Add kernel for GeGLU with approximate GELU (#3337)

csrc/activation_kernels.cu

@@ -33,12 +33,25 @@ template<typename T>
 __device__ __forceinline__ T gelu_kernel(const T& x) {
   // Equivalent to PyTorch GELU with 'none' approximation.
   // Refer to:
-  // https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L38
+  // https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L36-L38
   const float f = (float) x;
   constexpr float ALPHA = M_SQRT1_2;
   return (T) (f * 0.5f * (1.0f + ::erf(f * ALPHA)));
 }
 
+template<typename T>
+__device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
+  // Equivalent to PyTorch GELU with 'tanh' approximation.
+  // Refer to:
+  // https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L25-L30
+  const float f = (float) x;
+  constexpr float BETA = M_SQRT2 * M_2_SQRTPI * 0.5f;
+  constexpr float KAPPA = 0.044715;
+  float x_cube = f * f * f;
+  float inner = BETA * (f + KAPPA * x_cube);
+  return (T) (0.5f * f * (1.0f + ::tanhf(inner)));
+}
+
 } // namespace vllm
 
 // Launch activation and gating kernel.
@@ -73,6 +86,13 @@ void gelu_and_mul(
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_kernel);
 }
 
+void gelu_tanh_and_mul(
+  torch::Tensor& out,      // [..., d]
+  torch::Tensor& input)    // [..., 2 * d]
+{
+  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_tanh_kernel);
+}
+
 namespace vllm {
 
 // Element-wise activation kernel template.

csrc/ops.h

@@ -61,6 +61,10 @@ void gelu_and_mul(
   torch::Tensor& out,
   torch::Tensor& input);
 
+void gelu_tanh_and_mul(
+  torch::Tensor& out,
+  torch::Tensor& input);
+
 void gelu_new(
   torch::Tensor& out,
   torch::Tensor& input);

csrc/pybind.cpp

@@ -25,7 +25,11 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   ops.def(
     "gelu_and_mul",
     &gelu_and_mul,
-    "Activation function used in GeGLU.");
+    "Activation function used in GeGLU with `none` approximation.");
+  ops.def(
+    "gelu_tanh_and_mul",
+    &gelu_tanh_and_mul,
+    "Activation function used in GeGLU with `tanh` approximation.");
   ops.def(
     "gelu_new",
     &gelu_new,

tests/kernels/test_activation.py

@@ -16,7 +16,7 @@ CUDA_DEVICES = [
 ]
 
 
-@pytest.mark.parametrize("activation", [SiluAndMul, GeluAndMul])
+@pytest.mark.parametrize("activation", ["silu", "gelu", "gelu_tanh"])
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
 @pytest.mark.parametrize("d", D)
 @pytest.mark.parametrize("dtype", DTYPES)
@@ -24,7 +24,7 @@ CUDA_DEVICES = [
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
 def test_act_and_mul(
-    activation: Type[torch.nn.Module],
+    activation: str,
     num_tokens: int,
     d: int,
     dtype: torch.dtype,
@@ -36,7 +36,12 @@ def test_act_and_mul(
         torch.cuda.manual_seed(seed)
     torch.set_default_device(device)
     x = torch.randn(num_tokens, 2 * d, dtype=dtype)
-    layer = activation()
+    if activation == "silu":
+        layer = SiluAndMul()
+    elif activation == "gelu":
+        layer = GeluAndMul(approximate="none")
+    elif activation == "gelu_tanh":
+        layer = GeluAndMul(approximate="tanh")
     out = layer(x)
     ref_out = layer._forward(x)
     # The SiLU and GELU implementations are equivalent to the native PyTorch

vllm/model_executor/layers/activation.py

@@ -47,16 +47,25 @@ class GeluAndMul(nn.Module):
         return: (batch_size, seq_len, d) or (num_tokens, d)
     """
 
+    def __init__(self, approximate: str = "none"):
+        super().__init__()
+        self.approximate = approximate
+        if approximate not in ("none", "tanh"):
+            raise ValueError(f"Unknown approximate mode: {approximate}")
+
     def _forward(self, x: torch.Tensor) -> torch.Tensor:
         """PyTorch-native implementation equivalent to forward()."""
         d = x.shape[-1] // 2
-        return F.gelu(x[..., :d]) * x[..., d:]
+        return F.gelu(x[..., :d], approximate=self.approximate) * x[..., d:]
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         d = x.shape[-1] // 2
         output_shape = (x.shape[:-1] + (d, ))
         out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        if self.approximate == "none":
             ops.gelu_and_mul(out, x)
+        elif self.approximate == "tanh":
+            ops.gelu_tanh_and_mul(out, x)
         return out