This PR is part of an effort to speed up torch.onnx.export (#121422).
- Doing a reverse look-up in `symbol_dim_map` incurs a linear cost in number of symbols. This happens for each node, so incurs a quadratic cost to the whole export.
- Add a reverse look-up `dim_symbol_map` that is kept in parallel of `symbol_dim_map`. This avoids a linear time look-up, which creates a quadratic export time complexity.
- This is a highly pragmatic solution. If someone more familiar with the code base has a better solution, I'm interested to hear about it.
- Resolves (9) in #121422.
(partial fix of #121422)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123029
Approved by: https://github.com/justinchuby
Fix#82589
Why:
1. **full_check** works in `onnx::checker::check_model` function as it turns on **strict_mode** in `onnx::shape_inference::InferShapes()` which I think that was the intention of this part of code.
2. **strict_mode** catches failed shape type inference (invalid ONNX model from onnx perspective) and ONNXRUNTIME can't run these invalid models, as ONNXRUNTIME actually rely on ONNX shape type inference to optimize ONNX graph. Why we don't set it True for default? >>> some of existing users use other platform, such as caffe2 to run ONNX model which doesn't need valid ONNX model to run.
3. This PR doesn't change the original behavior of `check_onnx_proto`, but add a warning message for those models which can't pass strict shape type inference, saying the models would fail on onnxruntime.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83186
Approved by: https://github.com/justinchuby, https://github.com/thiagocrepaldi, https://github.com/jcwchen, https://github.com/BowenBao
Summary:
Remove code dup in import.cpp / export_modules.cpp such that
1. Only one copy of switching logic (detect flatbuffer / is_flatbuffer);
2. Move detection of includeness of flatbuffer to runtime (so no more macros)
This also reverts the dependency of import.cpp -> flatbuffer_loader.cpp to flatbuffer_loader.cpp -> import.cpp.
Differential Revision: D36926217
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79184
Approved by: https://github.com/zhxchen17
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:
Add ONNX exporter logging facility. Supporting both C++/Python logging api. Logging can be turned on/off. Logging output stream can be either set to `stdout` or `stderr`.
A few other changes:
* When exception is raised in passes, the current IR graph being processed will be logged.
* When exception is raised from `_jit_pass_onnx` (the pass that converts nodes from namespace `ATen` to `ONNX`), both ATen IR graph and ONNX IR graph under construction will be logged.
* Exception message for ConstantFolding is truncated to avoid being too verbose.
* Update the final printed IR graph with node name in ONNX ModelProto as node attribute. Torch IR Node does not have name. Adding this to printed IR graph helps debugging.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71342
Reviewed By: msaroufim
Differential Revision: D34433473
Pulled By: malfet
fbshipit-source-id: 4b137dfd6a33eb681a5f2612f19aadf5dfe3d84a
(cherry picked from commit 67a8ebed5192c266f604bdcca931df6fe589699f)
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 81b956c23a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67803
* Addresses comments from #63589
[ONNX] remove torch::onnx::PRODUCER_VERSION (#67107)
Use constants from version.h instead.
This simplifies things since we no longer have to update
PRODUCER_VERSION for each release.
Also add TORCH_VERSION to version.h so that a string is available for
this purpose.
[ONNX] Set `ir_version` based on opset_version. (#67128)
This increases the odds that the exported ONNX model will be usable.
Before this change, we were setting the IR version to a value which may
be higher than what the model consumer supports.
Also some minor clean-up in the test code:
* Fix string replacement.
* Use a temporary file so as to not leave files around in the test
current working directory.
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D32181306
Pulled By: malfet
fbshipit-source-id: 02f136d34ef8f664ade0bc1985a584f0e8c2b663
Co-authored-by: BowenBao <bowbao@microsoft.com>
Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Co-authored-by: Nikita Shulga <nshulga@fb.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65966
ghstack-source-id: 141594521
Support exportation of "interface methods" from submodule to a mobile module. "Interface methods" are defined as methods which might be dynamically called in a module therefore need to be exported anyway, like virtual functions in C++.
Before this change the algorithm of exportation is a simple iteration through all toplevel methods. Now since we have indirect calls, we need to recursively walkthrough the call graph to find all potentially used methods, which means the order we export methods might break in old runtimes, to guarantee forward compatibility we need to export toplevel methods first, then extra methods, in this order toplevel methods will always be found first.
NOTE that interface methods exportations are disabled by default in this diff. We need to call torch._C._enable_mobile_interface_call_export to actaully enable it.
Test Plan: buck test mode/dev //caffe2/test:jit -- --exact 'caffe2/test:jit - test_export_opnames_interface (jit.test_misc.TestMisc)'
Reviewed By: qihqi, iseeyuan
Differential Revision: D31326155
fbshipit-source-id: 5be7234cca07691f62648a85133b6db65e427b53
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66140
* Add new argument to export api to enable users specifying `nn.Module` classes that they wish to be exported as local function in ONNX model.
* Refactor `torch/csrc/jit/serialization/export.cpp`, and remove redundant `EncoderBase` class.
* ~~Contains changes from #63268~~
* Depends on #63716 to update onnx submodule.
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D31424098
fbshipit-source-id: c949d0b01c206c30b4182c2dd1a5b90e32b7a0d3
Co-authored-by: BowenBao <bowbao@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64382
* This `use_external_data_format` parameter is used for large models cannot be exported because of the 2GB protobuf limit.
* When `use_external_data_format` set to True, the model is exported in ONNX external data format, in which case some of the model parameters are stored in external binary files and not in the ONNX model file itself.
* This PR will set this paramter to DEPRECATED and check the model proto sizes by code instead of by user, if the sizes lager than 2GB, then `use_external_data_format = True` automatically.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D30905265
Pulled By: malfet
fbshipit-source-id: 82b4e17bfa6a8de2bfd700a5282c12f6835603cb
Co-authored-by: hwangdeyu <dejack953@outlook.com>
Summary:
1. Enable support for operators with default args and out args. For `torch.add(x, h, out=x)`, the number of specified arguments will be 3 instead of 4.
2. Bump bytecode version from 6 to 7
3. Implement backport_v7_to_v6 function. Also slightly refactor the local_thread to allow re-emit operators.
4. unittest to cover backport function
5. Update expect result from 4 to 3 in unit test DefaultArgsWithOutArg to cover the number of specified arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63651
ghstack-source-id: 138539912
Test Plan:
```
caffe2/test/cpp/jit:jit - LiteInterpreterTest.DefaultArgsWithOutArg
caffe2/test/cpp/jit:jit - LiteInterpreterTest.DefaultArgsPinvWithOutArg
caffe2/test/cpp/jit:jit - LiteInterpreterTest.BackPortByteCodeModelAllVersions
```
Reviewed By: raziel, tugsbayasgalan
Differential Revision: D30454080
fbshipit-source-id: 357c50b96682430675142d20d688d1f64e1de307
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61862
Modularize functions of parsing bytecode tables so that they can be used as needed in situations other than mobile lite interpreter.
* The decoupled functions are re-used by current lite interpreter loader.
* The bytecode can be serialized/deserialized from other formats.
* The decoupled functions have minimum dependencies on other PyTorch components.
Next:
Build a driver binary to include the parser and interpreter, but only has necessary dependency on other PyTorch components.
ghstack-source-id: 137867287
Test Plan:
As an example, a simple bytecode is parsed to a mobile function, and directly run in the added unit test, `RunTimeTest:ParseBytecode`. It contains basic control flow (if, else) and basic data orchestration (list construction).
CI
Reviewed By: larryliu0820
Differential Revision: D29798382
Pulled By: iseeyuan
fbshipit-source-id: 1c173a5f5d37097e3a97baec3f3e48e1eea1400f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61760
Remove all code that related to metadata.pkl creation including creating metadata.pkl, converting data from extra/mobile_info.json and extra/producer_info.json to metadata.pkl file.
Test Plan:
## Run buck commands:
- `cd` into `fbcode` then `buck build //caffe2/caffe2/fb/init:init`
- `cd` into `fbcode` then `buck build //caffe2/torch/fb/init:init`
- `buck build //xplat/caffe2:torch_mobile_core`
## Export a PyTorch lite/mobile model
- Run: `flow-cli canary users.xcheng16.pytorch_trainer.TestWorkflow --run-as-secure-group ai_mobile_platform --buck-target //fblearner/flow/projects/users/xcheng16:workflow` under `fbcode` on devserver.
- Resulted Model: metadata.pkl no longer exist
{F632063134}
Reviewed By: guangy10
Differential Revision: D29702943
fbshipit-source-id: ec7964f4aa3a8e09ccc20b1a7e2232f85931dd81
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59735
1. Fixes ABA storage identity problem during serialization for `torch.package` by keeping reference of serialized storages through lifetime of `PackageExporter` to prevent reuse of memory address. Achieved by extending logic used in solution to mobile's same issue.
2. Adds determinism to naming scheme of serialized storages in export code paths which utilize `tensor_cdata_naming_scheme`(introduced 2nd mapping in `StorageContext`, now maps `storage cdata ptr` -> `unique id`, `unique id` -> `c10::Storage`)
3. Additionally uses presence of a storage in the `StorageContext` instance as marker for if a storage has been serialized or not, removing the need to scan the `PythonStreamWriter` for presence of the storage's serialization file
Test Plan: Imported from OSS
Reviewed By: suo
Differential Revision: D29075276
Pulled By: Lilyjjo
fbshipit-source-id: 15a5c30b1de99c5bd7079388f2db9b6ece2eca12
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58852
Enable implicit operator versioning via number of arguments from Mobile.
1. By default, TS doesn't emit instructions for tailing default args and the provided number of specified args is serialized to bytecode. From interpreter the default values are fetched from operator schema. The implementation has been landed in #56845. Please refer to #56845 for details.
2. Since there is bytecode schema change, the bytecode version is bumped from 5 to 6.
3. The corresponding backport function is provided, for forward compatibility use. Note that because there is instruction change, a global flag is used as the switch to control the two versions.
Test Plan: Imported from OSS
Reviewed By: raziel
Differential Revision: D28789746
Pulled By: iseeyuan
fbshipit-source-id: 6e5f16460c79b2bd3312de02d0f57b79f50bf66b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55958
This PR refactors the existing ScriptModuleSerializer to be exposed to the public. Most of the code is the same, git just thinks it's different due to it being shifted over a white space. I commented on the actual changes that weren't due to the white space shifting
Test Plan: Imported from OSS
Reviewed By: suo
Differential Revision: D27832546
Pulled By: Lilyjjo
fbshipit-source-id: c73e33211e46fca56053aa45ea2b9a2803eab82c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56044
We want to be able to drop the dependence of full-jit deps in the auto-generated unit tests for 2 reasons:
1. Running bloaty on the auto-generated unit tests should be somewhat representative of the actual size.
2. The runtime environment of the auto-generated unit tests should be as close to the production environment as possible to ensure that we are running the tests in a production-like runtime.
Due to the dependece on full-jit, we aren't there yet. For the auto-generated tests, we probably don't need to depend on `_export_operator_list()` evetually, but for now we do since it is used to decide whether the model being run is a Metal GPU model or a CPU model, and gates whether the test runs that model or not.
Eventually, we can stop doing this in the test and do it in the codegen from PTM-CLI instead (by fetching the operators from that tool, and writing out to the BUCK file which backend(s) this model is targeting). However, that will take some time to land, so in the spirit of expediency, this change is being proposed.
Discussed this offline with iseeyuan
ghstack-source-id: 126656877
Test Plan: Build + BSB.
Reviewed By: iseeyuan
Differential Revision: D27694781
fbshipit-source-id: f31a2dfd40803c02f4fd19c45a3cc6fb9bdf9697
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52333
Export_opnames current documentation is a bit misleading. Change it to better clarify what it does.
ghstack-source-id: 121810264
Test Plan: n/a
Reviewed By: iseeyuan
Differential Revision: D26471803
fbshipit-source-id: 496d10b161c9a4076c4e12db8a0affafc4e1e359
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49385
Currently, the API to export operator lists accepts a `torch::jit::Module` object, and spits out an operator list. The operator list is practically used only for mobile. This is not ideal because the set of root operators may change by the time the model is subsequently optmized and exported for mobile.
What we need to to instead is glean the list of operators from the mobile model itself (`bytecode.pkl` specifically), and expose that instead.
Also updated the logic in `converter`.
### Before this change:
1. Get operator List from Torch Script Model
2. Convert to bytecode mobile model
### After this change:
1. Convert to bytecode mobile model
2. Use this converted mobile model to get the list of operators for each method on the model
ghstack-source-id: 118796752
Test Plan:
Added a unit test in `test_lite_interpreter.cpp` to ensure that all model referenced operators show up in the exported operator list. Also make `test_lite_interpreter.cpp` runnable from `xplat/caffe2/BUCK` since this is where the production code will be built from.
Verified that the list of operators produced before and after this change for an example model (segmentation) are the same.
{P147863234}
Also verified that the operator lists for BI-Xray model is different (we have been having problems with missing operators for this one): {P154903132}
Reviewed By: iseeyuan
Differential Revision: D24690094
fbshipit-source-id: 0426a6ef90456a811010cfe337c415882ae2deff
Summary:
* Support propagating `dim_param` in ONNX by encoding as `ShapeSymbol` in `SymbolicShape` of outputs. If export is called with `dynamic_axes` provided, shape inference will start with these axes set as dynamic.
* Add new test file `test_pytorch_onnx_shape_inference.py`, reusing all test cases from `test_pytorch_onnx_onnxruntime.py`, but focus on validating shape for all nodes in graph. Currently this is not enabled in the CI, since there are still quite some existing issues and corner cases to fix. The test is default to run only at opset 12.
* Bug fixes, such as div, _len, and peephole.cpp passes for PackPadded, and LogSoftmaxCrossEntropy.
* This PR depends on existing PR such as 44332.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44920
Reviewed By: eellison
Differential Revision: D23958398
Pulled By: bzinodev
fbshipit-source-id: 00479d9bd19c867d526769a15ba97ec16d56e51d
Summary:
Optimize export_onnx api to reduce string and model proto exchange in export.cpp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44332
Reviewed By: bwasti, eellison
Differential Revision: D23880129
Pulled By: bzinodev
fbshipit-source-id: 1d216d8f710f356cbba2334fb21ea15a89dd16fa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43584
1. add `metadata.pkl` to `.bc` file which includes the model info that we are interested in
2. load `metadata.pkl` as a attribute `unordered_map<string, string>` in the module
ghstack-source-id: 110730013
Test Plan:
- CI
```buck build //xplat/caffe2:jit_module_saving
```
```buck build //xplat/caffe2:torch_mobile_core
```
Reviewed By: xcheng16
Differential Revision: D23330080
fbshipit-source-id: 5d65bd730b4b566730930d3754fa1bf16aa3957e
Summary:
Original commit changeset: f368d00f7bae
Back out "[2/3][lite interpreter] add metadata when saving and loading models for mobile"
D23047144 (e37f871e87)
Pull Request: https://github.com/pytorch/pytorch/pull/43516
(Note: this ignores all push blocking failures!)
Test Plan: CI
Reviewed By: xcheng16
Differential Revision: D23304639
fbshipit-source-id: 970ca3438c1858f8656cbcf831ffee2c4a551110
Summary:
1. add `metadata.pkl` to `.bc` file which includes the model info that we are interested in
2. load `metadata.pkl` as a attribute `unordered_map<string, string>` in the module
Test Plan:
- CI
```buck build //xplat/caffe2:jit_module_saving
```
```buck build //xplat/caffe2:torch_mobile_core
```
Reviewed By: xcheng16
Differential Revision: D23047144
fbshipit-source-id: f368d00f7baef2d3d15f89473cdb146467aa1e0b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42133
Test Plan:
We save a module with module debugging information as follows.
```
import torch
m = torch.jit.load('./detect.pt')
# Save module without debug info
m._save_for_lite_interpreter('./detect.bc')
# Save module with debug info
m._save_for_lite_interpreter('./detect.bc', _save_debug_info_in_bytecode=True)
```
Size of the file without module debugging information: 4.508 MB
Size of the file with module debugging information: 4.512 MB
Reviewed By: kimishpatel
Differential Revision: D22803740
Pulled By: taivu1998
fbshipit-source-id: c82ea62498fde36a1cfc5b073e2cea510d3b7edb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38898
Pickling will pickle the tensor meta info, and its up to the jit
exporter or other upstream who use the pickler to decide how to write
the actual tensor data.
This PR make we call getWritableTensorData in upper level so that rpc
and TensorPipe can leverge it with only pickling tensor meta data without
converting the tensor from GPU to CPU.
Test Plan: Imported from OSS
Differential Revision: D21879866
Pulled By: wanchaol
fbshipit-source-id: 75f7ff4073e4ad15b6588973dcbdc48f97a8329f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
