feat: add SiLUT activation

deepmd/common.py

@@ -54,6 +54,7 @@
     "gelu",
     "gelu_tf",
     "silu",
+    "silut",
     "none",
     "linear",
 ]

deepmd/dpmodel/utils/network.py

@@ -325,6 +325,37 @@ def fn(x):
             # generated by GitHub Copilot
             return x / (1 + xp.exp(-x))
 
+        return fn
+    elif activation_function.startswith("silut") or activation_function.startswith(
+        "custom_silu"
+    ):
+
+        def sigmoid(x):
+            return 1 / (1 + np.exp(-x))
+
+        def silu(x):
+            return x * sigmoid(x)
+
+        def silu_grad(x):
+            sig = sigmoid(x)
+            return sig + x * sig * (1 - sig)
+
+        threshold = (
+            float(activation_function.split(":")[-1])
+            if ":" in activation_function
+            else 3.0
+        )
+        slope = float(silu_grad(threshold))
+        const = float(silu(threshold))
+
+        def fn(x):
+            xp = array_api_compat.array_namespace(x)
+            return xp.where(
+                x < threshold,
+                x * (1 / (1 + xp.exp(-x))),
+                xp.tanh(slope * (x - threshold)) + const,
+            )
+
         return fn
     elif activation_function.lower() in ("none", "linear"):
 

deepmd/pd/utils/utils.py

@@ -32,10 +32,47 @@
     )
 
 
+class SiLUT(paddle.nn.Layer):
+    def __init__(self, threshold=3.0):
+        super().__init__()
+
+        def sigmoid(x):
+            return 1 / (1 + np.exp(-x))
+
+        def silu(x):
+            return x * sigmoid(x)
+
+        def silu_grad(x):
+            sig = sigmoid(x)
+            return sig + x * sig * (1 - sig)
+
+        self.threshold = threshold
+        self.slope = float(silu_grad(threshold))
+        self.const = float(silu(threshold))
+
+    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
+        silu_part = F.silu(x)
+        mask = x >= self.threshold
+        if paddle.any(mask):
+            tanh_part = paddle.tanh(self.slope * (x - self.threshold)) + self.const
+            return paddle.where(x < self.threshold, silu_part, tanh_part)
+        else:
+            return silu_part
+
+
 class ActivationFn(paddle.nn.Layer):
     def __init__(self, activation: str | None):
         super().__init__()
         self.activation: str = activation if activation is not None else "linear"
+        if self.activation.lower().startswith(
+            "silut"
+        ) or self.activation.lower().startswith("custom_silu"):
+            threshold = (
+                float(self.activation.split(":")[-1]) if ":" in self.activation else 3.0
+            )
+            self.silut = SiLUT(threshold=threshold)
+        else:
+            self.silut = None
 
     def forward(self, x: paddle.Tensor) -> paddle.Tensor:
         """Returns the tensor after applying activation function corresponding to `activation`."""
@@ -53,6 +90,11 @@ def forward(self, x: paddle.Tensor) -> paddle.Tensor:
             return F.sigmoid(x)
         elif self.activation.lower() == "silu":
             return F.silu(x)
+        elif self.activation.lower().startswith(
+            "silut"
+        ) or self.activation.lower().startswith("custom_silu"):
+            assert self.silut is not None
+            return self.silut(x)
         elif self.activation.lower() == "linear" or self.activation.lower() == "none":
             return x
         else:

deepmd/pt/entrypoints/main.py

@@ -249,6 +249,7 @@ def train(
     output: str = "out.json",
 ) -> None:
     log.info("Configuration path: %s", input_file)
+    env.CUSTOM_OP_USE_JIT = True
     if LOCAL_RANK == 0:
         SummaryPrinter()()
     with open(input_file) as fin:

deepmd/pt/utils/env.py

@@ -34,6 +34,7 @@
 JIT = False
 CACHE_PER_SYS = 5  # keep at most so many sets per sys in memory
 ENERGY_BIAS_TRAINABLE = True
+CUSTOM_OP_USE_JIT = False
 
 PRECISION_DICT = {
     "float16": torch.float16,
@@ -76,6 +77,7 @@
 
 __all__ = [
     "CACHE_PER_SYS",
+    "CUSTOM_OP_USE_JIT",
     "DEFAULT_PRECISION",
     "DEVICE",
     "ENERGY_BIAS_TRAINABLE",

deepmd/pt/utils/utils.py

@@ -11,17 +11,169 @@
 import torch.nn.functional as F
 
 from deepmd.dpmodel.common import PRECISION_DICT as NP_PRECISION_DICT
+from deepmd.pt.utils import (
+    env,
+)
 
 from .env import (
     DEVICE,
 )
 from .env import PRECISION_DICT as PT_PRECISION_DICT
 
 
+def silut_forward(
+    x: torch.Tensor, threshold: float, slope: float, const_val: float
+) -> torch.Tensor:
+    sig = torch.sigmoid(x)
+    silu = x * sig
+    tanh_part = torch.tanh(slope * (x - threshold)) + const_val
+    return torch.where(x >= threshold, tanh_part, silu)
+
+
+def silut_backward(
+    x: torch.Tensor, grad_output: torch.Tensor, threshold: float, slope: float
+):
+    sig = torch.sigmoid(x)
+    grad_silu = sig * (1 + x * (1 - sig))
+
+    tanh_term = torch.tanh(slope * (x - threshold))
+    grad_tanh = slope * (1 - tanh_term.pow(2))
+
+    grad = torch.where(x >= threshold, grad_tanh, grad_silu)
+    return grad * grad_output, grad
+
+
+def silut_double_backward(
+    x: torch.Tensor,
+    grad_grad_output: torch.Tensor,
+    grad_output: torch.Tensor,
+    threshold: float,
+    slope: float,
+) -> torch.Tensor:
+    # Tanh branch
+    tanh_term = torch.tanh(slope * (x - threshold))
+    grad_grad = -2 * slope * slope * tanh_term * (1 - tanh_term * tanh_term)
+
+    # SiLU branch
+    sig = 1.0 / (1.0 + torch.exp(-x))
+    sig_prime = sig * (1 - sig)
+    silu_term = sig_prime * (2 + x * (1 - 2 * sig))
+
+    grad_grad = torch.where(x >= threshold, grad_grad, silu_term)
+
+    return grad_output * grad_grad * grad_grad_output
+
+
+class SiLUTScript(torch.nn.Module):
+    def __init__(self, threshold: float = 3.0):
+        super().__init__()
+        self.threshold = threshold
+
+        # Precompute parameters for the tanh replacement
+        sigmoid_threshold = 1 / (1 + np.exp(-threshold))
+        self.slope = float(
+            sigmoid_threshold + threshold * sigmoid_threshold * (1 - sigmoid_threshold)
+        )
+        self.const_val = float(threshold * sigmoid_threshold)
+        self.get_script_code()
+
+    def get_script_code(self):
+        silut_forward_script = torch.jit.script(silut_forward)
+        silut_backward_script = torch.jit.script(silut_backward)
+        silut_double_backward_script = torch.jit.script(silut_double_backward)
+
+        class SiLUTFunction(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, x, threshold, slope, const_val):
+                ctx.save_for_backward(x)
+                ctx.threshold = threshold
+                ctx.slope = slope
+                ctx.const_val = const_val
+                return silut_forward_script(x, threshold, slope, const_val)
+
+            @staticmethod
+            def backward(ctx, grad_output):
+                (x,) = ctx.saved_tensors
+                threshold = ctx.threshold
+                slope = ctx.slope
+
+                grad_input = SiLUTGradFunction.apply(x, grad_output, threshold, slope)
+                return grad_input, None, None, None
+
+        class SiLUTGradFunction(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, x, grad_output, threshold, slope):
+                ctx.threshold = threshold
+                ctx.slope = slope
+                grad_input, grad = silut_backward_script(
+                    x, grad_output, threshold, slope
+                )
+                ctx.save_for_backward(x, grad_output, grad)
+                return grad_input
+
+            @staticmethod
+            def backward(ctx, grad_grad_output):
+                (x, grad_output, grad) = ctx.saved_tensors
+                threshold = ctx.threshold
+                slope = ctx.slope
+
+                grad_input = silut_double_backward_script(
+                    x, grad_grad_output, grad_output, threshold, slope
+                )
+                return grad_input, grad * grad_grad_output, None, None
+
+        self.SiLUTFunction = SiLUTFunction
+
+    def forward(self, x):
+        return self.SiLUTFunction.apply(x, self.threshold, self.slope, self.const_val)
+
+
+class SiLUT(torch.nn.Module):
+    def __init__(self, threshold=3.0):
+        super().__init__()
+
+        def sigmoid(x):
+            return 1 / (1 + np.exp(-x))
+
+        def silu(x):
+            return x * sigmoid(x)
+
+        def silu_grad(x):
+            sig = sigmoid(x)
+            return sig + x * sig * (1 - sig)
+
+        self.threshold = threshold
+        self.slope = float(silu_grad(threshold))
+        self.const = float(silu(threshold))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        silu_part = F.silu(x)
+        mask = x >= self.threshold
+        if torch.any(mask):
+            tanh_part = torch.tanh(self.slope * (x - self.threshold)) + self.const
+            return torch.where(x < self.threshold, silu_part, tanh_part)
+        else:
+            return silu_part
+
+
 class ActivationFn(torch.nn.Module):
     def __init__(self, activation: Optional[str]) -> None:
         super().__init__()
         self.activation: str = activation if activation is not None else "linear"
+        if self.activation.lower().startswith(
+            "silut"
+        ) or self.activation.lower().startswith("custom_silu"):
+            threshold = (
+                float(self.activation.split(":")[-1]) if ":" in self.activation else 3.0
+            )
+            if env.CUSTOM_OP_USE_JIT:
+                # for efficient training but can not be jit
+                self.silut = SiLUTScript(threshold=threshold)
+            else:
+                # for jit freeze
+                self.silut = SiLUT(threshold=threshold)
+        else:
+            self.silut = None
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Returns the tensor after applying activation function corresponding to `activation`."""
@@ -41,6 +193,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             return torch.sigmoid(x)
         elif self.activation.lower() == "silu":
             return F.silu(x)
+        elif self.activation.lower().startswith(
+            "silut"
+        ) or self.activation.lower().startswith("custom_silu"):
+            assert self.silut is not None
+            return self.silut(x)
         elif self.activation.lower() == "linear" or self.activation.lower() == "none":
             return x
         else:

deepmd/tf/common.py

@@ -144,6 +144,47 @@ def silu(x: tf.Tensor) -> tf.Tensor:
     return x * tf.sigmoid(x)
 
 
+def get_silut(activation_function: str = "silut"):
+    import numpy as np
+
+    def sigmoid(x):
+        return 1 / (1 + np.exp(-x))
+
+    def silu(x):
+        return x * sigmoid(x)
+
+    def silu_grad(x):
+        sig = sigmoid(x)
+        return sig + x * sig * (1 - sig)
+
+    threshold = (
+        float(activation_function.split(":")[-1]) if ":" in activation_function else 3.0
+    )
+    slope = float(silu_grad(threshold))
+    const = float(silu(threshold))
+
+    def silut(x: tf.Tensor) -> tf.Tensor:
+        """The customized sigmoid-weighted linear unit with tanh.
+
+        Parameters
+        ----------
+        x : tf.Tensor
+            float Tensor to perform activation
+
+        Returns
+        -------
+        tf.Tensor
+            `x` with the SiLUT activation applied
+        """
+        return tf.where(
+            x < threshold,
+            x * tf.sigmoid(x),
+            tf.nn.tanh(slope * (x - threshold)) + const,
+        )
+
+    return silut
+
+
 ACTIVATION_FN_DICT = {
     "relu": tf.nn.relu,
     "relu6": tf.nn.relu6,
@@ -153,6 +194,7 @@ def silu(x: tf.Tensor) -> tf.Tensor:
     "gelu": gelu,
     "gelu_tf": gelu_tf,
     "silu": silu,
+    "silut": get_silut("silut"),
     "linear": lambda x: x,
     "none": lambda x: x,
 }
@@ -182,6 +224,8 @@ def get_activation_func(
     if activation_fn is None:
         activation_fn = "none"
     assert activation_fn is not None
+    if activation_fn.lower().startswith("silut"):
+        ACTIVATION_FN_DICT[activation_fn.lower()] = get_silut(activation_fn.lower())
     if activation_fn.lower() not in ACTIVATION_FN_DICT:
         raise RuntimeError(f"{activation_fn} is not a valid activation function")
     return ACTIVATION_FN_DICT[activation_fn.lower()]