feat(mge/trace): support dict return value processing in trace

GitOrigin-RevId: 5b1c08848b

imperative/python/megengine/jit/tracing.py

@@ -642,22 +642,24 @@ class trace:
             if self._capture_as_const:
                 self._process_inputs(*args, **kwargs)
             outputs = self.__wrapped__(*args, **kwargs)
-            transform = False
-            # outputs can be None
+            if self._capture_as_const:
+                self._process_outputs(outputs)
+
+            # outputs could be None
             if outputs is not None:
-                if not isinstance(outputs, collections.abc.Sequence):
-                    transform = True
-                    outputs = (outputs,)
-                for o in outputs:
+                list_outputs = outputs
+                if isinstance(outputs, collections.abc.Mapping):
+                    _, list_outputs = zip(*sorted(outputs.items()))
+                elif not isinstance(outputs, collections.abc.Sequence):
+                    list_outputs = (outputs,)
+
+                for o in list_outputs:
                     # if outputs are copied, then use the newest info in trace data structure
                     if o._copied:
                         self._active_tensors[o._mixin_handle] = TensorWeakRef(o)
                         if self._untraced and self._symbolic:
                             self._lazy_eval_tensors[o._mixin_handle] = TensorWeakRef(o)
-            if self._capture_as_const:
-                self._process_outputs(outputs)
-            if transform:
-                outputs = outputs[0]
+
             return outputs
 
     def dump(

imperative/python/test/unit/test_tracing.py

@@ -28,18 +28,32 @@ from megengine.module import Module
 from megengine.random import normal, uniform
 
 
-def test_trace():
-    for symbolic in [False, True]:
-
-        @trace(symbolic=symbolic)
-        def f(x):
+@pytest.mark.parametrize("trace_mode", [False, True])
+@pytest.mark.parametrize("return_mode", ["Value", "Tuple", "List", "Dict"])
+def test_trace(trace_mode, return_mode):
+    @trace(symbolic=trace_mode)
+    def f(x):
+        if return_mode == "Tuple":
+            return (-x,)
+        elif return_mode == "List":
+            return [-x]
+        elif return_mode == "Dict":
+            return {"neg": -x}
+        else:
             return -x
 
-        x = tensor([1])
-        y = f(x).numpy()
+    def get_numpy(y):
+        if return_mode == "Tuple" or return_mode == "List":
+            return y[0].numpy()
+        elif return_mode == "Dict":
+            return y["neg"].numpy()
+        return y.numpy()
 
-        for i in range(3):
-            np.testing.assert_equal(f(x).numpy(), y)
+    x = tensor([1])
+    y = get_numpy(f(x))
+
+    for i in range(3):
+        np.testing.assert_equal(get_numpy(f(x)), y)
 
 
 def test_output_copy_trace():
@@ -54,51 +68,46 @@ def test_output_copy_trace():
             x = F.exp(x)
             return x
 
-    net = Simple()
-
-    gm = GradManager().attach(net.parameters())
-    opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
-    data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
+    ys = {False: [], True: []}
 
-    @trace(symbolic=False)
-    def train_f1(d):
-        with gm:
-            loss = net(d)
-            gm.backward(loss)
-            opt.step().clear_grad()
-        return loss
+    for symbolic in [False, True]:
+        net = Simple()
+        gm = GradManager().attach(net.parameters())
+        opt = optim.SGD(net.parameters(), 1e-3, momentum=0.9)
+        data = tensor(np.arange(4).reshape(2, 2), dtype="float32")
 
-    @trace(symbolic=True)
-    def train_f2(d):
-        with gm:
-            loss = net(d)
-            gm.backward(loss)
-            opt.step().clear_grad()
-        return loss
+        @trace(symbolic=symbolic)
+        def train_func(d):
+            with gm:
+                loss = net(d)
+                gm.backward(loss)
+                opt.step().clear_grad()
+            return loss
 
-        for i in range(2):
-            y1 = train_f1(data).numpy()
-            y2 = train_f2(data).numpy()
-            np.testing.assert_equal(y1, y2)
+        for i in range(3):
+            y = train_func(data).numpy()
+            ys[symbolic].append(y)
 
+    for i in range(3):
+        np.testing.assert_equal(ys[False][i], ys[True][i])
 
-def test_exclude_from_trace():
-    for symbolic in [False, True]:
 
-        @trace(symbolic=symbolic)
-        def f(x):
-            x = -x
-            with exclude_from_trace():
-                if i % 2:
-                    x = -x
-            x = -x
-            return x
+@pytest.mark.parametrize("trace_mode", [False, True])
+def test_exclude_from_trace(trace_mode):
+    @trace(symbolic=trace_mode)
+    def f(x):
+        x = -x
+        with exclude_from_trace():
+            if i % 2:
+                x = -x
+        x = -x
+        return x
 
-        x = tensor([1])
+    x = tensor([1])
 
-        for i in range(3):
-            y = f(x).numpy()
-            np.testing.assert_equal(f(x).numpy(), y)
+    for i in range(3):
+        y = f(x).numpy()
+        np.testing.assert_equal(f(x).numpy(), y)
 
 
 def test_print_in_trace():
@@ -191,21 +200,20 @@ def test_dump_volatile():
     )
 
 
-def test_trace_profiler():
-    for symbolic in [False, True]:
-
-        @trace(symbolic=symbolic, profiling=True)
-        def f(x):
-            return -x
+@pytest.mark.parametrize("trace_mode", [False, True])
+def test_trace_profiler(trace_mode):
+    @trace(symbolic=trace_mode, profiling=True)
+    def f(x):
+        return -x
 
-        x = tensor([1])
-        y = f(x).numpy()
+    x = tensor([1])
+    y = f(x).numpy()
 
-        f(x)
-        f(x)  # XXX: has to run twice
+    f(x)
+    f(x)  # XXX: has to run twice
 
-        out = f.get_profile()
-        assert out.get("profiler")
+    out = f.get_profile()
+    assert out.get("profiler")
 
 
 @pytest.mark.skip(reason="force opt_level=0 when building graph")
@@ -306,20 +314,20 @@ def test_trace_cvt_bool():
         np.testing.assert_equal(f(x).numpy(), False)
 
 
-def test_trace_reshape():
-    for symbolic in [False, True]:
-        x1 = tensor(np.random.randn(2, 10, 10))
-        x2 = tensor(np.random.randn(4, 10, 10))
-        x3 = tensor(np.random.randn(8, 10, 10))
+@pytest.mark.parametrize("trace_mode", [False, True])
+def test_trace_reshape(trace_mode):
+    x1 = tensor(np.random.randn(2, 10, 10))
+    x2 = tensor(np.random.randn(4, 10, 10))
+    x3 = tensor(np.random.randn(8, 10, 10))
 
-        @trace(symbolic=symbolic, capture_as_const=True)
-        def f(x):
-            y = x.reshape(x.shape[0], 100)
-            return y
+    @trace(symbolic=trace_mode, capture_as_const=True)
+    def f(x):
+        y = x.reshape(x.shape[0], 100)
+        return y
 
-        f(x1)
-        f(x2)
-        f(x3)
+    f(x1)
+    f(x2)
+    f(x3)
 
 
 def test_trace_topk():
@@ -387,20 +395,20 @@ def test_raise_on_trace():
     assert catch_count == 1
 
 
-def test_trace_broadcast():
-    for symbolic in [False, True]:
-        x1 = tensor(np.random.randn(3, 1, 1))
-        x2 = tensor(np.random.randn(1, 4, 1))
-        x3 = tensor(np.random.randn(1, 1, 5))
+@pytest.mark.parametrize("trace_mode", [False, True])
+def test_trace_broadcast(trace_mode):
+    x1 = tensor(np.random.randn(3, 1, 1))
+    x2 = tensor(np.random.randn(1, 4, 1))
+    x3 = tensor(np.random.randn(1, 1, 5))
 
-        @trace(symbolic=symbolic, capture_as_const=True)
-        def f(x):
-            y = F.broadcast_to(x, (3, 4, 5))
-            return y
+    @trace(symbolic=trace_mode, capture_as_const=True)
+    def f(x):
+        y = F.broadcast_to(x, (3, 4, 5))
+        return y
 
-        f(x1)
-        f(x2)
-        f(x3)
+    f(x1)
+    f(x2)
+    f(x3)
 
 
 def test_trace_nms():
@@ -466,21 +474,20 @@ def test_slice():
     y + y
 
 
-def test_random():
+@pytest.mark.parametrize("shape_mode", [False, True])
+def test_random(shape_mode):
     def run_test(op):
-        for symbolic_shape in [True, False]:
-
-            @trace(symbolic=True, symbolic_shape=symbolic_shape)
-            def f():
-                out = op(size=[10, 10])
-                out_shape = out.shape
-                assert out_shape is not None
-                if not isinstance(out_shape, tuple):
-                    assert out.shape.numpy() is not None
-                return out
-
-            for _ in range(3):
-                f()
+        @trace(symbolic=True, symbolic_shape=shape_mode)
+        def f():
+            out = op(size=[10, 10])
+            out_shape = out.shape
+            assert out_shape is not None
+            if not isinstance(out_shape, tuple):
+                assert out.shape.numpy() is not None
+            return out
+
+        for _ in range(3):
+            f()
 
     run_test(uniform)
     run_test(normal)