[Kernel][CPU] Add Quick gelu to CPU

csrc/cpu/activation.cpp

@@ -59,6 +59,13 @@ FORCE_INLINE vec_op::FP32Vec8 gelu_fast_act(const vec_op::FP32Vec8& x) {
   return w3 * x * (ones + t);
 }
 
+FORCE_INLINE vec_op::FP32Vec8 gelu_quick_act(const vec_op::FP32Vec8& x) {
+  const vec_op::FP32Vec8 zeros(0.0);
+  const vec_op::FP32Vec8 ones(1.0);
+  const vec_op::FP32Vec8 w1(1.702f);
+  return x / (ones + (zeros - w1 * x).exp());
+}
+
 FORCE_INLINE vec_op::FP32Vec8 gelu_act(const vec_op::FP32Vec8& x) {
   const vec_op::FP32Vec8 ones(1.0);
   const vec_op::FP32Vec8 w1(M_SQRT1_2);
@@ -142,3 +149,15 @@ void gelu_fast(torch::Tensor& out, torch::Tensor& input) {
     CPU_KERNEL_GUARD_OUT(gelu_fast_impl)
   });
 }
+
+void gelu_quick(torch::Tensor& out, torch::Tensor& input) {
+  int num_tokens = input.numel() / input.size(-1);
+  int d = input.size(-1);
+
+  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "gelu_quick_impl", [&] {
+    CPU_KERNEL_GUARD_IN(gelu_quick_impl)
+    activation_kernel<scalar_t, gelu_quick_act, false>(
+        num_tokens, d, input.data_ptr<scalar_t>(), out.data_ptr<scalar_t>());
+    CPU_KERNEL_GUARD_OUT(gelu_quick_impl)
+  });
+}

csrc/cpu/torch_bindings.cpp

@@ -58,6 +58,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("gelu_fast(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_fast", torch::kCPU, &gelu_fast);
 
+  // Quick GELU implementation.
+  ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
+  ops.impl("gelu_quick", torch::kCPU, &gelu_quick);
+
   // Layernorm
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(

vllm/_ipex_ops.py

@@ -43,6 +43,9 @@ def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
     def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
         out.copy_(torch.nn.functional.gelu(x))
 
+    # TODO add implementation of gelu_quick here
+    # def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
+
     def paged_attention_v1(
         out: torch.Tensor,
         query: torch.Tensor,

vllm/model_executor/layers/activation.py

@@ -155,6 +155,9 @@ def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
         ops.gelu_quick(out, x)
         return out
 
+    # TODO implement forward_xpu for QuickGELU
+    # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+
 
 class ScaledActivation(nn.Module):
     """An activation function with post-scale parameters.