topk_softmax (#1592)

* topk_softmax

* remove some file

* fix atomix linear_offset

* address various comment, and change sfc get_index api to static(tuple)

include/ck_tile/host/reference/reference_topk.hpp

@@ -0,0 +1,124 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/host_tensor.hpp"
+#include <thread>
+#include <numeric>
+#include <functional>
+#include <utility>
+#include <algorithm>
+
+namespace ck_tile {
+
+/*
+    similiar to torch.topk()
+    x (Tensor) – the input tensor.
+    k (int) – the k in “top-k”
+    dim (int, optional) – the dimension to sort along
+    largest (bool, optional) – largest or smallest elements
+    sorted (bool, optional) – elements in sorted order or not
+
+    output:
+    y_values
+    y_indices
+
+    https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/TopKImpl.h
+*/
+template <typename DataType, typename IndexType = index_t>
+CK_TILE_HOST void reference_topk(const HostTensor<DataType>& x,
+                                 HostTensor<DataType>& y_values,
+                                 HostTensor<IndexType>& y_indices,
+                                 index_t k,
+                                 index_t dim  = -1,
+                                 bool largest = true,
+                                 bool sorted  = true)
+{
+    // rank must be the same
+    index_t rank = x.get_num_of_dimension();
+    assert(rank == y_values.get_num_of_dimension());
+    assert(rank == y_indices.get_num_of_dimension());
+    assert(dim == -1 || dim < rank);
+
+    index_t topk_dim     = dim == -1 ? (rank - 1) : dim;
+    index_t topk_src_len = x.get_length(topk_dim);
+    auto x_len           = x.get_lengths();
+
+    assert(k <= topk_src_len);
+    assert(k == y_values.get_length(topk_dim) && k == y_indices.get_length(topk_dim));
+
+    index_t n_parallel = x.get_element_size() / topk_src_len;
+
+    // clang-format off
+    auto f = [&](auto i_element) {
+        std::vector<size_t> topk_coord = [&](){
+            std::vector<size_t> t_(rank, 0);
+            size_t r = i_element;
+            for(index_t i = rank - 1; i >= 0; i--) {
+                if(i == topk_dim)          continue; // topk dim should be zero
+                t_[i] = r % x_len[i];      r = r / x_len[i];
+            }
+            return t_;
+        }();
+
+        using elem_t = std::pair<DataType, IndexType>;
+        std::vector<elem_t> q = [&](){
+            std::vector<elem_t> t_(topk_src_len);
+            for(index_t i = 0; i < topk_src_len; i++) {
+                auto c_ = topk_coord;  c_[topk_dim] = i;
+                t_[i].first = x(c_);   t_[i].second = i;
+            }
+            return t_;
+        }();
+
+        // run topk
+        if(largest) {
+            std::nth_element(q.begin(), q.begin() + k - 1, q.end(),
+            [](const elem_t& lhs, const elem_t& rhs) -> bool { return lhs.first > rhs.first; });
+            if(sorted) {
+                std::sort(q.begin(), q.begin() + k - 1,
+                [](const elem_t& lhs, const elem_t& rhs) -> bool { return lhs.first > rhs.first; });
+            }
+        } else {
+            std::nth_element(q.begin(), q.begin() + k - 1, q.end(),
+            [](const elem_t& lhs, const elem_t& rhs) -> bool { return lhs.first < rhs.first; });
+            if(sorted) {
+                std::sort(q.begin(), q.begin() + k - 1,
+                [](const elem_t& lhs, const elem_t& rhs) -> bool { return lhs.first < rhs.first; });
+            }
+        }
+
+        // write out
+        for(index_t i = 0; i < k; i++) {
+            auto c_ = topk_coord;  c_[topk_dim] = i;
+            y_values(c_) = q[i].first;  y_indices(c_) = q[i].second;
+        }
+    };
+    // clang-format on
+
+    make_ParallelTensorFunctor(f, n_parallel)(std::thread::hardware_concurrency());
+}
+
+// TODO: if using this method, the return tensor would be dense(no stride)
+template <typename DataType, typename IndexType = index_t>
+CK_TILE_HOST auto reference_topk(const HostTensor<DataType>& x,
+                                 index_t k,
+                                 index_t dim  = -1,
+                                 bool largest = true,
+                                 bool sorted  = true)
+{
+    auto lens          = x.get_lengths();
+    index_t target_dim = (dim == -1) ? (lens.size() - 1) : dim;
+    assert(target_dim < lens.size());
+    assert(k <= lens[target_dim]);
+    lens[target_dim] = k;
+    HostTensor<DataType> y_values(lens);
+    HostTensor<IndexType> y_indices(lens);
+
+    reference_topk<DataType, IndexType>(x, y_values, y_indices, k, dim, largest, sorted);
+
+    return ck_tile::make_tuple(y_values, y_indices);
+}
+} // namespace ck_tile