common.cu

#include "common.cuh"
#include "dispatch_utils.h"

#include <c10/cuda/CUDAGuard.h>

#ifndef USE_ROCM
  #include <cub/cub.cuh>
#else
  #include <hipcub/hipcub.hpp>
#endif

namespace vllm {

template <typename scalar_t>
__global__ void scaled_fp8_quant_kernel(FP8_TYPE* __restrict__ out,
                                        const scalar_t* __restrict__ input,
                                        const float* __restrict__ scale,
                                        int64_t num_elems) {
  int tid = blockDim.x * blockIdx.x + threadIdx.x;

  // Invert the scale so that we can use multiplications to avoid expensive
  // division.
  const float inverted_scale = 1.0f / (*scale);
  scaled_fp8_conversion_vec<scalar_t, true>(
      out, input, inverted_scale, num_elems, tid, blockDim.x * gridDim.x);
}

template <typename scalar_t>
__global__ void dynamic_per_token_scaled_fp8_quant_kernel(
    FP8_TYPE* __restrict__ out, float* __restrict__ scale,
    scalar_t const* __restrict__ input, float const* __restrict__ scale_ub,
    const int hidden_size) {
  float const min_scaling_factor = 1.0f / (FP8_E4M3_MAX * 512.f);

  int const tid = threadIdx.x;
  int const token_idx = blockIdx.x;

  // Use int64 to avoid overflowing an int32 when calculating this offset
  int64_t offset = static_cast<int64_t>(token_idx) * hidden_size;
  scalar_t const* __restrict__ token_input = &input[offset];
  FP8_TYPE* __restrict__ token_output = &out[offset];

  // For vectorization, token_input and token_output pointers need to be
  // aligned at 8-byte and 4-byte addresses respectively.
  bool const can_vectorize = hidden_size % 4 == 0;

  float absmax_val = 0.0f;
  if (can_vectorize) {
    absmax_val = thread_max_vec(token_input, hidden_size, tid, blockDim.x);
  } else {
    for (int i = tid; i < hidden_size; i += blockDim.x) {
      float const x = static_cast<float>(token_input[i]);
      absmax_val = max(absmax_val, fabs(x));
    }
  }

  using BlockReduce = cub::BlockReduce<float, 1024>;
  __shared__ typename BlockReduce::TempStorage reduceStorage;
  float const block_absmax_val_maybe =
      BlockReduce(reduceStorage).Reduce(absmax_val, cub::Max{}, blockDim.x);
  __shared__ float token_scale;
  if (tid == 0) {
    if (scale_ub) {
      token_scale = min(block_absmax_val_maybe, *scale_ub);
    } else {
      token_scale = block_absmax_val_maybe;
    }
    // token scale computation
    token_scale = max(token_scale / FP8_E4M3_MAX, min_scaling_factor);
    scale[token_idx] = token_scale;
  }
  __syncthreads();

  // Note that we don't use inverted scales so we can match FBGemm impl.
  if (can_vectorize) {
    scaled_fp8_conversion_vec<scalar_t, false>(
        token_output, token_input, token_scale, hidden_size, tid, blockDim.x);
  } else {
    for (int i = tid; i < hidden_size; i += blockDim.x) {
      token_output[i] = scaled_fp8_conversion<false>(
          static_cast<float>(token_input[i]), token_scale);
    }
  }
}

}  // namespace vllm

void static_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
                             torch::Tensor const& input,  // [..., d]
                             torch::Tensor const& scale)  // [1]
{
  int64_t num_tokens = input.numel() / input.size(-1);
  int64_t num_elems = input.numel();
  dim3 grid(num_tokens);
  dim3 block(1024);
  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  VLLM_DISPATCH_FLOATING_TYPES(
      input.scalar_type(), "scaled_fp8_quant_kernel", [&] {
        vllm::scaled_fp8_quant_kernel<scalar_t><<<grid, block, 0, stream>>>(
            out.data_ptr<FP8_TYPE>(), input.data_ptr<scalar_t>(),
            scale.data_ptr<float>(), num_elems);
      });
}

void dynamic_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
                              torch::Tensor const& input,  // [..., d]
                              torch::Tensor& scale)        // [1]
{
  int64_t num_tokens = input.numel() / input.size(-1);
  int64_t num_elems = input.numel();
  dim3 grid(num_tokens);
  dim3 block(1024);
  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  VLLM_DISPATCH_FLOATING_TYPES(
      input.scalar_type(), "scaled_fp8_quant_kernel", [&] {
        vllm::segmented_max_reduction<scalar_t><<<grid, block, 0, stream>>>(
            scale.data_ptr<float>(), input.data_ptr<scalar_t>(), num_elems);
        vllm::scaled_fp8_quant_kernel<scalar_t><<<grid, block, 0, stream>>>(
            out.data_ptr<FP8_TYPE>(), input.data_ptr<scalar_t>(),
            scale.data_ptr<float>(), num_elems);
      });
}

void dynamic_per_token_scaled_fp8_quant(
    torch::Tensor& out,          // [..., d]
    torch::Tensor const& input,  // [..., d]
    torch::Tensor& scales, std::optional<at::Tensor> const& scale_ub) {
  TORCH_CHECK(input.is_contiguous());
  TORCH_CHECK(out.is_contiguous());

  int const hidden_size = input.size(-1);
  int const num_tokens = input.numel() / hidden_size;
  dim3 const grid(num_tokens);
  dim3 const block(std::min(hidden_size, 1024));

  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  VLLM_DISPATCH_FLOATING_TYPES(
      input.scalar_type(), "dynamic_per_token_scaled_fp8_quant_kernel", [&] {
        vllm::dynamic_per_token_scaled_fp8_quant_kernel<scalar_t>
            <<<grid, block, 0, stream>>>(
                out.data_ptr<FP8_TYPE>(), scales.data_ptr<float>(),
                input.data_ptr<scalar_t>(),
                scale_ub.has_value() ? scale_ub->data_ptr<float>() : nullptr,
                hidden_size);
      });
}