223 lines
8.5 KiB
Plaintext
223 lines
8.5 KiB
Plaintext
#include <ATen/cuda/CUDAContext.h>
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#include <torch/all.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <cmath>
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#include "cuda_compat.h"
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#include "dispatch_utils.h"
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namespace vllm {
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template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&),
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bool act_first>
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__device__ __forceinline__ scalar_t compute(const scalar_t& x,
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const scalar_t& y) {
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return act_first ? ACT_FN(x) * y : x * ACT_FN(y);
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}
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// Activation and gating kernel template.
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template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&),
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bool act_first>
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__global__ void act_and_mul_kernel(
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scalar_t* __restrict__ out, // [..., d]
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const scalar_t* __restrict__ input, // [..., 2, d]
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const int d) {
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const int64_t token_idx = blockIdx.x;
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for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
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const scalar_t x = VLLM_LDG(&input[token_idx * 2 * d + idx]);
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const scalar_t y = VLLM_LDG(&input[token_idx * 2 * d + d + idx]);
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out[token_idx * d + idx] = compute<scalar_t, ACT_FN, act_first>(x, y);
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}
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}
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template <typename T>
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__device__ __forceinline__ T silu_kernel(const T& x) {
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// x * sigmoid(x)
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return (T)(((float)x) / (1.0f + expf((float)-x)));
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}
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template <typename T>
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__device__ __forceinline__ T gelu_kernel(const T& x) {
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// Equivalent to PyTorch GELU with 'none' approximation.
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// Refer to:
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// https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L36-L38
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const float f = (float)x;
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constexpr float ALPHA = M_SQRT1_2;
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return (T)(f * 0.5f * (1.0f + ::erf(f * ALPHA)));
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}
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template <typename T>
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__device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
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// Equivalent to PyTorch GELU with 'tanh' approximation.
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// Refer to:
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// https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L25-L30
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const float f = (float)x;
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constexpr float BETA = M_SQRT2 * M_2_SQRTPI * 0.5f;
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constexpr float KAPPA = 0.044715;
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float x_cube = f * f * f;
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float inner = BETA * (f + KAPPA * x_cube);
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return (T)(0.5f * f * (1.0f + ::tanhf(inner)));
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}
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} // namespace vllm
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// Launch activation and gating kernel.
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// Use ACT_FIRST (bool) indicating whether to apply the activation function
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// first.
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#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, ACT_FIRST) \
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int d = input.size(-1) / 2; \
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int64_t num_tokens = input.numel() / input.size(-1); \
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dim3 grid(num_tokens); \
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dim3 block(std::min(d, 1024)); \
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const at::cuda::OptionalCUDAGuard device_guard(device_of(input)); \
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); \
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VLLM_DISPATCH_FLOATING_TYPES( \
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input.scalar_type(), "act_and_mul_kernel", [&] { \
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vllm::act_and_mul_kernel<scalar_t, KERNEL<scalar_t>, ACT_FIRST> \
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<<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(), \
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input.data_ptr<scalar_t>(), d); \
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});
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void silu_and_mul(torch::Tensor& out, // [..., d]
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torch::Tensor& input) // [..., 2 * d]
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{
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LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, true);
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}
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void mul_and_silu(torch::Tensor& out, // [..., d]
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torch::Tensor& input) // [..., 2 * d]
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{
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// The difference between mul_and_silu and silu_and_mul is that mul_and_silu
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// applies the silu to the latter half of the input.
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LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, false);
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}
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void gelu_and_mul(torch::Tensor& out, // [..., d]
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torch::Tensor& input) // [..., 2 * d]
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{
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LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_kernel, true);
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}
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void gelu_tanh_and_mul(torch::Tensor& out, // [..., d]
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torch::Tensor& input) // [..., 2 * d]
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{
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LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_tanh_kernel, true);
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}
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namespace vllm {
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template <typename T>
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__device__ __forceinline__ T fatrelu_kernel(const T& x, const float threshold) {
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const float f = (float)x;
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return (T)(f > threshold ? f : 0.0f);
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}
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template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&, const float)>
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__global__ void act_and_mul_kernel_with_param(
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scalar_t* __restrict__ out, const scalar_t* __restrict__ input, const int d,
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const float param) {
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const int64_t token_idx = blockIdx.x;
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for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
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const scalar_t x = VLLM_LDG(&input[token_idx * 2 * d + idx]);
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const scalar_t y = VLLM_LDG(&input[token_idx * 2 * d + d + idx]);
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out[token_idx * d + idx] = ACT_FN(x, param) * y;
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}
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}
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} // namespace vllm
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#define LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(KERNEL, PARAM) \
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int d = input.size(-1) / 2; \
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int64_t num_tokens = input.numel() / input.size(-1); \
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dim3 grid(num_tokens); \
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dim3 block(std::min(d, 1024)); \
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const at::cuda::OptionalCUDAGuard device_guard(device_of(input)); \
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); \
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VLLM_DISPATCH_FLOATING_TYPES( \
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input.scalar_type(), "act_and_mul_kernel_with_param", [&] { \
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vllm::act_and_mul_kernel_with_param<scalar_t, KERNEL<scalar_t>> \
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<<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(), \
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input.data_ptr<scalar_t>(), d, \
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PARAM); \
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});
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void fatrelu_and_mul(torch::Tensor& out, // [..., d],
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torch::Tensor& input, // [..., 2 * d]
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double threshold) {
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LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(vllm::fatrelu_kernel, threshold);
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}
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namespace vllm {
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// Element-wise activation kernel template.
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template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
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__global__ void activation_kernel(
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scalar_t* __restrict__ out, // [..., d]
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const scalar_t* __restrict__ input, // [..., d]
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const int d) {
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const int64_t token_idx = blockIdx.x;
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for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
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const scalar_t x = VLLM_LDG(&input[token_idx * d + idx]);
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out[token_idx * d + idx] = ACT_FN(x);
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}
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}
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} // namespace vllm
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// Launch element-wise activation kernel.
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#define LAUNCH_ACTIVATION_KERNEL(KERNEL) \
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int d = input.size(-1); \
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int64_t num_tokens = input.numel() / d; \
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dim3 grid(num_tokens); \
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dim3 block(std::min(d, 1024)); \
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const at::cuda::OptionalCUDAGuard device_guard(device_of(input)); \
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); \
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VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "activation_kernel", [&] { \
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vllm::activation_kernel<scalar_t, KERNEL<scalar_t>> \
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<<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(), \
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input.data_ptr<scalar_t>(), d); \
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});
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namespace vllm {
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template <typename T>
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__device__ __forceinline__ T gelu_new_kernel(const T& x) {
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const float x3 = (float)(x * x * x);
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const T t = (T)tanhf((T)(0.79788456f * (float)(x + (T)(0.044715f * x3))));
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return ((T)0.5) * x * (((T)1.0) + t);
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}
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template <typename T>
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__device__ __forceinline__ T gelu_fast_kernel(const T& x) {
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const float f = (float)x;
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const T t =
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(T)tanhf(((T)(f * 0.79788456f)) * (((T)1.0) + (T)(0.044715f * f) * x));
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return ((T)0.5) * x * (((T)1.0) + t);
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}
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template <typename T>
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__device__ __forceinline__ T gelu_quick_kernel(const T& x) {
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// x * sigmoid(1.702 * x)
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return (T)(((float)x) / (1.0f + expf(-1.702f * (float)x)));
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}
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} // namespace vllm
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void gelu_new(torch::Tensor& out, // [..., d]
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torch::Tensor& input) // [..., d]
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{
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LAUNCH_ACTIVATION_KERNEL(vllm::gelu_new_kernel);
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}
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void gelu_fast(torch::Tensor& out, // [..., d]
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torch::Tensor& input) // [..., d]
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{
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LAUNCH_ACTIVATION_KERNEL(vllm::gelu_fast_kernel);
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}
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void gelu_quick(torch::Tensor& out, // [..., d]
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torch::Tensor& input) // [..., d]
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{
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LAUNCH_ACTIVATION_KERNEL(vllm::gelu_quick_kernel);
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}
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