feat(mge/functional): add softplus function

GitOrigin-RevId: 26c1ab7416

python_module/megengine/functional/__init__.py

@@ -72,6 +72,7 @@ from .nn import (
     roi_align,
     roi_pooling,
     softmax,
+    softplus,
     warp_perspective,
 )
 from .quantized import conv_bias_activation

python_module/megengine/functional/nn.py

@@ -18,7 +18,8 @@ from ..jit import barrier, mark_impure
 from ..random import uniform
 from ..utils.types import _pair, _pair_nonzero
 from .debug_param import get_conv_execution_strategy
-from .tensor import concat
+from .elemwise import exp, log
+from .tensor import concat, where
 from .utils import _decide_comp_node_and_comp_graph
 
 
@@ -267,6 +268,24 @@ def leaky_relu(inp: Tensor, negative_slope: float = 0.01) -> Tensor:
     )
 
 
+@wrap_io_tensor
+def softplus(inp: Tensor, beta: float = 1, threshold: float = 20) -> Tensor:
+    r"""
+    Performs the elementwise function:
+    
+    .. math::
+        
+        \mathsf{softplus}(x) = \log(1+\exp(\beta x)) / \beta.
+
+    For numerical stability the identity function is used when :math:`\beta x > \textrm{threshold}`.
+
+    """
+    mask = beta * inp <= threshold
+    out = log(1 + exp(beta * inp)) / beta
+    out = where(mask, out, inp)
+    return out
+
+
 @wrap_io_tensor
 def flatten(inp: Tensor, start_axis: int = 0, end_axis: int = -1) -> Tensor:
     r"""

python_module/test/unit/functional/test_functional.py

@@ -439,3 +439,19 @@ def test_conv_bias():
 
         run(10, 36, 8, 46, 26, 2, 2, 2, 1, 1, 2, False, "RELU")
         run(10, 36, 8, 46, 26, 2, 2, 2, 1, 1, 2, True, "RELU")
+
+
+def test_softplus():
+    x = np.arange(1000).astype(np.float32)
+    out = F.softplus(tensor(x))
+    mask = x <= 20
+    with np.errstate(over="ignore"):
+        expected = np.where(mask, np.log(1 + np.exp(x)), x)
+    assertTensorClose(out, expected)
+    beta = 2
+    out = F.softplus(tensor(x), beta=beta, threshold=30)
+    mask = beta * x <= 30
+    # ignore overflow
+    with np.errstate(over="ignore"):
+        expected = np.where(mask, np.log(1 + np.exp(x * beta)) / beta, x)
+    assertTensorClose(out, expected)