add silu op, test=develop (#32384)

paddle/fluid/framework/ir/is_test_pass.cc

@@ -35,7 +35,7 @@ void IsTestPass::ApplyImpl(ir::Graph* graph) const {
                   "hard_shrink", "hard_sigmoid", "relu6",
                   "soft_relu",   "swish",        "thresholded_relu",
                   "log",         "square",       "softplus",
-                  "softsign"};
+                  "softsign",    "silu"};
   for (const Node* n : graph->Nodes()) {
     if (n->IsOp()) {
       auto* op = n->Op();

paddle/fluid/operators/activation_op.cc

@@ -162,6 +162,12 @@ $$out = \\frac{1}{1 + e^{-x}}$$
 
 )DOC";
 
+UNUSED constexpr char SiluDoc[] = R"DOC(
+Silu Activation Operator
+
+$$out = x * \\frac{1}{1 + e^{-x}}$$
+)DOC";
+
 UNUSED constexpr char LogSigmoidDoc[] = R"DOC(
 Logsigmoid Activation Operator
 
@@ -697,6 +703,7 @@ It is recommended to use the defaults for this activation.
 };
 
 REGISTER_ACTIVATION_OP_MAKER(Sigmoid, SigmoidDoc);
+REGISTER_ACTIVATION_OP_MAKER(Silu, SiluDoc);
 REGISTER_ACTIVATION_OP_MAKER(LogSigmoid, LogSigmoidDoc);
 REGISTER_ACTIVATION_OP_MAKER(Exp, ExpDoc);
 REGISTER_ACTIVATION_OP_MAKER(Relu, ReluDoc);

paddle/fluid/operators/activation_op.h

@@ -258,6 +258,31 @@ struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; }
 };
 
+// silu(x) = x / (1 + exp(-x))
+template <typename T>
+struct SiluFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) const {
+    auto temp = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
+    out.device(d) = x * temp;
+  }
+};
+
+// silu'(x) = (1 / (1 + e^{-x}))  * (1 + out * e^{-x}))
+template <typename T>
+struct SiluGradFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Out, typename dOut,
+            typename dX>
+  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
+    auto temp1 = static_cast<T>(1) + (-x).exp();  // 1+e^(-x)
+    auto temp2 = x * (-x).exp();                  // x*e^(-x)
+    dx.device(d) = dout * ((static_cast<T>(1) / temp1) *
+                           (static_cast<T>(1) + (temp2 / temp1)));
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
+};
+
 // Originally: logsigmoid(x) = -log (1 + exp(-x))
 // For numerical stability, we can use the log-sum-exp trick:
 // https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/
@@ -2129,6 +2154,7 @@ struct LogGradGradFunctor : public BaseActivationFunctor<T> {
 
 #define FOR_EACH_ACTIVATION_OP(__macro)                                       \
   __macro(sigmoid, Sigmoid, SigmoidFunctor, SigmoidGradFunctor);              \
+  __macro(silu, Silu, SiluFunctor, SiluGradFunctor);                          \
   __macro(logsigmoid, LogSigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor);  \
   __macro(atan, Atan, AtanFunctor, AtanGradFunctor);                          \
   __macro(softshrink, SoftShrink, SoftShrinkFunctor, SoftShrinkGradFunctor);  \

python/paddle/fluid/layers/ops.py

@@ -27,6 +27,7 @@ __deprecated_func_name__ = {
 
 __activations_noattr__ = [
     'sigmoid',
+    'silu',
     'logsigmoid',
     'tanh_shrink',
     'softplus',
@@ -100,6 +101,20 @@ Examples:
 
 """)
 
+add_sample_code(globals()["silu"], r"""
+Examples:
+    .. code-block:: python
+
+        import paddle
+        import paddle.nn.functional as F
+
+        x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
+        out = F.silu(x)
+        print(out)
+        # [ 0.7310586 1.7615942 2.8577224, 3.9280552 ]
+
+""")
+
 add_sample_code(globals()["logsigmoid"], r"""
 Examples:
     .. code-block:: python

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

@@ -119,6 +119,72 @@ class TestSigmoid(TestActivation):
         self.check_grad(['X'], 'Out', max_relative_error=0.01)
 
 
+class TestSilu(TestActivation):
+    def setUp(self):
+        self.op_type = "silu"
+        self.init_dtype()
+
+        np.random.seed(1024)
+        x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
+        out = x / (np.exp(-x) + 1)
+
+        self.inputs = {'X': x}
+        self.outputs = {'Out': out}
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad(['X'], 'Out')
+
+
+class TestSiluAPI(unittest.TestCase):
+    # test paddle.nn.Silu, paddle.nn.functional.silu
+    def setUp(self):
+        self.x_np = np.random.uniform(-1, 1, [11, 17]).astype('float32')
+        self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def test_static_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.fluid.data('X', [11, 17])
+            out1 = F.silu(x)
+            m = paddle.nn.Silu()
+            out2 = m(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
+        out_ref = self.x_np / (1 + np.exp(-self.x_np))
+        for r in res:
+            self.assertEqual(np.allclose(out_ref, r), True)
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        out1 = F.silu(x)
+        m = paddle.nn.Silu()
+        out2 = m(x)
+        out_ref = self.x_np / (1 + np.exp(-self.x_np))
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+        paddle.enable_static()
+
+    def test_errors(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            # The input type must be Variable.
+            self.assertRaises(TypeError, F.silu, 1)
+            # The input dtype must be float16, float32, float64.
+            x_int32 = paddle.fluid.data(
+                name='x_int32', shape=[11, 17], dtype='int32')
+            self.assertRaises(TypeError, F.silu, x_int32)
+            # support the input dtype is float16
+            x_fp16 = paddle.fluid.data(
+                name='x_fp16', shape=[11, 17], dtype='float16')
+            F.silu(x_fp16)
+
+
 class TestLogSigmoid(TestActivation):
     def setUp(self):
         self.op_type = "logsigmoid"
@@ -2629,6 +2695,7 @@ def create_test_act_fp16_class(parent,
 
 create_test_act_fp16_class(TestActivation)
 create_test_act_fp16_class(TestSigmoid)
+create_test_act_fp16_class(TestSilu)
 create_test_act_fp16_class(TestLogSigmoid)
 create_test_act_fp16_class(TestTanh)
 create_test_act_fp16_class(TestTanhshrink)

python/paddle/nn/__init__.py

@@ -55,6 +55,7 @@ from .layer.activation import PReLU  #DEFINE_ALIAS
 from .layer.activation import ReLU  #DEFINE_ALIAS
 from .layer.activation import ReLU6  #DEFINE_ALIAS
 from .layer.activation import SELU  #DEFINE_ALIAS
+from .layer.activation import Silu  #DEFINE_ALIAS
 from .layer.activation import LeakyReLU  #DEFINE_ALIAS
 from .layer.activation import Sigmoid  #DEFINE_ALIAS
 from .layer.activation import Hardsigmoid  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -46,6 +46,7 @@ from .activation import relu_  #DEFINE_ALIAS
 from .activation import relu6  #DEFINE_ALIAS
 from .activation import selu  #DEFINE_ALIAS
 from .activation import sigmoid  #DEFINE_ALIAS
+from .activation import silu  #DEFINE_ALIAS
 # from .activation import soft_relu  #DEFINE_ALIAS
 from .activation import softmax  #DEFINE_ALIAS
 from .activation import softmax_  #DEFINE_ALIAS

python/paddle/nn/functional/activation.py

@@ -49,6 +49,7 @@ __all__ = [
     'softshrink',
     'softsign',
     'sigmoid',
+    'silu'
     'swish',
     'tanh',
     'tanh_',
@@ -761,6 +762,39 @@ def selu(x,
     return out
 
 
+def silu(x, name=None):
+    """
+    silu activation.
+    .. math:
+        silu(x) = \frac{x}{1 + e^{-x}}
+    
+    Parameters:
+        x (Tensor): The input Tensor with data type float32, float64.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+    
+    Returns:
+        A Tensor with the same data type and shape as ``x`` .
+    
+    Examples:
+        .. code-block:: python
+        import paddle
+        import paddle.nn.functional as F
+        
+        x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
+        out = F.silu(x) # [ 0.731059, 1.761594, 2.857722, 3.928055 ]
+    """
+
+    if in_dygraph_mode():
+        return core.ops.silu(x)
+
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'silu')
+    helper = LayerHelper("silu", **locals())
+    out = helper.create_variable_for_type_inference(x.dtype)
+    helper.append_op(type='silu', inputs={'X': x}, outputs={'Out': out})
+    return out
+
+
 def softmax(x, axis=-1, dtype=None, name=None):
     r"""
     This operator implements the softmax layer. The calculation process is as follows:

python/paddle/nn/layer/activation.py

@@ -27,6 +27,7 @@ __all__ = [
     'SELU',
     'LeakyReLU',
     'Sigmoid',
+    'Silu',
     'Hardsigmoid',
     'Softmax',
     'Softplus',
@@ -919,6 +920,44 @@ class ThresholdedReLU(layers.Layer):
         return 'threshold={}{}'.format(self._threshold, name_str)
 
 
+class Silu(layers.Layer):
+    """
+    Silu Activation.
+    .. math::
+
+        Silu(x) = \frac{x}{1 + e^{-x}}
+
+    Parameters:
+        x (Tensor): The input Tensor with data type float32, or float64.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Shape:
+        - input: Tensor with any shape.
+        - output: Tensor with the same shape as input.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
+            m = paddle.nn.Silu()
+            out = m(x) # [ 0.731059, 1.761594, 2.857722, 3.928055 ]
+    """
+
+    def __init__(self, name=None):
+        super(Silu, self).__init__()
+        self._name = name
+
+    def forward(self, x):
+        return F.silu(x, self._name)
+
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
+
 class LogSigmoid(layers.Layer):
     r"""
     LogSigmoid Activation.