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
ghstack dependencies: #164801, #164532
Builds on top of https://github.com/pytorch/pytorch/pull/163673 and https://github.com/pytorch/pytorch/pull/164174. This will be used in the followup PRs to apply regional inductor compilation.
The existing implementation let Dynamo trace into the `torch.fx.traceback.annotate`, but thats not what we want. We want Dynamo to essentially run the torch.fx.traceback.annotate function in eager, so that every Fx node created in Dynamo Fx graph has the custom meta node.
What does not work?
* We still have to set the context manager `torch.fx.traceback.preserve_node_meta()` in the user code because CI was unhappy. This can be fixed but with some perseverance.
* This does not work with graph breaks yet. But we can solve that problem, if needed, in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164678
Approved by: https://github.com/SherlockNoMad, https://github.com/jansel, https://github.com/xmfan
```
import torch
import torch.fx.traceback as fx_traceback
import torch.export
class M(torch.nn.Module):
def forward(self, x):
with fx_traceback.annotate({"pp_stage": 0}):
with fx_traceback.annotate({"fdsp_bucket": 0}):
x = x + 1
x = x - 2
with fx_traceback.annotate({"cuda_stream": 2, "fsdp_bucket": 1}):
x = x * 2
x = x / 3
return x
m = M()
with fx_traceback.preserve_node_meta():
ep = torch.export.export(m, (torch.randn(10),))
for node in ep.graph.nodes:
if node.op == "call_function":
print(f"{node.target}, {node.meta.get("custom", {})}")
```
prints
```
aten.add.Tensor, {'pp_stage': 0, 'fdsp_bucket': 0}
aten.sub.Tensor, {'pp_stage': 0}
aten.mul.Tensor, {'pp_stage': 0, 'cuda_stream': 2, 'fsdp_bucket': 1}
aten.div.Tensor, {}
```
TODOs:
- run_decomposition is failing
- Need to test with the new full graph capture + aot_export_joint apis
- Need to make the annotation propagate through autograd engine to reach the bw nodes. Sample impl here: https://github.com/pytorch/pytorch/pull/83558
- Edward want to restrict the key in custom field to be top-level singleton objects only
- also need to take care of metadata merging when passes are fusing nodes
Thanks @angelayi for contributing the dynamo fixes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163673
Approved by: https://github.com/albanD, https://github.com/angelayi
We are seeing crashes of the form
```
Traceback (most recent call last):
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/symbolic_convert.py", line 1487, in run
while self.step():
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/symbolic_convert.py", line 1348, in step
self.dispatch_table[inst.opcode](self, inst)
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/symbolic_convert.py", line 2437, in LOAD_ATTR
self._load_attr(inst)
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/symbolic_convert.py", line 2425, in _load_attr
result = BuiltinVariable(getattr).call_function(
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/variables/builtin.py", line 1347, in call_function
return handler(tx, args, kwargs)
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/variables/builtin.py", line 967, in <lambda>
tx, [v.realize() for v in args], kwargs
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/variables/builtin.py", line 967, in <listcomp>
tx, [v.realize() for v in args], kwargs
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/variables/lazy.py", line 72, in realize
self._cache.realize()
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/variables/lazy.py", line 33, in realize
self.vt = builder.VariableBuilder(tx, self.source)(self.value)
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/variables/builder.py", line 445, in __call__
vt = self._wrap(value)
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/variables/builder.py", line 1043, in _wrap
torch._dynamo.utils.store_user_object_weakref(value)
File "/packages/aps_ads_vm/launcher_multiapp-inplace#link-tree/torch/_dynamo/utils.py", line 4694, in store_user_object_weakref
user_obj_id_to_weakref[obj_id] = weakref.ref(obj)
torch._dynamo.exc.InternalTorchDynamoError: TypeError: cannot create weak reference to 'torch.Event' object
```
This pull request makes us gracefully graph break, vs explicitly crashing.
I've added a test which reproduces the issue. There is a side discussion re:
how did torch.Event support ever work here, since it appears you cannot take a
weakref to a torch.Event
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163168
Approved by: https://github.com/Lucaskabela, https://github.com/jansel
Note: Adding unit test for this is tricky as having errors in the specific unit test would cause test_utils.py to crash all together.
Tested as follows:
1. Added x = 1/0 after guarded_code = compile_inner(code, one_graph, hooks, transform) in convert_frame.py
2. Printed exception_stack_trace and got: ['Traceback (most recent call last):\n File "/data/users/jovian/pytorch/torch/_dynamo/convert_frame.py", line 1207, in _compile\n x = 1/0\n ~^~\nZeroDivisionError: division by zero\n']
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161096
Approved by: https://github.com/c00w
We add a logging around when an ID_MATCH guard is added at a place where inbuilt_inline_nn_modules would inline it. This is done with the aim of tagging recompiles that could be avoided by setting inbuilt_inline_nn_modules flag.
It will help us log and track the flag's adoption and potentially quantify saving in the the number of recompiles.
Differential Revision: D80075975
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160592
Approved by: https://github.com/anijain2305
As part of better engineering week, we would like to improve out type support to improve dev experience in dynamo
This PR adds strict typing support to a critical set of files for dynamo, `source.py` and the base `_guards.py`
Running
```
mypy torch/_dynamo/source.py torch/_guards.py --linecount-report /tmp/coverage_log
```
| -------- | Lines Unannotated | Lines Total | % lines covered | Funcs Unannotated | Funcs Total | % funcs covered |
| -------- | ------- | -------- | ------- | ------- | ------- | ------- |
| Main | 1227 | 2208 | 55.57% | 207 | 362 | 57.18% |
| This PR | 2217 | 2217 | 100.00% | 362 | 362 | 100.00% |
| Delta | +990 | +9 | +44.43% | +155 | 0 | +42.82% |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158397
Approved by: https://github.com/anijain2305
Collects some scattershot improvements made while attempting to enable training for AOTInductor. Non-typing changes are:
1. Swapping a few custom searches for the output node in an FX graph for calling `graph.output_node()`.
2. Removing two unused parameters from `torch.export._unlift._unlift`.
3. Switching handles to constants in `cpp_wrapper_cpu` to use C++ references for memory efficiency.
4. Cleaning out unused, unexported imports from `torch/export/__init__.py`, and adding one missing export to `__all__`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158075
Approved by: https://github.com/Skylion007
