roastduck commented on a change in pull request #6238:
URL: https://github.com/apache/incubator-tvm/pull/6238#discussion_r476316338
##########
File path: tests/python/unittest/test_tir_transform_hoist_if.py
##########
@@ -255,6 +259,488 @@ def test_multi_if():
('tir.For', 'i'): (('tir.IfThenElse', ('i',)),)}
verify_structure(new_stmt, expected_struct)
+def test_no_hoisting_1():
+ ib = tvm.tir.ir_builder.create()
+ data = ib.pointer("float32", name="data")
+ n = te.var("n")
+
+ with ib.for_range(0, 10, "i") as i:
+ with ib.for_range(0, 10, "j") as j:
+ with ib.for_range(0, 10, "k") as k:
+ with ib.if_scope(k >= 3):
+ data[i * 100 + j * 10 + k] = data[i * 100 + j * 10 + k] +
0.5
+
+ stmt = ib.get()
+ mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([], stmt))
+ new_stmt = tvm.tir.transform.HoistIfThenElse()(mod)["main"].body
+ tvm.ir.assert_structural_equal(new_stmt, stmt)
+
+ with tvm.transform.PassContext(config={
+ "tir.HoistIfThenElse": {"support_block_scope_hosting": True}
+ }):
+ new_stmt = tvm.tir.transform.HoistIfThenElse()(mod)["main"].body
+ tvm.ir.assert_structural_equal(new_stmt, stmt)
+
+def test_no_hoisting_2():
+ ib = tvm.tir.ir_builder.create()
+ data = ib.pointer("float32", name="data")
+ n = te.var("n")
+ x = te.var("x")
+
+ with ib.for_range(0, 10, "i") as i:
+ with ib.for_range(0, 10, "j") as j:
+ with ib.for_range(0, 10, "k") as k:
+ with ib.if_scope(i >= 3):
+ data[i * 100 + j * 10 + k] = data[i * 100 + j * 10 + k] +
0.3
+ data[i * 100 + j * 10 + k] = data[i * 100 + j * 10 + k] + 0.5
+
+ stmt = ib.get()
+ mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([], stmt))
+ new_stmt = tvm.tir.transform.HoistIfThenElse()(mod)["main"].body
+ tvm.ir.assert_structural_equal(new_stmt, stmt)
+
+ with tvm.transform.PassContext(config={
+ "tir.HoistIfThenElse": {"support_block_scope_hosting": True}
+ }):
+ new_stmt = tvm.tir.transform.HoistIfThenElse()(mod)["main"].body
+ tvm.ir.assert_structural_equal(new_stmt, stmt)
+
+def test_no_hoisting_3():
+ ib = tvm.tir.ir_builder.create()
+ dshape = (32, 64)
+ dshape_inner = (33, 63)
+ data = ib.pointer("float32", name="data")
+ l = te.var('l')
+ m = te.var('m')
+ n = te.var('n')
+
+ tx = te.thread_axis("threadIdx.x")
+ bx = te.thread_axis("blockIdx.x")
+ ib.scope_attr(tx, "thread_extent", dshape[0])
+ ib.scope_attr(bx, "thread_extent", dshape[1])
+ with ib.for_range(0, l, "i") as i:
+ with ib.for_range(0, m, "j") as j:
+ with ib.for_range(0, n, "k") as k:
+ ib.scope_attr(tx, "thread_extent", dshape_inner[0])
+ ib.scope_attr(bx, "thread_extent", dshape_inner[1])
Review comment:
For example, if we are doing a collaborative load on a GPU, we use a
`compute_at` schedule to insert the loading function into the computing
function. The loading function will become a local scope inside the computing
function. Then, the thread attributes of the loading function will stay in that
scope, which can be correctly hoisted.
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