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 pytorch#4: <unknown function> + 0x56c958 (0x7f0ecb73c958 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame pytorch#5: <unknown function> + 0x672286 (0x7f0ecb842286 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame pytorch#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 pytorch#7: <unknown function> + 0x65c6ac (0x7f0ecb82c6ac in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame pytorch#8: <unknown function> + 0x3c8ab4 (0x7f0f06bc0ab4 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) frame pytorch#9: <unknown function> + 0x3ad2c3 (0x7f0f06ba52c3 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) frame pytorch#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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