samplingTopKKernels.cu

/*
 * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
 * Copyright (c) 2021, NAVER Corp.  Authored by CLOVA.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include <stdexcept>
#ifndef CUDART_VERSION
#error CUDART_VERSION Undefined!
#elif (CUDART_VERSION >= 11050)
#include <cub/cub.cuh>
#else
#include "3rdparty/cub/cub.cuh"
#endif

#include "tensorrt_llm/common/logger.h"
#include "tensorrt_llm/common/reduceKernelUtils.cuh"
#include "tensorrt_llm/common/stringUtils.h"
#include "tensorrt_llm/kernels/samplingTopKKernels.h"

using namespace tensorrt_llm::common;

namespace tensorrt_llm
{
namespace kernels
{

__global__ void curandInitialize(curandState_t* state, const int size, const unsigned long long random_seed)
{
    if (threadIdx.x + blockIdx.x * blockDim.x < size)
    {
        curand_init(random_seed, 0, 0, &state[blockIdx.x * blockDim.x + threadIdx.x]);
    }
}

void invokeCurandInitialize(
    curandState_t* state, const size_t batch_size, const unsigned long long random_seed, cudaStream_t stream)
{
    dim3 block(256);
    dim3 grid((int) (ceil(batch_size * 1.0 / 256)));
    curandInitialize<<<grid, block, 0, stream>>>(state, batch_size, random_seed);
}

__global__ void curandBatchInitialize(curandState_t* states, const int size, const unsigned long long* random_seeds)
{
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    if (idx < size)
    {
        curand_init(random_seeds[idx], 0, 0, &states[idx]);
    }
}

void invokeCurandBatchInitialize(
    curandState_t* states, const size_t batch_size, const unsigned long long* random_seeds, cudaStream_t stream)
{
    dim3 block(256);
    dim3 grid((int) (ceil(batch_size * 1.0 / 256)));
    curandBatchInitialize<<<grid, block, 0, stream>>>(states, batch_size, random_seeds);
}

template <typename T>
__global__ void addBiasEndMask(T* logits, const T* bias, const int* end_ids, const bool* finished, const int vocab_size,
    const int vocab_size_padded)
{
    int bid = blockIdx.x;
    bool finish = finished != nullptr ? finished[bid] : false;
    int offset = bid * vocab_size_padded;

    const bool IS_FP16 = std::is_same<T, half>::value;
    const T MAX_T_VAL = (IS_FP16) ? HALF_FLT_MAX : FLT_MAX;
    for (int tid = threadIdx.x; tid < vocab_size_padded; tid += blockDim.x)
    {
        if (tid >= vocab_size)
        {
            logits[offset + tid] = -MAX_T_VAL;
        }
        else if (finish)
        {
            logits[offset + tid] = (tid == end_ids[bid]) ? MAX_T_VAL : -MAX_T_VAL;
        }
        else
        {
            if (bias != nullptr)
            {
                logits[offset + tid] += bias[tid];
            }
        }
    }
}

template <typename T>
void invokeAddBiasEndMask(T* logits, const T* bias, const int* end_ids, const bool* finished, const int batch_size,
    const int vocab_size, const int vocab_size_padded, cudaStream_t stream)
{
    dim3 grid(batch_size);
    dim3 block(min(vocab_size_padded, 1024));
    /*n is the vocab_size, e.g., 30000, 7000.... vocab_size is usually very big.
     */
    addBiasEndMask<<<grid, block, 0, stream>>>(logits, bias, end_ids, finished, vocab_size, vocab_size_padded);
}

template void invokeAddBiasEndMask(float* logits, const float* bias, const int* end_ids, const bool* finished,
    const int batch_size, const int vocab_size, const int vocab_size_padded, cudaStream_t stream);

template void invokeAddBiasEndMask(half* logits, const half* bias, const int* end_ids, const bool* finished,
    const int batch_size, const int vocab_size, const int vocab_size_padded, cudaStream_t stream);

template <typename T, int BLOCK_SIZE_, int BLOCKS_PER_BEAM_>
__global__ void topk_stage1(const T* __restrict log_probs, T* tmp_log_probs, int* topk_tmp_id_buf, T* topk_tmp_val_buf,
    const bool* finished, const int max_top_k, const int* top_ks, const int vocab_size, const int* end_ids,
    const bool* skip_decode)
{
    typedef cub::BlockReduce<TopK_2<T>, BLOCK_SIZE_> BlockReduce;
    __shared__ typename BlockReduce::TempStorage temp_storage;

    const int tid = threadIdx.x;
    const int bid = blockIdx.x;

    const int batch_id = bid / BLOCKS_PER_BEAM_; // row id for log_probs
    if (skip_decode != nullptr && skip_decode[batch_id])
    {
        return;
    }
    const int block_lane = bid % BLOCKS_PER_BEAM_;                    // block id for a beam
    const int k = (top_ks != nullptr) ? top_ks[batch_id] : max_top_k; // batch_id = batch index

    const int tmp_log_buf_index = batch_id * vocab_size;
    const int tmp_topk_buf_index = batch_id * BLOCKS_PER_BEAM_ * max_top_k + block_lane * k;

    TopK_2<T> partial;
    const bool IS_FP16 = std::is_same<T, half>::value;
    const T MAX_T_VAL = (IS_FP16) ? HALF_FLT_MAX : FLT_MAX;

    if (finished != nullptr && finished[batch_id] == true)
    {
        if (tid < k)
        {
            const int index = tmp_topk_buf_index + tid;
            if (block_lane == 0 && tid == 0)
            {
                const int end_id = end_ids[batch_id];
                topk_tmp_id_buf[index] = tmp_log_buf_index + end_id;
                topk_tmp_val_buf[index] = log_probs[tmp_log_buf_index + end_id];
            }
            else
            {
                topk_tmp_id_buf[index] = -1;
                topk_tmp_val_buf[index] = -MAX_T_VAL;
            }
        }
        return;
    }

    for (int elem_id = tid + block_lane * BLOCK_SIZE_; elem_id < vocab_size; elem_id += BLOCK_SIZE_ * BLOCKS_PER_BEAM_)
    {
        int index = elem_id + tmp_log_buf_index;
        tmp_log_probs[index] = log_probs[index];
    }

    for (int ite = 0; ite < k; ite++)
    {
        partial.init();
#pragma unroll
        for (int elem_id = tid + block_lane * BLOCK_SIZE_; elem_id < vocab_size;
             elem_id += BLOCK_SIZE_ * BLOCKS_PER_BEAM_)
        {
            int index = elem_id + tmp_log_buf_index;
            partial.insert(tmp_log_probs[index], index);
        }

        TopK_2<T> total = BlockReduce(temp_storage).Reduce(partial, reduce_topk_op_2<T>);

        if (tid == 0)
        {
            const int index = tmp_topk_buf_index + ite;
            topk_tmp_id_buf[index] = total.p;
            topk_tmp_val_buf[index] = total.u;
            if (total.p >= 0)
            {
                tmp_log_probs[total.p] = -MAX_T_VAL;
            }
        }
        __syncthreads();
    }
}

template <typename T, int BLOCK_SIZE_, int BLOCKS_PER_BEAM_>
__global__ void topk_stage2_sampling(const int* __restrict topk_tmp_id_buf, T* topk_tmp_val_buf, int** ids,
    int* sequence_lengths, bool* finished, float* cum_log_probs, float* output_log_probs, const int max_top_k,
    const int* top_ks, const float top_p, const float* top_ps, curandState_t* curandstate, const int* end_ids,
    const int vocab_size, const bool* skip_decode)
{
    const bool IS_FP16 = std::is_same<T, half>::value;
    const T MAX_T_VAL = (IS_FP16) ? HALF_FLT_MAX : FLT_MAX;

    const int tid = threadIdx.x;
    const int batch_id = blockIdx.x;
    if (skip_decode != nullptr && skip_decode[batch_id])
    {
        return;
    }

    const int k = (top_ks != nullptr) ? top_ks[batch_id] : max_top_k;
    const float prob_threshold = (top_ps != nullptr) ? top_ps[batch_id] : top_p;
    const int size = k * BLOCKS_PER_BEAM_;
    const int stride = max_top_k * BLOCKS_PER_BEAM_;

    typedef cub::BlockReduce<TopK_2<float>, BLOCK_SIZE_> BlockReduce;
    __shared__ typename BlockReduce::TempStorage temp_storage;
    extern __shared__ char array[];
    __shared__ float rand_num;
    __shared__ float s_sum;
    __shared__ float s_max;
    T* s_val = topk_tmp_val_buf + batch_id * stride;
    int* s_id = reinterpret_cast<int*>(array);
    if (tid == 0)
    {
        s_sum = 0.0f;
    }
    TopK_2<float> partial;

    if (finished != nullptr && finished[batch_id] == true)
    {
        ids[batch_id][sequence_lengths[batch_id]] = end_ids[batch_id];
        return;
    }

    float* s_val2 = reinterpret_cast<float*>(s_id + k);
    for (int ite = 0; ite < k; ite++)
    {
        partial.init();
#pragma unroll
        for (int i = tid; i < size; i += BLOCK_SIZE_)
        {
            partial.insert((float) s_val[i], i);
        }

        TopK_2<float> total = BlockReduce(temp_storage).Reduce(partial, reduce_topk_op_2<float>);

        if (tid == 0)
        {
            if (ite == 0)
            {
                s_max = total.u;
            }
            s_id[ite] = total.p;
            s_val[total.p] = -MAX_T_VAL;

            // when cum_log_probs are computed, topk_tmp_val_buf (logits_buf_) are
            // already pre-processed by softmax_kernel
            if (cum_log_probs == nullptr && output_log_probs == nullptr)
            {
                total.u = __expf(total.u - s_max);
            }
            s_val2[ite] = total.u;
            s_sum += total.u;
        }
        __syncthreads();
    }

    if (tid == 0)
    {
        rand_num = (float) curand_uniform(curandstate + blockIdx.x) * prob_threshold * s_sum;
        for (int i = 0; i < k; i++)
        {
            float exp_logit = s_val2[i];
            rand_num = rand_num - exp_logit;
            if (rand_num <= 0.0f || i == k - 1)
            {
                ids[batch_id][sequence_lengths[batch_id]] = topk_tmp_id_buf[batch_id * stride + s_id[i]] % vocab_size;
                if (cum_log_probs != nullptr || output_log_probs != nullptr)
                {
                    float log_prob = logf(exp_logit);
                    if (cum_log_probs != nullptr)
                    {
                        cum_log_probs[batch_id] += log_prob;
                    }
                    if (output_log_probs != nullptr)
                    {
                        // 'output_log_probs' is the probability induced by the top-k
                        // sampling. We normalize the probability 'exp_logit' of the
                        // selected token by the probability 's_sum' of a set of top-k
                        // tokens, meaning the log_prob is the probability of the selected
                        // token, conditioned on the event that it is selected, i.e.,
                        //   log_prob = log P(i | i is in top-k) = log(exp_logit / s_sum).
                        output_log_probs[batch_id] = log_prob - logf(s_sum);
                    }
                }
                break;
            }
        }
        if (sequence_lengths != nullptr && finished != nullptr)
        {
            int seqlen = sequence_lengths[batch_id];
            finished[batch_id] = ids[batch_id][seqlen] == end_ids[batch_id];
            if (!finished[batch_id])
            {
                sequence_lengths[batch_id] = seqlen + 1;
            }
        }
    }
}

#define CASE_K(K_MAX, BLOCK_SIZE_1_, BLOCK_SIZE_2_, BLOCKS_PER_BEAM_)                                                  \
    topk_stage1<T, BLOCK_SIZE_1_, BLOCKS_PER_BEAM_>                                                                    \
        <<<batch_size * BLOCKS_PER_BEAM_, BLOCK_SIZE_1_, 0, stream>>>(log_probs, temp_log_probs, topk_tmp_id_buf,      \
            topk_tmp_val_buf, finished, max_top_k, top_ks, vocab_size, end_ids, skip_decode);                          \
    topk_stage2_sampling<T, BLOCK_SIZE_2_, BLOCKS_PER_BEAM_>                                                           \
        <<<batch_size, BLOCK_SIZE_2_, K_MAX * sizeof(int) + K_MAX * sizeof(float), stream>>>(topk_tmp_id_buf,          \
            topk_tmp_val_buf, ids, sequence_lengths, finished, cum_log_probs, output_log_probs, max_top_k, top_ks,     \
            top_p, top_ps, curandstate, end_ids, vocab_size, skip_decode);                                             \
    break;

template <typename T>
void invokeBatchTopKSampling(void* workspace, size_t& workspace_size, const T* log_probs, int** ids,
    int* sequence_lengths, bool* finished, float* cum_log_probs, float* output_log_probs, curandState_t* curandstate,
    const int max_top_k, const int* top_ks, const float top_p, const float* top_ps, const int vocab_size_padded,
    const int* end_ids, cudaStream_t stream, const int batch_size, const bool* skip_decode)
{
    TLLM_LOG_DEBUG("%s start", __PRETTY_FUNCTION__);

    // Not allow an ambiguous inputs top_p and top_ps.
    assert(top_p == 1.0f || top_ps == nullptr);
    const int vocab_size = vocab_size_padded;
    const int max_block_per_beam = 8;
    int temp_log_probs_buf_size = batch_size * vocab_size;                   // type float
    int topk_tmp_ids_buf_size = batch_size * max_top_k * max_block_per_beam; // type int
    int topk_tmp_val_buf_size = batch_size * max_top_k * max_block_per_beam; // type float

    // prevent memory misaligned address
    temp_log_probs_buf_size = (int) (ceil(temp_log_probs_buf_size / 4.)) * 4;
    topk_tmp_ids_buf_size = (int) (ceil(topk_tmp_ids_buf_size / 4.)) * 4;
    topk_tmp_val_buf_size = (int) (ceil(topk_tmp_val_buf_size / 4.)) * 4;

    if (workspace == nullptr)
    {
        workspace_size = sizeof(T) * temp_log_probs_buf_size + sizeof(int) * topk_tmp_ids_buf_size
            + sizeof(T) * topk_tmp_val_buf_size;
        return;
    }

    T* temp_log_probs = (T*) workspace;
    int* topk_tmp_id_buf = (int*) (temp_log_probs + temp_log_probs_buf_size);
    T* topk_tmp_val_buf = (T*) (topk_tmp_id_buf + topk_tmp_ids_buf_size);

    // TODO (bhsueh) need to support case top_k = [2, 17] (use different cases of max_top_k)
    int log_max_top_k(0);
    int recursor(max_top_k - 1);
    while (recursor >>= 1)
        ++log_max_top_k;
    switch (log_max_top_k)
    {
    case 0:
    case 1:
    case 2:
    case 3: // 0 < max_top_k <= 16
        CASE_K(16, 128, 128, 8);
    case 4: // 16 < max_top_k <= 32
        CASE_K(32, 256, 128, 8);
    case 5: // 32 < max_top_k <= 64
        CASE_K(64, 256, 256, 8);
    case 6:
    case 7:
    case 8:
    case 9: // 64 < max_top_k <= 1024
        CASE_K(1024, 256, 256, 8);
    default: throw std::domain_error(fmtstr("top-k kernel supports 1<=k<=1024 but got k=%d", max_top_k));
    }
}

#undef CASE_K

template void invokeBatchTopKSampling(void* workspace, size_t& workspace_size, const float* log_probs, int** ids,
    int* sequence_lengths, bool* finished_buf, float* cum_log_probs, float* output_log_probs,
    curandState_t* curandstate, const int max_top_k, const int* top_ks, const float top_p, const float* top_ps,
    const int vocab_size_padded, const int* end_ids, cudaStream_t stream, const int batch_size,
    const bool* skip_decode);

template void invokeBatchTopKSampling(void* workspace, size_t& workspace_size, const half* log_probs, int** ids,
    int* sequence_lengths, bool* finished_buf, float* cum_log_probs, float* output_log_probs,
    curandState_t* curandstate, const int max_top_k, const int* top_ks, const float top_p, const float* top_ps,
    const int vocab_size_padded, const int* end_ids, cudaStream_t stream, const int batch_size,
    const bool* skip_decode);

template <typename T>
void invokeTopKSampling(void* workspace, size_t& workspace_size, const T* log_probs, int** ids, int* sequence_lengths,
    bool* finished_buf, float* cum_log_probs, float* output_log_probs, curandState_t* curandstate, const int top_k,
    const float top_p, const int vocab_size_padded, const int* end_ids, cudaStream_t stream, const int batch_size,
    const bool* skip_decode)
{
    invokeBatchTopKSampling(workspace, workspace_size, log_probs, ids, sequence_lengths, finished_buf, cum_log_probs,
        output_log_probs, curandstate, top_k, nullptr, top_p, nullptr, vocab_size_padded, end_ids, stream, batch_size,
        skip_decode);
}

template void invokeTopKSampling(void* workspace, size_t& workspace_size, const float* log_probs, int** ids,
    int* sequence_lengths, bool* finished_buf, float* cum_log_probs, float* output_log_probs,
    curandState_t* curandstate, const int top_k, const float top_p, const int vocab_size_padded, const int* end_ids,
    cudaStream_t stream, const int batch_size, const bool* skip_decode);

template void invokeTopKSampling(void* workspace, size_t& workspace_size, const half* log_probs, int** ids,
    int* sequence_lengths, bool* finished_buf, float* cum_log_probs, float* output_log_probs,
    curandState_t* curandstate, const int top_k, const float top_p, const int vocab_size_padded, const int* end_ids,
    cudaStream_t stream, const int batch_size, const bool* skip_decode);

template <typename T>
void invokeTopKTopPSampling(void* workspace, size_t& workspace_size, int** output_ids, const T* logits,
    int* sequence_lengths, bool* finished_buf, float* cum_log_probs, float* output_log_probs,
    curandState_t* curandstate, const int batch_size, const int top_k, const float top_p, const int vocab_size_padded,
    const int* end_ids, cudaStream_t stream)
{
    // invokeTopKTopPSampling will be deprecated. Please use invokeTopKSampling
    // instead.
    invokeTopKSampling(workspace, workspace_size, logits, output_ids, sequence_lengths, finished_buf, cum_log_probs,
        output_log_probs, curandstate, top_k, top_p, vocab_size_padded, end_ids, stream, batch_size, nullptr);
}

template void invokeTopKTopPSampling(void* workspace, size_t& workspace_size, int** output_ids, const float* logits,
    int* sequence_lengths, bool* finished_buf, float* cum_log_probs, float* output_log_probs,
    curandState_t* curandstate, const int batch_size, const int top_k, const float top_p, const int vocab_size_padded,
    const int* end_ids, cudaStream_t stream);

template void invokeTopKTopPSampling(void* workspace, size_t& workspace_size, int** output_ids, const half* logits,
    int* sequence_lengths, bool* finished_buf, float* cum_log_probs, float* output_log_probs,
    curandState_t* curandstate, const int batch_size, const int top_k, const float top_p, const int vocab_size_padded,
    const int* end_ids, cudaStream_t stream);

} // namespace kernels
} // namespace tensorrt_llm