add slice broadcast

Author: cjld

python/jittor/__init__.py

@@ -9,7 +9,7 @@
 # file 'LICENSE.txt', which is part of this source code package.
 # ***************************************************************
 
-__version__ = '1.3.3.7'
+__version__ = '1.3.3.8'
 from jittor_utils import lock
 with lock.lock_scope():
     ori_int = int

python/jittor/src/ops/broadcast_to_op.cc

@@ -98,7 +98,7 @@ BroadcastToOp::BroadcastToOp(Var* x, NanoVector shape, NanoVector dims) : x(x),
 bool BroadcastToOp::need_broadcast(const Var* x, const NanoVector& shape) {
     if (x->shape.size() < shape.size()) return true;
     for (uint i=shape.size()-1, j=x->shape.size()-1; i<shape.size(); i--,j--)
-        if (x->shape[j]< 0 || (x->shape[j] != shape[i] && shape[i] != 1)) return true;
+        if ((x->shape[j] != shape[i] && shape[i] != 1)) return true;
     return false;
 }
 
@@ -154,8 +154,6 @@ void BroadcastToOp::infer_shape() {
         int64 zs;
         if ((xshape == 1 || yshape == 1) && (xshape != yshape)) {
             zs = xshape * yshape;
-        } else if (xshape < 0 || yshape < 0) {
-            zs = std::min(xshape, yshape);
         } else {
             CHECKop(xshape,==,yshape) << "Shape not match" << x->shape << yshapes << bcast_mask;
             zs = xshape;

python/jittor/src/ops/getitem_op.cc

@@ -108,11 +108,11 @@ void GetitemOp::infer_slices(
             for (int j=0; j<niv; j++) {
                 auto iv_shape_j = iv_shape[niv-j-1];
                 auto& out_shape_j = out_shape[first_oid_of_var+var_dim-j-1];
+                CHECK(out_shape_j == iv_shape_j || out_shape_j == 1 || iv_shape_j == 1) << "Shape not match " >> out_shape_j >> "!="
+                    >> iv_shape_j << "data shape:" << in_shape <<
+                    "slice shape:" << iv_shape;
                 if (out_shape_j == 1)
                     out_shape_j = iv_shape_j;
-                else
-                    ASSERT(out_shape_j == iv_shape_j || out_shape_j < 0 || iv_shape_j < 0)
-                        << out_shape_j << iv_shape_j << out_shape;
             }
         } else
         if (s.is_ellipsis()) {

python/jittor/src/ops/reshape_op.cc

@@ -41,9 +41,7 @@ void ReshapeOp::infer_shape() {
     CHECK(uncertain_dim <= 1) << "max number of -1 is 1, but get" << uncertain_dim << ".";
     int64_t x_items = x->num;
     auto yshape = shape;
-    if (x_items < 0) {
-        // pass if input is uncertain
-    } else if (uncertain_dim == 0) {
+    if (uncertain_dim == 0) {
         CHECKop(x_items,==,y_items) << "reshape shape is invalid for input of size";
     } else {
         CHECK(y_items != 0 && x_items % y_items == 0) << "reshape shape is invalid for input of size " << x_items;

python/jittor/src/ops/ternary_op.cc

@@ -58,7 +58,6 @@ void TernaryOp::infer_shape() {
         auto shape = std::min(xshape, std::min(yshape, cshape));
         auto shape2 = std::max(xshape, std::max(yshape, cshape));
         zshape.push_back(shape2);
-        if (shape < 0) continue;
         CHECK(shape==shape2) << "Shape not match" << x->shape << y->shape << cond->shape;
     }
     z->set_shape(zshape);

python/jittor/test/test_setitem.py

@@ -394,7 +394,39 @@ def test_dfs_memopt(self):
             jt.get_max_memory_treemap()
 
 
-
+    def test_setitem_bc(self):
+        a = jt.random([10,11,12])
+        b = a[jt.arange(3)[:,None],
+            jt.arange(4)[None,:]]
+        b.sync()
+        assert (a[:3, :4] == b).all()
+        
+        a = jt.random([10,11,12])
+        b = a[jt.arange(3)[:,None],
+            jt.arange(4)[None,:],
+            jt.arange(4)[None,:]]
+        nb = a.data[np.arange(3)[:,None],
+            np.arange(4)[None,:],
+            np.arange(4)[None,:]]
+        np.testing.assert_allclose(nb, b.data)
+        
+        a = jt.random([10,11,12])
+        b = a[jt.arange(3)[::-1,None],
+            jt.arange(4)[None,:],
+            jt.arange(4)[None,:]]
+        nb = a.data[np.arange(3)[::-1,None],
+            np.arange(4)[None,:],
+            np.arange(4)[None,:]]
+        np.testing.assert_allclose(nb, b.data)
+        
+        a = jt.random([10,11,12])
+        b = a[jt.arange(3)[::-1,None],
+            jt.arange(4)[None,:],
+            jt.arange(4)[None,::-1]]
+        nb = a.data[np.arange(3)[::-1,None],
+            np.arange(4)[None,:],
+            np.arange(4)[None,::-1]]
+        np.testing.assert_allclose(nb, b.data)