kparzysz-quic commented on code in PR #12854:
URL: https://github.com/apache/tvm/pull/12854#discussion_r984890169
##########
tests/python/contrib/test_hexagon/test_meta_schedule.py:
##########
@@ -209,3 +213,207 @@ def schedule_dense_for_tune(sch):
with hexagon_launcher.start_session() as session:
verify_dense(sch, target, M, N, K, session)
+
+
+# This is an example of a schedule found by vrmpy auto tensorization.
+# It gets 440 GFLOPS on SD888.
[email protected]_module
+class Module_vrmpy_auto_tensorize:
+ @T.prim_func
+ def main(
+ X: T.Buffer[(128, 768), "uint8"],
+ packedW: T.Buffer[(24, 192, 32, 4), "uint8"],
+ compute: T.Buffer[(128, 768), "int32"],
+ ) -> None:
+ T.func_attr({"global_symbol": "main", "tir.noalias": True})
+ for i0_0_i1_0_0_fused in T.parallel(
+ 512, annotations={"pragma_auto_unroll_max_step": 64,
"pragma_unroll_explicit": 1}
+ ):
+ for i0_1_init, i1_0_1_init, i0_2_init, i1_0_2_init in T.grid(2, 3,
1, 1):
+ with T.block("compute_o_init"):
+ i = T.axis.spatial(128, i0_0_i1_0_0_fused // 8 * 2 +
i0_1_init + i0_2_init)
+ j_o = T.axis.spatial(24, i1_0_2_init + i0_0_i1_0_0_fused %
8 * 3 + i1_0_1_init)
+ T.reads()
+ T.writes(compute[i, j_o * 32 : j_o * 32 + 32])
+ for i1_1 in T.vectorized(32):
+ with T.block("compute_init"):
+ j_i_init = T.axis.spatial(32, i1_1)
+ T.reads()
+ T.writes(compute[i, j_o * 32 + j_i_init])
+ compute[i, j_o * 32 + j_i_init] = 0
+ for i2_0_0, i0_1, i1_0_1, i2_0_1, i0_2, i1_0_2 in T.grid(32, 2, 3,
6, 1, 1):
+ with T.block("compute_o_update"):
+ i = T.axis.spatial(128, i0_0_i1_0_0_fused // 8 * 2 + i0_1
+ i0_2)
+ j_o = T.axis.spatial(24, i1_0_2 + i0_0_i1_0_0_fused % 8 *
3 + i1_0_1)
+ k_o = T.axis.reduce(192, i2_0_0 * 6 + i2_0_1)
+ T.reads(
+ compute[i, j_o * 32 : j_o * 32 + 32],
+ X[i, k_o * 4 : k_o * 4 + 4],
+ packedW[j_o, k_o, 0:32, 0:4],
+ )
+ T.writes(compute[i, j_o * 32 : j_o * 32 + 32])
+ A = T.match_buffer(
+ X[i, k_o * 4 : k_o * 4 + 4], [4], dtype="uint8",
offset_factor=1
+ )
+ B = T.match_buffer(
+ packedW[j_o, k_o, 0:32, 0:4], [32, 4], dtype="uint8",
offset_factor=1
+ )
+ C = T.match_buffer(
+ compute[i, j_o * 32 : j_o * 32 + 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_i32x32: T.int32x32 = T.reinterpret(B[0, 0:128],
dtype="int32x32")
+ C[0:32] = T.call_llvm_pure_intrin(
+ 4390, T.uint32(3), C[0:32], B_i32x32, A_i32,
dtype="int32x32"
+ )
+
+
[email protected]_hexagon
+def test_vrmpy_dense_auto_tensorize(hexagon_launcher):
+ if hexagon_launcher._serial_number == "simulator":
+ pytest.skip(msg="Tuning on simulator not supported.")
+
+ target_hexagon = tvm.target.hexagon("v68")
+ target = tvm.target.Target(target_hexagon, host=target_hexagon)
+
+ M, N, K = 128, 768, 768
+ workload = te.create_prim_func(dense(M, N, K))
+
+ sch_rules = [
+ schedule_rule.MultiLevelTilingWithIntrin(
+ VRMPY_u8u8i32_INTRIN,
+ structure="SRSRS",
+ tile_binds=None,
+ max_innermost_factor=64,
+ vector_load_lens=None,
+ reuse_read=None,
+ reuse_write=schedule_rule.ReuseType(
+ req="may",
+ levels=[1, 2],
+ scope="global",
+ ),
+ ),
+ schedule_rule.ParallelizeVectorizeUnroll(
+ max_jobs_per_core=16,
+ max_vectorize_extent=128,
+ unroll_max_steps=[0, 16, 64, 512],
+ unroll_explicit=True,
+ ),
+ ]
+
+ postprocs = [
+ postproc.RewriteParallelVectorizeUnroll(),
+ postproc.RewriteReductionBlock(),
+ postproc.RewriteTensorize(vectorize_init_loop=True),
+ ]
+
+ if True:
Review Comment:
Is this a leftover from something?
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