Remove -march=native from default flags

CMakeLists.txt

@@ -43,10 +43,6 @@ option(BUILD_SANDBOX "If on, builds the sandbox" OFF)
 
 set(INSTALL_PATH "" CACHE STRING "Path to install the libraries")
 
-SET(TYPE_ARCHITECTURE "native" CACHE STRING
-    "Choose the type of architecture to compile to in release mode."
-)
-
 ########## COMPILATION FLAGS ##########
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wextra -Weffc++ -Wno-non-virtual-dtor")
 set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Wextra")
@@ -57,14 +53,6 @@ if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" OR
 
     set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} -Og")
     set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -Og")
-
-    # Add -march=${TYPE_ARCHITECTURE} (-march=native by default) to the release
-    # build flags
-    set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -march=${TYPE_ARCHITECTURE}")
-    set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -march=${TYPE_ARCHITECTURE}")
-
-    set(CMAKE_C_FLAGS_RELWITHDEBINFO "${CMAKE_C_FLAGS_RELWITHDEBINFO} -march=${TYPE_ARCHITECTURE}")
-    set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -march=${TYPE_ARCHITECTURE}")
 endif()
 
 ########## START CONFIGURATION ##########

bindings/rascal/representations/spherical_covariants.py

@@ -314,10 +314,10 @@ def get_num_coefficients(self, n_species=1):
                         * self.hypers["max_angular"]
                     )
                 n_col *= 2 * self.hypers["covariant_lambda"] + 1
-                return int(n_col * n_species ** 2 * self.hypers["max_radial"] ** 2)
+                return int(n_col * n_species**2 * self.hypers["max_radial"] ** 2)
             else:
                 return (
-                    n_species ** 2
+                    n_species**2
                     * self.hypers["max_radial"] ** 2
                     * int(
                         (

bindings/rascal/utils/cg_utils.py

@@ -240,15 +240,12 @@ def compute_lambda_soap(spx, cg, lam, parity=0):
                 continue
             if l2 - l1 > lam or l2 + l1 < lam:
                 continue
-            lsoap[..., nl, :] = (
-                cg.combine_einsum(
-                    spx[..., lm_slice(l1)],
-                    spx[..., lm_slice(l2)],
-                    lam,
-                    combination_string="ian,iAN->ianAN",
-                )
-                * (1 if l2 == l1 else SQRT_2)
-            )
+            lsoap[..., nl, :] = cg.combine_einsum(
+                spx[..., lm_slice(l1)],
+                spx[..., lm_slice(l2)],
+                lam,
+                combination_string="ian,iAN->ianAN",
+            ) * (1 if l2 == l1 else SQRT_2)
             nl += 1
     return lsoap
 

bindings/rascal/utils/radial_basis.py

@@ -94,7 +94,7 @@ def gto_width(sigma):
     ---Returns---
     b: GTO (Gaussian) width
     """
-    return 1.0 / (2 * sigma ** 2)
+    return 1.0 / (2 * sigma**2)
 
 
 def gto_prefactor(n, sigma):
@@ -125,7 +125,7 @@ def gto(r, n, sigma):
     """
     b = gto_width(sigma)
     N = gto_prefactor(n, sigma)
-    return N * r ** (n + 1) * np.exp(-b * r ** 2)
+    return N * r ** (n + 1) * np.exp(-b * r**2)
 
 
 def gto_overlap(n, m, sigma_n, sigma_m):

requirements.txt

@@ -18,7 +18,7 @@ nbsphinx>=0.8.1
 pygments>=2.4.1
 # Developers tools for linting and formatting
 cpplint==1.5.5
-black==20.8b1
+black==22.3.0
 # Developers tools - data generation scripts
 ase
 mpmath

scripts/generate_modified_bessel_first_kind_ref_data.py

@@ -27,7 +27,7 @@ def sbesseli_complete_square(n, a, r, x):
     """i_n(2arx)*\exp{-ar^2}*\exp{-ax^2}"""
     z = 2 * a * r * x
     return float(
-        sqrt(pi / (2 * z)) * besseli(n + 0.5, z) * exp(-a * r ** 2) * exp(-a * x ** 2)
+        sqrt(pi / (2 * z)) * besseli(n + 0.5, z) * exp(-a * r**2) * exp(-a * x**2)
     )
 
 

scripts/generate_spherical_covariant_ref_data.py

@@ -146,7 +146,7 @@ def main(json_dump, save_kernel):
 
     x = get_feature_vector(test_hypers, frames)
     x0 = x.shape[0]
-    x = x.reshape((x0, 3, -1, (2 * lam + 1), nmax ** 2))
+    x = x.reshape((x0, 3, -1, (2 * lam + 1), nmax**2))
     x = x.transpose((0, 3, 1, 2, 4))
     x = x.reshape((x0 * (2 * lam + 1), -1))
     kernel = np.dot(x, x.T)