Add expand composite rule

python/paddle/fluid/tests/unittests/test_expand_v2_op.py

@@ -179,7 +179,7 @@ def test_check_output(self):
         self.check_output()
 
 
-#  Situation 56: input x is Integer
+#  Situation 6: input x is Integer
 class TestExpandV2OpInt64_t(OpTest):
     def setUp(self):
         self.op_type = "expand_v2"
@@ -332,6 +332,136 @@ def test_grad(self):
             self.func(p)
 
 
+# Situation 7: comp case, shape is a list(without tensor)
+class TestExpandV2CompOpRank1(OpTest):
+    def setUp(self):
+        self.op_type = "expand_v2"
+        self.prim_op_type = "comp"
+        self.init_data()
+        self.python_api = paddle.expand
+        self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
+        self.attrs = {'shape': self.shape}
+        output = np.tile(self.inputs['X'], self.expand_times)
+        self.outputs = {'Out': output}
+        self.enable_cinn = True
+
+    def init_data(self):
+        self.ori_shape = [100]
+        self.shape = [100]
+        self.expand_times = [1]
+
+    def test_check_output(self):
+        self.check_output(check_prim=True)
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out', check_prim=True)
+
+
+class TestExpandV2OpCompRank2_DimExpanding(TestExpandV2CompOpRank1):
+    def init_data(self):
+        self.ori_shape = [120]
+        self.shape = [2, 120]
+        self.expand_times = [2, 1]
+
+
+class TestExpandV2CompOpRank2(TestExpandV2CompOpRank1):
+    def init_data(self):
+        self.ori_shape = [1, 140]
+        self.shape = [12, 140]
+        self.expand_times = [12, 1]
+
+
+class TestExpandV2CompOpRank3_Corner(TestExpandV2CompOpRank1):
+    def init_data(self):
+        self.ori_shape = (2, 10, 5)
+        self.shape = (2, 10, 5)
+        self.expand_times = (1, 1, 1)
+
+
+class TestExpandV2CompOpRank4(TestExpandV2CompOpRank1):
+    def init_data(self):
+        self.ori_shape = (2, 4, 5, 7)
+        self.shape = (-1, -1, -1, -1)
+        self.expand_times = (1, 1, 1, 1)
+
+
+# Situation 8: comp case, shape is a tensor
+class TestExpandV2CompOpRank1_tensor(OpTest):
+    def setUp(self):
+        self.op_type = "expand_v2"
+        self.prim_op_type = "comp"
+        self.python_api = paddle.expand
+        self.init_data()
+
+        self.inputs = {
+            'X': np.random.random(self.ori_shape).astype("float64"),
+            'Shape': np.array(self.expand_shape).astype("int32"),
+        }
+        self.attrs = {}
+        output = np.tile(self.inputs['X'], self.expand_times)
+        self.outputs = {'Out': output}
+
+    def init_data(self):
+        self.ori_shape = [100]
+        self.expand_times = [2, 1]
+        self.expand_shape = [2, 100]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+# Situation 9: comp case, input x is Integer
+class TestExpandV2CompOpInteger(OpTest):
+    def setUp(self):
+        self.op_type = "expand_v2"
+        self.prim_op_type = "comp"
+        self.python_api = paddle.expand
+        self.inputs = {
+            'X': np.random.randint(10, size=(2, 4, 5)).astype("int32")
+        }
+        self.attrs = {'shape': [2, 4, 5]}
+        output = np.tile(self.inputs['X'], (1, 1, 1))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+#  Situation 10: comp case, input x is Bool
+class TestExpandV2CompOpBoolean(OpTest):
+    def setUp(self):
+        self.op_type = "expand_v2"
+        self.prim_op_type = "comp"
+        self.python_api = paddle.expand
+        self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
+        self.attrs = {'shape': [2, 4, 5]}
+        output = np.tile(self.inputs['X'], (1, 1, 1))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+#  Situation 11: comp case, input x is Integer
+class TestExpandV2CompOpInt64_t(OpTest):
+    def setUp(self):
+        self.op_type = "expand_v2"
+        self.prim_op_type = "comp"
+        self.python_api = paddle.expand
+        self.inputs = {
+            'X': np.random.randint(10, size=(2, 4, 5)).astype("int64")
+        }
+        self.attrs = {'shape': [2, 4, 5]}
+        output = np.tile(self.inputs['X'], (1, 1, 1))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()

python/paddle/incubate/autograd/composite_rules.py

@@ -192,6 +192,42 @@ def mean_composite(x, axis, keepdim):
     return divide(sum_x, norm)
 
 
+@REGISTER_COMPOSITE('expand_v2')
+def expand_v2_composite(x, shape):
+    """
+    define composite rule of op expnad_v2, expand_v2->expand
+    repeat_times = shape / x.shape
+    out = tile(x, repeat_times = repeat_times)
+    """
+    shape_in = x.shape
+    assert len(shape) >= len(shape_in)
+    dim_out = len(shape)
+    dim_in = len(shape_in)
+    if dim_out == 0:
+        return x
+    repeat_times = []
+    for i in range(dim_out):
+        offset = dim_out - i
+        dim = dim_in - offset
+        size_in = shape_in[dim] if dim >= 0 else 1
+        size_out = shape[i]
+        if size_out == -1:
+            assert dim >= 0
+            repeat = 1
+        else:
+            assert size_out % size_in == 0
+            repeat = int(size_out / size_in)
+        repeat_times.append(repeat)
+    if dim_in < dim_out:
+        shape_in_expand = []
+        for i in range(dim_out - dim_in):
+            shape_in_expand.append(1)
+        shape_in_expand.extend(shape_in)
+        x_reshape = reshape(x, shape_in_expand)
+        return tile(x_reshape, repeat_times=repeat_times)
+    return tile(x, repeat_times=repeat_times)
+
+
 @REGISTER_COMPOSITE('stack')
 def stack_composite(x, axis):
     """

python/paddle/incubate/autograd/primitives.py

@@ -57,6 +57,7 @@
 from paddle.tensor import sum  # noqa: F401
 from paddle.tensor import tan  # noqa: F401
 from paddle.tensor import tanh  # noqa: F401
+from paddle.tensor import tile  # noqa: F401
 from paddle.tensor import uniform  # noqa: F401
 from paddle.tensor import zeros  # noqa: F401
 from paddle.tensor.creation import assign  # noqa: F401
@@ -123,6 +124,7 @@
     'fill_constant',
     'reshape',
     'full',
+    'tile',
     'concat',
     'uniform',
     'greater_equal',