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92 lines
3.1 KiB
Markdown
92 lines
3.1 KiB
Markdown
```{warning}
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FIXME(all): I've moved this section (from how-to) to the basic section, because it must be read before the tutorials
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```
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# Compiling and Executing
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## Importing necessary components
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Everything you need to compile and execute homomorphic functions is included in a single module. You can import it like so:
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```python
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import concrete.numpy as hnp
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```
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## Defining a function to compile
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You need to have a python function that follows the [limits](../explanation/FHE_AND_FRAMEWORK_LIMITS.md) of the **Concrete Framework**. Here is a simple example:
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<!--python-test:cont-->
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```python
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def f(x, y):
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return x + y
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```
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## Compiling the function
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```{warning}
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FIXME(arthur): move to the new API
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FIXME(all): do you think guys we should just explain the new API or should we explain the two API's? I would say: just the new one, and maybe the old one in the dev section (but not ever sure).
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```
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To compile the function, you need to provide what are the inputs that it's expecting. In the example function above, `x` and `y` could be scalars or tensors (though, for now, only dot between tensors are supported), they can be encrypted or clear, they can be signed or unsigned, they can have different bit-widths. So, we need to know what they are beforehand. We can do that like so:
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<!--python-test:cont-->
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```python
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x = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
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y = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
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```
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In this configuration, both `x` and `y` are 3-bit unsigned integers, so they have the range of `[0, 2**3 - 1]`
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We also need an inputset. It is to determine the bit-widths of the intermediate results. It should be an iterable yielding tuples in the same order as the inputs of the function to compile. There should be at least 10 inputs in the input set to avoid warnings (except for functions with less than 10 possible inputs). The warning is there because the bigger the input set, the better the bounds will be.
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<!--python-test:cont-->
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```python
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inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1), (3, 2), (6, 1), (1, 7), (4, 5), (5, 4)]
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```
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Finally, we can compile our function to its homomorphic equivalent.
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<!--python-test:cont-->
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```python
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circuit = hnp.compile_numpy_function(
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f, {"x": x, "y": y},
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inputset=inputset,
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)
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```
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## Performing homomorphic evaluation
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You can use `.run(...)` method of `FHECircuit` returned by `hnp.compile_numpy_function(...)` to perform fully homomorphic evaluation. Here are some examples:
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<!--python-test:cont-->
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```python
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circuit.run(3, 4)
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# 7
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circuit.run(1, 2)
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# 3
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circuit.run(7, 7)
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# 14
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circuit.run(0, 0)
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# 0
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```
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```{caution}
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Be careful about the inputs, though.
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If you were to run with values outside the range of the inputset, the result might not be correct.
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```
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```{warning}
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FIXME(benoit): explain the API to encrypt, run_inference, decrypt, keygen etc when they are available
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```
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## Further reading
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- [Arithmetic Operations Tutorial](../tutorial/ARITHMETIC_OPERATIONS.md)
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- [Working With Floating Points Tutorial](../tutorial/WORKING_WITH_FLOATING_POINTS.md)
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- [Table Lookup Tutorial](../tutorial/TABLE_LOOKUP.md)
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- [Compiling a torch model](../tutorial/COMPILING_TORCH_MODEL.md)
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