cascade.cu

/*
 * Copyright (c) 2023 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/attention/cascade.cuh>

#include "pytorch_extension_utils.h"

using namespace flashinfer;

void merge_state(at::Tensor v_a, at::Tensor s_a, at::Tensor v_b, at::Tensor s_b,
                 at::Tensor v_merged, at::Tensor s_merged, int64_t cuda_stream) {
  CHECK_INPUT(v_a);
  CHECK_INPUT(s_a);
  CHECK_INPUT(v_b);
  CHECK_INPUT(s_b);
  auto device = v_a.device();
  CHECK_EQ(s_a.device(), device);
  CHECK_EQ(v_b.device(), device);
  CHECK_EQ(s_b.device(), device);
  CHECK_DIM(3, v_a);
  CHECK_DIM(2, s_a);
  CHECK_DIM(3, v_b);
  CHECK_DIM(2, s_b);
  CHECK_SHAPE(v_a, v_b);
  CHECK_SHAPE(s_a, s_b);
  CHECK_EQ(v_a.size(0), s_a.size(0));
  CHECK_EQ(v_a.size(1), s_b.size(1));
  unsigned int seq_len = v_a.size(0);
  unsigned int num_heads = v_a.size(1);
  unsigned int head_dim = v_a.size(2);

  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);

  bool success = DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(v_a.scalar_type(), c_type, [&] {
    cudaError_t status =
        MergeState(static_cast<c_type*>(v_a.data_ptr()), static_cast<float*>(s_a.data_ptr()),
                   static_cast<c_type*>(v_b.data_ptr()), static_cast<float*>(s_b.data_ptr()),
                   static_cast<c_type*>(v_merged.data_ptr()),
                   static_cast<float*>(s_merged.data_ptr()), seq_len, num_heads, head_dim, stream);
    TORCH_CHECK(status == cudaSuccess,
                "MergeState kernel launch failed: ", cudaGetErrorString(status));
    return true;
  });

  TORCH_CHECK(success, "MergeState kernel launch failed: unsupported data type");
}

void merge_state_in_place(at::Tensor v, at::Tensor s, at::Tensor v_other, at::Tensor s_other,
                          std::optional<at::Tensor> mask, int64_t cuda_stream) {
  CHECK_INPUT(v);
  CHECK_INPUT(s);
  CHECK_INPUT(v_other);
  CHECK_INPUT(s_other);
  auto device = v.device();
  CHECK_EQ(s.device(), device);
  CHECK_EQ(v_other.device(), device);
  CHECK_EQ(s_other.device(), device);
  CHECK_DIM(3, v);
  CHECK_DIM(2, s);
  CHECK_DIM(3, v_other);
  CHECK_DIM(2, s_other);
  CHECK_SHAPE(v, v_other);
  CHECK_SHAPE(s, s_other);
  CHECK_EQ(v.size(0), s.size(0));
  CHECK_EQ(v.size(1), s.size(1));
  uint8_t* mask_ptr = nullptr;
  if (mask.has_value()) {
    CHECK_DIM(1, mask.value());
    CHECK_EQ(v.size(0), mask.value().size(0));
    CHECK_EQ(mask.value().device(), device);
    mask_ptr = static_cast<uint8_t*>(mask.value().data_ptr());
  }
  unsigned int seq_len = v.size(0);
  unsigned int num_heads = v.size(1);
  unsigned int head_dim = v.size(2);

  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
  bool success = DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(v.scalar_type(), c_type, [&] {
    cudaError_t status = MergeStateInPlace(
        static_cast<c_type*>(v.data_ptr()), static_cast<float*>(s.data_ptr()),
        static_cast<c_type*>(v_other.data_ptr()), static_cast<float*>(s_other.data_ptr()), seq_len,
        num_heads, head_dim, mask_ptr, stream);
    TORCH_CHECK(status == cudaSuccess,
                "MergeStateInPlace kernel launch failed: ", cudaGetErrorString(status));
    return true;
  });

  TORCH_CHECK(success, "MergeStateInPlace kernel launch failed: unsupported data type");
}

void merge_states(at::Tensor v, at::Tensor s, at::Tensor v_merged, at::Tensor s_merged,
                  int64_t cuda_stream) {
  CHECK_INPUT(v);
  CHECK_INPUT(s);
  auto device = v.device();
  CHECK_EQ(s.device(), device);
  CHECK_DIM(4, v);
  CHECK_DIM(3, s);
  CHECK_EQ(v.size(0), s.size(0));
  CHECK_EQ(v.size(1), s.size(1));
  CHECK_EQ(v.size(2), s.size(2));
  unsigned int seq_len = v.size(0);
  unsigned int num_index_sets = v.size(1);
  unsigned int num_heads = v.size(2);
  unsigned int head_dim = v.size(3);

  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
  bool success = DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(v.scalar_type(), c_type, [&] {
    cudaError_t status = MergeStates(
        static_cast<c_type*>(v.data_ptr()), static_cast<float*>(s.data_ptr()),
        static_cast<c_type*>(v_merged.data_ptr()), static_cast<float*>(s_merged.data_ptr()),
        num_index_sets, seq_len, num_heads, head_dim, stream);
    TORCH_CHECK(status == cudaSuccess,
                "MergeStates kernel launch failed: ", cudaGetErrorString(status));
    return true;
  });

  TORCH_CHECK(success, "MergeStates kernel launch failed: unsupported data type");
}