BUG: make a variable volatile to work around clang compiler bug (#18030)

* BUG: make a variable volatile to work around clang compiler bug

* Adding comments for relevance

* TST: Adding test to check for no overflow warnings in log

Fixes #18005

numpy/core/src/umath/simd.inc.src

@@ -1549,7 +1549,11 @@ fma_get_exponent(__m256 x)
     __m256 denormal_mask = _mm256_cmp_ps(x, _mm256_set1_ps(FLT_MIN), _CMP_LT_OQ);
     __m256 normal_mask = _mm256_cmp_ps(x, _mm256_set1_ps(FLT_MIN), _CMP_GE_OQ);
 
-    __m256 temp1 = _mm256_blendv_ps(x, _mm256_set1_ps(0.0f), normal_mask);
+    /*
+     * It is necessary for temp1 to be volatile, a bug in clang optimizes it out which leads
+     * to an overflow warning in some cases. See https://github.com/numpy/numpy/issues/18005
+     */
+    volatile __m256 temp1 = _mm256_blendv_ps(x, _mm256_set1_ps(0.0f), normal_mask);
     __m256 temp = _mm256_mul_ps(temp1, two_power_100);
     x = _mm256_blendv_ps(x, temp, denormal_mask);
 
@@ -1576,7 +1580,11 @@ fma_get_mantissa(__m256 x)
     __m256 denormal_mask = _mm256_cmp_ps(x, _mm256_set1_ps(FLT_MIN), _CMP_LT_OQ);
     __m256 normal_mask = _mm256_cmp_ps(x, _mm256_set1_ps(FLT_MIN), _CMP_GE_OQ);
 
-    __m256 temp1 = _mm256_blendv_ps(x, _mm256_set1_ps(0.0f), normal_mask);
+    /*
+     * It is necessary for temp1 to be volatile, a bug in clang optimizes it out which leads
+     * to an overflow warning in some cases. See https://github.com/numpy/numpy/issues/18005
+     */
+    volatile __m256 temp1 = _mm256_blendv_ps(x, _mm256_set1_ps(0.0f), normal_mask);
     __m256 temp = _mm256_mul_ps(temp1, two_power_100);
     x = _mm256_blendv_ps(x, temp, denormal_mask);
 

numpy/core/tests/test_umath.py

@@ -926,6 +926,11 @@ def test_log_values(self):
             assert_raises(FloatingPointError, np.log, np.float32(-np.inf))
             assert_raises(FloatingPointError, np.log, np.float32(-1.0))
 
+        # See https://github.com/numpy/numpy/issues/18005 
+        with assert_no_warnings():
+            a = np.array(1e9, dtype='float32')
+            np.log(a)
+
     def test_sincos_values(self):
         with np.errstate(all='ignore'):
             x = [np.nan,  np.nan, np.nan, np.nan]