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* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
*
* 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 "src/turbomind/kernels/decoding_kernels.h"
#include "src/turbomind/kernels/reduce_kernel_utils.cuh"
#include "src/turbomind/utils/cuda_type_utils.cuh"
#include "src/turbomind/utils/cuda_utils.h"
#include "src/turbomind/utils/string_utils.h"
namespace turbomind {
// PROMPT_SRC: 0 --> no prompts, 1 --> from loaded prompts, 2 --> from request prompts
template<typename T>
__global__ void embeddingLookupPosEncoding(T* from_tensor,
const T* embedding_table,
const T* position_encoding,
const int* all_ids,
const int* padding_count,
const int* input_lengths,
const int local_token_num,
const int64_t hidden_units,
const int step,
const int max_input_length,
const int token_num,
const int ite,
const T scale)
{
// 1. lookup from embedding table
// 2. multiply scale
// 3. add the position encoding
const int id_offset = step * token_num + ite * local_token_num;
const bool use_padding_count = padding_count != nullptr;
const bool use_input_len = input_lengths != nullptr;
for (int64_t index = blockIdx.x * blockDim.x + threadIdx.x; index < local_token_num * hidden_units;
index += blockDim.x * gridDim.x) {
const int row_index = index / hidden_units;
const int col_index = index % hidden_units;
int step_offset = step;
if (use_padding_count) {
step_offset -= padding_count[row_index];
}
else if (use_input_len) {
step_offset -= max_input_length - input_lengths[row_index];
}
step_offset *= hidden_units;
T val = embedding_table[all_ids[id_offset + row_index] * hidden_units + col_index] * scale;
val = val + position_encoding[step_offset + col_index];
from_tensor[index] = val;
}
}
// No absolute position embedding
// PROMPT_SRC: 0 --> no prompts, 1 --> from loaded prompts, 2 --> from request prompts
template<typename T, int PROMPT_SRC>
__global__ void embeddingLookup(T* from_tensor,
const T* embedding_table,
const int* all_ids,
pPromptTuningParam<T> prompt_param,
const int local_token_num,
const int64_t hidden_units,
const int step,
const int token_num,
const int ite,
const int seq_len,
const T scale)
{
// 1. lookup from embedding table
// 2. multiply scale
const int id_offset = step * token_num + ite * local_token_num;
for (int64_t index = blockIdx.x * blockDim.x + threadIdx.x; index < local_token_num * hidden_units;
index += blockDim.x * gridDim.x) {
const int word_index = index / hidden_units;
const int word_index_row = word_index / seq_len; // batch_id
const int col_index = index % hidden_units;
const int input_id = all_ids == nullptr ? word_index : all_ids[id_offset + word_index];
const int prompt_id = input_id - prompt_param.p_prompt_tuning_id_start;
T embedding = (T)0.0f;
if (PROMPT_SRC > 0 && prompt_id >= 0) {
if (PROMPT_SRC == 1) {
// from loaded prompt embedding tables
embedding =
prompt_param.p_prompt_tuning_batch_weights[word_index_row][prompt_id * hidden_units + col_index];
}
else {
// from request prompt embedding
embedding =
prompt_param
.request_prompt_embedding[word_index_row * prompt_param.request_prompt_max_length * hidden_units
+ prompt_id * hidden_units + col_index];
}
}
else {
embedding = embedding_table[input_id * hidden_units + col_index];
}
from_tensor[index] = embedding * scale;
}
}
#define EMBEDDING_LOOKUP(PROMPT_SRC) \
embeddingLookup<T, PROMPT_SRC><<<grid, block, 0, stream>>>(from_tensor, \
embedding_table, \
all_ids, \
prompt_param, \
local_token_num, \
hidden_units, \
step, \
token_num, \
ite, \
seq_len, \
scale);
/* Adapter function for invokeEmbeddingLookupPosEncoding{PadCount,InputLen} */
template<typename T>
void invokeEmbeddingLookupPosEncoding(T* from_tensor,
const T* embedding_table,
const T* position_encoding,
const int* all_ids,
const int* padding_count,
const int* input_lengths,
pPromptTuningParam<T> prompt_param,
const int local_token_num,
const int hidden_units,
const T scale,
const int step,
const int max_input_length,
const int token_num,
const int ite,
const int seq_len,
cudaStream_t stream)
{
dim3 grid(min(local_token_num, 65536));
dim3 block(min(hidden_units, 1024));
if (position_encoding != nullptr) {
FT_CHECK_WITH_INFO(prompt_param.use_request_p_prompt_embedding == false
&& prompt_param.p_prompt_tuning_batch_weights == nullptr,
fmtstr("embeddingLookupPosEncoding still not support prompt tuning"));
embeddingLookupPosEncoding<T><<<grid, block, 0, stream>>>(from_tensor,
embedding_table,
position_encoding,
all_ids,
padding_count,
input_lengths,
local_token_num,
hidden_units,
step,
max_input_length,
token_num,
ite,
scale);
}
else {
if (prompt_param.use_request_p_prompt_embedding) {
EMBEDDING_LOOKUP(2);
}
else if (prompt_param.p_prompt_tuning_batch_weights != nullptr) {
EMBEDDING_LOOKUP(1);
}
else {
EMBEDDING_LOOKUP(0);
}
}
}
#undef EMBEDDING_LOOKUP
template<typename T>
void invokeEmbeddingLookupPosEncodingPadCount(T* from_tensor,
const T* embedding_table,
const T* position_encoding,
const int* all_ids,
const int* pad_count,
pPromptTuningParam<T> prompt_param,
const int local_token_num,
const int hidden_units,
const T scale,
const int step,
const int token_num,
const int ite,
const int seq_len,
cudaStream_t stream)
{
invokeEmbeddingLookupPosEncoding<T>(from_tensor,
embedding_table,
position_encoding,
all_ids,
pad_count,
nullptr,
prompt_param,
local_token_num,
hidden_units,
scale,
step,
0,
token_num,
ite,
seq_len,
stream);
}
#define INSTANTIATE_LOOKUP_POS_ENCODING_PAD_COUNT(T) \
template void invokeEmbeddingLookupPosEncodingPadCount(T* from_tensor, \
const T* embedding_table, \
const T* position_encoding, \
const int* all_ids, \
const int* pad_count, \
pPromptTuningParam<T> prompt_param, \
const int local_token_num, \
const int hidden_units, \
const T scale, \
const int step, \
const int token_num, \
const int ite, \
const int seq_len, \
cudaStream_t stream)
#ifdef ENABLE_FP32
INSTANTIATE_LOOKUP_POS_ENCODING_PAD_COUNT(float);
#endif
INSTANTIATE_LOOKUP_POS_ENCODING_PAD_COUNT(half);
#ifdef ENABLE_BF16
INSTANTIATE_LOOKUP_POS_ENCODING_PAD_COUNT(__nv_bfloat16);
#endif
#undef INSTANTIATE_LOOKUP_POS_ENCODING_PAD_COUNT
template<typename T>
__global__ void paddingEmbedding(T* padded_embedding_kernel,
T* padded_embedding_bias,
const T* embedding_kernel,
const T* embedding_bias,
const int64_t hidden_unit,
const int64_t vocab_size,
const int64_t vocab_size_padded)
{
for (int64_t id = threadIdx.x + blockIdx.x * blockDim.x; id < hidden_unit * vocab_size_padded;
id += blockDim.x * gridDim.x) {
int row_id = id / vocab_size_padded;
int col_id = id % vocab_size_padded;
if (col_id < vocab_size) {
padded_embedding_kernel[id] = embedding_kernel[row_id * vocab_size + col_id];
}
else {
padded_embedding_kernel[id] = (T)(0.0f);
}
}
for (int id = threadIdx.x + blockIdx.x * blockDim.x; id < vocab_size_padded; id += blockDim.x * gridDim.x) {
if (id < vocab_size) {
padded_embedding_bias[id] = embedding_bias[id];
}
else {
padded_embedding_bias[id] = (T)(0.0f);
}
}
}
template<typename T>
void invokePaddingEmbedding(T* padded_embedding_kernel,
T* padded_embedding_bias,
const T* embedding_kernel,
const T* embedding_bias,
const int hidden_unit,
const int vocab_size,
const int vocab_size_padded,
cudaStream_t stream)
{
dim3 block(512);
dim3 grid((int)(ceil(hidden_unit * vocab_size_padded / 512.)));
paddingEmbedding<<<grid, block, 0, stream>>>(padded_embedding_kernel,
padded_embedding_bias,
embedding_kernel,
embedding_bias,
hidden_unit,
vocab_size,
vocab_size_padded);
}
// template void invokePaddingEmbedding(float* padded_embedding_kernel,
// float* padded_embedding_bias,
// const float* embedding_kernel,
// const float* embedding_bias,
// const int hidden_unit,
// const int vocab_size,
// const int vocab_size_padded,
// cudaStream_t stream);
// template void invokePaddingEmbedding(half* padded_embedding_kernel,
// half* padded_embedding_bias,
// const half* embedding_kernel,
// const half* embedding_bias,
// const int hidden_unit,
// const int vocab_size,
// const int vocab_size_padded,
// cudaStream_t stream);
// #ifdef ENABLE_BF16
// template void invokePaddingEmbedding(__nv_bfloat16* padded_embedding_kernel,
// __nv_bfloat16* padded_embedding_bias,
// const __nv_bfloat16* embedding_kernel,
// const __nv_bfloat16* embedding_bias,
// const int hidden_unit,
// const int vocab_size,
// const int vocab_size_padded,
// cudaStream_t stream);
// #endif
template<typename T>
__global__ void paddingEmbeddingKernel(T* padded_embedding_kernel,
const T* embedding_kernel,
const int hidden_unit,
const int vocab_size,
const int vocab_size_padded)
{
for (int id = threadIdx.x + blockIdx.x * blockDim.x; id < hidden_unit * vocab_size_padded;
id += blockDim.x * gridDim.x) {
int row_id = id / hidden_unit;
int col_id = id % hidden_unit;
if (row_id < vocab_size) {
padded_embedding_kernel[id] = embedding_kernel[row_id * hidden_unit + col_id];
}
else {
padded_embedding_kernel[id] = (T)(0.0f);
}
}
}
template<typename T>
void invokePaddingEmbeddingKernel(T* padded_embedding_kernel,
const T* embedding_kernel,
const int hidden_unit,
const int vocab_size,
const int vocab_size_padded,
cudaStream_t stream)
{
dim3 block(512);
dim3 grid((int)(ceil(hidden_unit * vocab_size_padded / 512.)));
paddingEmbeddingKernel<<<grid, block, 0, stream>>>(
padded_embedding_kernel, embedding_kernel, hidden_unit, vocab_size, vocab_size_padded);
}
// template void invokePaddingEmbeddingKernel(float* padded_embedding_kernel,
// const float* embedding_kernel,
// const int hidden_unit,
// const int vocab_size,
// const int vocab_size_padded,
// cudaStream_t stream);
// template void invokePaddingEmbeddingKernel(half* padded_embedding_kernel,
// const half* embedding_kernel,
// const int hidden_unit,
// const int vocab_size,
// const int vocab_size_padded,
// cudaStream_t stream);
// #ifdef ENABLE_BF16
// template void invokePaddingEmbeddingKernel(__nv_bfloat16* padded_embedding_kernel,
// const __nv_bfloat16* embedding_kernel,
// const int hidden_unit,
// const int vocab_size,
// const int vocab_size_padded,
// cudaStream_t stream);
// #endif
template<typename T>
__global__ void plusScalar(T* buf, const T val, const int size)
{
for (int i = threadIdx.x + blockIdx.x * blockDim.x; i < size; i += blockDim.x * gridDim.x) {
buf[i] += val;
}
}
template<typename T>
void invokePlusScalar(T* buf, const T val, const int size, cudaStream_t stream)
{
dim3 block(min(256, size));
dim3 grid(ceil(size / 256.));
plusScalar<<<block, grid, 0, stream>>>(buf, val, size);
}
template void invokePlusScalar(int* buf, const int val, const int size, cudaStream_t stream);
} // namespace turbomind
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