# 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. """Tuning a single dense operator""" from collections import namedtuple import logging import os import tvm from tvm import autotvm from tvm.contrib.util import get_lower_ir import topi import vta import vta.testing env = vta.get_env() Workload = namedtuple("DenseWorkload", ['batch', 'in_filter', 'out_filter']) dense_wkls = [ ('lstm.dense.1', Workload(1, 256, 128)), ('lstm.dense.4', Workload(4, 256, 128)), ] @tvm.tag_scope(tag=topi.tag.ELEMWISE) def my_clip(x, a_min, a_max): """Unlike topi's current clip, put min and max into two stages.""" const_min = tvm.const(a_min, x.dtype) const_max = tvm.const(a_max, x.dtype) x = tvm.compute(x.shape, lambda *i: tvm.min(x(*i), const_max), name="clipA") x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB") return x def dense(N, CI, CO): data_shape = (N//env.BATCH, CI//env.BLOCK_IN, env.BATCH, env.BLOCK_IN) kernel_shape = (CO//env.BLOCK_OUT, CI//env.BLOCK_IN, env.BLOCK_OUT, env.BLOCK_IN) data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype) kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype) with tvm.target.vta(): res = topi.nn.dense(data, kernel, None, 'int32') res = topi.right_shift(res, 8) res = my_clip(res, 0, 127) res = topi.cast(res, "int8") if tvm.target.current_target().device_name == 'vta': s = topi.generic.schedule_dense([res]) else: s = tvm.create_schedule([res.op]) return s, [data, kernel, res] if __name__ == '__main__': # Logging config (for printing tuning log to the screen) logging.basicConfig() # logging.getLogger('autotvm').setLevel(logging.DEBUG) # Tuning log files log_file = "%s.dense.log" % (env.TARGET) # create tmp log file tmp_log_file = log_file + ".tmp" if os.path.exists(log_file): os.remove(log_file) # Get tracker info from env tracket_host = os.environ.get("TVM_TRACKER_HOST", None) tracket_port = os.environ.get("TVM_TRACKER_PORT", None) if not tracket_host or not tracket_port: print("Set your AutoTVM tracker node host and port variables to run the autotuner") exit() for idx, (wl_name, wl) in enumerate(dense_wkls): prefix = "[Task %2d/%2d] " % (idx, len(dense_wkls)) # Workload parameters N = wl.batch CI = wl.in_filter CO = wl.out_filter task = autotvm.task.create(dense, args=(N, CI, CO), target=tvm.target.vta(), target_host=env.target_host, template_key='direct') print(task.config_space) # Tune measure_option = autotvm.measure_option( builder=autotvm.LocalBuilder(), runner=autotvm.RPCRunner( env.TARGET, host=tracket_host, port=int(tracket_port), number=5, timeout=60, check_correctness=True)) # Run Tuner tuner = autotvm.tuner.RandomTuner(task) tuner.tune( n_trial=len(task.config_space), early_stopping=None, measure_option=measure_option, callbacks=[ autotvm.callback.progress_bar(len(task.config_space), prefix=prefix), autotvm.callback.log_to_file(tmp_log_file)]) # Pick best records to a cache file autotvm.record.pick_best(tmp_log_file, log_file) os.remove(tmp_log_file)