xref: /aosp_15_r20/external/pytorch/aten/src/ATen/native/cpu/Reduce.h (revision da0073e96a02ea20f0ac840b70461e3646d07c45)

#pragma once

#include <ATen/native/cpu/Loops.h>
#include <ATen/Parallel.h>
#include <c10/util/TypeList.h>
#include <c10/core/Scalar.h>
#include <c10/util/irange.h>

#include <sstream>
#include <type_traits>

namespace at { namespace native { inline namespace CPU_CAPABILITY {

using namespace vec;

#define VEC_LOOP_HEADER(func_t, data) \
  using scalar_t = typename function_traits<func_t>::result_type; \
  using Vec = Vectorized<scalar_t>; \
  char* out_ptr = data[0]; \
  (void) out_ptr;

// reduction that is contiguous over the input in dim 0
template <typename traits>
inline bool is_contiguous_reduction(const int64_t* strides) {
  return strides[0] == 0 &&
         strides[1] == sizeof(typename traits::arg2_t);
}

// reduction that is contiguous over the input in dim 1
template <typename traits>
inline bool is_outer_reduction(const int64_t* strides) {
  return strides[0] == 0 &&
         strides[2] == sizeof(typename traits::result_type) &&
         strides[3] == sizeof(typename traits::arg2_t);
}

template <typename func_t, typename vec_func_t>
inline void vectorized_reduction(char** data, int64_t n, int64_t stride,
                                        func_t op, vec_func_t vop, bool reduce) {
  VEC_LOOP_HEADER(func_t, data)
  const char* in1_ptr = data[1];
  Vec acc[4];
  for (const auto j : c10::irange(4)) {
    acc[j] = Vec::loadu(in1_ptr + j * Vec::size() * sizeof(scalar_t));
  }
  for (const auto i : c10::irange(1, n)) {
    const char* ptr = in1_ptr + stride * i;
    acc[0] = vop(acc[0], Vec::loadu(ptr + (0 * Vec::size() * sizeof(scalar_t))));
    acc[1] = vop(acc[1], Vec::loadu(ptr + (1 * Vec::size() * sizeof(scalar_t))));
    acc[2] = vop(acc[2], Vec::loadu(ptr + (2 * Vec::size() * sizeof(scalar_t))));
    acc[3] = vop(acc[3], Vec::loadu(ptr + (3 * Vec::size() * sizeof(scalar_t))));
  }
  if (reduce) {
    scalar_t buffer[Vec::size()];
    acc[0] = vop(vop(acc[0], acc[1]), vop(acc[2], acc[3]));
    acc[0].store(buffer);
    for (const auto j : c10::irange(1, Vec::size())) {
      buffer[0] = op(buffer[0], buffer[j]);
    }
    auto dst = (scalar_t*)out_ptr;
    *dst = op(*dst, buffer[0]);
  } else {
    for (const auto j : c10::irange(4)) {
      auto dst = out_ptr + j * Vec::size() * sizeof(scalar_t);
      acc[j] = vop(acc[j], Vec::loadu(dst));
      acc[j].store(dst);
    }
  }
}

template <typename F>
inline void UNARY_OUTER_LOOP(char* data[2], const int64_t strides[2], int64_t n, F f) {
  for (const auto j C10_UNUSED : c10::irange(n)) {
    f();
    data[0] += strides[0];
    data[1] += strides[1];
  }
}

// computes the reduction out = op(out, in)
template <typename func_t, typename vec_func_t>
inline void vectorized_inner_reduction(char** data, int64_t n, func_t op, vec_func_t vop) {
  VEC_LOOP_HEADER(func_t, data)
  int64_t vector_stride = 4 * Vec::size() * sizeof(scalar_t);
  int64_t count = n / (4 * Vec::size());
  if (count > 0) {
    vectorized_reduction(data, count, vector_stride, op, vop, /*reduce=*/true);
  }
  char* ptrs[3] = { data[0], data[0], data[1] };
  int64_t strides[] = { 0, 0, sizeof(scalar_t) };
  basic_loop(ptrs, strides, count * 4 * Vec::size(), n, op);
}

// computes the reduction out = op(out, in)
template <typename func_t, typename vec_func_t>
inline void vectorized_outer_reduction(char** data, int64_t inner_stride, int64_t size0, int64_t size1, func_t op, vec_func_t vop) {
  VEC_LOOP_HEADER(func_t, data)

  // reduce down each column of 4 * Vec::size() elements (128 or 256 bytes)
#if defined(CPU_CAPABILITY_AVX512)
  int64_t outer_stride[2] = { 256, 256 };
#else
  int64_t outer_stride[2] = { 128, 128 };
#endif
  UNARY_OUTER_LOOP(data, outer_stride, size1 / (4 * Vec::size()), [&] {
    vectorized_reduction(data, size0, inner_stride, op, vop, /*reduce=*/false);
  });

  // reduce down the remaining columns
  int64_t step[] = { sizeof(scalar_t), sizeof(scalar_t) };
  int64_t remaining = size1 % (4 * Vec::size());
  UNARY_OUTER_LOOP(data, step, remaining, [&] {
    char* ptrs[3] = { data[0], data[0], data[1] };
    int64_t strides[] = { 0, 0, inner_stride };
    basic_loop(ptrs, strides, 0, size0, op);
  });
}

template<typename traits, typename res_t>
static void set_result(const int index, const res_t result, const TensorIteratorBase &iter, const int num_outputs) {
  // static_assert(std::is_same<res_t, typename traits::arg2_t>::value, "data types must match");
  if (index < num_outputs) {
    char *out = (char *) iter.data_ptr(index);
    *(res_t *) out = result;
  }
}

template<typename traits, typename res_t>
static void set_results(const res_t result, const TensorIteratorBase &iter, const int num_outputs) {
  AT_ASSERT(num_outputs == 1);
  set_result<traits>(0, result, iter, num_outputs);
}

template<typename traits, std::size_t i = 0, typename... tuple_t>
inline typename std::enable_if<i == sizeof...(tuple_t), std::size_t>::type
for_each_in_tuple(const std::tuple<tuple_t...>& /*t*/, const TensorIteratorBase& /*iter*/, const int /*num_outputs*/) {
  return i;
}

template<typename traits, std::size_t i = 0, typename... tuple_t>
inline typename std::enable_if<i < sizeof...(tuple_t), std::size_t>::type
for_each_in_tuple(const std::tuple<tuple_t...>& t, const TensorIteratorBase &iter, const int num_outputs) {
  if (i < (size_t)num_outputs) {
    set_result<traits>(i, std::get<i>(t), iter, num_outputs);
    return for_each_in_tuple<traits, i + 1, tuple_t...>(t, iter, num_outputs);
  }
  return i;
}

template<typename traits, typename... res_t>
static void set_results(const std::tuple<res_t...>& result, const TensorIteratorBase &iter, const int num_outputs) {
  AT_ASSERT(num_outputs >= 1);
  std::size_t result_size = for_each_in_tuple<traits>(result, iter, num_outputs);
  AT_ASSERT((size_t)num_outputs == result_size);
}

template <typename T, typename... Args>
struct all_same : std::conjunction<
  std::is_same<T, Args>...
> {};

// data_t is the input/output data type.
// acc_t is a type that contains all the necessary data
// to continue reducing.
// index_t is a one-dimensional index
//
// ops_t is such that &ops_t::reduce, &ops_t::combine, and &ops_t::project exist and satisfy
// the following.
// reduce: (acc_t, data_t, index_t) -> acc_t adds one data point to the accumulated value.
// combine: (acc_t, acc_t) -> acc_t combines two accumulated values into one.
// project: acc_t -> out_t finishes the reduction, getting the required output.
//
// Additionally, acc_t must be default-constructible:
// acc_t {} is an identity for combine,
// and project(acc_t {}) is the value of the operation on zero elements.
//
// The point of `combine` is to support parallelization -
// the idea is to one sequence of `reduce` calls per thread of execution,
// and then to combine them at the end with `combine`.
//
// If there is more than one output element,
// our parallelization strategy is to use one thread for each of them,
// which means that `combine` will never be called.
//
// If, on the other hand, there is only one, then we split the input into
// into several pieces, reduce each separately, and then combine them.

template <typename ops_t, typename init_t>
void binary_kernel_reduce(TensorIteratorBase& iter, ops_t ops, init_t init) {
  using rf_t = decltype(&ops_t::reduce);
  using cf_t = decltype(&ops_t::combine);
  using pf_t = decltype(&ops_t::project);
  using r_traits = binary_function_traits<rf_t>;
  using c_traits = binary_function_traits<cf_t>;
  using p_traits = unary_function_traits<pf_t>;
  using acc_t = typename p_traits::arg1_t;
  using data_t = typename r_traits::arg2_t;
  static_assert(
    all_same<
      acc_t,
      init_t,
      typename r_traits::arg1_t,
      typename r_traits::result_type,
      typename c_traits::arg1_t,
      typename c_traits::arg2_t,
      typename c_traits::result_type>::value,
    "all accumulate types must match");
  static_assert(
    std::is_default_constructible<acc_t>::value,
    "the accumulate type must be default-constructible"
  );
  const int num_outputs = iter.noutputs();
  iter.foreach_reduced_elt([&ops, &init, num_outputs](TensorIteratorBase &sub_iter) {
    auto reduction_body = [&ops, &sub_iter, num_outputs](acc_t acc, int64_t begin, int64_t end) -> acc_t {
      int ntensors = sub_iter.ntensors();
      sub_iter.serial_for_each([&acc, &ops, num_outputs, ntensors, begin](char** data, const int64_t* strides, int64_t size) {
        AT_ASSERT(ntensors - num_outputs == 1);
        char *in = data[ntensors - 1];
        int64_t stride = strides[ntensors - 1];
        for (const auto i : c10::irange(size)) {
          acc = ops.reduce(acc, c10::load<data_t>(in), begin + i);
          in += stride;
        }
      }, {begin, end});
      return ops.translate_idx(acc, sub_iter.view_offsets()[0]);
    };
    acc_t total_acc = init;
    auto numel = sub_iter.numel();
    if (numel < at::internal::GRAIN_SIZE || at::get_num_threads() == 1 ||
        at::in_parallel_region()) {
      total_acc = reduction_body(total_acc, 0, numel);
    } else {
      int max_threads = at::get_num_threads();
      AT_ASSERT(max_threads > 0);
      static_assert(
        !std::is_same<acc_t, bool>::value,
        "Concurrently modifying different references into std::vector<bool> is UB."
      );
      std::vector<acc_t> buffer((unsigned)max_threads, init);
      at::parallel_for(0, numel, internal::GRAIN_SIZE,
        [&](int64_t begin, int64_t end) {
          auto& acc = buffer[at::get_thread_num()];
          acc = reduction_body(acc, begin, end);
        }
      );
      for (const auto i : c10::irange(max_threads)) {
        total_acc = ops.combine(total_acc, buffer[i]);
      }
    }
    set_results<r_traits>(ops.project(total_acc), sub_iter, num_outputs);
  });
}

template <typename func_t, typename vec_func_t>
void binary_kernel_reduce_vec(TensorIteratorBase& iter, func_t op, vec_func_t vop, double ident = 0) {
  using traits = binary_function_traits<func_t>;
  static_assert(
    all_same<
      typename traits::result_type,
      typename traits::arg1_t,
      typename traits::arg2_t>::value,
    "all types must match");

  iter.output_base().fill_(ident);
  iter.parallel_reduce([&](char** data, const int64_t* strides, int64_t size0, int64_t size1) {
    int64_t outer_strides[] = { strides[2], strides[3] };
    if (is_contiguous_reduction<traits>(strides)) {
      // input is contiguous in dim 0, output is reduced in dim 0
      UNARY_OUTER_LOOP(data, outer_strides, size1, [&] {
        vectorized_inner_reduction(data, size0, op, vop);
      });
    } else if (is_outer_reduction<traits>(strides)) {
      // input and output are contiguous in dim 1
      int64_t inner_stride = strides[1]; // stride of input in dim 0
      vectorized_outer_reduction(data, inner_stride, size0, size1, op, vop);
    } else {
      UNARY_OUTER_LOOP(data, outer_strides, size1, [&] {
        char* ptrs[3] = { data[0], data[0], data[1] };
        int64_t inner_strides[3] = { strides[0], strides[0], strides[1] };
        basic_loop(ptrs, inner_strides, 0, size0, op);
      });
    }
  });
}

// when reduction is on most inner dimension (dim 0 in TensorIterator)
// and input has contiguous most inner dimension, `binary_kernel_reduce_lastdim`
// can be used.
inline bool is_reduce_lastdim(TensorIteratorBase& iter) {
  return iter.num_reduce_dims() == 1 && iter.is_dim_reduced(0)
      && iter.ninputs() == 1 && iter.strides(1)[0] == iter.element_size(1);
}

template <typename reduce_func_t>
void binary_kernel_reduce_lastdim(TensorIteratorBase& iter, reduce_func_t reduce_op) {
  auto shape = iter.shape();
  int64_t dim_size = shape[0];
  int64_t grain_size = std::max((int64_t) 1, at::internal::GRAIN_SIZE / dim_size);
  TensorIterator sub_iter(iter);
  // create sub iterator to parallel on all non-reduce-dims
  sub_iter.narrow(0, 0, 1);
  auto loop = [&](char** data, const int64_t* strides, int64_t size) {
    char* out = data[0];
    char* in = data[1];
    for (int64_t i = 0; i < size; ++i) {
      reduce_op(out, in, dim_size);
      out += strides[0];
      in += strides[1];
    }
  };
  sub_iter.for_each(loop, grain_size);
}

}}}  // namespace at::native::<anonymous>