test_topi_group_conv2d.py 9.57 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
"""Example code to do group convolution."""

import numpy as np
import tvm
21
from tvm import te
22 23 24 25 26 27 28
from tvm import autotvm
from tvm.autotvm.task.space import FallbackConfigEntity
import topi
import topi.testing
from tvm.contrib.pickle_memoize import memoize
from topi.util import get_const_tuple

29
from common import get_all_backend, Int8Fallback
30 31


32 33 34 35 36 37
_group_conv2d_nchw_implement = {
    "generic": (topi.nn.group_conv2d_nchw, topi.generic.schedule_group_conv2d_nchw),
    "gpu": (topi.cuda.group_conv2d_nchw, topi.cuda.schedule_group_conv2d_nchw),
}


38 39 40 41 42 43 44
def verify_group_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation, groups, add_bias=False, add_relu=False):
    print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d, %d)" %
        (batch, in_channel, in_size, num_filter,
         kernel, stride, padding, dilation, groups))

    in_height = in_width = in_size

45 46 47
    A = te.placeholder((batch, in_channel, in_height, in_width), name='A')
    W = te.placeholder((num_filter, in_channel // groups, kernel, kernel), name='W')
    bias = te.placeholder((num_filter, 1, 1), name='bias')
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

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    bias_shape = get_const_tuple(bias.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_group_conv2d.verify_group_conv2d_nchw")
    def get_ref_data():
        a_np = np.random.uniform(size=a_shape).astype(dtype)
        w_np = np.random.uniform(size=w_shape).astype(dtype)
        b_np = np.random.uniform(size=bias_shape).astype(dtype)
        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, groups).astype(dtype)

        if add_bias:
            b_np = np.random.uniform(size=bias_shape).astype(dtype)
            c_np += b_np
        if add_relu:
            c_np = np.maximum(c_np, 0)

        return a_np, w_np, b_np, c_np

    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):
80 81
            fcompute, fschedule = topi.testing.dispatch(device, _group_conv2d_nchw_implement)
            C = fcompute(A, W, stride, padding, dilation, groups, dtype)
82 83 84 85
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
86
            s = fschedule([C])
87 88 89 90 91 92 93 94 95 96 97 98 99 100 101

        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_%d" %\
                (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation, groups))
            func(a, w, b, c)
        else:
            func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d_%d" % \
            (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation, groups))
            func(a, w, c)
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)

102
    for device in ["llvm", "cuda"]:
103 104 105 106 107 108 109 110 111 112 113 114 115
        check_device(device)


oc_block_factor = 4


def verify_group_conv2d_NCHWc_int8(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation, groups, add_bias=False, add_relu=False):
    print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d, %d)" %
        (batch, in_channel, in_size, num_filter,
         kernel, stride, padding, dilation, groups))

    in_height = in_width = in_size

116 117 118
    A = te.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='int8')
    W = te.placeholder((num_filter, in_channel // groups, kernel, kernel), name='W', dtype='int8')
    bias = te.placeholder((num_filter // oc_block_factor, 1, 1, oc_block_factor), name='bias',
119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
                            dtype='int8')

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    bias_shape = get_const_tuple(bias.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_group_conv2d.verify_group_conv2d_NCHWc_int8")
    def get_ref_data():
        a_np = np.random.randint(low=-128, high=127, size=a_shape).astype(dtype)
        w_np = np.random.randint(low=-128, high=128, size=w_shape).astype(dtype)
        b_np = np.random.uniform(size=bias_shape).astype(dtype)
        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, groups).astype(dtype)

        # convert to NCHWc
        _, _, out_height, out_width = c_np.shape
        c_np = c_np.reshape((batch, num_filter // oc_block_factor, oc_block_factor, \
                out_height, out_width)).transpose(0, 1, 3, 4, 2)

        if add_bias:
            b_np = np.random.uniform(size=bias_shape).astype(dtype)
            c_np += b_np
        if add_relu:
            c_np = np.maximum(c_np, 0)

        return a_np, w_np, b_np, c_np

    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
        if device == "cuda" and not tvm.contrib.nvcc.have_int8(ctx.compute_version):
            print("Skip because int8 intrinsics are not available")
            return

        print("Running on target: %s" % device)
        with tvm.target.create(device):
160
            C = topi.cuda.group_conv2d_NCHWc_int8(A, W, stride, padding, dilation, groups, dtype)
161 162 163 164
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
165
            s = topi.cuda.schedule_group_conv2d_NCHWc_int8([C])
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

        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_%d" %\
                (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation, groups))
            func(a, w, b, c)
        else:
            func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d_%d" % \
            (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation, groups))
            func(a, w, c)
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)

    for device in ["cuda"]:
        check_device(device)


def test_group_conv2d_nchw():
    # ResNeXt-50 workload
    verify_group_conv2d_nchw(1, 128, 56, 128, 3, 1, 1, 1, 32)
    verify_group_conv2d_nchw(1, 256, 56, 256, 3, 2, 1, 1, 32)
    verify_group_conv2d_nchw(1, 256, 28, 256, 3, 1, 1, 1, 32)
    verify_group_conv2d_nchw(1, 512, 28, 512, 3, 2, 1, 1, 32)
    verify_group_conv2d_nchw(1, 512, 14, 512, 3, 1, 1, 1, 32)
    verify_group_conv2d_nchw(1, 1024, 14, 1024, 3, 2, 1, 1, 32)
    verify_group_conv2d_nchw(1, 1024, 7, 1024, 3, 1, 1, 1, 32)

    # bias, relu
    verify_group_conv2d_nchw(1, 128, 56, 128, 3, 1, 1, 1, 32, add_relu=True)
    verify_group_conv2d_nchw(1, 128, 56, 128, 3, 1, 1, 1, 32, add_bias=True)
    verify_group_conv2d_nchw(1, 128, 56, 128, 3, 1, 1, 1, 32, add_relu=True,
                             add_bias=True)

    # dilation
202
    verify_group_conv2d_nchw(1, 128, 56, 128, 3, 1, 1, 2, 32)
203 204 205 206 207 208 209 210

    # batch size
    verify_group_conv2d_nchw(2, 128, 56, 128, 3, 1, 1, 1, 32)
    verify_group_conv2d_nchw(9, 128, 56, 128, 3, 1, 1, 1, 32)



def test_group_conv2d_NCHWc_int8():
211
    with Int8Fallback():
212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236
        # ResNeXt-50 workload
        verify_group_conv2d_NCHWc_int8(1, 128, 56, 128, 3, 1, 1, 1, 32)
        verify_group_conv2d_NCHWc_int8(1, 256, 56, 256, 3, 2, 1, 1, 32)
        verify_group_conv2d_NCHWc_int8(1, 256, 28, 256, 3, 1, 1, 1, 32)
        verify_group_conv2d_NCHWc_int8(1, 512, 28, 512, 3, 2, 1, 1, 32)
        verify_group_conv2d_NCHWc_int8(1, 512, 14, 512, 3, 1, 1, 1, 32)
        verify_group_conv2d_NCHWc_int8(1, 1024, 14, 1024, 3, 2, 1, 1, 32)
        verify_group_conv2d_NCHWc_int8(1, 1024, 7, 1024, 3, 1, 1, 1, 32)

        # bias, relu
        verify_group_conv2d_NCHWc_int8(1, 128, 56, 128, 3, 1, 1, 1, 32, add_relu=True)
        verify_group_conv2d_NCHWc_int8(1, 128, 56, 128, 3, 1, 1, 1, 32, add_bias=True)
        verify_group_conv2d_NCHWc_int8(1, 128, 56, 128, 3, 1, 1, 1, 32, add_relu=True,
                                       add_bias=True)
        # dilation
        verify_group_conv2d_NCHWc_int8(1, 128, 56, 128, 3, 1, 1, 2, 32)

        # batch size
        verify_group_conv2d_NCHWc_int8(2, 128, 56, 128, 3, 1, 1, 1, 32)
        verify_group_conv2d_NCHWc_int8(9, 128, 56, 128, 3, 1, 1, 1, 32)


if __name__ == "__main__":
    test_group_conv2d_nchw()
    test_group_conv2d_NCHWc_int8()