vectorization: improve treatment of unary minus

pystencils/backends/cbackend.py

@@ -546,6 +546,8 @@ class VectorizedCustomSympyPrinter(CustomSympyPrinter):
         elif isinstance(expr, cast_func):
             arg, data_type = expr.args
             if type(data_type) is VectorType:
+                # vector_memory_access is a cast_func itself so it should't be directly inside a cast_func
+                assert not isinstance(arg, vector_memory_access)
                 if isinstance(arg, sp.Tuple):
                     is_boolean = get_type_of_expression(arg[0]) == create_type("bool")
                     is_integer = get_type_of_expression(arg[0]) == create_type("int")

pystencils/cpu/vectorization.py

 import warnings
 from typing import Container, Union
 
+import numpy as np
 import sympy as sp
 from sympy.logic.boolalg import BooleanFunction
 
@@ -160,7 +161,6 @@ def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_a
 
 
 def mask_conditionals(loop_body):
-
     def visit_node(node, mask):
         if isinstance(node, ast.Conditional):
             cond = node.condition_expr
@@ -201,18 +201,32 @@ def insert_vector_casts(ast_node):
             new_arg = visit_expr(expr.args[0])
             base_type = get_type_of_expression(expr.args[0]).base_type if type(expr.args[0]) is vector_memory_access \
                 else get_type_of_expression(expr.args[0])
-            pw = sp.Piecewise((base_type.numpy_dtype.type(-1) * new_arg, new_arg < base_type.numpy_dtype.type(0)),
+            pw = sp.Piecewise((-new_arg, new_arg < base_type.numpy_dtype.type(0)),
                               (new_arg, True))
             return visit_expr(pw)
         elif expr.func in handled_functions or isinstance(expr, sp.Rel) or isinstance(expr, BooleanFunction):
+            default_type = 'double'
+            if expr.func is sp.Mul and expr.args[0] == -1:
+                # special treatment for the unary minus: make sure that the -1 has the same type as the argument
+                dtype = int
+                for arg in expr.args[1:]:
+                    if type(arg) is vector_memory_access and arg.dtype.base_type.is_float():
+                        dtype = arg.dtype.base_type.numpy_dtype.type
+                    elif type(arg) is TypedSymbol and type(arg.dtype) is VectorType and arg.dtype.base_type.is_float():
+                        dtype = arg.dtype.base_type.numpy_dtype.type
+                if dtype is not int:
+                    if dtype is np.float32:
+                        default_type = 'float'
+                    expr = sp.Mul(dtype(expr.args[0]), *expr.args[1:])
             new_args = [visit_expr(a) for a in expr.args]
-            arg_types = [get_type_of_expression(a) for a in new_args]
+            arg_types = [get_type_of_expression(a, default_float_type=default_type) for a in new_args]
             if not any(type(t) is VectorType for t in arg_types):
                 return expr
             else:
                 target_type = collate_types(arg_types)
-                casted_args = [cast_func(a, target_type) if t != target_type else a
-                               for a, t in zip(new_args, arg_types)]
+                casted_args = [
+                    cast_func(a, target_type) if t != target_type and not isinstance(a, vector_memory_access) else a
+                    for a, t in zip(new_args, arg_types)]
                 return expr.func(*casted_args)
         elif expr.func is sp.Pow:
             new_arg = visit_expr(expr.args[0])

pystencils/data_types.py

@@ -502,6 +502,9 @@ def get_type_of_expression(expr,
     from pystencils.astnodes import ResolvedFieldAccess
     from pystencils.cpu.vectorization import vec_all, vec_any
 
+    if default_float_type == 'float':
+        default_float_type = 'float32'
+
     if not symbol_type_dict:
         symbol_type_dict = defaultdict(lambda: create_type('double'))
 

pystencils/include/myintrin.h

@@ -36,6 +36,8 @@ QUALIFIERS __m128d _my_cvtepu64_pd(const __m128i x)
 {
 #ifdef __AVX512VL__
     return _mm_cvtepu64_pd(x);
+#elif defined(__clang__)
+    return __builtin_convertvector((uint64_t __attribute__((__vector_size__(16)))) x, __m128d);
 #else
     __m128i xH = _mm_srli_epi64(x, 32);
     xH = _mm_or_si128(xH, _mm_castpd_si128(_mm_set1_pd(19342813113834066795298816.)));          //  2^84
@@ -85,6 +87,8 @@ QUALIFIERS __m256d _my256_cvtepu64_pd(const __m256i x)
 {
 #ifdef __AVX512VL__
     return _mm256_cvtepu64_pd(x);
+#elif defined(__clang__)
+    return __builtin_convertvector((uint64_t __attribute__((__vector_size__(32)))) x, __m256d);
 #else
     __m256i xH = _mm256_srli_epi64(x, 32);
     xH = _mm256_or_si256(xH, _mm256_castpd_si256(_mm256_set1_pd(19342813113834066795298816.)));          //  2^84

pystencils_tests/test_random.py

@@ -73,7 +73,7 @@ def test_rng(target, rng, precision, dtype, t=124, offsets=(0, 0), keys=(0, 0),
             for x in range(dh.shape[0]):
                 for y in range(dh.shape[1]):
                     r = Philox(counter=t + (x + offset_values[0]) * 2 ** 32 + (y + offset_values[1]) * 2 ** 64,
-                               key=keys[0] + keys[1] * 2 ** 32, number=4, width=32)
+                               key=keys[0] + keys[1] * 2 ** 32, number=4, width=32, mode="sequence")
                     r.advance(-4, counter=False)
                     int_reference[x, y, :] = r.random_raw(size=4)
 

pystencils_tests/test_vectorization.py

@@ -33,22 +33,6 @@ def test_vector_type_propagation():
     np.testing.assert_equal(dst[1:-1, 1:-1], 2 * 10.0 + 3)
 
 
-def test_vectorized_abs():
-    arr = np.ones((2 ** 2 + 2, 2 ** 3 + 2))
-    arr[-3:, :] = -1
-
-    f, g = ps.fields(f=arr, g=arr)
-    update_rule = [ps.Assignment(g.center(), sp.Abs(f.center()))]
-
-    ast = ps.create_kernel(update_rule)
-    vectorize(ast, instruction_set=instruction_set)
-
-    func = ast.compile()
-    dst = np.zeros_like(arr)
-    func(g=dst, f=arr)
-    np.testing.assert_equal(np.sum(dst[1:-1, 1:-1]), 2 ** 2 * 2 ** 3)
-
-
 def test_aligned_and_nt_stores():
     domain_size = (24, 24)
     # create a datahandling object

pystencils_tests/test_vectorization_specific.py

 import pytest
 
 import numpy as np
+import sympy as sp
 
 import pystencils as ps
 from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
@@ -28,3 +29,23 @@ def test_vectorisation_varying_arch(instruction_set):
     kernel = ast.compile()
     kernel(f=arr)
     np.testing.assert_equal(arr, 2)
+
+
+@pytest.mark.parametrize('dtype', ('float', 'double'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+def test_vectorized_abs(instruction_set, dtype):
+    """Some instructions sets have abs, some don't.
+       Furthermore, the special treatment of unary minus makes this data type-sensitive too.
+    """
+    arr = np.ones((2 ** 2 + 2, 2 ** 3 + 2), dtype=np.float64 if dtype == 'double' else np.float32)
+    arr[-3:, :] = -1
+
+    f, g = ps.fields(f=arr, g=arr)
+    update_rule = [ps.Assignment(g.center(), sp.Abs(f.center()))]
+
+    ast = ps.create_kernel(update_rule, cpu_vectorize_info={'instruction_set': instruction_set})
+
+    func = ast.compile()
+    dst = np.zeros_like(arr)
+    func(g=dst, f=arr)
+    np.testing.assert_equal(np.sum(dst[1:-1, 1:-1]), 2 ** 2 * 2 ** 3)