# 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 conv2d transpose 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 # Get batch info from env env = vta.get_env() Workload = namedtuple("Conv2DTransposeWorkload", ['batch', 'height', 'width', 'in_filter', 'out_filter', 'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride']) dcgan_wkls = [ # dcgan ('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2)), ('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2)), ('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2)), ] @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 conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding): data_shape = (N//env.BATCH, CI//env.BLOCK_IN, H, W, env.BATCH, env.BLOCK_IN) kernel_shape = (CO//env.BLOCK_OUT, CI//env.BLOCK_IN, KH, KW, 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.conv2d_transpose_nchw( Input=data, Filter=kernel, strides=strides, padding=padding, out_dtype=env.acc_dtype) res = topi.right_shift(res, env.WGT_WIDTH) res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1) res = topi.cast(res, env.out_dtype) if tvm.target.current_target().device_name == 'vta': s = topi.generic.schedule_conv2d_transpose_nchw([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.conv2d_transpose.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 tracker_host = os.environ.get("TVM_TRACKER_HOST", None) tracker_port = os.environ.get("TVM_TRACKER_PORT", None) if not tracker_host or not tracker_port: print("Set your AutoTVM tracker node host and port variables to run the autotuner") exit() for idx, (wl_name, wl) in enumerate(dcgan_wkls): prefix = "[Task %2d/%2d] " % (idx, len(dcgan_wkls)) # Read in workload parameters N = wl.batch H = wl.height W = wl.width CI = wl.in_filter CO = wl.out_filter KH = wl.hkernel KW = wl.wkernel strides = (wl.hstride, wl.wstride) padding = (wl.hpad, wl.wpad) # Create task task = autotvm.task.create( conv2d_transpose, args=(N, CI, H, W, CO, KH, KW, strides, padding), 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=tracker_host, port=int(tracker_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)