Merge branch 'master' of https://github.com/Jittor/jittor

notebook/example.src.md

@@ -59,7 +59,7 @@ optim = nn.SGD (model.parameters(), learning_rate)
 
 for i,(x,y) in enumerate(get_data(n)):
     pred_y = model(x)
-    loss = ((pred_y - y)**2)
+    loss = jt.sqr(pred_y - y)
     loss_mean = loss.mean()
     optim.step (loss_mean)
     print(f"step {i}, loss = {loss_mean.data.sum()}")

notebook/meta_op.src.md

@@ -102,7 +102,7 @@ def conv(x, w):
     ])
     ww = w.broadcast_var(xx)
     yy = xx*ww
-    y = yy.sum([3,4,5]) # Kh, Kw, Kc
+    y = yy.sum([3,4,5]) # Kh, Kw, c
     return y
 
 # Let's disable tuner. This will cause jittor not to use mkl for convolution
@@ -150,7 +150,7 @@ xx = x.reindex([N,H-Kh+1,W-Kw+1,Kh,Kw,C,Kc], [
 ])
 ww = w.broadcast_var(xx)
 yy = xx*ww
-y = yy.sum([3,4,5]) # Kh, Kw, Kc
+y = yy.sum([3,4,5]) # Kh, Kw, C
 ```
 
 **After expansion:**

python/jittor/test/test_reindex_op.py

@@ -21,7 +21,7 @@ def conv(x, w):
     ])
     ww = w.broadcast_var(xx)
     yy = xx*ww
-    y = yy.sum([3,4,5]) # Kh, Kw, Kc
+    y = yy.sum([3,4,5]) # Kh, Kw, C
     return y, yy
 
 def conv_naive(x, w):
@@ -52,7 +52,7 @@ def conv_transpose(x, w):
     ], 0, ['(i1-i3)%2', '(i2-i4)%2'])
     ww = w.broadcast_var(xx)
     yy = xx*ww
-    y = yy.sum([3,4,5]) # Kh, Kw, Kc
+    y = yy.sum([3,4,5]) # Kh, Kw, C
     return y, yy
 
 def conv_transpose_naive(x, w):

src/ops/reindex_op.h

@@ -86,7 +86,7 @@ struct ReindexOp : Op {
         ])
         ww = w.broadcast_var(xx)
         yy = xx*ww
-        y = yy.sum([3,4,5]) # Kh, Kw, Kc
+        y = yy.sum([3,4,5]) # Kh, Kw, C
         return y, yy
     ```
      */