Support both Gluon 1 and 2 in the hybrid containers

python/mxnet/gluon/block.py

@@ -901,6 +901,7 @@ def forward(self, x):
     """
     def __init__(self):
         super(HybridBlock, self).__init__()
+        self._v2 = inspect.unwrap(self.hybrid_forward.__func__) is HybridBlock.hybrid_forward
         self._cached_graph = ()
         self._cached_op = None
         self._out_format = None
@@ -985,7 +986,7 @@ def _get_graph_v2(self, *args):
 
     def _get_graph(self, *args):
         if not self._cached_graph:
-            if inspect.unwrap(self.hybrid_forward.__func__) is not HybridBlock.hybrid_forward:
+            if not self._v2:
                 return self._get_graph_v1(*args)
             else:  # Gluon 2 based on deferred compute mode
                 return self._get_graph_v2(*args)
@@ -1282,7 +1283,7 @@ def _infer_attrs(self, infer_fn, attr, *args):
 
     def infer_shape(self, *args):
         """Infers shape of Parameters from inputs."""
-        if inspect.unwrap(self.hybrid_forward.__func__) is not HybridBlock.hybrid_forward:
+        if not self._v2:
             # Gluon 1 based on F:  hybrid_forward is defined by user
             self._infer_attrs('infer_shape', 'shape', *args)
         else:
@@ -1406,7 +1407,7 @@ def c_callback(name, op_name, array):
             cld()._monitor_all = monitor_all
 
     def __call__(self, x, *args):
-        if inspect.unwrap(self.hybrid_forward.__func__) is not HybridBlock.hybrid_forward:
+        if not self._v2:
             # Gluon 1 based on F:  hybrid_forward is defined by user
             return super().__call__(x, *args)
         else:  # Gluon 2 based on deferred compute mode

python/mxnet/gluon/nn/basic_layers.py

@@ -23,12 +23,13 @@
            'Flatten', 'Lambda', 'HybridLambda', 'Concatenate', 'HybridConcatenate', 'Identity']
 import warnings
 import uuid
+import inspect
 import numpy as np
 
 from .activations import Activation
 from ..block import Block, HybridBlock
 from ..utils import _indent
-from ... import ndarray as nd, symbol as sym, context
+from ... import ndarray as nd, np as mxnp, symbol as sym, context, _deferred_compute as dc
 from ...util import is_np_array
 from ..parameter import Parameter
 
@@ -111,15 +112,41 @@ class HybridSequential(HybridBlock):
         net.hybridize()
     """
     def __init__(self):
-        super(HybridSequential, self).__init__()
+        super().__init__()
         self._layers = []
+        self._v2_checked = False
 
     def add(self, *blocks):
         """Adds block on top of the stack."""
         for block in blocks:
             self._layers.append(block)
             self.register_child(block)
 
+    def __call__(self, *args, **kwargs):
+        if self._active  and not self._v2_checked and not dc.is_deferred_compute():
+            # If any of the child Blocks implements the Gluon 2 interface, the
+            # container must not pass a Symbol to them
+            if any(inspect.unwrap(chld().hybrid_forward.__func__) is
+                   HybridBlock.hybrid_forward for chld in self._children.values()):
+                self._v2 = True
+                self._v2_checked = True
+                self.forward = self._forward
+
+        return super().__call__(*args, **kwargs)
+
+
+    def _forward(self, x, *args):
+        for block in self._children.values():
+            x = block()(x, *args)
+            args = []
+            if isinstance(x, (tuple, list)):
+                args = x[1:]
+                x = x[0]
+        if args:
+            x = tuple([x] + list(args))
+        return x
+
+
     def hybrid_forward(self, F, x, *args):
         for block in self._children.values():
             x = block()(x, *args)
@@ -997,9 +1024,19 @@ class HybridConcatenate(HybridSequential):
         The axis on which to concatenate the outputs.
     """
     def __init__(self, axis=-1):
-        super(HybridConcatenate, self).__init__()
+        super().__init__()
         self.axis = axis
 
+    def _forward(self, x):
+        out = []
+        for block in self._children.values():
+            out.append(block()(x))
+        if is_np_array():
+            out = mxnp.concatenate(out, axis=self.axis)
+        else:
+            out = nd.concat(*out, dim=self.axis)
+        return out
+
     def hybrid_forward(self, F, x):
         out = []
         for block in self._children.values():

tests/python/unittest/test_gluon.py

@@ -1011,16 +1011,47 @@ def check_sequential(net):
     net.add(dense2)
     dense3 = gluon.nn.Dense(10)
     net.add(dense3)
+    net.initialize()
+
+    net(mx.nd.zeros((10, 10)))
+    net.hybridize()
+    assert net[1] is dense2
+    assert net[-1] is dense3
+    slc = net[1:3]
+    assert len(slc) == 2 and slc[0] is dense2 and slc[1] is dense3
+    assert isinstance(slc, type(net))
+
+def check_sequential_dc(net):
+    class MyBlock(mx.gluon.HybridBlock):
+        def __init__(self):
+            super().__init__()
+            self.dense = mx.gluon.nn.Dense(units=10, in_units=10)
+            self.weight = mx.gluon.Parameter('weight', shape=(10, ))
+
+        def forward(self, x):
+            return self.dense(x) + self.weight.data()
 
+    dense1 = MyBlock()
+    net.add(dense1)
+    dense2 = MyBlock()
+    net.add(dense2)
+    dense3 = MyBlock()
+    net.add(dense3)
+
+    net.initialize()
+    net.hybridize()
+    net(mx.nd.zeros((10, 10)))
     assert net[1] is dense2
     assert net[-1] is dense3
     slc = net[1:3]
     assert len(slc) == 2 and slc[0] is dense2 and slc[1] is dense3
     assert isinstance(slc, type(net))
 
+@pytest.mark.garbage_expected
 def test_sequential():
     check_sequential(gluon.nn.Sequential())
     check_sequential(gluon.nn.HybridSequential())
+    check_sequential_dc(gluon.nn.HybridSequential())
 
 def test_sequential_warning():
     with warnings.catch_warnings(record=True) as w:
@@ -3075,24 +3106,43 @@ def test_ModulatedDeformableConvolution():
     with mx.autograd.record():
         y = net(x)
 
-def test_concatenate():
+
+@pytest.mark.parametrize('dc', [True, False])
+@pytest.mark.parametrize('hybridize', [True, False])
+@pytest.mark.garbage_expected
+def test_concatenate(dc, hybridize):
+    if dc:
+        class MyBlock(mx.gluon.HybridBlock):
+            def __init__(self, units, activation=None, in_units=0):
+                super().__init__()
+                self.dense = mx.gluon.nn.Dense(units, activation=activation, in_units=in_units)
+
+            def forward(self, x):
+                return self.dense(x)
+    else:
+        MyBlock = nn.Dense
+
     model = nn.HybridConcatenate(axis=1)
-    model.add(nn.Dense(128, activation='tanh', in_units=10))
-    model.add(nn.Dense(64, activation='tanh', in_units=10))
-    model.add(nn.Dense(32, in_units=10))
+    model.add(MyBlock(128, activation='tanh', in_units=10))
+    model.add(MyBlock(64, activation='tanh', in_units=10))
+    model.add(MyBlock(32, in_units=10))
     model2 = nn.Concatenate(axis=1)
-    model2.add(nn.Dense(128, activation='tanh', in_units=10))
-    model2.add(nn.Dense(64, activation='tanh', in_units=10))
-    model2.add(nn.Dense(32, in_units=10))
+    model2.add(MyBlock(128, activation='tanh', in_units=10))
+    model2.add(MyBlock(64, activation='tanh', in_units=10))
+    model2.add(MyBlock(32, in_units=10))
 
     # symbol
-    x = mx.sym.var('data')
-    y = model(x)
-    assert len(y.list_arguments()) == 7
+    if not dc:
+        x = mx.sym.var('data')
+        y = model(x)
+        assert len(y.list_arguments()) == 7
 
     # ndarray
     model.initialize(mx.init.Xavier(magnitude=2.24))
     model2.initialize(mx.init.Xavier(magnitude=2.24))
+    if hybridize:
+        model.hybridize()
+        model2.hybridize()
     x = model(mx.nd.zeros((32, 10)))
     x2 = model2(mx.nd.zeros((32, 10)))
     assert x.shape == (32, 224)