Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72889
The script along with the GRAPH_EXPORT macro will allow for an easy way to extract IR from logs. One use case in this diff is to extract the fusion groups from nvfuser, so that the fusions can be tested individually.
Usage (e.g. for nvfuser test)
1. Write some test.py file that uses nvfuser
2. `PYTORCH_JIT_LOG_LEVEL=">>graph_fuser" python3 test.py 2>&1 | tee output.txt`
3. `python3 pytorch/scripts/jit/log_extract.py output.txt --nvfuser`
This will run with and without nvfuser to compare the output.
Alternatively, use `--output` to dump the IR so that it can be used in other applications.
Currently, only `--output` works (since generating input tensors is not supported)
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D34440189
Pulled By: davidberard98
fbshipit-source-id: fca0f619200ee37aba34bb39b69e6c640c263e26
(cherry picked from commit eb319166075db160f1628f0de545641fbecde8be)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73329
There is a quantization use case for having better alias analysis with function calls remaining. This does the relatively dumb approach of getting the inlined graph of each function call, and then analyzing that subgraph. Since we need a unique single analysis of every `Value*`, for every function call make a copy of the graph for every analysis past the first. This is relatively slow, but given the limited use case here should work well enough (and is no slower than calling the inlining pass).
cc vkuzo
Test Plan: Imported from OSS
Reviewed By: davidberard98
Differential Revision: D34451424
Pulled By: eellison
fbshipit-source-id: b7c7e54679d723f5ded1e11ffb32eb6d2176431d
(cherry picked from commit 81a42b31522b890311a3f512448b372c4ebbefd1)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68491
* Allows implementing symbolic functions for domains other than `aten`, for example `prim`, in symbolic_opset#.py.
* Allows symbolic function to access extra context if needed, through `SymbolicFunctionState`.
* Particularly, the `prim::PythonOp` special case can access node without the need of passing node through inputs. Updates will be made downstreams, and in a follow-up PR we will remove the previous workaround in exporter.
* `prim::Loop`, `prim::If`, etc are now moved outside of `_run_symbolic_function` from utils.py, and to symbolic_opset9.py.
Motivation for this change:
- Better maintainability and reducing complexity. Easier to add symbolic for operators, both simple and complex ones (that need additional context), without the former needing to know the existence of the latter.
- The design idea was long outdated. prim ops are no longer rare special cases, and they shouldn't all be handled inside `_run_symbolic_function`. As a result this function becomes too clumsy. There were also prim ops symbolic added in symbolic_opset#.py with signature `prim_[opname]`, creating separation and confusion.
Test Plan: Imported from OSS
Reviewed By: jansel
Differential Revision: D32483782
Pulled By: malfet
fbshipit-source-id: f9affc31b1570af30ffa6668da9375da111fd54a
Co-authored-by: BowenBao <bowbao@microsoft.com>
(cherry picked from commit 1e04ffd2fd)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71651
The only tests that regress are because chunk NYI, the other tests that I touched were passing just because the `assertAllFused` wasn't working correctly. That, and we're no longer compiling conv/matmul w dynamic shapes
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D33801500
Pulled By: eellison
fbshipit-source-id: 074118ab4a975b7db876a4fcdfb9483afb879e79
(cherry picked from commit abaa7948c1)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69645
As noted in code comment:
existing device operator is registered with input name `a`, which prevents torch.device(type="cuda") from working. add shim-layer here
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D33515231
Pulled By: eellison
fbshipit-source-id: c04af8158a9568a20cd5fbbbd573f6efab98fd60
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69579
This should help us avoid reference counting overhead on singleton Type subclasses without a major rewrite of the Type subsystem.
ghstack-source-id: 146643993
Test Plan:
Ran //caffe2/caffe2/fb/high_perf_models/pytorch/benchmark_framework_overheads:cpp_benchmark with arguments `--op empty -niter 40 --stressTestRecordFunction --captureRecordFunctionInputs` on devbig with turbo off.
Before:
```
I1206 13:47:15.037441 1201670 bench.cpp:144] Mean 0.737675
I1206 13:47:15.037463 1201670 bench.cpp:145] Median 0.736725
I1206 13:47:15.037468 1201670 bench.cpp:146] Min 0.722897
I1206 13:47:15.037473 1201670 bench.cpp:147] stddev 0.00508187
I1206 13:47:15.037482 1201670 bench.cpp:148] stddev / mean 0.00688903
```
After:
```
I1206 13:48:16.830123 1205612 bench.cpp:144] Mean 0.66988
I1206 13:48:16.830150 1205612 bench.cpp:145] Median 0.663956
I1206 13:48:16.830157 1205612 bench.cpp:146] Min 0.65986
I1206 13:48:16.830164 1205612 bench.cpp:147] stddev 0.0335928
I1206 13:48:16.830171 1205612 bench.cpp:148] stddev / mean 0.0501475
```
Static runtime startup is also improved; for CMF local_ro, time to initialize a predictor went from 10.01s to 9.59s.
(Note: I wish I had a production workload to demonstrate the advantage of this on. I tried ctr_mobile_feed local_ro net but it was neutral. Anything that manipulates types or List/Dict a lot might be promising.)
Reviewed By: suo
Differential Revision: D32923880
fbshipit-source-id: c82ed6689b3598e61047fbcb2149982173127ff0
Summary:
...because we don't like segfaults from Python (see test).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68253
Reviewed By: suo
Differential Revision: D32396747
Pulled By: gmagogsfm
fbshipit-source-id: a0925e8479702766e88176280985a63bc79e4f6a
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:
Some of the "no-ops" are not actually no-ops because they can change the dtype
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67688
Reviewed By: davidberard98
Differential Revision: D32104601
Pulled By: eellison
fbshipit-source-id: ccb99179a4b30fd20b5a9228374584f2cdc8ec21
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:
This PR is created to replace https://github.com/pytorch/pytorch/pull/53180 PR stack, which has all the review discussions. Reason for needing a replacement is due to a messy Sandcastle issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64234
Reviewed By: gmagogsfm
Differential Revision: D30656444
Pulled By: ansley
fbshipit-source-id: 77536c8bcc88162e2c72636026ca3c16891d669a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59814
Using opinfos to test shape analysis. By default, we just check that we don't give incorrect answers, and then if `assert_jit_shape_analysis` is true, tests that we correctly propagates the full shape. and it found a couple bugs {emoji:1f603}
Test Plan: Imported from OSS
Reviewed By: Krovatkin
Differential Revision: D30200058
Pulled By: eellison
fbshipit-source-id: 6226be87f5390277cfa5a1fffaa1b072d4bc8803
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61791
methods from forward
During inlining we attached InlinedCallstack to nodes being inlined. In
the process we attach moodule information as well, such that if
CallMethod is being inlined we know which class instance and class type
the method belongs to. However, CallMethod can be calling a method of
the same object to which the graph belongs. e.g.:
```
def forward(self, input):
x = input + 10
return forward_impl_(x, input)
```
Here forward_impl is method defined on the same class in which forward
is defined. Existing module hierarchy annotation will mislabel this as
unknown instance since the method is not associated with output of
GetAttr node (it would be we had called self.conv.forward_impl_ for
example).
Change in this PR reconciles this by creating a placeholder name "SELF"
for module instance indicating that you can traverse InlinedCallStack
backwards to find first node with name != SELF, which would be the name
of the object.
e.g.:
TOP(ResNet)::forward.SELF(ResNet)::_forward_impl.layer1(Sequential)::forward.0(BasicBlock)::forward.conv1(Conv2d)::forward.SELF(Conv2d)::_conv_forward
Test Plan:
Add test
Imported from OSS
Reviewed By: larryliu0820
Differential Revision: D29745443
fbshipit-source-id: 1525e41df53913341c4c36a56772454782a0ba93
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60244
Do 2GB size check for protocol buffer serialization at a later time, to avoid false alarming for cases like shape inference where no serialization actually happens.
Test Plan: Imported from OSS
Reviewed By: zou3519, ZolotukhinM
Differential Revision: D29494910
Pulled By: SplitInfinity
fbshipit-source-id: 4c36d26de9a94e5d6cf78f332d4dffc46588ebf0
Co-authored-by: BowenBao <bowbao@microsoft.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60384
Currently inlining module graph will drop module hierarchy info on Python side. Here we retrieve the module hierarchy from cpp side and expose it to a new Python API on Node called `moduleHierarchy()`.
Test Plan:
Usage:
```
torch._C._jit_pass_inline(module.graph)
torch._C._jit_pass_propagate_shapes_on_graph(module.graph)
node = module.graph.findNode("quantized::conv2d_relu")
'top(' + module.original_name + ').' + node.moduleHierarchy() + '.' + node.kind()
```
Output:
```
'top(QuantWrapper).module(FBNetHR).0(Sequential).xif0_0(ConvBNRelu).conv(ConvReLU2d).quantized::conv2d_relu'
```
Reviewed By: kimishpatel
Differential Revision: D29252169
fbshipit-source-id: 74163a87f919e061e5e75dfebc4c5cdbe8489d93
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55925
This sets up the initial handling of symbolic shapes. As in the test, it doesn't work perfectly yet because it needs a couple other optimization passes. The basic description is pretty simple: we resolve tensor dimension indices to the same Value *, and before extracting out the output Tensor shape we substitute in symbolic shapes. We don't substitute during optimization because they are represented as negative numbers so we don't want them inadvertently used in Constant prop or something else.
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D27750996
Pulled By: eellison
fbshipit-source-id: 6984e7276b578f96b00fc2025cef0e13f594b6e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54809
I'm going to post on dev-discuss soon with a more thorough explanation of the design and advantages of this shape analysis, so I'm leaving out that for now.
There is still a ton left to do, I'm posting this initial version so we can get something on master multiple can work on. List of many remaining steps to do:
- [ ] Add symbolic shapes support
- [ ] Bind shape functions for operators in C++
- [ ] Make classes of operators share the same shape function (e.g. pointwise, broadcast two inputs)
- [ ] Refactor APIs
- [ ] Only iteratively optimize shape function while a change has been made
- [ ] Expand coverage of coverage to common ops
- [ ] Add shape analysis pass on Graph that handles Ifs and Loops
- [ ] Allow concurrent reads to the operator map
- [ ] Successive applications of same inputs to same shape function (e.g. series of pointwise ops)
For this review, I am mostly looking for comments related to the implementation of symolic_shape_analysis.cpp, with the caveats listed above. I am not really looking for comments related to api/registration/graph level analysis as those are all planned to be changed. I am fine landing this as is or waiting until necessary components of the TODOs above are finished.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D27750998
Pulled By: eellison
fbshipit-source-id: 4338b99e8651df076291c6b781c0e36a1bcbec03
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54915
TorchScript and torch.package have different mangling schemes. To avoid
them interfering with each other, we should undo the torch.package
mangling before processing anything with TorchScript (since TS
independently makes sure that no names collide).
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D27410472
Pulled By: suo
fbshipit-source-id: d1cc013c532d9abb7fb9615122bc465ded4785bb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52881
**This PR adds:**
1. logic to parse complex constants (complex literals of the form `bj`)
2. logic to parse complex lists
3. support for complex constructors: `complex(tensor/int/float/bool, tensor/int/float/bool)`
4. Limited operator support
- `add`, `sub`, `mul`, `torch.tensor`, `torch.as_tensor`
**Follow-up work:**
1. Add complex support for unary and other registered ops.
2. support complex constructor with string as input (this is supported in Python eager mode).
3. Test all emitXYZ for all XYZ in `ir_emitter.cpp` (currently only emitConst, emitValueToTensor are tested). e.g., test loops etc.
4. onnx doesn't support complex tensors, so we should error out with a clear and descriptive error message.
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D27245059
Pulled By: anjali411
fbshipit-source-id: af043b5159ae99a9cc8691b5a8401503fa8d6f05
Summary:
Previously, the graph might have been delete while Python still has iterators, leading to segfaults.
This does not fully work for iterators from Nodes and Blocks as they may be invalidated when the owning graph goes out of scope. I will look into these separately.
Fixes https://github.com/pytorch/pytorch/issues/50454
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51951
Reviewed By: mrshenli
Differential Revision: D26352629
Pulled By: SplitInfinity
fbshipit-source-id: 67299b6cbf1ac7ab77f8703a0ca8f1162e03fcd4
Summary:
Previously we might have gotten segfaults and all, now it raises an exception.
Thread safety hasn't been an objective.
I have a followup to expand the Python interface for the API.
Fixes https://github.com/pytorch/pytorch/issues/49969.
wanchaol
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50326
Reviewed By: pbelevich
Differential Revision: D26096234
Pulled By: gmagogsfm
fbshipit-source-id: 5425772002eb4deb3830ed51eaa3964f22505840
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50870
**Summary**
Module attributes whose types cannot be determined based on annotations
or inference based on their values at script time are added to the
concrete type of the corresponding module as "failed attributes". Any
attempt to access them in scripted code produces an error with a message
explaining that the attribute could not be contributed to a
corresponding attribute on the TorchScript module. However, this error
is not more specific than that.
This commit modifies `infer_type` in `_recursive.py` so that it returns
`c10::InferredType` instead, which allows more information about typing
failures to be communicated to the caller through the `reason()` method
on this class. This information is appended to the hint added to the
module concrete type for failed attributes.
**Testing**
This commit adds a unit test to `test_module_containers.py` that checks
that extra information is provided about the reason for the failure
when a module attribute consisting of a list of `torch.nn.Module` fails to convert.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26091472
Pulled By: SplitInfinity
fbshipit-source-id: fcad6588b937520f250587f3d9e005662eb9af0d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50228
`fastmod -m 'expect(<((at|c10)::)?\w+Type>\(\)\s*)->'
'expectRef${1}.'`
Presuming it builds, this is a safe change: the result of `expect()`
wasn't being saved anywhere, so we didn't need it, so we can take a
reference instead of a new `shared_ptr`.
ghstack-source-id: 119782961
Test Plan: CI
Reviewed By: SplitInfinity
Differential Revision: D25837374
fbshipit-source-id: 86757b70b1520e3dbaa141001e7976400cdd3b08
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50074
Adds Conv-BN fusion for models that have been frozen. I haven't explicitly tested perf yet but it should be equivalent to the results from Chillee's PR [here](https://github.com/pytorch/pytorch/pull/476570) and [here](https://github.com/pytorch/pytorch/pull/47657#issuecomment-725752765). Click on the PR for details but it's a good speed up.
In a later PR in the stack I plan on making this optimization on by default as part of `torch.jit.freeze`. I will also in a later PR add a peephole so that there is not conv->batchnorm2d doesn't generate a conditional checking # dims.
Zino was working on freezing and left the team, so not really sure who should be reviewing this, but I dont care too much so long as I get a review �
Test Plan: Imported from OSS
Reviewed By: tugsbayasgalan
Differential Revision: D25856261
Pulled By: eellison
fbshipit-source-id: da58c4ad97506a09a5c3a15e41aa92bdd7e9a197
Summary:
`isCompleteTensor()` only returns true when both scalar type and shape is present. All dimensions in the shape must be static. This high requirement is unnecessary for many use cases such as when only rank or scalar type needs to be known.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48162
Reviewed By: malfet
Differential Revision: D25340823
Pulled By: bzinodev
fbshipit-source-id: 1fef61f44918f4339dd6654fb725b18cd58d99cf
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46779
Test Plan: Used it in some notebooks.
Reviewed By: suo
Differential Revision: D24574005
Pulled By: dreiss
fbshipit-source-id: 78ba7a2bdb859fef5633212b73c7a3eb2cfbc380
