[pypy-commit] pypy default: precision loss, big number * small number has a different result then doing the calculation in order, disable the feature

[plan_rich]

Author: Richard Plangger <planrichi at gmail.com>
Branch: 
Changeset: r88789:8096cd4c9209
Date: 2016-12-01 13:40 +0100
http://bitbucket.org/pypy/pypy/changeset/8096cd4c9209/

Log:	precision loss, big number * small number has a different result
	then doing the calculation in order, disable the feature

diff --git a/pypy/module/micronumpy/test/test_zjit.py b/pypy/module/micronumpy/test/test_zjit.py
--- a/pypy/module/micronumpy/test/test_zjit.py
+++ b/pypy/module/micronumpy/test/test_zjit.py
@@ -518,12 +518,10 @@
     def test_prod(self):
         result = self.run("prod")
         assert int(result) == 576
-        self.check_vectorized(1, 1)
 
     def test_prod_zero(self):
         result = self.run("prod_zero")
         assert int(result) == 0
-        self.check_vectorized(1, 1)
 
 
     def define_max():
diff --git a/pypy/module/pypyjit/test_pypy_c/test_micronumpy.py b/pypy/module/pypyjit/test_pypy_c/test_micronumpy.py
--- a/pypy/module/pypyjit/test_pypy_c/test_micronumpy.py
+++ b/pypy/module/pypyjit/test_pypy_c/test_micronumpy.py
@@ -76,7 +76,6 @@
     arith_comb = [
         ('sum','int', 1742, 1742, 1),
         ('sum','float', 2581, 2581, 1),
-        ('prod','float', 1, 3178, 1),
         ('prod','int', 1, 3178, 1),
         ('any','int', 1, 2239, 1),
         ('any','int', 0, 4912, 0),
diff --git a/rpython/jit/backend/llgraph/runner.py b/rpython/jit/backend/llgraph/runner.py
--- a/rpython/jit/backend/llgraph/runner.py
+++ b/rpython/jit/backend/llgraph/runner.py
@@ -1128,7 +1128,7 @@
                 value = sum(value)
             elif info.accum_operation == '*':
                 def prod(acc, x): return acc * x
-                value = reduce(prod, value, 1)
+                value = reduce(prod, value, 1.0)
             else:
                 raise NotImplementedError("accum operator in fail guard")
             values[i] = value
diff --git a/rpython/jit/metainterp/optimizeopt/schedule.py b/rpython/jit/metainterp/optimizeopt/schedule.py
--- a/rpython/jit/metainterp/optimizeopt/schedule.py
+++ b/rpython/jit/metainterp/optimizeopt/schedule.py
@@ -980,7 +980,6 @@
 class AccumPack(Pack):
     SUPPORTED = { rop.FLOAT_ADD: '+',
                   rop.INT_ADD:   '+',
-                  rop.FLOAT_MUL: '*',
                 }
 
     def __init__(self, nodes, operator, position):
diff --git a/rpython/jit/metainterp/optimizeopt/vector.py b/rpython/jit/metainterp/optimizeopt/vector.py
--- a/rpython/jit/metainterp/optimizeopt/vector.py
+++ b/rpython/jit/metainterp/optimizeopt/vector.py
@@ -847,6 +847,10 @@
                                      vecop, count)
                 oplist.append(vecop)
             elif pack.reduce_init() == 1:
+                # PRECISION loss, because the numbers are accumulated (associative, commutative properties must hold)
+                # you can end up a small number and a huge number that is finally multiplied. giving an
+                # inprecision result, thus this is disabled now
+                raise NotImplementedError
                 # multiply is only supported by floats
                 vecop = OpHelpers.create_vec_expand(ConstFloat(1.0), bytesize,
                                                     signed, count)
diff --git a/rpython/jit/metainterp/test/test_vector.py b/rpython/jit/metainterp/test/test_vector.py
--- a/rpython/jit/metainterp/test/test_vector.py
+++ b/rpython/jit/metainterp/test/test_vector.py
@@ -414,7 +414,9 @@
                              lambda a,b: lltype.intmask(lltype.intmask(a)+lltype.intmask(b)), lltype.Signed)
     small_floats = st.floats(min_value=-100, max_value=100, allow_nan=False, allow_infinity=False)
     test_vec_float_sum = vec_reduce(small_floats, lambda a,b: a+b, rffi.DOUBLE)
-    test_vec_float_prod = vec_reduce(small_floats, lambda a,b: a*b, rffi.DOUBLE)
+    # PRECISION loss, because the numbers are accumulated (associative, commutative properties must hold)
+    # you can end up a small number and a huge number that is finally multiplied losing precision
+    # test_vec_float_prod = vec_reduce(small_floats, lambda a,b: a*b, rffi.DOUBLE)
 
 
     def test_constant_expand(self):