test_pass_mac_count.py

# 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.
"""Unit tests for MAC counter."""
import tvm
from tvm import relay

def test_gemm():
    n = 512
    k = 1024
    m = 256
    dshape1 = (n, k)
    dshape2 = (m, k)
    data1 = relay.var("data1", shape=dshape1)
    data2 = relay.var("data2", shape=dshape2)
    gemm = relay.nn.dense(data1, data2)
    func = relay.Function([data1, data2],
                            relay.Tuple(tvm.convert([gemm])))
    func = relay.ir_pass.infer_type(func)
    compute_count = relay.ir_pass.get_total_mac_number(func)
    expect_count = n * m * k
    assert compute_count == expect_count

def test_conv():
    batch_size = 1
    input_channel = 3
    h = 224
    w = 224
    output_channel = 64
    kh = 7
    kw = 7
    h_padding = 1
    w_padding = 1
    oh = h + h_padding * 2 - kh + 1
    ow = w + w_padding * 2 - kw + 1
    dshape = (batch_size, input_channel, h, w)
    weight = relay.var("weight", shape=(output_channel, input_channel, kh, kw))
    data = relay.var("data", shape=dshape)
    conv2d = relay.nn.conv2d(
        data,
        weight,
        channels=output_channel,
        kernel_size=(kh, kw),
        padding=(1, 1))
    func = relay.Function([data, weight],
                            relay.Tuple(tvm.convert([conv2d])))
    func = relay.ir_pass.infer_type(func)
    compute_count = relay.ir_pass.get_total_mac_number(func)
    expect_count = batch_size * input_channel * oh * ow * output_channel * kh * kw
    assert compute_count == expect_count

def test_simple_network():
    batch_size = 1
    dshape = (batch_size, 64, 56, 56)
    weight_conv = relay.var("weight_conv", shape=(64, 64, 3, 3))
    data1 = relay.var("data1", shape=dshape)
    data2 = relay.var("data2", shape=dshape)
    weight_dense = relay.var("weight_dense", shape=(1, 56*56*64))

    conv2d_1 = relay.nn.conv2d(
        data1,
        weight_conv,
        channels=64,
        kernel_size=(3, 3),
        padding=(1, 1))
    conv2d_2 = relay.nn.conv2d(
        data2,
        weight_conv,
        channels=64,
        kernel_size=(3, 3),
        padding=(1, 1))
    add = relay.add(conv2d_1, conv2d_2)
    flattened = relay.nn.batch_flatten(add)
    dense_1 = relay.nn.dense(
        flattened,
        weight_dense)

    func = relay.Function([data1, data2, weight_conv, weight_dense],
                            relay.Tuple(tvm.convert([conv2d_1, conv2d_2,
                                                    dense_1, add, flattened])))
    func = relay.ir_pass.infer_type(func)
    # alter the CONV 2D data layout to test
    func = relay.ir_pass.alter_op_layout(func)
    func = relay.ir_pass.infer_type(func)
    compute_count = relay.ir_pass.get_total_mac_number(func)
    expect_count = 231411712
    assert compute_count == expect_count

if __name__ == "__main__":
    test_conv()
    test_gemm()
    test_simple_network()