remove with_stateless and only_stateless. Replace with variants: [method,function] #1
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zdevito
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Nov 14, 2017
* Fix CUDA 9 builds for Windows * Add msvc conditional flag * minor bug fix * minor bugs #1
zdevito
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Jan 23, 2018
Currently, index operation kernels work in "source/destination index-major order". (E.g., if thread count equals slice size, each thread will process slice #0 in lockstep, and then slice #1, and so on.) However, when elements inside each "slice" is separated by large strides (e.g., selecting columns of a matrix), it is better to switch to "elementInSlice-major order". For example, each thread can process element #0 of every slice, and then element #1 of every slice, and so on.
zdevito
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Sep 11, 2018
…orms we care about. (pytorch#11394) Summary: While the use of memcpy as part of the byte swapping sequence looks funky, all major compilers recognize and optimize this pattern reliably, resulting in essentially optimal code generation. For example, decodeUInt32LE goes from this on iOS arm64: > ldrb w8, [x0, #3] > ldrb w9, [x0, #2] > bfi w8, w9, #8, #8 > ldrb w9, [x0, #1] > bfi w8, w9, #16, #8 > ldrb w9, [x0] > bfi w8, w9, #24, #8 > mov x0, x8 > ret To this: > ldr w8, [x0] > rev w0, w8 > ret Pull Request resolved: pytorch#11394 Reviewed By: SsnL Differential Revision: D9728659 Pulled By: resistor fbshipit-source-id: 9afbd4adfad1d1fb7b01f1179e6707ee21fa726f
zdevito
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Nov 16, 2018
pytorch#14040) Summary: …2164)" This reverts commit 4b7c615. Pull Request resolved: pytorch#14040 Differential Revision: D13089531 Pulled By: yinghai fbshipit-source-id: 2114b36111dab6f179c02921bbc9bd382ef461bf
zdevito
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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
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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
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with_stateless: True === variants: [ method, function ]
only_stateless === variants: [ functions ]
nothing specified (i.e. default variants): variants: [ method ]
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