float32

example/sphere_pytorch.py

@@ -19,16 +19,18 @@
 
 import sasmodels.kerneltorch as kt
 
+device = torch.device('mps')
+
 def make_kernel(model, q_vectors):
     """Instantiate the python kernel with input *q_vectors*"""
-    q_input = kt.PyInput(q_vectors, dtype=torch.float64)
+    q_input = kt.PyInput(q_vectors, dtype=torch.float32)
     return kt.PyKernel(model.info, q_input)
 
 
 model = load_model('_spherepy')
-print(model.info)
 
-q = torch.logspace(-3, -1, 200)
+q = torch.logspace(-3, -1, 200).to(device)
+
 
 #qq = logspace(-3, -1, 200)
 
@@ -49,3 +51,5 @@ def make_kernel(model, q_vectors):
 Iq = call_kernel(kernel, pars)
 elapsed = time.time() - t0
 print('Computation time:', elapsed)
+
+print(Iq)

example/sphere_pytorch_prototype.py

@@ -8,7 +8,9 @@
 
 import sasmodels.kerneltorch as kt
 
+
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device('mps')
 
 print("device",device)
 
@@ -24,7 +26,7 @@ def make_kernel(model, q_vectors,device):
 print("q",q[6])
 kernel = model.make_kernel([q])
 
-pars = {'radius': 200, 'radius_pd': 0.1, 'radius_pd_n':100, 'scale': 2}
+pars = {'radius': 200, 'radius_pd': 0.1, 'radius_pd_n':10000, 'scale': 2}
 
 t_before = time.time()
 Iq = call_kernel(kernel, pars)

sasmodels/kerneltorch.py

@@ -87,7 +87,7 @@ def __init__(self, q_vectors, dtype):
             self.q[:, 1] = q_vectors[1]
         else:
             # Create empty tensor
-            self.q = torch.tensor(np.empty(self.nq, dtype=np.float64))
+            self.q = torch.tensor(np.empty(self.nq, dtype=np.float32))
             self.q[:self.nq] = q_vectors[0]
 
     def release(self):
@@ -124,7 +124,7 @@ def __init__(self, model_info, q_input, device):
         self.dtype = np.dtype('d')
         self.info = model_info
         self.q_input = q_input
-        self.res = np.empty(q_input.nq, np.float64)
+        self.res = np.empty(q_input.nq, np.float32)
         self.dim = '2d' if q_input.is_2d else '1d'
 
         partable = model_info.parameters
@@ -224,10 +224,10 @@ def _loops(parameters, form, form_volume, form_radius, nq, call_details,
     # mesh, we update the components with the polydispersity values before
     # calling the respective functions.
     n_pars = len(parameters)
-    parameters = torch.DoubleTensor(parameters).to(device)
+    parameters = torch.tensor(parameters, dtype=torch.float32).to(device)
 
     #parameters[:] = values[2:n_pars+2]
-    parameters[:] = torch.DoubleTensor(values[2:n_pars+2])
+    parameters[:] = torch.tensor(values[2:n_pars+2], dtype=torch.float32)
 
     print("parameters",parameters)
     if call_details.num_active == 0:
@@ -238,8 +238,8 @@ def _loops(parameters, form, form_volume, form_radius, nq, call_details,
 
     else:
         #transform to tensor flow
-        pd_value = torch.DoubleTensor(values[2+n_pars:2+n_pars + call_details.num_weights])
-        pd_weight = torch.DoubleTensor(values[2+n_pars + call_details.num_weights:])
+        pd_value = torch.tensor(values[2+n_pars:2+n_pars + call_details.num_weights], dtype=torch.float32)
+        pd_weight = torch.tensor(values[2+n_pars + call_details.num_weights:], dtype=torch.float32)
 
         #print("pd_value",pd_value)
         #print("pd_weight",pd_weight)
@@ -262,7 +262,7 @@ def _loops(parameters, form, form_volume, form_radius, nq, call_details,
         pd_length = call_details.pd_length[:call_details.num_active]
 
         #total = np.zeros(nq, np.float64)
-        total = torch.zeros(nq, dtype= torch.float64).to(device)
+        total = torch.zeros(nq, dtype= torch.float32).to(device)
 
         #print("ll", range(call_details.num_eval))
         #parallel for loop