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""" Tests that check if JIT-compiled numpy operations produce reasonably
good assembler
"""
import py
from rpython.jit.metainterp.test.support import LLJitMixin
from rpython.jit.backend.x86.test.test_basic import Jit386Mixin
from rpython.jit.metainterp.warmspot import reset_jit, get_stats
from rpython.jit.metainterp.jitprof import Profiler
from rpython.jit.metainterp import counter
from rpython.rlib.jit import Counters
from rpython.rlib.rarithmetic import intmask
from pypy.module.micronumpy import boxes
from pypy.module.micronumpy.compile import FakeSpace, Parser, InterpreterState
from pypy.module.micronumpy.base import W_NDimArray
from rpython.jit.backend.detect_cpu import getcpuclass
CPU = getcpuclass()
if not CPU.vector_ext:
py.test.skip("this cpu %s has no implemented vector backend" % CPU)
def get_profiler():
from rpython.jit.metainterp import pyjitpl
return pyjitpl._warmrunnerdesc.metainterp_sd.profiler
class TestNumpyJit(LLJitMixin):
enable_opts = "intbounds:rewrite:virtualize:string:earlyforce:pure:heap:unroll"
graph = None
interp = None
def setup_method(self, method):
if not self.CPUClass.vector_ext:
py.test.skip("needs vector extension to run (for now)")
def assert_float_equal(self, f1, f2, delta=0.0001):
assert abs(f1-f2) < delta
def setup_class(cls):
default = """
a = [1,2,3,4]
z = (1, 2)
c = a + b
sum(c) -> 1::1
a -> 3:1:2
"""
d = {}
p = Parser()
allcodes = [p.parse(default)]
for name, meth in cls.__dict__.iteritems():
if name.startswith("define_"):
code = meth()
d[name[len("define_"):]] = len(allcodes)
allcodes.append(p.parse(code))
cls.code_mapping = d
cls.codes = allcodes
def compile_graph(self):
if self.graph is not None:
return
space = FakeSpace()
codes = self.codes
def f(i):
interp = InterpreterState(codes[i])
interp.run(space)
if not len(interp.results):
raise Exception("need results")
w_res = interp.results[-1]
if isinstance(w_res, W_NDimArray):
i, s = w_res.create_iter()
w_res = i.getitem(s)
if isinstance(w_res, boxes.W_Float64Box):
return w_res.value
if isinstance(w_res, boxes.W_Float32Box):
return float(w_res.value)
elif isinstance(w_res, boxes.W_Int64Box):
return float(w_res.value)
elif isinstance(w_res, boxes.W_Int32Box):
return float(int(w_res.value))
elif isinstance(w_res, boxes.W_Int16Box):
return float(int(w_res.value))
elif isinstance(w_res, boxes.W_Int8Box):
return float(int(w_res.value))
elif isinstance(w_res, boxes.W_UInt64Box):
return float(intmask(w_res.value))
elif isinstance(w_res, boxes.W_UInt32Box):
return float(intmask(w_res.value))
elif isinstance(w_res, boxes.W_UInt16Box):
return float(intmask(w_res.value))
elif isinstance(w_res, boxes.W_UInt8Box):
return float(intmask(w_res.value))
elif isinstance(w_res, boxes.W_LongBox):
return float(w_res.value)
elif isinstance(w_res, boxes.W_BoolBox):
return float(w_res.value)
print "ERROR: did not implement return type for interpreter"
raise TypeError(w_res)
if self.graph is None:
interp, graph = self.meta_interp(f, [0],
listops=True,
listcomp=True,
backendopt=True,
graph_and_interp_only=True,
ProfilerClass=Profiler,
vec=True)
self.__class__.interp = interp
self.__class__.graph = graph
def check_vectorized(self, expected_tried, expected_success):
profiler = get_profiler()
tried = profiler.get_counter(Counters.OPT_VECTORIZE_TRY)
success = profiler.get_counter(Counters.OPT_VECTORIZED)
assert tried >= success
assert tried == expected_tried
assert success == expected_success
def run(self, name):
self.compile_graph()
profiler = get_profiler()
profiler.start()
reset_jit()
i = self.code_mapping[name]
retval = self.interp.eval_graph(self.graph, [i])
return retval
def define_float32_copy():
return """
a = astype(|30|, float32)
x1 = a -> 7
x2 = a -> 8
x3 = a -> 9
x4 = a -> 10
r = x1 + x2 + x3 + x4
r
"""
def test_float32_copy(self):
result = self.run("float32_copy")
assert int(result) == 7+8+9+10
self.check_vectorized(1, 1)
def define_int32_copy():
return """
a = astype(|30|, int32)
x1 = a -> 7
x2 = a -> 8
x3 = a -> 9
x4 = a -> 10
x1 + x2 + x3 + x4
"""
def test_int32_copy(self):
result = self.run("int32_copy")
assert int(result) == 7+8+9+10
self.check_vectorized(1, 1)
def define_float32_add():
return """
a = astype(|30|, float32)
b = a + a
b -> 15
"""
def test_float32_add(self):
result = self.run("float32_add")
self.assert_float_equal(result, 15.0 + 15.0)
self.check_vectorized(2, 2)
def define_float_add():
return """
a = |30|
b = a + a
b -> 17
"""
def test_float_add(self):
result = self.run("float_add")
self.assert_float_equal(result, 17.0 + 17.0)
self.check_vectorized(1, 1)
def define_uint_add():
return """
a = astype(|30|, uint64)
b = a + a
b -> 17
"""
def test_uint_add(self):
result = self.run("uint_add")
assert int(result) == 17+17
self.check_vectorized(2, 1)
def define_float32_add_const():
return """
a = astype(|30|, float32)
b = a + 77.345
b -> 29
"""
def test_float32_add_const(self):
result = self.run("float32_add_const")
self.assert_float_equal(result, 29.0 + 77.345)
self.check_vectorized(2, 2)
def define_float_add_const():
return """
a = |30| + 25.5
a -> 29
"""
def test_float_add_const(self):
result = self.run("float_add_const")
self.assert_float_equal(result, 29.0 + 25.5)
self.check_vectorized(1, 1)
def define_int_add_const():
return """
a = astype(|30|, int)
b = a + 1i
d = astype(|30|, int)
c = d + 2.0
x1 = b -> 7
x2 = b -> 8
x3 = c -> 11
x4 = c -> 12
x1 + x2 + x3 + x4
"""
def test_int_add_const(self):
result = self.run("int_add_const")
assert int(result) == 7+1+8+1+11+2+12+2
self.check_vectorized(2, 2)
def define_int_expand():
return """
a = astype(|30|, int)
c = astype(|1|, int)
c[0] = 16
b = a + c
x1 = b -> 7
x2 = b -> 8
x1 + x2
"""
def test_int_expand(self):
result = self.run("int_expand")
assert int(result) == 7+16+8+16
self.check_vectorized(2, 2)
def define_int32_expand():
return """
a = astype(|30|, int32)
c = astype(|1|, int32)
c[0] = 16i
b = a + c
x1 = b -> 7
x2 = b -> 8
x1 + x2
"""
def test_int32_expand(self):
result = self.run("int32_expand")
assert int(result) == 7+16+8+16
self.check_vectorized(2, 1)
def define_int16_expand():
return """
a = astype(|30|, int16)
c = astype(|1|, int16)
c[0] = 16i
b = a + c
d = b -> 7:15
sum(d)
"""
def test_int16_expand(self):
result = self.run("int16_expand")
i = 8
assert int(result) == i*16 + sum(range(7,7+i))
# currently is is not possible to accum for types with < 8 bytes
self.check_vectorized(3, 0)
def define_int8_expand():
return """
a = astype(|30|, int8)
c = astype(|1|, int8)
c[0] = 8i
b = a + c
d = b -> 0:17
sum(d)
"""
def test_int8_expand(self):
result = self.run("int8_expand")
assert int(result) == 17*8 + sum(range(0,17))
# does not pay off to cast float64 -> int8
# neither does sum
# a + c should work, but it is given as a parameter
# thus the accum must handle this!
self.check_vectorized(3, 0)
def define_int32_add_const():
return """
a = astype(|30|, int32)
b = a + 1i
d = astype(|30|, int32)
c = d + 2.0
x1 = b -> 7
x2 = b -> 8
x3 = c -> 11
x4 = c -> 12
x1 + x2 + x3 + x4
"""
def test_int32_add_const(self):
result = self.run("int32_add_const")
assert int(result) == 7+1+8+1+11+2+12+2
self.check_vectorized(2, 2)
def define_float_mul_array():
return """
a = astype(|30|, float)
b = astype(|30|, float)
c = a * b
x1 = c -> 7
x2 = c -> 8
x3 = c -> 11
x4 = c -> 12
x1 + x2 + x3 + x4
"""
def test_float_mul_array(self):
result = self.run("float_mul_array")
assert int(result) == 7*7+8*8+11*11+12*12
self.check_vectorized(2, 2)
def define_int32_mul_array():
return """
a = astype(|30|, int32)
b = astype(|30|, int32)
c = a * b
x1 = c -> 7
x2 = c -> 8
x3 = c -> 11
x4 = c -> 12
x1 + x2 + x3 + x4
"""
def test_int32_mul_array(self):
result = self.run("int32_mul_array")
assert int(result) == 7*7+8*8+11*11+12*12
self.check_vectorized(2, 2)
def define_float32_mul_array():
return """
a = astype(|30|, float32)
b = astype(|30|, float32)
c = a * b
x1 = c -> 7
x2 = c -> 8
x3 = c -> 11
x4 = c -> 12
x1 + x2 + x3 + x4
"""
def test_float32_mul_array(self):
result = self.run("float32_mul_array")
assert int(result) == 7*7+8*8+11*11+12*12
self.check_vectorized(2, 2)
def define_conversion():
return """
a = astype(|30|, int8)
b = astype(|30|, int)
c = a + b
sum(c)
"""
def test_conversion(self):
result = self.run("conversion")
assert result == sum(range(30)) + sum(range(30))
self.check_vectorized(4, 2) # only sum and astype(int) succeed
def define_sum():
return """
a = |30|
sum(a)
"""
def test_sum(self):
result = self.run("sum")
assert result == sum(range(30))
self.check_vectorized(1, 0)
def define_sum_int():
return """
a = astype(|65|,int)
sum(a)
"""
def test_sum_int(self):
result = self.run("sum_int")
assert result == sum(range(65))
self.check_vectorized(2, 2)
def define_sum_multi():
return """
a = |30|
b = sum(a)
c = |60|
d = sum(c)
b + d
"""
def test_sum_multi(self):
result = self.run("sum_multi")
assert result == sum(range(30)) + sum(range(60))
self.check_vectorized(1, 0)
def define_sum_float_to_int16():
return """
a = |30|
sum(a,int16)
"""
def test_sum_float_to_int16(self):
result = self.run("sum_float_to_int16")
assert result == sum(range(30))
def define_sum_float_to_int32():
return """
a = |30|
sum(a,int32)
"""
def test_sum_float_to_int32(self):
result = self.run("sum_float_to_int32")
assert result == sum(range(30))
def define_sum_float_to_float32():
return """
a = |30|
sum(a,float32)
"""
def test_sum_float_to_float32(self):
result = self.run("sum_float_to_float32")
assert result == sum(range(30))
self.check_vectorized(1, 1)
def define_sum_float_to_uint64():
return """
a = |30|
sum(a,uint64)
"""
def test_sum_float_to_uint64(self):
result = self.run("sum_float_to_uint64")
assert result == sum(range(30))
self.check_vectorized(1, 0) # unsigned
def define_cumsum():
return """
a = |30|
b = cumsum(a)
b -> 5
"""
def test_cumsum(self):
result = self.run("cumsum")
assert result == 15
def define_axissum():
return """
a = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
b = sum(a,0)
b -> 1
"""
def test_axissum(self):
result = self.run("axissum")
assert result == 30
# XXX note - the bridge here is fairly crucial and yet it's pretty
# bogus. We need to improve the situation somehow.
self.check_vectorized(1, 0)
def define_reduce():
return """
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sum(a)
"""
def test_reduce_compile_only_once(self):
self.compile_graph()
reset_jit()
i = self.code_mapping['reduce']
# run it twice
retval = self.interp.eval_graph(self.graph, [i])
assert retval == sum(range(1,11))
retval = self.interp.eval_graph(self.graph, [i])
assert retval == sum(range(1,11))
# check that we got only one loop
assert len(get_stats().loops) == 1
self.check_vectorized(2, 0)
def test_reduce_axis_compile_only_once(self):
self.compile_graph()
reset_jit()
i = self.code_mapping['axissum']
# run it twice
retval = self.interp.eval_graph(self.graph, [i])
retval = self.interp.eval_graph(self.graph, [i])
# check that we got only one loop
assert len(get_stats().loops) == 1
self.check_vectorized(3, 0)
def define_prod():
return """
a = [1,2,3,4,1,2,3,4]
prod(a)
"""
def define_prod_zero():
return """
a = [1,2,3,4,1,2,3,0]
prod(a)
"""
def test_prod(self):
result = self.run("prod")
assert int(result) == 576
def test_prod_zero(self):
result = self.run("prod_zero")
assert int(result) == 0
def define_max():
return """
a = |30|
a[13] = 128.0
max(a)
"""
def test_max(self):
result = self.run("max")
assert result == 128
self.check_vectorized(1, 0)
def define_min():
return """
a = |30|
a[13] = -128
min(a)
"""
def test_min(self):
result = self.run("min")
assert result == -128
self.check_vectorized(1, 0)
def define_any():
return """
a = astype([0,0,0,0,0,0,0,1,0,0,0],int8)
any(a)
"""
def define_any_int():
return """
a = astype([0,0,0,0,256,0,0,0,0,0,0],int16)
any(a)
"""
def define_any_ret_0():
return """
a = astype([0,0,0,0,0,0,0,0,0,0,0],int64)
any(a)
"""
def define_float_any():
return """
a = [0,0,0,0,0,0,0,0.1,0,0,0]
any(a)
"""
def define_float32_any():
return """
a = astype([0,0,0,0,0,0,0,0.1,0,0,0], float32)
any(a)
"""
def test_any_float(self):
result = self.run("float_any")
assert int(result) == 1
self.check_vectorized(1, 1)
def test_any_float32(self):
result = self.run("float32_any")
assert int(result) == 1
self.check_vectorized(2, 2)
def test_any(self):
result = self.run("any")
assert int(result) == 1
self.check_vectorized(2, 1)
def test_any_int(self):
result = self.run("any_int")
assert int(result) == 1
self.check_vectorized(2, 1)
def test_any_ret_0(self):
result = self.run("any_ret_0")
assert int(result) == 0
self.check_vectorized(2, 2)
def define_all():
return """
a = astype([1,1,1,1,1,1,1,1],int32)
all(a)
"""
def define_all_int():
return """
a = astype([1,100,255,1,3,1,1,1],int32)
all(a)
"""
def define_all_ret_0():
return """
a = astype([1,1,1,1,1,0,1,1],int32)
all(a)
"""
def define_float_all():
return """
a = [1,1,1,1,1,1,1,1]
all(a)
"""
def define_float32_all():
return """
a = astype([1,1,1,1,1,1,1,1],float32)
all(a)
"""
def test_all_float(self):
result = self.run("float_all")
assert int(result) == 1
self.check_vectorized(1, 1)
def test_all_float32(self):
result = self.run("float32_all")
assert int(result) == 1
self.check_vectorized(2, 2)
def test_all(self):
result = self.run("all")
assert int(result) == 1
self.check_vectorized(2, 2)
def test_all_int(self):
result = self.run("all_int")
assert int(result) == 1
self.check_vectorized(2, 2)
def test_all_ret_0(self):
result = self.run("all_ret_0")
assert int(result) == 0
self.check_vectorized(2, 2)
def define_logical_xor_reduce():
return """
a = [1,1,1,1,1,1,1,1]
logical_xor_reduce(a)
"""
def test_logical_xor_reduce(self):
result = self.run("logical_xor_reduce")
assert result == 0
self.check_vectorized(0, 0) # TODO reduce
def define_already_forced():
return """
a = |30|
b = a + 4.5
b -> 5 # forces
c = b * 8
c -> 5
"""
def test_already_forced(self):
result = self.run("already_forced")
assert result == (5 + 4.5) * 8
self.check_vectorized(2, 2)
def define_ufunc():
return """
a = |30|
b = unegative(a)
b -> 3
"""
def test_ufunc(self):
result = self.run("ufunc")
assert result == -3
self.check_vectorized(1, 1)
def define_specialization():
return """
a = |30|
b = a + a
c = unegative(b)
c -> 3
d = a * a
unegative(d)
d -> 3
d = a * a
unegative(d)
d -> 3
d = a * a
unegative(d)
d -> 3
d = a * a
unegative(d)
d -> 3
"""
def test_specialization(self):
result = self.run("specialization")
assert result == (3*3)
self.check_vectorized(3, 3)
def define_multidim():
return """
a = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
b = a + a
b -> 1 -> 1
"""
def test_multidim(self):
result = self.run('multidim')
assert result == 8
self.check_vectorized(1, 1)
def define_broadcast():
return """
a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
b = [1, 2, 3, 4]
c = a + b
c -> 1 -> 2
"""
def test_broadcast(self):
result = self.run("broadcast")
assert result == 10
self.check_vectorized(1, 0) # TODO check on broadcast
def define_setslice():
return """
a = |30|
b = |10|
b[1] = 5.5
a[0:30:3] = b
a -> 3
"""
def test_setslice(self):
result = self.run("setslice")
assert result == 5.5
self.check_vectorized(1, 1)
def define_virtual_slice():
return """
a = |30|
c = a + a
d = c -> 1:20
d -> 1
"""
def test_virtual_slice(self):
result = self.run("virtual_slice")
assert result == 4
self.check_vectorized(1, 1)
def define_flat_iter():
return '''
a = |30|
b = flat(a)
c = b + a
c -> 3
'''
def test_flat_iter(self):
result = self.run("flat_iter")
assert result == 6
self.check_vectorized(1, 1)
def define_flat_getitem():
return '''
a = |30|
b = flat(a)
b -> 4: -> 6
'''
def test_flat_getitem(self):
result = self.run("flat_getitem")
assert result == 10.0
self.check_vectorized(1,1)
def define_flat_setitem():
return '''
a = |30|
b = flat(a)
b[4:] = a->:26
a -> 5
'''
def test_flat_setitem(self):
result = self.run("flat_setitem")
assert result == 1.0
self.check_vectorized(1,0) # TODO this can be improved
def define_dot():
return """
a = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
b = [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]
c = dot(a, b)
c -> 1 -> 2
"""
def test_dot(self):
result = self.run("dot")
assert result == 184
self.check_trace_count(4)
self.check_vectorized(1,1)
def define_argsort():
return """
a = |30|
argsort(a)
a->6
"""
def test_argsort(self):
result = self.run("argsort")
assert result == 6
self.check_vectorized(1,1) # vec. setslice
def define_where():
return """
a = [1, 0, 1, 0]
x = [1, 2, 3, 4]
y = [-10, -20, -30, -40]
r = where(a, x, y)
r -> 3
"""
def test_where(self):
result = self.run("where")
assert result == -40
def define_searchsorted():
return """
a = [1, 4, 5, 6, 9]
b = |30| -> ::-1
c = searchsorted(a, b)
c -> -1
"""
def test_searchsorted(self):
result = self.run("searchsorted")
assert result == 0
self.check_trace_count(6)
def define_int_mul_array():
return """
a = astype(|30|, int32)
b = astype(|30|, int32)
c = a * b
x1 = c -> 7
x2 = c -> 8
x3 = c -> 11
x4 = c -> 12
x1 + x2 + x3 + x4
"""
def test_int_mul_array(self):
# note that int64 mul has not packed machine instr
# for SSE4 thus int32
result = self.run("int_mul_array")
assert int(result) == 7*7+8*8+11*11+12*12
self.check_vectorized(2, 2)
def define_slice():
return """
a = |30|
b = a -> ::3
c = b + b
c -> 3
"""
def test_slice(self):
result = self.run("slice")
assert result == 18
self.check_vectorized(1,1)
def define_multidim_slice():
return """
a = [[1, 2, 3, 4], [3, 4, 5, 6], [5, 6, 7, 8], [7, 8, 9, 10], [9, 10, 11, 12], [11, 12, 13, 14], [13, 14, 15, 16], [16, 17, 18, 19]]
b = a -> ::2
c = b + b
d = c -> 1
d -> 1
"""
def test_multidim_slice(self):
result = self.run('multidim_slice')
assert result == 12
self.check_trace_count(3)
# ::2 creates a view object -> needs an inner loop
# that iterates continous chunks of the matrix
self.check_vectorized(1,0)
def define_dot_matrix():
return """
mat = |16|
m = reshape(mat, [4,4])
vec = [0,1,2,3]
a = dot(m, vec)
a -> 3
"""
def test_dot_matrix(self):
result = self.run("dot_matrix")
assert int(result) == 86
self.check_vectorized(1, 1)
# NOT WORKING
def define_pow():
return """
a = |30| ** 2
a -> 29
"""
def test_pow(self):
result = self.run("pow")
assert result == 29 ** 2
self.check_trace_count(1)
def define_pow_int():
return """
a = astype(|30|, int)
b = astype([2], int)
c = a ** b
c -> 15
"""
def test_pow_int(self):
result = self.run("pow_int")
assert result == 15 ** 2
self.check_trace_count(4) # extra one for the astype
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