_graph
         self._apply_func = self._derivate_operator_func
+        self.merged = False
 
     def forward(self, data_dict: DATA_DICT):
         # use cache
@@ -893,6 +894,9 @@ def _expr_to_callable_nodes(
                 # skip sdf function since it is always already given in data_dict
                 if callable_nodes_group[i][j].expr.args[0].name == "sdf":
                     continue
+                # skip merged node
+                if callable_nodes_group[i][j].merged:
+                    continue
 
                 candidate_pos = [[i, j]]
                 for ii in range(len(callable_nodes_group)):
@@ -904,6 +908,9 @@ def _expr_to_callable_nodes(
                         # skip same node
                         if i == ii and j == jj:
                             continue
+                        # skip merged node
+                        if callable_nodes_group[ii][jj].merged:
+                            continue
 
                         # has same function item
                         if (
@@ -932,17 +939,24 @@ def _expr_to_callable_nodes(
                 f" fuse node sequence: {fused_node_seq} at position: ([{gid0}][{nid0}])"
             )
 
+            # mark merged node
+            for i, (gid, nid) in enumerate(candidate_pos):
+                assert isinstance(callable_nodes_group[gid][nid], DerivativeNode)
+                callable_nodes_group[gid][nid].merged = True
+
             # replace first mergable node with fused node sequence(packed in list)
             # then mask the rest merged node to None(except [gid0, nid0])
-            for i, (gid, nid) in enumerate(candidate_pos):
-                if i == 0:
-                    callable_nodes_group[gid0][nid0] = fused_node_seq
-                else:
-                    # keep the end node of each group to avoid generating empty callable
-                    # node sequence, this will not effect performance since cache strategy
-                    # in Node.forward
-                    if nid != len(callable_nodes_group[gid]) - 1:
-                        callable_nodes_group[gid][nid] = None
+            for i, (gid, nid) in enumerate(candidate_pos[1:]):
+                # keep the end node of each group to avoid generating empty callable
+                # node sequence, this will not effect performance since cache strategy
+                # in Node.forward
+                if nid != len(callable_nodes_group[gid]) - 1:
+                    callable_nodes_group[gid][nid] = None
+
+            if nid0 == len(callable_nodes_group[gid0]) - 1:
+                callable_nodes_group[gid0].insert(nid0, fused_node_seq)
+            else:
+                callable_nodes_group[gid0][nid0] = fused_node_seq
 
             # re-organize callable_nodes_group, remove None element and unpack list
             for i in range(len(callable_nodes_group)):

test/utils/test_symbolic.py

@@ -88,5 +88,63 @@ def test_multi_model_and_sdf():
     assert np.allclose(out_var_tensor.numpy(), out_var_reference.numpy())
 
 
+def test_complicated_symbolic():
+    x_ten = paddle.randn([32, 1])
+    x_ten.stop_gradient = False
+    y_ten = paddle.randn([32, 1])
+    y_ten.stop_gradient = False
+    z_ten = paddle.randn([32, 1])
+    z_ten.stop_gradient = False
+
+    input_data = {
+        "x": x_ten,
+        "y": y_ten,
+        "z": z_ten,
+    }
+    x_sp, y_sp, z_sp = ppsci.equation.PDE.create_symbols("x y z")
+    f = sp.Function("f")(x_sp, y_sp, z_sp)
+    # g = sp.Function("g")(x_sp, y_sp, z_sp)
+    model_f = ppsci.arch.MLP((x_sp.name, y_sp.name, z_sp.name), (f.name,), 3, 6)
+    # model_g = ppsci.arch.MLP((x_sp.name, y_sp.name, z_sp.name), (f.name,), 3, 6)
+
+    for test_id in range(100):
+
+        def random_derivative(state):
+            ret = f
+            for k in range(4):
+                if state & (1 << k):
+                    ret = ret.diff(x_sp)
+                else:
+                    ret = ret.diff(y_sp)
+            return ret
+
+        state1 = np.random.randint(0, 1 << 4)
+        state2 = np.random.randint(0, 1 << 4)
+        state3 = np.random.randint(0, 1 << 4)
+        state4 = np.random.randint(0, 1 << 4)
+        targets = [
+            random_derivative(state1),
+            random_derivative(state2),
+            random_derivative(state3),
+            random_derivative(state4),
+        ]
+        eqs_fuse = ppsci.lambdify(
+            targets,
+            model_f,
+            fuse_derivative=True,
+        )
+        eqs_expected = ppsci.lambdify(
+            targets,
+            model_f,
+            fuse_derivative=True,
+        )
+
+        for i in range(len(targets)):
+            output_fuse = eqs_fuse[i](input_data)
+            output_expected = eqs_expected[i](input_data)
+            assert np.allclose(output_fuse.numpy(), output_expected.numpy())
+        ppsci.autodiff.clear()
+
+
 if __name__ == "__main__":
     pytest.main()