[math] Refactor taichi operators

brainpy/_src/dnn/linear.py

@@ -570,7 +570,7 @@ def __init__(
       sharding: Optional[Sharding] = None,
       mode: Optional[bm.Mode] = None,
       name: Optional[str] = None,
-      method: str = 'cusparse',
+      method: str = None,
       transpose: bool = True,
   ):
     super().__init__(name=name, mode=mode, conn=conn, weight=weight, sharding=sharding, transpose=transpose)
@@ -580,8 +580,7 @@ def update(self, x):
     if x.ndim == 1:
       return bm.sparse.csrmv(self.weight, self.indices, self.indptr, x,
                              shape=(self.conn.pre_num, self.conn.post_num),
-                             transpose=self.transpose,
-                             method=self.method)
+                             method=self.method, transpose=self.transpose)
     elif x.ndim > 1:
       shapes = x.shape[:-1]
       x = bm.flatten(x, end_dim=-2)
@@ -593,9 +592,7 @@ def update(self, x):
   def _batch_csrmv(self, x):
     return bm.sparse.csrmv(self.weight, self.indices, self.indptr, x,
                            shape=(self.conn.pre_num, self.conn.post_num),
-                           transpose=self.transpose,
-                           method=self.method)
-
+                           method=self.method, transpose=self.transpose)
 
 class EventCSRLinear(_CSRLayer):
   r"""Synaptic matrix multiplication with event CSR sparse computation.
@@ -646,7 +643,6 @@ def _batch_csrmv(self, x):
                           shape=(self.conn.pre_num, self.conn.post_num),
                           transpose=self.transpose)
 
-
 @numba.njit(nogil=True, fastmath=True, parallel=False)
 def _cpu_csr_on_pre_update(w, indices, indptr, spike, trace, w_min, w_max, out_w):
   out_w[:] = w
@@ -659,7 +655,6 @@ def _cpu_csr_on_pre_update(w, indices, indptr, spike, trace, w_min, w_max, out_w
         # out_w[k] = np.clip(out_w[k] + trace[j], w_min, w_max)
         out_w[k] = np.minimum(np.maximum(out_w[k] + trace[j], w_min), w_max)
 
-
 csr_on_pre_update_prim = bm.XLACustomOp(_cpu_csr_on_pre_update)
 
 
@@ -671,7 +666,6 @@ def csr_on_pre_update(w, indices, indptr, spike, trace, w_min=None, w_max=None):
   return csr_on_pre_update_prim(w, indices, indptr, spike, trace, w_min, w_max,
                                 outs=[jax.ShapeDtypeStruct(w.shape, w.dtype)])[0]
 
-
 @numba.njit(nogil=True, fastmath=True, parallel=False)
 def _cpu_csc_on_pre_update(w, post_ids, indptr, w_ids, spike, trace, w_min, w_max, out_w):
   out_w[:] = w
@@ -697,6 +691,7 @@ def csc_on_post_update(w, post_ids, indptr, w_ids, spike, trace, w_min=None, w_m
                                 outs=[jax.ShapeDtypeStruct(w.shape, w.dtype)])[0]
 
 
+
 class CSCLinear(Layer):
   r"""Synaptic matrix multiplication with CSC sparse computation.
 
@@ -1080,7 +1075,7 @@ def __init__(
       mode: Optional[bm.Mode] = None,
       name: Optional[str] = None,
       transpose: bool = False,
-      atomic: bool = False,
+      atomic: bool = True,
   ):
     super().__init__(name=name, mode=mode)
 
@@ -1161,7 +1156,7 @@ def __init__(
       mode: Optional[bm.Mode] = None,
       name: Optional[str] = None,
       transpose: bool = False,
-      atomic: bool = False,
+      atomic: bool = True,
   ):
     super().__init__(name=name, mode=mode)
 
@@ -1239,7 +1234,7 @@ def __init__(
       seed: Optional[int] = None,
       sharding: Optional[Sharding] = None,
       transpose: bool = False,
-      atomic: bool = False,
+      atomic: bool = True,
       mode: Optional[bm.Mode] = None,
       name: Optional[str] = None,
   ):

brainpy/_src/dnn/tests/test_linear.py

@@ -213,6 +213,5 @@ def test_EventJitFPNormalLinear(self, prob, w_mu, w_sigma, shape):
     self.assertTrue(y2.shape == shape + (200,))
     bm.clear_buffer_memory()
 
-
 if __name__ == '__main__':
   absltest.main()

brainpy/_src/math/event/__init__.py

@@ -1,5 +1,4 @@
 
 from ._info_collection import *
 from ._csr_matvec import *
-from ._csr_matvec_taichi import *