|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 1, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [ |
| 8 | + { |
| 9 | + "name": "stdout", |
| 10 | + "output_type": "stream", |
| 11 | + "text": [ |
| 12 | + "1.6.0a0+77b4e2d\n" |
| 13 | + ] |
| 14 | + } |
| 15 | + ], |
| 16 | + "source": [ |
| 17 | + "import torch \n", |
| 18 | + "import time\n", |
| 19 | + "\n", |
| 20 | + "print(torch.__version__)\n", |
| 21 | + "\n", |
| 22 | + "warmup_iter_raw = 1000\n", |
| 23 | + "prof_iter_raw = 1000\n", |
| 24 | + "\n", |
| 25 | + "def test_convtranspose2d(bs, c, hw, ks, stride, pad, outpad, dilation):\n", |
| 26 | + " warmup_iter = warmup_iter_raw\n", |
| 27 | + " prof_iter = prof_iter_raw\n", |
| 28 | + " \n", |
| 29 | + " if c >= 256: \n", |
| 30 | + " warmup_iter //= 10 \n", |
| 31 | + " prof_iter //= 10 \n", |
| 32 | + " \n", |
| 33 | + " print(bs, c, hw, ks, stride, pad, outpad, dilation)\n", |
| 34 | + " x = torch.randn(bs, c, hw, hw, device='cuda', dtype=torch.half, requires_grad=True) \n", |
| 35 | + " conv = torch.nn.ConvTranspose2d(\n", |
| 36 | + " in_channels=c, \n", |
| 37 | + " out_channels=c, \n", |
| 38 | + " kernel_size=ks, \n", |
| 39 | + " stride=stride, \n", |
| 40 | + " padding=pad, \n", |
| 41 | + " output_padding=outpad, \n", |
| 42 | + " groups=c,\n", |
| 43 | + " bias=False, \n", |
| 44 | + " dilation=dilation\n", |
| 45 | + " ).half().cuda()\n", |
| 46 | + "\n", |
| 47 | + " y:torch.Tensor = conv(x)\n", |
| 48 | + " g = torch.ones_like(y)\n", |
| 49 | + "\n", |
| 50 | + " for warm_up in range(warmup_iter): \n", |
| 51 | + " y = conv(x)\n", |
| 52 | + " y.backward(g)\n", |
| 53 | + "\n", |
| 54 | + " torch.cuda.synchronize()\n", |
| 55 | + " ts = time.time() \n", |
| 56 | + "\n", |
| 57 | + " for it in range(prof_iter): \n", |
| 58 | + " y = conv(x)\n", |
| 59 | + "\n", |
| 60 | + " torch.cuda.synchronize()\n", |
| 61 | + " te = time.time()\n", |
| 62 | + "\n", |
| 63 | + " t_forward = (te-ts)/prof_iter\n", |
| 64 | + " print(f'forward {t_forward: .3e}')\n", |
| 65 | + "\n", |
| 66 | + " ts = time.time()\n", |
| 67 | + " torch.cuda.synchronize() \n", |
| 68 | + "\n", |
| 69 | + " for it in range(prof_iter): \n", |
| 70 | + " y.backward(g, retain_graph=True)\n", |
| 71 | + "\n", |
| 72 | + " torch.cuda.synchronize()\n", |
| 73 | + " te = time.time()\n", |
| 74 | + "\n", |
| 75 | + " t_backward = (te-ts)/prof_iter\n", |
| 76 | + " print(f'backward {t_backward: .3e}')\n", |
| 77 | + "\n", |
| 78 | + " print(f'total {t_forward + t_backward: .3e}')\n", |
| 79 | + " \n", |
| 80 | + " print()" |
| 81 | + ] |
| 82 | + }, |
| 83 | + { |
| 84 | + "cell_type": "code", |
| 85 | + "execution_count": 2, |
| 86 | + "metadata": {}, |
| 87 | + "outputs": [], |
| 88 | + "source": [ |
| 89 | + "def test(): \n", |
| 90 | + " print('bs c hw ks stride pad outpad dilation\\n')\n", |
| 91 | + " # def test_convtranspose2d(bs, c, hw, ks, stride, pad, outpad, dilation)\n", |
| 92 | + " \n", |
| 93 | + " test_convtranspose2d(32, 128, 7, 1, 2, 0, 0, 2)\n", |
| 94 | + " test_convtranspose2d(32, 128, 7, 1, 1, 0, 0, 2)\n", |
| 95 | + " test_convtranspose2d(8, 128, 14, 1, 1, 0, 0, 3)\n", |
| 96 | + " test_convtranspose2d(1, 128, 7, 1, 3, 0, 1, 2)\n", |
| 97 | + " \n", |
| 98 | + " test_convtranspose2d(32, 512, 7, 1, 3, 0, 2, 2)\n", |
| 99 | + " test_convtranspose2d(8, 512, 14, 3, 2, 1, 1, 1)\n", |
| 100 | + " test_convtranspose2d(32, 256, 7, 1, 3, 0, 1, 1)\n", |
| 101 | + " test_convtranspose2d(1, 512, 14, 3, 3, 1, 1, 1)" |
| 102 | + ] |
| 103 | + }, |
| 104 | + { |
| 105 | + "cell_type": "code", |
| 106 | + "execution_count": 15, |
| 107 | + "metadata": { |
| 108 | + "scrolled": false |
| 109 | + }, |
| 110 | + "outputs": [ |
| 111 | + { |
| 112 | + "name": "stdout", |
| 113 | + "output_type": "stream", |
| 114 | + "text": [ |
| 115 | + "master\n", |
| 116 | + "bs c hw ks stride pad outpad dilation\n", |
| 117 | + "\n", |
| 118 | + "32 128 7 1 2 0 0 2\n", |
| 119 | + "forward 7.415e-05\n", |
| 120 | + "backward 2.432e-03\n", |
| 121 | + "total 2.506e-03\n", |
| 122 | + "\n", |
| 123 | + "32 128 7 1 1 0 0 2\n", |
| 124 | + "forward 7.270e-05\n", |
| 125 | + "backward 3.238e-03\n", |
| 126 | + "total 3.311e-03\n", |
| 127 | + "\n", |
| 128 | + "8 128 14 1 1 0 0 3\n", |
| 129 | + "forward 7.214e-05\n", |
| 130 | + "backward 3.232e-03\n", |
| 131 | + "total 3.304e-03\n", |
| 132 | + "\n", |
| 133 | + "1 128 7 1 3 0 1 2\n", |
| 134 | + "forward 7.211e-05\n", |
| 135 | + "backward 3.110e-03\n", |
| 136 | + "total 3.182e-03\n", |
| 137 | + "\n", |
| 138 | + "32 512 7 1 3 0 2 2\n", |
| 139 | + "forward 1.974e-01\n", |
| 140 | + "backward 3.896e-01\n", |
| 141 | + "total 5.870e-01\n", |
| 142 | + "\n", |
| 143 | + "8 512 14 3 2 1 1 1\n", |
| 144 | + "forward 8.204e-02\n", |
| 145 | + "backward 1.598e-01\n", |
| 146 | + "total 2.418e-01\n", |
| 147 | + "\n", |
| 148 | + "32 256 7 1 3 0 1 1\n", |
| 149 | + "forward 9.928e-02\n", |
| 150 | + "backward 1.947e-01\n", |
| 151 | + "total 2.940e-01\n", |
| 152 | + "\n", |
| 153 | + "1 512 14 3 3 1 1 1\n", |
| 154 | + "forward 3.833e-02\n", |
| 155 | + "backward 8.089e-02\n", |
| 156 | + "total 1.192e-01\n", |
| 157 | + "\n" |
| 158 | + ] |
| 159 | + } |
| 160 | + ], |
| 161 | + "source": [ |
| 162 | + "print('master')\n", |
| 163 | + "test()" |
| 164 | + ] |
| 165 | + }, |
| 166 | + { |
| 167 | + "cell_type": "code", |
| 168 | + "execution_count": 3, |
| 169 | + "metadata": {}, |
| 170 | + "outputs": [ |
| 171 | + { |
| 172 | + "name": "stdout", |
| 173 | + "output_type": "stream", |
| 174 | + "text": [ |
| 175 | + "without dilation check\n", |
| 176 | + "bs c hw ks stride pad outpad dilation\n", |
| 177 | + "\n", |
| 178 | + "32 128 7 1 2 0 0 2\n", |
| 179 | + "forward 9.304e-05\n", |
| 180 | + "backward 2.358e-03\n", |
| 181 | + "total 2.451e-03\n", |
| 182 | + "\n", |
| 183 | + "32 128 7 1 1 0 0 2\n", |
| 184 | + "forward 7.569e-05\n", |
| 185 | + "backward 3.104e-03\n", |
| 186 | + "total 3.180e-03\n", |
| 187 | + "\n", |
| 188 | + "8 128 14 1 1 0 0 3\n", |
| 189 | + "forward 7.390e-05\n", |
| 190 | + "backward 3.123e-03\n", |
| 191 | + "total 3.197e-03\n", |
| 192 | + "\n", |
| 193 | + "1 128 7 1 3 0 1 2\n", |
| 194 | + "forward 7.711e-05\n", |
| 195 | + "backward 2.982e-03\n", |
| 196 | + "total 3.059e-03\n", |
| 197 | + "\n", |
| 198 | + "32 512 7 1 3 0 2 2\n", |
| 199 | + "forward 2.085e-04\n", |
| 200 | + "backward 1.152e-02\n", |
| 201 | + "total 1.173e-02\n", |
| 202 | + "\n", |
| 203 | + "8 512 14 3 2 1 1 1\n", |
| 204 | + "forward 1.887e-04\n", |
| 205 | + "backward 1.424e-04\n", |
| 206 | + "total 3.311e-04\n", |
| 207 | + "\n", |
| 208 | + "32 256 7 1 3 0 1 1\n", |
| 209 | + "forward 1.264e-04\n", |
| 210 | + "backward 3.502e-03\n", |
| 211 | + "total 3.629e-03\n", |
| 212 | + "\n", |
| 213 | + "1 512 14 3 3 1 1 1\n", |
| 214 | + "forward 7.291e-05\n", |
| 215 | + "backward 1.455e-04\n", |
| 216 | + "total 2.184e-04\n", |
| 217 | + "\n" |
| 218 | + ] |
| 219 | + } |
| 220 | + ], |
| 221 | + "source": [ |
| 222 | + "print('without dilation check')\n", |
| 223 | + "test()" |
| 224 | + ] |
| 225 | + }, |
| 226 | + { |
| 227 | + "cell_type": "code", |
| 228 | + "execution_count": null, |
| 229 | + "metadata": {}, |
| 230 | + "outputs": [], |
| 231 | + "source": [] |
| 232 | + } |
| 233 | + ], |
| 234 | + "metadata": { |
| 235 | + "kernelspec": { |
| 236 | + "display_name": "Python 3.7.6 64-bit", |
| 237 | + "language": "python", |
| 238 | + "name": "python37664bitfce950e88ea94256bae6c6f663f53e68" |
| 239 | + }, |
| 240 | + "language_info": { |
| 241 | + "codemirror_mode": { |
| 242 | + "name": "ipython", |
| 243 | + "version": 3 |
| 244 | + }, |
| 245 | + "file_extension": ".py", |
| 246 | + "mimetype": "text/x-python", |
| 247 | + "name": "python", |
| 248 | + "nbconvert_exporter": "python", |
| 249 | + "pygments_lexer": "ipython3", |
| 250 | + "version": "3.7.6" |
| 251 | + } |
| 252 | + }, |
| 253 | + "nbformat": 4, |
| 254 | + "nbformat_minor": 2 |
| 255 | +} |
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