wehub-resource-sync
120 lines
3.9 KiB
Markdown
120 lines
3.9 KiB
Markdown
# Contributing guidelines
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## Style
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#### C++ coding style
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Follow [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html), with max-line-length extended to 120.
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Run `cpplint` before committing code.
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#### Python coding style
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Follow [Google Python Style Guide,](https://google.github.io/styleguide/pyguide.html) with max-line-length extended to 120. Exceptions are allowed if it feels more natural to follow Pytorch style. For example, Pytorch allows import relative path, also class name.
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Run `pylint` before committing code. It doesn't mean every issue has to be corrected nor check has to be manually disabled. Just make sure you are aware of the remaining issues and you are comfort with all of them. But, don't leave lint error there. Disable it explicitly if it is not a real error.
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Install `pylint`
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```bash
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pip install pylint
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```
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To check a file with `pylint`:
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```bash
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pylint --rcfile=.pylintrc myfile.py
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```
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#### Yapf
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[yapf](https://github.com/google/yapf/) is an auto format tool owned by Google (not a Google product). To save the time of arguing code style during code review, use yapf to format the code is a good option. Note that it doesn't reformat comment.
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Install `yapf`
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```bash
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pip install yapf
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```
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Format code with yapf
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```bash
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yapf myfile.py --style .style.yapf
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```
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There are Sublime and Vim plugins.
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## Test
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Use [googletest](https://github.com/google/googletest) for c++ code.
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Use [pytest](https://docs.pytest.org/en/latest/) for python code.
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To run all the tests:
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```shell
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pytest --verbose
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```
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To run a particular test file
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```shell
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pytest --verbose mytestfile.py
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```
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## Conventions
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Quantization is a very overloaded word, many things related to it can create a lot of confusions. Let's try to avoid confusions as much as possible by following existing conventions. Generally, if there is a similar Tensorflow or numpy function, follow its convention. Though Tensorflow uses `quantized`, `quantization` and `quant`, let's stick with the shortest one only.
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### Naming
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##### Function and class name
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When developing quantized version of a function or module, add`Quant` to class name, add `quant_` to function name, e.g.
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```python
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class Linear(...)
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class QuantLinear(...)
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def linear(...)
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def quant_linear(...)
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```
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##### Variable name
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Add prefix `quant_mode_`, `num_bits_` etc. to name of tensors will be quantized, e.g.
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```python
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def matmul(a, b)
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def quant_matmul(a, b)
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```
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Don't use prefix/suffix `weight` or `act` if tensor being quantized doesn't have them explicitly in name. From function's perspective, it takes tensors, not necessarily weight and activation tensors. e.g. `a` and `b` of `matmul` can both be either weight or activation.
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##### Quantization mode
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There only convention here we can adopt is `per_channel`. Other things, like there is no convention to follow of per row/column scale of matrix multiply. And though we usually absolute max value based scaling factor, there are other ways to decide it, like KL-divergence.
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Our API design is flexible enough to support any granularity of quantization. The main concept is `axis`.
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```python
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# axis=None means per tensor
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# For 2d convolution weight with layout KCRS, axis=(1, 2, 3) means perchannel quantization
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# more example below
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QUANT_DESC_8BIT_PER_TENSOR = QuantDescriptor(num_bits=8)
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QUANT_DESC_UNSIGNED_8BIT_PER_TENSOR = QuantDescriptor(num_bits=8, unsigned=True)
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QUANT_DESC_8BIT_CONV1D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0))
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QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0))
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QUANT_DESC_8BIT_CONV3D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0))
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QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW = QuantDescriptor(num_bits=8, axis=(0))
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QUANT_DESC_8BIT_CONVTRANSPOSE1D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1))
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QUANT_DESC_8BIT_CONVTRANSPOSE2D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1))
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QUANT_DESC_8BIT_CONVTRANSPOSE3D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1))
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```
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### Misc
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