Change:
From : Happened because of some change in the environment as it is also affecting historical builds. cc
This issue seems to come from within libuv, a dependency of TensorPipe. First guess: there's two conflicting versions of libuv (e.g., a preinstalled one and the one shipped as a submodule of TensorPipe), and we build the one shipped by TensorPipe but somehow it ends up including the headers of the other one? In particular, the constant it's complaining about was in , which became part of libuv 1.41.0, which is the version that TensorPipe bundles. But it seems those jobs are installing version 1.40.0 via conda: So the question is why are the conda headers picked up and prioritized? I feel this will require some CMake digging, and it's possible that the fix will have to land in libuv's upstream CMake files. Ugh.
BTW, if you want to validate this theory you can try setting this flag to : Which will cause TensorPipe to use the preinstalled libuv instead of building its own one. But please don't land this "workaround", as it will only lead to users coming to complain that they couldn't build PyTorch because their system has a libuv version that is too old for what TensorPipe needs.
this is definitely what is happening: libuv-1.40 is installed from conda, which does not have in the header: But I'm still confused why it started to happen just now [Edit] Can it be update that happened 4 hours ago?
Finally found a PR that caused a regression: (and testing were clearly regressed in CI: )

Discussion:
- **c_n** (num_layers * num_directions, batch, hidden_size): tensor containing the cell state for t=seq_len Attributes: <del> weight_ih_l[k] : the learnable input-hidden weights of the k-th layer `(W_ir|W_ii|W_in)`, of shape `(input_size x 3*hidden_size)` weight_hh_l[k] : the learnable hidden-hidden weights of the k-th layer `(W_hr|W_hi|W_hn)`, of shape `(hidden_size x 3*hidden_size)` bias_ih_l[k] : the learnable input-hidden bias of the k-th layer `(b_ir|b_ii|b_in)`, of shape `(3*hidden_size)` bias_hh_l[k] : the learnable hidden-hidden bias of the k-th layer `(W_hr|W_hi|W_hn)`, of shape `(3*hidden_size)` </del> <ins> weight_ih_l[k] : the learnable input-hidden weights of the k-th layer `(W_ii|W_if|W_ig|W_io)`, of shape `(input_size x 4*hidden_size)` weight_hh_l[k] : the learnable hidden-hidden weights of the k-th layer `(W_hi|W_hf|W_hg|W_ho)`, of shape `(hidden_size x 4*hidden_size)` bias_ih_l[k] : the learnable input-hidden bias of the k-th layer `(b_ii|b_if|b_ig|b_io)`, of shape `(4*hidden_size)` bias_hh_l[k] : the learnable hidden-hidden bias of the k-th layer `(W_hi|W_hf|W_hg|b_ho)`, of shape `(4*hidden_size)` </ins> Examples::

Attributes: weight_ih_l[k] : the learnable input-hidden weights of the k-th layer (W_ir|W_ii|W_in), of shape <del> (input_size x 3*hidden_size) </del> <ins> `(input_size x 3*hidden_size)` </ins> weight_hh_l[k] : the learnable hidden-hidden weights of the k-th layer (W_hr|W_hi|W_hn), of shape <del> (hidden_size x 3*hidden_size) bias_ih_l[k] : the learnable input-hidden bias of the k-th layer (b_ir|b_ii|b_in), of shape (3*hidden_size) bias_hh_l[k] : the learnable hidden-hidden bias of the k-th layer (W_hr|W_hi|W_hn), of shape (3*hidden_size) </del> <ins> `(hidden_size x 3*hidden_size)` bias_ih_l[k] : the learnable input-hidden bias of the k-th layer (b_ir|b_ii|b_in), of shape `(3*hidden_size)` bias_hh_l[k] : the learnable hidden-hidden bias of the k-th layer (W_hr|W_hi|W_hn), of shape `(3*hidden_size)` </ins> Examples:: >>> rnn = nn.GRU(10, 20, 2)