Symbolic shapes compile time on full CI with inductor is horribly long (even though our aot_eager local runs seemed to suggest that the added latency was only 10s per model.) To patch over the problem for now, run the benchmark suite with dynamic batch only. This should absolve a lot of sins.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97912
Approved by: https://github.com/janeyx99, https://github.com/desertfire
The purpose of this API is to execute a few large components of work:
1) Refactor all the internals of plumbing dynamic dimension information after dynamo to be stateless
2) Decouple allocation controls around dynamic dimensions from verification
3) For (2), for allocation, create an enum that dictates whether we are in DUCK (default today), STATIC (aka assume_static_default in the past), or DYNAMIC (aka user constrained, do not duck shape)
4) For (2), for verification, we separate out the list of dynamic ranges entirely from allocation. This means shape_env does not tracking for what we verify on, and instead, it is the callers job to invoke produce_guards() with the various things they want verified, specifically, with the valid ranges. We do use constrain ranges to refine value ranges when doing analysis.
5) We have decided, therefore, as an extension of (4) to double down on "late" checks versus "eager" checks, primarily because the mechanisms for gathering what actually matters happens during guards, and should be a purview of the caller seeking guards, not the shape env. However, for dynamo, these structures are essentially one and the same.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96699
Approved by: https://github.com/avikchaudhuri, https://github.com/ezyang
Twice this week I have had people confuse "operator defined with Python
operator registration aka torch.library" and "PyOperator which is used
to define control flow operators and other operators that cannot be
represented in JIT schema." Renaming PyOperator for clarity.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97493
Approved by: https://github.com/SherlockNoMad
The purpose of this PR is to remove reliance on argument positions in dedup guards, AND extend the functionality to params.
A version of this PR was stamped prior https://github.com/pytorch/pytorch/pull/95831 - but was kinda gross, because it was based on an underlying PR that did way too much with source names.
This PR leaves most of that alone, in favor of just reusing the same name standardization logic that dynamo module registration does.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96774
Approved by: https://github.com/ezyang
Summary:
Verified the changes to catch unspecialized int/floats being added as additional graph in D44037548 prior to RP(https://github.com/pytorch/pytorch/pull/95621).
However, with #95621 the issue to be solved originally is no longer valid because int & float in `forward` will always be specialized in export. This RP is to add the assertion anyway *(though not be hit unless there is a regression)* to immediately catch the attempt to add unspecialized int/float to additional graphargs
Test Plan:
Example of the error message would look like:
```
Dynamo attempts to add additional input: value=9.999999747378752e-06, source=NNModuleSource(inner=AttrSource(base=NNModuleSource(inner=AttrSource(base=LocalInputSource(local_name='self', pos=0), member='torch_module')), member='eps'))
```
Passed all export tests
```
Buck UI: https://www.internalfb.com/buck2/fea72653-5549-47e7-a9bf-740eb86a8e26
Test UI: https://www.internalfb.com/intern/testinfra/testrun/8725724422167257
RE: reSessionID-7b3470b1-c293-4c4a-9671-dd0b7a2839b8 Up: 6.0 KiB Down: 0 B
Jobs completed: 101. Time elapsed: 115.7s.
Tests finished: Pass 98. Fail 0. Fatal 0. Skip 0. 0 builds failed
```
Differential Revision: D44075910
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96786
Approved by: https://github.com/tugsbayasgalan, https://github.com/ezyang
OK, so this PR used to be about reducing the number of constants we specialize on, but it turns out that unspecialization was ~essentially never used (because we still constant specialized way too aggressively) and I ended up having to fix a bunch of issues to actually get tests to pass. So this PR is now "make int unspecialization actually work". As part of this, I have to turn off unspecialization by default, as there are still latent bugs in inductor.
The general strategy is that an unspecialized int is represented as a SymInt. Representing it as a 0d tensor (which is what the code used to do) is untenable: (1) we often need unspecialized ints to participate in size computations, but we have no way of propagating sympy expressions through tensor compute, and (2) a lot of APIs work when passed SymInt, but not when passed a Tensor. However, I continue to represent Numpy scalars as Tensors, as they are rarely used for size computation and they have an explicit dtype, so they are more accurately modeled as 0d tensors.
* I folded in the changes from https://github.com/pytorch/pytorch/pull/95099 as I cannot represent unspecialized ints as SymInts without also turning on dynamic shapes. This also eliminates the necessity for test_unspec.py, as toggling specialization without dynamic shapes doesn't do anything. As dynamic shapes defaults to unspecializing, I just deleted this entirely; for the specialization case, I rely on regular static shape tests to catch it. (Hypothetically, we could also rerun all the tests with dynamic shapes, but WITH int/float specialization, but this seems... not that useful? I mean, I guess export wants it, but I'd kind of like our Source heuristic to improve enough that export doesn't have to toggle this either.)
* Only 0/1 integers get specialized by default now
* A hodgepodge of fixes. I'll comment on the PR about them.
Fixes https://github.com/pytorch/pytorch/issues/95469
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95621
Approved by: https://github.com/jansel, https://github.com/Chillee
This PR allows us to reuse the static per tensor decision making we make at fake tensorification time. We can use this to avoid setting up dynamic dim guards later if the tensor was never a candidate.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95566
Approved by: https://github.com/ezyang
The idea is to make it a little more obvious which branch you're going to go down in a subset of cases, and make it easier to detect if you've accidentally shadowed one condition with another (the reason I wrote this in the first place.) The type dictionary also makes it harder for people to accidentally use isinstance when they should have used istype.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95476
Approved by: https://github.com/jansel
My intention is to collapse all of the istype() and isinstance() and object identity tests into a more structured form involving a dict lookup. To do this conveniently, I need every continuation to be expressible in a single expression. Thus, all multi-line wrap methods are moved. This is code motion only, no logic changes.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95472
Approved by: https://github.com/Skylion007
Some of these changes are semantics preserving, some are not. Please review carefully.
* Use `istype(x, y)` over `type(x) is y`
* Use istype over isinstance in frozenset. If the user subclassed the type in question, we must treat it as a user defined class as it may have custom behavior
* The `isinstance(value, (int, float))` condition for `wrap_unspecialized_primitive` is dead-ish; direct int/float values are caught earlier istype check. Technically however, if you subclassed int/float it would pass through, however this is almost assuredly not intended behavior
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95471
Approved by: https://github.com/Skylion007
Tweak dynamo behavior in 2 places when calling nn.Modules,
to route the call to __call__ instead of .forward(), since
__call__ is the codepath that eager users hit and will dispatch
to hooks correctly.
(1) inside NNModuleVariable.call_function, which covers the common case
of calling a module from code dynamo is already tracing
(2) at the OptimizedModule layer, which is the entrypoint
into a top-level nn.Module dynamo is about to compile
This exposes a new bug: NNModuleVariable used to special-case calling
module.forward() (which is a method) as a UserFunctionVariable with an extra
'self' arg. After tracing into module.__call__, there is no longer a special
case for the eventual call into .forward, and it gets wrapped in a
UserDefinedObjectVariable following standard behavior of ._wrap(). UDOV can't be
called, so this broke some tests.
- Fix: add a new special case in _wrap() that treats methods as a UserDefinedMethod
instead of UserDefinedObjectVariable. Now, the forward method can be called.
Also, fix NNModuleVar.call_method routing forward back to __call__
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92125
Approved by: https://github.com/ezyang, https://github.com/jansel, https://github.com/voznesenskym
There is a fast way to implement a guard for an empty dict, which is to check its bool() value.
However, we can't use this guard in general, since we can only safely apply it at runtime if the runtime value actually is a dict (or, another type that works with 'bool' in the same way). A counterexample is when a tensor is passed instead of a dict, and throws on bool() operator.
So we can put a type check in the guard, but that is slow enough it defeats the purpose.
Instead, we note that for the case of NNModuleVariables (which are specialized NNModules not unspecialized ones), we already have a hook in place to invalidate the guards if setattr is called. I am claiming that setattr is the only way that the type of a property on an NNModule could change. If I'm right, then it's safe to (a) only use this guard for NNModuleVariables, (b) not do a type check inside the guard.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95248
Approved by: https://github.com/voznesenskym
I applied some flake8 fixes and enabled checking for them in the linter. I also enabled some checks for my previous comprehensions PR.
This is a follow up to #94323 where I enable the flake8 checkers for the fixes I made and fix a few more of them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94601
Approved by: https://github.com/ezyang
Historically, we work out `size_hint` by working it out on the fly by doing a substitution on the sympy expression with the `var_to_val` mapping. With this change, we also maintain the hint directly on SymNode (in `expr._hint`) and use it in lieu of Sympy substitution when it is available (mostly guards on SymInt, etc; in particular, in idiomatic Inductor code, we typically manipulate Sympy expressions directly and so do not have a way to conveniently maintain hints.)
While it's possible this will give us modest performance improvements, this is not the point of this PR; the goal is to make it easier to carefully handle unbacked SymInts, where hints are expected not to be available. You can now easily test if a SymInt is backed or not by checking `symint.node.hint is None`.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94201
Approved by: https://github.com/voznesenskym
Previously, Dynamo faked support for item() when `capture_scalar_outputs` was True by representing it internally as a Tensor. With dynamic shapes, this is no longer necessary; we can represent it directly as a SymInt/SymFloat. Do so. Doing this requires you to use dynamic shapes; in principle we could support scalar outputs WITHOUT dynamic shapes but I won't do this unless someone hollers for it.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Differential Revision: [D42885775](https://our.internmc.facebook.com/intern/diff/D42885775)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93150
Approved by: https://github.com/voznesenskym
**Motivation**
When adding support for default args (#90575), a lot of VariableTrackers missing sources were encountered. Currently, in a lot of cases it seems OK to skip the source for VariableTrackers created (especially during inlining), but that assumption breaks down when inlining functions with default arguments.
**Summary** of changes
- propagate the self.source of the VariableBuilder to the new variables being built, which seems like it was an omission previously
- Add SuperSource to track usages of super(), so that SuperVariables can support function calls with default args
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91729
Approved by: https://github.com/ezyang
