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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73855
Calling the clock is one of the most expensive parts of profiling. We can reduce the profiling overhead by using `rdtsc` instead. The tradeoff is that we have to measure and convert. (shift and scale)
Test Plan: I added a cpp unit test with *very* aggressive anti-flake measures. I also ran the overhead benchmark (9 replicates) with `--stressTestKineto` (0.94 -> 0.89 us) and `--stressTestKineto --kinetoProfileMemory` (1.27 -> 1.17 us)
Reviewed By: chaekit
Differential Revision: D34231071
fbshipit-source-id: e3b3dd7580d93bcc783e87c7f2fc726cb74f4df8
(cherry picked from commit e8be9f8160793c6ee35d5af02bca3e01703e377d)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73409
We can do better than `vector` or `deque`, and it's sufficiently important to the hot path to justify a custom container. (This is part of the larger queue refactor, but this is a standalone drop-in replacement so we don't need to wait.)
Test Plan: It's a pretty simple container type, so I just added a few cpp tests for emplace and read back. I also ran the overhead benchmark (replicates=9) with both `--stressTestKineto` (0.99 -> 0.94 us) and `--stressTestKineto --kinetoProfileMemory` (1.36 -> 1.27 us).
Reviewed By: swolchok
Differential Revision: D34231072
fbshipit-source-id: ed57299729d444d59cf843a0d38a3ee2240eeec1
(cherry picked from commit 43907948f3a8d2137244e7bb59f43999bd660917)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73486
conv and linear packed params were previously defined in ATen/native/quantized/cpu/conv_packed_params.h> and ATen/native/quantized/cpu/packed_params.h>. These two files have been merged into one and has been relocated to ATen/native/quantized/cpu/packed_params.h>.
Differential Revision:
D34513286
D34513286
Test Plan: Imported from OSS
Reviewed By: dagitses
Pulled By: dzdang
fbshipit-source-id: 813845af7ea9449e316ab7822efe7460f0bd0d88
(cherry picked from commit 2f627561f27f81977ff73b8863c5e9e719dc4c60)
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:
RFC: https://github.com/pytorch/rfcs/pull/40
This PR (re)introduces python codegen for unboxing wrappers. Given an entry of `native_functions.yaml` the codegen should be able to generate the corresponding C++ code to convert ivalues from the stack to their proper types. To trigger the codegen, run
```
tools/jit/gen_unboxing.py -d cg/torch/share/ATen
```
Merged changes on CI test. In https://github.com/pytorch/pytorch/issues/71782 I added an e2e test for static dispatch + codegen unboxing. The test exports a mobile model of mobilenetv2, load and run it on a new binary for lite interpreter: `test/mobile/custom_build/lite_predictor.cpp`.
## Lite predictor build specifics
1. Codegen: `gen.py` generates `RegisterCPU.cpp` and `RegisterSchema.cpp`. Now with this PR, once `static_dispatch` mode is enabled, `gen.py` will not generate `TORCH_LIBRARY` API calls in those cpp files, hence avoids interaction with the dispatcher. Once `USE_LIGHTWEIGHT_DISPATCH` is turned on, `cmake/Codegen.cmake` calls `gen_unboxing.py` which generates `UnboxingFunctions.h`, `UnboxingFunctions_[0-4].cpp` and `RegisterCodegenUnboxedKernels_[0-4].cpp`.
2. Build: `USE_LIGHTWEIGHT_DISPATCH` adds generated sources into `all_cpu_cpp` in `aten/src/ATen/CMakeLists.txt`. All other files remain unchanged. In reality all the `Operators_[0-4].cpp` are not necessary but we can rely on linker to strip them off.
## Current CI job test coverage update
Created a new CI job `linux-xenial-py3-clang5-mobile-lightweight-dispatch-build` that enables the following build options:
* `USE_LIGHTWEIGHT_DISPATCH=1`
* `BUILD_LITE_INTERPRETER=1`
* `STATIC_DISPATCH_BACKEND=CPU`
This job triggers `test/mobile/lightweight_dispatch/build.sh` and builds `libtorch`. Then the script runs C++ tests written in `test_lightweight_dispatch.cpp` and `test_codegen_unboxing.cpp`. Recent commits added tests to cover as many C++ argument type as possible: in `build.sh` we installed PyTorch Python API so that we can export test models in `tests_setup.py`. Then we run C++ test binary to run these models on lightweight dispatch enabled runtime.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69881
Reviewed By: iseeyuan
Differential Revision: D33692299
Pulled By: larryliu0820
fbshipit-source-id: 211e59f2364100703359b4a3d2ab48ca5155a023
(cherry picked from commit 58e1c9a25e3d1b5b656282cf3ac2f548d98d530b)
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:
There are various possible approaches, but the approach chosen minimizes disruption to source control blame.
Addresses:
```
error: Function _ZN23FunctionalTest_Pad_Test8TestBodyEv is too big to optimize [-Werror,-Wignored-optimization-argument]
```
Test Plan: buck2 build mode/opt caffe2/test/cpp/api:functional
Reviewed By: jamesr66a
Differential Revision: D34027291
fbshipit-source-id: 9dfd771ad56d3d4bc0d41b38b04654c8dae7c006
(cherry picked from commit d43b5a7ed6)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72875
This diff contains changes from several PRs landed to lazy_tensor_staging branch.
* generating 'fallback' overrides for each codegenned op, useful for debugging
* supports operators which are missing aten:: symbols for op names, instead using their string counterpart
* makes the IR class a base class instead of hardcoding the assumption of TS
It also resolves lint issues and in particular cleans up the following:
* {Type}s shouldn't be passed into isValueType, and using the catch-all base class of CType is nicer than specifying a list of types.
Fixes#72852
Test Plan: test manually on lazy_tensor_staging branch
Reviewed By: shunting314
Differential Revision: D34250357
fbshipit-source-id: aa7d589f605055d5d02bc77c77fa6f1182ff7497
(cherry picked from commit 2f8f5e4971)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73028
A typical use case for `TensorExprKernel` is to create the kernel once and call it multiple times, possibly in parallel. For the parallel calls to work, we need to ensure that the run() method calls do not change any state in `TensorExprKernel`.
Before this change, the `run()` method was modifying the sizes and strides vectors when dynamic shapes were present. This manifested as a data race when running a model with Static Runtime.
ghstack-source-id: 149398820
Test Plan:
```
buck build mode/dev-asan //caffe2/test/cpp/tensorexpr:tensorexpr
./buck-out/dev/gen/caffe2/test/cpp/tensorexpr/tensorexpr --gtest_filter="DynamicShapes.MultiThreadedExecution"
```
Reviewed By: eellison
Differential Revision: D34287960
fbshipit-source-id: d311f3c5a66c5d5de4e1deaeaa01816b53e9906e
(cherry picked from commit 161568bfae)
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 0f1b335e5b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72730
This diff contains changes from several PRs landed to lazy_tensor_staging branch.
- generating 'fallback' overrides for each codegenned op, useful for debugging
- supports operators which are missing aten:: symbols for op names, instead using their string counterpart
- makes the IR class a base class instead of hardcoding the assumption of TS
Test Plan: tested on lazy_tensor_staging branch
Reviewed By: desertfire
Differential Revision: D34178476
fbshipit-source-id: 7190b2e0d82b4eb1f4510c858c24446c6df3f9d0
(cherry picked from commit 6713d3f0ef)
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 1d276baca3)
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 b47cc935ee)
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:
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 e009bc5c4e)
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 0b3f5d73e8)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72390
This class didn't add much value and only caused more boilerplate code.
This change removes the class and updates all the use cases with
uses of `ExprHandle`.
A side effect of this change is different names in loop variables, which
caused massive mechanical changes in our tests.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D34030296
Pulled By: ZolotukhinM
fbshipit-source-id: 2ba4e313506a43ab129a10d99e72b638b7d40108
(cherry picked from commit c2ec46a058)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72389
This is an NFC change that just prepares the code for the upcoming
deletion of `DimArg` class. This change makes `Compute` and `Reduce`
APIs to use `ExprHandle` everywhere.
There should be no observable behavior change from this PR.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D34030295
Pulled By: ZolotukhinM
fbshipit-source-id: 3fd035b6a6bd0a07ccfa92e118819478ae85412a
(cherry picked from commit 1b0a4b6fac)
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 21cfd80123)
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 5e18809350)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72009
This simplifies the Stats interface by merging IntervalStat and FixedCountStat into a single Stat w/ a specific window size duration and an optional max samples per window. This allows for the original intention of having comparably sized windows (for statistical purposes) while also having a consistent output bandwidth.
Test Plan:
```
buck test //caffe2/test:monitor //caffe2/test/cpp/monitor:monitor
```
Reviewed By: kiukchung
Differential Revision: D33822956
fbshipit-source-id: a74782492421be613a1a8b14341b6fb2e8eeb8b4
(cherry picked from commit 293b94e0b4)
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 a97fe0556d)
Summary: Reland for D33282878 (911d527b87) . Land backend change first to maintain FC. Will wait for 2 weeks after this diff is in. And than land the front-end change in next diff.
Test Plan:
test in next diff
time buck test mode/dev-nosan fblearner/flow/projects/langtech/translation:tests -- test_e2e_base_training
Reviewed By: gmagogsfm
Differential Revision: D33342547
fbshipit-source-id: b3dee9a4bdfd78103848c12629e5fccafdd621e3
(cherry picked from commit ae1935f1af)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71186
So far we've only supported scalar inputs, but couldn't handle scalar outputs
or intermediates. This PR adds it.
Scalar outputs are returned as 0-dim tensors. If the kernel is invoked on a
stack of IValues, we correctly convert the results to scalar IValues when
needed. If the kernel is invoked with a vector of void* pointers, everything
works out of the box without any conversions.
Lowerings for scalar operators are a bit tricky. Usual lowerings return a pair
<Buf, Stmt> (aka Tensor), but for scalar operators we also want to have the
corresponding Var that the lowering function supposedly creates (in theory we
could just use Loads and Stores, but I'm worried it can affect performance as
there is no guarantee this will be optimized by LLVM). So, what we do here to
work around this is we return a fake buf + stmt that sets the corresponding
var. Then outside of the lowering we create a real buffer and generate a Store
to it with the value from the variable we passed as the base handle of the fake
buf. This real buffer is then treated as usual by the rest of the system and we
can use it if we need to return this scalar value as a kernel output. If we do
not need to return it, then the Store will be deleted by the DCE pass.
Differential Revision:
D33539324
D33539324
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: ab4524b9820ce204f106effcf6232ed33d4ee223
(cherry picked from commit 7faa0939f0)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71597
Problem: _jit_to_backend overrides get/set state. This means any attributes added to the module after lowering will not be preserved after serialization. For edge workflows the biggest problem here is it breaks bundled_inputs.
Solution?:
Real quick and easy way to handle issues with to_backend overriding get/set state. Wraps the lowered module in another module and has forwarding functions for the api specified in 'method_compile_spec'.
The tradeoff with this approach is now the actual workhorse of the module is 1 layer deep which might make debugging slightly grosser/more difficult/confusing. The other approach Martin David and I talked about would be to only lower the portions that require custom get/set state logic. This leaves the top level the same, and only specific backened internals are changed. Personally I'm not sure how much that really addresses the debugging concern all that well. It seems like if you cracked the model open you'd still run into similar amounts of confusion with a lot of the variables and logic referenced coming from another module.
The other concern with this approach is whether or not 'compile_spec' specifies the public api of the module (since thats our source of truth for this wrapper). While it may not be enforced, it certainly seems to be true by convention and the to_backend api already uses it as a source of truth for all functions that get generated in the resulting module. I say we just formally commit to this (compile spec keys being functions) being the contract of the api instead of just assuming it to be the case and then having weird behavior if its not.
Test Plan:
New Unit Test
CI to check for existing behavior and contracts.
manually tested in a notebook with bundled inputs.
{P475790313}
Reviewed By: raziel
Differential Revision: D33694257
fbshipit-source-id: 9ff27db421eba41bac083dff11a22e9e40a36970
(cherry picked from commit 91ef49977e)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68693
Generation of python bindings for native functions is split over 8
different files. One for each namespace, with the torch namespace
split into 3 shards, and methods in their own file as well. This
change ensures that editing any single (non-method) operator only
causes one of these files to be rebuilt.
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D32596270
Pulled By: albanD
fbshipit-source-id: 0570ec69e7476b8f1bc21138ba18fe8f95ebbe3f
(cherry picked from commit ba0fc71a3a)
Summary:
The block and thread extent calculations in `cuda_codegen` should be using `int64_t` instead of `int`. The updated test, `test_dynamic_shapes`, fails without this change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71428
Reviewed By: samdow
Differential Revision: D33640374
Pulled By: navahgar
fbshipit-source-id: 64c340ad2a9a1fa1fe066cf1c5dfc3b546b7be6d
(cherry picked from commit 6ea546ce11)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70613
This refactors `at::detail::empty_cpu` to use only `TensorBase` so you
can construct tensors without including `Tensor.h`. It also adds a
`TensorOptions` version to reduce friction in operators moving from
the `at::empty` API.
Test Plan: Imported from OSS
Reviewed By: samdow
Differential Revision: D33623682
Pulled By: ngimel
fbshipit-source-id: 7a7b08bc2ed06830a3d698197a0c8389a096dc1d
(cherry picked from commit 2e17ad0bbd)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71291
This commit removes torch::lazy::convertShapes since it's no longer used.
In addition, it replaces a numel logic within LTCTensorImpl.
Test Plan:
./build/bin/test_lazy
CI in lazy_tensor_staging branch
Reviewed By: wconstab
Differential Revision: D33575084
Pulled By: alanwaketan
fbshipit-source-id: b104ef39fd552822e1f4069eab2cb942d48423a6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71275
Currently it's taking more than 10 minutes to run the conformance test. Instead we should use parametrized test to shard into test segments so that they can run in parallel.
ghstack-source-id: 146990608
Test Plan:
```
[zhxchen17@devbig560.ftw3 /data/users/zhxchen17/fbsource/fbcode] buck test mode/dev-tsan //caffe2/test/cpp/jit:jit -- -r 'LiteInterpreterDynamicTypeTestFixture'
Building... 34.9 sec (99%) 12110/12111 jobs, 0/12111 updated
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: ebea52b3-7c7f-46be-9f69-18e2e7b040cc
Trace available for this run at /tmp/tpx-20220113-113635.717778/trace.log
RemoteExecution session id: reSessionID-ebea52b3-7c7f-46be-9f69-18e2e7b040cc-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/4222124735827748
✓ ListingSuccess: caffe2/test/cpp/jit:jit : 431 tests discovered (11.173)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/0 (51.331)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/1 (65.614)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/3 (76.875)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/5 (77.271)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/4 (78.871)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/6 (78.984)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/7 (84.068)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/2 (85.198)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/8 (88.815)
✓ Pass: caffe2/test/cpp/jit:jit - Conformance/LiteInterpreterDynamicTypeTestFixture.Conformance/9 (90.332)
Summary
Pass: 10
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/4222124735827748
```
Reviewed By: qihqi
Differential Revision: D33570442
fbshipit-source-id: 5c49e03b0f88068d444c84b4adeaaf45433ce1fa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69853
We can implement this overload more efficiently.
ghstack-source-id: 146924693
Test Plan:
patched alias_analysis tests
Time reported to initialize a predictor by static runtime when given ctr_mobile_feed local_ro net is 9.5s instead of 10.5s.
Reviewed By: mikeiovine
Differential Revision: D33039731
fbshipit-source-id: 52559d678e9eb00e335b9e0db304e7a5840ea397
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70201
Included functions:
save_mobile_module -> saves a mobile::Module to flatbuffer
load_mobile_module_from_file -> loads a flatbuffer into mobile::Module
parse_mobile_module -> parses from bytes or deserialized flatbuffer module object
Compared to previous attempts, this diff only adds flatbuffer to cmake target and leaves fbcode/xplat ones unchanged.
Test Plan: unittest
Reviewed By: malfet, gmagogsfm
Differential Revision: D33239362
fbshipit-source-id: b9ca36b83d6af2d78cc50b9eb9e2a6fa7fce0763
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67781
Update `LLVMCodeGen` in NNC to use the given kernel function name while emitting code.
This was earlier committed as D31445799 (c30dc52739) and got reverted as part of a stack of diffs that included a cache for `PyTorchLLVMJIT`, which was the likely culprit.
Test Plan:
```
buck test mode/opt //caffe2/test/cpp/tensorexpr:tensorexpr -- --exact 'caffe2/test/cpp/tensorexpr:tensorexpr - LLVM.CodeGenKernelFuncName'
```
Reviewed By: ZolotukhinM, bdhirsh
Differential Revision: D32145958
fbshipit-source-id: 5f4e0400c4fa7cabce5b91e6de2a294fa0cad88e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69567
This exposes torch.monitor events and stats via pybind11 to the underlying C++ implementation.
* The registration interface is a tad different since it takes a lambda function in Python where as in C++ it's a full class.
* This has a small amount of changes to the counter interfaces since there's no way to create an initializer list at runtime so they now also take a vector.
* Only double based stats are provided in Python since it's intended more for high level stats where float imprecision shouldn't be an issue. This can be changed down the line if need arises.
```
events = []
def handler(event):
events.append(event)
handle = register_event_handler(handler)
log_event(Event(type="torch.monitor.TestEvent", timestamp=datetime.now(), metadata={"foo": 1.0}))
```
D32969391 is now included in this diff.
This cleans up the naming for events. type is now name, message is gone, and metadata is renamed data.
Test Plan: buck test //caffe2/test:monitor //caffe2/test/cpp/monitor:monitor
Reviewed By: kiukchung
Differential Revision: D32924141
fbshipit-source-id: 563304c2e3261a4754e40cca39fc64c5a04b43e8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70464
Add handling of strided input tensors to dynamic fusion. This is done with the same set of input striding specializations as https://github.com/pytorch/pytorch/pull/60684/:
```
S_ONE, // STRIDE_ONE: packed
S_CONT, // STRIDE_CONTIGUOUS: stride[i + 1] * sizes[i + 1]
S_TRAN_CONT, // STRIDE_TRANSPOSED_CONTIGUOUS: stride[i-1] * sizes[i-1]
S_AS_ARG, // STRIDE_AS_ARG: stride passed in as runtime value
```
and then two additional specializations for a) contiguous tensor and b) channels-last tensor. channels-last is a common case and we should optimize for it. additionally, tensors natively store whether they are contiguous/channels-last contiguous, which makes it faster to check if tensors follow this pattern.
Output striding will be done in a follow up.
The striding is stored on both the TensorGroup node and on the guard node. The striding descriptors are stored as a vector of strings on the node for debugability and to make use of storing ivalues as attributes on nodes.
As an example:
```
%8 : Double(10, 11, 12, 13, strides=[1716, 1, 143, 11], requires_grad=0, device=cpu) = prim::TensorExprGroup_0[symbolic_shape_inputs=[-37, -36, -35, -34], striding_inputs_desc=[["TENSOR_CONT_CHANNELS_LAST"]](%x, %24, %23, %22, %21)```
```
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D33458649
Pulled By: eellison
fbshipit-source-id: c42616d3c683d70f6258180d23d3841a31a6030d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71144
This wasn't being used anywhere. It was originally intended for the SR flow but we're doing something else now.
Test Plan: Imported from OSS
Reviewed By: navahgar, ZolotukhinM
Differential Revision: D33521061
Pulled By: eellison
fbshipit-source-id: 0574698a2b7409df6feb703f81e806d886225307
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70509
TypeFactory will construct DynamicType when building on Edge platforms. We use this facility to make FunctionSchema return DynamicType all the time for OptionalType. We don't explicitly use DynamicTypeFactory everywhere because that requires too many changes and will split the entire aten codebase.
ghstack-source-id: 146818621
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D33306737
fbshipit-source-id: d7ce00b438f7c03b43945d578280cfd254b1f634
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70233
Make type parser to produce DynamicType for all base types which don't have type arguments, and return DynamicType pointer for IValue::type().
ghstack-source-id: 146818622
Test Plan: no behavior change.
Reviewed By: iseeyuan
Differential Revision: D33137219
fbshipit-source-id: 1612c924f5619261ebb21359936309b41b2754f5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70212
Use DynamicType instead of ListType all over the place in Lite Interpreter. Namely we need to modify the following places:
1. Type parser which produces the Type constants.
2. IValue::type() which returns reflected Type from IValues.
3. Helper functions to construct the container value.
4. Typechecks which test whether a type instance is a particular container type.
ghstack-source-id: 146818619
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D33176931
fbshipit-source-id: 9144787f5fc4778538e5c665946974eb6171a2e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70202
Use DynamicType instead of DictType all over the place in Lite Interpreter. Namely we need to modify the following places:
1. Type parser which produces the Type constants.
2. IValue::type() which returns reflected Type from IValues.
3. Helper functions to construct the container value.
4. Typechecks which test whether a type instance is a particular container type.
ghstack-source-id: 146735648
Test Plan: no behavior change.
Reviewed By: iseeyuan
Differential Revision: D33137257
fbshipit-source-id: 971bf431658c422ea9353cc32cdab66e98876e9d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70871
We had previously handled reusing memory in the optimized kernel execution path, but not yet handled it if we hit the unoptimized fallback.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D33458652
Pulled By: eellison
fbshipit-source-id: 4eb62181ed02c95813a99638f5e2d0f9347b5c08
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70338
Today Unpickler is used by both server and mobile for deserializing model, and it always fallback to mobile parser when there's no type resolver provided by user. However this is not intended as server and mobile type parser supports different things. In this diff we provide a default fallback using script parser and opt it out for all mobile cases.
ghstack-source-id: 146727330
(Note: this ignores all push blocking failures!)
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D33284352
fbshipit-source-id: 997c4f110b36eee6596e8f23f6a87bf91a4197ed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68137
A small step to replace existing OptionalType usage to DynamicType in Edge runtime.
ghstack-source-id: 146670520
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D32264617
fbshipit-source-id: 62d3ffad40901842deac19ca2098ea5ca132e718
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69482
Add a test to enumerate a number of JIT type combinations and see if their subtyping behavior is preserved in the new DynamicType system.
ghstack-source-id: 146670526
Test Plan: buck test mode/opt //caffe2/test/cpp/jit:jit -- --exact 'caffe2/test/cpp/jit:jit - LiteInterpreterTest.DynamicType'
Reviewed By: gmagogsfm
Differential Revision: D32891263
fbshipit-source-id: 728211b39778e93db011b69b0a4047df78a8fc5b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70535
This also fixes handling of inputs that happen to be outputs (they
require copy).
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D33399116
Pulled By: ZolotukhinM
fbshipit-source-id: 9845838eb653b82ae47b527631b51893990d5319
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66515
These passes should not be used generally as they change API of the
model's forward method, but they help experimenting with the model and
ironing out all the kinks before it can be compiled properly. In the
long run ideally we should provide a better way to enable such
experiments.
Differential Revision:
D31590862
D31590862
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 74ded34c6c871d4cafa29f43dc27c7e71daff8fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70339
When a python program is translated to TorchScript, the python exception type is dropped. This makes users's life hard when they need to categorize errors based more than only exception message.
Here we make the change so when we raise a python exception, we record the fully qualified class name for the exception. Later on when the TorchScript is interpreted, a special exception CustomJITException is thrown. User can get the python class name from CustomJITException::getPythonClassName .
Note that, this diff does not customize the mapping from C++ exception to Python exception. It's left to the users to do whatever mapping they want.
Code under scripts/shunting are just my own experimental code. I can split them out if requested.
ghstack-source-id: 146221879
Test Plan: buck test mode/opt //caffe2/test:jit
Reviewed By: gmagogsfm
Differential Revision: D33282878
fbshipit-source-id: 910f67a764519f1053a48589d1a34df69001525d
