For a custom op
```
@torch.library.custom_op("my_lib::foo", mutates_args={})
def foo(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return x + y
```
ppl could call `torch.ops.my_lib.foo()` or directly call `foo()` in the `forward` of an `nn.Module`
These two calling conventions will lead to the same node in the output graph, but different stack traces.
When directly calling `foo()`, the displayed stack_trace in the graph will be
```
# File: .../pytorch/torch/_library/custom_ops.py:687 in __call__, code: return self._opoverload(*args, **kwargs)
```
This is not useful so we filter it out.
```
python test/functorch/test_aot_joint_with_descriptors.py -k test_custom_op_stack_trace
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165693
Approved by: https://github.com/SherlockNoMad, https://github.com/williamwen42
Three fixes:
1. When doing t[u0] +=1 if u0 is unbacked we could allocate a new unbacked symbol during the the indexing of t[u0] (when we fake trace setitem), namely because meta_select does allocate a new unbacked symbol for the storage offset when we do not know if u0>=0 or u0<0. but the output size/stride of setitem(), does not depend on that new symbol. it's self consumed in setitem so we shall ignore it.
2. Also when we trace through generalized_scatter the applications of the views could allocate unbacked symints
but those do not effect final output, we also shall ignore them.
3.Before accessing strides in lowering we shall materialize.
Address https://github.com/pytorch/pytorch/issues/114293 and https://github.com/pytorch/pytorch/issues/131911
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164341
Approved by: https://github.com/bobrenjc93
Eager AC/SAC reapplies the mutations (like global dict mutations) in the backward during the recomputation of forward. torch.compile has no easy way to reapply python mutations in the backward. But many users might be ok to skip reapplication of side effects in the backward. They can set this config flag to accept this eager and compile divergence.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165775
Approved by: https://github.com/zou3519
ghstack dependencies: #165734
Summary: This starts writing the compiler_config metadata into logger
Test Plan:
Modified existing test case to make sure this is not null.
(Also eyeballed what we're logging tomake sure it's reasonable
Reviewed By: masnesral
Differential Revision: D84014636
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165581
Approved by: https://github.com/masnesral
Summary:
This stores information on where fx graphs come from, which makes it
significantly easier to debug.
One outstanding question
1) I only stored the kernel stack traces, do we also want the node mappings?
Test Plan:
I wrote a explicit logging test which makes a module, fx traces it, compiles it, and makes sure the logging infomration shows up.
```
clr@devvm17763 ~/fbsource/fbcode/caffe2/test/dynamo
% buck2 test @//mode/opt fbcode//caffe2/test/dynamo:test_dynamo -- test_utils
File changed: fbsource//xplat/caffe2/test/dynamo/test_utils.py
File changed: fbcode//caffe2/test/dynamo/test_utils.py
Buck UI: https://www.internalfb.com/buck2/528dea32-2416-4a62-a1ec-39f3c0efdd2e
Test UI: https://www.internalfb.com/intern/testinfra/testrun/13229324015574003
Network: Up: 0B Down: 0B
Executing actions. Remaining 0/2
Command: test.
Time elapsed: 17.3s
Tests finished: Pass 16. Fail 0. Fatal 0. Skip 0. Build failure 0
```
Rollback Plan:
Differential Revision: D82037582
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162669
Approved by: https://github.com/yushangdi
Not sure what exactly we want to have in the message, but that's easy to adjust. I tried to find a reliable test to reproduce this message (happens only when a guard fails right after it's created), but I ended up mocking a `guard_manager.check` function to return `False` to trigger this behavior. I think that's fine, because any other case that we pick (like datetime.now()), we want to patch one day anyway, so every time we make the next patch, will need to chase for another repro test
@williamwen42
Fixes#164990
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165242
Approved by: https://github.com/williamwen42
Summary: If a function is wrapped with functools, we should not look at the wrapped function signature but rather the wrapper, since we need to construct the frame for the top level function here.
Test Plan: test_decorated_function_with_functools_wrap_aot
Differential Revision: D84626752
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165454
Approved by: https://github.com/yiming0416
Creates the fork/join stream ops. These ops are passthrough ops which mutate all of their args (without actually performing any computation on them) so that during functionalization, implicit dependencies are added on all of their args. This allows us to prevent reordering during our pre/post grad graph passes.
Make custom ops inplace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162900
Approved by: https://github.com/anijain2305
ghstack dependencies: #163027, #162899, #163028
Stores streams in a global object look table that maps a dynamo selected index to objects. This index is generated during tracing, and at runtime, a helper function is called from the bytecode to populate this map.
This differs from the previous implementation that simply mapped IDs to the associated objects. This required specialization on the IDs of the specific objects, while this new approach does not.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162899
Approved by: https://github.com/anijain2305
ghstack dependencies: #163027
Verify the deterministic mode with torch.compile benchmark scripts.
Here is what my testing script does (pasted in the end):
- run a model in default mode, save it's result
- run the model again in default mode, but distort the benchmarking results. Compare it with the saved result.
- Do the above again in deterministic mode.
I tried to test a few modes
- BertForMaskedLM and GoogleFnet: I can repro the numeric change by distorting the benchnmark result in the default mode. The non-determinism is gone in the deterministic mode
- DistillGPT2: I can not repro the numeric change by distorting the benchmarking result in the default mode. It does not surprise me much. Reduction order change does not always cause numeric change.
```
model=GoogleFnet
export TORCHINDUCTOR_WRITE_ARE_DETERMINISTIC_ALGORITHMS_ENABLED=0
export TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 # disable autotune cache
export TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE=0
export TORCHINDUCTOR_FX_GRAPH_CACHE=0
export TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_shunting/
export TORCHINDUCTOR_BENCHMARK_KERNEL=1
export TORCHINDUCTOR_UNIQUE_KERNEL_NAMES=1
export INDUCTOR_TEST_DISABLE_FRESH_CACHE=1
# Non deterministic mode
# --float32 rather than --amp to make it easier to repro non-deterministic
echo "Save results for non-deterministic mode"
python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --save-model-outputs-to=/tmp/saved-non-deterministic.pkl
echo "Compare results with distorted benchmarking in non-deterministic mode"
TORCHINDUCTOR_DISTORT_BENCHMARKING_RESULT=inverse python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --compare-model-outputs-with=/tmp/saved-non-deterministic.pkl
echo "Save results for deterministic mode"
TORCHINDUCTOR_DETERMINISTIC=1 python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --save-model-outputs-to=/tmp/saved-deterministic.pkl
echo "Compare results with distorted benchmarking in deterministic mode"
TORCHINDUCTOR_DETERMINISTIC=1 TORCHINDUCTOR_DISTORT_BENCHMARKING_RESULT=inverse python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --compare-model-outputs-with=/tmp/saved-deterministic.pkl
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164904
Approved by: https://github.com/jansel, https://github.com/v0i0
