我正在尝试为WebGL后端编译用TVM(python)优化的神经网络,遵循[TVM文档中给出的示例]。(https://github.com/dmlc/nnvm/blob/master/tutorials/from_mxnet_to_webgl.py)。
我只更改了代码中的变量run_deploy_web
,并将其设置为顶层True
,以便运行所需的示例。你知道吗
问题是我得到了错误:
Loading pretrained resnet model from MXNet...
- Model loaded!
Compiling the neural network...
[11:23:05] /home/SERILOCAL/n.perto/Documents/tvm/src/runtime/opengl/opengl_device_api.cc:227: OpenGL initialized, version = 4.3 (Core Profile) Mesa 17.2.8
Traceback (most recent call last):
File "from_mxnet_to_webgl.py", line 515, in <module>
deploy_web()
File "from_mxnet_to_webgl.py", line 462, in deploy_web
params=params)
File "from_mxnet_to_webgl.py", line 243, in compile_net
params=params)
File "/home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/compiler/build_module.py", line 321, in build
graph = graph.apply("GraphCompile")
File "/home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/graph.py", line 250, in apply
check_call(_LIB.NNGraphApplyPasses(self.handle, npass, cpass, ctypes.byref(ghandle)))
File "/home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/_base.py", line 91, in check_call
raise NNVMError(py_str(_LIB.NNGetLastError()))
nnvm._base.NNVMError: TVMError: Check failed: allow_null: No available targets are compatible with this triple. target_triple=asmjs-unknown-emscripten
Stack trace:
File "/home/SERILOCAL/n.perto/Documents/tvm/src/codegen/llvm/llvm_common.cc", line 162
[bt] (0) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x8eff6d) [0x7f7200ff7f6d]
[bt] (1) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x8e6ad5) [0x7f7200feead5]
[bt] (2) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x8e529b) [0x7f7200fed29b]
[bt] (3) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x274f6e) [0x7f720097cf6e]
[bt] (4) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x195158) [0x7f720089d158]
[bt] (5) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(TVMFuncCall+0x61) [0x7f720101f001]
raise get_last_ffi_error()
File "/home/SERILOCAL/n.perto/Documents/tvm/python/tvm/_ffi/_ctypes/function.py", line 206, in __call__
return _Build(lowered_func, target)
File "/home/SERILOCAL/n.perto/Documents/tvm/python/tvm/codegen.py", line 36, in build_module
mhost = codegen.build_module(fhost_all, str(target_host))
File "/home/SERILOCAL/n.perto/Documents/tvm/python/tvm/build_module.py", line 623, in build
return tvm.build(funcs, target=target, target_host=target_host)
File "/home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/compiler/build_module.py", line 140, in _build
rv = local_pyfunc(*pyargs)
File "/home/SERILOCAL/n.perto/Documents/tvm/python/tvm/_ffi/_ctypes/function.py", line 71, in cfun
Stack trace:
[bt] (0) /home/SERILOCAL/n.perto/Documents/tvm/build/libtvm.so(+0x913b7b) [0x7f720101bb7b]
[bt] (1) /home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/../../lib/libnnvm_compiler.so(nnvm::compiler::GraphCompile(nnvm::Graph const&)+0x28bd) [0x7f71fa47e08d]
[bt] (2) /home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/../../lib/libnnvm_compiler.so(std::_Function_handler<nnvm::Graph (nnvm::Graph), nnvm::Graph (*)(nnvm::Graph const&)>::_M_invoke(std::_Any_data const&, nnvm::Graph&&)+0x20) [0x7f71fa4788b0]
[bt] (3) /home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/../../lib/libnnvm_compiler.so(nnvm::ApplyPasses(nnvm::Graph, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&)+0x32b) [0x7f71fa4ab95b]
[bt] (4) /home/SERILOCAL/n.perto/Documents/tvm/nnvm/python/nnvm/../../lib/libnnvm_compiler.so(NNGraphApplyPasses+0x348) [0x7f71fa4a1ef8]
[bt] (5) /home/SERILOCAL/n.perto/.anaconda3/envs/tf/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7f72030abec0]
[bt] (6) /home/SERILOCAL/n.perto/.anaconda3/envs/tf/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7f72030ab87d]
[bt] (7) /home/SERILOCAL/n.perto/.anaconda3/envs/tf/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce) [0x7f7205c12ede]
[bt] (8) /home/SERILOCAL/n.perto/.anaconda3/envs/tf/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x13915) [0x7f7205c13915]
这行应该是问题所在:
nnvm._base.NNVMError: TVMError: Check failed: allow_null: No available targets are compatible with this triple. target_triple=asmjs-unknown-emscripten
Emscripten以前已随sdk工具一起安装:
emsdk install latest
emsdk activate latest
source emsdk_env.sh
(一切都是在sane会话中完成的,否则我将源代码脚本来初始化环境)
脚本中尝试构建某些代码的部分如下所示:
# As usual, compile the model.
graph, lib, params = compile_net(
net,
target_host="llvm -target=asmjs-unknown-emscripten -system-lib",
target="opengl",
data_shape=data_shape,
params=params)
我没有配置更多关于emscripten sdk的内容。 在fastcomp的文档中,据说它与llvm紧密集成,所以我假设在从sdk工具运行安装之后,它应该可以无缝地工作,但是我认为它需要更多的配置。你知道吗
它也可能是tvm文档中的pyhton示例,它已经过时了,只需要很少的修改就可以使用新版本的工具链。你知道吗
由于我从未使用过这些工具,所以我对所有这些工具的协同工作感到有些不知所措,不知道应该在哪里查找错误,您的帮助将不胜感激。你知道吗
谢谢。你知道吗
目前没有回答
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