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
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/24136
This diff aims to reduce the execution of benchmark_all_test which runs all the supported operator benchmarks. In the default run, only one shape of each operator will be benchmarked. The rest of the benchmarks can be triggered with tag_filter flag.
Reviewed By: hl475
Differential Revision: D16736448
fbshipit-source-id: 33bd86f6fc2610f87f24240ad559fb11d3e35e89
