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artiq
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compiler: stop using deprecated numpy.float

This commit is contained in:
Sebastien Bourdeauducq 2021-06-15 10:48:34 +08:00
parent ae137d1c9e
commit 9741e4aa43
2 changed files with 6 additions and 6 deletions
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4
artiq/compiler/embedding.py
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@ -167,10 +167,10 @@ class ASTSynthesizer:
coerced = bool(value)
return asttyped.NameConstantT(value=coerced, type=typ,
loc=self._add(repr(coerced)))
elif value is numpy.float:
elif value is float:
typ = builtins.fn_float()
return asttyped.NameConstantT(value=None, type=typ,
loc=self._add("numpy.float"))
loc=self._add("float"))
elif value is numpy.int32:
typ = builtins.fn_int32()
return asttyped.NameConstantT(value=None, type=typ,

8
artiq/test/coredevice/test_numpy.py
View File

@ -72,13 +72,13 @@ class CompareHostDeviceTest(ExperimentCase):
# randomised tests instead.
# TODO: Provoke overflows, division by zero, etc., and compare results.
args = [(typ(a), typ(b)) for a, b in [(0, 1), (3, 2), (11, 6)]
for typ in [numpy.int32, numpy.int64, numpy.float]]
for typ in [numpy.int32, numpy.int64, numpy.float64]]
for op in ELEM_WISE_BINOPS:
for arg in args:
self._test_binop("a" + op + "b", *arg)
def test_scalar_matrix_binops(self):
for typ in [numpy.int32, numpy.int64, numpy.float]:
for typ in [numpy.int32, numpy.int64, numpy.float64]:
scalar = typ(3)
matrix = numpy.array([[4, 5, 6], [7, 8, 9]], dtype=typ)
for op in ELEM_WISE_BINOPS:
@ -88,7 +88,7 @@ class CompareHostDeviceTest(ExperimentCase):
self._test_binop(code, matrix, matrix)
def test_matrix_mult(self):
for typ in [numpy.int32, numpy.int64, numpy.float]:
for typ in [numpy.int32, numpy.int64, numpy.float64]:
mat_a = numpy.array([[1, 2, 3], [4, 5, 6]], dtype=typ)
mat_b = numpy.array([[7, 8], [9, 10], [11, 12]], dtype=typ)
self._test_binop("a @ b", mat_a, mat_b)
@ -139,7 +139,7 @@ class _MatrixMult(EnvExperiment):
def build(self):
self.setattr_device("core")
self.imat = numpy.arange(4, dtype=numpy.int64).reshape((2, 2))
self.fmat = numpy.arange(4, dtype=numpy.float).reshape((2, 2))
self.fmat = numpy.arange(4, dtype=numpy.float64).reshape((2, 2))
@kernel
def run(self):