相关 issue：https://github.com/PaddlePaddle/Anakin/issues/527

在 https://github.com/PaddlePaddle/Anakin/issues/527 里面 @yiicy 说

感谢指出问题，我们最开始的GEMM借鉴了compute library，现在sgemm.cpp里的接口已经弃用了，统一使用sgemm_prepacked.cpp里的接口，实现上也做了相应的变化，sgemm_prepacked.cpp的xblock将按照l2 cache大小划分

我理解为你们承认了你们使用了 acl 的开源代码，那么请遵守它们的 MIT 协议。没有人关心你们的 block 怎么划分

在同一个 issue 里面，@throneclay 说

没人回复这个就关了，如果有问题可以随时跟我们联系

我和 @izp001 都进一步回复了，请你们也正面给出回应

I am curious about how you implement depthwise plugin layer in TRT by yourselves? The MobileNet v1, v2 test results do not make sense to me. Why so little latency difference between batchsize=1 and 2?

Bumps protobuf from 3.1.0 to 3.15.0.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

I'm trying to convert fluid model to anakin, but I got an error in the very beginning. Hope you can help me.

Issue:

ERROR: flag 'logtostderr' was defined more than once (in files '/tmp/glog-20171216-78363-w0y7q0/glog-0.3.5/src/logging.cc' and '/Users/python2/yangjiabin/PR/Paddle/build/third_party/glog/src/extern_glog/src/logging.cc').

ps: /Users/python2/yangjiabin/... this is not my path.

Env: laptop: MacOSX 10.12.6 fluid model: MobileNetV1: model+params (pretrained models of paddlepaddle official) Anakin branch: developing (already up to date)

config.yaml:

OPTIONS:
  Framework: FLUID
  SavePath: ./model/mobilenet_v1/output
  ResultName: MobileNetV1_fluid
  Config:
    LaunchBoard: ON
    Server:
      ip: 127.0.0.1
      port: 6007
    OptimizedGraph:
      enable: ON
      path: ./model/mobilenet_v1/output/MobileNetV1_fluid.anakin.bin
  LOGGER:
    LogToPath: ./model/mobilenet_v1/log/
    WithColor: ON

TARGET:
  CAFFE:
    # path to proto files
    ProtoPaths:
      - 
    PrototxtPath: 
    ModelPath: 
    Remark:  # Generally no need to modify.

  FLUID:
    # path of fluid inference model
    Debug: NULL                            # Generally no need to modify.
    ModelPath: /Users/xxx/MobileNetV1/fluid
    NetType:                               # Generally no need to modify.

Any comments will be appreciated. Thanks in advance!

I followed the anakin docker guide guide.

and issue the command

sudo ./anakin_docker_build_and_run.sh -p NVIDIA-GPU -o Ubuntu -m Build

error occur:

-- cudnn include header is CUDNN_INCLUDE_DIR-NOTFOUND/cudnn.h
-- cudnn library is /usr/lib/x86_64-linux-gnu/libcudnn.so/libcudnn.so
CMake Error at cmake/cuda.cmake:126 (message):
  Could not find cudnn library in:
Call Stack (most recent call first):
  cmake/gather.cmake:16 (anakin_find_cudnn)
  CMakeLists.txt:178 (include)


-- Found CUDA: /usr/local/cuda (found suitable version "8.0", minimum required is "7.5") 
-- Building fat-bin for cuda code !
--  `--support arch :  3.5
--  `--support arch :  5.0
--  `--support arch :  6.0
--  `--support arch :  6.1
-- Looking for include file pthread.h
-- Looking for include file pthread.h - found
-- Looking for pthread_create
-- Looking for pthread_create - not found
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE  
-- Found PROTOBUF: /usr/local/lib/libprotobuf.so  
-- Found protobuf in /usr/local/include
-- 
-- ================================ configuration ==================================
-- General:
--   anakin version            : 0.1.2
--   System                    : Linux
--   C++ compiler              : /usr/bin/c++
--   C flags                   : 
--   CXX flags                 :  -std=c++11 -fPIC -ldl -lrt -W  -pthread    -Wno-unused-variable      -fpermissive  -fdiagnostics-show-option -Wno-undef -Wno-narrowing -Wno-unknown-pragmas -Wno-delete-non-virtual-dtor -Wno-comment -Wno-sign-compare -Wno-ignored-qualifiers -Wno-enum-compare -O3 -DNDEBUG
--   Build type                : Release;FORCE
--   Build cross plantform     : ON
--   Build anakin fp32         : ON
-- 
--   Build shared libs         : YES
--   Build with unit test      : YES
-- 
--   Enable verbose message    : NO
--   Enable noisy warnings     : NO
--   Disable all warnings      : YES
-- 
--   Use local logger          : logger
--   Use google protobuf       : ON
--   Use local Unit test       : aktest
--   USE_OPENCV                : OFF
--   USE_BOOST                 : OFF
--   USE_OPENMP                : 
-- 
-- Cuda:
--   USE_CUDA                  : ON
--     |--CUDA version         : 8.0
--     `--NVCC flags           :   -Xcompiler -fPIC -O3 -std=c++11 --default-stream per-thread -Wno-deprecated-gpu-targets  --generate-code arch=compute_35,code=sm_35  --generate-code arch=compute_50,code=sm_50  --generate-code arch=compute_60,code=sm_60  --generate-code arch=compute_61,code=sm_61 
--   USE_CUBLAS                : ON
--   USE_CURAND                : ON
--   USE_CUFFT                 : ON
--   USE_CUDNN                 : ON
--     `--Cudnn version        : 
-- 
--   USE_OPENCL                : OFF
-- 
--   SELECT_GPU_PLACE          : YES
-- 
--   Configuation path         : /Anakin/gpu_build_sm61/anakin_config.h
-- ================================ End ==================================
-- Configuring incomplete, errors occurred!
See also "/Anakin/gpu_build_sm61/CMakeFiles/CMakeOutput.log".
See also "/Anakin/gpu_build_sm61/CMakeFiles/CMakeError.log".
make: *** No targets specified and no makefile found.  Stop.
sha256:694bca37cbee627f47bfc46e71af099147bb61045fddf6ec3abd1beeab69d00d

WAN| 08:46:02.00618| 6.902s| main_thread| parser.cpp:138] Parsing in edges of node : relu5_4 FTL| 08:46:02.00618| 6.902s| main_thread| graph_base.inl:57] Check failed: (this->has_vertex(arc.bottom()) && this->has_vertex(arc.top())) The arc's top or bottom is not vertex! *** Check failed: (this->has_vertex(arc.bottom()) && this->has_vertex(arc.top())) fatal error: stack trace: *** 6 0x4a3329 ./anakin_demo() [0x4a3329] 5 0x7febe6ff2830 __libc_start_main + 240 [??:0] 4 0x4a420b ./anakin_demo() [0x4a420b] 3 0x7febe8878e96 anakin::graph::Graph<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1, (anakin::Precision)0>::load(std::string) + 202 [??:0] 2 0x7febe877e99a anakin::Status anakin::parser::load<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1, (anakin::Precision)0>(anakin::graph::Graph<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1, (anakin::Precision)0>, std::string&) + 69 [??:0] 1 0x7febe877f4d2 anakin::Status anakin::parser::load<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1, (anakin::Precision)0>(anakin::graph::Graph<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1, (anakin::Precision)0>, char const*) + 2656 [??:0] 0 0x4e5cbd anakin::graph::GraphBase<std::string, std::shared_ptr<anakin::graph::Node<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1, (anakin::Precision)0> >, anakin::saber::Tensor<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1, anakin::saber::NCHW>*, anakin::graph::Edge<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1> >::add_in_arc(anakin::graph::Edge<anakin::saber::TargetType<(anakin::saber::TargetTypeEnum)1>, (anakin::saber::DataType)1>&) + 451 [??:?]

Hi, I have a new question,

Does Anakin support “variadic” input shapes? For example, in frame 1, I have one standard 224224 image as input. But in frame 2, I have another image of any arbitrary size like 456789. I would like to know if Anakin could support.

Thank you~ ~

Hi!, I am interested in the Anakin project.

I would like to know if there are any supports & benchmark results for int8? If not, will there be any? thx!

based on ubuntu Dockerfile, and changed gcc version to 4.8.5. error message as follows.

Linking CXX executable ../../output/unit_test/test_saber_buffer_NV /usr/bin/ld: ../../output/libanakin.so.2.0.1: undefined reference to symbol 'cudnnGetRNNLinLayerBiasParams' /usr/lib/gcc/x86_64-linux-gnu/4.8/../../../x86_64-linux-gnu/libcudnn.so: error adding symbols: DSO missing from command line collect2: error: ld returned 1 exit status test/CMakeFiles/test_TargetWrapper.dir/build.make:93: recipe for target '../output/unit_test/test_TargetWrapper' failed make[2]: *** [../output/unit_test/test_TargetWrapper] Error 1 CMakeFiles/Makefile2:757: recipe for target 'test/CMakeFiles/test_TargetWrapper.dir/all' failed make[1]: *** [test/CMakeFiles/test_TargetWrapper.dir/all] Error 2

使用convert.py转换TensorFlow（1.12版本）模型时报错如下： tensor.type=7 Traceback (most recent call last): File "converter.py", line 113, in graph = Graph(config) File "/data0/xikali/tangram_run/anakin/tools/external_converter_v2/parser/graph.py", line 46, in init self.graph_io = self.parser() File "/data0/xikali/tangram_run/anakin/tools/external_converter_v2/parser/tensorflow/parser_tf.py", line 27, in call med_graph = self._conver_tf_2_med() File "/data0/xikali/tangram_run/anakin/tools/external_converter_v2/parser/tensorflow/parser_tf.py", line 41, in _conver_tf_2_med return parser.parse() File "/data0/xikali/tangram_run/anakin/tools/external_converter_v2/parser/tensorflow/parse_tf_2_med.py", line 201, in parse nodes = self._parse_tf_node(tf_graph, {}) File "/data0/xikali/tangram_run/anakin/tools/external_converter_v2/parser/tensorflow/parse_tf_2_med.py", line 73, in _parse_tf_node anakin_tensor = tf_to_anakin_tensor(node.get_attr(a)) File "/data0/xikali/tangram_run/anakin/tools/external_converter_v2/parser/tensorflow/tf_trans_util.py", line 94, in tf_to_anakin_tensor new_type = TF_TO_ANAKIN_DTYPE[tensor.dtype] KeyError: 7 看上去应该是无法支持tensor.dtype是string类型的？已经删除了所有string类型的特征，但是tf中Assert的op一定是包含string类型的tensor，这个如何解决呢？

my config:

> OPTIONS:
>     Framework: CAFFE
>     SavePath: ./output
>     ResultName: face_r100
>     Config:
>         LaunchBoard: ON
>         Server:
>             ip: 0.0.0.0
>             port: 8888
>         OptimizedGraph:
>             enable: OFF
>             path: ./googlenet.paddle_inference_model.bin.saved
>     LOGGER:
>         LogToPath: ./log/
>         WithColor: ON
> 
> TARGET:
>     CAFFE:
>         # path of fluid inference model
>         Debug: NULL                            # Generally no need to modify.
>         PrototxtPath: ./model/model.prototxt        # The upper path of a fluid inference model.
>         ModelPath: ./model/model.caffmodel        # The upper path of a fluid inference model.
>         NetType:        

Traceback (most recent call last): File "converter.py", line 79, in graph = Graph(config) File "/root/Anakin/tools/external_converter_v2/parser/graph.py", line 26, in init raise NameError('ERROR: GrapProtoIO not support %s model.' % (config.framework)) NameError: ERROR: GrapProtoIO not support CAFFE model.

Bumps protobuf from 3.1.0 to 3.18.3.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Absolute Path Traversal due to incorrect use of send_file call

A path traversal attack (also known as directory traversal) aims to access files and directories that are stored outside the web root folder. By manipulating variables that reference files with “dot-dot-slash (../)” sequences and its variations or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system including application source code or configuration and critical system files. This attack is also known as “dot-dot-slash”, “directory traversal”, “directory climbing” and “backtracking”.

Root Cause Analysis

The os.path.join call is unsafe for use with untrusted input. When the os.path.join call encounters an absolute path, it ignores all the parameters it has encountered till that point and starts working with the new absolute path. Please see the example below.

>>> import os.path
>>> static = "path/to/mySafeStaticDir"
>>> malicious = "/../../../../../etc/passwd"
>>> os.path.join(t,malicious)
'/../../../../../etc/passwd'

Since the "malicious" parameter represents an absolute path, the result of os.path.join ignores the static directory completely. Hence, untrusted input is passed via the os.path.join call to flask.send_file can lead to path traversal attacks.

In this case, the problems occurs due to the following code : https://github.com/PaddlePaddle/Anakin/blob/5fd68a6cc4c4620cd1a30794c1bf06eebd3f4730/docs/api_on_web/init.py#L26

Here, the filename parameter is attacker controlled. This parameter passes through the unsafe os.path.join call making the effective directory and filename passed to the send_file call attacker controlled. This leads to a path traversal attack.

Proof of Concept

The bug can be verified using a proof of concept similar to the one shown below.

curl --path-as-is 'http://<domain>///../../../../etc/passwd"'

Remediation

This can be fixed by preventing flow of untrusted data to the vulnerable send_file function. In case the application logic necessiates this behaviour, one can either use the werkzeug.utils.safe_join to join untrusted paths or replace flask.send_file calls with flask.send_from_directory calls.

References

• OWASP Path Traversal
• github/securitylab#669

This bug was found using CodeQL by Github