junrushao commented on code in PR #13301:
URL: https://github.com/apache/tvm/pull/13301#discussion_r1019703907
##########
tests/python/unittest/test_tir_transform_lower_cross_thread_reduction.py:
##########
@@ -487,6 +487,149 @@ def lowered_single_reduction_loop_with_block_predicate(
)
[email protected]_func
+def single_reduction_loop_with_tensorize(
+ input_A: T.Buffer[(1, 64, 7, 7, 32), "uint8"],
+ input_B: T.Buffer[(16, 64, 1, 1, 8, 32, 4), "int8"],
+ output: T.Buffer[(1, 16, 7, 7, 32), "int32"],
+) -> None:
+ # body
+ # with T.block("root")
+ for i1, i2, i3, i4, i5 in T.grid(16, 4, 98, 2, 32):
+ with T.block("compute_o"):
+ n = T.axis.spatial(1, 0)
+ oc_chunk = T.axis.spatial(16, i1)
+ oh = T.axis.spatial(7, (i2 * 6272 + i3 * 64 + i4 * 32 + i5) //
3584)
+ ow = T.axis.spatial(7, (i2 * 6272 + i3 * 64 + i4 * 32 + i5) % 3584
// 512)
+ kh = T.axis.reduce(1, 0)
+ kw = T.axis.reduce(1, 0)
+ ic_outer = T.axis.reduce(64, (i2 * 6272 + i3 * 64 + i4 * 32 + i5)
% 512 // 8)
+ ic_f_inner = T.axis.reduce(8, (i2 * 6272 + i3 * 64 + i4 * 32 + i5)
% 8)
+ T.reads(
+ input_A[n, ic_outer, oh + kh, ow + kw, ic_f_inner * 4 :
ic_f_inner * 4 + 4],
+ input_B[oc_chunk, ic_outer, kh, kw, ic_f_inner, 0:32, 0:4],
+ )
+ T.writes(output[n, oc_chunk, oh, ow, 0:32])
+ with T.init():
+ for x in T.serial(32):
+ with T.block("compute_init"):
+ oc_block_i_init = T.axis.spatial(32, x)
+ T.reads()
+ T.writes(output[n, oc_chunk, oh, ow, oc_block_i_init])
+ output[n, oc_chunk, oh, ow, oc_block_i_init] = 0
+ with T.block("compute_o"):
+ T.reads(
+ output[n, oc_chunk, oh, ow, 0:32],
+ input_A[n, ic_outer, oh + kh, ow + kw, ic_f_inner * 4 :
ic_f_inner * 4 + 4],
+ input_B[oc_chunk, ic_outer, kh, kw, ic_f_inner, 0:32, 0:4],
+ )
+ T.writes(output[n, oc_chunk, oh, ow, 0:32])
+ A = T.match_buffer(
+ input_A[n, ic_outer, oh + kh, ow + kw, ic_f_inner * 4 :
ic_f_inner * 4 + 4],
+ [4],
+ dtype="uint8",
+ offset_factor=1,
+ )
+ B = T.match_buffer(
+ input_B[oc_chunk, ic_outer, kh, kw, ic_f_inner, 0:32, 0:4],
+ [32, 4],
+ dtype="int8",
+ offset_factor=1,
+ )
+ C = T.match_buffer(
+ output[n, oc_chunk, oh, ow, 0:32], [32], dtype="int32",
offset_factor=1
+ )
+ A_u8x4: T.uint8x4 = A[0:4]
+ A_i32: T.int32 = T.reinterpret(A_u8x4, dtype="int32")
+ B_i8x128 = B[0, 0:128]
+ B_i32x32: T.int32x32 = T.reinterpret(B_i8x128,
dtype="int32x32")
+ C[0:32] = T.call_llvm_pure_intrin(
+ 4217, T.uint32(3), C[0:32], T.broadcast(A_i32, 32),
B_i32x32, dtype="int32x32"
+ )
+
+
[email protected]_func
+def nested_reduction_loop_with_inner_match_buffers(
+ in0: T.Buffer[(4, 16), "int8"],
+ in1: T.Buffer[(4, 16), "int8"],
+ out: T.Buffer[(4, 4), "int32"],
+) -> None:
+ # body
+ # with T.block("root")
+ for y in T.serial(4):
+ with T.block("C"):
+ yi = T.axis.spatial(4, y)
+ T.reads(in0[yi, 0:16], in1[yi, 0:16])
+ T.writes(out[yi, 0:4])
+ for x in T.serial(4):
+ xr = T.axis.reduce(4, x)
+ with T.init():
+ for i in T.serial(4):
+ with T.block("C_init"):
+ ii = T.axis.spatial(4, i)
+ T.reads()
+ T.writes(out[yi, ii])
+ out[yi, ii] = 0
+ with T.block("C"):
+ T.reads(
+ out[yi, xr],
+ in0[yi, yi * 4 + xr : yi * 4 + xr + 4],
+ in1[yi, yi * 4 + xr : yi * 4 + xr + 4],
+ )
+ T.writes(out[yi, xr])
+ A = T.match_buffer(
+ in0[yi, yi * 4 + xr : yi * 4 + xr + 4], [4],
dtype="int8", offset_factor=1
+ )
+ B = T.match_buffer(
+ in1[yi, yi * 4 + xr : yi * 4 + xr + 4], [4],
dtype="int8", offset_factor=1
+ )
+ C = T.match_buffer(out[yi, xr], [1], dtype="int32",
offset_factor=1)
+ A_i8x4: T.int8x4 = A[0:4]
+ A_i32: T.int32 = T.reinterpret(A_i8x4, dtype="int32")
+ B_i8x4: T.int8x4 = B[0:4]
+ B_i32: T.int32 = T.reinterpret(B_i8x4, dtype="int32")
+ C[0] = A_i32 + B_i32 + C[0]
+
+
[email protected]_func
+def nested_reduction_loop_with_outer_match_buffers(
+ in0: T.Buffer[(4, 16), "int8"],
+ in1: T.Buffer[(4, 16), "int8"],
+ out: T.Buffer[(4, 4), "int32"],
+) -> None:
+ # body
+ # with T.block("root")
+ for y in T.serial(4):
+ with T.block("C"):
+ yi = T.axis.spatial(4, y)
+ T.reads(in0[yi, 0:16], in1[yi, 0:16])
+ T.writes(out[yi, 0:4])
+ A = T.match_buffer(in0[yi, 0:16], [16], dtype="int8",
offset_factor=1)
+ B = T.match_buffer(in1[yi, 0:16], [16], dtype="int8",
offset_factor=1)
+ C = T.match_buffer(out[yi, 0:4], [4], dtype="int32",
offset_factor=1)
+ for x in T.serial(4):
+ xr = T.axis.reduce(4, x)
+ with T.init():
+ for i in T.serial(4):
+ with T.block("C_init"):
+ ii = T.axis.spatial(4, i)
+ T.reads()
+ T.writes(out[yi, ii])
+ out[yi, ii] = 0
+ with T.block("C"):
Review Comment:
If we print out the testcase using
`nested_reduction_loop_with_outer_match_buffers.show()`, then the TIR looks
like:
```python
# from tvm.script import tir as T
@T.prim_func
def func(in0: T.Buffer[(4, 16), "int8"], in1: T.Buffer[(4, 16), "int8"],
out: T.Buffer[(4, 4), "int32"]):
# body
# with T.block("root")
for y in T.serial(4):
with T.block("C"):
yi = T.axis.spatial(4, y)
xr = T.axis.reduce(4, x)
T.reads(in0[yi, 0 : 16], in1[yi, 0 : 16])
T.writes(out[yi, 0 : 4])
A = T.match_buffer(in0[yi, 0 : 16], [16], dtype="int8",
offset_factor=1)
B = T.match_buffer(in1[yi, 0 : 16], [16], dtype="int8",
offset_factor=1)
C = T.match_buffer(out[yi, 0 : 4], [4], dtype="int32",
offset_factor=1)
with T.init():
for i in T.serial(4):
with T.block("C_init"):
ii = T.axis.spatial(4, i)
T.reads()
T.writes(out[yi, ii])
out[yi, ii] = 0
for x in T.serial(4):
with T.block("C"):
T.reads(out[yi, xr], in0[yi, yi * 4 + xr : yi * 4 + xr +
4], in1[yi, yi * 4 + xr : yi * 4 + xr + 4])
T.writes(out[yi, xr])
A_i8x4: T.int8x4 = A[yi * 4 + xr:yi * 4 + xr + 4]
A_i32: T.int32 = T.reinterpret(A_i8x4, dtype="int32")
B_i8x4: T.int8x4 = B[yi * 4 + xr:yi * 4 + xr + 4]
B_i32: T.int32 = T.reinterpret(B_i8x4, dtype="int32")
C[xr] = A_i32 + B_i32 + C[xr]
```
where we may see some use-before-def issues
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