test_topi_conv2d_NCHWc.py 9.33 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
# 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.
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
"""Test for NCHW[x]c convolution"""

import numpy as np
import tvm
from tvm import autotvm
import topi
import topi.testing
from tvm.contrib.pickle_memoize import memoize
from topi.util import get_const_tuple

from common import get_all_backend

def _transform_data(data, bn):
    # NCHW -> NCHW[x]c
    batch_size, channel, height, width = data.shape
32 33
    data = np.reshape(data, (batch_size, channel//bn, bn, height, width))
    data = np.transpose(data, (0, 1, 3, 4, 2))
34 35 36 37 38
    return data

def _transform_kernel(kernel, ic_bn, oc_bn):
    # OIHW -> OIHW[x]i[x]o
    out_channel, in_channel, kh, kw = kernel.shape
39 40
    kernel = np.reshape(kernel, (out_channel//oc_bn, oc_bn, in_channel//ic_bn, ic_bn, kh, kw))
    kernel = np.transpose(kernel, (0, 2, 4, 5, 3, 1))
41 42 43 44 45
    return kernel

def _transform_bias(bias, bn):
    # [num_filter, 1, 1] -> [num_filter//bn, 1, 1, bn]
    num_filter, h, w = bias.shape
46 47
    bias = np.reshape(bias, (num_filter//bn, bn, h, w))
    bias = np.transpose(bias, (0, 2, 3, 1))
48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
    return bias

def verify_conv2d_NCHWc(batch, in_channel, in_size, num_filter, kernel, stride,
                        padding, dilation=1, add_bias=False, add_relu=False, dtype="float32"):
    print("Workload: (%d, %d, %d, %d, %d, %d, %d)" %
          (batch, in_channel, in_size, num_filter, kernel, stride, padding))

    in_height = in_width = in_size

    # for testing functionality,
    # we choose arbitrary block size that can divide the channel,
    # regardless of the performance.
    oc_block = 1
    for bn in range(16, 0, -1):
        if num_filter % bn == 0:
            oc_block = bn
            break

    ic_block = 1
    for bn in range(oc_block, 0, -1):
        if in_channel % bn == 0:
            ic_block = bn
            break

    A = tvm.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='A')
    W = tvm.placeholder((num_filter//oc_block, in_channel//ic_block, kernel, kernel, ic_block, oc_block), name='W')
    bias = tvm.placeholder((num_filter//oc_block, 1, 1, oc_block), name='bias')

    @memoize("topi.tests.test_topi_conv2d_NCHWc.verify_conv2d_NCHWc")
    def get_ref_data():
        a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype)
        w_np = np.random.uniform(size=(num_filter, in_channel, kernel, kernel)).astype(dtype)
        b_np = np.random.uniform(size=(num_filter, 1, 1)).astype(dtype)
81 82
        dw_np = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation))
        c_np = topi.testing.conv2d_nchw_python(a_np, dw_np, stride, padding)
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
        if add_bias:
            c_np += b_np
        if add_relu:
            c_np = np.maximum(c_np, 0)
        return _transform_data(a_np, ic_block), _transform_kernel(w_np, ic_block, oc_block), \
               _transform_bias(b_np, oc_block), _transform_data(c_np, oc_block)

    a_np, w_np, b_np, c_np = get_ref_data()

    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        with tvm.target.create(device):
            C = topi.nn.conv2d_NCHWc(A, W, (stride, stride), (padding, padding),
100
                                     (dilation, dilation),
101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123
                                     layout='NCHW%dc'%ic_block,
                                     out_layout="NCHW%dc"%oc_block,
                                     out_dtype=dtype)
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
            s = topi.generic.schedule_conv2d_NCHWc([C])

        a = tvm.nd.array(a_np, ctx)
        w = tvm.nd.array(w_np, ctx)
        b = tvm.nd.array(b_np, ctx)
        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)
        if add_bias:
            func = tvm.build(s, [A, W, bias, C], device,
                             name="relu_%d_%d_%d_%d_%d_%d_%d_%d" %
                                  (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
            func(a, w, b, c)
        else:
            func = tvm.build(s, [A, W, C], device,
                             name="relu_%d_%d_%d_%d_%d_%d_%d_%d" %
                                  (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
            func(a, w, c)
124
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-3)
125 126 127 128 129 130 131

    # test llvm only for now since conv2d_NCHWc implement is missing in other backend.
    for device in ["llvm"]:
        with autotvm.tophub.context(device):  # load tophub pre-tuned parameters
            check_device(device)


132
def test_conv2d_NCHWc():
133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151
    # ResNet18 workloads
    verify_conv2d_NCHWc(1,   3, 224,  64, 7, 2, 3)
    verify_conv2d_NCHWc(1,  64,  56,  64, 3, 1, 1)
    verify_conv2d_NCHWc(1,  64,  56,  64, 1, 1, 0)
    verify_conv2d_NCHWc(1,  64,  56, 128, 3, 2, 1)
    verify_conv2d_NCHWc(1,  64,  56, 128, 1, 2, 0)
    verify_conv2d_NCHWc(1, 128,  28, 128, 3, 1, 1)
    verify_conv2d_NCHWc(1, 128,  28, 256, 3, 2, 1)
    verify_conv2d_NCHWc(1, 128,  28, 256, 1, 2, 0)
    verify_conv2d_NCHWc(1, 256,  14, 256, 3, 1, 1)
    verify_conv2d_NCHWc(1, 256,  14, 512, 3, 2, 1)
    verify_conv2d_NCHWc(1, 256,  14, 512, 1, 2, 0)
    verify_conv2d_NCHWc(1, 512,   7, 512, 3, 1, 1)

    # bias, relu
    verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_relu=True)
    verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_bias=True)
    verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_bias=True, add_relu=True)

152 153
    # dilation
    verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, dilation=2)
154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218

    # batch size
    verify_conv2d_NCHWc(4, 64, 56, 64, 3, 1, 1)
    verify_conv2d_NCHWc(9, 64, 56, 64, 3, 1, 1)

    # weird workloads
    verify_conv2d_NCHWc(2, 2, 2, 2, 2, 2, 2)
    verify_conv2d_NCHWc(3, 3, 3, 3, 3, 3, 3)
    verify_conv2d_NCHWc(4, 4, 4, 4, 4, 4, 4)
    verify_conv2d_NCHWc(5, 5, 5, 5, 5, 5, 5)
    verify_conv2d_NCHWc(6, 6, 6, 6, 6, 6, 6)

    # disable these tests due to some bugs of llvm with nvptx
    # verify_conv2d_NCHWc(1, 1, 1, 1, 1, 1, 1, dilation=1)
    # verify_conv2d_NCHWc(1, 1, 1, 1, 1, 1, 1, dilation=2)
    # verify_conv2d_NCHWc(2, 13, 71, 59, 3, 1, 1)

    # inception v3 workloads
    verify_conv2d_NCHWc(1,    3, 299,  32, 3, 2, 0)
    verify_conv2d_NCHWc(1,   32, 149,  32, 3, 1, 0)
    verify_conv2d_NCHWc(1,   32, 147,  64, 3, 1, 1)
    verify_conv2d_NCHWc(1,   64,  73,  80, 1, 1, 0)
    verify_conv2d_NCHWc(1,   80,  73, 192, 3, 1, 0)
    verify_conv2d_NCHWc(1,  192,  35,  64, 1, 1, 0)
    verify_conv2d_NCHWc(1,  192,  35,  48, 1, 1, 0)
    verify_conv2d_NCHWc(1,   48,  35,  64, 5, 1, 2)
    verify_conv2d_NCHWc(1,   64,  35,  96, 3, 1, 1)
    verify_conv2d_NCHWc(1,   96,  35,  96, 3, 1, 1)
    verify_conv2d_NCHWc(1,  192,  35,  32, 1, 1, 0)
    verify_conv2d_NCHWc(1,  256,  35,  64, 1, 1, 0)
    verify_conv2d_NCHWc(1,  256,  35,  48, 1, 1, 0)
    verify_conv2d_NCHWc(1,  288,  35,  64, 1, 1, 0)
    verify_conv2d_NCHWc(1,  288,  35,  48, 1, 1, 0)
    verify_conv2d_NCHWc(1,  288,  35, 384, 3, 2, 0)
    verify_conv2d_NCHWc(1,   96,  35,  96, 3, 2, 0)
    verify_conv2d_NCHWc(1,  768,  17, 192, 1, 1, 0)
    verify_conv2d_NCHWc(1,  768,  17, 128, 1, 1, 0)
    verify_conv2d_NCHWc(1,  128,  17, 128, 1, 1, 0)
    verify_conv2d_NCHWc(1,  128,  17, 192, 7, 1, 3)
    verify_conv2d_NCHWc(1,  128,  17, 128, 7, 1, 3)
    verify_conv2d_NCHWc(1,  128,  17, 192, 1, 1, 0)
    verify_conv2d_NCHWc(1,  768,  17, 160, 1, 1, 0)
    verify_conv2d_NCHWc(1,  160,  17, 160, 1, 1, 0)
    verify_conv2d_NCHWc(1,  160,  17, 192, 7, 1, 3)
    verify_conv2d_NCHWc(1,  160,  17, 160, 7, 1, 3)
    verify_conv2d_NCHWc(1,  160,  17, 192, 1, 1, 0)
    verify_conv2d_NCHWc(1,  192,  17, 192, 1, 1, 0)
    verify_conv2d_NCHWc(1,  192,  17, 192, 7, 1, 3)
    verify_conv2d_NCHWc(1,  192,  17, 320, 3, 2, 0)
    verify_conv2d_NCHWc(1,  192,  17, 192, 3, 2, 0)
    verify_conv2d_NCHWc(1, 1280,   8, 320, 1, 1, 0)
    verify_conv2d_NCHWc(1, 1280,   8, 384, 1, 1, 0)
    verify_conv2d_NCHWc(1,  384,   8, 384, 1, 1, 0)
    verify_conv2d_NCHWc(1,  384,   8, 384, 3, 1, 1)
    verify_conv2d_NCHWc(1, 1280,   8, 448, 1, 1, 0)
    verify_conv2d_NCHWc(1,  448,   8, 384, 3, 1, 1)
    verify_conv2d_NCHWc(1, 1280,   8, 192, 1, 1, 0)
    verify_conv2d_NCHWc(1, 2048,   8, 320, 1, 1, 0)
    verify_conv2d_NCHWc(1, 2048,   8, 384, 1, 1, 0)
    verify_conv2d_NCHWc(1, 2048,   8, 448, 1, 1, 0)
    verify_conv2d_NCHWc(1, 2048,   8, 192, 1, 1, 0)
    verify_conv2d_NCHWc(1, 1024,  19,  84, 3, 1, 1)
    verify_conv2d_NCHWc(1, 2048,  10, 126, 3, 1, 1)
    verify_conv2d_NCHWc(1,  512,   5, 126, 3, 1, 1)
    verify_conv2d_NCHWc(1,  256,   3, 126, 3, 1, 1)
219 220

if __name__ == "__main__":
221
    test_conv2d_NCHWc()