wehub-resource-sync
204 lines
7.3 KiB
Plaintext
204 lines
7.3 KiB
Plaintext
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "helper.h"
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__device__ bool is_in_end_v3(const int64_t id, const int64_t *end_ids, int length) {
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bool flag = false;
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for (int i = 0; i < length; i++) {
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if (id == end_ids[i]) {
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return true;
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}
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}
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return flag;
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}
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template <int THREADBLOCK_SIZE>
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__global__ void update_inputs_kernel_v2(
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bool *not_need_stop,
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int64_t *step_idx,
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bool *stop_flags,
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int *seq_lens_this_time,
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int *seq_lens_encoder,
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int *seq_lens_decoder,
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int64_t *next_tokens,
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int64_t *kwargs_next_tokens,
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int64_t *input_ids,
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const int64_t *end_ids,
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const int64_t *stop_nums,
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const bool *is_block_step,
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const int64_t *max_dec_len,
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const int bsz,
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const int max_bsz,
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const int input_ids_stride,
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const int end_length,
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const int Flag_truncated_return_eos) {
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int thread_idx = threadIdx.x;
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bool output_len_truncated = false;
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// update step_idx and stop_flags
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if (thread_idx < max_bsz) {
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bool stop_flag = stop_flags[thread_idx];
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if (!stop_flag) {
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step_idx[thread_idx] += 1;
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}
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if (step_idx[thread_idx] >= max_dec_len[thread_idx]) {
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stop_flags[thread_idx] = true;
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output_len_truncated = true;
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}
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}
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__syncthreads();
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// update inputs
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if (thread_idx < bsz) {
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if (stop_flags[thread_idx]) {
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if (seq_lens_this_time[thread_idx] == 0) {
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next_tokens[thread_idx] = -1;
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} else {
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if (!Flag_truncated_return_eos && output_len_truncated) {
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// output len truncated will not return eos for rl.
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kwargs_next_tokens[thread_idx] = next_tokens[thread_idx];
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}else{
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next_tokens[thread_idx] = end_ids[0];
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kwargs_next_tokens[thread_idx] = end_ids[0];
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}
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}
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} else {
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kwargs_next_tokens[thread_idx] = next_tokens[thread_idx];
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}
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if (is_in_end_v3(next_tokens[thread_idx], end_ids, end_length)) {
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stop_flags[thread_idx] = true;
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}
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}
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typedef cub::BlockReduce<int64_t, THREADBLOCK_SIZE> BlockReduce;
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__shared__ typename BlockReduce::TempStorage temp_storage;
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bool stop_flag_now = false;
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int64_t stop_flag_now_int = 0;
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if (thread_idx < max_bsz) {
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if (thread_idx < bsz) {
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stop_flag_now = stop_flags[thread_idx];
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if (is_block_step[thread_idx]) {
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stop_flag_now_int=0;
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} else {
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stop_flag_now_int = static_cast<int64_t>(stop_flag_now);
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}
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} else {
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stop_flag_now_int = 1;
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}
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}
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if (thread_idx < bsz) {
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const int seq_len_this_time = seq_lens_this_time[thread_idx];
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const int seq_len_encoder = seq_lens_encoder[thread_idx];
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const int seq_len_decoder = seq_lens_decoder[thread_idx];
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seq_lens_decoder[thread_idx] = stop_flag_now ? 0 : (seq_len_encoder > 0 ? (seq_len_encoder + seq_len_decoder) : seq_len_decoder + 1);
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seq_lens_this_time[thread_idx] = stop_flag_now ? 0 : 1;
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seq_lens_encoder[thread_idx] = 0;
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int64_t *input_ids_now = input_ids + thread_idx * input_ids_stride;
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input_ids_now[0] = next_tokens[thread_idx];
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}
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__syncthreads();
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int64_t stop_sum = BlockReduce(temp_storage).Sum(stop_flag_now_int);
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if (thread_idx == 0) {
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not_need_stop[0] = stop_sum < stop_nums[0];
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}
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}
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void UpdateInputesV2(const paddle::Tensor& stop_flags,
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const paddle::Tensor& step_idx,
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const paddle::Tensor& not_need_stop, // cpu
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const paddle::Tensor& seq_lens_this_time,
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const paddle::Tensor& seq_lens_encoder,
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const paddle::Tensor& seq_lens_decoder,
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const paddle::Tensor& max_dec_len,
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const paddle::Tensor& input_ids,
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const paddle::Tensor& stop_nums,
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const paddle::Tensor& next_tokens,
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const paddle::Tensor& is_block_step,
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const paddle::Tensor& end_ids,
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const paddle::Tensor& kwargs_next_tokens) {
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const int max_bsz = stop_flags.shape()[0];
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const int now_bsz = seq_lens_this_time.shape()[0];
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const int input_ids_stride = input_ids.shape()[1];
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const int end_length = end_ids.shape()[0];
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auto not_need_stop_gpu = not_need_stop.copy_to(stop_flags.place(), false);
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int Flag_truncated_return_eos = 1;
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if (const char* inference_truncated_return_eos_env_p =
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std::getenv("INFERENCE_TRUNCATED_RETURN_EOS")) {
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std::string inference_truncated_return_eos_env_str(
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inference_truncated_return_eos_env_p);
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int inference_truncated_return_eos_from_env =
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std::stoi(inference_truncated_return_eos_env_str);
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Flag_truncated_return_eos = inference_truncated_return_eos_from_env;
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}
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update_inputs_kernel_v2<1024><<<1, 1024, 0, input_ids.stream()>>>(
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const_cast<bool*>(not_need_stop_gpu.data<bool>()),
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const_cast<int64_t*>(step_idx.data<int64_t>()),
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const_cast<bool*>(stop_flags.data<bool>()),
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const_cast<int*>(seq_lens_this_time.data<int>()),
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const_cast<int*>(seq_lens_encoder.data<int>()),
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const_cast<int*>(seq_lens_decoder.data<int>()),
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const_cast<int64_t*>(next_tokens.data<int64_t>()),
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const_cast<int64_t*>(kwargs_next_tokens.data<int64_t>()),
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const_cast<int64_t*>(input_ids.data<int64_t>()),
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end_ids.data<int64_t>(),
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stop_nums.data<int64_t>(),
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is_block_step.data<bool>(),
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max_dec_len.data<int64_t>(),
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now_bsz,
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max_bsz,
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input_ids_stride,
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end_length,
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Flag_truncated_return_eos
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);
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auto not_need_stop_cpu = not_need_stop_gpu.copy_to(not_need_stop.place(), false);
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bool *not_need_stop_data = const_cast<bool*>(not_need_stop.data<bool>());
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not_need_stop_data[0] = not_need_stop_cpu.data<bool>()[0];
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}
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PD_BUILD_OP(update_inputs_v2)
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.Inputs({"stop_flags",
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"step_idx",
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"not_need_stop",
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"seq_lens_this_time",
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"seq_lens_encoder",
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"seq_lens_decoder",
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"max_dec_len",
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"input_ids",
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"stop_nums",
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"next_tokens",
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"is_block_step",
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"end_ids",
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"kwargs_next_tokens"})
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.Outputs({"not_need_stop_out",
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"seq_lens_this_time_out",
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"seq_lens_encoder_out",
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"seq_lens_decoder_out",
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"input_ids_out",
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"next_tokens_out",
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"kwargs_next_tokens_out",
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"step_idx_out"})
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.SetInplaceMap({{"not_need_stop", "not_need_stop_out"},
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{"seq_lens_this_time", "seq_lens_this_time_out"},
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{"seq_lens_encoder", "seq_lens_encoder_out"},
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{"seq_lens_decoder", "seq_lens_decoder_out"},
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{"input_ids", "input_ids_out"},
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{"next_tokens", "next_tokens_out"},
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{"kwargs_next_tokens", "kwargs_next_tokens_out"},
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{"step_idx", "step_idx_out"}})
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.SetKernelFn(PD_KERNEL(UpdateInputesV2)); |