Add imperative python tracer

paddle/fluid/imperative/layer.h

@@ -205,6 +205,7 @@ class OpBase {
       : op_desc_(nullptr),
         forward_id_(-1),
         backward_id_(-1),
+        trace_id_(-1),
         place_(platform::CPUPlace()) {}
 
   virtual ~OpBase() {
@@ -225,6 +226,7 @@ class OpBase {
   // Note: each fwd op corresponds to a vector of bwd ops.
   std::vector<framework::OpDesc*> grad_op_descs_;
   int backward_id_;
+  int trace_id_;
 
   platform::Place place_;
 

paddle/fluid/pybind/pybind.cc

@@ -193,6 +193,16 @@ PYBIND11_MODULE(core, m) {
             }
           },
           py::return_value_policy::reference)
+      .def_property("_trace_id",
+                    [](const imperative::OpBase &self) {
+                      pybind11::gil_scoped_release release;
+                      return self.trace_id_;
+                    },
+                    [](imperative::OpBase &self, int trace_id) {
+                      pybind11::gil_scoped_release release;
+                      self.trace_id_ = trace_id;
+                    },
+                    py::return_value_policy::reference)
       .def_property(
           "forward_id",
           [](const imperative::OpBase &self) { return self.forward_id_; },

python/paddle/fluid/framework.py

@@ -1201,13 +1201,13 @@ class Block(object):
             raise ValueError("Var {0} is not found recursively".format(name))
 
     def _clear_block(self):
-        # TODO(minqiyang): move this to backward_hooks
-        self.desc._clear_block()
+        assert _in_imperative_mode()
 
-        for name in self.vars.keys():
-            assert self.vars[name].persistable
+        # TODO(minqiyang): move this to Variable and Operator's __del__
+        self.desc._clear_block()
 
-        del self.ops[:]
+        assert len(self.vars) == 0
+        assert len(self.ops) == 0
 
     def all_parameters(self):
         return list(self.iter_parameters())
@@ -1345,26 +1345,13 @@ class Block(object):
             #
             # TODO(minqiyang): add op stop_gradient support in static mode too.
             # currently, we only support stop_gradient in imperative mode.
-            self._trace_op(op, kwargs.get("stop_gradient", False))
-        self.ops.append(op)
+            _imperative_tracer().trace_op(op,
+                                          kwargs.get("stop_gradient", False))
+        else:
+            self.ops.append(op)
 
         return op
 
-    def _trace_op(self, op, stop_gradient=False):
-        backward_refs = _imperative_tracer().trace(
-            op.iop, op.inputs, op.outputs, self.desc,
-            _imperative_current_expected_place_, stop_gradient)
-
-        # TODO(minqiyang): support backward_hooks to eager remove backward_refs
-        op.backward_refs = defaultdict(list)
-        for k, v in six.iteritems(op.inputs):
-            if k in backward_refs:
-                op.backward_refs[k] = op.inputs[k]
-
-        for k, v in six.iteritems(op.outputs):
-            if k in backward_refs:
-                op.backward_refs[k] = op.outputs[k]
-
     def _insert_op(self, index, *args, **kwargs):
         """
         Insert a Operator according to the giving arguments.
@@ -1417,9 +1404,11 @@ class Block(object):
             inputs=kwargs.get("inputs", None),
             outputs=kwargs.get("outputs", None),
             attrs=kwargs.get("attrs", None))
-        self.ops.insert(0, op)
         if _in_imperative_mode():
-            self._trace_op(op, kwargs.get("stop_gradient", False))
+            _imperative_tracer().trace_op(op,
+                                          kwargs.get("stop_gradient", False))
+        else:
+            self.ops.insert(0, op)
         return op
 
     def _sync_with_cpp(self):

python/paddle/fluid/imperative/__init__.py

@@ -23,7 +23,11 @@ from .layers import *
 from . import nn
 from .nn import *
 
+from . import tracer
+from .tracer import *
+
 __all__ = []
 __all__ += layers.__all__
 __all__ += base.__all__
 __all__ += nn.__all__
+__all__ += tracer.__all__

python/paddle/fluid/imperative/base.py

@@ -16,6 +16,7 @@ import numpy as np
 
 from paddle.fluid import core
 from paddle.fluid import framework
+from .tracer import Tracer
 
 __all__ = ['enabled', 'guard', 'to_variable']
 
@@ -28,7 +29,7 @@ def enabled():
 def guard(place=None):
     train = framework.Program()
     startup = framework.Program()
-    tracer = core.Tracer(train.current_block().desc)
+    tracer = Tracer(train.current_block().desc)
 
     if place is None:
         if core.is_compiled_with_cuda():