issue/36 - Migrate cuda ramdom sample to metax, but compile and run too slow

src/infiniop/ops/random_sample/maca/random_sample_kernel.h

+#include "../../../devices/maca/maca_kernel_common.h"
+#include "infinicore.h"
+#include <hccub/device/device_radix_sort.cuh>
+#include <hccub/device/device_reduce.cuh>
+#include <hccub/device/device_scan.cuh>
+
+namespace op::random_sample::maca {
+
+// ↓↓↓ 重新封装 cub api，减少模板参数，方便调用
+
+template <class T>
+static hcError_t argMax_(
+    cub::KeyValuePair<int, T> *kv_pair,
+    const T *logits,
+    int n,
+    void *workspace_ptr,
+    size_t &workspace_len,
+    hcStream_t stream) {
+    return cub::DeviceReduce::ArgMax(
+        workspace_ptr, workspace_len,
+        logits, kv_pair, n,
+        stream);
+}
+
+template <class Tval, class Tidx>
+static hcError_t radixSort(
+    void *workspace_ptr, size_t &workspace_len,
+    const Tval *key_in, Tval *key_out,
+    const Tidx *val_in, Tidx *val_out,
+    int n,
+    hcStream_t stream) {
+    return cub::DeviceRadixSort::SortPairsDescending(
+        workspace_ptr, workspace_len,
+        key_in, key_out,
+        val_in, val_out,
+        n,
+        0, sizeof(Tval) * 8,
+        stream);
+}
+
+template <class T>
+static hcError_t inclusiveSum(
+    void *workspace_ptr, size_t &workspace_len,
+    T *data, int n,
+    hcStream_t stream) {
+    return cub::DeviceScan::InclusiveSum(
+        workspace_ptr, workspace_len,
+        data, data, n,
+        stream);
+}
+
+// ↑↑↑ 重新封装 cub api，减少模板参数，方便调用
+// ↓↓↓ 计算 workspace
+
+// 地址对齐到 256
+static constexpr size_t align256(size_t size) {
+    return (size + 255) & (~255);
+}
+
+template <class Tidx, class Tval>
+utils::Result<size_t> calculateWorkspace(size_t n_) {
+    const auto n = static_cast<int>(n_);
+
+    size_t argmax;
+    CHECK_MACA(argMax_<Tval>(
+        nullptr, nullptr, n,
+        nullptr, argmax,
+        nullptr));
+    // 前 256 字节用于 kv pair
+    argmax += 256;
+
+    // indices
+    size_t size_random = align256(sizeof(Tidx) * n);
+    // sorted
+    size_random += align256(sizeof(Tval) * n);
+    // indices_out
+    size_random += align256(sizeof(Tidx) * n);
+    // cub device api
+    size_t size_radix_sort;
+    CHECK_MACA((radixSort<Tval, Tidx>(
+        nullptr, size_radix_sort,
+        nullptr, nullptr,
+        nullptr, nullptr,
+        n,
+        nullptr)));
+
+    size_t size_inclusive_sum;
+    CHECK_MACA(inclusiveSum<Tval>(
+        nullptr, size_inclusive_sum,
+        nullptr, n,
+        nullptr));
+    size_random += cub::Max()(size_radix_sort, size_inclusive_sum);
+
+    return utils::Result<size_t>(cub::Max()(argmax, size_random));
+}
+
+// ↑↑↑ 计算 workspace
+// ↓↓↓ 通过特化将 fp16_t 转换为 half
+
+template <class Tval>
+struct CudaTval {
+    using Type = Tval;
+};
+
+template <>
+struct CudaTval<fp16_t> {
+    using Type = half;
+};
+
+// ↑↑↑ 通过特化将 fp16_t 转换为 half
+// ↓↓↓ 用于采样过程的小型 kernel
+
+// maca toolkit 11.x 带的 cub::DeviceReduce::ArgMax 只接受 cub::KeyValuePair<int, Tval> 输出。
+// 这个 kernel 用于取出序号
+template <class Tidx, class Tval>
+static __global__ void castIdx(Tidx *result, const cub::KeyValuePair<int, Tval> *kv_pair) {
+    *result = kv_pair->key;
+}
+
+// 填充排序要求的序号数组
+template <class Tidx>
+static __global__ void fillIndices(Tidx *indices, int n) {
+    int i = blockIdx.x * blockDim.x + threadIdx.x;
+    if (i < n) {
+        indices[i] = i;
+    }
+}
+
+// random sample 使用的 softmax 可以简化为一个基本的线性映射
+// 由于已经排序，最大值就是第一个数字
+// 第一个数字需要被多个 block 读取，不能写
+template <class T>
+static __global__ void partialSoftmaxKernel(
+    T *__restrict__ data, int n,
+    float temperature) {
+    int i = blockIdx.x * blockDim.x + threadIdx.x;
+    if (0 < i && i < n) {
+        float max = __ldg(data);
+        data[i] = (T)expf(((float)data[i] - max) / temperature);
+    }
+}
+
+// 将第一个数字写成 1，即 exp(0)
+template <class T>
+static __global__ void setSoftmaxMaxKernel(
+    T *__restrict__ data) {
+    *data = 1;
+}
+
+// 直接 for 循环遍历采样
+// 这个 kernel 仅用于避免将数据拷贝到 cpu
+template <class Tval, class Tidx>
+static __global__ void randomSampleKernel(
+    Tidx *__restrict__ result,
+    const Tval *__restrict__ sorted,
+    const Tidx *__restrict__ indices_out,
+    size_t n,
+    float random, float topp, size_t topk) {
+    topk = cub::Min()(topk, n);
+    auto p = (Tval)(random * cub::Min()(topp * (float)sorted[n - 1], (float)sorted[topk - 1]));
+    for (size_t i = 0;; ++i) {
+        if ((sorted[i]) >= p) {
+            *result = indices_out[i];
+            return;
+        }
+    }
+}
+
+// ↑↑↑ 用于采样过程的小型 kernel
+
+struct Algo {
+    int block_size;
+
+    template <class Tidx, class Tval_>
+    infiniStatus_t argmax(
+        void *workspace, size_t workspace_size,
+        void *result, const void *probs, size_t n,
+        void *stream_) const {
+
+        using Tval = typename CudaTval<Tval_>::Type;
+
+        auto stream = (hcStream_t)stream_;
+        auto logits = (Tval *)probs;
+        auto kv_pair = (cub::KeyValuePair<int, Tval> *)workspace;
+        workspace = (void *)((char *)workspace + 256);
+        workspace_size -= 256;
+
+        argMax_(
+            kv_pair,
+            logits,
+            n,
+            workspace,
+            workspace_size, stream);
+        castIdx<<<1, 1, 0, stream>>>((Tidx *)result, kv_pair);
+
+        return INFINI_STATUS_SUCCESS;
+    }
+
+    template <class Tidx, class Tval_>
+    infiniStatus_t random(
+        void *workspace_, size_t workspace_size,
+        void *result_, const void *probs, size_t n,
+        float random_val, float topp, int topk, float temperature,
+        void *stream_) const {
+
+        using Tval = typename CudaTval<Tval_>::Type;
+
+        auto stream = (hcStream_t)stream_;
+        auto logits = (Tval *)probs;
+        auto result = (Tidx *)result_;
+
+        auto workspace = reinterpret_cast<size_t>(workspace_);
+        auto workspace_end = workspace + workspace_size;
+
+        auto indices = reinterpret_cast<Tidx *>(workspace);
+        workspace += align256(sizeof(Tidx) * n);
+
+        auto sorted = reinterpret_cast<Tval *>(workspace);
+        workspace += align256(sizeof(Tval) * n);
+
+        auto indices_out = reinterpret_cast<Tidx *>(workspace);
+        workspace += align256(sizeof(Tidx) * n);
+
+        workspace_ = reinterpret_cast<void *>(workspace);
+        workspace_size = workspace_end - workspace;
+
+        auto block = cub::Min()((size_t)block_size, n);
+        auto grid = (n + block - 1) / block;
+        // sort
+        fillIndices<<<grid, block, 0, stream>>>(indices, n);
+        CHECK_MACA(radixSort(
+            workspace_, workspace_size,
+            logits, sorted,
+            indices, indices_out,
+            n,
+            stream));
+        // softmax
+        partialSoftmaxKernel<<<grid, block, 0, stream>>>(sorted, n, temperature);
+        setSoftmaxMaxKernel<<<1, 1, 0, stream>>>(sorted);
+        // sum
+        CHECK_MACA(inclusiveSum(
+            workspace_, workspace,
+            sorted, n,
+            stream));
+        // sample
+        randomSampleKernel<<<1, 1, 0, stream>>>(
+            result,
+            sorted, indices_out, n,
+            random_val, topp, topk);
+        return INFINI_STATUS_SUCCESS;
+    }
+};
+
+} // namespace op::random_sample::maca

src/infiniop/ops/random_sample/maca/random_sample_maca.h

+#ifndef __RANDOM_SAMPLE_MACA_H__
+#define __RANDOM_SAMPLE_MACA_H__
+
+#include "../random_sample.h"
+
+DESCRIPTOR(maca)
+
+#endif // __RANDOM_SAMPLE_MACA_H__

src/infiniop/ops/random_sample/maca/random_sample_maca.maca

+#include "../../../devices/maca/common_maca.h"
+#include "../../../devices/maca/maca_handle.h"
+#include "../info.h"
+#include "random_sample_kernel.h"
+#include "random_sample_maca.h"
+
+namespace op::random_sample::maca {
+
+struct Descriptor::Opaque {
+    std::shared_ptr<device::maca::Handle::Internal> internal;
+};
+
+Descriptor::~Descriptor() {
+    delete _opaque;
+}
+
+infiniStatus_t Descriptor::create(
+    infiniopHandle_t handle_,
+    Descriptor **desc_ptr,
+    infiniopTensorDescriptor_t result_desc,
+    infiniopTensorDescriptor_t probs_desc) {
+    auto handle = reinterpret_cast<device::maca::Handle *>(handle_);
+
+    auto result = RandomSampleInfo::create(result_desc, probs_desc);
+    CHECK_RESULT(result);
+
+    auto info = result.take();
+    size_t workspace_size;
+
+#define CASE_P(CASE, Tidx, Tval)                                        \
+    case CASE: {                                                        \
+        auto workspace_result = calculateWorkspace<Tidx, Tval>(info.n); \
+        CHECK_RESULT(workspace_result);                                 \
+        workspace_size = workspace_result.take();                       \
+    } break
+
+#define CASE_I(CASE, Tidx)                          \
+    case CASE:                                      \
+        switch (info.dt_p) {                        \
+            CASE_P(INFINI_DTYPE_F16, Tidx, half);   \
+            CASE_P(INFINI_DTYPE_F32, Tidx, float);  \
+            CASE_P(INFINI_DTYPE_F64, Tidx, double); \
+        default:                                    \
+            abort();                                \
+        }                                           \
+        break
+
+    switch (info.dt_i) {
+        CASE_I(INFINI_DTYPE_I8, int8_t);
+        CASE_I(INFINI_DTYPE_I16, int16_t);
+        CASE_I(INFINI_DTYPE_I32, int32_t);
+        CASE_I(INFINI_DTYPE_I64, int64_t);
+        CASE_I(INFINI_DTYPE_U8, uint8_t);
+        CASE_I(INFINI_DTYPE_U16, uint16_t);
+        CASE_I(INFINI_DTYPE_U32, uint32_t);
+        CASE_I(INFINI_DTYPE_U64, uint64_t);
+    default:
+        abort();
+    }
+
+#undef CASE_I
+#undef CASE_P
+
+    *desc_ptr = new Descriptor(
+        info,
+        workspace_size,
+        new Opaque{handle->internal()},
+        handle->device, handle->device_id);
+    return INFINI_STATUS_SUCCESS;
+}
+
+size_t Descriptor::minWorkspaceSize() const {
+    return _min_workspace_size;
+}
+
+infiniStatus_t Descriptor::calculate(
+    void *workspace,
+    size_t workspace_size,
+    void *result,
+    const void *probs,
+    float random_val,
+    float topp,
+    int topk,
+    float temperature,
+    void *stream) const {
+
+    if (workspace_size < _min_workspace_size) {
+        return INFINI_STATUS_INSUFFICIENT_WORKSPACE;
+    }
+
+    auto block_size = _opaque->internal->blockSizeX();
+
+    Calculate::calculate<Algo>(
+        Algo{block_size}, _info, workspace, workspace_size,
+        result, probs,
+        random_val, topp, topk, temperature,
+        stream);
+
+    return INFINI_STATUS_SUCCESS;
+}
+
+} // namespace op::random_sample::maca

src/infiniop/ops/random_sample/operator.cc

@@ -8,6 +8,9 @@
 #ifdef ENABLE_CUDA_API
 #include "cuda/random_sample_cuda.cuh"
 #endif
+#ifdef ENABLE_METAX_API
+#include "maca/random_sample_maca.h"
+#endif
 
 __C infiniStatus_t infiniopCreateRandomSampleDescriptor(
     infiniopHandle_t handle,
@@ -31,6 +34,9 @@ __C infiniStatus_t infiniopCreateRandomSampleDescriptor(
 #ifdef ENABLE_CUDA_API
         CREATE(INFINI_DEVICE_NVIDIA, cuda);
 #endif
+#ifdef ENABLE_METAX_API
+        CREATE(INFINI_DEVICE_METAX, maca);
+#endif
 
     default:
         return INFINI_STATUS_DEVICE_TYPE_NOT_SUPPORTED;
@@ -58,6 +64,9 @@ __C infiniStatus_t infiniopGetRandomSampleWorkspaceSize(
 #ifdef ENABLE_CUDA_API
         GET(INFINI_DEVICE_NVIDIA, cuda);
 #endif
+#ifdef ENABLE_METAX_API
+        GET(INFINI_DEVICE_METAX, maca);
+#endif
 
     default:
         return INFINI_STATUS_DEVICE_TYPE_NOT_SUPPORTED;
@@ -95,6 +104,9 @@ __C infiniStatus_t infiniopRandomSample(
 #ifdef ENABLE_CUDA_API
         CALCULATE(INFINI_DEVICE_NVIDIA, cuda);
 #endif
+#ifdef ENABLE_METAX_API
+        CALCULATE(INFINI_DEVICE_METAX, maca);
+#endif
 
     default:
         return INFINI_STATUS_DEVICE_TYPE_NOT_SUPPORTED;
@@ -119,6 +131,9 @@ __C infiniStatus_t infiniopDestroyRandomSampleDescriptor(
 #ifdef ENABLE_CUDA_API
         DELETE(INFINI_DEVICE_NVIDIA, cuda);
 #endif
+#ifdef ENABLE_METAX_API
+        DELETE(INFINI_DEVICE_METAX, maca);
+#endif
 
     default:
         return INFINI_STATUS_DEVICE_TYPE_NOT_SUPPORTED;