101 lines
1.7 KiB
Python
101 lines
1.7 KiB
Python
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"""
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Usecases of recursive functions in the CUDA target, many derived from
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numba/tests/recursion_usecases.py.
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Some functions are compiled at import time, hence a separate module.
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"""
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from numba import cuda
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@cuda.jit("i8(i8)", device=True)
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def fib1(n):
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if n < 2:
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return n
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# Note the second call does not use a named argument, unlike the CPU target
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# usecase
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return fib1(n - 1) + fib1(n - 2)
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def make_fib2():
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@cuda.jit("i8(i8)", device=True)
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def fib2(n):
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if n < 2:
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return n
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return fib2(n - 1) + fib2(n - 2)
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return fib2
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fib2 = make_fib2()
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@cuda.jit
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def type_change_self(x, y):
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if x > 1 and y > 0:
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return x + type_change_self(x - y, y)
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else:
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return y
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# Implicit signature
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@cuda.jit(device=True)
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def fib3(n):
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if n < 2:
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return n
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return fib3(n - 1) + fib3(n - 2)
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# Run-away self recursion
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@cuda.jit(device=True)
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def runaway_self(x):
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return runaway_self(x)
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@cuda.jit(device=True)
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def raise_self(x):
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if x == 1:
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raise ValueError("raise_self")
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elif x > 0:
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return raise_self(x - 1)
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else:
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return 1
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@cuda.jit(debug=True, opt=False)
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def raise_self_kernel(x):
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raise_self(x)
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def make_optional_return_case(jit=lambda x: x):
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@jit
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def foo(x):
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if x > 5:
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return x - 1
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else:
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return
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@jit
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def bar(x):
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out = foo(x)
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if out is None:
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return out
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elif out < 8:
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return out
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else:
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return x * bar(out)
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return bar
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def make_growing_tuple_case(jit=lambda x: x):
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# From issue #4387
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@jit
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def make_list(n):
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if n <= 0:
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return None
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return (n, make_list(n - 1))
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return make_list
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