conv isn't fast yet
MODIFY
tinygrad/utils.py
tinygrad/utils.py
@@ -33,37 +33,50 @@ def fetch_mnist():
# write them fast and the convs will be fast?
@lru_cache
-def get_im2col_indexes(oy, ox, cin, H, W):
+def get_im2col_index(oy, ox, cin, H, W):
idxc = np.tile(np.arange(cin).repeat(H*W), oy*ox)
idxy = np.tile(np.arange(H).repeat(W), oy*ox*cin) + np.arange(oy).repeat(ox*cin*H*W)
idxx = np.tile(np.arange(W), oy*ox*cin*H) + np.tile(np.arange(ox), oy).repeat(cin*H*W)
- return idxc, idxy, idxx
+
+ # why return 3 index when we can return 1?
+ OY, OX = oy+(H-1), ox+(W-1)
+ idx = idxc * OY * OX + idxy * OX + idxx
+ return idx
def im2col(x, H, W):
bs,cin,oy,ox = x.shape[0], x.shape[1], x.shape[2]-(H-1), x.shape[3]-(W-1)
- ic, iy, ix = get_im2col_indexes(oy, ox, cin, H, W)
- tx = x[:, ic, iy, ix]
+ idx = get_im2col_index(oy, ox, cin, H, W)
+ tx = x.reshape(bs, -1)[:, idx]
+
+ """
+ # this is slower
+ tx = np.empty((bs, oy, ox, cin*W*H), dtype=x.dtype)
+ for Y in range(oy):
+ for X in range(ox):
+ tx[:, Y, X] = x[:, :, Y:Y+H, X:X+W].reshape(bs, -1)
+ """
+
return tx.reshape(-1, cin*W*H)
def col2im(tx, H, W, OY, OX):
oy, ox = OY-(H-1), OX-(W-1)
bs = tx.shape[0] // (oy * ox)
cin = tx.shape[1] // (H * W)
- x = np.zeros((bs, cin, OY, OX), dtype=tx.dtype)
"""
# col2im is just im2col in reverse
- tx = tx.reshape(bs, -1)
- ic, iy, ix = get_im2col_indexes(oy, ox, cin, H, W)
- np.add.at(x, (slice(None), ic, iy, ix), tx)
+ x = np.zeros((bs, cin*OY*OX), dtype=tx.dtype)
+ idx = get_im2col_index(oy, ox, cin, H, W)
+ np.add.at(x, (slice(None), idx), tx.reshape(bs, -1))
"""
# sadly, this is faster
+ x = np.zeros((bs, cin, OY, OX), dtype=tx.dtype)
tx = tx.reshape(bs, oy, ox, cin, H, W)
for Y in range(oy):
for X in range(ox):
x[:, :, Y:Y+H, X:X+W] += tx[:, Y, X]
- return x
+ return x.reshape(bs, cin, OY, OX)

conv isn't fast yet