test_topi_conv2d_winograd.py 4.84 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
"""Example code to do convolution."""

import numpy as np
import tvm
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


hlu1 committed
29 30
def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False,
        devices=['cuda', 'llvm -device=arm_cpu', 'opencl -device=mali']):
31
    print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

    in_height = in_width = in_size

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

    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_conv2d_nchw.verify_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)
        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):
67
            C = topi.nn.conv2d(A, W, stride, padding, dilation, layout='NCHW', out_dtype=dtype)
68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
            s = topi.generic.schedule_conv2d_nchw([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)
84
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
85 86


hlu1 committed
87
    for device in devices:
88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
        check_device(device)


class WinogradFallback(autotvm.FallbackContext):
    def _query_inside(self, target, workload):
        key = (target, workload)
        if key in self.memory:
            return self.memory[key]
        cfg = FallbackConfigEntity()
        cfg.template_key = 'winograd'
        self.memory[key] = cfg
        return cfg


def test_conv2d_nchw():
    autotvm.DispatchContext.current.silent = True

    with WinogradFallback():
        # resnet 18 workloads
        verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1)
        verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1)
        verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1)
        verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1)

        # batch size = 2
        verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1)

        # relu, bias
        verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_bias=True)
        verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_relu=True)
        verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_relu=True, add_bias=True)

        # werid workloads
        verify_conv2d_nchw(1, 1, 1, 1, 3, 1, 1)
        verify_conv2d_nchw(3, 3, 3, 3, 3, 1, 1)
        verify_conv2d_nchw(2, 13, 71, 59, 3, 1, 1)

if __name__ == "__main__":
    test_conv2d_nchw()