implement expand as using tile (#51577)

python/paddle/fluid/tests/unittests/test_expand_as_v2_op.py

@@ -24,6 +24,7 @@ import paddle.fluid as fluid
 class TestExpandAsBasic(OpTest):
     def setUp(self):
         self.op_type = "expand_as_v2"
+        self.prim_op_type = "comp"
         self.python_api = paddle.expand_as
         x = np.random.rand(100).astype("float64")
         target_tensor = np.random.rand(2, 100).astype("float64")
@@ -34,15 +35,16 @@ class TestExpandAsBasic(OpTest):
         self.outputs = {'Out': output}
 
     def test_check_output(self):
-        self.check_output(check_eager=True)
+        self.check_output(check_eager=True, check_prim=True)
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out', check_eager=True)
+        self.check_grad(['X'], 'Out', check_eager=True, check_prim=True)
 
 
 class TestExpandAsOpRank2(TestExpandAsBasic):
     def setUp(self):
         self.op_type = "expand_as_v2"
+        self.prim_op_type = "comp"
         self.python_api = paddle.expand_as
         x = np.random.rand(10, 12).astype("float64")
         target_tensor = np.random.rand(10, 12).astype("float64")
@@ -56,6 +58,7 @@ class TestExpandAsOpRank2(TestExpandAsBasic):
 class TestExpandAsOpRank3(TestExpandAsBasic):
     def setUp(self):
         self.op_type = "expand_as_v2"
+        self.prim_op_type = "comp"
         self.python_api = paddle.expand_as
         x = np.random.rand(2, 3, 20).astype("float64")
         target_tensor = np.random.rand(2, 3, 20).astype("float64")
@@ -69,6 +72,7 @@ class TestExpandAsOpRank3(TestExpandAsBasic):
 class TestExpandAsOpRank4(TestExpandAsBasic):
     def setUp(self):
         self.op_type = "expand_as_v2"
+        self.prim_op_type = "comp"
         self.python_api = paddle.expand_as
         x = np.random.rand(1, 1, 7, 16).astype("float64")
         target_tensor = np.random.rand(4, 6, 7, 16).astype("float64")
@@ -84,6 +88,7 @@ class TestExpandAsOpRank5(TestExpandAsBasic):
 
     def setUp(self):
         self.op_type = "expand_as_v2"
+        self.prim_op_type = "comp"
         self.python_api = paddle.expand_as
         x = np.random.rand(1, 1, 7, 16).astype("int64")
         target_tensor = np.random.rand(4, 6, 7, 16).astype("float64")

python/paddle/incubate/autograd/composite_rules.py

@@ -225,6 +225,43 @@ def expand_v2_composite(x, shape):
     return tile(x, repeat_times=repeat_times)
 
 
+@REGISTER_COMPOSITE('expand_as_v2')
+def expand_as_v2_composite(x, y, target_shape):
+    """
+    define composite rule of op expnad_as_v2, expand_as_v2->expand_as
+    repeat_times = target_shape / x.shape
+    out = tile(x, repeat_times = repeat_times)
+    """
+    shape_in = x.shape
+    if y is not None:
+        target_shape = y.shape
+    assert target_shape is not None
+    dim_out = len(target_shape)
+    dim_in = len(shape_in)
+    assert dim_in <= dim_out and dim_out >= 0
+    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 = target_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):
     """