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130 lines
3.1 KiB
Markdown
130 lines
3.1 KiB
Markdown
# Table Lookup
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In this tutorial, we are going to go over the ways to perform direct table lookups in **Concrete Framework**. Please read [Compiling and Executing](../basics/compiling_and_executing.md) before reading further to see how you can compile the functions below.
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## Direct table lookup
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**Concrete Framework** provides a special class to allow direct table lookups. Here is how to use it:
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```python
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import concrete.numpy as hnp
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table = hnp.LookupTable([2, 1, 3, 0])
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def f(x):
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return table[x]
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```
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where
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- `x = "encrypted"` scalar
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results in
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<!--python-test:skip-->
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```python
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circuit.run(0) == 2
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circuit.run(1) == 1
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circuit.run(2) == 3
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circuit.run(3) == 0
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```
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Moreover, direct lookup tables can be used with tensors where the same table lookup is applied to each value in the tensor, so
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- `x = "encrypted"` tensor of shape `(2, 3)`
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results in
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<!--python-test:skip-->
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```python
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input = np.array([[0, 1, 3], [2, 3, 1]], dtype=np.uint8)
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circuit.run(input) == [[2, 1, 0], [3, 0, 1]]
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```
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## Direct Multi Table Lookup
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Sometimes you may want to apply a different lookup table to each value in a tensor. That's where direct multi lookup table becomes handy. Here is how to use it:
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<!--python-test:skip-->
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```python
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import concrete.numpy as hnp
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squared = hnp.LookupTable([i ** 2 for i in range(4)])
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cubed = hnp.LookupTable([i ** 3 for i in range(4)])
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table = hnp.MultiLookupTable([
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[squared, cubed],
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[squared, cubed],
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[squared, cubed],
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])
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def f(x):
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return table[x]
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```
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where
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- `x = "encrypted"` tensor of shape `(3, 2)`
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results in
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<!--python-test:skip-->
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```python
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input = np.array([[2, 3], [1, 2], [3, 0]], dtype=np.uint8)
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circuit.run(input) == [[4, 27], [1, 8], [9, 0]]
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```
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Basically, we applied `squared` table to the first column and `cubed` to the second one.
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## Fused table lookup
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Direct tables are tedious to prepare by hand. When possible, **Concrete Framework** fuses the floating point operations into table lookups automatically. There are some limitations on fusing operations, which you can learn more about on the next tutorial, [Working With Floating Points](./working_with_floating_points.md).
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Here is an example function that results in fused table lookup:
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<!--python-test:skip-->
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```python
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def f(x):
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return 127 - (50 * (np.sin(x) + 1)).astype(np.uint32) # astype is to go back to integer world
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```
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where
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- `x = "encrypted"` scalar
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results in
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<!--python-test:skip-->
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```python
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circuit.run(0) == 77
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circuit.run(1) == 35
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circuit.run(2) == 32
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circuit.run(3) == 70
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circuit.run(4) == 115
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circuit.run(5) == 125
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circuit.run(6) == 91
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circuit.run(7) == 45
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```
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Initially, the function is converted to this operation graph
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and after floating point operations are fused, we get the following operation graph
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Internally, it uses the following lookup table
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<!--python-test:skip-->
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```python
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table = hnp.LookupTable([50, 92, 95, 57, 12, 2, 36, 82])
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```
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which is calculated by:
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<!--python-test:skip-->
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```python
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[(50 * (np.sin(x) + 1)).astype(np.uint32) for x in range(2 ** 3)]
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```
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