masahi commented on code in PR #14603:
URL: https://github.com/apache/tvm/pull/14603#discussion_r1164050103
##########
tests/python/relax/test_transform_fuse_tir.py:
##########
@@ -883,5 +883,125 @@ def main(
_check(Before, Expected)
+def test_same_buffer_multiple_read():
+ @I.ir_module
+ class Module:
+ @T.prim_func
+ def concatenate(
+ rxplaceholder: T.Buffer((T.int64(1), T.int64(4), T.int64(64),
T.int64(64)), "float32"),
+ rxplaceholder_1: T.Buffer(
+ (T.int64(1), T.int64(4), T.int64(64), T.int64(64)), "float32"
+ ),
+ T_concat: T.Buffer((T.int64(2), T.int64(4), T.int64(64),
T.int64(64)), "float32"),
+ ):
+ T.func_attr({"op_pattern": 2, "tir.noalias": T.bool(True)})
+ for ax0, ax1, ax2, ax3 in T.grid(T.int64(2), T.int64(4),
T.int64(64), T.int64(64)):
+ with T.block("T_concat"):
+ v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0,
ax1, ax2, ax3])
+ T.reads(
+ rxplaceholder_1[v_ax0 - T.int64(1), v_ax1, v_ax2,
v_ax3],
+ rxplaceholder[v_ax0, v_ax1, v_ax2, v_ax3],
Review Comment:
Note that in this test case, `rxplaceholder_1` and `rxplaceholder` refer to
the same buffer `inp_0`. But they are used with different access patterns.
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