We guard on key order
1) When a key is a non-constant object
2) When we actually need key order - like .values, .items etc
For dicts/OrderedDicts that do not require key order guarding, we just rely on usual `GuardManger + DictGetItemGuardAccessor`. This is faster than going through the `list(d.keys())` based design for OrderedDicts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124779
Approved by: https://github.com/jansel
For code like following:
```python
import torch
def f():
a = {"a": torch.randn(2, 2)}
a.clear()
return a
torch.compile(f, backend="eager", fullgraph=True)()
```
We have a graph break before the pr:
```
torch._dynamo.exc.Unsupported: call_method ConstDictVariable() clear [] {}
```
Test Plan:
Added new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119197
Approved by: https://github.com/jansel, https://github.com/anijain2305
Make variables in dict lazy and remove DICT_KEYS guard.
We build the keys of a dict depth-first and we rely on the guards of
each element in the dict to create the correct guards. This allows us to
remove the rather buggy DICT_KEYS guard and make the guard lazy.
The guards are not completely lazy yet, as we instantiate them in
`_HashableTracker._eq_impl` but it should be possible to make them
truly lazy.
Also, adding new types to the supported types within keys should be less
error prone.
This is marginally less efficient when we graph break, but in turn we
should graph break much less. It also makes the dicts code easier to maintain
(removes `is_hashable_python_var`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117625
Approved by: https://github.com/jansel, https://github.com/peterbell10, https://github.com/anijain2305
ghstack dependencies: #117982, #118098, #117983
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.
Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
Was debugging an export issue, and currently when `key` does not exist in `self.items`, the error message is
```
File "/opt/pytorch/torch/_dynamo/variables/dicts.py", line 208, in getitem_const
return self.items[key]
~~~~~~~~~~^^^^^
torch._dynamo.exc.InternalTorchDynamoError: <torch._dynamo.variables.dicts.ConstDictVariable._HashableTracker object at 0x7fd7697cbf90>
```
This PR changes it to be the following.
```
File "/data/users/angelayi/pytorch/torch/_dynamo/variables/dicts.py", line 199, in getitem_const
raise KeyError(arg.value)
torch._dynamo.exc.InternalTorchDynamoError: shape
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117902
Approved by: https://github.com/williamwen42
This prepares the PR where we implement sets in terms of dicts.
To do so, rather than storing internally a dictionary that maps literals
to VariableTrackers, it stores (pretty much) a dictionary from VTs to VTs.
To do so, keys are wrapped in an opaque internal class _Hashable.
The Hashable class is opaque on purpose so that it fails hard if
if it inadvertently leaks back into user code.
We also found and fixed a number of latent bugs and inconsistencies
in the way dynamo checked what can be a dict key. More generally, we
make much clearer what are the things that need to be modified to add
a new supported key type to Dicts.
Fixes [#107595](https://www.internalfb.com/tasks?t=107595)
Fixes [#111603](https://www.internalfb.com/tasks?t=111603)
Re-PR of https://github.com/pytorch/pytorch/pull/111196 sadly due to reverts, we could not reuse @lezcano's original PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116785
Approved by: https://github.com/mlazos
1. Removes calls to `replace_all` and `clone` and makes VTs mutable.
2. Properly handles Tuple Iterator mutation. Previously TupleIterator variables would only be properly reconstructed if they were advanced at least once in a frame. On calls to `next`, the source information would be lost (due to constructing a new iterator without using builder), which would ensure that during codegen the variable would be reconstructed from scratch. Now that VTs are mutated, the source is never lost, so we need to properly track mutation and handle it by replaying calls to `next` at the end of the modified bytecode.
3. Added test for checking iadd side effects, this was missing in our unit test coverage.
4. Fixed two incorrect sources, DelayGraphBreakVariable, and UserMethodVariable both relied on setting the source to AttrSource(parent, name) at the callsite of `var_getattr`.
5. Fixed a bug in inplace adding for lists, it would set the resulting VariableTracker's source to `None` which would utilize a different reconstruct path in codegen. Now this is handled explicitly by reconstructing vars when allow_cache=`False`, so that during side effect replay, the mutated var is correctly updated.
In subsequent PRs:
* Refactoring side effect tracking to be significantly simpler (I think we only need an `is_modified` flag)
* Refactor `next_variables` iterator to match the signature of `next`
* Remove all references to `options` in the code
* Refactor VTs representing mutable collections to implement their own mutation update handling
* Remove clone and/or make it specific to lists for creating slices
* Add mutation tracking/replay for sets
* Add mutation tracking/replay for iter.py
* Removing setting source in builder (it's set at the top level after a var is returned)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113725
Approved by: https://github.com/jansel
This prepares the PR where we implement sets in terms of dicts.
To do so, rather than storing internally a dictionary that maps literals
to VariableTrackers, it stores (pretty much) a dictionary from VTs to VTs.
To do so, keys are wrapped in an opaque internal class `_Hashable`.
The Hashable class is opaque on purpose so that it fails hard if
if it inadvertently leaks back into user code.
We also found and fixed a number of latent bugs and inconsistencies
in the way dynamo checked what can be a dict key. More generally, we
make much clearer what are the things that need to be modified to add
a new supported key type to Dicts.
Fixes https://github.com/pytorch/pytorch/issues/107595
Fixes https://github.com/pytorch/pytorch/issues/111603
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111196
Approved by: https://github.com/jansel
Currently custom VariableTrackers exist for classes that live outside of pytorch.
For these cases dynamo currently eagerly imports the module to get the class
object to compare against.
This instead uses `sys.modules.get("module.path")` such that the module is never
imported by dynamo itself, but if the user has imported the module then we will
still access the module and grab the type we need to compare against.
I noticed this issue because importing `KeyedJaggedTensor` fails half-way
through if `fbgemm_gpu` has been built with an incompatible PyTorch version, in
which case it retries the import again each time!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112319
Approved by: https://github.com/lezcano, https://github.com/ezyang
Summary:
As of https://github.com/pytorch/pytorch/pull/103192, dynamo
supports code that creates OrderedDict instances using kwargs
for the key-value pairs rather than passing a dict literal.
But custom dicts (for example subclasses of OrderedDict) follow
a different codepath so that we can check for conditions such
as a custom `__init__` that need to force a graph break.
This commit allows kwargs for custom dict constructors - if the
args are empty and the class is not also a dataclass (which is
the case that, for example, a
`transformers.modeling_outputs.ModelOutput` instance will wind
up hitting) then treat the kwargs as the key-value pairs.
NOTE: For this to behave 100% correctly, we are relying on
the fact that python dicts behave like ordered dicts so that they
preserve the kwargs' ordering. Technically it is not guaranteed that
future versions of Python will respect this; if that behavior changes
we would need to ensure that dynamo uses OrderedDict for kwargs all
the way down in order to handle special cases like OrderedDict where
the kwargs' ordering does matter.
Test Plan:
```
pytest test/dynamo/test_functions.py
```
I also verified that the new test fails without the changes to
`dicts.py`.
Reviewers: yanboliang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112513
Approved by: https://github.com/yanboliang
`sys.modules` is currently treated as a constant dictionary and any reference to
it will result in guards on the full contents of `sys.modules`. This instead
adds a specialized variable tracker which tries to guard only on the modules
referenced by the code. e.g.
```
sys.modules["operator"].add(x, x)
```
will generate the guard
```
___dict_contains('operator', G['sys'].modules)
```
It does this with special support for `__contains__` `__getitem__` and `.get`
which are probably the most commonly used with `sys.modules`. For anything else
we just fall back to building the dict tracker as normal.
While accessing `sys.modules` may seem unusual, it actually comes up when
inlining the `warnings.catch_warnings` context manager which internally accesses
`sys.modules["warnings"]`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110990
Approved by: https://github.com/ezyang
Fix: #107315
This PR enables dynamo to trace through the `pytree` API by inlining its functions. In
order to do so, a few details of `pytree` had to be changed.
In summary, this PR:
- Introduces `TreeSpecVariable` for representing `TreeSpec` instances
- Specializes `<type>.__bases__` call, returning a `TupleVariable`
- Enables the call to `id` builtin function for every variable that implements
`as_python_constant` method
- Specializes `ConstantVariable.call_method` for its (un)flatten functions
- Implements `UserDefinedObjectVariable.as_python_constant`
- Modifies `pytree` by:
- Make `SUPPORTED_NODES` a map of ids (instead of types) to `NodeDef`
- Removed `functools.wraps` function, since it can't be inlined
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108533
Approved by: https://github.com/ezyang, https://github.com/voznesenskym
ghstack dependencies: #109201
before the PR, for HF's ModelOutput class, we use dicts.py/DataClassVariable with our own implementation on __getItem__, __setAttr__, __setItem__. There is a risk that ModelOutput logic may change since it is a user code
after the PR, we inline __getItem__, __setAttr__, __setItem__ using dicts.py/CustomizedDictVariable so the logic always keep AA
unit test
* python test/dynamo/test_model_output.py -k test_HF_bert_model_output
test on HF benchmark
* python benchmarks/dynamo/huggingface.py -d cuda --inference --accuracy --progress --inductor --print-dataframe-summary 2>&1
* all metric are the same before/after the PR, including pass rate, unique_graphs, graph_breaks, unique_graph_breaks
* before the PR: P790393916
* after the PR: P790368991
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105044
Approved by: https://github.com/jansel
Marks all params/optimizer state as static addresses and a finalizer which cleans up the graph attributes when the optimizer goes out of scope.
**Note: this does not mark grads as static because this will increase memory usage significantly
There are two cases:
1. The upstream graph is cudagraphed - this case will work fine OOTB
2. The upstream graph is not cudagraphed - in this case, there will be a lot of copies introduced from the upstream (to copy the grads) into cudagraphed-owned memory, unless the user explicitly marks the grads as static. If the user does this, this will also require not deallocating the grads in zero_grad() (either the mod or optimizer version) by setting them to zero vs None. There is a PR (https://github.com/pytorch/pytorch/pull/107853) in flight to throw an error if zero_grad attempts to set static grads to None.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107504
Approved by: https://github.com/eellison
Fixes https://github.com/pytorch/pytorch/pull/102577#issuecomment-1650905536
Serializing to json is more stable, and renamed the API:
```
# Takes in a treespec and returns the serialized treespec as a string. Also optionally takes in a protocol version number.
def treespec_dumps(treespec: TreeSpec, protocol: Optional[int] = None) -> str:
# Takes in a serialized treespec and outputs a TreeSpec
def treespec_loads(data: str) -> TreeSpec:
```
If users want to register their own serialization format for a given pytree, they can go through the `_register_treespec_serializer` API which optionally takes in a `getstate` and `setstate` function.
```
_register_treespec_serializer(type_, *, getstate, setstate)
# Takes in the context, and outputs a json-dumpable context
def getstate(context: Context) -> DumpableContext:
# Takes in a json-dumpable context, and reconstructs the original context
def setstate(dumpable_context: DumpableContext) -> Context:
```
We will serialize to the following dataclass, and then json.dump this it to string.
```
class TreeSpec
type: Optional[str] # a string name of the type. null for the case of a LeafSpec
context: Optional[Any] # optional, a json dumpable format of the context
children_specs: List[TreeSpec],
}
```
If no getstate/setstate function is registered, we will by default serialize the context using `json.dumps/loads`. We will also serialize the type through `f"{typ.__module__}.{typ.__name__}"`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106116
Approved by: https://github.com/zou3519
