# 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=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition import tvm import numpy as np from topi.x86.tensor_intrin import dot_16x1x16_int8_int8_int16 def benchmark_fc_int8_acc16(): m = 128 n = 128 k = 128 X = tvm.placeholder((m, k), name='X', dtype="uint8") W = tvm.placeholder((n, k), name='W', dtype="int8") peak = 512/16*2*2*2 gops_per_mm = 2*n*m*k print("Peak {} Gops/s \n".format(peak)) def verify(target="llvm -mcpu=skylake-avx512"): if not tvm.module.enabled(target): print("skip because %s is not enabled..." % target) return ctx = tvm.context(target, 0) X = tvm.placeholder((m, k), name='X', dtype="uint8") W = tvm.placeholder((n, k), name='W', dtype="int8") pc = dot_16x1x16_int8_int8_int16() ak = tvm.reduce_axis((0, k), name='k') packedW = tvm.placeholder((n/128, 128*(k/2), 2), name='packedW', dtype="int8") t_fc = tvm.compute((m, n), lambda i, j: tvm.sum(X[i, ak].astype("int16") * packedW[j/128, (ak/2)*128+j%128, ak%2].astype("int16"), axis=ak), name="F") t_sch = tvm.create_schedule(t_fc.op) a_x, a_y = t_fc.op.axis a_k, = t_fc.op.reduce_axis a_yo, a_yi = t_sch[t_fc].split(a_y, factor=128) a_ko, a_ki = t_sch[t_fc].split(a_k, factor=2) a_xo, a_xi = t_sch[t_fc].split(a_x, factor=128) a_koo, a_koi = t_sch[t_fc].split(a_ko, factor=32) t_sch[t_fc].reorder(a_yo, a_xo, a_koo, a_xi, a_koi, a_yi, a_ki) t_sch[t_fc].tensorize(a_yi, pc) # print(tvm.lower(t_sch, [X, packedW, t_fc], simple_mode=True)) t_func = tvm.build(t_sch, [X, packedW, t_fc], target, name="intrinsic") t_evaluator = t_func.time_evaluator(t_func.entry_name, ctx, number=10) # generate the plain data a_ = np.random.uniform(1, 10, size=(m, k)).astype("uint8") b_ = np.random.uniform(1, 10, size=(n, k)).astype("int8") packW = np.random.uniform(1, 10, size=(n/128, 128*(k/2), 2)).astype("int8") # This occurs in pre_compute stage for r_idx in range(n/128): for s_idx in range(128*(k/2)): for t_idx in range(2): packW[r_idx][s_idx][t_idx] = b_[r_idx*128+s_idx%128][s_idx/128*2+t_idx] x = tvm.nd.array(a_, ctx) w = tvm.nd.array(packW, ctx) y = tvm.nd.array(np.zeros((m, n), dtype="int16"), ctx) result = t_evaluator(x, w, y) gops_per_sec = gops_per_mm/result.mean/1e9 tvm.testing.assert_allclose( y.asnumpy(), np.dot(a_, b_.T), rtol=1e-5) print('Tensorization: running time: {:.3f} ms, {:.2f} Gops/s, effiency: {:.2f}.'.format(result.mean*1000, gops_per_sec, gops_per_sec/peak)) t_func.export_library("gemm_tensorize.o") verify() if __name__ == "__main__": benchmark_fc_int8_acc16()