Add flatten composite rule

python/paddle/fluid/tests/unittests/prim/model/test_resnet_prim_cinn.py

@@ -159,6 +159,7 @@ def test_cinn(self):
         not paddle.is_compiled_with_cinn(), "padle is not compiled with CINN"
     )
     def test_prim_cinn(self):
+        core._set_prim_forward_blacklist("flatten_contiguous_range")
         dy2st_prim_cinn = train(
             to_static=True, enable_prim=True, enable_cinn=True
         )

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

@@ -25,21 +25,25 @@ def setUp(self):
         self.python_api = paddle.flatten
         self.python_out_sig = ["Out"]
         self.op_type = "flatten_contiguous_range"
+        self.prim_op_type = "comp"
         self.start_axis = 0
         self.stop_axis = -1
         self.init_test_case()
         self.inputs = {"X": np.random.random(self.in_shape).astype("float64")}
         self.init_attrs()
+        self.enable_cinn = False
         self.outputs = {
             "Out": self.inputs["X"].reshape(self.new_shape),
             "XShape": np.random.random(self.in_shape).astype("float32"),
         }
 
     def test_check_output(self):
-        self.check_output(no_check_set=["XShape"], check_eager=True)
+        self.check_output(
+            no_check_set=["XShape"], 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)
 
     def init_test_case(self):
         self.in_shape = (3, 2, 5, 4)

python/paddle/incubate/autograd/composite_rules.py

@@ -191,6 +191,32 @@ def mean_composite(x, axis, keepdim):
     return divide(sum_x, norm)
 
 
+@REGISTER_COMPOSITE('flatten_contiguous_range')
+def flatten_contiguous_range_composite(x, start_axis, stop_axis):
+    """
+    define composite rule of op flatten, flatten_contiguous_range -> flatten.
+    CINN doesn't need xshape for backward pass, return none instead of xshape.
+    shape_out is the parameter of reshape, get from start_axis and stop_axis.
+    out = reshape(x, shape=shape_out), xshape
+    """
+    shape_in = x.shape
+    start_dim = start_axis if len(shape_in) != 0 else 0
+    end_dim = stop_axis if len(shape_in) != 0 else 0
+    assert start_dim <= end_dim
+    if len(shape_in) == 0 or start_dim == end_dim:
+        return reshape(x, shape=shape_in), None
+    slice_numel = 1
+    for i in range(start_dim, end_dim + 1):
+        slice_numel *= shape_in[i]
+    shape_out = []
+    for i in range(start_dim):
+        shape_out.append(shape_in[i])
+    shape_out.append(slice_numel)
+    for i in range(end_dim + 1, len(shape_in)):
+        shape_out.append(shape_in[i])
+    return reshape(x, shape=shape_out), None
+
+
 @REGISTER_COMPOSITE('dropout')
 def dropout_composite(x, seed_tensor, p, is_test, mode, seed, fix_seed):
     """define composite rule of op dropout.