mirror of https://github.com/m-labs/artiq.git
test: remove test_numpy (NAC3TODO)
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from artiq.experiment import *
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import numpy
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import scipy.special
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from artiq.test.hardware_testbench import ExperimentCase
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from artiq.compiler.targets import CortexA9Target
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from artiq.compiler import math_fns
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class _RunOnDevice(EnvExperiment):
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def build(self):
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self.setattr_device("core")
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@kernel
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def run_on_kernel_unary(self, a, callback, numpy, scipy):
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self.run(a, callback, numpy, scipy)
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@kernel
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def run_on_kernel_binary(self, a, b, callback, numpy, scipy):
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self.run(a, b, callback, numpy, scipy)
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# Binary operations supported for scalars and arrays of any dimension, including
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# broadcasting.
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ELEM_WISE_BINOPS = ["+", "*", "//", "%", "**", "-", "/"]
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class CompareHostDeviceTest(ExperimentCase):
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def _test_binop(self, op, a, b):
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exp = self.create(_RunOnDevice)
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exp.run = kernel_from_string(["a", "b", "callback", "numpy", "scipy"],
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"callback(" + op + ")",
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decorator=portable)
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checked = False
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def with_host_result(host):
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def with_both_results(device):
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nonlocal checked
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checked = True
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self.assertTrue(
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numpy.allclose(host, device, equal_nan=True),
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"Discrepancy in binop test for '{}': Expexcted ({}, {}) -> {}, got {}"
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.format(op, a, b, host, device))
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exp.run_on_kernel_binary(a, b, with_both_results, numpy, scipy)
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exp.run(a, b, with_host_result, numpy, scipy)
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self.assertTrue(checked, "Test did not run")
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def _test_unaryop(self, op, a):
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exp = self.create(_RunOnDevice)
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exp.run = kernel_from_string(["a", "callback", "numpy", "scipy"],
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"callback(" + op + ")",
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decorator=portable)
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checked = False
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def with_host_result(host):
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def with_both_results(device):
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nonlocal checked
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checked = True
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self.assertTrue(
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numpy.allclose(host, device, equal_nan=True),
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"Discrepancy in unaryop test for '{}': Expexcted {} -> {}, got {}"
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.format(op, a, host, device))
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exp.run_on_kernel_unary(a, with_both_results, numpy, scipy)
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exp.run(a, with_host_result, numpy, scipy)
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self.assertTrue(checked, "Test did not run")
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def test_scalar_scalar_binops(self):
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# Some arbitrarily chosen arguments of different types. Could be turned into
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# randomised tests instead.
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# TODO: Provoke overflows, division by zero, etc., and compare results.
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args = [(typ(a), typ(b)) for a, b in [(0, 1), (3, 2), (11, 6)]
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for typ in [numpy.int32, numpy.int64, numpy.float64]]
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for op in ELEM_WISE_BINOPS:
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for arg in args:
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self._test_binop("a" + op + "b", *arg)
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def test_scalar_matrix_binops(self):
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for typ in [numpy.int32, numpy.int64, numpy.float64]:
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scalar = typ(3)
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matrix = numpy.array([[4, 5, 6], [7, 8, 9]], dtype=typ)
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for op in ELEM_WISE_BINOPS:
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code = "a" + op + "b"
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self._test_binop(code, scalar, matrix)
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self._test_binop(code, matrix, scalar)
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self._test_binop(code, matrix, matrix)
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def test_matrix_mult(self):
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for typ in [numpy.int32, numpy.int64, numpy.float64]:
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mat_a = numpy.array([[1, 2, 3], [4, 5, 6]], dtype=typ)
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mat_b = numpy.array([[7, 8], [9, 10], [11, 12]], dtype=typ)
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self._test_binop("a @ b", mat_a, mat_b)
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def test_unary_math_fns(self):
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names = [
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a for a, _ in math_fns.unary_fp_intrinsics + math_fns.unary_fp_runtime_calls
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]
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exp = self.create(_RunOnDevice)
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if exp.core.target_cls != CortexA9Target:
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names.remove("exp2")
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names.remove("log2")
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names.remove("trunc")
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for name in names:
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op = "numpy.{}(a)".format(name)
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# Avoid 0.5, as numpy.rint's rounding mode currently doesn't match.
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self._test_unaryop(op, 0.51)
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self._test_unaryop(op, numpy.array([[0.3, 0.4], [0.51, 0.6]]))
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def test_unary_scipy_fns(self):
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names = [name for name, _ in math_fns.scipy_special_unary_runtime_calls]
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if self.create(_RunOnDevice).core.target_cls != CortexA9Target:
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names.remove("gamma")
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for name in names:
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op = "scipy.special.{}(a)".format(name)
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self._test_unaryop(op, 0.5)
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self._test_unaryop(op, numpy.array([[0.3, 0.4], [0.5, 0.6]]))
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def test_binary_math_fns(self):
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names = [name for name, _ in math_fns.binary_fp_runtime_calls]
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exp = self.create(_RunOnDevice)
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if exp.core.target_cls != CortexA9Target:
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names.remove("fmax")
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names.remove("fmin")
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for name in names:
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code = "numpy.{}(a, b)".format(name)
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# Avoid 0.5, as numpy.rint's rounding mode currently doesn't match.
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self._test_binop(code, 1.0, 2.0)
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self._test_binop(code, 1.0, numpy.array([2.0, 3.0]))
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self._test_binop(code, numpy.array([1.0, 2.0]), 3.0)
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self._test_binop(code, numpy.array([1.0, 2.0]), numpy.array([3.0, 4.0]))
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class _MatrixMult(EnvExperiment):
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"""Regression test for GitHub #1578 (ICE when mixing different matrix multiplication
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types in one kernel).
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"""
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def build(self):
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self.setattr_device("core")
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self.imat = numpy.arange(4, dtype=numpy.int64).reshape((2, 2))
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self.fmat = numpy.arange(4, dtype=numpy.float64).reshape((2, 2))
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@kernel
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def run(self):
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self.verify(self.imat, self.imat, self.imat @ self.imat)
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self.verify(self.imat, self.fmat, self.imat @ self.fmat)
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self.verify(self.fmat, self.imat, self.fmat @ self.imat)
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self.verify(self.fmat, self.fmat, self.fmat @ self.fmat)
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def verify(self, a, b, ab):
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if not numpy.allclose(a @ b, ab):
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raise ValueError("Mismatch for {} @ {}", a, b)
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class TestMatrixMult(ExperimentCase):
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def test_multiple_matrix_mult_types(self):
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self.create(_MatrixMult).run()
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