fix(mgb/trace): finalize when exception raise

GitOrigin-RevId: b8ffd00a7ea29add26a2a3d6275ea9f29d877908

imperative/python/megengine/jit/tracing.py

@@ -125,6 +125,9 @@ class trace:
         self._graph_opt_level = opt_level
         self._tensor_shape = tensor_shape
 
+        self._reset()
+
+    def _reset(self):
         self._untraced = True
         self._tinfo = []  # handle -> TensorInfo
         self._seq = []
@@ -257,31 +260,63 @@ class trace:
     def _record_const(self, op, outputs):
         pass
 
-    @contextlib.contextmanager
-    def _setup(self):
+    def _set_active(self, active: bool):
         global active_trace
+        if active:
             if active_trace:
                 raise NotImplementedError("sorry, not implemented: nested trace")
             active_trace = self
+        else:
+            assert active_trace is self
+            active_trace = None
 
-        if self._untraced:
+    def _init_trace(self, symbolic: bool):
         apply.enable(apply_with_tracing)
         apply.enable(apply_const_with_tracing)
-            if self._symbolic:
+        if symbolic:
             apply.enable(apply_symbolic_mode)
             apply.enable(apply_const_symbolic_mode)
             self._lazy_eval_graph = G.Graph()
+
+    def _take_escaped_tensors(self):
+        escaped_tensors = tuple(self._active_tensors)
+        self._active_tensors.clear()
+        return escaped_tensors
+
+    def _lazy_eval(self, lazy_eval_graph, lazy_eval_tensors):
+        active_lazy_eval_tensors = []
+        visited = set()
+        readers = []
+        for x in lazy_eval_tensors:
+            x = x()
+            if x is None or x in visited:
+                continue
+            reader = G.OutputNode(x._LazyEvalTensor__varnode).outputs[0]
+            readers.append(reader)
+            active_lazy_eval_tensors.append(x)
+            visited.add(x)
+        self._apply_graph_options(lazy_eval_graph)
+        lazy_eval_graph.compile(*readers)
+        lazy_eval_graph()
+        for r, x in zip(readers, active_lazy_eval_tensors):
+            assign_raw_tensor(x, as_raw_tensor(r.op.get_value()))
+
+    @contextlib.contextmanager
+    def _setup(self):
+        interrupted = False
+
+        def do_enter():
+            self._set_active(True)
+            if self._untraced:
+                self._init_trace(self._symbolic)
             else:
                 apply.enable(apply_compiled_mode)
                 if self._graph is None:
                     self._compile()
                 self._graph.execute()
 
-        yield
-
-        escaped_tensors = tuple(self._active_tensors)
-        self._active_tensors.clear()
-
+        def do_finalize():
+            escaped_tensors = self._take_escaped_tensors()
             if self._untraced:
                 for x in escaped_tensors:
                     info = self._tinfo[x._TraceMixin__handle]
@@ -290,44 +325,52 @@ class trace:
                 if self._inputs_to_restore:
                     for x in self._inputs_to_restore:
                         x._TraceMixin__restore()
-            if self._symbolic:
+                if self._symbolic and self._lazy_eval_tensors:
                     # eval lazy eval tensors
-                if self._lazy_eval_tensors:
-                    lazy_eval_tensors = []
-                    visited = set()
-                    readers = []
-                    for x in self._lazy_eval_tensors:
-                        x = x()
-                        if x is None or x in visited:
-                            continue
-                        reader = G.OutputNode(x._LazyEvalTensor__varnode).outputs[0]
-                        readers.append(reader)
-                        lazy_eval_tensors.append(x)
-                        visited.add(x)
-                    self._apply_graph_options(self._lazy_eval_graph)
-                    self._lazy_eval_graph.compile(*readers)
-                    self._lazy_eval_graph()
-                    for r, x in zip(readers, lazy_eval_tensors):
-                        assign_raw_tensor(x, as_raw_tensor(r.op.get_value()))
+                    self._lazy_eval(self._lazy_eval_graph, self._lazy_eval_tensors)
                     self._lazy_eval_graph = None
                     self._lazy_eval_tensors = None
                 self._untraced = False
             else:
-            if self._pc != len(self._seq):
-                raise TraceMismatchError("premature end")
+                # compiled_tensor leaks
+                if self._pc == len(self._seq):
                     for x in escaped_tensors:
+                        try:
                             assign_raw_tensor(x, as_raw_tensor(x._dev_tensor()))
+                        except TraceMismatchError:
+                            # TraceMismatchError thrown in do_exit
+                            pass
                     self._graph.wait()
                     self._reset_exec_env()
-            self._pc = 0
 
+            # reset status
+            self._pc = 0
             self._tensor_remaps = None
             apply.disable(apply_with_tracing)
             apply.disable(apply_const_with_tracing)
             apply.disable(apply_symbolic_mode)
             apply.disable(apply_const_symbolic_mode)
             apply.disable(apply_compiled_mode)
-        active_trace = None
+            self._set_active(False)
+
+        def do_exit():
+            if not self._untraced and self._pc != len(self._seq):
+                raise TraceMismatchError("premature end")
+            if not self._symbolic or not self._untraced:
+                for x in self._active_tensors:
+                    x._dev_tensor()
+
+        try:
+            do_enter()
+            yield
+            do_exit()
+        except:
+            interrupted = True
+            raise
+        finally:
+            do_finalize()
+            if interrupted:
+                self._reset()
 
     def _begin_excluded_region(self):
         if self._capture_as_const:

imperative/python/test/unit/test_tracing.py

@@ -307,3 +307,36 @@ def test_trace_warp_perspective():
 
     for i in range(1):
         f(x, M)
+
+
+def test_raise_on_trace():
+    step_count = 0
+    catch_count = 0
+    bad_step = 10
+
+    class CatchMe(Exception):
+        pass
+
+    a = tensor([1, 2, 3, 4])
+    b = tensor([5, 6, 7, 8])
+    c = tensor([9, 0, 1, 2])
+
+    @trace
+    def add_abc(a, b, c):
+        print("Hello")
+        ps = a + b
+        result = ps + c
+        if step_count == bad_step:
+            raise CatchMe("catch me")
+        return result
+
+    for i in range(100):
+        try:
+            d = add_abc(a, b, c)
+        except CatchMe as e:
+            catch_count += 1
+        else:
+            np.testing.assert_equal(d.numpy(), (a + b + c).numpy())
+        step_count += 1
+
+    assert catch_count == 1