Done via
```
git grep -l 'SymbolicIntNode' | xargs sed -i 's/SymbolicIntNode/SymIntNodeImpl/g'
```
Reasoning for the change:
* Sym is shorter than Symbolic, and consistent with SymInt
* You usually will deal in shared_ptr<...>, so we're going to
reserve the shorter name (SymIntNode) for the shared pointer.
But I don't want to update the Python name, so afterwards I ran
```
git grep -l _C.SymIntNodeImpl | xargs sed -i 's/_C.SymIntNodeImpl/_C.SymIntNode/'
```
and manually fixed up the binding code
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82350
Approved by: https://github.com/Krovatkin
- toTypeInferredIValue will throw an error when given an empty container because it isn't able to tell what kind of container it is. Thus empty containers are ignored in addArgumentValue/s, overlaps, and is_alias_of.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81786
Approved by: https://github.com/davidberard98
- Modify the is_mutable(size_t index) overload to become is_mutable(const SchemaArgument& argument) due to cases where one might want to check the mutability of either input or output arguments.
- Refactored all calls to the function to use this new overload
- Tested through is_mutable() tests in test_schema_info.cpp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81784
Approved by: https://github.com/davidberard98
- Modified is_mutable python binding to accept a string instead of a string_view for better python compatibility.
- Modified argument value adding python bindings to deal with input/self edge case due to inconsistencies in how the first variable is named.
- Modified _is_alias_of and created _contains_alias_of python bindings to accurately find out if values are aliasing, or contain an alias.
- Fixed is_mutable implementation to cover all ops that have mutable optional arguments. (These are all the ops that have the optional arguments 'running_mean' and 'running_var' along with either 'train', 'training' or 'use_input_stats.'
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81782
Approved by: https://github.com/davidberard98
Fix torch.save _open_zipfile_writer optimization that uses a c++ stream when `f` is a os.PathLike.
This fastpath requires that we don't `open()` in python if possible, so don't do it unconditionally.
Fix PyTorchStreamWriter construction binding that takes a buffer object.
Use py::memoryview instead of py::bytes as the former doesn't copy the data.
Validated with a trivial benchmark that calls torch.save in a loop 20x with a 10M elements float32 tensor
either on cpu or cuda. Saved to /dev/null.
Tried two variants 'str' and 'open'
In 'str' we pass the string "/dev/null" to torch.save.
In 'open' we pass `open("/dev/null", "wb")` to torch.save.
Timing in seconds.
Before this patch:
str-cpu :: 0.757
open-cpu :: 0.757
str-cuda :: 1.367
open-cuda :: 1.366
After this patch:
str-cpu :: 0.256
open-cpu :: 0.251
str-cuda :: 0.896
open-cuda :: 0.834
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80404
Approved by: https://github.com/jamesr66a
This PR adds support for `SymInt`s in python. Namely,
* `THPVariable_size` now returns `sym_sizes()`
* python arg parser is modified to parse PyObjects into ints and `SymbolicIntNode`s
* pybind11 bindings for `SymbolicIntNode` are added, so size expressions can be traced
* a large number of tests added to demonstrate how to implement python symints.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78135
Approved by: https://github.com/ezyang
Original PR: #77295
Original commit message:
On GPU, conv errors if not all its inputs have the same dtype.
In the case of autocasting during freezing, what we see is:
1) inputs to conv are casted to half
2) inputs to batchnorm are not casted, so many are still floats
3) we try to fold conv + batchnorm, by finding different weight and bias such that conv(input, new_weight, new_bias) is equivalent to the original conv -> batchnorm.
If conv previously had an optional bias, then during freezing we will temporarily create a zero-valued bias as a placeholder for conv_bias. We want to construct it to have the same dtype as the weight input to conv, to avoid errors on GPU.
Reland changes:
There's a memory leak from cuda caching allocator that is a side effect of this fix. The memory leak causes the test to fail, though for some reason it didn't fail on CI in the last PR. This skips the tests for now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77617
Approved by: https://github.com/eellison
On GPU, conv errors if not all its inputs have the same dtype.
In the case of autocasting during freezing, what we see is:
1) inputs to conv are casted to half
2) inputs to batchnorm are not casted, so many are still floats
3) we try to fold conv + batchnorm, by finding different weight and bias such that conv(input, new_weight, new_bias) is equivalent to the original conv -> batchnorm.
If conv previously had an optional bias, then during freezing we will temporarily create a zero-valued bias as a placeholder for conv_bias. We want to construct it to have the same dtype as the weight input to conv, to avoid errors on GPU.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77295
Approved by: https://github.com/eellison
