tmoreau89 commented on a change in pull request #4421: [VTA] Bringing group convolution support URL: https://github.com/apache/incubator-tvm/pull/4421#discussion_r351115493
########## File path: vta/scripts/tune_group_conv2d.py ########## @@ -0,0 +1,156 @@ +# 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 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("GroupConv2DWorkload", + ['batch', 'height', 'width', 'in_filter', 'out_filter', 'groups', + 'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride']) + +# Mobilenet (grouped variant) workloads +mobilenet_wkls = [ + ('mobilenet.D1', Workload(env.BATCH, 112, 112, 32, 32, 2, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D2', Workload(env.BATCH, 112, 112, 64, 64, 4, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D3', Workload(env.BATCH, 56, 56, 128, 128, 8, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D4', Workload(env.BATCH, 56, 56, 128, 128, 8, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D5', Workload(env.BATCH, 28, 28, 256, 256, 16, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D6', Workload(env.BATCH, 28, 28, 256, 256, 16, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D7', Workload(env.BATCH, 14, 14, 512, 512, 32, 3, 3, 1, 1, 1, 1)), + ('mobilenet.D8', Workload(env.BATCH, 14, 14, 512, 512, 32, 3, 3, 1, 1, 2, 2)), + ('mobilenet.D9', Workload(env.BATCH, 7, 7, 1024, 1024, 64, 3, 3, 1, 1, 1, 1)), +] + [email protected]_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 group_conv2d(N, CI, H, W, CO, KH, KW, strides, padding, dilation, group): + + CI_G = CI // groups + data_shape = (N//env.BATCH, CI//env.BLOCK_IN, H, W, env.BATCH, env.BLOCK_IN) + kernel_shape = (CO//env.BLOCK_OUT, CI_G//env.BLOCK_IN, KH, KW, env.BLOCK_OUT, env.BLOCK_IN) + bias_shape = (N//env.BATCH, CO//env.BLOCK_OUT, 1, 1, env.BATCH, env.BLOCK_OUT) + + data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype) + kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype) + bias = tvm.placeholder(bias_shape, name="bias", dtype=env.acc_dtype) + + with tvm.target.vta(): + res = topi.nn.group_conv2d_nchw( + data, + kernel, + strides, + padding, + dilation, + groups, + env.acc_dtype) + res = topi.right_shift(res, env.WGT_WIDTH) + res = topi.add(res, bias) + 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_group_conv2d_nchw([res]) + else: + s = tvm.create_schedule([res.op]) + + return s, [data, kernel, bias, res] + +if __name__ == '__main__': + + # Logging config (for printing tuning log to the screen) + logging.basicConfig() + # logging.getLogger('autotvm').setLevel(logging.DEBUG) Review comment: Yes indeed ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
