/* Inference for Llama-2 Transformer model in pure C
 * With added CUDA support initially drawing from
 * https://github.com/ankan-ban/llama2.cu/blob/master/llama2.cu
 * and structured in a way that hopefully makes keeping it
 * up-to-date straightforward.
 */

#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <time.h>
#include <math.h>
#include <string.h>
#include <fcntl.h>
#include <assert.h>
#include <future>
#if defined _WIN32
    #include "win.h"
#else
    #include <unistd.h>
    #include <sys/mman.h>
#endif
#include "llama2.h"

#ifdef USE_CUDA
#include <cuda_runtime.h>
#include <cub/cub.cuh>
#include <cublas_v2.h>

// Each CUDA function call should be checked for errors.
#define CUCHK(err) cuda_check((err), __FILE__, __LINE__)
inline void cuda_check(cudaError_t error_code, const char *file, int line)
{
    if (error_code != cudaSuccess)
    {
        fprintf(stderr, "CUDA Error %d: %s. In file '%s' on line %d\n", error_code, cudaGetErrorString(error_code), file, line);
        fflush(stderr);
        exit(error_code);
    }
}

// cublasHandle_t g_cublas_handle = nullptr;

// void create_cublas_handle() {
//     cublasStatus_t stat = cublasCreate(&g_cublas_handle);  // FIXME cublasDestroy
//     if (stat != CUBLAS_STATUS_SUCCESS) {
//         printf ("CUBLAS initialization failed\n");
//         exit(EXIT_FAILURE);
//     }
// }
// void destroy_cublas_handle() {
//     cublasStatus_t stat = cublasDestroy(g_cublas_handle);
//     if (stat != CUBLAS_STATUS_SUCCESS) {
//         printf ("CUBLAS initialization failed\n");
//         exit(EXIT_FAILURE);
//     }
// }
#endif

// ----------------------------------------------------------------------------
// Transformer model

typedef struct {
    int dim; // transformer dimension
    int hidden_dim; // for ffn layers
    int n_layers; // number of layers
    int n_heads; // number of query heads
    int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
    int vocab_size; // vocabulary size, usually 256 (byte-level)
    int seq_len; // max sequence length
} Config;

// CUDA NOTE: The TransformerWeights structure will be stored on the host, 
// but all of the pointers in the structure will point to data on the GPU.
// The checkpoint file is mmap-ed to the host and the weights portion 
// is allocated on and copied to the GPU.  Then, memory_map_weights() updates  
// these structure pointers to point to the proper location.  Happily, this
// function is the same for both C and CUDA.
typedef struct {
    // token embedding table
    float* token_embedding_table;    // (vocab_size, dim)
    // weights for rmsnorms
    float* rms_att_weight; // (layer, dim) rmsnorm weights
    float* rms_ffn_weight; // (layer, dim)
    // weights for matmuls. note dim == n_heads * head_size
    float* wq; // (layer, dim, n_heads * head_size)
    float* wk; // (layer, dim, n_kv_heads * head_size)
    float* wv; // (layer, dim, n_kv_heads * head_size)
    float* wo; // (layer, n_heads * head_size, dim)
    // weights for ffn
    float* w1; // (layer, hidden_dim, dim)
    float* w2; // (layer, dim, hidden_dim)
    float* w3; // (layer, hidden_dim, dim)
    // final rmsnorm
    float* rms_final_weight; // (dim,)
    // (optional) classifier weights for the logits, on the last layer
    float* wcls;
} TransformerWeights;

// CUDA NOTE: The RunState structure will be stored on the host, but all of the
// pointers in the structure will point to data on the GPU, created via
// cudaMalloc.  The exception is logits which is the final result of the
// transformer & is copied from the GPU as the last step in the transformer
// and is used by the host.
typedef struct {
    // current wave of activations
    float *x; // activation at current time stamp (dim,)
    float *xb; // same, but inside a residual branch (dim,)
    float *xb2; // an additional buffer just for convenience (dim,)
    float *hb; // buffer for hidden dimension in the ffn (hidden_dim,)
    float *hb2; // buffer for hidden dimension in the ffn (hidden_dim,)
    float *q; // query (dim,)
    float *k; // key (dim,)
    float *v; // value (dim,)
    float *att; // buffer for scores/attention values (n_heads, seq_len)
#ifdef USE_CUDA
    float *logits_gpu; // output logits in GPU
#endif
    float *logits; // output logits in CPU
    // kv cache
    float* key_cache;   // (layer, seq_len, dim)
    float* value_cache; // (layer, seq_len, dim)
} RunState;

typedef struct {
    Config config; // the hyperparameters of the architecture (the blueprint)
    TransformerWeights weights; // the weights of the model
    RunState state; // buffers for the "wave" of activations in the forward pass
    // some more state needed to properly clean up the memory mapping (sigh)
    int fd; // file descriptor for memory mapping
    float* data; // memory mapped data pointer
    ssize_t file_size; // size of the checkpoint file in bytes
} Transformer;

#ifdef USE_CUDA
void malloc_run_state(RunState* s, Config* p) {
    // we calloc instead of malloc to keep valgrind happy
    int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
    CUCHK(cudaMalloc((void**)&s->x, p->dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->xb, p->dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->xb2, p->dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->hb, p->hidden_dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->hb2, p->hidden_dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->q, p->dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->key_cache, p->n_layers * p->seq_len * kv_dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->value_cache, p->n_layers * p->seq_len * kv_dim * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->att, p->n_heads * p->seq_len * sizeof(float)));
    CUCHK(cudaMalloc((void**)&s->logits_gpu, p->vocab_size * sizeof(float)));
    s->logits = (float *)calloc(p->vocab_size, sizeof(float));
    // ensure all mallocs went fine
    if (!s->x || !s->xb || !s->xb2 || !s->hb || !s->hb2 || !s->q
     || !s->key_cache || !s->value_cache || !s->att || !s->logits_gpu || !s->logits) {
        fprintf(stderr, "malloc failed!\n");
        exit(EXIT_FAILURE);
    }
}
#else
void malloc_run_state(RunState* s, Config* p) {
    // we calloc instead of malloc to keep valgrind happy
    int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
    s->x = (float *)calloc(p->dim, sizeof(float));
    s->xb = (float *)calloc(p->dim, sizeof(float));
    s->xb2 = (float *)calloc(p->dim, sizeof(float));
    s->hb = (float *)calloc(p->hidden_dim, sizeof(float));
    s->hb2 = (float *)calloc(p->hidden_dim, sizeof(float));
    s->q = (float *)calloc(p->dim, sizeof(float));
    s->key_cache = (float *)calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
    s->value_cache = (float *)calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
    s->att = (float *)calloc(p->n_heads * p->seq_len, sizeof(float));
    s->logits = (float *)calloc(p->vocab_size, sizeof(float));
    // ensure all mallocs went fine
    if (!s->x || !s->xb || !s->xb2 || !s->hb || !s->hb2 || !s->q
     || !s->key_cache || !s->value_cache || !s->att || !s->logits) {
        fprintf(stderr, "malloc failed!\n");
        exit(EXIT_FAILURE);
    }
}
#endif

#ifdef USE_CUDA
void free_run_state(RunState* s) {
    CUCHK(cudaFree(s->x));
    CUCHK(cudaFree(s->xb));
    CUCHK(cudaFree(s->xb2));
    CUCHK(cudaFree(s->hb));
    CUCHK(cudaFree(s->hb2));
    CUCHK(cudaFree(s->q));
    CUCHK(cudaFree(s->att));
    CUCHK(cudaFree(s->logits_gpu));
    free(s->logits);
    CUCHK(cudaFree(s->key_cache));
    CUCHK(cudaFree(s->value_cache));
}
#else
void free_run_state(RunState* s) {
    free(s->x);
    free(s->xb);
    free(s->xb2);
    free(s->hb);
    free(s->hb2);
    free(s->q);
    free(s->att);
    free(s->logits);
    free(s->key_cache);
    free(s->value_cache);
}
#endif

void memory_map_weights(TransformerWeights *w, Config* p, float* ptr, int shared_weights) {
    int head_size = p->dim / p->n_heads;
    // make sure the multiplications below are done in 64bit to fit the parameter counts of 13B+ models
    unsigned long long n_layers = p->n_layers;
    w->token_embedding_table = ptr;
    ptr += p->vocab_size * p->dim;
    w->rms_att_weight = ptr;
    ptr += n_layers * p->dim;
    w->wq = ptr;
    ptr += n_layers * p->dim * (p->n_heads * head_size);
    w->wk = ptr;
    ptr += n_layers * p->dim * (p->n_kv_heads * head_size);
    w->wv = ptr;
    ptr += n_layers * p->dim * (p->n_kv_heads * head_size);
    w->wo = ptr;
    ptr += n_layers * (p->n_heads * head_size) * p->dim;
    w->rms_ffn_weight = ptr;
    ptr += n_layers * p->dim;
    w->w1 = ptr;
    ptr += n_layers * p->dim * p->hidden_dim;
    w->w2 = ptr;
    ptr += n_layers * p->hidden_dim * p->dim;
    w->w3 = ptr;
    ptr += n_layers * p->dim * p->hidden_dim;
    w->rms_final_weight = ptr;
    ptr += p->dim;
    ptr += p->seq_len * head_size / 2; // skip what used to be freq_cis_real (for RoPE)
    ptr += p->seq_len * head_size / 2; // skip what used to be freq_cis_imag (for RoPE)
    w->wcls = shared_weights ? w->token_embedding_table : ptr;
}

void read_checkpoint(char* checkpoint, Config* config, TransformerWeights* weights,
                     int* fd, float** data, ssize_t* file_size) {
    FILE *file = fopen(checkpoint, "rb");
    if (!file) { fprintf(stderr, "Couldn't open file %s\n", checkpoint); exit(EXIT_FAILURE); }
    // read in the config header
    if (fread(config, sizeof(Config), 1, file) != 1) { exit(EXIT_FAILURE); }
    // negative vocab size is hacky way of signaling unshared weights. bit yikes.
    int shared_weights = config->vocab_size > 0 ? 1 : 0;
    config->vocab_size = abs(config->vocab_size);
    // figure out the file size
    fseek(file, 0, SEEK_END); // move file pointer to end of file
    *file_size = ftell(file); // get the file size, in bytes
    fclose(file);
    // memory map the Transformer weights into the data pointer
    *fd = open(checkpoint, O_RDONLY); // open in read only mode
    if (*fd == -1) { fprintf(stderr, "open failed!\n"); exit(EXIT_FAILURE); }
    *data = (float *)mmap(NULL, *file_size, PROT_READ, MAP_PRIVATE, *fd, 0);
    if (*data == MAP_FAILED) { fprintf(stderr, "mmap failed!\n"); exit(EXIT_FAILURE); }
#ifdef USE_CUDA
    // allocate & copy mmap data to the gpu first
    // TODO: allocate & copy just a portion to the GPU if the weights are too big
    // to fit in the GPU, then copy the data only as needed while running.
    float* weights_ptr;
    size_t weights_size = *file_size - sizeof(Config);
    CUCHK(cudaMalloc((void**)&weights_ptr, weights_size));
    CUCHK(cudaMemcpy(weights_ptr, *data + sizeof(Config)/sizeof(float), weights_size, cudaMemcpyHostToDevice));
#else
    float* weights_ptr = *data + sizeof(Config)/sizeof(float);
#endif
    memory_map_weights(weights, config, weights_ptr, shared_weights);
}

void build_transformer(Transformer *t, char* checkpoint_path) {
    // read in the Config and the Weights from the checkpoint
    read_checkpoint(checkpoint_path, &t->config, &t->weights, &t->fd, &t->data, &t->file_size);
    // allocate the RunState buffers
    malloc_run_state(&t->state, &t->config);
}

void free_transformer(Transformer* t) {
    // close the memory mapping
    if (t->data != MAP_FAILED) { munmap(t->data, t->file_size); }
    if (t->fd != -1) { close(t->fd); }
#ifdef USE_CUDA
    // we cudaMalloc a region of memory, then hand the address to
    // the token_embedding_table field.  Free it here.
    CUCHK(cudaFree(t->weights.token_embedding_table));
#endif
    // free the RunState buffers
    free_run_state(&t->state);
}

// ----------------------------------------------------------------------------
// neural net blocks; the dynamics of the Transformer

#ifdef USE_CUDA
// Utility routine to divide a into ceiling of b parts
int divUp(int a, int b) {
    return (a - 1) / b + 1;
}

const int num_threads_lrg = 1024;
const int num_threads_med = 256;

__global__ void rmsnorm_kernel(float* o, float* x, float* weight, int size, int elementsPerThread) {
    // parallel reduction of sum of squares via CUB
    float ss = 0.0f;
    for (int i = 0; i < elementsPerThread; i++) {
        int j = threadIdx.x + i * num_threads_lrg;
        if (j < size)
            ss += x[j] * x[j];
    }
    using BlockReduce = cub::BlockReduce<float, num_threads_lrg>;
    __shared__ typename BlockReduce::TempStorage temp;
    ss = BlockReduce(temp).Sum(ss);

    // serialization point to calculate normalization factor 
    __shared__ float shared_ss;
    if (threadIdx.x == 0) {
        ss /= size;
        ss += 1e-5f;
        ss = 1.0f / sqrtf(ss);
        shared_ss = ss;
    }
    __syncthreads();
    ss = shared_ss;

    // normalize and scale
    for (int i = 0; i < elementsPerThread; i++) {
        int j = threadIdx.x + i * num_threads_lrg;
        if (j < size) {
            o[j] = weight[j] * (ss * x[j]);
        }
    }
}
void rmsnorm(float* o, float* x, float* weight, int size) {
    int elementsPerThread = divUp(size, num_threads_lrg);
    rmsnorm_kernel <<<1, num_threads_lrg >>> (o, x, weight, size, elementsPerThread);
}
#else
void rmsnorm(float* o, float* x, float* weight, int size) {
    // calculate sum of squares
    float ss = 0.0f;
    for (int j = 0; j < size; j++) {
        ss += x[j] * x[j];
    }
    ss /= size;
    ss += 1e-5f;
    ss = 1.0f / sqrtf(ss);
    // normalize and scale
    for (int j = 0; j < size; j++) {
        o[j] = weight[j] * (ss * x[j]);
    }
}
#endif

#ifdef USE_CUDA
__device__ void softmax_gpu(float* __restrict__ x, int size) {
    int tid = threadIdx.x;
    int step = blockDim.x;

    // find max value (for numerical stability)
    float max_val = tid < size ? x[tid] : 0;
    for (int i = tid + step; i < size; i += step) {
        if (x[i] > max_val) {
            max_val = x[i];
        }
    }
    using BlockReduce = cub::BlockReduce<float, num_threads_lrg>;
    __shared__ typename BlockReduce::TempStorage temp;
    __shared__ float shared_val;
    max_val = BlockReduce(temp).Reduce(max_val, cub::Max());
    if (threadIdx.x == 0) {
        shared_val = max_val;
    }
    __syncthreads();
    max_val = shared_val;

    // exp and sum
    float sum = 0.0f;
    for (int i = tid; i < size; i += step) {
        x[i] = expf(x[i] - max_val);
        sum += x[i];
    }
    sum = BlockReduce(temp).Sum(sum);
    if (threadIdx.x == 0) {
        shared_val = sum;
    }
    __syncthreads();
    sum = shared_val;

    // normalize
    for (int i = tid; i < size; i += step) {
        x[i] /= sum;
    }
}
#endif
void softmax(float* x, int size) {
    // find max value (for numerical stability)
    float max_val = x[0];
    for (int i = 1; i < size; i++) {
        if (x[i] > max_val) {
            max_val = x[i];
        }
    }
    // exp and sum
    float sum = 0.0f;
    for (int i = 0; i < size; i++) {
        x[i] = expf(x[i] - max_val);
        sum += x[i];
    }
    // normalize
    for (int i = 0; i < size; i++) {
        x[i] /= sum;
    }
}

#ifdef USE_CUDA
// Use cuBLAS for matmul to leverage this included, high-performance library.
void matmul(cublasHandle_t handle, float* xout, float* x, float* w, int n, int d) {
    // W (d,n) @ x (n,) -> xout (d,)
    // W is stored in this order: (n=0,d=0), (n=1,d=0), (n=2,d=0), ... 
    // so W is n x d in cublas terms & we'll need to transpose.
    // Sgemv does y = alpha * op(A) * x + beta * y (modifying y)
    //   where op can transpose the matrix A
    // Translating to our local vars, that is
    // xout = 1.0*op(w)*x + 0.0*xout
    float alpha = 1.0f;
    float beta = 0.0f; // when this is 0, xout will not be used for input
    cublasSgemv(handle, CUBLAS_OP_T, n, d, &alpha, w, n, x, 1, &beta, xout, 1);
}
#else
void matmul(float* xout, float* x, float* w, int n, int d) {
    // W (d,n) @ x (n,) -> xout (d,)
    // by far the most amount of time is spent inside this little function
    int i;
    #pragma omp parallel for private(i)
    for (i = 0; i < d; i++) {
        float val = 0.0f;
        for (int j = 0; j < n; j++) {
            val += w[i * n + j] * x[j];
        }
        xout[i] = val;
    }
}
#endif

// Additional neural net blocks (brought out from transformer function)
#ifdef USE_CUDA
__global__ void RoPe_rotation_kernel(int pos, float *sq, float *sk, int kv_dim, int head_size) {
    int i = threadIdx.x * 2 + blockIdx.x * head_size;
    int head_dim = i % head_size;
    float freq = 1.0f / powf(10000.0f, head_dim / (float)head_size);
    float val = pos * freq;
    float fcr = cosf(val);
    float fci = sinf(val);
    int rotn = i < kv_dim ? 2 : 1; // how many vectors? 2 = q & k, 1 = q only
    for (int v = 0; v < rotn; v++) {
        float* vec = v == 0 ? sq : sk; // the vector to rotate (query or key)
        float v0 = vec[i];
        float v1 = vec[i+1];
        vec[i]   = v0 * fcr - v1 * fci;
        vec[i+1] = v0 * fci + v1 * fcr;
    }
}
void RoPe_rotation(int pos, RunState* s, int dim, int kv_dim, int head_size) {
    RoPe_rotation_kernel <<<dim/head_size, head_size/2 >>> (pos, s->q, s->k, kv_dim, head_size);
}
#else
void RoPe_rotation(int pos, RunState* s, int dim, int kv_dim, int head_size) { //s->q, s->k, freq_cis_real_row, freq_cis_imag_row, p->n_heads, head_size) {
    for (int i = 0; i < dim; i+=2) {
        int head_dim = i % head_size;
        float freq = 1.0f / powf(10000.0f, head_dim / (float)head_size);
        float val = pos * freq;
        float fcr = cosf(val);
        float fci = sinf(val);
        int rotn = i < kv_dim ? 2 : 1; // how many vectors? 2 = q & k, 1 = q only
        for (int v = 0; v < rotn; v++) {
            float* vec = v == 0 ? s->q : s->k; // the vector to rotate (query or key)
            float v0 = vec[i];
            float v1 = vec[i+1];
            vec[i]   = v0 * fcr - v1 * fci;
            vec[i+1] = v0 * fci + v1 * fcr;
        }
    }
}
#endif

#ifdef USE_CUDA
// TODO refactor vs C code
__global__ void multi_head_attention_kernel(int pos, int seq_len, float *sq, float *satt, float *sxb, float *key_cache, float *value_cache, int kv_dim, int kv_mul, int head_size, int loff) {
    int h = blockIdx.x;
    // get the query vector for this head
    float* q = sq + h * head_size;
    // attention scores for this head
    float* att = satt + h * seq_len;
    // iterate over all timesteps, including the current one 
    // In CUDA, each thread does a small portion of the calc
    for (int t = threadIdx.x; t <= pos; t += blockDim.x) {
        // get the key vector for this head and at this timestep
        float* k = key_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
        // calculate the attention score as the dot product of q and k
        float score = 0.0f;
        for (int i = 0; i < head_size; i++) {
            score += q[i] * k[i];
        }
        score /= sqrtf(head_size);
        // save the score to the attention buffer
        att[t] = score;
    }
    // above was this threads portion of the iteration.  wait for all threads to finish
    __syncthreads();

    // softmax the scores to get attention weights, from 0..pos inclusively
    softmax_gpu(att, pos + 1);
    __syncthreads();

    // weighted sum of the values, store back into xb
    // NOTE: by swapping the order of the for loops (vs. C) a simpler
    // version of the code accomplishes the same task and fits more
    // naturally with the CUDA way of subdividing the problem.
    float* xb = sxb + h * head_size;
    for (int i = threadIdx.x; i < head_size; i += blockDim.x) {
        float val = 0.0f;
        for (int t = 0; t <= pos; t++) {
            // get the value vector for this head and at this timestep
            float* v = value_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
            // get the attention weight for this timestep
            float a = att[t];
            val += a * v[i];
        }
        xb[i] = val;
    }
}
void multi_head_attention(int pos, Config* p, RunState* s, int kv_dim, int kv_mul, int head_size, int loff) {
    multi_head_attention_kernel <<<p->n_heads, num_threads_lrg>>> (pos, p->seq_len, s->q, s->att, s->xb, s->key_cache, s->value_cache, kv_dim, kv_mul, head_size, loff);
}
#else
void multi_head_attention(int pos, Config* p, RunState* s, int kv_dim, int kv_mul, int head_size, int loff) {
    int h;
    #pragma omp parallel for private(h)
    for (h = 0; h < p->n_heads; h++) {
        // get the query vector for this head
        float* q = s->q + h * head_size;
        // attention scores for this head
        float* att = s->att + h * p->seq_len;
        // iterate over all timesteps, including the current one
        for (int t = 0; t <= pos; t++) {
            // get the key vector for this head and at this timestep
            float* k = s->key_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
            // calculate the attention score as the dot product of q and k
            float score = 0.0f;
            for (int i = 0; i < head_size; i++) {
                score += q[i] * k[i];
            }
            score /= sqrtf(head_size);
            // save the score to the attention buffer
            att[t] = score;
        }

        // softmax the scores to get attention weights, from 0..pos inclusively
        softmax(att, pos + 1);

        // weighted sum of the values, store back into xb
        float* xb = s->xb + h * head_size;
        memset(xb, 0, head_size * sizeof(float));
        for (int t = 0; t <= pos; t++) {
            // get the value vector for this head and at this timestep
            float* v = s->value_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
            // get the attention weight for this timestep
            float a = att[t];
            // accumulate the weighted value into xb
            for (int i = 0; i < head_size; i++) {
                xb[i] += a * v[i];
            }
        }
    }
}
#endif

#ifdef USE_CUDA
__global__ void f_silu_elementwise_mul_w3_kernel(float *shb, float *shb2, int hidden_dim) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < hidden_dim) {
        float val = shb[i];
        // silu(x)=x*σ(x), where σ(x) is the logistic sigmoid
        val *= (1.0f / (1.0f + expf(-val)));
        // elementwise multiply with w3(x)
        val *= shb2[i];
        shb[i] = val;
    }
}
void f_silu_elementwise_mul_w3(RunState *s, int hidden_dim) {
    f_silu_elementwise_mul_w3_kernel<<<divUp(hidden_dim, num_threads_med), num_threads_med>>>(s->hb, s->hb2, hidden_dim);
}
#else
void f_silu_elementwise_mul_w3(RunState *s, int hidden_dim) {
    for (int i = 0; i < hidden_dim; i++) {
        float val = s->hb[i];
        // silu(x)=x*σ(x), where σ(x) is the logistic sigmoid
        val *= (1.0f / (1.0f + expf(-val)));
        // elementwise multiply with w3(x)
        val *= s->hb2[i];
        s->hb[i] = val;
    }
}
#endif

#ifdef USE_CUDA
__global__ void accum_kernel(float* a, float* b, int size) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < size) {
        a[i] += b[i];
    }
}
void accum(float *a, float *b, int size) {
    accum_kernel<<<divUp(size, num_threads_med), num_threads_med>>>(a,b,size);
}
#else
void accum(float *a, float *b, int size) {
    for (int i = 0; i < size; i++) {
        a[i] += b[i];
    }
}
#endif

#ifdef USE_CUDA
float* forward(Transformer* transformer, int token, int pos, cublasHandle_t handle) {
#else
float* forward(Transformer* transformer, int token, int pos) {
#endif
    // a few convenience variables
    Config* p = &transformer->config;
    TransformerWeights* w = &transformer->weights;
    RunState* s = &transformer->state;
    float *x = s->x;
    int dim = p->dim;
    int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
    int kv_mul = p->n_heads / p->n_kv_heads; // integer multiplier of the kv sharing in multiquery
    int hidden_dim =  p->hidden_dim;
    int head_size = dim / p->n_heads;

    // copy the token embedding into x
    float* content_row = w->token_embedding_table + token * dim;
#ifdef USE_CUDA
    CUCHK(cudaMemcpy(x, content_row, dim*sizeof(*x), cudaMemcpyDeviceToDevice));
#else
    memcpy(x, content_row, dim*sizeof(*x));
#endif

    // forward all the layers
    for(unsigned long long l = 0; l < p->n_layers; l++) {

        // attention rmsnorm
        rmsnorm(s->xb, x, w->rms_att_weight + l*dim, dim);

        // key and value point to the kv cache
        int loff = l * p->seq_len * kv_dim; // kv cache layer offset for convenience
        s->k = s->key_cache + loff + pos * kv_dim;
        s->v = s->value_cache + loff + pos * kv_dim;

        // qkv matmuls for this position
#ifdef USE_CUDA
        matmul(handle, s->q, s->xb, w->wq + l*dim*dim, dim, dim);
        matmul(handle, s->k, s->xb, w->wk + l*dim*kv_dim, dim, kv_dim);
        matmul(handle, s->v, s->xb, w->wv + l*dim*kv_dim, dim, kv_dim);
#else
        matmul(s->q, s->xb, w->wq + l*dim*dim, dim, dim);
        matmul(s->k, s->xb, w->wk + l*dim*kv_dim, dim, kv_dim);
        matmul(s->v, s->xb, w->wv + l*dim*kv_dim, dim, kv_dim);
#endif
        // RoPE relative positional encoding: complex-valued rotate q and k in each head
        RoPe_rotation(pos, s, dim, kv_dim, head_size);

        // multihead attention. iterate over all heads
        multi_head_attention(pos, p, s, kv_dim, kv_mul, head_size, loff);

        // final matmul to get the output of the attention
#ifdef USE_CUDA
        matmul(handle, s->xb2, s->xb, w->wo + l*dim*dim, dim, dim);
#else
        matmul(s->xb2, s->xb, w->wo + l*dim*dim, dim, dim);
#endif

        // residual connection back into x
        accum(x, s->xb2, dim);

        // ffn rmsnorm
        rmsnorm(s->xb, x, w->rms_ffn_weight + l*dim, dim);

        // Now for FFN in PyTorch we have: self.w2(F.silu(self.w1(x)) * self.w3(x))
        // first calculate self.w1(x) and self.w3(x)
#ifdef USE_CUDA
        matmul(handle, s->hb, s->xb, w->w1 + l*dim*hidden_dim, dim, hidden_dim);
        matmul(handle, s->hb2, s->xb, w->w3 + l*dim*hidden_dim, dim, hidden_dim);
#else
        matmul(s->hb, s->xb, w->w1 + l*dim*hidden_dim, dim, hidden_dim);
        matmul(s->hb2, s->xb, w->w3 + l*dim*hidden_dim, dim, hidden_dim);
#endif

        // SwiGLU non-linearity
        f_silu_elementwise_mul_w3(s, hidden_dim);

        // final matmul to get the output of the ffn
#ifdef USE_CUDA
        matmul(handle, s->xb, s->hb, w->w2 + l*dim*hidden_dim, hidden_dim, dim);
#else
        matmul(s->xb, s->hb, w->w2 + l*dim*hidden_dim, hidden_dim, dim);
#endif

        // residual connection
        accum(x, s->xb, dim);
    }

    // final rmsnorm
    rmsnorm(x, x, w->rms_final_weight, dim);

    // classifier into logits
#ifdef USE_CUDA
    matmul(handle, s->logits_gpu, x, w->wcls, p->dim, p->vocab_size);
    CUCHK(cudaMemcpy(s->logits, s->logits_gpu, p->vocab_size * sizeof(float), cudaMemcpyDeviceToHost));
#else
    matmul(s->logits, x, w->wcls, p->dim, p->vocab_size);
#endif 
    return s->logits;
}

// ----------------------------------------------------------------------------
// The Byte Pair Encoding (BPE) Tokenizer that translates strings <-> tokens

typedef struct {
    char *str;
    int id;
} TokenIndex;

typedef struct {
    char** vocab;
    float* vocab_scores;
    TokenIndex *sorted_vocab;
    int vocab_size;
    unsigned int max_token_length;
    unsigned char byte_pieces[512]; // stores all single-byte strings
} Tokenizer;

int compare_tokens(const void *a, const void *b) {
    return strcmp(((TokenIndex*)a)->str, ((TokenIndex*)b)->str);
}

void build_tokenizer(Tokenizer* t, char* tokenizer_path, int vocab_size) {
    // i should have written the vocab_size into the tokenizer file... sigh
    t->vocab_size = vocab_size;
    // malloc space to hold the scores and the strings
    t->vocab = (char**)malloc(vocab_size * sizeof(char*));
    t->vocab_scores = (float*)malloc(vocab_size * sizeof(float));
    t->sorted_vocab = NULL; // initialized lazily
    for (int i = 0; i < 256; i++) {
        t->byte_pieces[i * 2] = (unsigned char)i;
        t->byte_pieces[i * 2 + 1] = '\0';
    }
    // read in the file
    FILE *file = fopen(tokenizer_path, "rb");
    if (!file) { fprintf(stderr, "couldn't load %s\n", tokenizer_path); exit(EXIT_FAILURE); }
    if (fread(&t->max_token_length, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
    int len;
    for (int i = 0; i < vocab_size; i++) {
        if (fread(t->vocab_scores + i, sizeof(float), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE);}
        if (fread(&len, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
        t->vocab[i] = (char *)malloc(len + 1);
        if (fread(t->vocab[i], len, 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
        t->vocab[i][len] = '\0'; // add the string terminating token
    }
    fclose(file);
}

void free_tokenizer(Tokenizer* t) {
    for (int i = 0; i < t->vocab_size; i++) { free(t->vocab[i]); }
    free(t->vocab);
    free(t->vocab_scores);
    free(t->sorted_vocab);
}

char* decode(Tokenizer* t, int prev_token, int token) {
    char *piece = t->vocab[token];
    // following BOS (1) token, sentencepiece decoder strips any leading whitespace (see PR #89)
    if (prev_token == 1 && piece[0] == ' ') { piece++; }
    // careful, some tokens designate raw bytes, and look like e.g. '<0x01>'
    // parse this and convert and return the actual byte
    unsigned char byte_val;
    if (sscanf(piece, "<0x%02hhX>", &byte_val) == 1) {
        piece = (char*)t->byte_pieces + byte_val * 2;
    }
    return piece;
}

void safe_printf(char *piece) {
    // piece might be a raw byte token, and we only want to print printable chars or whitespace
    // because some of the other bytes can be various control codes, backspace, etc.
    if (piece == NULL) { return; }
    if (piece[0] == '\0') { return; }
    if (piece[1] == '\0') {
        unsigned char byte_val = piece[0];
        if (!(isprint(byte_val) || isspace(byte_val))) {
            return; // bad byte, don't print it
        }
    }
    printf("%s", piece);
}

int str_lookup(char *str, TokenIndex *sorted_vocab, int vocab_size) {
    // efficiently find the perfect match for str in vocab, return its index or -1 if not found
#if defined USE_CUDA && defined _WIN32
    // CUDA on Windows was not capable of handling the syntax below
    TokenIndex tok;
    tok.str = str;
#else
    TokenIndex tok = { .str = str }; // acts as the key to search for
#endif
    TokenIndex *res = (TokenIndex *)bsearch(&tok, sorted_vocab, vocab_size, sizeof(TokenIndex), compare_tokens);
    return res != NULL ? res->id : -1;
}

void encode(Tokenizer* t, char *text, int8_t bos, int8_t eos, int *tokens, int *n_tokens) {
    // encode the string text (input) into an upper-bound preallocated tokens[] array
    // bos != 0 means prepend the BOS token (=1), eos != 0 means append the EOS token (=2)
    if (text == NULL) { fprintf(stderr, "cannot encode NULL text\n"); exit(EXIT_FAILURE); }

    if (t->sorted_vocab == NULL) {
        // lazily malloc and sort the vocabulary
        t->sorted_vocab = (TokenIndex *)malloc(t->vocab_size * sizeof(TokenIndex));
        for (int i = 0; i < t->vocab_size; i++) {
            t->sorted_vocab[i].str = t->vocab[i];
            t->sorted_vocab[i].id = i;
        }
        qsort(t->sorted_vocab, t->vocab_size, sizeof(TokenIndex), compare_tokens);
    }

    // create a temporary buffer that will store merge candidates of always two consecutive tokens
    // *2 for concat, +1 for null terminator +2 for UTF8 (in case max_token_length is 1)
    char* str_buffer = (char *)malloc((t->max_token_length*2 +1 +2) * sizeof(char));
    size_t str_len = 0;

    // start at 0 tokens
    *n_tokens = 0;

    // add optional BOS (=1) token, if desired
    if (bos) tokens[(*n_tokens)++] = 1;

    // add_dummy_prefix is true by default
    // so prepend a dummy prefix token to the input string, but only if text != ""
    // TODO: pretty sure this isn't correct in the general case but I don't have the
    // energy to read more of the sentencepiece code to figure out what it's doing
    if (text[0] != '\0') {
        int dummy_prefix = str_lookup((char *)" ", t->sorted_vocab, t->vocab_size);
        tokens[(*n_tokens)++] = dummy_prefix;
    }

    // Okay UTF-8 time. This will get messy. Here is the reference from Wikipedia:
    // Code point ↔ UTF-8 conversion
    // First code point	Last code point	Byte 1	Byte 2	Byte 3	Byte 4
    // U+0000	U+007F	    0xxxxxxx
    // U+0080	U+07FF	    110xxxxx	10xxxxxx
    // U+0800	U+FFFF	    1110xxxx	10xxxxxx	10xxxxxx
    // U+10000	U+10FFFF    11110xxx	10xxxxxx	10xxxxxx	10xxxxxx

    // process the raw (UTF-8) byte sequence of the input string
    for (char *c = text; *c != '\0'; c++) {

        // reset buffer if the current byte is ASCII or a leading byte
        // 0xC0 is 11000000, so (*c & 0xC0) keeps the first 2 bits and zeros the rest
        // 0x80 is 10000000
        // in UTF-8, all continuation bytes start with "10" in first two bits
        // so in English this is: "if this byte is not a continuation byte"
        if ((*c & 0xC0) != 0x80) {
            // this byte must be either a leading byte (11...) or an ASCII char (0x...)
            // => reset our location, as we're starting a new UTF-8 codepoint
            str_len = 0;
        }

        // append the current byte to the buffer
        str_buffer[str_len++] = *c; // ++ is post-increment, incremented after this line
        str_buffer[str_len] = '\0';

        // while the next character is a continuation byte, continue appending
        // but if there are too many of them, just stop to avoid overruning str_buffer size.
        if ((*(c+1) & 0xC0) == 0x80 && str_len < 4) {
            continue;
        }

        // ok c+1 is not a continuation byte, so we've read in a full codepoint
        int id = str_lookup(str_buffer, t->sorted_vocab, t->vocab_size);

        if (id != -1) {
            // we found this codepoint in vocab, add it as a token
            tokens[(*n_tokens)++] = id;
        } else {
            // byte_fallback encoding: just encode each byte as a token
            // +3 is here because the first 3 vocab elements are <unk>, <s>, </s>
            // so the individual bytes only start at index 3
            for (int i=0; i < str_len; i++) {
                tokens[(*n_tokens)++] = (unsigned char)str_buffer[i] + 3;
            }
        }
        str_len = 0; // protect against a sequence of stray UTF8 continuation bytes
    }

    // merge the best consecutive pair each iteration, according the scores in vocab_scores
    while (1) {
        float best_score = -1e10;
        int best_id = -1;
        int best_idx = -1;

        for (int i=0; i < (*n_tokens-1); i++) {
            // check if we can merge the pair (tokens[i], tokens[i+1])
            sprintf(str_buffer, "%s%s", t->vocab[tokens[i]], t->vocab[tokens[i+1]]);
            int id = str_lookup(str_buffer, t->sorted_vocab, t->vocab_size);
            if (id != -1 && t->vocab_scores[id] > best_score) {
                // this merge pair exists in vocab! record its score and position
                best_score = t->vocab_scores[id];
                best_id = id;
                best_idx = i;
            }
        }

        if (best_idx == -1) {
            break; // we couldn't find any more pairs to merge, so we're done
        }

        // merge the consecutive pair (best_idx, best_idx+1) into new token best_id
        tokens[best_idx] = best_id;
        // delete token at position best_idx+1, shift the entire sequence back 1
        for (int i = best_idx+1; i < (*n_tokens-1); i++) {
            tokens[i] = tokens[i+1];
        }
        (*n_tokens)--; // token length decreased
    }

    // add optional EOS (=2) token, if desired
    if (eos) tokens[(*n_tokens)++] = 2;

    free(str_buffer);
}

// ----------------------------------------------------------------------------
// The Sampler, which takes logits and returns a sampled token
// sampling can be done in a few ways: greedy argmax, sampling, top-p sampling

typedef struct {
    float prob;
    int index;
} ProbIndex; // struct used when sorting probabilities during top-p sampling

typedef struct {
    int vocab_size;
    ProbIndex* probindex; // buffer used in top-p sampling
    float temperature;
    float topp;
    unsigned long long rng_state;
} Sampler;

int sample_argmax(float* probabilities, int n) {
    // return the index that has the highest probability
    int max_i = 0;
    float max_p = probabilities[0];
    for (int i = 1; i < n; i++) {
        if (probabilities[i] > max_p) {
            max_i = i;
            max_p = probabilities[i];
        }
    }
    return max_i;
}

int sample_mult(float* probabilities, int n, float coin) {
    // sample index from probabilities (they must sum to 1!)
    // coin is a random number in [0, 1), usually from random_f32()
    float cdf = 0.0f;
    for (int i = 0; i < n; i++) {
        cdf += probabilities[i];
        if (coin < cdf) {
            return i;
        }
    }
    return n - 1; // in case of rounding errors
}

int compare(const void* a, const void* b) {
    ProbIndex* a_ = (ProbIndex*) a;
    ProbIndex* b_ = (ProbIndex*) b;
    if (a_->prob > b_->prob) return -1;
    if (a_->prob < b_->prob) return 1;
    return 0;
}

int sample_topp(float* probabilities, int n, float topp, ProbIndex* probindex, float coin) {
    // top-p sampling (or "nucleus sampling") samples from the smallest set of
    // tokens that exceed probability topp. This way we never sample tokens that
    // have very low probabilities and are less likely to go "off the rails".
    // coin is a random number in [0, 1), usually from random_f32()

    int n0 = 0;
    // quicksort indices in descending order of probabilities
    // values smaller than (1 - topp) / (n - 1) cannot be part of the result
    // so for efficiency we crop these out as candidates before sorting
    const float cutoff = (1.0f - topp) / (n - 1);
    for (int i = 0; i < n; i++) {
        if (probabilities[i] >= cutoff) {
            probindex[n0].index = i;
            probindex[n0].prob = probabilities[i];
            n0++;
        }
    }
    qsort(probindex, n0, sizeof(ProbIndex), compare);

    // truncate the list where cumulative probability exceeds topp
    float cumulative_prob = 0.0f;
    int last_idx = n0 - 1; // in case of rounding errors consider all elements
    for (int i = 0; i < n0; i++) {
        cumulative_prob += probindex[i].prob;
        if (cumulative_prob > topp) {
            last_idx = i;
            break; // we've exceeded topp by including last_idx
        }
    }

    // sample from the truncated list
    float r = coin * cumulative_prob;
    float cdf = 0.0f;
    for (int i = 0; i <= last_idx; i++) {
        cdf += probindex[i].prob;
        if (r < cdf) {
            return probindex[i].index;
        }
    }
    return probindex[last_idx].index; // in case of rounding errors
}

void build_sampler(Sampler* sampler, int vocab_size, float temperature, float topp, unsigned long long rng_seed) {
    sampler->vocab_size = vocab_size;
    sampler->temperature = temperature;
    sampler->topp = topp;
    sampler->rng_state = rng_seed;
    // buffer only used with nucleus sampling; may not need but it's ~small
    sampler->probindex = (ProbIndex *)malloc(sampler->vocab_size * sizeof(ProbIndex));
}

void free_sampler(Sampler* sampler) {
    free(sampler->probindex);
    sampler->probindex = NULL;
}

unsigned int random_u32(unsigned long long *state) {
    // xorshift rng: https://en.wikipedia.org/wiki/Xorshift#xorshift.2A
    *state ^= *state >> 12;
    *state ^= *state << 25;
    *state ^= *state >> 27;
    return (*state * 0x2545F4914F6CDD1Dull) >> 32;
}
float random_f32(unsigned long long *state) { // random float32 in [0,1)
    return (random_u32(state) >> 8) / 16777216.0f;
}

int sample(Sampler* sampler, float* logits) {
    // sample the token given the logits and some hyperparameters
    int next;
    if (sampler->temperature == 0.0f) {
        // greedy argmax sampling: take the token with the highest probability
        next = sample_argmax(logits, sampler->vocab_size);
    } else {
        // apply the temperature to the logits
        for (int q=0; q<sampler->vocab_size; q++) { logits[q] /= sampler->temperature; }
        // apply softmax to the logits to get the probabilities for next token
        softmax(logits, sampler->vocab_size);
        // flip a (float) coin (this is our source of entropy for sampling)
        float coin = random_f32(&sampler->rng_state);
        // we sample from this distribution to get the next token
        if (sampler->topp <= 0 || sampler->topp >= 1) {
            // simply sample from the predicted probability distribution
            next = sample_mult(logits, sampler->vocab_size, coin);
        } else {
            // top-p (nucleus) sampling, clamping the least likely tokens to zero
            next = sample_topp(logits, sampler->vocab_size, sampler->topp, sampler->probindex, coin);
        }
    }
    return next;
}

// ----------------------------------------------------------------------------
// utilities: time

long time_in_ms() {
    // return time in milliseconds, for benchmarking the model speed
    struct timespec time;
    clock_gettime(CLOCK_REALTIME, &time);
    return time.tv_sec * 1000 + time.tv_nsec / 1000000;
}

// ----------------------------------------------------------------------------
// generation loop

// void generate(Transformer *transformer, Tokenizer *tokenizer, Sampler *sampler, char *prompt, int steps) {
//     char *empty_prompt = (char *)"";
//     if (prompt == NULL) { prompt = empty_prompt; }

//     // encode the (string) prompt into tokens sequence
//     int num_prompt_tokens = 0;
//     int* prompt_tokens = (int*)malloc((strlen(prompt)+3) * sizeof(int)); // +3 for '\0', ?BOS, ?EOS
//     encode(tokenizer, prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
//     if (num_prompt_tokens < 1) {
//         fprintf(stderr, "something is wrong, expected at least 1 prompt token\n");
//         exit(EXIT_FAILURE);
//     }

//     // start the main loop
//     long start = 0;  // used to time our code, only initialized after first iteration
//     int next;        // will store the next token in the sequence
//     int token = prompt_tokens[0]; // kick off with the first token in the prompt
//     int pos = 0;     // position in the sequence
//     while (pos < steps) {

//         // forward the transformer to get logits for the next token
//         float* logits = forward(transformer, token, pos);

//         // advance the state machine
//         if (pos < num_prompt_tokens - 1) {
//             // if we are still processing the input prompt, force the next prompt token
//             next = prompt_tokens[pos + 1];
//         } else {
//             // otherwise sample the next token from the logits
//             next = sample(sampler, logits);
//         }
//         pos++;

//         // data-dependent terminating condition: the BOS (=1) token delimits sequences
//         if (next == 1) { break; }

//         // print the token as string, decode it with the Tokenizer object
//         char* piece = decode(tokenizer, token, next);
//         safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
//         fflush(stdout);
//         token = next;

//         // init the timer here because the first iteration can be slower
//         if (start == 0) { start = time_in_ms(); }
//     }
//     printf("\n");

//     // report achieved tok/s (pos-1 because the timer starts after first iteration)
//     if (pos > 1) {
//         long end = time_in_ms();
//         fprintf(stderr, "achieved tok/s: %f\n", (pos-1) / (double)(end-start)*1000);
//     }

//     free(prompt_tokens);
// }

// void read_stdin(const char* guide, char* buffer, size_t bufsize) {
//     // read a line from stdin, up to but not including \n
//     printf("%s", guide);
//     if (fgets(buffer, bufsize, stdin) != NULL) {
//         size_t len = strlen(buffer);
//         if (len > 0 && buffer[len - 1] == '\n') {
//             buffer[len - 1] = '\0'; // strip newline
//         }
//     }
// }

// // ----------------------------------------------------------------------------
// // chat loop
// // I manually inspected the tokens for a few chat conversations compared to
// // python reference and that seemed ok, but this was not thoroughly tested and
// // is not safely implemented, it's more a proof of concept atm.

// void chat(Transformer *transformer, Tokenizer *tokenizer, Sampler *sampler,
//           char *cli_user_prompt, char *cli_system_prompt, int steps) {

//     // buffers for reading the system prompt and user prompt from stdin
//     // you'll notice they are soomewhat haphazardly and unsafely set atm
//     char system_prompt[512];
//     char user_prompt[512];
//     char rendered_prompt[1152];
//     int num_prompt_tokens = 0;
//     int* prompt_tokens = (int*)malloc(1152 * sizeof(int));
//     int user_idx;

//     // start the main loop
//     int8_t user_turn = 1; // user starts
//     int next;        // will store the next token in the sequence
//     int token;       // stores the current token to feed into the transformer
//     int prev_token;
//     int pos = 0;     // position in the sequence
//     while (pos < steps) {

//         // when it is the user's turn to contribute tokens to the dialog...
//         if (user_turn) {
//             // get the (optional) system prompt at position 0
//             if (pos == 0) {
//                 // at position 0, the user can also contribute a system prompt
//                 if (cli_system_prompt == NULL) {
//                     // system prompt was not passed in, attempt to get it from stdin
//                     read_stdin("Enter system prompt (optional): ", system_prompt, sizeof(system_prompt));
//                 } else {
//                     // system prompt was passed in, use it
//                     strcpy(system_prompt, cli_system_prompt);
//                 }
//             }
//             // get the user prompt
//             if (pos == 0 && cli_user_prompt != NULL) {
//                 // user prompt for position 0 was passed in, use it
//                 strcpy(user_prompt, cli_user_prompt);
//             } else {
//                 // otherwise get user prompt from stdin
//                 read_stdin("User: ", user_prompt, sizeof(user_prompt));
//             }
//             // render user/system prompts into the Llama 2 Chat schema
//             if (pos == 0 && system_prompt[0] != '\0') {
//                 char system_template[] = "[INST] <<SYS>>\n%s\n<</SYS>>\n\n%s [/INST]";
//                 sprintf(rendered_prompt, system_template, system_prompt, user_prompt);
//             } else {
//                 char user_template[] = "[INST] %s [/INST]";
//                 sprintf(rendered_prompt, user_template, user_prompt);
//             }
//             // encode the rendered prompt into tokens
//             encode(tokenizer, rendered_prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
//             user_idx = 0; // reset the user index
//             user_turn = 0;
//             printf("Assistant: ");
//         }

//         // determine the token to pass into the transformer next
//         if (user_idx < num_prompt_tokens) {
//             // if we are still processing the input prompt, force the next prompt token
//             token = prompt_tokens[user_idx++];
//         } else {
//             // otherwise use the next token sampled from previous turn
//             token = next;
//         }
//         // EOS (=2) token ends the Assistant turn
//         if (token == 2) { user_turn = 1; }

//         // forward the transformer to get logits for the next token
//         float* logits = forward(transformer, token, pos);
//         next = sample(sampler, logits);
//         pos++;

//         if (user_idx >= num_prompt_tokens && next != 2) {
//             // the Assistant is responding, so print its output
//             char* piece = decode(tokenizer, token, next);
//             safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
//             fflush(stdout);
//         }
//         if (next == 2) { printf("\n"); }
//     }
//     printf("\n");
//     free(prompt_tokens);
// }

typedef struct {
    Transformer transformer;
    Tokenizer tokenizer;
    Sampler sampler;
    int *output;    // buffer to store the output tokens(max_tokens + 1)
    int output_idx; // current index in the output buffer(0 ... max_tokens - 1)
    int gen_idx;    // generated tokens(0 ... max_tokens)
    int finished;
#ifdef USE_CUDA
    cublasHandle_t g_cublas_handle;
#endif
} llama2_ctx;

void *llama2_init(char *model_path, char *tokenizer_path) {
    llama2_ctx *ctx = (llama2_ctx *)malloc(sizeof(llama2_ctx));
    build_transformer(&ctx->transformer, model_path);
    build_tokenizer(&ctx->tokenizer, tokenizer_path, ctx->transformer.config.vocab_size);
    ctx->output = NULL;
#ifdef USE_CUDA
    cublasStatus_t stat = cublasCreate(&ctx->g_cublas_handle);  // FIXME cublasDestroy
    if (stat != CUBLAS_STATUS_SUCCESS) {
        printf ("CUBLAS initialization failed\n");
        exit(EXIT_FAILURE);
    }
#endif
    return ctx;
}

void llama2_free(void *ctx) {
    llama2_ctx *c = (llama2_ctx *)ctx;
    free_transformer(&c->transformer);
    free_tokenizer(&c->tokenizer);
    if (c->sampler.probindex != NULL)
        free_sampler(&c->sampler);
#ifdef USE_CUDA
    cublasStatus_t stat = cublasDestroy(c->g_cublas_handle);
    if (stat != CUBLAS_STATUS_SUCCESS) {
        printf ("CUBLAS destroy failed\n");
        exit(EXIT_FAILURE);
    }
#endif
    if (c->output != NULL)
        free(c->output);
}

void llama2_generate_loop(llama2_ctx *ctx, int *prompt_tokens, int num_prompt_tokens, int steps, int *output_tokens) {
    // printf("generate loop started\n");
    // start the main loop
    // long start = 0;  // used to time our code, only initialized after first iteration
    int next;        // will store the next token in the sequence
    int token = prompt_tokens[0]; // kick off with the first token in the prompt
    int pos = 0;     // position in the sequence
    while (pos < steps) {

        // forward the transformer to get logits for the next token
#ifdef USE_CUDA
        float* logits = forward(&ctx->transformer, token, pos, ctx->g_cublas_handle);
#else
        float* logits = forward(&ctx->transformer, token, pos);
#endif
        // advance the state machine
        if (pos < num_prompt_tokens - 1) {
            // if we are still processing the input prompt, force the next prompt token
            next = prompt_tokens[pos + 1];
        } else {
            // otherwise sample the next token from the logits
            next = sample(&ctx->sampler, logits);
        }
        // printf("current gen idx: %d, %d\n", ctx->gen_idx, next);
        if (pos == num_prompt_tokens - 1)
            output_tokens[ctx->gen_idx] = token;
        if (pos >= num_prompt_tokens - 1)
            output_tokens[ctx->gen_idx++ + 1] = next;
        pos++;
        token = next;

        // EOS (=2) token ends the Assistant turn
        if (next == 2)
            break;
    }
    // report achieved tok/s (pos-1 because the timer starts after first iteration)
    // if (pos > 1) {
    //     long end = time_in_ms();
    //     fprintf(stderr, "achieved tok/s: %f\n", (pos-1) / (double)(end-start)*1000);
    // }
    ctx->finished = 1;
    free(prompt_tokens);
    free_sampler(&ctx->sampler);
    // printf("generate loop finished\n");
}

int llama2_generate(void *ctx, char *prompt, int steps, float temperature, float topp, int seed) {
    llama2_ctx *c = (llama2_ctx *)ctx;
    build_sampler(&c->sampler, c->transformer.config.vocab_size, temperature, topp, seed);
    char *empty_prompt = (char *)"";
    if (prompt == NULL) { prompt = empty_prompt; }
    // encode the (string) prompt into tokens sequence
    int num_prompt_tokens = 0;
    int* prompt_tokens = (int*)malloc((strlen(prompt)+3) * sizeof(int)); // +3 for '\0', ?BOS, ?EOS
    encode(&c->tokenizer, prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
    if (num_prompt_tokens < 1) {
        fprintf(stderr, "something is wrong, expected at least 1 prompt token\n");
        return 1;
    }
    if (num_prompt_tokens >= steps) {
        fprintf(stderr, "prompt tokens exceeds max token length\n");
        return 1;
    }
    c->output = (int *)malloc((steps + 1) * sizeof(int));
    c->gen_idx = 0;
    c->output_idx = 0;
    c->finished = 0;
    std::thread t(llama2_generate_loop, c, prompt_tokens, num_prompt_tokens, steps, c->output);
    t.detach();
    return 0;
}

char *llama2_get_last(void *ctx) {
    llama2_ctx *c = (llama2_ctx *)ctx;
    assert(c->output != NULL);  // shouldn't be called again after finished
    while(!c->finished && c->output_idx >= c->gen_idx) {
        // printf("current idx: %d, %d\n", c->output_idx, c->gen_idx);
        usleep(100000);
    }   // wait for next token
    if (c->finished && c->output_idx >= c->gen_idx) {
        free(c->output);
        c->output = NULL;
        return NULL;
    }
    // printf("current idx: %d, %d, finished:%d\n", c->output_idx, c->gen_idx, c->finished);
    char *piece = decode(&c->tokenizer, c->output[c->output_idx], c->output[c->output_idx + 1]);
    c->output_idx++;
    return piece;
}

void llama2_tokenize(void *ctx, char *text, int8_t bos, int8_t eos, int *tokens, int *n_tokens) {
    llama2_ctx *c = (llama2_ctx *)ctx;
    encode(&c->tokenizer, text, bos, eos, tokens, n_tokens);
}