|
| 1 | +/* |
| 2 | + * Copyright (c) Meta Platforms, Inc. and affiliates. |
| 3 | + * All rights reserved. |
| 4 | + * |
| 5 | + * This source code is licensed under the BSD-style license found in the |
| 6 | + * LICENSE file in the root directory of this source tree. |
| 7 | + */ |
| 8 | + |
| 9 | +#include <cmath> |
| 10 | +#include <tuple> |
| 11 | + |
| 12 | +#include <executorch/runtime/kernel/kernel_includes.h> |
| 13 | + |
| 14 | +namespace torch { |
| 15 | +namespace executor { |
| 16 | +namespace native { |
| 17 | +namespace { |
| 18 | + |
| 19 | +bool check_topk_args( |
| 20 | + const Tensor& in, |
| 21 | + int64_t k, |
| 22 | + int64_t dim, |
| 23 | + Tensor& values, |
| 24 | + Tensor& indices) { |
| 25 | + ET_LOG_AND_RETURN_IF_FALSE(tensors_have_same_dtype(in, values)); |
| 26 | + ET_LOG_AND_RETURN_IF_FALSE(indices.scalar_type() == ScalarType::Long); |
| 27 | + ET_LOG_AND_RETURN_IF_FALSE(tensor_has_dim(in, dim)); |
| 28 | + if (dim < 0) { |
| 29 | + dim += nonzero_dim(in); |
| 30 | + } |
| 31 | + ET_LOG_MSG_AND_RETURN_IF_FALSE( |
| 32 | + k >= 0 && k <= nonempty_size(in, dim), "selected index k out of range"); |
| 33 | + return true; |
| 34 | +} |
| 35 | + |
| 36 | +bool get_topk_target_size( |
| 37 | + const Tensor& in, |
| 38 | + int64_t k, |
| 39 | + int64_t dim, |
| 40 | + Tensor::SizesType* target_size, |
| 41 | + size_t* target_dim) { |
| 42 | + *target_dim = in.dim(); |
| 43 | + for (size_t i = 0; i < *target_dim; ++i) { |
| 44 | + if (i == dim) { |
| 45 | + target_size[i] = k; |
| 46 | + } else { |
| 47 | + target_size[i] = in.size(i); |
| 48 | + } |
| 49 | + } |
| 50 | + return true; |
| 51 | +} |
| 52 | + |
| 53 | +template <typename CTYPE, typename elem_t = std::pair<CTYPE, int64_t>> |
| 54 | +void perform_topk( |
| 55 | + const Tensor& in, |
| 56 | + int64_t k, |
| 57 | + int64_t dim, |
| 58 | + bool largest, |
| 59 | + bool sorted, |
| 60 | + Tensor& values, |
| 61 | + Tensor& indices, |
| 62 | + elem_t* queue) { |
| 63 | + const CTYPE* const in_data = in.const_data_ptr<CTYPE>(); |
| 64 | + CTYPE* values_data = values.mutable_data_ptr<CTYPE>(); |
| 65 | + long* indices_data = indices.mutable_data_ptr<long>(); |
| 66 | + |
| 67 | + if (in.dim() == 0) { |
| 68 | + values_data[0] = in_data[0]; |
| 69 | + indices_data[0] = 0; |
| 70 | + return; |
| 71 | + } |
| 72 | + |
| 73 | + if (k == 0) { |
| 74 | + return; |
| 75 | + } |
| 76 | + |
| 77 | + const size_t outer_size = getLeadingDims(in, dim); |
| 78 | + |
| 79 | + const size_t dim_size = in.size(dim); |
| 80 | + const size_t dim_stride = in.strides()[dim]; |
| 81 | + |
| 82 | + const size_t outer_stride_in = dim_size * dim_stride; |
| 83 | + const size_t outer_stride_out = k * dim_stride; |
| 84 | + |
| 85 | + bool use_partial_sort = k * 64 <= dim_size; |
| 86 | + |
| 87 | + // Loop through all outer dimensions |
| 88 | + for (size_t outer_idx = 0; outer_idx < outer_size; ++outer_idx) { |
| 89 | + size_t outer_in = outer_idx * outer_stride_in; |
| 90 | + size_t outer_out = outer_idx * outer_stride_out; |
| 91 | + // Loop through all inner dimensions |
| 92 | + for (size_t inner_idx = 0; inner_idx < dim_stride; ++inner_idx) { |
| 93 | + size_t base_in = outer_in + inner_idx; |
| 94 | + size_t base_out = outer_out + inner_idx; |
| 95 | + |
| 96 | + // Populate the queue with the values from the input tensor |
| 97 | + for (size_t i = 0; i < dim_size; ++i) { |
| 98 | + size_t in_ix = base_in + i * dim_stride; |
| 99 | + queue[i].first = in_data[in_ix]; |
| 100 | + queue[i].second = i; |
| 101 | + } |
| 102 | + |
| 103 | + // Perform topk on the queue |
| 104 | + if (use_partial_sort) { |
| 105 | + if (largest) { |
| 106 | + std::partial_sort( |
| 107 | + queue, |
| 108 | + queue + k, |
| 109 | + queue + dim_size, |
| 110 | + [](const elem_t& x, const elem_t& y) -> bool { |
| 111 | + return ( |
| 112 | + (std::isnan(x.first) && !std::isnan(y.first)) || |
| 113 | + (x.first > y.first)); |
| 114 | + }); |
| 115 | + } else { |
| 116 | + std::partial_sort( |
| 117 | + queue, |
| 118 | + queue + k, |
| 119 | + queue + dim_size, |
| 120 | + [](const elem_t& x, const elem_t& y) -> bool { |
| 121 | + return ( |
| 122 | + (!std::isnan(x.first) && std::isnan(y.first)) || |
| 123 | + (x.first < y.first)); |
| 124 | + }); |
| 125 | + } |
| 126 | + } else { |
| 127 | + if (largest) { |
| 128 | + std::nth_element( |
| 129 | + queue, |
| 130 | + queue + k - 1, |
| 131 | + queue + dim_size, |
| 132 | + [](const elem_t& x, const elem_t& y) -> bool { |
| 133 | + return ( |
| 134 | + (std::isnan(x.first) && !std::isnan(y.first)) || |
| 135 | + (x.first > y.first)); |
| 136 | + }); |
| 137 | + if (sorted) { |
| 138 | + std::sort( |
| 139 | + queue, |
| 140 | + queue + k - 1, |
| 141 | + [](const elem_t& x, const elem_t& y) -> bool { |
| 142 | + return ( |
| 143 | + (std::isnan(x.first) && !std::isnan(y.first)) || |
| 144 | + (x.first > y.first)); |
| 145 | + }); |
| 146 | + } |
| 147 | + } else { |
| 148 | + std::nth_element( |
| 149 | + queue, |
| 150 | + queue + k - 1, |
| 151 | + queue + dim_size, |
| 152 | + [](const elem_t& x, const elem_t& y) -> bool { |
| 153 | + return ( |
| 154 | + (!std::isnan(x.first) && std::isnan(y.first)) || |
| 155 | + (x.first < y.first)); |
| 156 | + }); |
| 157 | + if (sorted) { |
| 158 | + std::sort( |
| 159 | + queue, |
| 160 | + queue + k - 1, |
| 161 | + [](const elem_t& x, const elem_t& y) -> bool { |
| 162 | + return ( |
| 163 | + (!std::isnan(x.first) && std::isnan(y.first)) || |
| 164 | + (x.first < y.first)); |
| 165 | + }); |
| 166 | + } |
| 167 | + } |
| 168 | + } |
| 169 | + |
| 170 | + // Write the topk values and indices to the output tensors |
| 171 | + for (size_t i = 0; i < k; ++i) { |
| 172 | + size_t out_ix = base_out + i * dim_stride; |
| 173 | + |
| 174 | + values_data[out_ix] = queue[i].first; |
| 175 | + indices_data[out_ix] = queue[i].second; |
| 176 | + } |
| 177 | + } |
| 178 | + } |
| 179 | +} |
| 180 | + |
| 181 | +void* allocate_temp_memory(RuntimeContext& ctx, size_t size) { |
| 182 | + Result<void*> temp_mem_res = ctx.allocate_temp(size); |
| 183 | + return temp_mem_res.ok() ? temp_mem_res.get() : nullptr; |
| 184 | +} |
| 185 | + |
| 186 | +} // namespace |
| 187 | + |
| 188 | +std::tuple<Tensor&, Tensor&> topk_values( |
| 189 | + RuntimeContext& ctx, |
| 190 | + const Tensor& in, |
| 191 | + int64_t k, |
| 192 | + int64_t dim, |
| 193 | + bool largest, |
| 194 | + bool sorted, |
| 195 | + Tensor& values, |
| 196 | + Tensor& indices) { |
| 197 | + auto out = std::tuple<Tensor&, Tensor&>({values, indices}); |
| 198 | + |
| 199 | + ET_KERNEL_CHECK( |
| 200 | + ctx, check_topk_args(in, k, dim, values, indices), InvalidArgument, out); |
| 201 | + |
| 202 | + if (dim < 0) { |
| 203 | + dim += nonzero_dim(in); |
| 204 | + } |
| 205 | + |
| 206 | + // @lint-ignore CLANGTIDY facebook-hte-CArray |
| 207 | + Tensor::SizesType target_size[kTensorDimensionLimit]; |
| 208 | + size_t target_dim = 0; |
| 209 | + get_topk_target_size(in, k, dim, target_size, &target_dim); |
| 210 | + |
| 211 | + ET_KERNEL_CHECK( |
| 212 | + ctx, |
| 213 | + resize_tensor(values, {target_size, target_dim}) == Error::Ok, |
| 214 | + InvalidArgument, |
| 215 | + out); |
| 216 | + |
| 217 | + ET_KERNEL_CHECK( |
| 218 | + ctx, |
| 219 | + resize_tensor(indices, {target_size, target_dim}) == Error::Ok, |
| 220 | + InvalidArgument, |
| 221 | + out); |
| 222 | + |
| 223 | + constexpr auto name = "topk.values"; |
| 224 | + |
| 225 | + if (in.numel() == 0 || (k == 0 && in.dim() > 0)) { |
| 226 | + return out; |
| 227 | + } |
| 228 | + |
| 229 | + bool temp_mem_allocated = false; |
| 230 | + |
| 231 | + ET_SWITCH_REALH_TYPES(in.scalar_type(), ctx, name, CTYPE, [&]() { |
| 232 | + using elem_t = std::pair<CTYPE, int64_t>; |
| 233 | + size_t temp_mem_size = nonempty_size(in, dim) * sizeof(elem_t); |
| 234 | + |
| 235 | + elem_t* queue = (elem_t*)allocate_temp_memory(ctx, temp_mem_size); |
| 236 | + if (queue == nullptr) { |
| 237 | + return; |
| 238 | + } |
| 239 | + temp_mem_allocated = true; |
| 240 | + |
| 241 | + perform_topk<CTYPE>(in, k, dim, largest, sorted, values, indices, queue); |
| 242 | + }); |
| 243 | + |
| 244 | + ET_KERNEL_CHECK(ctx, temp_mem_allocated, MemoryAllocationFailed, out); |
| 245 | + |
| 246 | + return out; |
| 247 | +} |
| 248 | + |
| 249 | +} // namespace native |
| 250 | +} // namespace executor |
| 251 | +} // namespace torch |
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