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* support mm prefix caching * update code * fix mm_hashes * support encoder cache * add encoder cache * update code * update encoder cache * fix features bug * fix worker bug * support processor cache, need to optimize yet * refactor multimodal data cache * update code * update code * update v1 scheduler * update code * update code * update codestyle * support turn off processor cache and encoder cache * update pre-commit * fix code * solve review * update code * update code * update test case * set processor cache in GiB * update test case * support mm prefix caching for qwen model * fix code style check * update pre-commit * fix unit test * fix unit test * add ci test case * fix rescheduled bug * change text_after_process to prompt_tokens * fix unit test * fix chat template * change model path * [EP] fix adapter bugs (#4572) * Update expert_service.py * Update common_engine.py * Update expert_service.py * fix v1 hang bug (#4573) * fix import image_ops error on some platforms (#4559) * [CLI]Update parameters in bench latecy cli tool and fix collect-env cli tool (#4558) * add collect-env * del files * [Graph Optimization] Add dy_runnable and introduce cudagraph_switch_threshold for cudagraph mode switching (#4578) * add new branch for sot * reorder * fix batch bug * [XPU]Moe uses a new operator (#4585) * [XPU]Moe uses a new operator * [XPU]Moe uses a new operator * update response * [Feature] Support Paddle-OCR (#4396) * init * update code * fix code style & disable thinking * adapt for common_engine.update_mm_requests_chunk_size * use 3d rope * use flash_attn_unpadded * opt siglip * update to be compatible with the latest codebase * fix typo * optim OCR performance * fix bug * fix bug * fix bug * fix bug * normlize name * modify xpu rope * revert logger * fix bug * fix bug * fix bug * support default_v1 * optim performance * fix bug --------- Co-authored-by: root <root@szzj-acg-tge1-fdda9.szzj.baidu.com> Co-authored-by: zhangyue66 <zhangyue66@baidu.com> * [DataProcessor] add reasoning_tokens into usage info (#4520) * add reasoning_tokens into usage info initial commit * add unit tests * modify unit test * modify and add unit tests * fix unit test * move steam usage to processor * modify processor * modify test_logprobs * modify test_logprobs.py * modify stream reasoning tokens accumulation * fix unit test * perf: Optimize task queue communication from engine to worker (#4531) * perf: Optimize task queue communication from engine to worker * perf: get_tasks to numpy * perf: get_tasks remove to_numpy * fix: request & replace ENV * remove test_e2w_perf.py * fix code style --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> * Clean up ports after processing results (#4587) * [CI] Add /re-run command in PR comments to restart failed CI workflows (#4593) * [Others] api server exits when worker process is dead (#3271) * [fix] fix terminal hangs when worker process is dead * [chore] change sleep time of monitor * [chore] remove redundant comments * update docs --------- Co-authored-by: ApplEOFDiscord <wwy640130@163.com> Co-authored-by: ApplEOFDiscord <31272106+ApplEOFDiscord@users.noreply.github.com> Co-authored-by: ltd0924 <32387785+ltd0924@users.noreply.github.com> Co-authored-by: yinwei <yinwei_hust@163.com> Co-authored-by: JYChen <zoooo0820@qq.com> Co-authored-by: qwes5s5 <45442318+qwes5s5@users.noreply.github.com> Co-authored-by: Ryan <zihaohuang@aliyun.com> Co-authored-by: yyssys <atyangshuang@foxmail.com> Co-authored-by: ming1753 <61511741+ming1753@users.noreply.github.com> Co-authored-by: root <root@szzj-acg-tge1-fdda9.szzj.baidu.com> Co-authored-by: zhangyue66 <zhangyue66@baidu.com> Co-authored-by: kxz2002 <115912648+kxz2002@users.noreply.github.com> Co-authored-by: SunLei <sunlei5788@gmail.com> Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> Co-authored-by: Zhang Yulong <35552275+ZhangYulongg@users.noreply.github.com> Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com> Co-authored-by: 李泳桦 <39643373+liyonghua0910@users.noreply.github.com>
128 lines
4.4 KiB
Python
128 lines
4.4 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 base64
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from io import BytesIO
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from pathlib import Path
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import numpy as np
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import numpy.typing as npt
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from .base import MediaIO
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# TODO 多模数据处理
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# try:
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# import librosa
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# except ImportError:
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# librosa = PlaceholderModule("librosa") # type: ignore[assignment]
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# try:
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# import soundfile
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# except ImportError:
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# soundfile = PlaceholderModule("soundfile") # type: ignore[assignment]
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def resample_audio(
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audio: npt.NDArray[np.floating],
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*,
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orig_sr: float,
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target_sr: float,
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) -> npt.NDArray[np.floating]:
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"""
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将音频数据从原始采样率(`orig_sr`)重采样到目标采样率(`target_sr`)。
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Args:
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audio (npt.NDArray[np.floating]): 带有单通道浮点型音频数据的 numpy ndarray,形状为 `(samples,)`。
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orig_sr (float): 音频数据的原始采样率。
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target_sr (float): 需要转换到的目标采样率。
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Returns:
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npt.NDArray[np.floating]: 带有单通道浮点型音频数据的 numpy ndarray,形状为 `(samples,)`,已经被重采样到目标采样率。
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Raises:
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None.
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"""
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import librosa
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return librosa.resample(audio, orig_sr=orig_sr, target_sr=target_sr)
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class AudioMediaIO(MediaIO[tuple[npt.NDArray, float]]):
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def load_bytes(self, data: bytes) -> tuple[npt.NDArray, float]:
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"""
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加载字节数据,返回音频信号和采样率。
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参数:
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data (bytes) - 字节数据,包含音频文件的内容。
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返回值(tuple):
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(array, float) - 第一个元素是一个numpy数组,表示音频信号,第二个元素是一个浮点数,表示采样率。
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如果解码失败,则返回 None。
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"""
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import librosa
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return librosa.load(BytesIO(data), sr=None)
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def load_base64(
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self,
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media_type: str,
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data: str,
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) -> tuple[npt.NDArray, float]:
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"""
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将 base64 编码的字符串转换为 numpy 数组和尺度。
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Args:
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media_type (str): 媒体类型,例如 'image/jpeg'、'image/png' 等。
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data (str): base64 编码的字符串,表示图像或其他二进制数据。
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Returns:
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tuple[npt.NDArray, float]: 包含以下两个元素:
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- npt.NDArray: 形状为(H,W,C)的 numpy 数组,表示图像或其他二进制数据。
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- float: 图像的尺度,单位为像素。
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Raises:
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ValueError: 当 media_type 不是有效的媒体类型时引发。
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"""
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return self.load_bytes(base64.b64decode(data))
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def load_file(self, filepath: Path) -> tuple[npt.NDArray, float]:
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"""
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加载音频文件,返回音频数据和采样率。
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参数:
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filepath (Path): 音频文件路径(Path类型)。
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返回值:
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tuple[npt.NDArray, float]:包含两个元素的元组,第一个是音频数据(npt.NDArray类型),
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第二个是采样率(float类型)。
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"""
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import librosa
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return librosa.load(filepath, sr=None)
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def encode_base64(self, media: tuple[npt.NDArray, float]) -> str:
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"""
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将音频数据和采样率转换为Base64编码的字符串。
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参数:
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media (tuple[numpy.ndarray, float]): 包含音频数据和采样率的元组,其中音频数据是一个numpy数组,采样率是一个浮点数。
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返回值 (str): Base64编码的字符串,表示音频数据和采样率。
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"""
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audio, sr = media
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with BytesIO() as buffer:
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import soundfile
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soundfile.write(buffer, audio, sr, format="WAV")
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data = buffer.getvalue()
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return base64.b64encode(data).decode("utf-8")
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