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[Feature] support prompt repetition_penalty (#2806)
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ming1753
2025-07-17 12:05:52 +08:00
committed by GitHub
parent 7dfd2ea052
commit 1f15ca21e4
8 changed files with 305 additions and 64 deletions
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170
custom_ops/gpu_ops/token_penalty_multi_scores.cu
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@@ -20,16 +20,16 @@ __global__ inline void min_length_logits_process(T *logits,
const int64_t *min_len,
const int64_t *eos_token_id,
const int64_t bs,
const int64_t length,
const int64_t end_length) {
const int64_t vocab_size,
const int64_t eos_len) {
int bi = threadIdx.x;
if (bi >= bs) return;
if (cur_len[bi] < 0) {
return;
}
if (cur_len[bi] < min_len[bi]) {
for (int i = 0; i < end_length; i++) {
logits[bi * length + eos_token_id[i]] = -1e10;
for (int i = 0; i < eos_len; i++) {
logits[bi * vocab_size + eos_token_id[i]] = -1e10;
}
}
}
@@ -41,61 +41,83 @@ __global__ inline void min_length_logits_process<half>(
const int64_t *min_len,
const int64_t *eos_token_id,
const int64_t bs,
const int64_t length,
const int64_t end_length) {
const int64_t vocab_size,
const int64_t eos_len) {
int bi = threadIdx.x;
if (bi >= bs) return;
if (cur_len[bi] < 0) {
return;
}
if (cur_len[bi] < min_len[bi]) {
for (int i = 0; i < end_length; i++) {
logits[bi * length + eos_token_id[i]] = -1e4;
for (int i = 0; i < eos_len; i++) {
logits[bi * vocab_size + eos_token_id[i]] = -1e4;
}
}
}
__global__ void update_repeat_times(const int64_t *pre_ids,
const int64_t *prompt_ids,
const int64_t *prompt_len,
const int64_t *cur_len,
int *repeat_times,
int *is_repeated,
const int64_t bs,
const int64_t length,
const int64_t length_id) {
int bi = blockIdx.x;
const int64_t vocab_size,
const int64_t max_dec_len,
const int64_t max_model_len) {
int64_t bi = blockIdx.x;
if (cur_len[bi] < 0) {
return;
}
int tid = threadIdx.x;
const int64_t *pre_ids_now = pre_ids + bi * length_id;
int *repeat_times_now = repeat_times + bi * length;
for (int i = tid; i < length_id; i += blockDim.x) {
int64_t id = pre_ids_now[i];
if (id < 0) break;
atomicAdd(&repeat_times_now[id], 1);
const int64_t prompt_len_now = prompt_len[bi];
int64_t tid = threadIdx.x;
const int64_t *prompt_now = prompt_ids + bi * max_model_len;
const int64_t *pre_ids_now = pre_ids + bi * max_dec_len;
int *repeat_times_now = repeat_times + bi * vocab_size;
int *is_repeated_now = is_repeated + bi * vocab_size;
const int64_t loop_len = prompt_len_now > max_dec_len ? prompt_len_now : max_dec_len;
for (int64_t i = tid; i < loop_len; i += blockDim.x) {
if (i < max_dec_len) {
int64_t id = pre_ids_now[i];
if (id >= 0) {
atomicAdd(&repeat_times_now[id], 1);
atomicAdd(&is_repeated_now[id], 1);
}
}
if (i < prompt_len_now) {
int64_t id = prompt_now[i];
if (id >= 0) {
atomicAdd(&is_repeated_now[id], 1);
}
}
}
}
template <typename T>
__global__ void update_value_by_repeat_times(const int *repeat_times,
const int *is_repeated,
const T *penalty_scores,
const T *frequency_score,
const T *presence_score,
const float *temperatures,
T *logits,
const int64_t bs,
const int64_t length) {
const int64_t vocab_size) {
int bi = blockIdx.x;
int tid = threadIdx.x;
T *logits_now = logits + bi * length;
const int *repeat_times_now = repeat_times + bi * length;
T *logits_now = logits + bi * vocab_size;
const int *repeat_times_now = repeat_times + bi * vocab_size;
const int *is_repeated_now = is_repeated + bi * vocab_size;
float alpha = static_cast<float>(penalty_scores[bi]);
float beta = static_cast<float>(frequency_score[bi]);
float gamma = static_cast<float>(presence_score[bi]);
for (int i = tid; i < length; i += blockDim.x) {
for (int i = tid; i < vocab_size; i += blockDim.x) {
int times = repeat_times_now[i];
float logit_now = static_cast<float>(logits_now[i]);
if (times != 0) {
if (is_repeated_now[i] != 0) {
logit_now = logit_now < 0 ? logit_now * alpha : logit_now / alpha;
}
if (times != 0) {
logit_now = logit_now - times * beta - gamma;
}
logits_now[i] = static_cast<T>(logit_now / temperatures[bi]);
@@ -106,20 +128,22 @@ template <typename T>
__global__ void ban_bad_words(T *logits,
const int64_t *bad_words_list,
const int64_t bs,
const int64_t length,
const int64_t bad_words_length) {
const int64_t vocab_size,
const int64_t bad_words_len) {
const int bi = blockIdx.x;
int tid = threadIdx.x;
T *logits_now = logits + bi * length;
for (int i = tid; i < bad_words_length; i += blockDim.x) {
T *logits_now = logits + bi * vocab_size;
for (int i = tid; i < bad_words_len; i += blockDim.x) {
const int64_t bad_words_token_id = bad_words_list[i];
if (bad_words_token_id >= length || bad_words_token_id < 0) continue;
if (bad_words_token_id >= vocab_size || bad_words_token_id < 0) continue;
logits_now[bad_words_token_id] = -1e10;
}
}
template <paddle::DataType D>
void token_penalty_multi_scores_kernel(const paddle::Tensor &pre_ids,
const paddle::Tensor &prompt_ids,
const paddle::Tensor &prompt_len,
const paddle::Tensor &logits,
const paddle::Tensor &penalty_scores,
const paddle::Tensor &frequency_score,
@@ -141,12 +165,15 @@ void token_penalty_multi_scores_kernel(const paddle::Tensor &pre_ids,
std::vector<int64_t> shape = logits.shape();
auto repeat_times =
paddle::full(shape, 0, paddle::DataType::INT32, pre_ids.place());
auto is_repeated =
paddle::full(shape, 0, paddle::DataType::INT32, pre_ids.place());
int64_t bs = shape[0];
int64_t length = shape[1];
int64_t length_id = pre_ids.shape()[1];
int64_t length_bad_words = bad_tokens.shape()[0];
int64_t end_length = eos_token_id.shape()[0];
int64_t vocab_size = shape[1];
int64_t max_dec_len = pre_ids.shape()[1];
int64_t bad_words_len = bad_tokens.shape()[0];
int64_t eos_len = eos_token_id.shape()[0];
int64_t max_model_len = prompt_ids.shape()[1];
int block_size = (bs + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE;
min_length_logits_process<<<1, block_size, 0, cu_stream>>>(
@@ -156,10 +183,10 @@ void token_penalty_multi_scores_kernel(const paddle::Tensor &pre_ids,
min_len.data<int64_t>(),
eos_token_id.data<int64_t>(),
bs,
length,
end_length);
vocab_size,
eos_len);
block_size = (length_id + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE;
block_size = (max_dec_len + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE;
#ifdef PADDLE_WITH_COREX
block_size = std::min(block_size, 512);
#else
@@ -167,13 +194,17 @@ void token_penalty_multi_scores_kernel(const paddle::Tensor &pre_ids,
#endif
update_repeat_times<<<bs, block_size, 0, cu_stream>>>(
pre_ids.data<int64_t>(),
prompt_ids.data<int64_t>(),
prompt_len.data<int64_t>(),
cur_len.data<int64_t>(),
repeat_times.data<int>(),
is_repeated.data<int>(),
bs,
length,
length_id);
vocab_size,
max_dec_len,
max_model_len);
block_size = (length + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE;
block_size = (vocab_size + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE;
#ifdef PADDLE_WITH_COREX
block_size = std::min(block_size, 512);
#else
@@ -181,6 +212,7 @@ void token_penalty_multi_scores_kernel(const paddle::Tensor &pre_ids,
#endif
update_value_by_repeat_times<DataType_><<<bs, block_size, 0, cu_stream>>>(
repeat_times.data<int>(),
is_repeated.data<int>(),
reinterpret_cast<DataType_ *>(
const_cast<data_t *>(penalty_scores.data<data_t>())),
reinterpret_cast<DataType_ *>(
@@ -191,9 +223,9 @@ void token_penalty_multi_scores_kernel(const paddle::Tensor &pre_ids,
reinterpret_cast<DataType_ *>(
const_cast<data_t *>(logits.data<data_t>())),
bs,
length);
vocab_size);
block_size = (length_bad_words + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE;
block_size = (bad_words_len + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE;
#ifdef PADDLE_WITH_COREX
block_size = std::min(block_size, 512);
#else
@@ -204,11 +236,13 @@ void token_penalty_multi_scores_kernel(const paddle::Tensor &pre_ids,
const_cast<data_t *>(logits.data<data_t>())),
bad_tokens.data<int64_t>(),
bs,
length,
length_bad_words);
vocab_size,
bad_words_len);
}
void TokenPenaltyMultiScores(const paddle::Tensor &pre_ids,
const paddle::Tensor &prompt_ids,
const paddle::Tensor &prompt_len,
const paddle::Tensor &logits,
const paddle::Tensor &penalty_scores,
const paddle::Tensor &frequency_scores,
@@ -222,6 +256,8 @@ void TokenPenaltyMultiScores(const paddle::Tensor &pre_ids,
case paddle::DataType::BFLOAT16: {
return token_penalty_multi_scores_kernel<
paddle::DataType::BFLOAT16>(pre_ids,
prompt_ids,
prompt_len,
logits,
penalty_scores,
frequency_scores,
@@ -233,30 +269,34 @@ void TokenPenaltyMultiScores(const paddle::Tensor &pre_ids,
eos_token_id);
}
case paddle::DataType::FLOAT16: {
return token_penalty_multi_scores_kernel<paddle::DataType::FLOAT16>(
pre_ids,
logits,
penalty_scores,
frequency_scores,
presence_scores,
temperatures,
bad_tokens,
cur_len,
min_len,
eos_token_id);
return token_penalty_multi_scores_kernel<
paddle::DataType::FLOAT16>(pre_ids,
prompt_ids,
prompt_len,
logits,
penalty_scores,
frequency_scores,
presence_scores,
temperatures,
bad_tokens,
cur_len,
min_len,
eos_token_id);
}
case paddle::DataType::FLOAT32: {
return token_penalty_multi_scores_kernel<paddle::DataType::FLOAT32>(
pre_ids,
logits,
penalty_scores,
frequency_scores,
presence_scores,
temperatures,
bad_tokens,
cur_len,
min_len,
eos_token_id);
return token_penalty_multi_scores_kernel<
paddle::DataType::FLOAT32>(pre_ids,
prompt_ids,
prompt_len,
logits,
penalty_scores,
frequency_scores,
presence_scores,
temperatures,
bad_tokens,
cur_len,
min_len,
eos_token_id);
}
default: {
PD_THROW(
@@ -269,6 +309,8 @@ void TokenPenaltyMultiScores(const paddle::Tensor &pre_ids,
PD_BUILD_STATIC_OP(get_token_penalty_multi_scores)
.Inputs({"pre_ids",
"prompt_ids",
"prompt_len",
"logits",
"penalty_scores",
"frequency_scores",