【Prim】support higher order autodiff for dy2static+composite

paddle/fluid/prim/utils/static/composite_grad_desc_maker.h

@@ -350,10 +350,7 @@ class CompositeGradOpMakerBase {
   framework::VarDesc* SingleOutputGrad(const std::string& name) const {
     auto* var = this->SingleForwardOutput(name);
     if (!var) {
-      PADDLE_THROW(platform::errors::InvalidArgument(
-          "GetSingleOutputGrad for %s_grad faild, if it is Optional input,"
-          "please use GetOptionalSingleOutputGrad replaced. ",
-          name));
+      return nullptr;
     }
     auto var_name = var->Name();
     auto grad_var_name = framework::GradVarName(var_name);
@@ -371,7 +368,7 @@ class CompositeGradOpMakerBase {
       return StaticCompositeContext::Instance().GetBlock()->FindVar(
           grad_var_name);
     } else {
-      return StaticCompositeContext::Instance().GetBlock()->Var(grad_var_name);
+      return nullptr;
     }
   }
 

python/paddle/fluid/backward.py

@@ -28,6 +28,8 @@
 
 from collections.abc import Sequence
 
+import re
+
 __all__ = [
     'append_backward',
     'gradients',
@@ -459,10 +461,14 @@ def _strip_grad_suffix_(name):
     """
     Strip the grad suffix from the given variable name
     e.g. x@GRAD ==> x
+         x@GRAD@GRAD ==> x
          y@GRAD@RENAME@1 ==> y
+         z@GRAD_slice_0@GRAD ==> z@GRAD_slice_0
     """
-    pos = name.find(core.grad_var_suffix())
-    new_name = name[:pos] if pos != -1 else name
+    pos = re.search(f'{core.grad_var_suffix()}$', name) or re.search(
+        f'{core.grad_var_suffix()}@', name
+    )
+    new_name = name[: pos.start()] if pos is not None else name
     new_pos = name.rfind('grad/')
     return new_name[new_pos + 5 :] if new_pos != -1 else new_name
 
@@ -1343,15 +1349,17 @@ def update_distop_context(
 
     if core._is_bwd_prim_enabled():
         composite_block = program.clone().current_block()
-        # Infer shape for operators whose output haven't been created.
+        # Create output and infer shape for operators whose output haven't
+        # been created.
         for op in composite_block.ops:
-            if not all(
-                tuple(
-                    composite_block._find_var_recursive(arg)
-                    for arg in op.output_arg_names
-                )
-            ):
-                infershape_for_composite(composite_block, op.desc)
+            for name in op.output_arg_names:
+                if not (
+                    composite_block.desc.has_var_recursive(name.encode())
+                    or name == core.empty_var_name()
+                ):
+                    composite_block.create_var(name=name)
+            op.desc.infer_var_type(composite_block.desc)
+            op.desc.infer_shape(composite_block.desc)
 
     # add grad_op_desc by reversed ops
     for op in reversed(ops):
@@ -1492,27 +1500,36 @@ def find_op_index(block_desc, cur_op_desc):
                 or name in input_grad_names_set
             )
             is_append_grad = False
+
+            # NOTE: In primitive mode, the intermediate variable generated by
+            # decompositing raw grad op are not satisfied the rule of 'XX@GRAD',
+            # which will cause it be pruned according to current pruning logic.
+            # For simplicity, we treate all prmitive operators as one raw
+            # operator, and keep the pruning logic consistent with currently
+            # logic. The drawback of this solution is may lead to some primitive
+            # operators are not pruned, which is needed to fixed.
+            # FIXME: Optimize pruning logic from the perspective of whole graph.
             input_grad_names = []
             for op_desc in grad_op_desc:
                 input_grad_names += [
                     name
                     for name in op_desc.input_arg_names()
                     if is_grad_name(name)
                 ]
+
+            # some code of gradient ops, like increment, are not very
+            # standard, there is no @GRAD in these ops' inputs.
             if len(input_grad_names) == 0:
                 is_append_grad = True
-                break
-
-            for op_desc in grad_op_desc:
-
-                # some code of gradient ops, like increment, are not very
-                # standard, there is no @GRAD in these ops' inputs.
+                continue
 
-                if _some_in_set_(input_grad_names, input_grad_names_set):
+            if _some_in_set_(input_grad_names, input_grad_names_set):
+                is_append_grad = True
+                for op_desc in grad_op_desc:
                     grad_op_descs.append(op_desc)
-                    is_append_grad = True
                     for name in op_desc.output_arg_names():
                         input_grad_names_set.add(name)
+
             if is_append_grad:
                 grad_to_var.update(op_grad_to_var)
         else:
@@ -1774,17 +1791,19 @@ def infershape_for_composite(block, grad_op_desc):
         op_desc.check_attrs()
         op_desc.infer_var_type(block.desc)
         op_desc.infer_shape(block.desc)
-        for arg in op_desc.output_arg_names():
-            if arg in new_vars:
-                _infer_var_data_type_shape_(arg, block)
-
         grad_op_desc.copy_from(op_desc)
 
-    # NOTE: Some operator doesn't infer dtype correctly, this patch set the
-    # grad_var dtype same with corresponding forward variable.
-    for arg in grad_op_desc.output_arg_names():
-        if arg in new_vars:
-            _infer_var_data_type_shape_(arg, block)
+    if not framework.OpProtoHolder.instance().has_op_proto(grad_op_desc.type()):
+        # NOTE: Some raw fluid grad operators which hadn't been decomposed may not
+        # implement InferVarType method, such as elementwise_xx_grad, and it will
+        # cause the dtype or shape of corresponding cotangent incorrect. This
+        # patch set the cotangent dtype and shape same with corresponding
+        # forward variable. For primitive operators, we have ensure all
+        # InferVarType method to be executed correctly in PR#52818, we skip
+        # this patch for primitive operators.
+        for arg in grad_op_desc.output_arg_names():
+            if arg in new_vars:
+                _infer_var_data_type_shape_(arg, block)
 
 
 def _rename_grad_(

python/paddle/fluid/tests/unittests/test_calc_gradient.py

@@ -20,6 +20,8 @@
 from paddle import fluid
 from paddle.fluid.backward import calc_gradient
 
+paddle.enable_static()
+
 
 class TestCalcGradient(unittest.TestCase):
     def test_calc_gradient(self):

test/cpp/prim/test_static_prim.cc

@@ -206,6 +206,11 @@ TEST(StaticPrim, TanhBackwardComposite) {
   auto* forward_opdesc = target_block->AllOps()[0];
   std::unordered_map<std::string, std::string> grad_to_var;
   std::vector<framework::BlockDesc*> grad_sub_block;
+  Tensor out_grad = prim::empty<prim::DescTensor>(
+      shape, phi::DataType::FLOAT32, paddle::Place());
+  framework::VarDesc* out_grad_desc =
+      static_cast<prim::DescTensor*>(out_grad.impl().get())->get_ptr();
+  target_block->RenameVar(out_grad_desc->Name(), "b@GRAD");
   std::vector<std::unique_ptr<framework::OpDesc>> grad_ops =
       std::move(framework::OpInfoMap::Instance()
                     .Get(forward_opdesc->Type())
@@ -288,6 +293,11 @@ TEST(StaticCompositeGradMaker, TestMutiInputMethod) {
   auto* forward_opdesc = target_block->AllOps()[0];
   std::unordered_map<std::string, std::string> grad_to_var;
   std::vector<framework::BlockDesc*> grad_sub_block;
+  Tensor out_grad = prim::empty<prim::DescTensor>(
+      shape, phi::DataType::FLOAT32, paddle::Place());
+  framework::VarDesc* out_grad_desc =
+      static_cast<prim::DescTensor*>(out_grad.impl().get())->get_ptr();
+  target_block->RenameVar(out_grad_desc->Name(), "out@GRAD");
   auto test = TestCompositeGradMaker(*forward_opdesc,
                                      std::unordered_set<std::string>(),
                                      &grad_to_var,
@@ -353,6 +363,19 @@ TEST(StaticCompositeGradMaker, TestMutiOutputMethod) {
   auto* forward_opdesc = target_block->AllOps()[0];
   std::unordered_map<std::string, std::string> grad_to_var;
   std::vector<framework::BlockDesc*> grad_sub_block;
+
+  Tensor out1_grad = prim::empty<prim::DescTensor>(
+      shape, phi::DataType::FLOAT32, paddle::Place());
+  framework::VarDesc* out1_grad_desc =
+      static_cast<prim::DescTensor*>(out1_grad.impl().get())->get_ptr();
+  target_block->RenameVar(out1_grad_desc->Name(), "out1@GRAD");
+
+  Tensor out2_grad = prim::empty<prim::DescTensor>(
+      shape, phi::DataType::FLOAT32, paddle::Place());
+  framework::VarDesc* out2_grad_desc =
+      static_cast<prim::DescTensor*>(out2_grad.impl().get())->get_ptr();
+  target_block->RenameVar(out2_grad_desc->Name(), "out2@GRAD");
+
   auto test = TestCompositeGradMaker(*forward_opdesc,
                                      std::unordered_set<std::string>(),
                                      &grad_to_var,

test/prim/test_comp_get_grad_op_desc_prim_enabled.py

@@ -67,6 +67,11 @@ def setUpClass(cls):
                 for n, vs in cls.outputs.items()
             },
         )
+
+        for _, outs in cls.outputs.items():
+            for out in outs:
+                block.create_var(name=out + core.grad_var_suffix())
+
         cls.fwd = block.ops[0].desc
 
     @classmethod

test/prim/test_comp_skip_op_set.py

@@ -46,6 +46,11 @@ def setUp(self):
                 for n, vs in self.outputs.items()
             },
         )
+
+        for _, outs in self.outputs.items():
+            for out in outs:
+                block.create_var(name=out + core.grad_var_suffix())
+
         self.fwd = block.ops[0].desc
 
     def tearDown(self):