Summary:
Pytorch unitest hangs when jitting the Tensor kernel. The problem exists for LLVM version >= 18 due to this upstream change: 45bb45f2ae
`IRBuilderBase::CreateCall` will insert the instruction into the BasicBlock by default. And we don't need to explicitly insert the instruction when compiling the tensor kernel.
Test Plan:
## Test with the release toolchain
```
buck test 'mode/dev' //caffe2/test:jit -- --exact 'caffe2/test:jit - test_concat_invariant (test_jit_fuser_te.TestTEFuserDynamic)'
```
## Test with the Buckified toolchain
Apply this D71046097 to select the LLVM libraries.
```
# Build tests
buck build 'mode/dev-asan' //caffe2/test:jit --show-output
```
```
# Run test (Change HASH and paths accordingly)
HASH="b755f1c435832a1e"
ENABLE_FLATBUFFER=0 FB_OVERRIDE_PYBIND11_GIL_INCREF_DECREF_CHECK=1 MKL_NUM_THREADS=1 NO_MULTIPROCESSING_SPAWN=0 OMP_NUM_THREADS=1 PYTORCH_TEST=1 PYTORCH_TEST_FBCODE=1 PYTORCH_TEST_WITH_ASAN=1 PYTORCH_TEST_WITH_DEV_DBG_ASAN=1 PYTORCH_TEST_WITH_TSAN=0 PYTORCH_TEST_WITH_UBSAN=1 SKIP_TEST_BOTTLENECK=1 TENSORPIPE_TLS_DATACENTER=test_dc TEST_PILOT=True TPX_IS_TEST_EXECUTION=true TPX_TIMEOUT_SEC=6000 \
buck-out/v2/gen/$HASH/caffe2/test/__jit__/jit.par --test-filter test_jit_fuser_te.TestTEFuserDynamic.test_concat_invariant
```
Differential Revision: D71046799
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149058
Approved by: https://github.com/dcci, https://github.com/Skylion007
Latest LLVM introduced two changes related to the `Triple` usage that causes build failures when building pytorch.
## Failure in llvm_codegen.cpp:
Triple is stored in Modules instead of the string: 979c275097
## Failure in llvm_jit.cpp:
Triple argument is removed from LLJITBuilder::... : b18e5b6a36
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148833
Approved by: https://github.com/Skylion007
Fix
```
/usr/bin/../lib64/gcc/x86_64-pc-linux-gnu/14.2.1/../../../../include/c++/14.2.1/bits/unique_ptr.h:91:16: error: invalid application of 'sizeof' to an incomplete type 'torch::jit::AliasDb::WriteRegistry'
91 | static_assert(sizeof(_Tp)>0,
| ^~~~~~~~~~~
/usr/bin/../lib64/gcc/x86_64-pc-linux-gnu/14.2.1/../../../../include/c++/14.2.1/bits/unique_ptr.h:399:4: note: in instantiation of member function 'std::default_delete<torch::jit::AliasDb::WriteRegistry>::operator()' requested here
399 | get_deleter()(std::move(__ptr));
| ^
../torch/csrc/jit/ir/alias_analysis.cpp:200:10: note: in instantiation of member function 'std::unique_ptr<torch::jit::AliasDb::WriteRegistry>::~unique_ptr' requested here
200 | AliasDb::~AliasDb() = default;
| ^
../torch/csrc/jit/ir/alias_analysis.cpp:200:23: note: in defaulted destructor for 'torch::jit::AliasDb' first required here
200 | AliasDb::~AliasDb() = default;
| ^
../torch/csrc/jit/ir/alias_analysis.h:298:10: note: forward declaration of 'torch::jit::AliasDb::WriteRegistry'
298 | struct WriteRegistry;
| ^
1 error generated.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148758
Approved by: https://github.com/Skylion007
By decorating the header with `C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wmismatched-new-delete")`
that will suppress following (when building against ancient llvm-9)
```
In file included from /var/lib/jenkins/workspace/torch/csrc/jit/tensorexpr/llvm_codegen.cpp:24:
/opt/llvm/include/llvm/IR/IRBuilder.h: In member function 'llvm::LoadInst* llvm::IRBuilder<T, Inserter>::CreateLoad(llvm::Type*, llvm::Value*, const llvm::Twine&) [with T = llvm::ConstantFolder; Inserter = llvm::IRBuilderDefaultInserter]':
/opt/llvm/include/llvm/IR/IRBuilder.h:1581:19: error: 'static void llvm::User::operator delete(void*)' called on pointer returned from a mismatched allocation function [-Werror=mismatched-new-delete]
1581 | return Insert(new LoadInst(Ty, Ptr), Name);
| ^~~~~~~~~~~~~~~~~~~~~
/opt/llvm/include/llvm/IR/IRBuilder.h:1581:19: note: returned from 'static void* llvm::UnaryInstruction::operator new(size_t)'
```
Probably a reasonable followup will be to disable NNC testing all-together, as project has been in a maintenance mode for a while now
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148763
Approved by: https://github.com/Skylion007, https://github.com/ZainRizvi, https://github.com/atalman
ghstack dependencies: #148739
Summary:
LLVM has a warning `-Wunused-value` which we treat as an error because it's so often diagnostic of a code issue. Unused values often indicate a programming mistake, but can also just be unnecessary cruft that harms readability and performance.
For questions/comments, contact r-barnes.
- If you approve of this diff, please use the "Accept & Ship" button :-)
Test Plan: Sandcastle
Differential Revision: D69945678
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147555
Approved by: https://github.com/Skylion007, https://github.com/eqy
Summary:
Seeing errors like when testing sigmoid for inline_cvr and perevent_cvr models.
```
terminate called after throwing an instance of 'c10::Error'
what(): forward() Expected a value of type 'Dict[int, Tuple[Tensor, Tensor, Tensor]]' for argument 'event_based_features' but instead found type 'Dict[Any, Any]'.
```
Let empty dict pass type check.
please, do NOT use any of the following flags, those are result of manual interventions in other parts of the system, misuse of them can be very painful for both detect and recover:
Test Plan:
```
MODEL_ENTITY_ID=691508446
SNAPSHOT_ID=0
OTHER_MODEL_ENTITY_ID=649645886
OTHER_SNAPSHOT_ID=0
MODULE=local
buck2 run mode/opt caffe2/torch/fb/model_transform/fx2trt/packaging:load_net_predictor -- \
--loadMode=BenchmarkAB \
--inputNetFile=/data/users/${USER}/models/${MODEL_ENTITY_ID}/${SNAPSHOT_ID}/${MODEL_ENTITY_ID}_${SNAPSHOT_ID}${suffix} \
--otherNetFile=/data/users/${USER}/models/${OTHER_MODEL_ENTITY_ID}/${OTHER_SNAPSHOT_ID}/${OTHER_MODEL_ENTITY_ID}_${OTHER_SNAPSHOT_ID}${suffix} \
--moduleName=${module} \
--submodToDevice "" \
--benchmarkDontRebatchSamples=true \
--sampleInputFilePath=/data/users/${USER}/models/${MODEL_ENTITY_ID}/${SNAPSHOT_ID}/archive_.predictor.disagg.gpu.local/data/sample_inputs/local.pt
```
Reviewed By: yjhao
Differential Revision: D69871393
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147480
Approved by: https://github.com/henryoier, https://github.com/jeanschmidt
Some of the windows files (fused_kernels.cpp or temp_file.h) contain code that fail to compile when this flag is enabled when built with clang-cl.
This PR resolves the issue by ensuring that even if we build with clang-cl, it doesn't include those flags on windows.
Alternatively if needed, I can fix the files mentioned to pass under this flag.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146981
Approved by: https://github.com/cyyever, https://github.com/Skylion007
Summary:
When Static Runtime graph node has sub-blocks, the memory planner does not consider sub-blocks' inputs as a node's input in memory planner. As the result, such nodes' inputs' lifetime is incorrect and corresponding tensor memory is released earlier than required and causes errors.
Differential Revision: D69195886
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146855
Approved by: https://github.com/swolchok
Summary:
`c10::AttributeError` is not automatically converted to Python AttributeError, it needs some special macros (e.g. `HANDLE_TH_ERRORS`).
Some Python functions like `hasattr` rely on the type of the throw exception to be correct.
We don't need the fully generality of those macros, so just do a targeted error type conversion here.
Test Plan: added unit test
Differential Revision: D69197217
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146516
Approved by: https://github.com/zdevito
## Background
This PR adds `torch.utils.serialization.config.load.calculate_storage_offsets`. This option relies on the previous PR in this stack, where storage order was changed to non lexicographical. A `.format_version` entry was added to the zipfile and `calculate_storage_offsets` will only work on checkpoints with `.format_version`.
When this is turned on, for `torch.load(mmap=True)`, offsets of each storage record (other than the 0th storage will be calculated instead of relying on `miniz` APIs to determine this).
The existing APIs will issue multiple random reads (reading the end of central directory record, then reading the zipfile header for the record) to determine the storage offset where the record starts. This can greatly degrade `torch.load(mmap=True)` performance for non-filesystem cases.
6aaae9d78f/caffe2/serialize/inline_container.cc (L589-L605)
## How does this work
The format for the checkpoint is as such
```
archive_name/
|_ data.pkl
|_.format_version
|_byteorder
|_data/
|_ 0
|_ 1
|_ 2
|_ ...
|_
```
Each `data/i` record represents a storage, where storages are written in the order that the Pickler encounters them.
For each storage, our `persistent_load` logic saves the following metadata to the pickle file `dtype, numel, key, location` where `numel` is the number of bytes in the storage.
Note that we always use `miniz` writer in the zip64 mode per [here](7796e308d0/caffe2/serialize/inline_container.cc (L701)) A zipfile record written by miniz looks as such
```
---------------- ----------------- ------------------- ---------------- --------- ------------------------------
| 30 byte header | n byte filename | zip64_extra_data | m byte padding | storage | 16 or 24 byte local dir footer |
---------------- ----------------- ------------------- ---------------- --------- ------------------------------
```
- The header size (30) is given by [`MZ_ZIP_LOCAL_DIR_HEADER_SIZE`](https://github.com/pytorch/pytorch/blob/main/third_party/miniz-3.0.2/miniz.c?fbclid=IwZXh0bgNhZW0CMTEAAR2O8Vysd--UoSCxW70gabXIS1dbz733oHwuUQ5_Ff1hY2WU6PL2i6CSH4A_aem_J9oaU2HpDeWtJKOU9EnVqw#L3290)
- filename will be `"{archive_name}/{filepath}"`
- `zip64_extra_data` is determined by [`mz_zip_writer_create_zip64_extra_data`](7796e308d0/third_party/miniz-3.0.2/miniz.c (L6202)). Note that [we only create zip64_extra_data if storage_size >= 0xFFFFFFFF or the offset of the start of the header >= 0xFFFFFFFF](7796e308d0/third_party/miniz-3.0.2/miniz.c (L6519-L6524))
- `m` is determined by [`getPadding`](7796e308d0/caffe2/serialize/inline_container.cc (L254)), which accounts for filename, zip64_extra_data to determine `m` such that the start of `storage` is aligned to 64 bytes. The `m` bytes will always start with `F B padding_size" as the first 4 bytes
- The local dir footer size is determined based on [this snippet ](7796e308d0/third_party/miniz-3.0.2/miniz.c (L6610-L6632)): if the buffer size is 0 it is skipped. If the zip64_extra_data was created, it is 24, otherwise it is 16.
When `torch.utils.serialization.config.load.calculate_storage_offsets` is set we do the following
- We keep track of where the "cursor" is in the file using `current_offset`, after each persistent_load call, it will be at the offset where the header for the next record starts
- for the 0th storage, "data/0", we use the regular get_record_offset to determine the start of the storage
- for any other storage, (where the storages will be in order encountered by the unpickler, 0, 1, 2, 3, ...) we use `get_record_offset_no_read`, which re-uses the `getPadding` logic to determine the offset of the storage
- Note that `load_tensor` will only ever be called again with the same key if the storage's `._data_ptr()` is 0 [[pointer1](https://github.com/pytorch/pytorch/blob/main/torch/serialization.py#L1917-L1918)][[pointer2](https://github.com/pytorch/pytorch/blob/main/torch/serialization.py#L1936-L1937)], so we cache the offsets for this edge case
- After each storage, if the storage is non-zero, we account for the local dir footer based on the logic described above
## Testing strategy
The agreed upon testing strategy was as follows:
- Add debug code gated by an environment flag `TORCH_SERIALIZATION_DEBUG` that will run this offset calculation logic and verify it against getRecordOffset for each storage (when mmap=False)
- This flag is set throughout CI, which means that every time `torch.load` is called, the offset calculation logic is implicitly being tested.
Differential Revision: [D67673026](https://our.internmc.facebook.com/intern/diff/D67673026)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143880
Approved by: https://github.com/albanD
ghstack dependencies: #143879
## Background
This PR adds `torch.utils.serialization.config.load.calculate_storage_offsets`. This option relies on the previous PR in this stack, where storage order was changed to non lexicographical. A `.format_version` entry was added to the zipfile and `calculate_storage_offsets` will only work on checkpoints with `.format_version`.
When this is turned on, for `torch.load(mmap=True)`, offsets of each storage record (other than the 0th storage will be calculated instead of relying on `miniz` APIs to determine this).
The existing APIs will issue multiple random reads (reading the end of central directory record, then reading the zipfile header for the record) to determine the storage offset where the record starts. This can greatly degrade `torch.load(mmap=True)` performance for non-filesystem cases.
6aaae9d78f/caffe2/serialize/inline_container.cc (L589-L605)
## Testing strategy
The agreed upon testing strategy was as follows:
- Add debug code gated by an environment flag `TORCH_SERIALIZATION_DEBUG` that will run this offset calculation logic and verify it against getRecordOffset for each storage (when mmap=False)
- This flag is set throughout CI, which means that every time `torch.load` is called, the offset calculation logic is implicitly being tested.
Differential Revision: [D67673026](https://our.internmc.facebook.com/intern/diff/D67673026)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143880
Approved by: https://github.com/albanD
ghstack dependencies: #143879
Summary:
This PR is generated from a meta internal Diff, aiming to resolve a crash from a race condition on the dictionary.
Test Plan:
Build and run
Print out the count/name/value of the dictionary and see if the values are get/set/removed correctly.
Observe the print statement on app start within IG
@diff-train-skip-merge
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143418
Approved by: https://github.com/shoumikhin
