This PR relands #108238 that was closed as stale due to CLA issues and also because the CI check has marked the PR as not mergeable.
Repro 1:
```python
import torch
def f(x):
return x[x > 0]
jf = torch.jit.trace(f, torch.tensor(2., device="cuda"))
```
Error:
```bash
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/pytorch/torch/jit/_trace.py", line 874, in trace
traced = torch._C._create_function_from_trace(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<stdin>", line 2, in f
RuntimeError: NYI: Named tensors are not supported with the tracer
```
Repro2:
```python
import torch
import torch.nn.functional as F
from torch import nn
import copy
class Net(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs):
x = copy.deepcopy(inputs) # RuntimeError: NYI: Named tensors are not supported with the tracer
x = F.relu(x)
return x
model = Net()
images = torch.randn(8, 28, 28)
torch.jit.trace(model, images)
```
Error 2:
```bash
Traceback (most recent call last):
File "/opt/pytorch/test_deepcopy.py", line 18, in <module>
File "/opt/pytorch/torch/jit/_trace.py", line 806, in trace
return trace_module(
^^^^^^^^^^^^^
File "/opt/pytorch/torch/jit/_trace.py", line 1074, in trace_module
module._c._create_method_from_trace(
File "/opt/pytorch/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/pytorch/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/pytorch/torch/nn/modules/module.py", line 1501, in _slow_forward
result = self.forward(*input, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/pytorch/test_deepcopy.py", line 12, in forward
x = F.relu(x)
^^^^^^^^^^
File "/opt/conda/envs/ptca/lib/python3.11/copy.py", line 153, in deepcopy
y = copier(memo)
^^^^^^^^^^^^
File "/opt/pytorch/torch/_tensor.py", line 122, in __deepcopy__
new_storage = self._typed_storage()._deepcopy(memo)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/pytorch/torch/storage.py", line 847, in _deepcopy
return self._new_wrapped_storage(copy.deepcopy(self._untyped_storage, memo))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/ptca/lib/python3.11/copy.py", line 153, in deepcopy
y = copier(memo)
^^^^^^^^^^^^
File "/opt/pytorch/torch/storage.py", line 112, in __deepcopy__
new_storage = self.clone()
^^^^^^^^^^^^
File "/opt/pytorch/torch/storage.py", line 126, in clone
return type(self)(self.nbytes(), device=self.device).copy_(self)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: NYI: Named tensors are not supported with the tracer
```
----
#48054 RuntimeError: NYI: Named tensors are not supported with the tracer
#49538 jit tracer doesn't work with unflatten layer
#31591 when i try to export a pytorch model to ONNX, got RuntimeError: output of traced region did not have observable data dependence with trace inputs; this probably indicates your program cannot be understood by the tracer.
- This bug was closed but exists. Multiple comments on it still showing error. This is addressed
Likely fixes the following issues (but untested)
#63297 Named tensor in tracer
#2323 [Bug] torch.onnx.errors.UnsupportedOperatorError when convert mask2former to onnx
Fix zero dimensioned tensors when used with jit.trace They are currently assigned an empty set for names {} this is not the same as "no name" so jit.trace bails with
"NYI: Named tensors are not supported with the tracer"
This happens when I am trying to save a non-trivial model as onnx but the simplest repro I have seen is 48054 above which has been added as test/jit/test_zero_dim_tensor_trace.py
Test plan:
New unit test added
Broken scenarios tested locally
CI
Fixes#48054
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118393
Approved by: https://github.com/zou3519
This was discussed in feedback from the original version of my "reorder proxy/fake" PR. This PR allows calls to `tensor.untyped_storage()` to **always** return a python storage object to the user. Previously, we would error loudly if we detected that the storage had a null dataptr.
Instead, I updated the python bindings for the python storage methods that I saw involve data access, to throw an error later, only if you try to access those methods (e.g. `storage.data_ptr()` will now raise an error if the data ptr is null).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107417
Approved by: https://github.com/albanD, https://github.com/ezyang, https://github.com/zou3519
Proposal of two float8 variants - e5m2 and e4m3 - based on https://arxiv.org/pdf/2209.05433.pdf
Hide all Float8 operator implementations behind `#if !defined(C10_MOBILE)` guard to keep Android build size almost unchanged
TODO:
- Refactor duplicated code
- Cleanup unbalanced pragma pop in dtype utils
- Add native implementation on the CUDA size
Co-authored-by: Nikita Shulga <nshulga@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104242
Approved by: https://github.com/albanD
Proposal of two float8 variants - e5m2 and e4m3 - based on https://arxiv.org/pdf/2209.05433.pdf
Hide all Float8 operator implementations behind `#if !defined(C10_MOBILE)` guard to keep Android build size almost unchanged
TODO:
- Refactor duplicated code
- Cleanup unbalanced pragma pop in dtype utils
- Add native implementation on the CUDA size
Co-authored-by: Nikita Shulga <nshulga@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104242
Approved by: https://github.com/albanD
This PR addresses #101690. This PR implement faster data elements swap in `_StorageBase` using C++ rather than using Python.
This PR helps such a situation that a large model saved on a little-endian machine will be loaded on a big-endian machine.
TODO:
- [x] Add test cases
- [x] Add performance comparison before and after the PR
- [ ] (Optional) Investigate further opportunities for performance improvements by [SIMDization](https://dev.to/wunk/fast-array-reversal-with-simd-j3p)
Fixes#101690
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101925
Approved by: https://github.com/mikaylagawarecki
Complete the implementation of the interface is_pinned() of untyped storage class for privateuse1.
And refactor the implementation in typed storage by untyped_storage.is_pinned().
Hi, @ezyang
This is another improvement of untyped storage for privateuse1, can you take a moment to review it? Thanks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100868
Approved by: https://github.com/kurtamohler, https://github.com/ezyang
To implement the warning when transitioning reshape to copy-on-write
storage, we want to be able to detect a write to one view family
following by a read or a write to another one that shares the same
copy-on-write storage.
Because we have historically not been strict about the mutability of
our data pointers, any warning we have would likely be far too
aggressive.
Therefore, this is the first PR in a long series to ensure a strict
distinction between mutable and const data accessors in TensorBase,
TensorImpl, Storage, and StorageImpl.
The rough plan is to give the mutable accessor a new name that is
explicit about mutation, this will also force us to rewrite any code
that really needs a mutation.
Differential Revision: [D44409928](https://our.internmc.facebook.com/intern/diff/D44409928/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97647
Approved by: https://github.com/ezyang
Summary:
This is part 1 of the effort to support `share_memory_()` in C++ aten library.
This allows C++ code to in place replace the tensor storage to shm based.
For now fd based shm is the only implementation supported to simplify memory management in general.
This first part intentionally avoids public api changes (to `TensorBase`, see comments in `StorageUtil.h`) such that we can get the core features usable outside pt/csrc first. The API addition to `Tensor` or `TensorBase` would involve more distracting changes and make the change harder to review.
Test Plan:
```
buck test caffe2:StorageUtils_test
```
Differential Revision: D43467616
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95228
Approved by: https://github.com/ezyang
