Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75201
In this diff:
1. Bump supported version to 9, which will serve as a placeholder for upcoming version bump to v9 for flatbuffer format migration.
2. Implements backport from v9 flatbuffer file to v8 pickle file.
ghstack-source-id: 153225189
(Note: this ignores all push blocking failures!)
Test Plan:
fb:
```
cd ~/fbsource/fbcode/ && buck test -c fbcode.caffe2_enable_flatbuffer=1 caffe2/test/cpp/jit:jit -- LiteInterpreterTest.BackPortByteCodeModelAllVersions
Parsing buck files: finished in 0.7 sec
Downloaded 0/25 artifacts, 0.00 bytes, 100.0% cache miss (for updated rules)
Building: finished in 20.7 sec (100%) 21783/21783 jobs, 5/21783 updated
cd ~/fbsource/fbcode/ && buck test caffe2/test/cpp/jit:jit -- FlatbufferTest.FlatbufferBackPortTest
Parsing buck files: finished in 0.7 sec
Building: finished in 4.5 sec (100%) 12972/53298 jobs, 0/53298 updated
Total time: 5.3 sec
More details at https://www.internalfb.com/intern/buck/build/b658d597-d358-4293-97cb-28e7612b96e8
BUILD SUCCEEDED
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: 35d5542d-6ee3-4c28-be10-1d822c7a6fef
Trace available for this run at /tmp/tpx-20220308-090347.891303-35d5542d-6ee3-4c28-be10-1d822c7a6fef/trace.log
RemoteExecution session id: reSessionID-35d5542d-6ee3-4c28-be10-1d822c7a6fef-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/8444249379196000
✓ ListingSuccess: caffe2/test/cpp/jit:jit : 490 tests discovered (22.838)
✓ Pass: caffe2/test/cpp/jit:jit - FlatbufferTest.FlatbufferBackPortTest (0.289)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/8444249379196000
```
Reviewed By: iseeyuan
Differential Revision: D34702597
fbshipit-source-id: 5c203c29d13360d7934ce6e57557739e7038c05e
(cherry picked from commit 6189e08a2bd968fdab636f77cb6bd73d6c36beb2)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57775
The minimum supported bytecode version is updated from 3 to 4. We no longer support version 3 bytecode models.
Why?
* There are hacky codes in operator loading, that performs differently on one operator on the global bytecode version 3. Instead operator related metadata should be passed (for example, in #56845). To allow future development, we remove the hacky way first.
* The bytecode version was bumped from 3 to 4 more than half a year ago. Since all the production models are all bumped to version 4, it's not practical to keep and maintain version 3. The risk to deprecate version 3 is low.
Test Plan: Imported from OSS
Reviewed By: raziel
Differential Revision: D28270791
Pulled By: cccclai
fbshipit-source-id: 70b1bd6352fdaae5f8d2173b81578d77018c8e44
(cherry picked from commit 3e930fa381cd01f3705116795c6426df992372fc)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75244
Original commit changeset: d653a5af662a
Original Phabricator Diff: D35060736 (d9d34922a0)
Test Plan: Model loading test, verified that D35060736 (d9d34922a0) will cause the torch::save => torch::load failure.
Reviewed By: yinghai, jianyuh
Differential Revision: D35387009
fbshipit-source-id: 9d176992d402d57779e2af3d905b3c1538335298
(cherry picked from commit 6c8cc0d3b8a88b15e35702d70e18bbae8aa4628a)
Summary:
update the upgrader models by hacking backport logic - copy everything in the model and only rewrite the bytecode version to 4 in D35265596
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75120
ghstack-source-id: 152823046
Test Plan: CI
Reviewed By: qihqi
Differential Revision: D35321154
fbshipit-source-id: 333158bd0fd9b4819b3b7cf47d80c285934adf3e
(cherry picked from commit 74bb2da73a4d18f448b8486772643eac89eb759a)
Summary:
This PR introduces `SymInt` type to Pytorch which will be used by LTC and AOTAutograd for tracing size arithmetic and tests.
`SymInt` is a C++ union structure [int64_t, SymbolicIntNode*] that wraps around an int64_t field where the value of the field could be an index into a list of `shared_ptr<SymbolicIntNode>` or a real int.
This PR doesn't add any support for actually tracing symbolic ints. i.e. data_ for now can only contain real ints.
```
Goal 1: just to show we can add a type to PyTorch core. (wraps int) LANDEABLE
Finalize the naming - symint
Want the name to be short
Does invoke “size” - NO
SInt/SymInt/SymbolicInt
SInt could mean signed int
sym_int or symint or SymInt (originally it was “int”; capitalized implies object semantics, whereas lowercase implies value semantics)
JIT schema - symint
C++ - symint
```
See more details here: https://docs.google.com/document/d/1iiLNwR5ohAsw_ymfnOpDsyF6L9RTUaHMpD8 (d843f63f2a)YLw-jxEw
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74861
Reviewed By: qihqi, ngimel
Differential Revision: D35226230
Pulled By: Krovatkin
fbshipit-source-id: 34acf342bd50fcaa4d8d5dd49c2fd6a98823a5b3
(cherry picked from commit 218643f63ef181cabb92d13a6e837eb64f2dda3c)
Summary:
Things changed in this PR that requires review:
test/forward_backward_compatibility/check_forward_backward_compatibility.py
Our previous function overload extension names were wrong and has been updated in this PR, hence the compatibility list updated.
nvfuser code updates with bug fixes towards failures we encountered in OpInfoTests as well as failures reported by AOTAutograd team.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73627
Reviewed By: Chillee
Differential Revision: D34765458
Pulled By: davidberard98
fbshipit-source-id: c81f3d6a1b723fb3a8ba419b7f82227f70440ca7
(cherry picked from commit b6a2c362c37051e44fac31687b2fe272f776551e)
This is a technical revert of 6d36bbde7e to reconcile it with e50478c02592597f12b8490ec5496f76c7d8b8cc (which is the same + lint changes applied)
Should be skipped during import
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74580
Handle Flatbuffer-serialized parameters.
Make `_load_parameters()` detect the input data format and use the correct deserializer to load the parameters.
Also, rename `BytecodeDeserializer` to `IValueUnpickler` to make it clear that it unpickles an `IValue` and doesn't have anything to do with bytecode.
ghstack-source-id: 152487890
Test Plan:
New unit test shows a successful round trip from _save_parameters() to _load_parameters() using flatbuffers.
```
$ buck test //xplat/caffe2:test_lite_trainer //xplat/caffe2:test_lite_trainer_pickle_and_flatbuffer
Building: finished in 0.5 sec (100%) 346/346 jobs, 0/346 updated
Total time: 0.6 sec
Testing: finished in 0.5 sec (26 PASS/0 FAIL)
BUILD SUCCEEDED
RESULTS FOR //xplat/caffe2:test_lite_trainer //xplat/caffe2:test_lite_trainer_pickle_and_flatbuffer
PASS <100ms 13 Passed 0 Skipped 0 Failed //xplat/caffe2:test_lite_trainer
PASS <100ms 13 Passed 0 Skipped 0 Failed //xplat/caffe2:test_lite_trainer_pickle_and_flatbuffer
TESTS PASSED
```
Reviewed By: qihqi
Differential Revision: D34488913
fbshipit-source-id: 8d2c0b895699f3b336115d33bf96d49cbf9245d2
(cherry picked from commit 319345deff260826197f8cdf5ac03071b412c72f)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74579
Now that we can convert a module to a flatbuffer, update `_save_parameters()` to optionally write to that format.
Also, rename the internal `ScriptModuleSerializer` class to `IValuePickler` to make it more clear that a) it's pickle-specific, and b) it serializes IValues, not Modules.
ghstack-source-id: 152487889
Test Plan:
New unit test shows that we can produce Flatbuffer-formatted output.
```
$ buck test //xplat/caffe2:test_lite_trainer //xplat/caffe2:test_lite_trainer_pickle_and_flatbuffer
Building: finished in 0.5 sec (100%) 346/346 jobs, 0/346 updated
Total time: 0.6 sec
Testing: finished in 0.5 sec (26 PASS/0 FAIL)
BUILD SUCCEEDED
RESULTS FOR //xplat/caffe2:test_lite_trainer //xplat/caffe2:test_lite_trainer_pickle_and_flatbuffer
PASS <100ms 13 Passed 0 Skipped 0 Failed //xplat/caffe2:test_lite_trainer
PASS <100ms 13 Passed 0 Skipped 0 Failed //xplat/caffe2:test_lite_trainer_pickle_and_flatbuffer
TESTS PASSED
```
A new test in later commit D34488913 tests the full round trip.
Reviewed By: qihqi
Differential Revision: D34408538
fbshipit-source-id: eea183c31b5e1b2b75a65f384d8a479223a4ae72
(cherry picked from commit de310a15422b65fb7e443f7005d287d9f5f586bc)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74186
Make the execution settings mutable on function_impl so that we can set it for running op decompositions. Add mapping to function objects and show example in test of executing op decompositions.
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D34938125
Pulled By: eellison
fbshipit-source-id: adf108b2f6c1bd166910c6d7b94245661d67ce0d
(cherry picked from commit 9957e33803002d9e71abe4ff802769270b6960d3)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74012
This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor).
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D34938124
Pulled By: eellison
fbshipit-source-id: cf7a45416457942b872322cab47d871a8336bdb5
(cherry picked from commit 9c600eb9ad0f2173f003e511268e97584edae36d)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73875
Previously we had a few settings:
- getExecutor - which toggled between Profiling Executor and Legacy
- getGraphOptimize - if true, overrides PE/Legacy to run with simple executor (no optimizations)
and then...
- getProfilingMode - which would set PE to 0 specializtions.
The last mode is redundant with getGraphOptimize, we should just remove it and use getGraphOptimize in these cases. It would lead to potentially invalid combinations of logic - what does mean if getProfilingMode is true but getExecutor is set to false ? This would lead to a bug in specialize_autograd_zero in this case, see: https://github.com/pytorch/pytorch/blob/master/torch%2Fcsrc%2Fjit%2Fpasses%2Fspecialize_autogradzero.cpp#L93.
The tests here are failing but get fixed with the PR above it, so i'll squash for landing.
Test Plan: Imported from OSS
Reviewed By: cpuhrsch
Differential Revision: D34938130
Pulled By: eellison
fbshipit-source-id: 1a9c0ae7f6d1cfddc2ed3499a5af611053ae5e1b
(cherry picked from commit cf69ce3d155ba7d334022c42fb2cee54bb088c23)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74594
Extending `_save_for_mobile` and `_load_for_mobile` to support faltbuffer format with additional optional argument which is set to pick pickle by default.
Adding new binary target with suffix `_pickle_and_flatbuffer` to help migration.
Size test in D34909502 shows the size has regressed by ~40K but after removing pickle and comparing lite_predictors we have ~120K size measure that we will achieve when deprecating pickle and moving to flatbuffer
**BEFORE:**
```lang=mermaid
graph TD;
torch_core-->torch_mobile_deserialize;
torch_mobile_core-->torch_mobile_deserialize;
jit_module_saving-->torch_core;
jit_module_saving-->torch_mobile_core;
torch_mobile_deserialize-->caffe2_serialize;
torch_mobile_deserialize-->torch_mobile_module;
caffe2_serialize-->miniz;
flatbuffer_loader-->mobile_bytecode;
flatbuffer_serializer-->mobile_bytecode;
mobile_bytecode-->flatbuffer_2.0;
flatbuffer_loader-->torch_mobile_module;
flatbuffer_serializer-->torch_mobile_module;
```
**AFTER:**
```lang=mermaid
graph TD;
torch_core-->torch_mobile_deserialize;
torch_mobile_core-->torch_mobile_deserialize;
jit_module_saving-->torch_core;
jit_module_saving-->torch_mobile_core;
torch_mobile_deserialize-->caffe2_serialize;
torch_mobile_deserialize-->torch_mobile_module;
caffe2_serialize-->miniz;
flatbuffer_loader-->mobile_bytecode;
flatbuffer_serializer-->mobile_bytecode;
mobile_bytecode-->flatbuffer_2.0;
torch_mobile_deserialize_pickle_and_flatbuffer-->|new| flatbuffer_loader;
torch_mobile_deserialize_pickle_and_flatbuffer-->|new| torch_mobile_deserialize;
torch_mobile_core_pickle_and_flatbuffer-->|new| torch_mobile_deserialize_pickle_and_flatbuffer;
torch_core_pickle_and_flatbuffer-->|new| torch_mobile_deserialize_pickle_and_flatbuffer;
jit_module_saving_pickle_and_flatbuffer-->|new| torch_core_pickle_and_flatbuffer;
jit_module_saving_pickle_and_flatbuffer-->|new| torch_mobile_core_pickle_and_flatbuffer;
flatbuffer_serializer-->torch_mobile_module;
jit_module_saving_pickle_and_flatbuffer-->|new|jit_module_saving;
jit_module_saving_pickle_and_flatbuffer-->|new|flatbuffer_serializer;
flatbuffer_loader-->torch_mobile_module;
```
Original commit changeset: 780dfb6fd6ba
Original Phabricator Diff: D34805092 (284b2b7135)
ghstack-source-id: 152044801
(Note: this ignores all push blocking failures!)
Test Plan:
CI
```
~/fbsource/fbcode] cd ~/fbsource/fbcode/ && buck test -c fbcode.caffe2_enable_flatbuffer=1 //caffe2/test/cpp/jit:jit -- FlatbufferTest.ExtraFiles
Parsing buck files: finished in 0.9 sec
Building: finished in 5.3 sec (100%) 12992/54304 jobs, 0/54304 updated
Total time: 6.2 sec
More details at https://www.internalfb.com/intern/buck/build/2b387fff-f813-4cfa-b53f-eb2378630d4e
BUILD SUCCEEDED
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: f93a84d6-e7ce-41a0-a97f-0ef3fa6d199d
Trace available for this run at /tmp/tpx-20220323-134108.766518-f93a84d6-e7ce-41a0-a97f-0ef3fa6d199d/trace.log
RemoteExecution session id: reSessionID-f93a84d6-e7ce-41a0-a97f-0ef3fa6d199d-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/4503599723101693
✓ ListingSuccess: caffe2/test/cpp/jit:jit : 486 tests discovered (19.122)
✓ Pass: caffe2/test/cpp/jit:jit - FlatbufferTest.ExtraFiles (0.187)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/4503599723101693
```
Similar Build Deps Dags
```
[pavithran@devvm5216.vll0 /data/users/pavithran/fbsource] buck query 'allpaths(//xplat/caffe2:torch_mobile_all_ops_pickle_and_flatbuffer, //xplat/caffe2:torch_mobile_deserialize_pickle_and_flatbuffer)' --output-format dot-compact | pastry
P486770901: https://www.internalfb.com/intern/paste/P486770901/
[pavithran@devvm5216.vll0 /data/users/pavithran/fbsource] buck query 'allpaths(//xplat/caffe2:torch_mobile_all_ops, //xplat/caffe2:torch_mobile_deserialize)' --output-format dot-compact | pastry
P486771278: https://www.internalfb.com/intern/paste/P486771278/
```
pickle_and_flatbuffer: https://www.internalfb.com/intern/dgw/graph/?build_id=P486770901
pickle: https://www.internalfb.com/intern/dgw/graph/?build_id=P486771278
Reviewed By: iseeyuan
Differential Revision: D35067157
fbshipit-source-id: 9044259c17a2e0da79bd6aedb28efbdfd57e23e0
(cherry picked from commit f738069ec3a72e79da56172741d027de514e9e5f)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74119
implemented function to generate source as ExtraFilesMap and constants
wrote function to construct jit module given (ivalue, source,
constant) tripple.
Test Plan: unittest
Reviewed By: pavithranrao
Differential Revision: D34803945
fbshipit-source-id: 2edc798407fe68294cb4c3c7516f5bd143df88c3
(cherry picked from commit 35e54e166b8f0f5cfe8f08c07866b59ae61ee79d)
Summary:
This diff is reverting D34455360 (61d6c43864)
D34455360 (61d6c43864) is making the following tests to fail and this revert diff is either the revert of the blame diff or the revert of the stack of diffs that need to be reverted to revert the blame diff
Tests affected:
- https://www.internalfb.com/intern/test/562950004334605/
Multisect link:
https://www.internalfb.com/intern/testinfra/multisect/756170
Test Plan: NA
Reviewed By: zhxchen17
Differential Revision: D34596156
fbshipit-source-id: a465bca0094db3caf6130c80f1ed49eea981359b
(cherry picked from commit ef5e5578c64ce9827570757fb016aafa9c782c6a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73707
Add a helper function to detect the file format from the first bytes of a data file or stream. This will be necessary during the migration from Pickle-serialized modules to Flatbuffer-serialized modules.
ghstack-source-id: 150384317
Test Plan:
Existing tests for ZIP+Pickle continue to pass.
New unit tests pass:
```
cd xplat && buck test //xplat/caffe2:test_lite_trainer //xplat/caffe2:test_lite_interpreter
Building: finished in 26.6 sec (100%) 3180/3180 jobs, 571/3180 updated
Total time: 32.2 sec
Testing: finished in 07:08.3 min (89 PASS/0 FAIL)
BUILD SUCCEEDED
RESULTS FOR //xplat/caffe2:test_lite_interpreter //xplat/caffe2:test_lite_trainer
PASS 421.1s 81 Passed 0 Skipped 0 Failed //xplat/caffe2:test_lite_interpreter
PASS 103ms 8 Passed 0 Skipped 0 Failed //xplat/caffe2:test_lite_trainer
TESTS PASSED
```
Reviewed By: iseeyuan
Differential Revision: D34527859
fbshipit-source-id: ff2d1eabc2f8be1de2e44709c878e2d1a373f0df
(cherry picked from commit 5c394848346ab9e374c9e7eed479ad70ed09a7ae)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73368
debug_pkl file inside of pytorch's .pt file consists of a list of SourceRanges. Each SourceRange points to a Source which is a stack track, filename, and start, end numbers. Those are emitted in debug_pkl file as strings.
Since many SourceRange shares the same source, the string for trace can be deduped.
The newer format saves a set of unique traces in a tuple, then each SourceRange will save the offset of it's trace w.r.t. position in that tuple. (i.e. manually applying dictionary compression).
The above helps with smaller file size. On loading, if we copy each trace to Source as string the runtime memory would still blowup.
To mitigate this, we use SourceView directly instead of source which will take the reference of string inside of Deserializer and make that into string_view. This is safe because Deserializer is hold by Unpickler by shared_ptr, and Unpickler is also hold by shared_ptr by another Source object. That Source object will be alive during the model construction.
Test Plan:
unit test
Took original file (312271638_930.predictor.disagg.local); loaded with `torch.jit.load` save again with `torch.jit.save`. Unzip both, look at contents:
```
[qihan@devvm5585.vll0 ~]$ du archive -h
4.0K archive/xl_model_weights
3.7M archive/extra
8.0K archive/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K archive/code/__torch__/caffe2/torch/fb/model_transform
8.0K archive/code/__torch__/caffe2/torch/fb
8.0K archive/code/__torch__/caffe2/torch
8.0K archive/code/__torch__/caffe2
20M archive/code/__torch__/torch/fx/graph_module
20M archive/code/__torch__/torch/fx
8.0K archive/code/__torch__/torch/classes
20M archive/code/__torch__/torch
20M archive/code/__torch__
20M archive/code
2.7M archive/constants
35M archive
[qihan@devvm5585.vll0 ~]$ du resaved -h
4.0K resaved/extra
8.0K resaved/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K resaved/code/__torch__/caffe2/torch/fb/model_transform
8.0K resaved/code/__torch__/caffe2/torch/fb
8.0K resaved/code/__torch__/caffe2/torch
8.0K resaved/code/__torch__/caffe2
1.3M resaved/code/__torch__/torch/fx/graph_module
1.3M resaved/code/__torch__/torch/fx
8.0K resaved/code/__torch__/torch/classes
1.4M resaved/code/__torch__/torch
1.4M resaved/code/__torch__
1.4M resaved/code
2.7M resaved/constants
13M resaved
[qihan@devvm5585.vll0 ~]$
```
Reviewed By: gmagogsfm
Differential Revision: D34455360
fbshipit-source-id: 8cc716f9bba7183746b1b4ecc33a2de34ac503b9
(cherry picked from commit f1a04730fc9ac8fdab6c8e4c44cb5529e42090e4)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73270
Together with open registration of NNC lowerings this should make possible to add support for custom operators, including internal fb-ops
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D34451275
Pulled By: eellison
fbshipit-source-id: ae8ae2deb93caa6770e738217461e65853897b55
(cherry picked from commit ea6b7e8a6d8f970a20e68d02eefc5c951e32aa07)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72951
Extend flatbuffer to support extra files map
Flatbuffer schema has extra files. The users can write extra files by providing a `map<string, string>` which will be part of the flatbuffer model asset and and can be loaded back similar to pickle.
ghstack-source-id: 149622799
Test Plan:
fb:
```[pavithran@devvm5216.vll0 ~/fbsource/fbcode] cd ~/fbsource/fbcode/ && buck test caffe2/test/cpp/jit:jit -- FlatbufferTest.ExtraFiles
Parsing buck files: finished in 0.7 sec
Downloaded 0/8 artifacts, 0.00 bytes, 100.0% cache miss (for updated rules)
Building: finished in 20.0 sec (100%) 22343/22343 jobs, 4/22343 updated
Total time: 20.7 sec
More details at https://www.internalfb.com/intern/buck/build/7dba5034-d623-4a1e-afa1-b0e809df7066
BUILD SUCCEEDED
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: 9c1ac1e0-a8c0-4a62-95df-8f49695aa7d1
Trace available for this run at /tmp/tpx-20220216-144630.207992/trace.log
RemoteExecution session id: reSessionID-9c1ac1e0-a8c0-4a62-95df-8f49695aa7d1-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/7318349470518809
✓ ListingSuccess: caffe2/test/cpp/jit:jit : 468 tests discovered (17.211)
✓ Pass: caffe2/test/cpp/jit:jit - FlatbufferTest.ExtraFiles (0.169)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/7318349470518809````
Reviewed By: iseeyuan
Differential Revision: D34286346
fbshipit-source-id: 4e09ab25b8ed6af6f8923db3aab046c255f13bb8
(cherry picked from commit ce8d88e22a360b25253d8a75f428d523fa88a79a)
Summary:
As far as I can tell theres no CI that actually runs the lean_runtime. This should add it I think. (Is this directory covered by CI?) Next up is to create some test for min_runtime_lib
(Note: this ignores all push blocking failures!)
Test Plan: buck test :lean_runtime_delegate_flatbuffer_test
Reviewed By: iseeyuan
Differential Revision: D34255148
fbshipit-source-id: b44693220e93869edd984bbcd17d33db4007a4ea
(cherry picked from commit 0a4a6b5bd2b4a1f8cce8bc1c4a22dad9539631c1)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72596
debug_pkl file inside of pytorch's .pt file consists of a list of SourceRanges. Each SourceRange points to a Source which is a stack track, filename, and start, end numbers. Those are emitted in debug_pkl file as strings.
Since many SourceRange shares the same source, the string for trace can be deduped.
The newer format saves a set of unique traces in a tuple, then each SourceRange will save the offset of it's trace w.r.t. position in that tuple. (i.e. manually applying dictionary compression).
The above helps with smaller file size. On loading, if we copy each trace to Source as string the runtime memory would still blowup.
To mitigate this, we use SourceView directly instead of source which will take the reference of string inside of Deserializer and make that into string_view. This is safe because Deserializer is hold by Unpickler by shared_ptr, and Unpickler is also hold by shared_ptr by another Source object. That Source object will be alive during the model construction.
Test Plan:
unit test
Took original file (312271638_930.predictor.disagg.local); loaded with `torch.jit.load` save again with `torch.jit.save`. Unzip both, look at contents:
```
[qihan@devvm5585.vll0 ~]$ du archive -h
4.0K archive/xl_model_weights
3.7M archive/extra
8.0K archive/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K archive/code/__torch__/caffe2/torch/fb/model_transform
8.0K archive/code/__torch__/caffe2/torch/fb
8.0K archive/code/__torch__/caffe2/torch
8.0K archive/code/__torch__/caffe2
20M archive/code/__torch__/torch/fx/graph_module
20M archive/code/__torch__/torch/fx
8.0K archive/code/__torch__/torch/classes
20M archive/code/__torch__/torch
20M archive/code/__torch__
20M archive/code
2.7M archive/constants
35M archive
[qihan@devvm5585.vll0 ~]$ du resaved -h
4.0K resaved/extra
8.0K resaved/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K resaved/code/__torch__/caffe2/torch/fb/model_transform
8.0K resaved/code/__torch__/caffe2/torch/fb
8.0K resaved/code/__torch__/caffe2/torch
8.0K resaved/code/__torch__/caffe2
1.3M resaved/code/__torch__/torch/fx/graph_module
1.3M resaved/code/__torch__/torch/fx
8.0K resaved/code/__torch__/torch/classes
1.4M resaved/code/__torch__/torch
1.4M resaved/code/__torch__
1.4M resaved/code
2.7M resaved/constants
13M resaved
[qihan@devvm5585.vll0 ~]$
```
Reviewed By: JasonHanwen
Differential Revision: D33994011
fbshipit-source-id: 8e6224c6e942e91c3403f686c8f0937d1002ed41
(cherry picked from commit a7014dd4029308c95007f362a57c31796d686647)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72587
This pattern frequently appears in a few graphs:
```
%result = prim::If(%condition)
block0():
-> (%a)
block1():
-> (%b)
```
This is slow, particularly in static runtime. Static runtime creates memory planners/block runners for each sub-block, which eats up a lot of memory and introduces a lot of extra overhead for this relatively simple operation.
This diff introduces a new op that replaces nodes like the above with a single op meant to act like a ternary operator:
```
%result = prim::IfThenElse(%condition, %a, %b)
```
Test Plan: New unit tests
Reviewed By: eellison
Differential Revision: D34091789
fbshipit-source-id: eb6a8c460c39b4c019a1f4ab1f3f1e5b6edc400c
(cherry picked from commit 0f1b335e5b83f402bda2dcdd9ecb411e0b67c651)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72899
Reland D33282878 (911d527b87). This is the frontend change.
ghstack-source-id: 149204031
Test Plan: Refer to D33282878 (911d527b87). Also check CI
Reviewed By: gmagogsfm
Differential Revision: D34252127
fbshipit-source-id: 27b17ddd4d05d904eb91fd9ee094d9121f00e388
(cherry picked from commit 1d276baca308110ac40111ccd622400b3bbdc864)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70471
Reland D33282878 (911d527b87). This is the frontend change.
ghstack-source-id: 149114933
Test Plan: Refer to D33282878 (911d527b87). Also check CI
Reviewed By: gmagogsfm
Differential Revision: D33342569
fbshipit-source-id: 57984ac67ae2c56c38f72d3b1fb69105901fb472
(cherry picked from commit b47cc935ee1fd7aa63aa453a323a637bc2c22f3c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71662
backport v8 to v7 to support promoted ops as instruction
a flag to help export as instruction from v8 and export as operators for v7 and below
Test Plan:
```
buck test caffe2/test/cpp/jit:jit -- LiteInterpreterTest.BackPortByteCodeModelAllVersions
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/5629499620570927
✓ ListingSuccess: caffe2/test/cpp/jit:jit : 461 tests discovered (15.693)
✓ Pass: caffe2/test/cpp/jit:jit - LiteInterpreterTest.BackPortByteCodeModelAllVersions (2.712)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/5629499620570927
```
```
buck run mode/opt //caffe2/torch/fb/mobile/upgrader_codegen:upgrader_codegen
buck test mode/opt //caffe2/test:upgrader_codegen -- mobile.test_upgrader_codegen.TestLiteScriptModule
Parsing buck files: finished in 0.8 sec
Downloaded 0/2 artifacts, 0.00 bytes, 100.0% cache miss (for updated rules)
Building: finished in 01:39.4 min (100%) 11031/11031 jobs, 2/11031 updated
Total time: 01:40.2 min
More details at https://www.internalfb.com/intern/buck/build/a8b0e417-019c-44ba-be6b-23379411a965
BUILD SUCCEEDED
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: 44fbfa66-cce8-4277-82ac-f89d79558581
Trace available for this run at /tmp/tpx-20220202-160956.915412/trace.log
RemoteExecution session id: reSessionID-44fbfa66-cce8-4277-82ac-f89d79558581-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/281475200877601
✓ ListingSuccess: caffe2/test:upgrader_codegen : 1 tests discovered (1.249)
✓ Pass: caffe2/test:upgrader_codegen - test_generate_bytecode (mobile.test_upgrader_codegen.TestLiteScriptModule) (1.365)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/281475200877601
```
Reviewed By: iseeyuan
Differential Revision: D33719098
fbshipit-source-id: e2d2b23d298f98e4d4fcdfc344f7b8c6f92cff26
(cherry picked from commit 81b956c23abc19489b69eee986721252474d00dc)
Summary:
Things changed in this PR that requires review:
1. aten/src/ATen/core/interned_strings.h
2. torch/csrc/jit/ir/alias_analysis.h : exposing createValue to allow efficient mutation
3. torch/csrc/jit/runtime/symbolic_shape_registry.cpp : added gelu/tanh/erf in registry
4. torch/jit/_script.py : throws scripting model sees autocast as decorator since it's not supported
nvfuser code update:
1. codegen improvements and performance tuning
2. integration bug fixes for shape expression logic
3. kernel segmentation update to address perf regression from horizontal fusion
4. scalar cpu tensor promotion to support inter-device operation between cpu scalar tensor and cuda tensor
Things reverted from local changes:
aten::gelu with approximation (tracked in PR: https://github.com/pytorch/pytorch/pull/61439)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72127
Reviewed By: HamidShojanazeri
Differential Revision: D34113233
Pulled By: jbschlosser
fbshipit-source-id: b82cde32b71e324eca0ea57cb8c9f9647278ca74
(cherry picked from commit e009bc5c4e943211c4953e6fdf7c9913fa66b3c9)
Summary:
Went through all these files and the design doc to understand the to_backend api. Figured I could add some comments to these files to make the apis a little clearer for those that come after.
(Note: this ignores all push blocking failures!)
Test Plan: na
Reviewed By: raziel, larryliu0820
Differential Revision: D34221989
fbshipit-source-id: 699fcbd8714bfb6b58c6c0bf0e5fbc019d2ef6f8
(cherry picked from commit 0b3f5d73e8de216b4402aed2f2b80be8781f145b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70532
This adds profiling to Optional[Tensor] types
First, in profiling_record.cpp, profiling nodes are added to Optional[Tensor] inputs. The nodes record
(a) whether or not any `None` types are encountered, and
(b) of the Tensor types, what's the most specific type matching all of non-null tensors that were encoutered (shape, dtype, etc.)
In tensorexpr_fuser, when specializing types based on the profiled information, an Optional[Tensor] type will always be Optional[], but the Tensor type contained in the optional type can be specialized (e.g. `Optional[Float(2x2x2, cpu, etc)]`)
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33714748
Pulled By: davidberard98
fbshipit-source-id: 93c819054450de7ac84b112de1012c0c12e34120
(cherry picked from commit 21cfd8012396cf4cc8437cb1dbf6b3846720d1b9)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72349
1. Interface call'd methods need to be registered to class. Previously all interface calls are inlined so there was no such problem.
2. parseDoubleList and parseBoolList got reversed when refactoring.
Test Plan:
1. Get ASR's test model at
```
mkdir ~/asr1 && cd ~/asr1
fbpkg fetch speech.tuna.milan.ondevice.en_us
```
2. Convert model:
```
cd ~/fbsource
buck run //xplat/caffe2/fb/lite_predictor:convert_model -- --model=$HOME/asr1/pytorchmodel.pt --output_name=$HOME/asr1/pytorchmodel.ff
```
3. Ran lite_predictor_flatbuffer
```
buck run //xplat/caffe2/fb/lite_predictor:lite_predictor_flatbuffer -- --model=$HOME/asr1/pytorchmodel.ff --method_to_call=encode_src --method_to_generate_input=get_all_bundled_inputs_for_encode_src
```
See perf metric generated (means loading and inference succeeded).
Reviewed By: gmagogsfm, zhxchen17
Differential Revision: D33959746
fbshipit-source-id: 24671e1189438119f477032eb6c29bd7736e74ca
(cherry picked from commit 5e188093506aaf5ba64728364474ebde6e4d3d7c)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71666
When JIT autodiff is constructing a gradient computation graph, it will only add gradients for tensors that require_grad. Previously, require_grad information was **not** propagated to the subgraph that autodiff used; as a result, autodiff would calculate *all* gradients, even if requires_grad had never been set during profiling runs. In certain cases, this can lead to performance issues. For example, during training, the gradient of the input data is not needed, but is still computed.
This propagates requires_grad to the subgraph passed into autodiff, so that autodiff will not compute unnecessary gradients.
Test: `./bin/test_jit --gtest_filter="AutodiffRemoveUnusedGradientsTest.Linear"`
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D33725304
Pulled By: davidberard98
fbshipit-source-id: ca7ab4c9a6a26f94f93aff2d5a4135e125323ba1
(cherry picked from commit a97fe0556da1d74d04250c7cbcd1b8e9d8b41ebe)
