jiebi/CodeConvo huggingface dataset:
Code change:
See Docker run fails with , which is due to the fact that is specified in docker container: Which results in: CI cc
Check that adding to manywheels image results in a failure:
Tentatively marking hi-pri since nightly testing are failing
This is resolved, here is the successful run:

Related discussion:
text: std::vector<at::Tensor> unpack(at::TensorList tl, const char *name, int pos) const; std::vector<at::Tensor> unpack_idxs(at::TensorList tl, const char *name, int pos) const; <del> Variable as_variable(Tensor tensor) const; std::tuple<Variable, Variable> as_variable(std::tuple<Tensor, Tensor> tensor) const; std::tuple<Variable, Variable, Variable> as_variable(std::tuple<Tensor, Tensor, Tensor> tensor) const; std::tuple<Variable, Variable, Variable, Variable> as_variable(std::tuple<Tensor, Tensor, Tensor, Tensor> tensor) const; std::vector<Variable> as_variable(TensorList tensor) const; Variable maybe_wrap(Tensor data, const Variable & self, bool inplace) const; </del> private: at::Type* baseType; std::string str;
Code change:
After merging cuda 12.4 for nightly. I observe following failures: libtorch-cuda124-shared-with-deps-cxx11-abi-build: Build Official Docker Images 124: We need to resolve these before continuing adding CUDA 12.4 CI workflows 0 cc
attempt 3 worked for libtorch build issue:
For Docker image, related discussion:
I will instance a new bug for the docker job failure.

Related discussion:
text: :members: <ins> Padding Layers -------------- :hidden:`ReflectionPad2d` ~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ReflectionPad2d :members: :hidden:`ReplicationPad2d` ~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ReplicationPad2d :members: :hidden:`ReplicationPad3d` ~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ReplicationPad3d :members: :hidden:`ZeroPad2d` ~~~~~~~~~~~~~~~~~~~ .. autoclass:: ZeroPad2d :members: :hidden:`ConstantPad2d` ~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: ConstantPad2d :members: </ins> Non-linear Activations ----------------------------------
Code change:
After merging cuda 12.4 for nightly. I observe following failures: libtorch-cuda124-shared-with-deps-cxx11-abi-build: Build Official Docker Images 124: We need to resolve these before continuing adding CUDA 12.4 CI workflows 0 cc
attempt 3 worked for libtorch build issue:
For Docker image, related discussion:
I will instance a new bug for the docker job failure.

Related discussion:
text: self.value = value def forward(self, input): <del> return F_ConstantPad2d(pad=self.padding, value=self.value)(input) </del> <ins> return F.pad(input, self.padding, 'constant', self.value) </ins> def __repr__(self): return self.__class__.__name__ + ' ' + str(self.padding)
Code change:
Here, I can't export trivial net with sort() using dynamoexport(). Legacy () works fine, dynamoexport errors out : Unsupported FX nodes: {'callfunction': ['']}. Pytorch nightly 06/03 cc
a missing op, could you take this?
Not sure if related:
Hmmm. Ideally, ONNX should add a sort op. It doesn't have one. I wonder what the legacy exporter converts it to.
should fix this issue.

Related discussion:
text: (Cross, (), ((S, 3), (S, 3))), (Cross, (), ((S, 3, S), (S, 3, S), 1), 'dim'), (Inverse, (), ((S, S),), '', (), [skipIfNoLapack]), <ins> (Gesv, (), ((S, S), (S, S)), '', (), [skipIfNoLapack]), </ins> (Clone, (), ((S, M, S),)), (Squeeze, (), ((S, 1, M, 1),)), # TODO: enable neg dim checks
Code change:
Here, I can't export trivial net with sort() using dynamoexport(). Legacy () works fine, dynamoexport errors out : Unsupported FX nodes: {'callfunction': ['']}. Pytorch nightly 06/03 cc
a missing op, could you take this?
Not sure if related:
Hmmm. Ideally, ONNX should add a sort op. It doesn't have one. I wonder what the legacy exporter converts it to.
should fix this issue.

Related discussion:
text: ('cross', (S, 3), ((S, 3),)), ('cross', (S, 3, S), ((S, 3, S), 1), 'dim'), ('inverse', (S, S), (), '', (), [skipIfNoLapack]), <ins> ('gesv', (S, S), ((S, S),), '', (), [skipIfNoLapack]), </ins> ('clone', (S, M, S), ()), ('eq', (S, S, S), ((S, S, S),)), ('ne', (S, S, S), ((S, S, S),)),
Code change:
Here, I can't export trivial net with sort() using dynamoexport(). Legacy () works fine, dynamoexport errors out : Unsupported FX nodes: {'callfunction': ['']}. Pytorch nightly 06/03 cc
a missing op, could you take this?
Not sure if related:
Hmmm. Ideally, ONNX should add a sort op. It doesn't have one. I wonder what the legacy exporter converts it to.
should fix this issue.

Related discussion:
text: def backward(ctx, grad_output): inverse, = ctx.saved_variables return -torch.mm(inverse.t(), torch.mm(grad_output, inverse.t())) <ins> class Gesv(Function): @staticmethod def forward(ctx, b, a): # TODO see if one can backprop through LU X, LU = torch.gesv(b, a) ctx.save_for_backward(X, a) ctx.mark_non_differentiable(LU) return X, LU @staticmethod def backward(ctx, grad_output, grad_LU=None): X, a = ctx.saved_variables grad_b, _ = torch.gesv(grad_output, a.t()) grad_a = -torch.mm(grad_b, X.t()) return grad_b, grad_a </ins>
Code change:
Here, I can't export trivial net with sort() using dynamoexport(). Legacy () works fine, dynamoexport errors out : Unsupported FX nodes: {'callfunction': ['']}. Pytorch nightly 06/03 cc
a missing op, could you take this?
Not sure if related:
Hmmm. Ideally, ONNX should add a sort op. It doesn't have one. I wonder what the legacy exporter converts it to.
should fix this issue.

Related discussion:
text: def inverse(self): return Inverse.apply(self) <ins> def gesv(self, a): return Gesv.apply(self, a) </ins> def multinomial(self, num_samples=1, with_replacement=False): return Multinomial(num_samples, with_replacement)(self)
Code change:
Previously, worked to install CPU version explicitly. However, does not work. Are the whl files for 2.4 missing? N/A cc
Hi, this link is deprecated since 2.4. Please use the official installation guide instead.
Still it is not available for macosx x86 platform
Hi and Please use official install commands from For linux its: MacOS x86 binaries where deprecated since release 2.3.0.
Closing as resolved. We previously used the command in original post for building dockerfiles for specific versions. I have now replaced it with:
This still installs torch 2.2.2 but not 2.4.0 On Monday, August 19, 2024 at 10:41:25 PM GMT+5:30, Mihir Patel Closing as resolved. We previously used the command in original post for building dockerfiles for specific versions. I have now replaced it with: pip${PYTHONVERSION} install --no-cache-dir --find-links  torch=={CUDAVERSIONTAG} — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID:
This still installs torch 2.2.2 but not 2.4.0
python --version Python 3.12.5 pip3 install torch torchvision torchaudio --index-url Collecting torch Downloading (151.0 MB) 151.0/151.0 MB 11.3 MB/s eta 0:00:00 Successfully installed torch-2.2.2 torchaudio-2.2.2 torchvision-0.17.2 but not 2.4.0

Related discussion:
text: >>> hx = Variable(torch.randn(3, 20)) >>> output = [] >>> for i in range(6): <del> ...     hx = rnn(input, hx) ...     output[i] = hx </del> <ins> ...     hx = rnn(input[i], hx) ...     output.append(hx) </ins> """ def __init__(self, input_size, hidden_size, bias=True, nonlinearity="tanh"):