Add export for __and__ & __or__ (#17894)

BowenBao · facebook-github-bot · commit fa189641b554 · 2019-05-16T13:52:06.000-07:00
Summary: In onnx spec, the supported input/output type for `And` and `Or` is `Bool` only. Thus in exporting, cast to/from `Bool` is inserted for input/output. Pull Request resolved: #17894 Reviewed By: zrphercule Differential Revision: D15103148 Pulled By: houseroad fbshipit-source-id: 3e1068ea236c743260d42882fb11f0e3a21707e6
diff --git a/test/onnx/test_pytorch_onnx_caffe2.py b/test/onnx/test_pytorch_onnx_caffe2.py
@@ -1651,6 +1651,24 @@ def forward(self, input):
                 return view_by_prim_shape(input)
         self.run_model_test(PrimShapeModel(), train=False, input=x, batch_size=BATCH_SIZE)
 
+    def test_and(self):
+        class AndModel(torch.nn.Module):
+            def forward(self, x, y):
+                return x & y
+
+        x = torch.randint(0, 1, (3, 5))
+        y = torch.randint(0, 1, (3, 5))
+        self.run_model_test(AndModel(), train=False, input=(x, y), batch_size=BATCH_SIZE)
+
+    def test_or(self):
+        class OrModel(torch.nn.Module):
+            def forward(self, x, y):
+                return x | y
+
+        x = torch.randint(0, 1, (3, 5))
+        y = torch.randint(0, 1, (3, 5))
+        self.run_model_test(OrModel(), train=False, input=(x, y), batch_size=BATCH_SIZE)
+
 # a bit of metaprogramming to set up all the rnn tests
 
 
diff --git a/torch/onnx/symbolic_helper.py b/torch/onnx/symbolic_helper.py
@@ -247,9 +247,9 @@ def _set_opset_version(opset_version):
     'Int': torch.onnx.TensorProtoDataType.INT32,
     'Long': torch.onnx.TensorProtoDataType.INT64,
     'Short': torch.onnx.TensorProtoDataType.INT16,
+    'Bool': torch.onnx.TensorProtoDataType.BOOL,
 }
 
-
 scalar_name_to_pytorch = {
     'uint8_t': 'Byte',
     'int8_t': 'Char',
diff --git a/torch/onnx/symbolic_opset9.py b/torch/onnx/symbolic_opset9.py
@@ -681,10 +681,22 @@ def upsample_bilinear2d(g, input, output_size, align_corners):
                 mode_s="linear")
 
 
-def wrap_logical_op_with_cast_to_uint8(func):
-    def wrap_with_cast(g, input, other):
-        return g.op("Cast", func(g, input, other), to_i=sym_help.cast_pytorch_to_onnx['Byte'])
-    return wrap_with_cast
+def wrap_logical_op_with_cast_to(to_type):
+    def decorator(fn):
+        def wrap_with_cast(g, input, other):
+            return g.op("Cast", fn(g, input, other), to_i=sym_help.cast_pytorch_to_onnx[to_type])
+        return wrap_with_cast
+    return decorator
+
+
+def wrap_logical_op_with_cast_to_and_from(to_type):
+    def decorator(fn):
+        def wrap_with_cast(g, input, other):
+            to_cast_func = globals()['_cast_{}'.format(to_type)]
+            from_cast_func = wrap_logical_op_with_cast_to(input.type().scalarType())(fn)
+            return from_cast_func(g, to_cast_func(g, input, False), to_cast_func(g, other, False))
+        return wrap_with_cast
+    return decorator
 
 
 def wrap_logical_op_with_negation(func):
@@ -693,18 +705,18 @@ def wrap_with_not(g, input, other):
     return wrap_with_not
 
 
-@wrap_logical_op_with_cast_to_uint8
+@wrap_logical_op_with_cast_to('Byte')
 def eq(g, self, other):
     return g.op("Equal", self, other)
 
 
-@wrap_logical_op_with_cast_to_uint8
+@wrap_logical_op_with_cast_to('Byte')
 @wrap_logical_op_with_negation
 def ne(g, self, other):
     return g.op("Equal", self, other)
 
 
-@wrap_logical_op_with_cast_to_uint8
+@wrap_logical_op_with_cast_to('Byte')
 def gt(g, input, other):
     return gt_impl(g, input, other)
 
@@ -714,7 +726,7 @@ def gt_impl(g, input, other):
     return g.op("Greater", input, sym_help._if_scalar_type_as(g, other, input))
 
 
-@wrap_logical_op_with_cast_to_uint8
+@wrap_logical_op_with_cast_to('Byte')
 def lt(g, input, other):
     return lt_impl(g, input, other)
 
@@ -724,20 +736,30 @@ def lt_impl(g, input, other):
     return g.op("Less", input, sym_help._if_scalar_type_as(g, other, input))
 
 
-@wrap_logical_op_with_cast_to_uint8
+@wrap_logical_op_with_cast_to('Byte')
 @wrap_logical_op_with_negation
 def ge(g, input, other):
     other = sym_help._maybe_get_scalar(other)
     return lt_impl(g, input, sym_help._if_scalar_type_as(g, other, input))
 
 
-@wrap_logical_op_with_cast_to_uint8
+@wrap_logical_op_with_cast_to('Byte')
 @wrap_logical_op_with_negation
 def le(g, input, other):
     other = sym_help._maybe_get_scalar(other)
     return gt_impl(g, input, sym_help._if_scalar_type_as(g, other, input))
 
 
+@wrap_logical_op_with_cast_to_and_from('Bool')
+def __and_(g, input, other):
+    return g.op('And', input, other)
+
+
+@wrap_logical_op_with_cast_to_and_from('Bool')
+def __or_(g, input, other):
+    return g.op('Or', input, other)
+
+
 def where(g, condition, self, other):
     return g.op("Where", condition, self, other)
 
