The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] [Current PR] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [ ] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [ ] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
Differential Revision: [D36792967](https://our.internmc.facebook.com/intern/diff/D36792967/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36792967/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78712
Approved by: https://github.com/jerryzh168
Summary:
As this diff shows, currently there are a couple hundred instances of raw `noqa` in the codebase, which just ignore all errors on a given line. That isn't great, so this PR changes all existing instances of that antipattern to qualify the `noqa` with respect to a specific error code, and adds a lint to prevent more of this from happening in the future.
Interestingly, some of the examples the `noqa` lint catches are genuine attempts to qualify the `noqa` with a specific error code, such as these two:
```
test/jit/test_misc.py:27: print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
test/jit/test_misc.py:28: print(f"format blank") # noqa F541
```
However, those are still wrong because they are [missing a colon](https://flake8.pycqa.org/en/3.9.1/user/violations.html#in-line-ignoring-errors), which actually causes the error code to be completely ignored:
- If you change them to anything else, the warnings will still be suppressed.
- If you add the necessary colons then it is revealed that `E261` was also being suppressed, unintentionally:
```
test/jit/test_misc.py:27:57: E261 at least two spaces before inline comment
test/jit/test_misc.py:28:35: E261 at least two spaces before inline comment
```
I did try using [flake8-noqa](https://pypi.org/project/flake8-noqa/) instead of a custom `git grep` lint, but it didn't seem to work. This PR is definitely missing some of the functionality that flake8-noqa is supposed to provide, though, so if someone can figure out how to use it, we should do that instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56272
Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI run (before this PR was finished) failed:
- https://github.com/pytorch/pytorch/runs/2365189927
Reviewed By: janeyx99
Differential Revision: D27830127
Pulled By: samestep
fbshipit-source-id: d6dcf4f945ebd18cd76c46a07f3b408296864fcb
Summary: D24747035 (1478e5ec2a) removes the entry point of `nnq.functional.relu`. Adjust op benchmark to `torch.nn.ReLU` accordingly.
Test Plan: buck run caffe2/benchmarks/operator_benchmark/pt:qactivation_test -- --use_jit --iterations 1 --warmup_iterations 1
Reviewed By: mingzhe09088
Differential Revision: D24961625
fbshipit-source-id: 5ed0ec7fa6d8cfefc8e7fc8324cf9a2a3e59de90
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47767
This diff implements the functionality of running benchmark on mobile on top of operator_benchmark framework. It does so through a few steps:
1. create a scripted module from existing benchmark case.
2. run mobile specific optimization pass on the scripted module
3. run the scripted module on AiBench by calling its Python API
A small change in the way of writing a benchmark case is introduced so that both local and mobile run can share the same interface. The change is about having inputs as arguments of the `forward` function, so that mobile optimization pass can be run successfully (otherwise everything will be optimized away by constant propagation).
Test Plan:
## local op_bench run
buck run caffe2/benchmarks/operator_benchmark:benchmark_all_test -- --iterations 1 --warmup_iterations 1
buck run caffe2/benchmarks/operator_benchmark:benchmark_all_test -- --iterations 1 --warmup_iterations 1 --use_jit
Exceptions: `py_module` op in `FakeQuantizePerTensorBaseOpBenchmark` and `FakeQuantizePerChannelBaseOpBenchmark` under JIT mode. These tests also failed in the base version
```
RuntimeError:
Module 'FakeQuantizePerChannelOpBenchmark' has no attribute 'op_func' (This function exists as an attribute on the Python module, but we failed to compile it to a TorchScript function.
The error stack is reproduced here:
Python builtin <built-in method apply of FunctionMeta object at 0x619000c652a0> is currently not supported in Torchscript:
File "/data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/pt/quantization_test#link-tree/quantization_test.py", line 260
quant_min: int, quant_max: int
):
return _LearnableFakeQuantizePerChannelOp.apply(input, scale, zero_point, axis, quant_min, quant_max, 1.0)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
:
File "/data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/pt/quantization_test#link-tree/quantization_test.py", line 313
axis: int, quant_min: int, quant_max: int
):
return self.op_func(input, scale, zero_point, axis, quant_min, quant_max)
~~~~~~~~~~~~ <--- HERE
```
`_consume_op` typing mismatch: chunk, split, qobserver, sort in qunary. These will be fixed in D24774105
## OSS test
python3 -m benchmark_all_test --iterations 1 --warmup_iterations 1 --use_jit
python3 -m benchmark_all_test --iterations 1 --warmup_iterations 1
## saved module graph
```
module __torch__.mobile_benchmark_utils.OpBenchmarkMobile {
parameters {
}
attributes {
training = True
num_iters = 1
benchmark = <__torch__.pt.add_test.___torch_mangle_4.AddBenchmark object at 0x6070001b8b50>
}
methods {
method forward {
graph(%self : __torch__.mobile_benchmark_utils.OpBenchmarkMobile):
%12 : None = prim::Constant() # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/mobile_benchmark_utils.py:9:4
%4 : bool = prim::Constant[value=1]() # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/mobile_benchmark_utils.py:10:8
%1 : int = prim::GetAttr[name="num_iters"](%self)
= prim::Loop(%1, %4) # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/mobile_benchmark_utils.py:10:8
block0(%i : int):
%6 : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark = prim::GetAttr[name="benchmark"](%self)
%7 : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark = prim::GetAttr[name="benchmark"](%self)
%self.inputs_tuple : (Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu), Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu)) = prim::Constant[value=({0.48884}, {0.809042})]()
%9 : Tensor, %10 : Tensor = prim::TupleUnpack(%self.inputs_tuple)
%23 : int = prim::Constant[value=1]()
%24 : Tensor = aten::add(%9, %10, %23) # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/pt/add_test.py:39:15
-> (%4)
return (%12)
}
}
submodules {
module __torch__.pt.add_test.___torch_mangle_4.AddBenchmark {
parameters {
}
attributes {
mobile_optimized = True
}
methods {
method forward {
graph(%self : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark,
%input_one.1 : Tensor,
%input_two.1 : Tensor):
%3 : int = prim::Constant[value=1]()
%4 : Tensor = aten::add(%input_one.1, %input_two.1, %3) # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/pt/add_test.py:39:15
return (%4)
}
method get_inputs {
graph(%self : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark):
%self.inputs_tuple : (Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu), Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu)) = prim::Constant[value=({0.48884}, {0.809042})]()
return (%self.inputs_tuple)
}
}
submodules {
}
}
}
}
```
Reviewed By: kimishpatel
Differential Revision: D24322214
fbshipit-source-id: 335317eca4f40c4083883eb41dc47caf25cbdfd1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42756
Similar to ELU, CELU was also broken in the quantized benchmark, fixing.
Test Plan:
```
cd benchmarks/operator_benchmark
python -m pt.qactivation_test
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23010863
fbshipit-source-id: 203e63f9cff760af6809f6f345b0d222dc1e9e1b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42318
We forgot to update this benchmark when quantized elu's signature
changed to require observation, fixing.
Test Plan:
```
cd benchmarks/operator_benchmark
python -m pt.qactivation_test
```
Imported from OSS
Reviewed By: supriyar
Differential Revision: D22845251
fbshipit-source-id: 1443f6f0deac695715b1f2bd47f0f22b96dc72ca
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36980
Missed this on the original diff, fixing. Create the output tensor directly instead of quantizing it.
Test Plan:
tests still pass
microbenchmarks show a 2x performance improvment for int8:
https://gist.github.com/vkuzo/3b321b428e4c38e805000961c263286b (this
will depend on input size)
Imported from OSS
Differential Revision: D21185970
fbshipit-source-id: 5b9e93d9f9ac05a8120532bd03ad347541a132c2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35731
Changes relu and relu6 to point to the functional implementations here.
The previous behavior tested the time to create the module, but didn't actually run the
function (I noticed this when adding the new input sizes and seeing
the measured time not change).
Test Plan:
run the benchmark, the time now changes as expected with input size for
these.
Imported from OSS
Differential Revision: D20875542
fbshipit-source-id: 3a6278a7a861437d613c1e30698a58175a8e8555
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35729
* there were a few quantized activations which had implementations but not benchmarks, adds them
* adds the input sizes from `unary_tests.py` here, so we can compare fairly from fp to quantized implementations of activations
Test Plan:
```
python -m pt.qactivation_test
```
Imported from OSS
Differential Revision: D20875544
fbshipit-source-id: f55a66422233b96f0791c85b05476596d5d72b5d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34820
Adds quantized version of hardswish, for common quantized operator coverage.
Note:
* we carry over scale and zero_point from the input to the output, because the
range of the output is unbounded if x > 0
* we also skip the .out function to not allow the user to specify a custom
scale+zp (flexible on this).
Test Plan:
```
python test/test_quantized.py
https://gist.github.com/vkuzo/f9b579315ed7f5fdb24839e3218d8465
```
Imported from OSS
Differential Revision: D20472905
fbshipit-source-id: 0f2a83e9f5f7b43485fa46caf30e756dc5d492a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34959
Adds quantized implementation of hardsigmoid.
Original PR was https://github.com/pytorch/pytorch/pull/34607 and had to
be reverted for a test breakage, trying again.
Test Plan:
tests
benchmarks
Imported from OSS
Differential Revision: D20514212
fbshipit-source-id: cc7ae3b67757e2dde5c313c05ce60a0f2625d961
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34607
Adds quantized version of hardsigmoid activation.
Note: not implementing the _ and .out versions is
currently intended, because the implementation changes the scale and
zp and it's nice to not allow the user to specify scale
and zp. Lmk if we should handle this differently.
Test Plan:
tests
benchmarks
Imported from OSS
Differential Revision: D20480546
fbshipit-source-id: 9febcb44afd920125ed2ca4900492f0b712078ea
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34267
Adds quantized ELU.
Test Plan:
```
python test/test_quantized.py TestQuantizedOps.test_qelu
```
still need to benchmark, saving that for after the review comments
Imported from OSS
Differential Revision: D20370953
fbshipit-source-id: fe941bf966f72dd9eee2c4b2ef45fe7afb50c866
