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// File from https://github.com/mlpen/YOSO/blob/main/encoders/backbones/efficient_attentions/yoso/yoso_v1/cuda/fast_lsh_cumulation_cuda.cu
#include "fast_lsh_cumulation_cuda.h"
#include "common_cuda_device.h"
#include "common_cuda.h"
#include "common.h"
#include <stdio.h>
//////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////
inline __device__ void fast_hadamard_transform(float *vector_buffer, int vector_dim, int dim_idx) {
int stride = vector_dim / 2;
while (stride > (WARP_SIZE / 2)) {
__syncthreads();
int sign = 1 - ((dim_idx / stride) % 2) * 2;
float val1 = vector_buffer[dim_idx];
float val2 = vector_buffer[dim_idx + sign * stride];
__syncthreads();
vector_buffer[dim_idx] = float(sign) * val1 + val2;
stride = stride / 2;
}
float val = vector_buffer[dim_idx];
#pragma unroll
for (stride = (WARP_SIZE / 2); stride > 0; stride = stride / 2) {
int sign = 1 - ((dim_idx / stride) % 2) * 2;
val = float(sign) * val + __shfl_xor_sync(FULL_MASK, val, stride);
}
vector_buffer[dim_idx] = val;
}
__global__ void fast_hash_ver1_cuda_kernel(
int *mask, // [batch_size, num_vector]
float *vector, // [batch_size, num_vector, vector_dim]
int *Dmat, // [batch_size, 3, num_part, vector_dim]
int *hash_code, // [batch_size, num_vector, num_hash_f]
int batch_size,
int num_vector,
int vector_dim,
int num_part,
int num_hash_f,
int hash_code_len
) {
int batch_idx = blockIdx.z;
int vector_idx = blockIdx.y;
int part_idx = blockIdx.x;
int dim_idx = threadIdx.x;
int batch_idx__vector_idx = batch_idx * num_vector + vector_idx;
if (mask[batch_idx__vector_idx] == 0) {
return;
}
extern __shared__ float buffer[];
float *vector_buffer = buffer;
vector_buffer[dim_idx] = vector[batch_idx__vector_idx * vector_dim + dim_idx];
vector_buffer[dim_idx] = vector_buffer[dim_idx] * (float)Dmat[((batch_idx * 3 + 0) * num_part + part_idx) * vector_dim + dim_idx];
fast_hadamard_transform(vector_buffer, vector_dim, dim_idx);
vector_buffer[dim_idx] = vector_buffer[dim_idx] * (float)Dmat[((batch_idx * 3 + 1) * num_part + part_idx) * vector_dim + dim_idx];
fast_hadamard_transform(vector_buffer, vector_dim, dim_idx);
vector_buffer[dim_idx] = vector_buffer[dim_idx] * (float)Dmat[((batch_idx * 3 + 2) * num_part + part_idx) * vector_dim + dim_idx];
fast_hadamard_transform(vector_buffer, vector_dim, dim_idx);
int num_hash_per_part = vector_dim / hash_code_len;
if (hash_code_len == 8 || hash_code_len == 16) {
int code = select(vector_buffer[dim_idx] > 0, 1 << (dim_idx % hash_code_len), 0);
for (int offset = 1; offset < hash_code_len; offset = offset * 2) {
code += __shfl_xor_sync(FULL_MASK, code, offset);
}
if (dim_idx % hash_code_len == 0) {
int hash_f_idx = part_idx * num_hash_per_part + dim_idx / hash_code_len;
if (hash_f_idx < num_hash_f) {
hash_code[batch_idx__vector_idx * num_hash_f + hash_f_idx] = code;
}
}
} else {
vector_buffer[dim_idx] = select(vector_buffer[dim_idx] > 0, 1 << (dim_idx % hash_code_len), 0);
__syncthreads();
if (dim_idx < num_hash_per_part) {
int code = 0;
for (int i = 0; i < hash_code_len; i++) {
code += vector_buffer[dim_idx * hash_code_len + i];
}
int hash_f_idx = part_idx * num_hash_per_part + dim_idx;
if (hash_f_idx < num_hash_f) {
hash_code[batch_idx__vector_idx * num_hash_f + hash_f_idx] = code;
}
}
}
}
__global__ void lsh_cumulation_ver1_step1_cuda_kernel(
int *key_mask, // [batch_size, num_key]
int *key_hash_code, // [batch_size, num_key, num_hash_f]
float *value, // [batch_size, num_key, value_dim]
float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE]
int batch_size,
int num_hash_f,
int hashtable_capacity,
int num_key,
int value_dim,
int offset_warp
) {
int warp_thread_idx = threadIdx.x;
int batch_idx = blockIdx.y;
int key_idx = blockIdx.x * blockDim.y + threadIdx.y;
int batch_idx__key_idx = batch_idx * num_key + key_idx;
if (key_mask[batch_idx__key_idx] == 0) {
return;
}
if (num_hash_f > WARP_SIZE) {
float warp_value = value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx];
for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) {
int warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_start + warp_thread_idx];
#pragma unroll
for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset);
int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode;
atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value);
}
}
} else {
float warp_value = value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx];
int warp_hashcode = 0;
if (warp_thread_idx < num_hash_f) {
warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + warp_thread_idx];
}
for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx);
int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode;
atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value);
}
}
}
__global__ void lsh_cumulation_ver1_step2_cuda_kernel(
int *query_mask, // [batch_size, num_query]
int *query_hash_code, // [batch_size, num_query, num_hash_f]
float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE]
float *cumulation_value, // [batch_size, num_query, value_dim]
int batch_size,
int num_hash_f,
int hashtable_capacity,
int num_query,
int value_dim,
int offset_warp
) {
int warp_thread_idx = threadIdx.x;
int batch_idx = blockIdx.y;
int query_idx = blockIdx.x * blockDim.y + threadIdx.y;
int batch_idx__query_idx = batch_idx * num_query + query_idx;
if (query_mask[batch_idx__query_idx] == 0) {
return;
}
if (num_hash_f > WARP_SIZE) {
float warp_value = 0;
for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) {
int warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + hash_f_start + warp_thread_idx];
#pragma unroll
for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset);
int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode;
warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx];
}
}
cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] = warp_value / float(num_hash_f);
} else {
float warp_value = 0;
int warp_hashcode = 0;
if (warp_thread_idx < num_hash_f) {
warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + warp_thread_idx];
}
for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx);
int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode;
warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx];
}
cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] = warp_value / float(num_hash_f);
}
}
__global__ void lsh_weighted_cumulation_ver1_step1_cuda_kernel(
int *key_mask, // [batch_size, num_key]
int *key_hash_code, // [batch_size, num_key, num_hash_f]
float *key_weight, // [batch_size, num_key, weight_dim]
float *value, // [batch_size, num_key, value_dim]
float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE]
int batch_size,
int num_hash_f,
int hashtable_capacity,
int num_key,
int value_dim,
int weight_dim,
int offset_warp,
int weight_idx
) {
int warp_thread_idx = threadIdx.x;
int batch_idx = blockIdx.y;
int key_idx = blockIdx.x * blockDim.y + threadIdx.y;
int batch_idx__key_idx = batch_idx * num_key + key_idx;
if (key_mask[batch_idx__key_idx] == 0) {
return;
}
if (num_hash_f > WARP_SIZE) {
float warp_value = key_weight[batch_idx__key_idx * weight_dim + weight_idx] * value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx];
for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) {
int warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_start + warp_thread_idx];
#pragma unroll
for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset);
int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode;
atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value);
}
}
} else {
float warp_value = key_weight[batch_idx__key_idx * weight_dim + weight_idx] * value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx];
int warp_hashcode = 0;
if (warp_thread_idx < num_hash_f) {
warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + warp_thread_idx];
}
for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx);
int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode;
atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value);
}
}
}
__global__ void lsh_weighted_cumulation_ver1_step2_cuda_kernel(
int *query_mask, // [batch_size, num_query]
int *query_hash_code, // [batch_size, num_query, num_hash_f]
float *query_weight, // [batch_size, num_query, weight_dim]
float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE]
float *cumulation_value, // [batch_size, num_query, value_dim]
int batch_size,
int num_hash_f,
int hashtable_capacity,
int num_query,
int value_dim,
int weight_dim,
int offset_warp,
int weight_idx
) {
int warp_thread_idx = threadIdx.x;
int batch_idx = blockIdx.y;
int query_idx = blockIdx.x * blockDim.y + threadIdx.y;
int batch_idx__query_idx = batch_idx * num_query + query_idx;
if (query_mask[batch_idx__query_idx] == 0) {
return;
}
if (num_hash_f > WARP_SIZE) {
float warp_value = 0;
for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) {
int warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + hash_f_start + warp_thread_idx];
#pragma unroll
for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset);
int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode;
warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx];
}
}
float warp_weight = query_weight[batch_idx__query_idx * weight_dim + weight_idx];
cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] += warp_weight * warp_value / float(num_hash_f);
} else {
float warp_value = 0;
int warp_hashcode = 0;
if (warp_thread_idx < num_hash_f) {
warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + warp_thread_idx];
}
for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) {
int current_hashcode = warp_hashcode;
current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx);
int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode;
warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx];
}
float warp_weight = query_weight[batch_idx__query_idx * weight_dim + weight_idx];
cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] += warp_weight * warp_value / float(num_hash_f);
}
}
__global__ void count_sort_step1_cuda_kernel(
int *key_mask, // [batch_size, num_key]
int *key_hash_code, // [batch_size, num_key, num_hash_f]
int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity]
int batch_size,
int num_hash_f,
int hashtable_capacity,
int num_key
) {
int batch_idx = blockIdx.y;
int key_idx = blockIdx.x * blockDim.y + threadIdx.y;
int hash_f_idx = threadIdx.x;
int batch_idx__key_idx = batch_idx * num_key + key_idx;
if (key_mask[batch_idx__key_idx] == 0) {
return;
}
int hash_code = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_idx];
atomicAdd(&count_sort_table[(batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + hash_code], 1);
}
__global__ void count_sort_step2_cuda_kernel(
int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity]
int batch_size,
int num_hash_f,
int hashtable_capacity
) {
int batch_idx = blockIdx.y;
int hash_f_idx = blockIdx.x;
int num_threads = blockDim.x;
int thread_id = threadIdx.x;
int batch_idx__hash_f_idx = batch_idx * num_hash_f + hash_f_idx;
extern __shared__ float buffer[];
int *table_buffer = (int*)buffer;
if (thread_id == 0) {
table_buffer[0] = 0;
}
copy_data<int>(&count_sort_table[batch_idx__hash_f_idx * hashtable_capacity], &table_buffer[1], hashtable_capacity - 1, num_threads, thread_id);
for (int table_idx_start = 0; table_idx_start < hashtable_capacity; table_idx_start = table_idx_start + num_threads) {
int thread_value = table_buffer[table_idx_start + thread_id];
int next_thread_value = 0;
for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) {
next_thread_value = __shfl_up_sync(FULL_MASK, thread_value, offset);
if (thread_id % WARP_SIZE >= offset) {
thread_value = thread_value + next_thread_value;
}
}
table_buffer[table_idx_start + thread_id] = thread_value;
}
__syncthreads();
if (hashtable_capacity > WARP_SIZE) {
if (thread_id < WARP_SIZE) {
for (int table_idx_start = WARP_SIZE; table_idx_start < hashtable_capacity; table_idx_start = table_idx_start + WARP_SIZE) {
table_buffer[table_idx_start + thread_id] += table_buffer[table_idx_start - 1];
}
}
}
copy_data<int>(table_buffer, &count_sort_table[batch_idx__hash_f_idx * hashtable_capacity], hashtable_capacity, num_threads, thread_id);
}
__global__ void count_sort_step3_cuda_kernel(
int *key_mask, // [batch_size, num_key]
int *key_hash_code, // [batch_size, num_key, num_hash_f]
int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity]
int *key_sorted_idxes, // [batch_size, num_hash_f, num_key]
int batch_size,
int num_hash_f,
int hashtable_capacity,
int num_key
) {
int batch_idx = blockIdx.y;
int key_idx = blockIdx.x * blockDim.y + threadIdx.y;
int hash_f_idx = threadIdx.x;
int batch_idx__key_idx = batch_idx * num_key + key_idx;
if (key_mask[batch_idx__key_idx] == 0) {
return;
}
int batch_idx__hash_f_idx = batch_idx * num_hash_f + hash_f_idx;
int hash_code = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_idx];
int sort_idx = atomicAdd(&count_sort_table[batch_idx__hash_f_idx * hashtable_capacity + hash_code], 1);
key_sorted_idxes[batch_idx__hash_f_idx * num_key + sort_idx] = key_idx;
}
__global__ void extract_query_info_cuda_kernel(
int *query_mask, // [batch_size, num_query]
int *query_hash_code, // [batch_size, num_query, num_hash_f]
int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity]
int *query_info, // [batch_size, num_query, 2, num_hash_f]
int batch_size,
int num_hash_f,
int hashtable_capacity,
int num_query
) {
int batch_idx = blockIdx.y;
int query_idx = blockIdx.x * blockDim.y + threadIdx.y;
int hash_f_idx = threadIdx.x;
int batch_idx__query_idx = batch_idx * num_query + query_idx;
if (query_mask[batch_idx__query_idx] == 0) {
return;
}
int hash_code = query_hash_code[batch_idx__query_idx * num_hash_f + hash_f_idx];
int batch_idx__hash_f_idx__hash_code = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + hash_code;
int key_offset = select(hash_code == 0, 0, count_sort_table[batch_idx__hash_f_idx__hash_code - 1]);
int key_count = count_sort_table[batch_idx__hash_f_idx__hash_code] - key_offset;
query_info[batch_idx__query_idx * 2 * num_hash_f + hash_f_idx] = key_offset;
query_info[(batch_idx__query_idx * 2 + 1) * num_hash_f + hash_f_idx] = key_count;
}
__global__ void lsh_weighted_cumulation_ver2_step2_cuda_kernel(
int *query_mask, // [batch_size, num_query]
int *query_info, // [batch_size, num_query, 2, num_hash_f]
int *key_sorted_idxes, // [batch_size, num_hash_f, num_key]
float *query_weight, // [batch_size, num_query, weight_dim]
float *key_weight, // [batch_size, num_key, weight_dim]
float *value, // [batch_size, num_key, value_dim]
float *cumulation_value, // [batch_size, num_query, value_dim]
int batch_size,
int num_hash_f,
int num_query,
int num_key,
int value_dim,
int weight_dim
) {
int batch_idx = blockIdx.z;
int hash_f_idx = blockIdx.y;
int query_idx = blockIdx.x;
int num_threads = blockDim.y * blockDim.x;
int thread_id = threadIdx.y * blockDim.x + threadIdx.x;
int num_warps = blockDim.y;
int warp_idx = threadIdx.y;
int warp_thread_idx = threadIdx.x;
int batch_idx__query_idx = batch_idx * num_query + query_idx;
if (query_mask[batch_idx__query_idx] == 0) {
return;
}
int key_offset = query_info[batch_idx__query_idx * 2 * num_hash_f + hash_f_idx];
int key_count = query_info[(batch_idx__query_idx * 2 + 1) * num_hash_f + hash_f_idx];
if (key_count == 0) {
return;
}
extern __shared__ float buffer[];
if (key_count == 1) {
if (warp_idx == 0) {
int key_idx = key_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_key + key_offset];
int batch_idx__key_idx = batch_idx * num_key + key_idx;
float weight = 0;
for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) {
int weight_dim_idx = weight_offset + warp_thread_idx;
float val = query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx] * key_weight[batch_idx__key_idx * weight_dim + weight_dim_idx];
#pragma unroll
for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) {
val += __shfl_xor_sync(FULL_MASK, val, offset);
}
weight = weight + val;
}
weight = weight / float(num_hash_f);
for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) {
int value_dim_idx = value_offset + warp_thread_idx;
float val = value[batch_idx__key_idx * value_dim + value_dim_idx];
atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val);
}
}
} else {
float *weight_buffer = buffer;
int *key_idxes_buffer = (int*)&buffer[weight_dim];
copy_data_nonblocking<float>(&query_weight[batch_idx__query_idx * weight_dim], weight_buffer, weight_dim, num_threads, thread_id);
while (key_count > 0) {
int work_size = min(WARP_SIZE, key_count);
copy_data_nonblocking<int>(&key_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_key + key_offset], key_idxes_buffer, work_size, num_threads, thread_id);
__syncthreads();
for (int work_offset = 0; work_offset < WARP_SIZE; work_offset = work_offset + num_warps) {
int work_idx = work_offset + warp_idx;
if (work_idx < key_count) {
int key_idx = key_idxes_buffer[work_idx];
int batch_idx__key_idx = batch_idx * num_key + key_idx;
float weight = 0;
for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) {
int weight_dim_idx = weight_offset + warp_thread_idx;
float val = weight_buffer[weight_dim_idx] * key_weight[batch_idx__key_idx * weight_dim + weight_dim_idx];
#pragma unroll
for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) {
val += __shfl_xor_sync(FULL_MASK, val, offset);
}
weight = weight + val;
}
weight = weight / float(num_hash_f);
for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) {
int value_dim_idx = value_offset + warp_thread_idx;
float val = value[batch_idx__key_idx * value_dim + value_dim_idx];
atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val);
}
}
}
key_count = key_count - work_size;
key_offset = key_offset + work_size;
}
}
}
__global__ void lsh_weighted_cumulation_ver3_step2_cuda_kernel(
int *query_sorted_idxes, // [batch_size, num_hash_f, num_query]
int *key_mask, // [batch_size, num_key]
int *key_info, // [batch_size, num_key, 2, num_hash_f]
float *query_weight, // [batch_size, num_query, weight_dim]
float *key_weight, // [batch_size, num_key, weight_dim]
float *value, // [batch_size, num_key, value_dim]
float *cumulation_value, // [batch_size, num_query, value_dim]
int batch_size,
int num_hash_f,
int num_query,
int num_key,
int value_dim,
int weight_dim
) {
int batch_idx = blockIdx.z;
int hash_f_idx = blockIdx.y;
int key_idx = blockIdx.x;
int num_threads = blockDim.y * blockDim.x;
int thread_id = threadIdx.y * blockDim.x + threadIdx.x;
int num_warps = blockDim.y;
int warp_idx = threadIdx.y;
int warp_thread_idx = threadIdx.x;
int batch_idx__key_idx = batch_idx * num_key + key_idx;
if (key_mask[batch_idx__key_idx] == 0) {
return;
}
int query_offset = key_info[batch_idx__key_idx * 2 * num_hash_f + hash_f_idx];
int query_count = key_info[(batch_idx__key_idx * 2 + 1) * num_hash_f + hash_f_idx];
if (query_count == 0) {
return;
}
extern __shared__ float buffer[];
if (query_count == 1) {
if (warp_idx == 0) {
int query_idx = query_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_query + query_offset];
int batch_idx__query_idx = batch_idx * num_query + query_idx;
float weight = 0;
for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) {
int weight_dim_idx = weight_offset + warp_thread_idx;
float val = key_weight[batch_idx__key_idx * weight_dim + weight_dim_idx] * query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx];
#pragma unroll
for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) {
val += __shfl_xor_sync(FULL_MASK, val, offset);
}
weight = weight + val;
}
weight = weight / float(num_hash_f);
for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) {
int value_dim_idx = value_offset + warp_thread_idx;
float val = value[batch_idx__key_idx * value_dim + value_dim_idx];
atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val);
}
}
} else {
float *weight_buffer = buffer;
float *value_buffer = &buffer[weight_dim];
int *query_idxes_buffer = (int*)&buffer[weight_dim + value_dim];
copy_data_nonblocking<float>(&key_weight[batch_idx__key_idx * weight_dim], weight_buffer, weight_dim, num_threads, thread_id);
copy_data_nonblocking<float>(&value[batch_idx__key_idx * value_dim], value_buffer, value_dim, num_threads, thread_id);
while (query_count > 0) {
int work_size = min(WARP_SIZE, query_count);
copy_data_nonblocking<int>(&query_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_query + query_offset], query_idxes_buffer, work_size, num_threads, thread_id);
__syncthreads();
for (int work_offset = 0; work_offset < WARP_SIZE; work_offset = work_offset + num_warps) {
int work_idx = work_offset + warp_idx;
if (work_idx < query_count) {
int query_idx = query_idxes_buffer[work_idx];
int batch_idx__query_idx = batch_idx * num_query + query_idx;
float weight = 0;
for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) {
int weight_dim_idx = weight_offset + warp_thread_idx;
float val = weight_buffer[weight_dim_idx] * query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx];
#pragma unroll
for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) {
val += __shfl_xor_sync(FULL_MASK, val, offset);
}
weight = weight + val;
}
weight = weight / float(num_hash_f);
for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) {
int value_dim_idx = value_offset + warp_thread_idx;
float val = value_buffer[value_dim_idx];
atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val);
}
}
}
query_count = query_count - work_size;
query_offset = query_offset + work_size;
}
}
}
__global__ void lsh_weighted_cumulation_ver4_step2_cuda_kernel(
int *query_sorted_idxes, // [batch_size, num_hash_f, num_query]
int *key_mask, // [batch_size, num_key]
int *key_info, // [batch_size, num_key, 2, num_hash_f]
float *query_weight, // [batch_size, num_query, weight_dim]
float *key_weight, // [batch_size, num_key, weight_dim]
float *value, // [batch_size, num_key, value_dim]
float *cumulation_value, // [batch_size, num_query, value_dim]
int batch_size,
int num_hash_f,
int num_query,
int num_key,
int value_dim,
int weight_dim
) {
int batch_idx = blockIdx.y;
int key_idx = blockIdx.x;
int num_threads = blockDim.y * blockDim.x;
int thread_id = threadIdx.y * blockDim.x + threadIdx.x;
int num_warps = blockDim.y;
int warp_idx = threadIdx.y;
int warp_thread_idx = threadIdx.x;
int batch_idx__key_idx = batch_idx * num_key + key_idx;
if (key_mask[batch_idx__key_idx] == 0) {
return;
}
extern __shared__ float buffer[];
float *weight_buffer = buffer;
float *value_buffer = &buffer[weight_dim];
int *key_info_buffer = (int*)&buffer[weight_dim + value_dim];
copy_data_nonblocking<float>(&key_weight[batch_idx__key_idx * weight_dim], weight_buffer, weight_dim, num_threads, thread_id);
copy_data_nonblocking<float>(&value[batch_idx__key_idx * value_dim], value_buffer, value_dim, num_threads, thread_id);
copy_data_nonblocking<int>(&key_info[batch_idx__key_idx * 2 * num_hash_f], key_info_buffer, 2 * num_hash_f, num_threads, thread_id);
int *query_offset_buffer = key_info_buffer;
int *query_count_buffer = &key_info_buffer[num_hash_f];
const int hashtable_size = 1024 + OPTIMAL_THREADS_PER_BLOCK;
__shared__ int hashtable_query[hashtable_size];
__shared__ int hashtable_count[hashtable_size];
__shared__ int inserted_query[hashtable_size];
__shared__ int query_counter[1];
int hash_f_idx_base = 0;
while (true) {
init_buffer_nonblocking<int>(EMPTY_VALUE, hashtable_query, hashtable_size, num_threads, thread_id);
init_buffer_nonblocking<int>(0, hashtable_count, hashtable_size, num_threads, thread_id);
init_buffer_nonblocking<int>(EMPTY_VALUE, inserted_query, hashtable_size, num_threads, thread_id);
init_buffer_nonblocking<int>(0, query_counter, 1, num_threads, thread_id);
__syncthreads();
while (hash_f_idx_base < num_hash_f) {
int hash_f_idx = hash_f_idx_base + warp_idx;
int batch_idx__hash_f_idx = batch_idx * num_hash_f + hash_f_idx;
int stop_flag = 0;
int query_offset = query_offset_buffer[hash_f_idx];
int query_count = query_count_buffer[hash_f_idx];
while (query_count > 0) {
int work_size = min(query_count, WARP_SIZE);
// try inserting query to set and check whether the query is new
int found_new_query = 0;
int query_idx = -1;
if (warp_thread_idx < work_size) {
query_idx = query_sorted_idxes[batch_idx__hash_f_idx * num_query + query_offset + warp_thread_idx];
int slot = set_insert<int>(hashtable_query, hashtable_size, query_idx);
if (slot >= 0) {
found_new_query = atomicAdd(&hashtable_count[slot], 1) == 0;
}
}
// compute cumulative offset
int position_offset = found_new_query;
int next_position_offset = 0;
#pragma unroll
for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) {
next_position_offset = __shfl_up_sync(FULL_MASK, position_offset, offset);
if (thread_id % WARP_SIZE >= offset) {
position_offset = position_offset + next_position_offset;
}
}
// get the inserted query list end index
int inserted_query_base = 0;
if (thread_id % WARP_SIZE == WARP_SIZE - 1) {
inserted_query_base = atomicAdd(query_counter, position_offset);
}
inserted_query_base = __shfl_sync(FULL_MASK, inserted_query_base, WARP_SIZE - 1);
// insert new queries to list
int insert_idx = inserted_query_base + position_offset - 1;
if (found_new_query) {
inserted_query[insert_idx] = query_idx;
}
// remove inserted queries from list
query_offset_buffer[hash_f_idx] += work_size;
query_count_buffer[hash_f_idx] -= work_size;
query_offset += work_size;
query_count -= work_size;
// if list is almost full, stop inserting
if (inserted_query_base + OPTIMAL_THREADS_PER_BLOCK > hashtable_size) {
stop_flag = 1;
break;
}
}
if (stop_flag) {
break;
}
hash_f_idx_base = hash_f_idx_base + num_warps;
}
__syncthreads();
int num_distint_query = query_counter[0];
if (num_distint_query > 0) {
for (int idx_base = 0; idx_base < num_distint_query; idx_base = idx_base + num_warps) {
int idx = idx_base + warp_idx;
if (idx < num_distint_query) {
int query_idx = inserted_query[idx];
int batch_idx__query_idx = batch_idx * num_query + query_idx;
int slot = set_lookup<int>(hashtable_query, hashtable_size, query_idx);
int duplicate_count = hashtable_count[slot];
float weight = 0;
for (int weight_idx_base = 0; weight_idx_base < weight_dim; weight_idx_base = weight_idx_base + WARP_SIZE) {
int weight_dim_idx = weight_idx_base + warp_thread_idx;
float val = weight_buffer[weight_dim_idx] * query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx];
#pragma unroll
for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) {
val += __shfl_xor_sync(FULL_MASK, val, offset);
}
weight = weight + val;
}
weight = (float)duplicate_count * weight / float(num_hash_f);
for (int value_idx_base = 0; value_idx_base < value_dim; value_idx_base = value_idx_base + WARP_SIZE) {
int value_dim_idx = value_idx_base + warp_thread_idx;
float val = value_buffer[value_dim_idx];
atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val);
}
}
}
} else {
// all computation is completed if num_distint_query == 0
break;
}
__syncthreads();
}
}
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