This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
Summary:
- Python interface to call StaticRuntime::runAsync API
- creates a custom executor with execution on inter-op thread pool
- test cases for different async graph scenarios like multiple forks, nested forks, exception handling
Test Plan:
- local tests
buck test mode/opt caffe2/test:static_runtime
buck test mode/opt caffe2/benchmarks/static_runtime/fb:test_fb_operators
buck test mode/opt caffe2/benchmarks/static_runtime:static_runtime_cpptest
- OSS CI tests
Differential Revision: D37471859
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80407
Approved by: https://github.com/tenpercent
Summary:
- Exception handling was not performed in forked subgraph execution
- forked subgraph runtime can throw runtime exception. Future returned by prim::fork needs to handle exceptions so that aten::wait handles it.
Test Plan:
local test cases:
- buck test caffe2/benchmarks/static_runtime/fb:test_fb_operators
- buck test mode/opt caffe2/benchmarks/static_runtime:static_runtime_cpptest
- buck test mode/opt caffe2/test:static_runtime
Async execution of the subgraph is tested by adding pytorch profiler hooks on the StaticRuntime execution via below code. Async execution in threadpool is verfiied by checking trace
with profile(activities=[ProfilerActivity.CPU]) as prof:
static_runtime_module(inputs)
prof.export_chrome_trace("trace.json")
Differential Revision: D37072493
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79292
Approved by: https://github.com/mikeiovine
Summary:
basic implementation of prim::fork and aten::wait
- current implementation uses interpreter to call the forked subgraph
- interpreter call to be replaced in future
- Added custom test cases for fork/wait procedures in the graph
Test Plan:
custom tests are created in test_static_runtime.py file for verification of static_runtime output compared to reference pytorch output.
test command
- buck run caffe2/test:static_runtime
- buck run caffe2/benchmarks/static_runtime:static_runtime_cpptest
- buck test caffe2/benchmarks/static_runtime/fb:test_fb_operators
Differential Revision: D36881214
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78780
Approved by: https://github.com/tenpercent
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71854
Support `prim::CreateObject` - this is a native interpreter instruction, so we can't fall back to the JIT for this op.
Test Plan: New unit test exercises creating and modifying custom objects
Reviewed By: d1jang
Differential Revision: D33783759
fbshipit-source-id: 8185ff71b5d441597d712a5d4aab7fc4dddf7034
(cherry picked from commit bd3f52d8e2cd8e20a8d66e2d2b802c1d92088e4e)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67467
Unit tests for static runtime in the dper multi-env tests for cpu and scripted (including fx-traced + scripted) models. Only turn it on for single_operators_tests that are in the inline_cvr local/local_ro/remote_ro model for now.
Will have another diff that turns this on by default and explicitly disables for certain tests.
Test Plan: buck test dper3/dper3/modules/low_level_modules/tests:single_operators_test
Reviewed By: hlu1, houseroad
Differential Revision: D30870488
fbshipit-source-id: 382daec8dbcb95135cdd43e7b84a1d23b445d27c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67001
The overload of `operator()` taking `std::vector<at::Tensor>` was only used for testing. In a diff following this one, I will add a new overload that takes `std::vector<c10::IValue> args` and no `kwargs` so we can avoid default-constructing `kwargs` everywhere.
This new overload will probably take a forwarding reference, so to avoid problems with overloading on forwarding reference and simplify the interface, it's best to remove this unused one.
Test Plan:
`buck test caffe2/benchmarks/static_runtime/...`
`buck test caffe2/test:static_runtime`
Reviewed By: hlu1
Differential Revision: D31821990
fbshipit-source-id: 6d2e4a75ca4abe6e262651532eb96c3b274c6f4a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61505
The handling of `self` in static runtime was previously incorrect. This diff fixed that issue, since self is essential to prim::GetAttr/SetAttr. After all, most of the time we're getting and setting attributes from self, the torch script module.
Reviewed By: ajyu
Differential Revision: D29350173
fbshipit-source-id: 6e62add4cda517ef8cd6c315d4cb0595e7d531fb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55342
The fusion stuff is pretty hard to debug. Given that we're not shipping this part of the stack any time soon, let's temporarily disable them and re-enable them when somebody has the cycles to debug them.
Test Plan: Verified that the tests are now disabled
Reviewed By: ajyu
Differential Revision: D27578573
fbshipit-source-id: cb8d7c9339f7c1700b7653b0231cf570996995ff
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51564
Constructor logic was spread throughout InferenceModule and StaticRuntime. This diff unifies the two. After a lot of discussion on this diff D25961626 it became apparent that `clone` is uglier than a cheap StaticRuntime.
This means StaticRuntime is effectively StaticModule and the only code in the new StaticRuntime is the `run` functions.
```
graph, schema = PrepareForStaticModule(torchscript_module)
sm = StaticModule(graph, schema, options)
sm(inputs)
// or create many cheap runtimes with the module
sr = StaticRuntime(sm)
sr(inputs)
```
Changelist:
- Rename InferenceModule StaticModule
- Move all logic for construction into StaticModule
- Create a new StaticRuntime that only has a unique memory planner (everything else is in StaticModule)
- Update comments with explanation
- Propagate all changes to predictor integration
- Propagate all changes to python integration
- Change semantics to be a bit more PyTorch-standard (no "run" calls, no "get_" getters).
Test Plan:
buck test //caffe2/test:static_runtime
buck test caffe2/benchmarks/static_runtime:static_runtime_cpptest
Reviewed By: hlu1
Differential Revision: D25592967
fbshipit-source-id: 8233bed03137ce129137af2d44bce0095033ef0f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46896
The idea of the memory model is quite similar to that of BlackBoxPredictor, however, it's more complicated in pt due to 1) tensor views that share storage with storage refcount bumps but with different TensorImpls, 2) tensors sharing the same TensorImpl and the same storage, but with no refcount bump of the StorageImpl, 3) data types such as TensorList and Tuples that have Tensors in them, 4) need to support non-out/out variant mix while we move the aten ops to out variants.
As a result, I have to make the following adjustments:
1) remove tensors in output Tuples from internal blob list;
2) for memory allocation/deallocation, get candidate Tensors from the outputs of ops with out variant, extract StorageImpls from the Tensors, dedup, and remove output tensor StorageImpls, and get the final list of blobs for memory planning;
3) during the clean_up_memory pass, clean up memory held by the StorageImpls as well as Tensors/Lists/Tuples in IValues that don't participate in memory planning to reduce overall memory usage
Risk:
PyTorch team is planning to deprecate the current resize_outout api, which we do rely on. This is a pretty big risk.
https://www.internalfb.com/intern/diffusion/FBS/browsefile/master/fbcode/caffe2/aten/src/ATen/native/Resize.cpp?commit=6457b329847607553d34e788a3a7092f41f38895&lines=9-23
Test Plan:
```
buck test //caffe2/test:static_runtime
buck test //caffe2/benchmarks/static_runtime:static_runtime_cpptest
buck test //caffe2/caffe2/fb/predictor:pytorch_predictor_test
```
Benchmarks:
```
MKL_NUM_THREADS=1 OMP_NUM_THREADS=1 numactl -m 0 -C 13 \
buck-out/opt/gen/caffe2/caffe2/fb/predictor/ptvsc2_predictor_bench \
--scripted_model=/home/hlu/ads/adindexer/adindexer_ctr_mobilefeed/pt/merge/traced_precomputation.pt \
--pt_inputs=/home/hlu/ads/adindexer/adindexer_ctr_mobilefeed/pt/merge/container_precomputation_bs1.pt \
--iters=1000 --warmup_iters=10000 --num_threads=1 --pt_enable_static_runtime=true \
--pt_cleanup_activations=true --pt_enable_out_variant=false
```
|pt_cleanup_activations |pt_enable_out_variant |old ms/iter |new ms/iter |
|--- |--- |--- |--- |
|0 |0 |0.31873 |0.30228 |
|0 |1 |0.30018 |0.29184 |
|1 |0 |0.35246 |0.31895 |
|1 |1 |0.35742 |0.30417 |
Reviewed By: bwasti, raziel
Differential Revision: D24471854
fbshipit-source-id: 4ac37dca7d2a0c362120a7f02fd3995460c9a55c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45639
`StaticRuntime::run_individual` is to mimic the caffe2 operator benchmark `SimpleNet::TEST_Benchmark`, so we can accurate information on the operator breakdown. We found that the PyTorch AutogradProfiler adds a lot of overhead to small models, such as the adindexer precomputation_merge net, 100% for batch_size 1, 33% for batch_size 20. This implementation adds very little overhead, as shown in the test plan.
Test Plan: Test results are fb internal only.
Reviewed By: yinghai, dzhulgakov
Differential Revision: D24012088
fbshipit-source-id: f32eb420aace93e2de421a15e4209fce6a3d90f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43647
Nothing fancy, just a basic implementation of the graph executor without using stack machine.
Reviewed By: bwasti
Differential Revision: D23208413
fbshipit-source-id: e483bb6ad7ba8591bbe1767e669654d82f42c356
Summary:
This PR whitelists and simplifies graphs to help with development later on. Key to note in this PR is the use of both a pattern substitution and the registration of custom operators. This will likely be one of the main optimization types done in this folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43024
Reviewed By: hlu1
Differential Revision: D23114262
Pulled By: bwasti
fbshipit-source-id: e25aa3564dcc8a2b48cfd1561b3ee2a4780ae462
Summary:
The premise of this approach is that a small subset of neural networks are well represented by a data flow graph. The README contains more information.
The name is subject to change, but I thought it was a cute reference to fire.
suo let me know if you'd prefer this in a different spot. Since it lowers a JIT'd module directly I assumed the JIT folder would be appropriate. There is no exposed Python interface yet (but is mocked up in `test_accelerant.py`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42753
Reviewed By: zou3519
Differential Revision: D23043771
Pulled By: bwasti
fbshipit-source-id: 5353731e3aae31c08b5b49820815da98113eb551
