forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 1
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Method/Function overlap #4
Comments
Actually just split lt into lt and ltT, check that we can backport into pytorch. Also do for all comparison operators.... |
Did that ^ |
zdevito
pushed a commit
that referenced
this issue
Feb 22, 2019
Summary: Currently there is a mismatch in naming between Python BatchNorm `running_var` and C++ BatchNorm `running_variance`, which causes JIT model parameters loading to fail (pytorch/vision#728 (comment)): ``` terminate called after throwing an instance of 'c10::Error' what(): No such serialized tensor 'running_variance' (read at /home/shahriar/Build/pytorch/torch/csrc/api/src/serialize/input-archive.cpp:27) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x85 (0x7f2d92d32f95 in /usr/local/lib/libc10.so) frame #1: torch::serialize::InputArchive::read(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, at::Tensor&, bool) + 0xdeb (0x7f2d938551ab in /usr/local/lib/libtorch.so.1) frame #2: torch::nn::Module::load(torch::serialize::InputArchive&) + 0x98 (0x7f2d9381cd08 in /usr/local/lib/libtorch.so.1) frame #3: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1) frame #4: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1) frame #5: torch::nn::operator>>(torch::serialize::InputArchive&, std::shared_ptr<torch::nn::Module> const&) + 0x32 (0x7f2d9381c7b2 in /usr/local/lib/libtorch.so.1) frame #6: <unknown function> + 0x2b16c (0x5645f4d1916c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #7: <unknown function> + 0x27a3c (0x5645f4d15a3c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #8: <unknown function> + 0x2165c (0x5645f4d0f65c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #9: <unknown function> + 0x1540b (0x5645f4d0340b in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #10: __libc_start_main + 0xf3 (0x7f2d051dd223 in /usr/lib/libc.so.6) frame #11: <unknown function> + 0x1381e (0x5645f4d0181e in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) ``` Renaming C++ BatchNorm `running_variance` to `running_var` should fix this problem. This is a BC-breaking change, but it should be easy for end user to rename `running_variance` to `running_var` in their call sites. Pull Request resolved: pytorch#17371 Reviewed By: goldsborough Differential Revision: D14172775 Pulled By: yf225 fbshipit-source-id: b9d3729ec79272a8084269756f28a8f7c4dd16b6
ailzhang
pushed a commit
that referenced
this issue
Apr 9, 2019
Summary: Tracing models which attempts to return this in-place value doesn't turn out well. I haven't run any tests to confirm the results to be honest, but regardless of the outcome, the operation happens in-place, so it should work as before. Sample output from traced model attempting to set `max_norm` on `Embedding`: ``` a leaf Variable that requires grad has been used in an in-place operation. (check_inplace at /pytorch/torch/csrc/autograd/VariableTypeUtils.h:49) frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f0ecc5cc021 in /usr/local/lib/python3.7/site-packages/torch/lib/libc10.so) frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f0ecc5cb8ea in /usr/local/lib/python3.7/site-packages/torch/lib/libc10.so) frame #2: <unknown function> + 0x38ab2f (0x7f0ecb55ab2f in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #3: torch::autograd::VariableType::embedding_renorm_(at::Tensor&, at::Tensor const&, double, double) const + 0x76 (0x7f0ecb5b5966 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #4: <unknown function> + 0x56c958 (0x7f0ecb73c958 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #5: <unknown function> + 0x672286 (0x7f0ecb842286 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #6: torch::jit::InterpreterState::run(std::vector<c10::IValue, std::allocator<c10::IValue> >&) + 0x22 (0x7f0ecb83d842 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #7: <unknown function> + 0x65c6ac (0x7f0ecb82c6ac in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #8: <unknown function> + 0x3c8ab4 (0x7f0f06bc0ab4 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) frame #9: <unknown function> + 0x3ad2c3 (0x7f0f06ba52c3 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) frame #10: <unknown function> + 0x11663e (0x7f0f0690e63e in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame pytorch#39: python_call + 0x11 (0x5563c3c521c1 in uwsgi) frame pytorch#40: uwsgi_request_wsgi + 0x100 (0x5563c3c54410 in uwsgi) frame pytorch#41: wsgi_req_recv + 0xac (0x5563c3becabc in uwsgi) frame pytorch#42: simple_loop_run + 0xc4 (0x5563c3c35be4 in uwsgi) frame pytorch#43: simple_loop + 0x10 (0x5563c3c35a00 in uwsgi) frame pytorch#44: uwsgi_ignition + 0x241 (0x5563c3c3a3a1 in uwsgi) frame pytorch#45: uwsgi_worker_run + 0x275 (0x5563c3c3ec35 in uwsgi) frame pytorch#46: <unknown function> + 0x8f22c (0x5563c3c3f22c in uwsgi) frame pytorch#47: <unknown function> + 0x3c13e (0x5563c3bec13e in uwsgi) frame pytorch#48: __libc_start_main + 0xf1 (0x7f0f138922e1 in /lib/x86_64-linux-gnu/libc.so.6) frame pytorch#49: _start + 0x2a (0x5563c3bec16a in uwsgi) : operation failed in interpreter: op_version_set = 0 def forward(self, input_1: Tensor) -> Tensor: _0 = torch.norm(self.item_embedding.weight, 2, 1, True) _1 = torch.div(self.item_embedding.weight, _0) m_weight = torch.t(_1) input_2 = torch.contiguous(input_1) weight_1 = torch.embedding_renorm_(self.item_embedding.weight, input_2, 1., 2.) ~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE x = torch.embedding(weight_1, input_2, -1, False, False) input_3 = torch.div(x, torch.norm(x, 2, 2, True)) max_batch_size = ops.prim.NumToTensor(torch.size(input_3, 0)) hx = torch.zeros([2, int(max_batch_size), 70], dtype=6, layout=0, device=torch.device("cpu")) _2 = [self.lstm_layer.weight_ih_l0, self.lstm_layer.weight_hh_l0, self.lstm_layer.weight_ih_l1, self.lstm_layer.weight_hh_l1] input_4, _3, _4 = torch.lstm(input_3, [hx, hx], _2, False, 2, 0.10000000000000001, False, False, True) input = torch.matmul(input_4, torch.t(self.rnn2item.weight)) tastevec = torch.div(input, torch.norm(input, 2, 2, True)) outputs = torch.matmul(tastevec, m_weight) ``` Pull Request resolved: pytorch#18684 Differential Revision: D14782041 Pulled By: ezyang fbshipit-source-id: 7b2fc19b7d5b6600263644498bb728319a19f39d
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Some functions have different declarations for method/function variants:
However, they overlap in name and arguments causing conflicts. In the case where a function is method only we should add a suffix to the name e.g. "lt_method" in Type, and have the Tensor method call that one to avoid this.
The text was updated successfully, but these errors were encountered: