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support speculate_limit_thinking_content_length_v2 (#4428)
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* support speculate_limit_thinking_content_length_v2 * fix * fix import
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@@ -678,6 +678,16 @@ void SpeculateVerify(
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const paddle::Tensor &actual_draft_token_nums, const paddle::Tensor &topp,
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int max_seq_len, int verify_window, bool enable_topp, bool benchmark_mode, bool accept_all_drafts);
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void SpeculateLimitThinkingContentLengthV2(
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const paddle::Tensor& next_tokens,
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const paddle::Tensor& max_think_lens,
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const paddle::Tensor& step_idx,
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const paddle::Tensor& limit_think_status,
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const paddle::Tensor& accept_num,
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const paddle::Tensor& seq_lens_decoder,
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const int64_t think_end_id,
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const int64_t line_break_id);
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void SpeculateUpdate(const paddle::Tensor &seq_lens_encoder,
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const paddle::Tensor &seq_lens_decoder,
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const paddle::Tensor ¬_need_stop,
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@@ -1245,6 +1255,8 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
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m.def("speculate_verify",&SpeculateVerify, "speculate_verify function");
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m.def("speculate_limit_thinking_content_length_v2",&SpeculateLimitThinkingContentLengthV2, "speculate limit thinking content length function");
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m.def("speculate_update",&SpeculateUpdate, "Speculate Update Kernel");
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m.def("speculate_set_value_by_flags_and_idx",&SpeculateSetValueByFlagsAndIdx, "speculate_set_value_by_flags_and_idx function");
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@@ -0,0 +1,159 @@
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// Copyright (c) 2025 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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#include "paddle/extension.h"
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// status == 0: 正常生成阶段
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// status == 1: 替换阶段
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// status == 2: 替换结束阶段
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// status == 3: 思考结束阶段
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__global__ void speculate_limit_thinking_content_length_kernel_v2(
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int64_t* next_tokens,
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const int* max_think_lens,
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int64_t* step_idx,
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int* limit_think_status,
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int* accept_num,
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int* seq_lens_decoder,
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const int64_t think_end_id,
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const int64_t line_break_id,
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const int tokens_per_step,
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const int bs) {
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int bid = threadIdx.x;
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if (bid >= bs) return;
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const int original_accept_num = accept_num[bid];
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if (original_accept_num <= 0) return;
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// 如果该序列未启用思考功能,则直接返回,默认值为 -1,表示不限制思考长度
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const int max_think_len = max_think_lens[bid];
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if (max_think_len < 0) return;
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int current_limit_think_status = limit_think_status[bid];
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// 如果在回复阶段, 且已经触发停止标志, 则直接返回, 无需多余执行.
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if (current_limit_think_status == 3) {
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return;
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}
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int new_accept_num = original_accept_num;
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const int64_t current_base_step = step_idx[bid] - original_accept_num + 1;
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for (int token_offset = 0; token_offset < original_accept_num;
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token_offset++) {
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const int token_idx = bid * tokens_per_step + token_offset;
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int64_t next_token = next_tokens[token_idx];
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const int64_t current_step = current_base_step + token_offset;
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bool condition_triggered = false;
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// ======================= 思考阶段控制 =======================
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// 阶段 1: 仍在思考 (status == 0), 检查是否需要强制结束
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// 阶段 2: 在替换 (status == 1), 检查是否替换结束
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if (current_limit_think_status <= 1) {
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// 当开启思考长度控制时,检查是否超时
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if (current_step == max_think_len) {
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// 强制将当前token替换为结束思考的token
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next_token = line_break_id;
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current_limit_think_status = 1;
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condition_triggered = true; // 因为修改了token,需要截断
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} else if (current_step == max_think_len + 1) {
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// 强制将当前token替换为结束思考的token
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next_token = think_end_id;
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current_limit_think_status = 1;
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condition_triggered = true; // 因为修改了token,需要截断
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} else if (current_step == max_think_len + 2) {
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// 强制将当前token替换为结束思考的token
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next_token = line_break_id;
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current_limit_think_status = 1;
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condition_triggered = true; // 因为修改了token,需要截断
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} else if (current_step == max_think_len + 3) {
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// 强制将当前token替换为结束思考的token
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next_token = line_break_id;
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// 将状态推进到 1, 表示 "正在结束思考"
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current_limit_think_status = 2;
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condition_triggered = true; // 因为修改了token,需要截断
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}
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}
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// ======================= 思考结束处理 =======================
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// 阶段 3: 检查是否已满足结束思考的条件 (status == 0 || status == 2)
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// 这种情况会处理两种场景:
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// 1. status == 0: 模型可能自己生成了 </think>
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// 2. status == 2: 上一阶段强制注入了 \n</think>\n\n
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if (current_limit_think_status == 0) {
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if (next_token == think_end_id) {
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// 确认思考结束,将状态推进到 3 (响应阶段)
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current_limit_think_status = 3;
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}
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}
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if (current_limit_think_status == 2) {
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// 确认思考结束,将状态推进到 3 (响应阶段)
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current_limit_think_status = 3;
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}
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next_tokens[token_idx] = next_token;
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if (condition_triggered) {
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new_accept_num = token_offset + 1;
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break;
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}
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}
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// 更新全局状态
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int discarded_tokens = original_accept_num - new_accept_num;
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if (discarded_tokens > 0) {
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step_idx[bid] -= discarded_tokens;
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seq_lens_decoder[bid] -= discarded_tokens;
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}
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accept_num[bid] = new_accept_num;
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limit_think_status[bid] = current_limit_think_status;
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}
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void SpeculateLimitThinkingContentLengthV2(
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const paddle::Tensor& next_tokens,
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const paddle::Tensor& max_think_lens,
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const paddle::Tensor& step_idx,
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const paddle::Tensor& limit_think_status,
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const paddle::Tensor& accept_num,
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const paddle::Tensor& seq_lens_decoder,
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const int64_t think_end_id,
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const int64_t line_break_id) {
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const int batch_size = next_tokens.shape()[0];
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const int tokens_per_step = next_tokens.shape()[1];
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speculate_limit_thinking_content_length_kernel_v2<<<1, 1024>>>(
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const_cast<int64_t*>(next_tokens.data<int64_t>()),
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max_think_lens.data<int>(),
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const_cast<int64_t*>(step_idx.data<int64_t>()),
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const_cast<int*>(limit_think_status.data<int>()),
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const_cast<int*>(accept_num.data<int>()),
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const_cast<int*>(seq_lens_decoder.data<int>()),
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think_end_id,
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line_break_id,
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tokens_per_step,
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batch_size);
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}
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PD_BUILD_STATIC_OP(speculate_limit_thinking_content_length_v2)
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.Inputs({"next_tokens",
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"max_think_lens",
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"step_idx",
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"limit_think_status",
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"accept_num",
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"seq_lens_decoder"})
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.Attrs({"think_end_id: int64_t", "line_break_id: int64_t"})
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.Outputs({"next_tokens_out"})
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.SetInplaceMap({{"next_tokens", "next_tokens_out"}})
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.SetKernelFn(PD_KERNEL(SpeculateLimitThinkingContentLengthV2));
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@@ -37,7 +37,7 @@ __global__ void speculate_set_value_by_flag_and_id(int64_t *pre_ids_all,
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const int seq_len_dec = seq_lens_decoder[tid];
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const int seq_len_enc = seq_lens_encoder[tid];
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if (seq_len_dec == 0 && seq_len_enc == 0) return; // stoped
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if (step_idx[tid] >= 0) {
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if (step_idx[tid] > 0) {
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for (int i = 0; i < accept_num[tid]; i++) {
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pre_ids_all_now[step_idx[tid] - i] =
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accept_tokens_now[accept_num[tid] - 1 - i];
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@@ -39,6 +39,13 @@ from fastdeploy.model_executor.layers.sample.ops import (
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from fastdeploy.platforms import current_platform
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from fastdeploy.worker.output import LogprobsTensors, SamplerOutput
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if current_platform.is_cuda():
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from fastdeploy.model_executor.ops.gpu import (
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speculate_limit_thinking_content_length_v2,
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speculate_verify,
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top_p_candidates,
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)
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def top_p_normalize_probs_paddle(
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probs: paddle.Tensor,
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@@ -396,11 +403,9 @@ class SpeculativeSampler(nn.Layer):
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max_model_len: int,
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share_inputs: List[paddle.Tensor],
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accept_all_drafts: bool = False,
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think_end_id: int = -1,
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line_break_id: int = -1,
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) -> paddle.Tensor:
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""" """
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from fastdeploy.model_executor.ops.gpu import speculate_verify, top_p_candidates
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logits = apply_speculative_penalty_multi_scores(
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sampling_metadata.pre_token_ids,
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logits,
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@@ -455,6 +460,18 @@ class SpeculativeSampler(nn.Layer):
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accept_all_drafts,
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)
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if think_end_id > 0 and line_break_id > 0:
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speculate_limit_thinking_content_length_v2(
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share_inputs["accept_tokens"],
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share_inputs["max_think_lens"],
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share_inputs["step_idx"],
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share_inputs["limit_think_status"],
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share_inputs["accept_num"],
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share_inputs["seq_lens_decoder"],
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think_end_id,
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line_break_id,
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)
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return None
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