Migrate swiglu from incubate to nn.functional[fluid_ops]

python/paddle/distributed/auto_parallel/high_level_api.py

@@ -479,7 +479,7 @@ def to_distributed(
             ...         )
 
             ...     def forward(self, x):
-            ...         x = paddle.incubate.nn.functional.swiglu(
+            ...         x = paddle.nn.functional.swiglu(
             ...             self.gate_proj(x), self.up_proj(x)
             ...         )
             ...         out = self.down_proj(x)

python/paddle/distributed/auto_parallel/static/pir_pass.py

@@ -1592,7 +1592,7 @@ def fuse_attention_ffn_qkv_pass(
         if add_gate is not None and add_up is not None:
             fused_o = paddle.add(fused_o, fused_bias)
             fused_o.get_defining_op().copy_attrs_from(add_gate)
-        out = paddle.incubate.nn.functional.swiglu(fused_o)
+        out = paddle.nn.functional.swiglu(fused_o)
         out.get_defining_op().copy_attrs_from(pat[-1])
         pat[-1].result(0).replace_all_uses_with(out)
 

python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py

@@ -756,7 +756,7 @@ def build(self):
     def apply(hidden_states, gate_weight, up_weight, down_weight):
         gate = paddle.matmul(hidden_states, gate_weight)
         up = paddle.matmul(hidden_states, up_weight)
-        tmp = paddle.incubate.nn.functional.swiglu(gate, up)
+        tmp = paddle.nn.functional.swiglu(gate, up)
         out = paddle.matmul(tmp, down_weight)
         return out
 

python/paddle/incubate/nn/functional/__init__.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
 from .batched_gemm import batched_gemm
 from .blha_get_max_len import blha_get_max_len
 from .block_multihead_attention import (
@@ -109,7 +110,6 @@
     "masked_multihead_attention",
     "blha_get_max_len",
     "block_multihead_attention",
-    "swiglu",
     "moe_combine",
     "expand_modality_expert_id",
     "cal_aux_loss",

python/paddle/incubate/nn/functional/swiglu.py

@@ -16,13 +16,20 @@
 from typing import TYPE_CHECKING
 
 from paddle import _C_ops
+from paddle.utils import deprecated
 
-from ....framework import LayerHelper, in_dynamic_or_pir_mode
+from ....framework import in_dynamic_or_pir_mode
 
 if TYPE_CHECKING:
     from paddle import Tensor
 
 
+@deprecated(
+    since="3.3.0",
+    update_to="paddle.nn.functional.swiglu",
+    level=1,
+    reason="paddle.incubate.nn.functional.swiglu will be removed in future. Please use paddle.nn.functional.swiglu instead.",
+)
 def swiglu(
     x: Tensor, y: Tensor | None = None, name: str | None = None
 ) -> Tensor:
@@ -56,10 +63,3 @@ def swiglu(
     """
     if in_dynamic_or_pir_mode():
         return _C_ops.swiglu(x, y)
-    else:
-        helper = LayerHelper("swiglu", **locals())
-        out = helper.create_variable_for_type_inference(dtype=x.dtype)
-        helper.append_op(
-            type="swiglu", inputs={"x": x, "y": y}, outputs={"out": out}
-        )
-        return out

python/paddle/nn/functional/__init__.py

@@ -46,6 +46,7 @@
     softplus,
     softshrink,
     softsign,
+    swiglu,
     swish,
     tanh,
     tanh_,
@@ -207,6 +208,7 @@
     'softsign',
     'sigmoid',
     'silu',
+    'swiglu',
     'swish',
     'mish',
     'tanh',

python/paddle/nn/functional/activation.py

@@ -1820,3 +1820,38 @@ def gumbel_softmax(
         attrs={'temperature': temperature, 'hard': hard, 'axis': axis},
     )
     return out
+
+
+def swiglu(
+    x: Tensor, y: Tensor | None = None, name: str | None = None
+) -> Tensor:
+    """
+    This function performs SwiGLU activation to the input Tensor.
+
+    .. math::
+
+        out = silu(x) * y when y is not None
+        out = silu(xs[0]) * xs[1] when y is None, where xs = paddle.chunk(x, 2, axis=-1)
+
+    Args:
+        x (Tensor): The first input Tensor of SwiGLU.
+        y (Tensor, optional): The second input Tensor of SwiGLU. Default: None.
+        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+
+    Returns:
+        A Tensor with the same data type with x and y.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+            >>> import paddle.nn.functional as F
+            >>> x = paddle.to_tensor([1, 2], dtype='float32')
+            >>> out1, out2 = F.swiglu(x), F.swiglu(x, x)
+            >>> print(out1, out2)
+            Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
+                   [1.46211720]) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
+                   [0.73105860, 3.52318811])
+    """
+    if in_dynamic_or_pir_mode():
+        return _C_ops.swiglu(x, y)

test/auto_parallel/hybrid_strategy/to_distributed_api_for_llama.py

@@ -233,9 +233,7 @@ def __init__(
         )
 
     def forward(self, x):
-        x = paddle.incubate.nn.functional.swiglu(
-            self.gate_proj(x), self.up_proj(x)
-        )
+        x = paddle.nn.functional.swiglu(self.gate_proj(x), self.up_proj(x))
         out = self.down_proj(x)
         return out
 

test/ir/pir/cinn/llama_test_model.py

@@ -21,7 +21,7 @@
 import paddle
 import paddle.nn.functional as F
 from paddle import nn
-from paddle.incubate.nn.functional import swiglu
+from paddle.nn.functional import swiglu
 
 sys.path.append(dirname(__file__))
 

test/ir/pir/cinn/symbolic/test_llama_group_swiglu.py

@@ -35,7 +35,7 @@ def __init__(self):
         super().__init__()
 
     def forward(self, x, y):
-        out = paddle.incubate.nn.functional.swiglu(x, y)
+        out = paddle.nn.functional.swiglu(x, y)
 
         return out
 

test/legacy_test/test_fused_swiglu_weighted_bwd_op.py

@@ -17,7 +17,7 @@
 import numpy as np
 
 import paddle
-from paddle.incubate.nn.functional import swiglu
+from paddle.nn.functional import swiglu
 
 
 class TestFusedWeightedSwigluBwd(unittest.TestCase):

test/legacy_test/test_swiglu.py

@@ -31,7 +31,7 @@
     DistTensorSpec,
     TensorDistAttr,
 )
-from paddle.incubate.nn.functional import swiglu as fused_swiglu_impl
+from paddle.nn.functional import swiglu as fused_swiglu_impl
 
 
 def swiglu(x, y, out_grad):
@@ -71,13 +71,13 @@ def swiglu(x, y, out_grad):
     return ret
 
 
-def fused_swiglu(x, y, out_grad):
+def fused_swiglu(x, y, out_grad, swiglu_func=fused_swiglu_impl):
     x = x.detach().clone()
     x.stop_gradient = False
     if y is not None:
         y = y.detach().clone()
         y.stop_gradient = False
-    out = fused_swiglu_impl(x, y)
+    out = swiglu_func(x, y)
     out.backward(out_grad)
 
     output_dtype = x.dtype
@@ -106,14 +106,20 @@ def fused_swiglu(x, y, out_grad):
 
 
 class TestSwiGLUDygraph(unittest.TestCase):
+    def fused_swiglu(self, x, y, out_grad):
+        return fused_swiglu(x, y, out_grad)
+
+    def fused_swiglu_impl(self, x, y=None):
+        return fused_swiglu_impl(x, y)
+
     def check_dygraph_impl(self, device, shape, dtype):
         x = paddle.randn(shape, dtype=dtype)
         y = paddle.randn(shape, dtype=dtype)
         out_grad = paddle.randn(shape, dtype=dtype)
 
         ret1 = swiglu(x, y, out_grad)
-        ret2 = fused_swiglu(x, y, out_grad)
-        ret3 = fused_swiglu(paddle.concat([x, y], axis=-1), None, out_grad)
+        ret2 = self.fused_swiglu(x, y, out_grad)
+        ret3 = self.fused_swiglu(paddle.concat([x, y], axis=-1), None, out_grad)
 
         atol, rtol = tol_map[dtype]
         err_msg = (
@@ -152,8 +158,8 @@ def check_static_graph(self, shape, dtype="float32"):
             shape=[*shape[:-1], shape[-1] * 2],
             dtype=dtype,
         )
-        out1 = fused_swiglu_impl(x, y)
-        out2 = fused_swiglu_impl(concated_x)
+        out1 = self.fused_swiglu_impl(x, y)
+        out2 = self.fused_swiglu_impl(concated_x)
 
         concated_x_np = np.random.random(concated_x.shape).astype(dtype)
         x_np, y_np = np.split(concated_x_np, 2, axis=-1)

test/prim/pir_prim/test_prim_sub_graph_dynamic_shape.py

@@ -120,11 +120,11 @@ def batch_norm_net4(x, y, z):
 
 
 def swiglu_net1(x, y):
-    return paddle.incubate.nn.functional.swiglu(x, y)
+    return paddle.nn.functional.swiglu(x, y)
 
 
 def swiglu_net2(x):
-    return paddle.incubate.nn.functional.swiglu(x)
+    return paddle.nn.functional.swiglu(x)
 
 
 def squared_l2_norm_net(x):

test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py

@@ -159,11 +159,11 @@ def sum_net5(x):
 
 
 def swiglu_net1(x, y):
-    return paddle.incubate.nn.functional.swiglu(x, y)
+    return paddle.nn.functional.swiglu(x, y)
 
 
 def swiglu_net2(x):
-    return paddle.incubate.nn.functional.swiglu(x)
+    return paddle.nn.functional.swiglu(x)
 
 
 def swish_net(x):

test/xpu/test_swiglu_op_xpu.py

@@ -18,7 +18,7 @@
 
 import paddle
 import paddle.nn.functional as F
-from paddle.incubate.nn.functional import swiglu as fused_swiglu_impl
+from paddle.nn.functional import swiglu as fused_swiglu_impl
 
 
 def swiglu(x, y, out_grad):