Export ROCm/rocm-libraries@2d4a3223cb

2026-05-05 22:22:27 +00:00 · 2026-03-11 23:03:20 -04:00
commit e6cd3f1e3f
6330 changed files with 1132789 additions and 0 deletions
--- a/include/ck_tile/host/reference/reference_batched_gemm.hpp
+++ b/include/ck_tile/host/reference/reference_batched_gemm.hpp
@@ -0,0 +1,90 @@
+// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+// SPDX-License-Identifier: MIT
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/host_tensor.hpp"
+#include <thread>
+
+namespace ck_tile {
+
+template <typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename CDataType,
+          typename AElementOp   = ck_tile::identity,
+          typename BElementOp   = ck_tile::identity,
+          typename ACCElementOp = ck_tile::identity>
+CK_TILE_HOST void reference_batched_gemm(const HostTensor<ADataType>& a_b_m_k,
+                                         const HostTensor<BDataType>& b_b_n_k,
+                                         HostTensor<CDataType>& c_b_m_n,
+                                         const AElementOp& a_element_op     = {},
+                                         const BElementOp& b_element_op     = {},
+                                         const ACCElementOp& acc_element_op = {})
+{
+    const int N = b_b_n_k.mDesc.get_lengths()[1];
+    const int K = b_b_n_k.mDesc.get_lengths()[2];
+
+    auto f = [&](auto batch, auto m) {
+        for(int n = 0; n < N; ++n)
+        {
+            AccDataType v_acc = 0;
+
+            for(int k = 0; k < K; ++k)
+            {
+                ADataType v_a = a_element_op(a_b_m_k(batch, m, k));
+                BDataType v_b = b_element_op(b_b_n_k(batch, n, k));
+
+                v_acc += ck_tile::type_convert<AccDataType>(v_a) *
+                         ck_tile::type_convert<AccDataType>(v_b);
+            }
+
+            c_b_m_n(batch, m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
+        }
+    };
+
+    make_ParallelTensorFunctor(f, c_b_m_n.mDesc.get_lengths()[0], c_b_m_n.mDesc.get_lengths()[1])(
+        std::thread::hardware_concurrency());
+}
+template <typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename CDataType,
+          typename AElementOp   = ck_tile::idx_identity,
+          typename BElementOp   = ck_tile::idx_identity,
+          typename ACCElementOp = ck_tile::idx_identity>
+CK_TILE_HOST void reference_batched_quant_gemm(const HostTensor<ADataType>& a_b_m_k,
+                                               const HostTensor<BDataType>& b_b_n_k,
+                                               HostTensor<CDataType>& c_b_m_n,
+                                               const AElementOp& a_element_op     = {},
+                                               const BElementOp& b_element_op     = {},
+                                               const ACCElementOp& acc_element_op = {})
+{
+    const int N = b_b_n_k.mDesc.get_lengths()[1];
+    const int K = b_b_n_k.mDesc.get_lengths()[2];
+
+    auto f = [&](auto batch, auto m) {
+        for(int n = 0; n < N; ++n)
+        {
+            AccDataType v_acc = 0;
+
+            for(int k = 0; k < K; ++k)
+            {
+                AccDataType v_a = ck_tile::type_convert<AccDataType>(
+                    a_element_op(std::make_tuple(batch, m, k), a_b_m_k(batch, m, k)));
+                AccDataType v_b = ck_tile::type_convert<AccDataType>(
+                    b_element_op(std::make_tuple(batch, n, k), b_b_n_k(batch, n, k)));
+
+                v_acc += v_a * v_b;
+            }
+
+            c_b_m_n(batch, m, n) = ck_tile::type_convert<CDataType>(
+                acc_element_op(std::make_tuple(batch, m, n), v_acc));
+        }
+    };
+
+    make_ParallelTensorFunctor(f, c_b_m_n.mDesc.get_lengths()[0], c_b_m_n.mDesc.get_lengths()[1])(
+        std::thread::hardware_concurrency());
+}
+} // namespace ck_tile