activation.cu

/*
 * Copyright (c) 2024 by FlashInfer team.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
#include <flashinfer/activation.cuh>

#include "pytorch_extension_utils.h"

using namespace flashinfer;

__device__ __forceinline__ float silu(const float& val) {
  return val / (1.0f + __expf(-val));
}

__device__ __forceinline__ float gelu(const float& val) {
  constexpr float kAlpha = M_SQRT1_2;
  return val * 0.5f * (1.0f + ::erf(val * kAlpha));
}

__device__ __forceinline__ float gelu_tanh(const float& val) {
  const float cdf = 0.5f * (1.0f + math::tanh((0.7978845608028654f * (val + 0.044715f * val * val * val))));
  return val * cdf;
}

void silu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream) {
  int d = input.size(-1) / 2;
  int64_t num_tokens = input.numel() / input.size(-1);
  dim3 grid(num_tokens);

  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
  DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input.scalar_type(), c_type, [&] {
    uint32_t vec_size = 16 / sizeof(c_type);
    dim3 block(std::min(d / vec_size, 1024U));
    flashinfer::activation::act_and_mul_kernel<c_type, silu>
        <<<grid, block, 0, stream>>>(static_cast<c_type*>(out.data_ptr()), static_cast<c_type*>(input.data_ptr()), d);

    return true;
  });
}

void gelu_tanh_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream) {
  int d = input.size(-1) / 2;
  int64_t num_tokens = input.numel() / input.size(-1);
  dim3 grid(num_tokens);

  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
  DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input.scalar_type(), c_type, [&] {
    uint32_t vec_size = 16 / sizeof(c_type);
    dim3 block(std::min(d / vec_size, 1024U));
    flashinfer::activation::act_and_mul_kernel<c_type, gelu_tanh>
        <<<grid, block, 0, stream>>>(static_cast<c_type*>(out.data_ptr()), static_cast<c_type*>(input.data_ptr()), d);

    return true;
  });
}

void gelu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream) {
  int d = input.size(-1) / 2;
  int64_t num_tokens = input.numel() / input.size(-1);
  dim3 grid(num_tokens);

  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
  DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input.scalar_type(), c_type, [&] {
    uint32_t vec_size = 16 / sizeof(c_type);
    dim3 block(std::min(d / vec_size, 1024U));
    flashinfer::activation::act_and_mul_kernel<c_type, gelu>
        <<<grid, block, 0, stream>>>(static_cast<c_type*>(out.data_ptr()), static_cast<c_type*>(input.data_ptr()), d);

    return true;
  });
}