support MXNet _minimum and _maximum (#2709)

nnvm/python/nnvm/frontend/mxnet.py

@@ -286,6 +286,12 @@ def _lrn(inputs, attrs):
     new_attrs['size'] = _required_attr(attrs, 'nsize')
     return _get_nnvm_op(op_name)(*inputs, **new_attrs)
 
+def _minimum(inputs, attrs):
+    return _get_nnvm_op('broadcast_min')(*inputs, **attrs)
+
+def _maximum(inputs, attrs):
+    return _get_nnvm_op('broadcast_max')(*inputs, **attrs)
+
 def _ones(_, attrs):
     op_name = 'ones'
     return _get_nnvm_op(op_name)(**attrs)
@@ -330,6 +336,8 @@ _convert_map = {
     '_rminus_scalar': _rename('__rsub_scalar__'),
     '_contrib_MultiBoxPrior' : _rename('multibox_prior'),
     '_contrib_MultiBoxDetection' : _contrib_multibox_detection,
+    '_minimum'      : _minimum,
+    '_maximum'      : _maximum,
     '_ones'         : _ones,
     '_zeros'        : _zeros,
     'argmax'        : _argmax,

nnvm/tests/python/frontend/mxnet/test_forward.py

@@ -227,6 +227,68 @@ def test_forward_slice():
     mx_sym = mx.sym.slice(data, begin=(-1, 1), end=(-3, 4), step=(-1, 2))
     verify_mxnet_frontend_impl(mx_sym, (3, 4), (2, 2))
 
+def test_forward_maximum():
+    a = mx.sym.var('a')
+    b = mx.sym.var('b')
+    dshape = (10, 20)
+    dtype = 'float32'
+    mx_sym = mx.sym._internal._maximum(a, b)
+    np_a = np.random.uniform(size=dshape).astype(dtype)
+    np_b = np.random.uniform(size=dshape).astype(dtype)
+    mx_a = mx.nd.array(np_a)
+    mx_b = mx.nd.array(np_b)
+    mod = mx.mod.Module(mx_sym, label_names=None, data_names=['a', 'b'])
+    mod.bind(data_shapes=[('a', dshape), ('b', dshape)], for_training=False)
+    mod.init_params()
+    args, auxs = mod.get_params()
+    mx_out = mx.nd._internal._maximum(mx_a, mx_b).asnumpy()
+    out_shape = dshape
+    new_sym, params = frontend.from_mxnet(mx_sym, args, auxs)
+    shape_dict = {'a': dshape, 'b': dshape}
+    for target, ctx in ctx_list():
+        with nnvm.compiler.build_config(opt_level=3):
+            graph, lib, params = nnvm.compiler.build(new_sym, target, shape_dict, params=params)
+        m = graph_runtime.create(graph, lib, ctx)
+        # set inputs
+        m.set_input("a", tvm.nd.array(np_a))
+        m.set_input("b", tvm.nd.array(np_b))
+        m.set_input(**params)
+        m.run()
+        # get outputs
+        tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()
+        tvm.testing.assert_allclose(mx_out, tvm_out, rtol=1e-5, atol=1e-5)
+
+def test_forward_minimum():
+    a = mx.sym.var('a')
+    b = mx.sym.var('b')
+    dshape = (10, 20)
+    dtype = 'float32'
+    mx_sym = mx.sym._internal._minimum(a, b)
+    np_a = np.random.uniform(size=dshape).astype(dtype)
+    np_b = np.random.uniform(size=dshape).astype(dtype)
+    mx_a = mx.nd.array(np_a)
+    mx_b = mx.nd.array(np_b)
+    mod = mx.mod.Module(mx_sym, label_names=None, data_names=['a', 'b'])
+    mod.bind(data_shapes=[('a', dshape), ('b', dshape)], for_training=False)
+    mod.init_params()
+    args, auxs = mod.get_params()
+    mx_out = mx.nd._internal._minimum(mx_a, mx_b).asnumpy()
+    out_shape = dshape
+    new_sym, params = frontend.from_mxnet(mx_sym, args, auxs)
+    shape_dict = {'a': dshape, 'b': dshape}
+    for target, ctx in ctx_list():
+        with nnvm.compiler.build_config(opt_level=3):
+            graph, lib, params = nnvm.compiler.build(new_sym, target, shape_dict, params=params)
+        m = graph_runtime.create(graph, lib, ctx)
+        # set inputs
+        m.set_input("a", tvm.nd.array(np_a))
+        m.set_input("b", tvm.nd.array(np_b))
+        m.set_input(**params)
+        m.run()
+        # get outputs
+        tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()
+        tvm.testing.assert_allclose(mx_out, tvm_out, rtol=1e-5, atol=1e-5)
+
 
 if __name__ == '__main__':
     test_forward_mlp()
@@ -251,4 +313,6 @@ if __name__ == '__main__':
     test_forward_argmin()
     test_forward_where()
     test_forward_slice()
+    test_forward_maximum()
+    test_forward_minimum()