88 lines
3.3 KiB
C++
88 lines
3.3 KiB
C++
#pragma once
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#include <cstdint>
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#include <c10/core/ScalarType.h>
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#include <ATen/cuda/CUDAConfig.h>
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// NOTE: These templates are intentionally not defined in this header,
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// which aviods re-compiling them for each translation unit. If you get
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// a link error, you need to add an explicit instantiation for your
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// types in cub.cu
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namespace at::cuda::cub {
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inline int get_num_bits(uint64_t max_key) {
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int num_bits = 1;
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while (max_key > 1) {
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max_key >>= 1;
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num_bits++;
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}
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return num_bits;
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}
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namespace detail {
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// radix_sort_pairs doesn't interact with value_t other than to copy
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// the data, so we can save template instantiations by reinterpreting
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// it as an opaque type.
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template <int N> struct alignas(N) OpaqueType { char data[N]; };
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template<typename key_t, int value_size>
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void radix_sort_pairs_impl(
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const key_t *keys_in, key_t *keys_out,
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const OpaqueType<value_size> *values_in, OpaqueType<value_size> *values_out,
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int64_t n, bool descending, int64_t begin_bit, int64_t end_bit);
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} // namespace detail
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template<typename key_t, typename value_t>
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void radix_sort_pairs(
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const key_t *keys_in, key_t *keys_out,
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const value_t *values_in, value_t *values_out,
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int64_t n, bool descending=false, int64_t begin_bit=0, int64_t end_bit=sizeof(key_t)*8) {
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static_assert(std::is_trivially_copyable<value_t>::value ||
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AT_ROCM_ENABLED(), // ROCm incorrectly fails this check for vector types
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"radix_sort_pairs value type must be trivially copyable");
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// Make value type opaque, so all inputs of a certain size use the same template instantiation
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using opaque_t = detail::OpaqueType<sizeof(value_t)>;
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static_assert(sizeof(value_t) <= 8 && (sizeof(value_t) & (sizeof(value_t) - 1)) == 0,
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"This size of value_t is not instantiated. Please instantiate it in cub.cu"
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" and modify this check.");
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static_assert(sizeof(value_t) == alignof(value_t), "Expected value_t to be size-aligned");
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detail::radix_sort_pairs_impl(
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keys_in, keys_out,
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reinterpret_cast<const opaque_t*>(values_in),
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reinterpret_cast<opaque_t*>(values_out),
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n, descending, begin_bit, end_bit);
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}
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template<typename key_t>
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void radix_sort_keys(
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const key_t *keys_in, key_t *keys_out,
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int64_t n, bool descending=false, int64_t begin_bit=0, int64_t end_bit=sizeof(key_t)*8);
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// NOTE: Intermediate sums will be truncated to input_t precision
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template <typename input_t, typename output_t>
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void inclusive_sum_truncating(const input_t *input, output_t *output, int64_t n);
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template <typename scalar_t>
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void inclusive_sum(const scalar_t *input, scalar_t *output, int64_t n) {
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return inclusive_sum_truncating(input, output, n);
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}
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// NOTE: Sums are done is common_type<input_t, output_t>
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template <typename input_t, typename output_t>
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void exclusive_sum_in_common_type(const input_t *input, output_t *output, int64_t n);
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template <typename scalar_t>
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void exclusive_sum(const scalar_t *input, scalar_t *output, int64_t n) {
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return exclusive_sum_in_common_type(input, output, n);
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}
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void mask_exclusive_sum(const uint8_t *mask, int64_t *output_idx, int64_t n);
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inline void mask_exclusive_sum(const bool *mask, int64_t *output_idx, int64_t n) {
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return mask_exclusive_sum(
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reinterpret_cast<const uint8_t*>(mask), output_idx, n);
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}
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} // namespace at::cuda::cub
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