speed up sum

extra/cherry.py

@@ -242,6 +242,8 @@ def cherry_reduceop(inp, op, axis):
     osize = [1]
     dimlist = [np.prod(inp.shape), 1]
     redlist = [True, False]
+    #dimlist = [1, np.prod(inp.shape)]
+    #redlist = [False, True]
   else:
     osize = np.array(inp.shape)
     osize[list(axis)] = 1
@@ -268,24 +270,42 @@ def cherry_reduceop(inp, op, axis):
       if i not in axis:
         nosize.append(osize[i])
     osize = nosize
-    if redlist[-1] != False:
-      dimlist = dimlist+[1]
-      redlist = redlist+[False]
-    if len(dimlist)%2 == 1:
-      dimlist = [1]+dimlist
-      redlist = [not redlist[0]]+redlist
 
   osize = tuple(osize)
-  print(op, inp.shape, axis, "->", osize, dimlist, redlist)
+  print("reduce", op, inp.shape, axis, "->", osize, dimlist, redlist)
   inslot, outslot = 0, 2
   cherry_dmar(SLOT(inslot), inp)
 
   # dimlist is always the inshape
   # redlist is always [False, True, False, True, ...., True, False]
 
+  # special case if redlist ends with True
+  if redlist[-1] == True:
+    print("special case redlist[-1] == True")
+    outside = int(np.prod(dimlist[:-1]))
+    for l in range(0, outside, SZ):
+      reduce_size = min(SZ, outside-l)
+      j = 0
+      while j < dimlist[-1]:
+        riski_load(Reg.MATMUL_INPUT,
+          SLOT(inslot) + l*dimlist[-1] + j,
+          stride_y=1, stride_x=dimlist[-1],
+          len_y=min(SZ if j == 0 else SZ-1, dimlist[-1]-j),
+          len_x=reduce_size,
+          zero=j==0, skip_first=j!=0)
+        reduceops[op]()
+        riski_mov(Reg.MATMUL_INPUT, Reg.MATMUL_OUTPUT)  # move the first row
+        j += SZ if j == 0 else SZ-1
+      riski_store(Reg.MATMUL_OUTPUT, SLOT(outslot) + l, len_y=1, len_x=reduce_size)
+    # remove last dimension
+    redlist = redlist[:-1]
+    dimlist = dimlist[:-1]
+    inslot, outslot = outslot, inslot
+
   # do the reduce merges one at a time
-  while True:
-    print(dimlist)
+  while len(dimlist) >= 2:
+    print("proc", dimlist, redlist)
+
     for l in range(0, int(np.prod(dimlist[:-2]))):
       for k in range(0, dimlist[-1], SZ):
         reduce_size = min(SZ, dimlist[-1]-k)
@@ -302,13 +322,13 @@ def cherry_reduceop(inp, op, axis):
           riski_mov(Reg.MATMUL_INPUT, Reg.MATMUL_OUTPUT)  # move the first row
           j += SZ if j == 0 else SZ-1
         riski_store(Reg.MATMUL_OUTPUT, SLOT(outslot) + l*dimlist[-1] + k, len_y=1, len_x=reduce_size)
-    if len(dimlist) == 2:
+    inslot, outslot = outslot, inslot
+    if len(dimlist) <= 2:
       break
     # merge [False, True] into [False]
     dimlist = dimlist[:-3] + [dimlist[-3]*dimlist[-1]]
-    inslot, outslot = outslot, inslot
 
-  return cherry_dmaw(SLOT(outslot), osize)
+  return cherry_dmaw(SLOT(inslot), osize)
 
 def cherry_unop(x, op):
   cherry_dmar(SLOT(0), x)

test/test_ops.py

@@ -83,6 +83,8 @@ class TestOps(unittest.TestCase):
     helper_test_op([(3,3,45,65), (3,3,65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
   def test_sum(self):
     helper_test_op([(45,3)], lambda x: x.sum(), Tensor.sum)
+    helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=3), lambda x: Tensor.sum(x, axis=3))
+    helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(1,3)), lambda x: Tensor.sum(x, axis=(1,3)))
     helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(0,2)), lambda x: Tensor.sum(x, axis=(0,2)))
     helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=(1,2)), lambda x: Tensor.sum(x, axis=(1,2)))
     helper_test_op([(3,4,5,6)], lambda x: x.sum(axis=1), lambda x: Tensor.sum(x, axis=1))