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* Update serving_chat.py * Update serving_completion.py * Update serving_completion.py * mv connection_manager init * [BugFix] fix kv cache * fix format * [Feature] support clear data --------- Co-authored-by: Yuanle Liu <yuanlehome@163.com> Co-authored-by: RAM <gstian5555@outlook.com>
600 lines
25 KiB
Python
600 lines
25 KiB
Python
"""
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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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"""
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import copy
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import os
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import threading
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import time
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import traceback
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import weakref
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from collections import Counter
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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from fastdeploy import envs
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from fastdeploy.engine.request import CompletionOutput, RequestMetrics, RequestOutput
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from fastdeploy.inter_communicator import IPCSignal
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from fastdeploy.metrics.metrics import main_process_metrics
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from fastdeploy.platforms import current_platform
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from fastdeploy.utils import llm_logger, spec_logger
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from fastdeploy.worker.output import LogprobsLists
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RECOVERY_STOP_SIGNAL = -3
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MAX_BSZ = 512
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K = 20
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MAX_DRAFT_TOKENS = 6
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SPECULATE_MAX_BSZ = 256
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class TokenProcessor:
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"""
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get Token/Score from Paddle inference engine
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"""
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def __init__(self, cfg, cached_generated_tokens, engine_worker_queue, split_connector):
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import paddle
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paddle.device.set_device("cpu")
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self.cfg = cfg
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self.cached_generated_tokens = cached_generated_tokens
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self.resource_manager = None
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self.engine_worker_queue = engine_worker_queue
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self.tokens_counter = Counter()
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self.split_connector = split_connector
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self.speculative_decoding = self.cfg.speculative_config.method is not None
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self.use_logprobs = self.cfg.model_config.enable_logprob
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if self.speculative_decoding:
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self.output_tokens = paddle.full(
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shape=[SPECULATE_MAX_BSZ * MAX_DRAFT_TOKENS + SPECULATE_MAX_BSZ + 2],
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fill_value=2,
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dtype="int64",
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)
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elif self.use_logprobs:
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self.output_tokens = paddle.full(shape=[MAX_BSZ * (K + 1) + 2, 1], fill_value=2, dtype="int64")
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self.output_scores = paddle.full(shape=[MAX_BSZ * (K + 1), 1], fill_value=0.0, dtype="float32")
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self.output_ranks = paddle.full(shape=[MAX_BSZ], fill_value=0, dtype="int64")
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else:
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self.output_tokens = paddle.full(shape=[MAX_BSZ + 2, 1], fill_value=2, dtype="int64")
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self.worker = None
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self.statics_start_time = time.time()
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self.number_of_tasks = 0
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self.number_of_input_tokens = 0
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self.number_of_output_tokens = 0
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self.total_step = 0
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self.speculative_stats_step = 0
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self.num_draft_tokens = 0
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self.num_accepted_tokens = 0
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self.num_emitted_tokens = 0
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self.max_num_emitted_tokens = 0
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self.num_rest_requests_per_head = [
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0,
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] * MAX_DRAFT_TOKENS
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self.num_accept_requests_per_head = [
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0,
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] * MAX_DRAFT_TOKENS
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prefill_time_data = np.zeros([100], dtype=np.float32)
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self.prefill_time_signal = IPCSignal(
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name="prefill_time_signal",
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array=prefill_time_data,
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dtype=np.float32,
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suffix=os.getpid(),
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create=True,
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)
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self.executor = ThreadPoolExecutor(max_workers=1)
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self.prefill_result_status = dict()
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self._finalizer = weakref.finalize(self, self._cleanup_resources)
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def _cleanup_resources(self):
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"""Cleaning up shared memory resources"""
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if hasattr(self, "prefill_time_signal"):
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self.prefill_time_signal.clear()
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if hasattr(self, "executor"):
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self.executor.shutdown(wait=False)
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def set_resource_manager(self, resource_manager):
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"""
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set ResourceManager
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Args:
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resource_manager (ResourceManager)
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"""
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assert self.resource_manager is None, "The resource manager is not None, cannot set again."
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self.resource_manager = resource_manager
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def run(self):
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"""
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start thread to get tokens
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"""
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assert self.resource_manager is not None, "The resource manager is None, cannot run."
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if self.worker is not None:
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raise Exception("Worker is already running!")
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self.worker = threading.Thread(target=self.process_sampling_results)
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self.worker.daemon = True
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self.worker.start()
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def process_sampling_results(self):
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"""
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read tokens from paddle inference engine and process
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"""
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if current_platform.is_xpu():
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from fastdeploy.model_executor.ops.xpu import get_output
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elif current_platform.is_iluvatar():
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from fastdeploy.model_executor.ops.iluvatar import get_output
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elif current_platform.is_gcu():
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from fastdeploy.model_executor.ops.gcu import get_output
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else:
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from fastdeploy.model_executor.ops.gpu import (
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get_output,
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get_output_ep,
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get_output_topk,
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speculate_get_output,
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)
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rank_id = self.cfg.parallel_config.local_data_parallel_id
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while True:
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try:
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is_blocking = True
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if self.speculative_decoding:
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speculate_get_output(self.output_tokens, rank_id, is_blocking, False)
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if self.output_tokens[0] == -2:
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continue
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else:
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if (
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self.cfg.parallel_config.enable_expert_parallel
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and self.cfg.parallel_config.data_parallel_size > 1
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):
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get_output_ep(self.output_tokens, rank_id, is_blocking)
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else:
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if self.use_logprobs:
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get_output_topk(
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self.output_tokens,
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self.output_scores,
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self.output_ranks,
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K,
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rank_id,
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is_blocking,
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)
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else:
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get_output(self.output_tokens, rank_id, is_blocking)
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if self.output_tokens[0, 0] == -2:
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continue
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llm_logger.debug(f"rank_id {rank_id} self.output_tokens[0, 0] {self.output_tokens[0, 0]}")
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self._process_prefill_metrics()
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self._process_batch_output()
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except Exception as e:
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llm_logger.info(f"while get input_data error: {e} {traceback.format_exc()!s}")
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def _process_prefill_metrics(self):
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"""Asynchronous processing prefill time indicators"""
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def process_metrics():
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try:
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current_index = 0
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while current_index < len(self.prefill_time_signal.value):
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prefill_time = self.prefill_time_signal.value[current_index]
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if prefill_time > 0:
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main_process_metrics.request_prefill_time.observe(prefill_time)
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self.prefill_time_signal.value[current_index] = 0
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current_index += 1
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except Exception as e:
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llm_logger.error(f"Error processing prefill metrics: {e}, {str(traceback.format_exc())}")
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self.executor.submit(process_metrics)
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def postprocess(self, batch_result):
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"""
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single post-processing function
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Args:
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batch_result (list): batch results
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"""
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try:
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self.cached_generated_tokens.put_results(batch_result)
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except Exception as e:
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llm_logger.error(f"Error in TokenProcessor's postprocess: {e}, {str(traceback.format_exc())}")
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def _recycle_resources(self, task_id, index, task, result=None, is_prefill=False):
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"""
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recycle resources
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"""
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if is_prefill:
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while True:
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finished_task_ids = self.engine_worker_queue.get_finished_req()
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if len(finished_task_ids) > 0:
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for finished_task_id in finished_task_ids:
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llm_logger.info(f"finished_task_id: {finished_task_id}")
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self.prefill_result_status[finished_task_id[0]] = finished_task_id[1]
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if task_id in self.prefill_result_status:
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self.split_connector.send_first_token(task.disaggregate_info, [result])
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self.resource_manager.stop_flags[index] = True
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self.resource_manager.tasks_list[index] = None
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self.resource_manager._recycle_block_tables(task)
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if self.prefill_result_status[task_id] != "finished":
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result.error_code = 400
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result.error_message = f"{task_id} failed to {self.prefill_result_status[task_id]}"
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del self.resource_manager.req_dict[task_id]
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break
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else:
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time.sleep(0.002)
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else:
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if envs.ENABLE_V1_KVCACHE_SCHEDULER:
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self.resource_manager.finish_requests_async(task_id)
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else:
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self.resource_manager.stop_flags[index] = True
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self.resource_manager.tasks_list[index] = None
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self.resource_manager._recycle_block_tables(task)
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task_used_block_num = sum([len(task.block_tables) if task else 0 for task in self.resource_manager.tasks_list])
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main_process_metrics.available_gpu_block_num.set(
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self.resource_manager.total_block_number() - task_used_block_num
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)
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main_process_metrics.batch_size.set(
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self.resource_manager.max_num_seqs - self.resource_manager.available_batch()
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)
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if task_id in self.tokens_counter:
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del self.tokens_counter[task_id]
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def _compute_speculative_status(self):
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# TODO(liuzichang): Supplement more statistics
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interval = 10
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if self.speculative_stats_step % interval == 0:
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accept_ratio = 1 - self.total_step * 1.0 / self.number_of_output_tokens
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spec_logger.info(
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f"Speculate global accept ratio(Accept draft_tokens/Generated tokens): {accept_ratio}"
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f" total step: {self.total_step}. total output token num: {self.number_of_output_tokens}"
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f" avarage accept len: {self.number_of_output_tokens / self.total_step}"
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)
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if self.cfg.speculative_config.method in ["mtp"]:
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single_head_acceptance_rates = []
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for head in range(self.cfg.speculative_config.num_speculative_tokens):
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if self.num_rest_requests_per_head[head] != 0:
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single_head_acceptance_rates.append(
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self.num_accept_requests_per_head[head] / self.num_rest_requests_per_head[head]
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)
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else:
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single_head_acceptance_rates.append(0)
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spec_logger.info(f" Single head accept ratio: {single_head_acceptance_rates}")
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if self.number_of_output_tokens > 1000000:
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self.number_of_output_tokens = 0
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self.total_step = 0
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self.speculative_stats_step += 1
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def _process_batch_output(self):
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"""
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batch post-processing function
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"""
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tokens = self.output_tokens.numpy()
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scores = None
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ranks = None
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if self.cfg.speculative_config.method:
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batch = self.output_tokens[1]
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accept_num = tokens[2 : batch + 2]
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self._record_speculative_decoding_mertics(accept_num)
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elif self.use_logprobs:
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batch = self.output_tokens[1, 0]
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tokens = tokens[2 : batch * (K + 1) + 2].reshape([batch, K + 1])[:, : (K + 1)]
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scores = self.output_scores[: batch * (K + 1)].numpy().reshape([batch, K + 1])[:, : (K + 1)]
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ranks = self.output_ranks[:batch].numpy()
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else:
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batch = self.output_tokens[1, 0]
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tokens = tokens[2 : batch + 2]
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batch_result = list()
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if envs.ENABLE_V1_KVCACHE_SCHEDULER:
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need_to_be_reschedule_req_ids = list(self.resource_manager.to_be_rescheduled_request_id_set)
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for request_id in need_to_be_reschedule_req_ids:
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if self.resource_manager.requests[request_id].idx >= (
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batch - 1
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): # No more token generated for preempted request
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self.resource_manager.reschedule_preempt_task(request_id)
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for i in range(batch):
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if self.resource_manager.stop_flags[i]:
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continue
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recovery_stop = False
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task = self.resource_manager.tasks_list[i]
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task_id = task.request_id
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if self.cfg.speculative_config.method:
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token_ids = tokens[
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2
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+ SPECULATE_MAX_BSZ
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+ i * MAX_DRAFT_TOKENS : 2
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+ SPECULATE_MAX_BSZ
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+ i * MAX_DRAFT_TOKENS
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+ accept_num[i]
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].tolist()
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if len(token_ids) == 0 or token_ids[-1] <= 0:
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continue
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else:
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token_id = int(tokens[i, 0])
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token_ids = [token_id]
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recovery_stop = token_id == RECOVERY_STOP_SIGNAL
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if recovery_stop:
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llm_logger.info(f"recovery stop signal found at task {task_id}")
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if not recovery_stop and token_id < 0:
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if envs.ENABLE_V1_KVCACHE_SCHEDULER:
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if task_id in self.resource_manager.to_be_rescheduled_request_id_set:
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self.resource_manager.reschedule_preempt_task(task_id)
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continue
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if task.get("prefill_chunk_info", None) is not None:
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prefill_chunk_num = task.get("prefill_chunk_num", 0)
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task.prefill_chunk_num = prefill_chunk_num + 1
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if task.prefill_chunk_num < len(task.prefill_chunk_info):
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continue
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self.total_step += 1
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current_time = time.time()
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if self.tokens_counter[task_id] == 0:
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metrics = RequestMetrics(
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arrival_time=task.arrival_time,
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inference_start_time=task.inference_start_time,
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model_execute_time=time.time() - task.inference_start_time,
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first_token_time=time.time() - task.inference_start_time,
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time_in_queue=task.schedule_start_time - task.preprocess_end_time,
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preprocess_cost_time=task.preprocess_end_time - task.preprocess_start_time,
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request_start_time=task.arrival_time,
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)
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self._record_first_token_metrics(task, current_time)
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else:
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metrics = RequestMetrics(
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arrival_time=time.time(),
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request_start_time=task.arrival_time,
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model_execute_time=time.time() - task.inference_start_time,
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)
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self.number_of_output_tokens += len(token_ids)
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self._record_metrics(task, current_time, token_ids)
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result = RequestOutput(
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request_id=task_id,
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outputs=CompletionOutput(
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index=i,
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send_idx=self.tokens_counter[task_id],
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token_ids=[],
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draft_token_ids=[],
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),
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finished=False,
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metrics=metrics,
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)
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if self.tokens_counter[task_id] == 0:
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if task.messages is not None:
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result.prompt = task.messages
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result.num_cached_tokens = task.num_cached_tokens
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is_prefill = task.disaggregate_info is not None and task.disaggregate_info["role"] == "prefill"
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if is_prefill and len(token_ids) > 1:
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result.outputs.draft_token_ids = copy.deepcopy(token_ids)
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for token_id in token_ids:
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self.tokens_counter[task_id] += 1
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if token_id != RECOVERY_STOP_SIGNAL:
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result.outputs.token_ids.append(token_id)
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task.output_token_ids.append(token_id)
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if self.use_logprobs:
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result.outputs.logprob = float(scores[i, 0])
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# Construct top_logprobs
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topk_token_ids = tokens[i, :].tolist()
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topk_logprobs = scores[i, :].tolist()
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sampled_rank = ranks[i].item()
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result.outputs.top_logprobs = LogprobsLists(
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logprob_token_ids=[topk_token_ids],
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logprobs=[topk_logprobs],
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sampled_token_ranks=[sampled_rank],
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)
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if token_id in task.eos_token_ids or is_prefill or recovery_stop:
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result.finished = True
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if recovery_stop:
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result.error_msg = "Recover is not supported, the result is incomplete!"
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llm_logger.info(
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f"Request: {task_id} finished, number of " f"generated tokens: {self.tokens_counter[task_id]}."
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)
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llm_logger.info(
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f"Request: {task_id} token ratio: {self.tokens_counter[task_id] / (time.time() - task.inference_start_time)}"
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)
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llm_logger.info(f"{self.resource_manager.info()}")
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if self.cfg.speculative_config.method:
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self._compute_speculative_status()
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if not is_prefill:
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self._record_completion_metrics(task, current_time)
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self._recycle_resources(task_id, i, task, result, is_prefill)
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break
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if not is_prefill or self.cfg.scheduler_config.name == "splitwise":
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batch_result.append(result)
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self.postprocess(batch_result)
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def _record_metrics(self, task, current_time, token_ids):
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"""Record all metrics for a task"""
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if hasattr(task, "last_token_time") and task.last_token_time is not None:
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token_gen_time = current_time - task.last_token_time
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main_process_metrics.time_per_output_token.observe(token_gen_time)
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task.last_token_time = current_time
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# Record generation metrics
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main_process_metrics.generation_tokens_total.inc(len(token_ids))
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def _record_first_token_metrics(self, task, current_time):
|
|
"""Record metrics for first token"""
|
|
task.first_token_time = current_time
|
|
main_process_metrics.first_token_latency.set(current_time - task.inference_start_time)
|
|
main_process_metrics.time_to_first_token.observe(current_time - task.inference_start_time)
|
|
main_process_metrics.request_queue_time.observe(task.schedule_start_time - task.preprocess_end_time)
|
|
|
|
def _record_completion_metrics(self, task, current_time):
|
|
"""Record metrics when request completes"""
|
|
if hasattr(task, "first_token_time"):
|
|
decode_time = current_time - task.first_token_time
|
|
main_process_metrics.request_decode_time.observe(decode_time)
|
|
|
|
main_process_metrics.num_requests_running.dec(1)
|
|
main_process_metrics.request_success_total.inc()
|
|
main_process_metrics.infer_latency.set(current_time - task.inference_start_time)
|
|
main_process_metrics.request_inference_time.observe(current_time - task.inference_start_time)
|
|
main_process_metrics.request_generation_tokens.observe(self.tokens_counter[task.request_id])
|
|
|
|
def clear_data(self):
|
|
if envs.ENABLE_V1_KVCACHE_SCHEDULER:
|
|
self.resource_manager.clear_data()
|
|
for i in range(self.cfg.max_num_seqs):
|
|
if self.resource_manager.stop_flags[i]:
|
|
continue
|
|
task = self.resource_manager.tasks_list[i]
|
|
result = RequestOutput(
|
|
request_id=task.request_id,
|
|
outputs=CompletionOutput(
|
|
index=i,
|
|
send_idx=self.tokens_counter[task.request_id],
|
|
token_ids=task.eos_token_ids,
|
|
draft_token_ids=[],
|
|
),
|
|
finished=True,
|
|
metrics=RequestMetrics(
|
|
arrival_time=time.time(),
|
|
request_start_time=task.arrival_time,
|
|
),
|
|
)
|
|
is_prefill = task.disaggregate_info is not None and task.disaggregate_info["role"] == "prefill"
|
|
self._recycle_resources(task.request_id, i, task, result, is_prefill)
|
|
llm_logger.warning(f"clear data for task {task.request_id}")
|
|
|
|
def _record_speculative_decoding_mertics(self, accept_num):
|
|
"""Record metrics of speculative decoding"""
|
|
if not hasattr(main_process_metrics, "spec_decode_draft_acceptance_rate"):
|
|
main_process_metrics._init_speculative_metrics(
|
|
self.cfg.speculative_config.method,
|
|
self.cfg.speculative_config.num_speculative_tokens,
|
|
)
|
|
|
|
real_accept_num = [x for x in accept_num if x != 0]
|
|
num_accepted_tokens = sum([x - 1 for x in real_accept_num])
|
|
self.num_accepted_tokens += num_accepted_tokens
|
|
num_emitted_tokens = sum(real_accept_num)
|
|
self.num_emitted_tokens += num_emitted_tokens
|
|
|
|
main_process_metrics.spec_decode_num_accepted_tokens_total.inc(num_accepted_tokens)
|
|
main_process_metrics.spec_decode_num_emitted_tokens_total.inc(num_emitted_tokens)
|
|
|
|
if self.cfg.speculative_config.method in ["ngram"]:
|
|
main_process_metrics.spec_decode_draft_acceptance_rate.set(
|
|
self.num_accepted_tokens / self.num_emitted_tokens
|
|
)
|
|
|
|
if self.cfg.speculative_config.method in ["mtp"]:
|
|
num_draft_tokens = len(real_accept_num) * self.cfg.speculative_config.num_speculative_tokens
|
|
self.num_draft_tokens += num_draft_tokens
|
|
|
|
self.max_num_emitted_tokens += len(real_accept_num) * (
|
|
self.cfg.speculative_config.num_speculative_tokens + 1
|
|
)
|
|
|
|
main_process_metrics.spec_decode_draft_acceptance_rate.set(
|
|
self.num_accepted_tokens / self.num_draft_tokens
|
|
)
|
|
main_process_metrics.spec_decode_efficiency.set(self.num_emitted_tokens / self.max_num_emitted_tokens)
|
|
main_process_metrics.spec_decode_num_draft_tokens_total.inc(num_draft_tokens)
|
|
|
|
num_rest_requests = len(real_accept_num)
|
|
for head in range(self.cfg.speculative_config.num_speculative_tokens):
|
|
num_accept_requests = len([x for x in real_accept_num if x >= head + 2])
|
|
# Accumulate the number of requests for each head
|
|
self.num_accept_requests_per_head[head] += num_accept_requests
|
|
self.num_rest_requests_per_head[head] += num_rest_requests
|
|
# Update the rest requests for each head
|
|
num_rest_requests = num_accept_requests
|
|
# Calculate the acceptance rate for each head
|
|
if self.num_rest_requests_per_head[head] != 0:
|
|
single_head_acceptance_rate = (
|
|
self.num_accept_requests_per_head[head] / self.num_rest_requests_per_head[head]
|
|
)
|
|
else:
|
|
single_head_acceptance_rate = 0
|
|
main_process_metrics.spec_decode_draft_single_head_acceptance_rate[head].set(
|
|
single_head_acceptance_rate
|
|
)
|
|
|
|
|
|
class WarmUpTokenProcessor(TokenProcessor):
|
|
"""
|
|
Warmup Processor
|
|
"""
|
|
|
|
def __init__(self, cfg):
|
|
super().__init__(cfg)
|
|
self._is_running = True
|
|
self._is_blocking = True
|
|
|
|
def postprocess(self, batch_result):
|
|
pass
|
|
|
|
def process_sampling_results(self):
|
|
"""
|
|
get output from model and process it
|
|
"""
|
|
|
|
if current_platform.is_xpu():
|
|
from fastdeploy.model_executor.ops.xpu import get_output
|
|
elif current_platform.is_iluvatar():
|
|
from fastdeploy.model_executor.ops.iluvatar import get_output
|
|
else:
|
|
from fastdeploy.model_executor.ops.gpu import (
|
|
get_output,
|
|
speculate_get_output,
|
|
)
|
|
|
|
while self._is_running:
|
|
try:
|
|
rank_id = 0
|
|
if self.speculative_decoding:
|
|
speculate_get_output(self.output_tokens, rank_id, self._is_blocking)
|
|
if self.output_tokens[0] == -2:
|
|
continue
|
|
else:
|
|
get_output(self.output_tokens, rank_id, self._is_blocking)
|
|
|
|
if self.output_tokens[0, 0] == -2:
|
|
continue
|
|
self._process_batch_output()
|
|
except Exception as e:
|
|
llm_logger.info(f"while get input_data error: {e} {traceback.format_exc()!s}")
|
|
|
|
def stop(self):
|
|
"""
|
|
stop warm up thread
|
|
"""
|
|
self._is_running = False
|
|
self.worker.join()
|
|
llm_logger.info("warm up thread stop")
|
|
del self.worker
|