mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
Sync v2.0 version of code to github repo
This commit is contained in:
Jiang-Jia-Jun
@@ -13,18 +13,28 @@
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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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from paddlenlp.utils.env import MAX_BSZ, MAX_DRAFT_TOKENS, SPECULATE_MAX_BSZ
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import numpy as np
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from fastdeploy.engine.request import (CompletionOutput, RequestMetrics,
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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.utils import llm_logger
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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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RECOVERY_STOP_SIGNAL = -3
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MAX_BSZ = 512
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MAX_DRAFT_TOKENS = 6
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SPECULATE_MAX_BSZ = 256
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class TokenProcessor(object):
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@@ -32,20 +42,23 @@ class TokenProcessor(object):
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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):
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def __init__(self, cfg, cached_generated_tokens, engine_worker_queue,
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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.is_speculate_decoding = False
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if self.is_speculate_decoding:
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self.speculative_decoding = self.cfg.speculative_config.method is not None
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if self.speculative_decoding:
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self.output_tokens = paddle.full(shape=[
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SPECULATE_MAX_BSZ * MAX_DRAFT_TOKENS + SPECULATE_MAX_BSZ + 2, 1
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SPECULATE_MAX_BSZ * MAX_DRAFT_TOKENS + SPECULATE_MAX_BSZ + 2
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],
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fill_value=2,
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dtype="int64")
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@@ -60,6 +73,24 @@ class TokenProcessor(object):
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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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prefill_time_data = np.zeros([100], dtype=np.float32)
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self.prefill_time_signal = IPCSignal(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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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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@@ -88,39 +119,61 @@ class TokenProcessor(object):
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"""
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read tokens from paddle inference engine and process
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"""
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from fastdeploy.model_executor.models import \
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inference_runner_supported_models
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if self.cfg.model_config.architectures not in inference_runner_supported_models \
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and "ErnieMoEVLForCausalLM" not in self.cfg.model_config.architectures:
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from paddlenlp_ops import get_output, speculate_get_output
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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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else:
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os.environ["ELLM_LOG_LEVEL"] = "3"
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use_pip_eff_llm = os.getenv('USE_PIP_EFF_LLM')
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if use_pip_eff_llm is None:
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from fastdeploy.model_executor.ops.gpu import (
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get_output, speculate_get_output)
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else:
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from efficientllm.ops.gpu import (get_output,
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speculate_get_output)
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from fastdeploy.model_executor.ops.gpu import (
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get_output, get_output_ep, speculate_get_output)
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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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rank_id = 0
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is_blocking = True
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if self.is_speculate_decoding:
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if self.speculative_decoding:
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speculate_get_output(self.output_tokens, rank_id,
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is_blocking)
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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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get_output(self.output_tokens, rank_id, is_blocking)
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if self.cfg.parallel_config.enable_expert_parallel and \
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self.cfg.parallel_config.data_parallel_size > 1:
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get_output_ep(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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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(
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f"rank_id {rank_id} self.output_tokens[0, 0] {self.output_tokens[0, 0]}"
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)
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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("while get input_data error: {0} {1}".format(
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e, str(traceback.format_exc())))
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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[
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current_index]
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if prefill_time > 0:
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main_process_metrics.request_prefill_time.observe(
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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}")
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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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@@ -128,57 +181,116 @@ class TokenProcessor(object):
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Args:
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batch_result (list): batch results
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"""
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self.cached_generated_tokens.put_results(batch_result)
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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}")
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def _recycle_resources(self, task_id, index, task):
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def _recycle_resources(self,
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task_id,
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index,
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task,
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result=None,
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is_prefill=False):
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"""
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recycle resources
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"""
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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.block_tables)
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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(
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f"finished_task_id: {finished_task_id}")
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self.prefill_result_status[
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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(
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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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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 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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self.speculative_stats_step += 1
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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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)
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if self.cfg.speculative_config.method in ["mtp"] and \
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self.cfg.speculative_config.num_speculative_tokens == 1:
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single_head_accep_ratio = accept_ratio / (1 - accept_ratio)
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spec_logger.info(
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f" Single head accept ratio: {single_head_accep_ratio}")
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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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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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batch = self.output_tokens[1, 0]
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if not self.is_speculate_decoding:
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tokens = tokens[2:batch + 2]
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else:
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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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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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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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if not self.is_speculate_decoding:
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token_ids = [int(tokens[i, 0])]
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else:
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token_ids = tokens[
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2 + SPECULATE_MAX_BSZ + i * MAX_DRAFT_TOKENS:2 +
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SPECULATE_MAX_BSZ + i * MAX_DRAFT_TOKENS +
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accept_num[i, 0],
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0,
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].tolist()
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if any(token_id < 0 for token_id in token_ids):
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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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if self.cfg.enable_chunked_prefill:
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if task.get("prefill_token_num", None) is None:
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task.set("prefill_token_num", task.token_chunk_size)
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else:
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task.prefill_token_num += task.token_chunk_size
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if task.prompt_token_ids_len > task.prefill_token_num:
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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[2 + SPECULATE_MAX_BSZ +
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i * MAX_DRAFT_TOKENS:2 + SPECULATE_MAX_BSZ +
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i * MAX_DRAFT_TOKENS +
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accept_num[i]].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(
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f"recovery stop signal found at task {task_id}",
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f"token_ids: {token_ids}")
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if not recovery_stop and token_id < 0:
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continue
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task_id = task.request_id
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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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@@ -192,41 +304,45 @@ class TokenProcessor(object):
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preprocess_cost_time=task.preprocess_end_time -
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task.preprocess_start_time)
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main_process_metrics.time_to_first_token.observe(
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current_time - task.inference_start_time)
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main_process_metrics.request_queue_time.observe(
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metrics.time_in_queue)
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self._record_first_token_metrics(task, current_time)
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else:
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if hasattr(task, 'last_token_time'
|
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) 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(
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token_gen_time)
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task.last_token_time = current_time
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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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)
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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(request_id=task_id,
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outputs=CompletionOutput(index=i,
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token_ids=[]),
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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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finished=False,
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metrics=metrics)
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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.prompt_token_ids = task.prompt_token_ids
|
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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[
|
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"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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|
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for token_id in token_ids:
|
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self.tokens_counter[task_id] += 1
|
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result.outputs.token_ids.append(token_id)
|
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if token_id in task.eos_token_ids:
|
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if token_id != RECOVERY_STOP_SIGNAL:
|
||||
result.outputs.token_ids.append(token_id)
|
||||
if token_id in task.eos_token_ids or is_prefill or recovery_stop:
|
||||
result.finished = True
|
||||
result.prompt = task.prompt
|
||||
result.prompt_token_ids = task.prompt_token_ids
|
||||
if recovery_stop:
|
||||
result.outputs.token_ids.append(task.eos_token_ids[0])
|
||||
result.error_msg = "Recover is not supported, the result is incomplete!"
|
||||
llm_logger.info(
|
||||
f"Request: {task_id} finished, number of "
|
||||
f"generated tokens: {self.tokens_counter[task_id]}.")
|
||||
@@ -234,19 +350,50 @@ class TokenProcessor(object):
|
||||
f"Request: {task_id} token ratio: {self.tokens_counter[task_id] / (time.time() - task.inference_start_time)}"
|
||||
)
|
||||
llm_logger.info(f"{self.resource_manager.info()}")
|
||||
llm_logger.info(
|
||||
f"Speculate accept ratio: {1 - self.total_step * 1.0 / self.number_of_output_tokens}"
|
||||
f" total step: {self.total_step}. total_output_token_num: {self.number_of_output_tokens}"
|
||||
)
|
||||
self._recycle_resources(task_id, i, task)
|
||||
main_process_metrics.num_requests_running.dec(1)
|
||||
main_process_metrics.request_inference_time.observe(
|
||||
current_time - task.inference_start_time)
|
||||
if self.cfg.speculative_config.method:
|
||||
self._compute_speculative_status()
|
||||
if not is_prefill:
|
||||
self._record_completion_metrics(task, current_time)
|
||||
self._recycle_resources(task_id, i, task, result,
|
||||
is_prefill)
|
||||
break
|
||||
batch_result.append(result)
|
||||
if not is_prefill or self.cfg.scheduler_config.name == "splitwise":
|
||||
batch_result.append(result)
|
||||
|
||||
self.postprocess(batch_result)
|
||||
|
||||
def _record_metrics(self, task, current_time, token_ids):
|
||||
"""Record all metrics for a task"""
|
||||
if hasattr(task,
|
||||
'last_token_time') and task.last_token_time is not None:
|
||||
token_gen_time = current_time - task.last_token_time
|
||||
main_process_metrics.time_per_output_token.observe(token_gen_time)
|
||||
task.last_token_time = current_time
|
||||
|
||||
# Record generation metrics
|
||||
main_process_metrics.generation_tokens_total.inc(len(token_ids))
|
||||
|
||||
def _record_first_token_metrics(self, task, current_time):
|
||||
"""Record metrics for first token"""
|
||||
task.first_token_time = current_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.request_inference_time.observe(
|
||||
current_time - task.inference_start_time)
|
||||
main_process_metrics.request_generation_tokens.observe(
|
||||
self.tokens_counter[task.request_id])
|
||||
|
||||
|
||||
class WarmUpTokenProcessor(TokenProcessor):
|
||||
"""
|
||||
@@ -265,32 +412,26 @@ class WarmUpTokenProcessor(TokenProcessor):
|
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"""
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get output from model and process it
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"""
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from fastdeploy.model_executor.models import \
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inference_runner_supported_models
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if self.cfg.model_config.architectures not in inference_runner_supported_models \
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and "ErnieMoEVLForCausalLM" not in self.cfg.model_config.architectures:
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from paddlenlp_ops import get_output, speculate_get_output
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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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else:
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os.environ["ELLM_LOG_LEVEL"] = "3"
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use_pip_eff_llm = os.getenv('USE_PIP_EFF_LLM')
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if use_pip_eff_llm is None:
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from fastdeploy.model_executor.ops.gpu import (
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get_output, speculate_get_output)
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else:
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from efficientllm.ops.gpu import (get_output,
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speculate_get_output)
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from fastdeploy.model_executor.ops.gpu import (
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get_output, speculate_get_output)
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while self._is_running:
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try:
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rank_id = 0
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if self.is_speculate_decoding:
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if self.speculative_decoding:
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speculate_get_output(self.output_tokens, rank_id,
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self._is_blocking)
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if self.output_tokens[0] == -2:
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continue
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else:
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get_output(self.output_tokens, rank_id, self._is_blocking)
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if self.output_tokens[0, 0] == -2:
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continue
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if self.output_tokens[0, 0] == -2:
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continue
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self._process_batch_output()
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except Exception as e:
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llm_logger.info("while get input_data error: {0} {1}".format(
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