adfwer233 commented on code in PR #15389: URL: https://github.com/apache/tvm/pull/15389#discussion_r1272937096
########## tests/python/dlight/test_gpu_matmul_tensorize.py: ########## @@ -0,0 +1,259 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=missing-docstring +import pytest + +import tvm.testing +from tvm import dlight as dl +from tvm.script import ir as I +from tvm.script import tir as T +from tvm.target import Target + + +class BaseBeforeAfter(tvm.testing.CompareBeforeAfter): + @pytest.fixture + def transform(self): + def transform(mod): + with Target("nvidia/geforce-rtx-2080-ti"): + return dl.ApplyDefaultSchedule(dl.gpu.Matmul())(mod) + return transform + + +class TestMatmulTensorize(BaseBeforeAfter): + # fmt: off + + @T.prim_func + def before(X: T.Buffer((256, 256), "float16"), W: T.Buffer((256, 256), "float16"), compute: T.Buffer((256, 256), "float16")): + T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)}) + # with T.block("root"): + for i, j, k in T.grid(256, 256, 256): + with T.block("compute"): + v_i, v_j, v_k = T.axis.remap("SSR", [i, j, k]) + T.reads(X[v_i, v_k], W[v_j, v_k]) + T.writes(compute[v_i, v_j]) + with T.init(): + compute[v_i, v_j] = T.float16(0) + compute[v_i, v_j] = compute[v_i, v_j] + X[v_i, v_k] * W[v_j, v_k] + + @T.prim_func + def expected(X: T.Buffer((256, 256), "float16"), W: T.Buffer((256, 256), "float16"), compute: T.Buffer((256, 256), "float16")): + T.func_attr({"global_symbol": "main", "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + # with T.block("root"): + X_reindex_shared_dyn = T.alloc_buffer((1, 256, 256), "float16", scope="shared.dyn") + W_reindex_shared_dyn = T.alloc_buffer((1, 256, 256), "float16", scope="shared.dyn") + X_reindex_shared_dyn_wmma_matrix_a = T.alloc_buffer((1, 256, 256), "float16", scope="wmma.matrix_a") + W_reindex_shared_dyn_wmma_matrix_b = T.alloc_buffer((1, 256, 256), "float16", scope="wmma.matrix_b") + compute_reindex_shared = T.alloc_buffer((1, 256, 256), "float16", scope="shared") + compute_reindex_shared_wmma_accumulator = T.alloc_buffer((1, 256, 256), "float16", scope="wmma.accumulator") + for ax0 in T.thread_binding(T.int64(1), thread="blockIdx.z"): + for ax1_0_0_ax2_0_0_fused in T.thread_binding(4, thread="blockIdx.x"): + for ax1_0_1_ax2_0_1_fused in T.thread_binding(4, thread="blockIdx.y"): + for ax2_0_2_ax1_0_2_fused in T.thread_binding(4, thread="threadIdx.y"): + for ax1_0_3_init, ax2_0_3_init in T.grid(2, 2): + with T.block("compute_o_init"): + v0_o = T.axis.spatial(T.int64(1), ax0) + v1_o = T.axis.spatial(16, ax1_0_0_ax2_0_0_fused * 4 + ax2_0_2_ax1_0_2_fused % 2 * 2 + ax1_0_3_init) + v2_o = T.axis.spatial(16, ax1_0_1_ax2_0_1_fused * 4 + ax2_0_2_ax1_0_2_fused // 2 * 2 + ax2_0_3_init) + T.reads() + T.writes(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + with T.block("compute_init_o"): + v1_i_init_o = T.axis.spatial(1, 0) + v2_i_init_o = T.axis.spatial(1, 0) + T.reads() + T.writes(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + C = T.match_buffer(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("C_s0", "C_s1"), scope="wmma.accumulator", offset_factor=16) + T.tvm_fill_fragment(C.data, 16, 16, 16, C.elem_offset // C.strides[0] // 16 * (C.strides[0] // 16) + C.elem_offset % C.strides[0] // 16, T.float32(0)) + for ax3_0_0 in range(8): + for ax0_ax1_fused_0 in range(2): + for ax0_ax1_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for ax0_ax1_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for ax0_ax1_fused_3 in T.vectorized(8): + with T.block("X_reindex_shared.dyn"): + v0 = T.axis.spatial(1, 0) + v1 = T.axis.spatial(256, ax1_0_0_ax2_0_0_fused * 64 + (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 * 256 + ax0_ax1_fused_2 * 8 + ax0_ax1_fused_3) // 32) + v2 = T.axis.spatial(256, ax3_0_0 * 32 + (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 * 256 + ax0_ax1_fused_2 * 8 + ax0_ax1_fused_3) % 32) + T.reads(X[v1, v2]) + T.writes(X_reindex_shared_dyn[v0, v1, v2]) + T.block_attr({"buffer_dim_align": [[0, 1, 16, 8]]}) + X_reindex_shared_dyn[v0, v1, v2] = X[v1, v2] + for ax0_ax1_fused_0 in range(2): + for ax0_ax1_fused_1 in T.thread_binding(4, thread="threadIdx.y"): + for ax0_ax1_fused_2 in T.thread_binding(32, thread="threadIdx.x"): + for ax0_ax1_fused_3 in T.vectorized(8): + with T.block("W_reindex_shared.dyn"): + v0 = T.axis.spatial(1, 0) + v1 = T.axis.spatial(256, ax1_0_1_ax2_0_1_fused * 64 + (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 * 256 + ax0_ax1_fused_2 * 8 + ax0_ax1_fused_3) // 32) + v2 = T.axis.spatial(256, ax3_0_0 * 32 + (ax0_ax1_fused_0 * 1024 + ax0_ax1_fused_1 * 256 + ax0_ax1_fused_2 * 8 + ax0_ax1_fused_3) % 32) + T.reads(W[v1, v2]) + T.writes(W_reindex_shared_dyn[v0, v1, v2]) + T.block_attr({"buffer_dim_align": [[0, 1, 16, 8]]}) + W_reindex_shared_dyn[v0, v1, v2] = W[v1, v2] + for ax3_0_1 in range(2): + for ax0_0 in T.unroll(2): + for ax1_0 in T.unroll(1): + with T.block("X_reindex_shared.dyn_wmma.matrix_a_o"): + v0_o = T.axis.spatial(1, 0) + v1_o = T.axis.spatial(16, ax1_0_0_ax2_0_0_fused * 4 + ax2_0_2_ax1_0_2_fused % 2 * 2 + ax0_0) + v2_o = T.axis.spatial(16, ax3_0_0 * 2 + ax3_0_1 + ax1_0) + T.reads(X_reindex_shared_dyn[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + T.writes(X_reindex_shared_dyn_wmma_matrix_a[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + A = T.match_buffer(X_reindex_shared_dyn[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("A_s0", "A_s1"), scope="shared.dyn", offset_factor=16) + C = T.match_buffer(X_reindex_shared_dyn_wmma_matrix_a[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("C_s0", "C_s1"), scope="wmma.matrix_a", offset_factor=16) + T.tvm_load_matrix_sync(C.data, 16, 16, 16, C.elem_offset // C.strides[0] // 16 * (C.strides[0] // 16) + C.elem_offset % C.strides[0] // 16, T.tvm_access_ptr(T.type_annotation("float16"), A.data, A.elem_offset, A.strides[0] * 16, 1), A.strides[0], "row_major") + for ax0_0 in T.unroll(2): + for ax1_0 in T.unroll(1): + with T.block("W_reindex_shared.dyn_wmma.matrix_b_o"): + v0_o = T.axis.spatial(1, 0) + v1_o = T.axis.spatial(16, ax1_0_1_ax2_0_1_fused * 4 + ax2_0_2_ax1_0_2_fused // 2 * 2 + ax0_0) + v2_o = T.axis.spatial(16, ax3_0_0 * 2 + ax3_0_1 + ax1_0) + T.reads(W_reindex_shared_dyn[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + T.writes(W_reindex_shared_dyn_wmma_matrix_b[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + A = T.match_buffer(W_reindex_shared_dyn[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("A_s0", "A_s1"), scope="shared.dyn", offset_factor=16) + C = T.match_buffer(W_reindex_shared_dyn_wmma_matrix_b[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("C_s0", "C_s1"), scope="wmma.matrix_b", offset_factor=16) + T.tvm_load_matrix_sync(C.data, 16, 16, 16, C.elem_offset // C.strides[0] // 16 * (C.strides[0] // 16) + C.elem_offset % C.strides[0] // 16, T.tvm_access_ptr(T.type_annotation("float16"), A.data, A.elem_offset, A.strides[0] * 16, 1), A.strides[0], "col_major") + for ax1_0_3, ax2_0_3 in T.grid(2, 2): + with T.block("compute_o_update"): + v0_o = T.axis.spatial(T.int64(1), ax0) + v1_o = T.axis.spatial(16, ax1_0_0_ax2_0_0_fused * 4 + ax2_0_2_ax1_0_2_fused % 2 * 2 + ax1_0_3) + v2_o = T.axis.spatial(16, ax1_0_1_ax2_0_1_fused * 4 + ax2_0_2_ax1_0_2_fused // 2 * 2 + ax2_0_3) + v3_o = T.axis.reduce(16, ax3_0_0 * 2 + ax3_0_1) + T.reads(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], X_reindex_shared_dyn_wmma_matrix_a[0, v1_o * 16:v1_o * 16 + 16, v3_o * 16:v3_o * 16 + 16], W_reindex_shared_dyn_wmma_matrix_b[0, v2_o * 16:v2_o * 16 + 16, v3_o * 16:v3_o * 16 + 16]) + T.writes(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + with T.block("compute_o"): + v1_i_o = T.axis.spatial(1, 0) + v2_i_o = T.axis.spatial(1, 0) + v3_i_o = T.axis.reduce(1, 0) + T.reads(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], X_reindex_shared_dyn_wmma_matrix_a[0, v1_o * 16:v1_o * 16 + 16, v3_o * 16:v3_o * 16 + 16], W_reindex_shared_dyn_wmma_matrix_b[0, v2_o * 16:v2_o * 16 + 16, v3_o * 16:v3_o * 16 + 16]) + T.writes(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + A = T.match_buffer(X_reindex_shared_dyn_wmma_matrix_a[0, v1_o * 16:v1_o * 16 + 16, v3_o * 16:v3_o * 16 + 16], (16, 16), "float16", strides=("A_s0", "A_s1"), scope="wmma.matrix_a", offset_factor=16) + B = T.match_buffer(W_reindex_shared_dyn_wmma_matrix_b[0, v2_o * 16:v2_o * 16 + 16, v3_o * 16:v3_o * 16 + 16], (16, 16), "float16", strides=("B_s0", "B_s1"), scope="wmma.matrix_b", offset_factor=16) + C = T.match_buffer(compute_reindex_shared_wmma_accumulator[0, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("C_s0", "C_s1"), scope="wmma.accumulator", offset_factor=16) + T.tvm_mma_sync(C.data, C.elem_offset // C.strides[0] // 16 * (C.strides[0] // 16) + C.elem_offset % C.strides[0] // 16, A.data, A.elem_offset // A.strides[0] // 16 * (A.strides[0] // 16) + A.elem_offset % A.strides[0] // 16, B.data, B.elem_offset // B.strides[0] // 16 * (B.strides[0] // 16) + B.elem_offset % B.strides[0] // 16, C.data, C.elem_offset // C.strides[0] // 16 * (C.strides[0] // 16) + C.elem_offset % C.strides[0] // 16) + for ax0_0, ax1_0 in T.grid(2, 2): + with T.block("compute_reindex_shared_wmma.accumulator_o"): + v0_o = T.axis.spatial(1, 0) + v1_o = T.axis.spatial(16, ax1_0_0_ax2_0_0_fused * 4 + ax2_0_2_ax1_0_2_fused % 2 * 2 + ax0_0) + v2_o = T.axis.spatial(16, ax1_0_1_ax2_0_1_fused * 4 + ax2_0_2_ax1_0_2_fused // 2 * 2 + ax1_0) + T.reads(compute_reindex_shared_wmma_accumulator[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + T.writes(compute_reindex_shared[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16]) + A = T.match_buffer(compute_reindex_shared_wmma_accumulator[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("A_s0", "A_s1"), scope="wmma.accumulator", offset_factor=16) + C = T.match_buffer(compute_reindex_shared[v0_o, v1_o * 16:v1_o * 16 + 16, v2_o * 16:v2_o * 16 + 16], (16, 16), "float16", strides=("C_s0", "C_s1"), scope="shared", offset_factor=16) + T.tvm_store_matrix_sync(A.data, 16, 16, 16, A.elem_offset // A.strides[0] // 16 * (A.strides[0] // 16) + A.elem_offset % A.strides[0] // 16, T.tvm_access_ptr(T.type_annotation("float16"), C.data, C.elem_offset, C.strides[0] * 16, 2), C.strides[0], "row_major") + for ax0_1, ax1 in T.grid(32, 32): + with T.block("compute_reindex_shared"): + v0 = T.axis.spatial(1, 0) + v1 = T.axis.spatial(256, ax1_0_0_ax2_0_0_fused * 64 + ax2_0_2_ax1_0_2_fused % 2 * 32 + ax0_1) + v2 = T.axis.spatial(256, ax1_0_1_ax2_0_1_fused * 64 + ax2_0_2_ax1_0_2_fused // 2 * 32 + ax1) + T.reads(compute_reindex_shared[v0, v1, v2]) + T.writes(compute[v1, v2]) + T.block_attr({"buffer_dim_align": [[0, 1, 16, 4]]}) + compute[v1, v2] = compute_reindex_shared[v0, v1, v2] + + # fmt: on + +class TestMatmulTensorizeTooSmall(BaseBeforeAfter): + # fmt: off + + @T.prim_func + def before(var_X: T.handle, W: T.Buffer((15, 256), "float16"), var_compute: T.handle): + T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)}) + m = T.int32() + X = T.match_buffer(var_X, (m, 256), "float16") + compute = T.match_buffer(var_compute, (m, 15)) + # with T.block("root"): + for i, j, k in T.grid(m, 15, 256): + with T.block("compute"): + v_i, v_j, v_k = T.axis.remap("SSR", [i, j, k]) + T.reads(X[v_i, v_k], W[v_j, v_k]) + T.writes(compute[v_i, v_j]) + with T.init(): + compute[v_i, v_j] = T.float32(0) + compute[v_i, v_j] = compute[v_i, v_j] + T.Cast("float32", X[v_i, v_k]) * T.Cast("float32", W[v_j, v_k]) + + @T.prim_func + def expected(var_X: T.handle, W: T.Buffer((15, 256), "float16"), var_compute: T.handle): + T.func_attr({"global_symbol": "main", "tir.is_scheduled": 1, "tir.noalias": T.bool(True)}) + m = T.int32() + X = T.match_buffer(var_X, (m, 256), "float16") + compute = T.match_buffer(var_compute, (m, 15)) + # with T.block("root"): + compute_reindex_pad_local = T.alloc_buffer((1, (T.Cast("int32", T.Cast("int64", m)) + 31) // 32 * 32, 64), scope="local") + X_reindex_pad_shared = T.alloc_buffer((1, (T.Cast("int32", T.Cast("int64", m)) + 31) // 32 * 32, 256), "float16", scope="shared") + W_reindex_pad_shared = T.alloc_buffer((1, 64, 256), "float16", scope="shared") + for ax0_ax2_0_fused in T.thread_binding(T.int64(1), thread="blockIdx.y"): + for ax1_0 in T.thread_binding((T.Cast("int32", T.Cast("int64", m)) + 31) // 32, thread="blockIdx.x"): + for ax2_1 in T.thread_binding(1, thread="vthread.y"): + for ax1_1 in T.thread_binding(1, thread="vthread.x"): + for ax2_2 in T.thread_binding(16, thread="threadIdx.y"): + for ax1_2 in T.thread_binding(8, thread="threadIdx.x", annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}): + for ax2_3_init, ax1_3_init in T.grid(4, 4): + with T.block("compute_init"): + v0 = T.axis.spatial(T.int64(1), T.int64(0)) + v1 = T.axis.spatial((T.Cast("int32", T.Cast("int64", m)) + 31) // 32 * 32, ax1_0 * 32 + ax1_1 * 32 + ax1_2 * 4 + ax1_3_init) + v2 = T.axis.spatial(64, ax2_1 * 64 + ax2_2 * 4 + ax2_3_init) + T.reads() + T.writes(compute_reindex_pad_local[0, v1, v2]) + compute_reindex_pad_local[0, v1, v2] = T.float32(0) + for ax3_0 in range(16): + for ax0_ax1_ax2_fused_0 in T.thread_binding(16, thread="threadIdx.y"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(8, thread="threadIdx.x"): + for ax0_ax1_ax2_fused_2 in range(2): + for ax0_ax1_ax2_fused_3 in T.vectorized(2): + with T.block("X_reindex_pad_shared"): + v0 = T.axis.spatial(1, 0) + v1 = T.axis.spatial((T.Cast("int32", T.Cast("int64", m)) + 31) // 32 * 32, ax1_0 * 32 + (ax0_ax1_ax2_fused_0 * 32 + ax0_ax1_ax2_fused_1 * 4 + ax0_ax1_ax2_fused_2 * 2 + ax0_ax1_ax2_fused_3) // 16) + v2 = T.axis.spatial(256, ax3_0 * 16 + (ax0_ax1_ax2_fused_0 * 32 + ax0_ax1_ax2_fused_1 * 4 + ax0_ax1_ax2_fused_2 * 2 + ax0_ax1_ax2_fused_3) % 16) + T.reads(X[v1, v2]) + T.writes(X_reindex_pad_shared[v0, v1, v2]) + T.block_attr({"buffer_dim_align": [[0, 1, 8, 2]]}) + X_reindex_pad_shared[v0, v1, v2] = T.if_then_else(v1 < m, X[v1, v2], T.float16(0)) + for ax0_ax1_ax2_fused_0 in T.thread_binding(16, thread="threadIdx.y"): + for ax0_ax1_ax2_fused_1 in T.thread_binding(8, thread="threadIdx.x"): + for ax0_ax1_ax2_fused_2 in range(4): + for ax0_ax1_ax2_fused_3 in T.vectorized(2): + with T.block("W_reindex_pad_shared"): + v0 = T.axis.spatial(1, 0) + v1 = T.axis.spatial(64, (ax0_ax1_ax2_fused_0 * 64 + ax0_ax1_ax2_fused_1 * 8 + ax0_ax1_ax2_fused_2 * 2 + ax0_ax1_ax2_fused_3) // 16) + v2 = T.axis.spatial(256, ax3_0 * 16 + (ax0_ax1_ax2_fused_0 * 64 + ax0_ax1_ax2_fused_1 * 8 + ax0_ax1_ax2_fused_2 * 2 + ax0_ax1_ax2_fused_3) % 16) + T.reads(W[v1, v2]) + T.writes(W_reindex_pad_shared[v0, v1, v2]) + T.block_attr({"buffer_dim_align": [[0, 1, 8, 2]]}) + W_reindex_pad_shared[v0, v1, v2] = T.if_then_else(v1 < 15, W[v1, v2], T.float16(0)) + for ax3_1, ax2_3, ax1_3 in T.grid(16, 4, 4): + with T.block("compute_update"): + v0 = T.axis.spatial(T.int64(1), T.int64(0)) + v1 = T.axis.spatial((T.Cast("int32", T.Cast("int64", m)) + 31) // 32 * 32, ax1_0 * 32 + ax1_1 * 32 + ax1_2 * 4 + ax1_3) + v2 = T.axis.spatial(64, ax2_1 * 64 + ax2_2 * 4 + ax2_3) + v3 = T.axis.reduce(256, ax3_0 * 16 + ax3_1) + T.reads(compute_reindex_pad_local[0, v1, v2], X_reindex_pad_shared[0, v1, v3], W_reindex_pad_shared[0, v2, v3]) + T.writes(compute_reindex_pad_local[0, v1, v2]) + compute_reindex_pad_local[0, v1, v2] = compute_reindex_pad_local[0, v1, v2] + T.Cast("float32", X_reindex_pad_shared[0, v1, v3]) * T.Cast("float32", W_reindex_pad_shared[0, v2, v3]) + for ax0, ax1, ax2_0 in T.grid(1, 4, 2): + for ax2_1_1 in T.vectorized(2): + with T.block("compute_reindex_pad_local"): + v0 = T.axis.spatial(1, ax0) + v1 = T.axis.spatial((T.Cast("int32", T.Cast("int64", m)) + 31) // 32 * 32, ax1_0 * 32 + ax1_2 * 4 + ax1) + v2 = T.axis.spatial(64, ax2_2 * 4 + ax2_0 * 2 + ax2_1_1) + T.reads(compute_reindex_pad_local[v0, v1, v2]) + T.writes(compute[v1, v2]) + if v1 < m and v2 < 15: + compute[v1, v2] = compute_reindex_pad_local[v0, v1, v2] + # fmt: on + + +if __name__ == "__main__": + tvm.testing.main() Review Comment: Sorry for that, I will format code to pass pylint test. -- This is an automated message from the Apache Git Service. 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