Fixes part of #96414
Replaces any calls to sizes, with sym_sizes. Still seeing an error with the repro script:
``` Bash
Exception raised from sizes_default at /scratch/drisspg/work/pytorch/c10/core/TensorImpl.h:635 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x7d (0x7f697f4a141d in /scratch/drisspg/work/pytorch/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, char const*) + 0xdd (0x7f697f49fbcd in /scratch/drisspg/work/pytorch/torch/lib/libc10.so)
frame #2: c10::TensorImpl::sizes_custom() const + 0x95 (0x7f697f4824c5 in /scratch/drisspg/work/pytorch/torch/lib/libc10.so)
frame #3: at::native::empty_like(at::Tensor const&, c10::optional<c10::ScalarType>, c10::optional<c10::Layout>, c10::optional<c10::Device>, c10::optional<bool>, c10::optional<c10::MemoryFormat>) + 0x92c (0x7f69809d18ac in /scratch/drisspg/work/pytorch/torch/lib/libtorch_cpu.so)
frame #4: <unknown function> + 0x23f5ce7 (0x7f698193bce7 in /scratch/drisspg/work/pytorch/torch/lib/libtorch_cpu.so)
```
still trying to track down this empty call
from the looks of it, might be coming from at::layer_norm?
the BT from lldb is 221 frames however, so lots of noise
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96674
Approved by: 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
Supports the following with dynamic shapes:
```python
for element in tensor:
# do stuff with element
```
Approach follows what's done when `call_range()` is invoked with dynamic shape inputs: guard on tensor size and continue tracing with a real size value from `dyn_dim0_size.evaluate_expr()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94326
Approved by: https://github.com/ezyang
**Introduces symbolic shape guards into dynamo.**
In this PR, we take the existing fake tensor infra and plumbing in dynamo and we start passing a shape_env around. This shape_env does not get plumbed down to middle layers / backend yet - it only collects expressions from frontend invocations at the moment. We then translate these expressions into guards at the point where we take other guards installed throughout dynamo - and add them to check_fn.
Part 1 of https://docs.google.com/document/d/1QJ-M4zfMkD-fjHIqW089RptjLl9EgozZGCceUbvmgfY/edit#
cc @jansel @lezcano @fdrocha @mlazos @soumith @yanboliang @penguinwu @anijain2305
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87570
Approved by: https://github.com/ezyang
