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
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
We have known for a while that we should in principle support SymBool as a separate concept from SymInt and SymFloat ( in particular, every distinct numeric type should get its own API). However, recent work with unbacked SymInts in, e.g., https://github.com/pytorch/pytorch/pull/90985 have made this a priority to implement. The essential problem is that our logic for computing the contiguity of tensors performs branches on the passed in input sizes, and this causes us to require guards when constructing tensors from unbacked SymInts. Morally, this should not be a big deal because, we only really care about the regular (non-channels-last) contiguity of the tensor, which should be guaranteed since most people aren't calling `empty_strided` on the tensor, however, because we store a bool (not a SymBool, prior to this PR it doesn't exist) on TensorImpl, we are forced to *immediately* compute these values, even if the value ends up not being used at all. In particular, even when a user allocates a contiguous tensor, we still must compute channels-last contiguity (as some contiguous tensors are also channels-last contiguous, but others are not.)
This PR implements SymBool, and makes TensorImpl use SymBool to store the contiguity information in ExtraMeta. There are a number of knock on effects, which I now discuss below.
* I introduce a new C++ type SymBool, analogous to SymInt and SymFloat. This type supports logical and, logical or and logical negation. I support the bitwise operations on this class (but not the conventional logic operators) to make it clear that logical operations on SymBool are NOT short-circuiting. I also, for now, do NOT support implicit conversion of SymBool to bool (creating a guard in this case). This does matter too much in practice, as in this PR I did not modify the equality operations (e.g., `==` on SymInt) to return SymBool, so all preexisting implicit guards did not need to be changed. I also introduced symbolic comparison functions `sym_eq`, etc. on SymInt to make it possible to create SymBool. The current implementation of comparison functions makes it unfortunately easy to accidentally introduce guards when you do not mean to (as both `s0 == s1` and `s0.sym_eq(s1)` are valid spellings of equality operation); in the short term, I intend to prevent excess guarding in this situation by unit testing; in the long term making the equality operators return SymBool is probably the correct fix.
* ~~I modify TensorImpl to store SymBool for the `is_contiguous` fields and friends on `ExtraMeta`. In practice, this essentially meant reverting most of the changes from https://github.com/pytorch/pytorch/pull/85936 . In particular, the fields on ExtraMeta are no longer strongly typed; at the time I was particularly concerned about the giant lambda I was using as the setter getting a desynchronized argument order, but now that I have individual setters for each field the only "big list" of boolean arguments is in the constructor of ExtraMeta, which seems like an acceptable risk. The semantics of TensorImpl are now that we guard only when you actually attempt to access the contiguity of the tensor via, e.g., `is_contiguous`. By in large, the contiguity calculation in the implementations now needs to be duplicated (as the boolean version can short circuit, but the SymBool version cannot); you should carefully review the duplicate new implementations. I typically use the `identity` template to disambiguate which version of the function I need, and rely on overloading to allow for implementation sharing. The changes to the `compute_` functions are particularly interesting; for most of the functions, I preserved their original non-symbolic implementation, and then introduce a new symbolic implementation that is branch-less (making use of our new SymBool operations). However, `compute_non_overlapping_and_dense` is special, see next bullet.~~ This appears to cause performance problems, so I am leaving this to an update PR.
* (Update: the Python side pieces for this are still in this PR, but they are not wired up until later PRs.) While the contiguity calculations are relatively easy to write in a branch-free way, `compute_non_overlapping_and_dense` is not: it involves a sort on the strides. While in principle we can still make it go through by using a data oblivious sorting network, this seems like too much complication for a field that is likely never used (because typically, it will be obvious that a tensor is non overlapping and dense, because the tensor is contiguous.) So we take a different approach: instead of trying to trace through the logic computation of non-overlapping and dense, we instead introduce a new opaque operator IsNonOverlappingAndDenseIndicator which represents all of the compute that would have been done here. This function returns an integer 0 if `is_non_overlapping_and_dense` would have returned `False`, and an integer 1 otherwise, for technical reasons (Sympy does not easily allow defining custom functions that return booleans). The function itself only knows how to evaluate itself if all of its arguments are integers; otherwise it is left unevaluated. This means we can always guard on it (as `size_hint` will always be able to evaluate through it), but otherwise its insides are left a black box. We typically do NOT expect this custom function to show up in actual boolean expressions, because we will typically shortcut it due to the tensor being contiguous. It's possible we should apply this treatment to all of the other `compute_` operations, more investigation necessary. As a technical note, because this operator takes a pair of a list of SymInts, we need to support converting `ArrayRef<SymNode>` to Python, and I also unpack the pair of lists into a single list because I don't know if Sympy operations can actually validly take lists of Sympy expressions as inputs. See for example `_make_node_sizes_strides`
* On the Python side, we also introduce a SymBool class, and update SymNode to track bool as a valid pytype. There is some subtlety here: bool is a subclass of int, so one has to be careful about `isinstance` checks (in fact, in most cases I replaced `isinstance(x, int)` with `type(x) is int` for expressly this reason.) Additionally, unlike, C++, I do NOT define bitwise inverse on SymBool, because it does not do the correct thing when run on booleans, e.g., `~True` is `-2`. (For that matter, they don't do the right thing in C++ either, but at least in principle the compiler can warn you about it with `-Wbool-operation`, and so the rule is simple in C++; only use logical operations if the types are statically known to be SymBool). Alas, logical negation is not overrideable, so we have to introduce `sym_not` which must be used in place of `not` whenever a SymBool can turn up. To avoid confusion with `__not__` which may imply that `operators.__not__` might be acceptable to use (it isn't), our magic method is called `__sym_not__`. The other bitwise operators `&` and `|` do the right thing with booleans and are acceptable to use.
* There is some annoyance working with booleans in Sympy. Unlike int and float, booleans live in their own algebra and they support less operations than regular numbers. In particular, `sympy.expand` does not work on them. To get around this, I introduce `safe_expand` which only calls expand on operations which are known to be expandable.
TODO: this PR appears to greatly regress performance of symbolic reasoning. In particular, `python test/functorch/test_aotdispatch.py -k max_pool2d` performs really poorly with these changes. Need to investigate.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92149
Approved by: https://github.com/albanD, https://github.com/Skylion007
We define specializations for pybind11 defined templates
(in particular, PYBIND11_DECLARE_HOLDER_TYPE) and consequently
it is important that these specializations *always* be #include'd
when making use of pybind11 templates whose behavior depends on
these specializations, otherwise we can cause an ODR violation.
The easiest way to ensure that all the specializations are always
loaded is to designate a header (in this case, torch/csrc/util/pybind.h)
that ensures the specializations are defined, and then add a lint
to ensure this header is included whenever pybind11 headers are
included.
The existing grep linter didn't have enough knobs to do this
conveniently, so I added some features. I'm open to suggestions
for how to structure the features better. The main changes:
- Added an --allowlist-pattern flag, which turns off the grep lint
if some other line exists. This is used to stop the grep
lint from complaining about pybind11 includes if the util
include already exists.
- Added --match-first-only flag, which lets grep only match against
the first matching line. This is because, even if there are multiple
includes that are problematic, I only need to fix one of them.
We don't /really/ need this, but when I was running lintrunner -a
to fixup the preexisting codebase it was annoying without this,
as the lintrunner overall driver fails if there are multiple edits
on the same file.
I excluded any files that didn't otherwise have a dependency on
torch/ATen, this was mostly caffe2 and the valgrind wrapper compat
bindings.
Note the grep replacement is kind of crappy, but clang-tidy lint
cleaned it up in most cases.
See also https://github.com/pybind/pybind11/issues/4099
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82552
Approved by: https://github.com/albanD
Summary:
Follow up to https://github.com/pytorch/pytorch/issues/68095
This also changes the files from the ATen folder to include c10's `Export.h` instead since they can't ever be exporting `TORCH_PYTHON_API`.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69585
Reviewed By: mrshenli
Differential Revision: D32958594
Pulled By: albanD
fbshipit-source-id: 1ec7ef63764573fa2b486928955e3a1172150061
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
This is something which I wrote because it was useful during my debugging sessions, but I think it might be generally useful to other people as well so I took the liberty of proposing an official `pytorch-gdb` extension.
`pytorch-gdb` is a gdb script written in python. Currently, it contains only one command: `torch-tensor-repr`, which prints a human-readable repr of an `at::Tensor` object. Example:
```
Breakpoint 1, at::native::neg (self=...) at [...]/pytorch/aten/src/ATen/native/UnaryOps.cpp:520
520 Tensor neg(const Tensor& self) { return unary_op_impl(self, at::neg_out); }
(gdb) # the default repr of 'self' is not very useful
(gdb) p self
$1 = (const at::Tensor &) 0x7ffff72ed780: {impl_ = {target_ = 0x5555559df6e0}}
(gdb) torch-tensor-repr self
Python-level repr of self:
tensor([1., 2., 3., 4.], dtype=torch.float64)
```
The idea is that by having an official place where to put these things, `pytorch-gdb` will slowly grow other useful features and make the pytorch debugging experience nicer and faster.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54339
Reviewed By: bdhirsh
Differential Revision: D27253674
Pulled By: ezyang
fbshipit-source-id: dba219e126cc2fe66b2d26740f3a8e3b886e56f5
Summary:
Following up on this: https://github.com/pytorch/pytorch/pull/35851 cross dtype storage copy is not being used internally, so I have not included cross dtype copy for complex.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35771
Differential Revision: D21319650
Pulled By: anjali411
fbshipit-source-id: 07c72996ee598eba0cf401ad61534494d6f5b5b3
Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
