[TOPI] Fix reduction fusion with injective input (#475)

topi/python/topi/cuda/reduction.py

@@ -34,6 +34,7 @@ def _schedule_reduce(op, sch):
 
         # Bind the axes to threads and blocks
         sch[data_out].bind(sch[data_out].op.reduce_axis[0], thread_x)
+        sch[data_out].set_store_predicate(thread_x.equal(0))
         sch[data_out].bind(outer_in, thread_y)
         sch[data_out].bind(bx, block_x)
     else:
@@ -57,17 +58,22 @@ def schedule_reduce(outs):
     """
     outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
     sch = tvm.create_schedule([x.op for x in outs])
-    def traverse(operator):
+
+    def traverse_before_reduce(operator):
         if tag.is_injective(operator.tag):
-            if operator not in sch.outputs:
-                sch[operator].compute_inline()
-            for tensor in operator.input_tensors:
-                if tensor.op.input_tensors:
-                    traverse(tensor.op)
+            sch[operator].compute_inline()
+        else:
+            raise RuntimeError("Unsupported operator: %s" % operator.tag)
+
+    def traverse_after_reduce(operator):
+        if tag.is_broadcast(operator.tag):
+            raise RuntimeError("Not yet support ewise after reduce")
         elif operator.tag == 'comm_reduce':
             _schedule_reduce(operator, sch)
+            for tensor in operator.input_tensors:
+                traverse_before_reduce(tensor.op)
         else:
             raise RuntimeError("Unsupported operator: %s" % operator.tag)
 
-    traverse(outs[0].op)
+    traverse_after_reduce(outs[0].op)
     return sch

topi/tests/python/test_topi_reduce.py

@@ -7,12 +7,13 @@ import topi
 def verify_reduce_map_ele(in_shape, axis, keepdims, type="sum"):
     # Build the logic and compile the function
     A = tvm.placeholder(shape=in_shape, name="A")
+    A1 = topi.exp(A)
     if type == "sum":
-        B = topi.sum(A, axis=axis, keepdims=keepdims)
+        B = topi.sum(A1, axis=axis, keepdims=keepdims)
     elif type == "max":
-        B = topi.max(A, axis=axis, keepdims=keepdims)
+        B = topi.max(A1, axis=axis, keepdims=keepdims)
     elif type == "min":
-        B = topi.min(A, axis=axis, keepdims=keepdims)
+        B = topi.min(A1, axis=axis, keepdims=keepdims)
     else:
         raise NotImplementedError
     s = topi.cuda.schedule_reduce(B)
@@ -23,15 +24,15 @@ def verify_reduce_map_ele(in_shape, axis, keepdims, type="sum"):
             return
         ctx = tvm.gpu(0) if device == "cuda" else tvm.cl(0)
         foo = tvm.build(s, [A, B], device, name="sum")
-
         # Test
-        in_npy = np.random.normal(size=in_shape).astype(np.float32)
+        in_npy = np.random.uniform(size=in_shape).astype(np.float32)
+        in_npy_map = np.exp(in_npy)
         if type == "sum":
-            out_npy = in_npy.sum(axis=axis, keepdims=keepdims)
+            out_npy = in_npy_map.sum(axis=axis, keepdims=keepdims)
         elif type == "max":
-            out_npy = in_npy.max(axis=axis, keepdims=keepdims)
+            out_npy = in_npy_map.max(axis=axis, keepdims=keepdims)
         elif type == "min":
-            out_npy = in_npy.min(axis=axis, keepdims=keepdims)
+            out_npy = in_npy_map.min(axis=axis, keepdims=keepdims)
         else:
             raise NotImplementedError