fused_rope_kernel.cu

// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
//
// 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 "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/aligned_vector.h"
#include "paddle/phi/kernels/fusion/gpu/fused_rope_utils.h"
namespace phi {
namespace fusion {

template <typename T, typename Context>
void FusedRopeKernel(const Context& dev_ctx,
                     const DenseTensor& q,
                     const paddle::optional<DenseTensor>& k,
                     const paddle::optional<DenseTensor>& v,
                     const paddle::optional<DenseTensor>& sin,
                     const paddle::optional<DenseTensor>& cos,
                     DenseTensor* out_q,
                     DenseTensor* out_k,
                     DenseTensor* out_v) {
  int numel = q.numel();
  if (numel <= 0) return;
  dev_ctx.template Alloc<T>(out_q);
  out_q->Resize(q.dims());
  // small size for broadcast
  auto batch_size = q.dims()[0];
  auto num_heads = q.dims()[2];
  auto head_dim = q.dims()[3];
  auto seq_len = q.dims()[1];
  PADDLE_ENFORCE_NE(head_dim % 2,
                    1,
                    phi::errors::InvalidArgument(
                        "The head_dim of input must be a multiple of 2."));

  constexpr const int vec_size = 2;

  auto config =
      phi::backends::gpu::GetGpuLaunchConfig1D(dev_ctx, numel, vec_size);

  int grid = config.block_per_grid.x;
  int block = config.thread_per_block.x;
  auto stream = dev_ctx.stream();

  phi::Array<T*, 3> outs_data;
  phi::Array<const T*, 3> ins_data;
  phi::Array<const T*, 2> sin_cos_data;

  ins_data[0] = q.data<T>();
  outs_data[0] = out_q->data<T>();
  int num_inputs = 0;

  if (k.get_ptr()) {
    dev_ctx.template Alloc<T>(out_k);
    out_k->Resize(q.dims());
    ins_data[1] = k->data<T>();
    outs_data[1] = out_k->data<T>();
    num_inputs++;
  }

  if (v.get_ptr()) {
    dev_ctx.template Alloc<T>(out_v);
    out_v->Resize(q.dims());
    ins_data[2] = v->data<T>();
    outs_data[2] = out_v->data<T>();
    num_inputs++;
  }

  using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
  MPType div_c = static_cast<MPType>(1.0f / head_dim);

  bool flag_sin_cos = false;

  if (sin.get_ptr() && cos.get_ptr()) {
    PADDLE_ENFORCE_EQ(sin.get_ptr()->dims(),
                      cos.get_ptr()->dims(),
                      phi::errors::InvalidArgument(
                          "The dims of sin and cos must be the same."));
    auto sin_dims = sin.get_ptr()->dims();
    int dims_size = sin_dims.size();
    PADDLE_ENFORCE_NE((dims_size == 2 || dims_size == 4),
                      false,
                      phi::errors::InvalidArgument(
                          "The dims of sin and cos must be 2 or 4."));
    if (dims_size == 4) {
      PADDLE_ENFORCE_NE(
          (sin_dims[0] == 1 && sin_dims[1] == 1),
          false,
          phi::errors::InvalidArgument(
              "The batch_size and num_heads of sin and cos must be 1."));
    }
    PADDLE_ENFORCE_NE(
        (sin_dims[dims_size - 1] == head_dim &&
         sin_dims[dims_size - 2] == seq_len),
        false,
        phi::errors::InvalidArgument("The seq_len and head_dim of sin and cos "
                                     "must be the same as those of q."));

    sin_cos_data[0] = sin->data<T>();
    sin_cos_data[1] = cos->data<T>();

    flag_sin_cos = true;
  }

  int sign = 1;
  VectorizedFusedRopeKernel<T, MPType, vec_size>
      <<<grid, block, 0, stream>>>(ins_data,
                                   sin_cos_data,
                                   flag_sin_cos,
                                   sign,
                                   batch_size,
                                   seq_len,
                                   num_heads,
                                   head_dim,
                                   outs_data,
                                   num_inputs,
                                   div_c);
}
}  // namespace fusion
}  // namespace phi

PD_REGISTER_KERNEL(fused_rotary_position_embedding,
                   GPU,
                   ALL_LAYOUT,
                   phi::fusion::FusedRopeKernel,
                   float,
                   double,
                   phi::dtype::float16,
                   phi::dtype::bfloat16){};