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xwang233xwang233
committedApr 22, 2020
naive update ref_grad
1 parent 98ef73c commit 34f1635

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‎avg-pool2d-channels-last/avg-pool2d-naive.ipynb

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.6.0a0+204b1cc\n",
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"n 1, c 1, h 14, w 14, ks 2\tchannels_last speedup, forward 0.994, backward 1.009\n",
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"n 1, c 1, h 14, w 14, ks 3\tchannels_last speedup, forward 0.959, backward 1.027\n",
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"n 1, c 1, h 14, w 14, ks 5\tchannels_last speedup, forward 0.953, backward 1.003\n",
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"n 1, c 1, h 14, w 14, ks 7\tchannels_last speedup, forward 0.972, backward 1.032\n",
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"n 1, c 1, h 28, w 28, ks 2\tchannels_last speedup, forward 0.937, backward 1.015\n",
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"n 1, c 1, h 28, w 28, ks 3\tchannels_last speedup, forward 0.943, backward 1.037\n",
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"n 1, c 1, h 28, w 28, ks 5\tchannels_last speedup, forward 0.941, backward 1.018\n",
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"n 1, c 1, h 28, w 28, ks 7\tchannels_last speedup, forward 0.941, backward 1.035\n",
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"n 1, c 1, h 56, w 56, ks 2\tchannels_last speedup, forward 0.936, backward 1.080\n",
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"n 1, c 1, h 56, w 56, ks 3\tchannels_last speedup, forward 0.893, backward 1.013\n",
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"n 1, c 1, h 56, w 56, ks 5\tchannels_last speedup, forward 0.942, backward 1.025\n",
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"1.6.0a0+64f5c4f\n",
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"n 1, c 1, h 14, w 14, ks 2\tchannels_last speedup, forward 0.931, backward 1.039\n",
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"n 1, c 1, h 14, w 14, ks 3\tchannels_last speedup, forward 0.954, backward 1.021\n",
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"n 1, c 1, h 14, w 14, ks 5\tchannels_last speedup, forward 0.941, backward 1.026\n",
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"n 1, c 1, h 14, w 14, ks 7\tchannels_last speedup, forward 0.955, backward 1.024\n",
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"n 1, c 1, h 28, w 28, ks 2\tchannels_last speedup, forward 0.943, backward 1.047\n",
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"n 1, c 1, h 28, w 28, ks 3\tchannels_last speedup, forward 0.931, backward 1.030\n",
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"n 1, c 1, h 28, w 28, ks 5\tchannels_last speedup, forward 0.943, backward 1.033\n",
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"n 1, c 1, h 28, w 28, ks 7\tchannels_last speedup, forward 0.965, backward 1.036\n",
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"n 1, c 1, h 56, w 56, ks 2\tchannels_last speedup, forward 0.952, backward 1.028\n",
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"n 1, c 1, h 56, w 56, ks 3\tchannels_last speedup, forward 0.895, backward 1.023\n",
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"n 1, c 1, h 56, w 56, ks 5\tchannels_last speedup, forward 0.957, backward 1.023\n",
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"n 1, c 1, h 56, w 56, ks 7\tchannels_last speedup, forward 0.950, backward 1.032\n",
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"n 1, c 1, h 128, w 128, ks 2\tchannels_last speedup, forward 0.975, backward 1.032\n",
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"n 1, c 1, h 128, w 128, ks 3\tchannels_last speedup, forward 0.932, backward 1.030\n",
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"n 1, c 1, h 128, w 128, ks 5\tchannels_last speedup, forward 0.938, backward 1.033\n",
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"n 1, c 1, h 128, w 128, ks 7\tchannels_last speedup, forward 0.944, backward 1.029\n",
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"n 1, c 1, h 256, w 256, ks 7\tchannels_last speedup, forward 0.947, backward 1.029\n",
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"n 1, c 32, h 14, w 14, ks 5\tchannels_last speedup, forward 0.929, backward 1.139\n",
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"n 1, c 32, h 14, w 14, ks 7\tchannels_last speedup, forward 0.918, backward 1.185\n",
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"n 1, c 32, h 128, w 128, ks 2\tchannels_last speedup, forward 0.916, backward 1.135\n",
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"n 32, c 32, h 28, w 28, ks 3\tchannels_last speedup, forward 0.940, backward 0.873\n",
101+
"n 32, c 32, h 28, w 28, ks 5\tchannels_last speedup, forward 0.917, backward 0.891\n",
102+
"n 32, c 32, h 28, w 28, ks 7\tchannels_last speedup, forward 0.929, backward 0.889\n",
103+
"n 32, c 32, h 56, w 56, ks 2\tchannels_last speedup, forward 0.832, backward 0.798\n",
104+
"n 32, c 32, h 56, w 56, ks 3\tchannels_last speedup, forward 0.881, backward 0.830\n",
105+
"n 32, c 32, h 56, w 56, ks 5\tchannels_last speedup, forward 0.964, backward 0.890\n",
106+
"n 32, c 32, h 56, w 56, ks 7\tchannels_last speedup, forward 0.945, backward 0.876\n",
107+
"n 32, c 32, h 128, w 128, ks 2\tchannels_last speedup, forward 0.763, backward 0.869\n",
108+
"n 32, c 32, h 128, w 128, ks 3\tchannels_last speedup, forward 0.768, backward 0.887\n",
109+
"n 32, c 32, h 128, w 128, ks 5\tchannels_last speedup, forward 0.931, backward 0.872\n",
110+
"n 32, c 32, h 128, w 128, ks 7\tchannels_last speedup, forward 0.909, backward 0.884\n",
111+
"n 32, c 32, h 256, w 256, ks 2\tchannels_last speedup, forward 0.757, backward 0.584\n",
112+
"n 32, c 32, h 256, w 256, ks 3\tchannels_last speedup, forward 0.757, backward 0.586\n",
113+
"n 32, c 32, h 256, w 256, ks 5\tchannels_last speedup, forward 0.848, backward 0.575\n",
114+
"n 32, c 32, h 256, w 256, ks 7\tchannels_last speedup, forward 0.872, backward 0.580\n"
115115
]
116116
},
117117
{
118118
"name": "stdout",
119119
"output_type": "stream",
120120
"text": [
121-
"n 32, c 64, h 14, w 14, ks 2\tchannels_last speedup, forward 0.942, backward 1.167\n",
122-
"n 32, c 64, h 14, w 14, ks 3\tchannels_last speedup, forward 0.941, backward 1.163\n",
123-
"n 32, c 64, h 14, w 14, ks 5\tchannels_last speedup, forward 0.945, backward 1.186\n",
124-
"n 32, c 64, h 14, w 14, ks 7\tchannels_last speedup, forward 0.934, backward 1.171\n",
125-
"n 32, c 64, h 28, w 28, ks 2\tchannels_last speedup, forward 0.849, backward 0.919\n",
126-
"n 32, c 64, h 28, w 28, ks 3\tchannels_last speedup, forward 0.924, backward 0.904\n",
127-
"n 32, c 64, h 28, w 28, ks 5\tchannels_last speedup, forward 0.925, backward 0.912\n",
128-
"n 32, c 64, h 28, w 28, ks 7\tchannels_last speedup, forward 0.921, backward 0.907\n",
129-
"n 32, c 64, h 56, w 56, ks 2\tchannels_last speedup, forward 0.800, backward 0.882\n",
130-
"n 32, c 64, h 56, w 56, ks 3\tchannels_last speedup, forward 0.864, backward 0.842\n",
131-
"n 32, c 64, h 56, w 56, ks 5\tchannels_last speedup, forward 0.963, backward 0.822\n",
132-
"n 32, c 64, h 56, w 56, ks 7\tchannels_last speedup, forward 0.957, backward 0.808\n",
133-
"n 32, c 64, h 128, w 128, ks 2\tchannels_last speedup, forward 0.755, backward 0.846\n",
134-
"n 32, c 64, h 128, w 128, ks 3\tchannels_last speedup, forward 0.756, backward 0.807\n",
135-
"n 32, c 64, h 128, w 128, ks 5\tchannels_last speedup, forward 0.897, backward 0.782\n",
136-
"n 32, c 64, h 128, w 128, ks 7\tchannels_last speedup, forward 0.874, backward 0.776\n",
137-
"n 32, c 64, h 256, w 256, ks 2\tchannels_last speedup, forward 0.746, backward 0.389\n",
138-
"n 32, c 64, h 256, w 256, ks 3\tchannels_last speedup, forward 0.744, backward 0.365\n",
139-
"n 32, c 64, h 256, w 256, ks 5\tchannels_last speedup, forward 0.852, backward 0.351\n",
140-
"n 32, c 64, h 256, w 256, ks 7\tchannels_last speedup, forward 0.882, backward 0.346\n",
141-
"n 64, c 1, h 14, w 14, ks 2\tchannels_last speedup, forward 0.878, backward 0.981\n",
142-
"n 64, c 1, h 14, w 14, ks 3\tchannels_last speedup, forward 0.934, backward 1.027\n",
143-
"n 64, c 1, h 14, w 14, ks 5\tchannels_last speedup, forward 0.947, backward 1.016\n",
144-
"n 64, c 1, h 14, w 14, ks 7\tchannels_last speedup, forward 0.937, backward 1.027\n",
145-
"n 64, c 1, h 28, w 28, ks 2\tchannels_last speedup, forward 0.940, backward 1.017\n",
146-
"n 64, c 1, h 28, w 28, ks 3\tchannels_last speedup, forward 0.933, backward 1.006\n",
147-
"n 64, c 1, h 28, w 28, ks 5\tchannels_last speedup, forward 0.940, backward 0.913\n",
148-
"n 64, c 1, h 28, w 28, ks 7\tchannels_last speedup, forward 0.953, backward 1.019\n",
149-
"n 64, c 1, h 56, w 56, ks 2\tchannels_last speedup, forward 0.947, backward 1.016\n",
150-
"n 64, c 1, h 56, w 56, ks 3\tchannels_last speedup, forward 0.954, backward 1.033\n",
151-
"n 64, c 1, h 56, w 56, ks 5\tchannels_last speedup, forward 0.958, backward 1.014\n",
152-
"n 64, c 1, h 56, w 56, ks 7\tchannels_last speedup, forward 0.954, backward 1.028\n",
153-
"n 64, c 1, h 128, w 128, ks 2\tchannels_last speedup, forward 0.948, backward 1.017\n",
154-
"n 64, c 1, h 128, w 128, ks 3\tchannels_last speedup, forward 0.941, backward 1.018\n",
155-
"n 64, c 1, h 128, w 128, ks 5\tchannels_last speedup, forward 0.946, backward 1.019\n",
156-
"n 64, c 1, h 128, w 128, ks 7\tchannels_last speedup, forward 0.937, backward 1.025\n",
157-
"n 64, c 1, h 256, w 256, ks 2\tchannels_last speedup, forward 0.999, backward 1.000\n",
158-
"n 64, c 1, h 256, w 256, ks 3\tchannels_last speedup, forward 0.995, backward 1.005\n",
159-
"n 64, c 1, h 256, w 256, ks 5\tchannels_last speedup, forward 0.996, backward 1.007\n",
160-
"n 64, c 1, h 256, w 256, ks 7\tchannels_last speedup, forward 0.999, backward 1.002\n",
161-
"n 64, c 32, h 14, w 14, ks 2\tchannels_last speedup, forward 0.912, backward 1.112\n",
162-
"n 64, c 32, h 14, w 14, ks 3\tchannels_last speedup, forward 0.919, backward 1.188\n",
163-
"n 64, c 32, h 14, w 14, ks 5\tchannels_last speedup, forward 0.916, backward 1.166\n",
164-
"n 64, c 32, h 14, w 14, ks 7\tchannels_last speedup, forward 0.928, backward 1.166\n",
165-
"n 64, c 32, h 28, w 28, ks 2\tchannels_last speedup, forward 0.856, backward 0.989\n",
166-
"n 64, c 32, h 28, w 28, ks 3\tchannels_last speedup, forward 0.916, backward 1.013\n",
167-
"n 64, c 32, h 28, w 28, ks 5\tchannels_last speedup, forward 0.943, backward 1.010\n",
168-
"n 64, c 32, h 28, w 28, ks 7\tchannels_last speedup, forward 0.922, backward 0.997\n",
169-
"n 64, c 32, h 56, w 56, ks 2\tchannels_last speedup, forward 0.794, backward 0.938\n",
170-
"n 64, c 32, h 56, w 56, ks 3\tchannels_last speedup, forward 0.853, backward 0.931\n",
171-
"n 64, c 32, h 56, w 56, ks 5\tchannels_last speedup, forward 0.961, backward 0.916\n",
172-
"n 64, c 32, h 56, w 56, ks 7\tchannels_last speedup, forward 0.960, backward 0.908\n",
173-
"n 64, c 32, h 128, w 128, ks 2\tchannels_last speedup, forward 0.759, backward 0.910\n",
174-
"n 64, c 32, h 128, w 128, ks 3\tchannels_last speedup, forward 0.761, backward 0.903\n",
175-
"n 64, c 32, h 128, w 128, ks 5\tchannels_last speedup, forward 0.901, backward 0.876\n",
176-
"n 64, c 32, h 128, w 128, ks 7\tchannels_last speedup, forward 0.881, backward 0.881\n",
177-
"n 64, c 32, h 256, w 256, ks 2\tchannels_last speedup, forward 0.756, backward 0.626\n",
178-
"n 64, c 32, h 256, w 256, ks 3\tchannels_last speedup, forward 0.750, backward 0.601\n",
179-
"n 64, c 32, h 256, w 256, ks 5\tchannels_last speedup, forward 0.843, backward 0.578\n",
180-
"n 64, c 32, h 256, w 256, ks 7\tchannels_last speedup, forward 0.871, backward 0.580\n",
181-
"n 64, c 64, h 14, w 14, ks 2\tchannels_last speedup, forward 0.926, backward 0.910\n",
182-
"n 64, c 64, h 14, w 14, ks 3\tchannels_last speedup, forward 0.933, backward 0.986\n",
183-
"n 64, c 64, h 14, w 14, ks 5\tchannels_last speedup, forward 0.927, backward 1.011\n",
184-
"n 64, c 64, h 14, w 14, ks 7\tchannels_last speedup, forward 0.933, backward 0.986\n",
185-
"n 64, c 64, h 28, w 28, ks 2\tchannels_last speedup, forward 0.775, backward 0.777\n",
186-
"n 64, c 64, h 28, w 28, ks 3\tchannels_last speedup, forward 0.916, backward 0.715\n",
187-
"n 64, c 64, h 28, w 28, ks 5\tchannels_last speedup, forward 1.028, backward 0.726\n",
188-
"n 64, c 64, h 28, w 28, ks 7\tchannels_last speedup, forward 1.085, backward 0.716\n",
189-
"n 64, c 64, h 56, w 56, ks 2\tchannels_last speedup, forward 0.753, backward 0.877\n",
190-
"n 64, c 64, h 56, w 56, ks 3\tchannels_last speedup, forward 0.838, backward 0.843\n",
191-
"n 64, c 64, h 56, w 56, ks 5\tchannels_last speedup, forward 0.958, backward 0.823\n",
192-
"n 64, c 64, h 56, w 56, ks 7\tchannels_last speedup, forward 0.960, backward 0.811\n",
193-
"n 64, c 64, h 128, w 128, ks 2\tchannels_last speedup, forward 0.750, backward 0.839\n",
194-
"n 64, c 64, h 128, w 128, ks 3\tchannels_last speedup, forward 0.746, backward 0.799\n",
195-
"n 64, c 64, h 128, w 128, ks 5\tchannels_last speedup, forward 0.884, backward 0.773\n",
196-
"n 64, c 64, h 128, w 128, ks 7\tchannels_last speedup, forward 0.854, backward 0.768\n",
197-
"n 64, c 64, h 256, w 256, ks 2\tchannels_last speedup, forward 0.744, backward 0.388\n",
198-
"n 64, c 64, h 256, w 256, ks 3\tchannels_last speedup, forward 0.741, backward 0.363\n",
199-
"n 64, c 64, h 256, w 256, ks 5\tchannels_last speedup, forward 0.848, backward 0.350\n",
200-
"n 64, c 64, h 256, w 256, ks 7\tchannels_last speedup, forward 0.878, backward 0.344\n"
121+
"n 32, c 64, h 14, w 14, ks 2\tchannels_last speedup, forward 0.924, backward 0.992\n",
122+
"n 32, c 64, h 14, w 14, ks 3\tchannels_last speedup, forward 0.925, backward 0.984\n",
123+
"n 32, c 64, h 14, w 14, ks 5\tchannels_last speedup, forward 0.926, backward 0.963\n",
124+
"n 32, c 64, h 14, w 14, ks 7\tchannels_last speedup, forward 0.930, backward 0.966\n",
125+
"n 32, c 64, h 28, w 28, ks 2\tchannels_last speedup, forward 0.873, backward 0.834\n",
126+
"n 32, c 64, h 28, w 28, ks 3\tchannels_last speedup, forward 0.931, backward 0.842\n",
127+
"n 32, c 64, h 28, w 28, ks 5\tchannels_last speedup, forward 0.918, backward 0.825\n",
128+
"n 32, c 64, h 28, w 28, ks 7\tchannels_last speedup, forward 0.924, backward 0.831\n",
129+
"n 32, c 64, h 56, w 56, ks 2\tchannels_last speedup, forward 0.792, backward 0.808\n",
130+
"n 32, c 64, h 56, w 56, ks 3\tchannels_last speedup, forward 0.857, backward 0.809\n",
131+
"n 32, c 64, h 56, w 56, ks 5\tchannels_last speedup, forward 0.957, backward 0.810\n",
132+
"n 32, c 64, h 56, w 56, ks 7\tchannels_last speedup, forward 0.983, backward 0.803\n",
133+
"n 32, c 64, h 128, w 128, ks 2\tchannels_last speedup, forward 0.755, backward 0.772\n",
134+
"n 32, c 64, h 128, w 128, ks 3\tchannels_last speedup, forward 0.759, backward 0.773\n",
135+
"n 32, c 64, h 128, w 128, ks 5\tchannels_last speedup, forward 0.898, backward 0.769\n",
136+
"n 32, c 64, h 128, w 128, ks 7\tchannels_last speedup, forward 0.874, backward 0.769\n",
137+
"n 32, c 64, h 256, w 256, ks 2\tchannels_last speedup, forward 0.745, backward 0.349\n",
138+
"n 32, c 64, h 256, w 256, ks 3\tchannels_last speedup, forward 0.743, backward 0.348\n",
139+
"n 32, c 64, h 256, w 256, ks 5\tchannels_last speedup, forward 0.852, backward 0.345\n",
140+
"n 32, c 64, h 256, w 256, ks 7\tchannels_last speedup, forward 0.885, backward 0.343\n",
141+
"n 64, c 1, h 14, w 14, ks 2\tchannels_last speedup, forward 0.945, backward 1.031\n",
142+
"n 64, c 1, h 14, w 14, ks 3\tchannels_last speedup, forward 0.953, backward 1.029\n",
143+
"n 64, c 1, h 14, w 14, ks 5\tchannels_last speedup, forward 0.965, backward 1.044\n",
144+
"n 64, c 1, h 14, w 14, ks 7\tchannels_last speedup, forward 0.960, backward 1.032\n",
145+
"n 64, c 1, h 28, w 28, ks 2\tchannels_last speedup, forward 0.938, backward 1.029\n",
146+
"n 64, c 1, h 28, w 28, ks 3\tchannels_last speedup, forward 0.938, backward 1.029\n",
147+
"n 64, c 1, h 28, w 28, ks 5\tchannels_last speedup, forward 0.948, backward 1.024\n",
148+
"n 64, c 1, h 28, w 28, ks 7\tchannels_last speedup, forward 0.953, backward 1.033\n",
149+
"n 64, c 1, h 56, w 56, ks 2\tchannels_last speedup, forward 0.953, backward 1.024\n",
150+
"n 64, c 1, h 56, w 56, ks 3\tchannels_last speedup, forward 0.949, backward 1.064\n",
151+
"n 64, c 1, h 56, w 56, ks 5\tchannels_last speedup, forward 0.943, backward 1.028\n",
152+
"n 64, c 1, h 56, w 56, ks 7\tchannels_last speedup, forward 0.959, backward 1.027\n",
153+
"n 64, c 1, h 128, w 128, ks 2\tchannels_last speedup, forward 0.956, backward 1.023\n",
154+
"n 64, c 1, h 128, w 128, ks 3\tchannels_last speedup, forward 0.963, backward 1.020\n",
155+
"n 64, c 1, h 128, w 128, ks 5\tchannels_last speedup, forward 0.965, backward 1.032\n",
156+
"n 64, c 1, h 128, w 128, ks 7\tchannels_last speedup, forward 0.954, backward 1.025\n",
157+
"n 64, c 1, h 256, w 256, ks 2\tchannels_last speedup, forward 1.000, backward 1.006\n",
158+
"n 64, c 1, h 256, w 256, ks 3\tchannels_last speedup, forward 0.995, backward 1.001\n",
159+
"n 64, c 1, h 256, w 256, ks 5\tchannels_last speedup, forward 1.003, backward 1.007\n",
160+
"n 64, c 1, h 256, w 256, ks 7\tchannels_last speedup, forward 0.994, backward 1.003\n",
161+
"n 64, c 32, h 14, w 14, ks 2\tchannels_last speedup, forward 0.935, backward 0.987\n",
162+
"n 64, c 32, h 14, w 14, ks 3\tchannels_last speedup, forward 0.926, backward 0.987\n",
163+
"n 64, c 32, h 14, w 14, ks 5\tchannels_last speedup, forward 0.926, backward 0.988\n",
164+
"n 64, c 32, h 14, w 14, ks 7\tchannels_last speedup, forward 0.929, backward 0.970\n",
165+
"n 64, c 32, h 28, w 28, ks 2\tchannels_last speedup, forward 0.859, backward 0.908\n",
166+
"n 64, c 32, h 28, w 28, ks 3\tchannels_last speedup, forward 0.926, backward 0.897\n",
167+
"n 64, c 32, h 28, w 28, ks 5\tchannels_last speedup, forward 0.937, backward 0.921\n",
168+
"n 64, c 32, h 28, w 28, ks 7\tchannels_last speedup, forward 0.930, backward 0.900\n",
169+
"n 64, c 32, h 56, w 56, ks 2\tchannels_last speedup, forward 0.799, backward 0.891\n",
170+
"n 64, c 32, h 56, w 56, ks 3\tchannels_last speedup, forward 0.853, backward 0.899\n",
171+
"n 64, c 32, h 56, w 56, ks 5\tchannels_last speedup, forward 0.958, backward 0.892\n",
172+
"n 64, c 32, h 56, w 56, ks 7\tchannels_last speedup, forward 0.974, backward 0.901\n",
173+
"n 64, c 32, h 128, w 128, ks 2\tchannels_last speedup, forward 0.760, backward 0.863\n",
174+
"n 64, c 32, h 128, w 128, ks 3\tchannels_last speedup, forward 0.757, backward 0.879\n",
175+
"n 64, c 32, h 128, w 128, ks 5\tchannels_last speedup, forward 0.900, backward 0.869\n",
176+
"n 64, c 32, h 128, w 128, ks 7\tchannels_last speedup, forward 0.876, backward 0.878\n",
177+
"n 64, c 32, h 256, w 256, ks 2\tchannels_last speedup, forward 0.755, backward 0.583\n",
178+
"n 64, c 32, h 256, w 256, ks 3\tchannels_last speedup, forward 0.748, backward 0.584\n",
179+
"n 64, c 32, h 256, w 256, ks 5\tchannels_last speedup, forward 0.843, backward 0.572\n",
180+
"n 64, c 32, h 256, w 256, ks 7\tchannels_last speedup, forward 0.871, backward 0.577\n",
181+
"n 64, c 64, h 14, w 14, ks 2\tchannels_last speedup, forward 0.936, backward 0.790\n",
182+
"n 64, c 64, h 14, w 14, ks 3\tchannels_last speedup, forward 0.925, backward 0.836\n",
183+
"n 64, c 64, h 14, w 14, ks 5\tchannels_last speedup, forward 0.935, backward 0.853\n",
184+
"n 64, c 64, h 14, w 14, ks 7\tchannels_last speedup, forward 0.926, backward 0.845\n",
185+
"n 64, c 64, h 28, w 28, ks 2\tchannels_last speedup, forward 0.782, backward 0.699\n",
186+
"n 64, c 64, h 28, w 28, ks 3\tchannels_last speedup, forward 0.925, backward 0.702\n",
187+
"n 64, c 64, h 28, w 28, ks 5\tchannels_last speedup, forward 1.036, backward 0.699\n",
188+
"n 64, c 64, h 28, w 28, ks 7\tchannels_last speedup, forward 1.077, backward 0.689\n",
189+
"n 64, c 64, h 56, w 56, ks 2\tchannels_last speedup, forward 0.755, backward 0.801\n",
190+
"n 64, c 64, h 56, w 56, ks 3\tchannels_last speedup, forward 0.840, backward 0.812\n",
191+
"n 64, c 64, h 56, w 56, ks 5\tchannels_last speedup, forward 0.952, backward 0.809\n",
192+
"n 64, c 64, h 56, w 56, ks 7\tchannels_last speedup, forward 0.963, backward 0.801\n",
193+
"n 64, c 64, h 128, w 128, ks 2\tchannels_last speedup, forward 0.751, backward 0.764\n",
194+
"n 64, c 64, h 128, w 128, ks 3\tchannels_last speedup, forward 0.749, backward 0.768\n",
195+
"n 64, c 64, h 128, w 128, ks 5\tchannels_last speedup, forward 0.883, backward 0.761\n",
196+
"n 64, c 64, h 128, w 128, ks 7\tchannels_last speedup, forward 0.859, backward 0.761\n",
197+
"n 64, c 64, h 256, w 256, ks 2\tchannels_last speedup, forward 0.745, backward 0.348\n",
198+
"n 64, c 64, h 256, w 256, ks 3\tchannels_last speedup, forward 0.741, backward 0.346\n",
199+
"n 64, c 64, h 256, w 256, ks 5\tchannels_last speedup, forward 0.849, backward 0.344\n",
200+
"n 64, c 64, h 256, w 256, ks 7\tchannels_last speedup, forward 0.881, backward 0.341\n"
201201
]
202202
}
203203
],
@@ -236,8 +236,10 @@
236236
" ref_out = ref_pool(ref_a)\n",
237237
"\n",
238238
" grad = torch.ones_like(out)\n",
239+
" ref_grad = grad.clone().contiguous()\n",
240+
" \n",
239241
" out.backward(grad, retain_graph=True)\n",
240-
" ref_out.backward(grad, retain_graph=True)\n",
242+
" ref_out.backward(ref_grad, retain_graph=True)\n",
241243
"\n",
242244
" assert((out==ref_out).all().item())\n",
243245
" assert((a.grad==ref_a.grad).all().item())\n",
@@ -298,7 +300,7 @@
298300
" ts = time.time() \n",
299301
"\n",
300302
" for _ in range(n_iter): \n",
301-
" ref_out.backward(grad, retain_graph=True)\n",
303+
" ref_out.backward(ref_grad, retain_graph=True)\n",
302304
" torch.cuda.synchronize() \n",
303305
"\n",
304306
" torch.cuda.synchronize() \n",
@@ -338,7 +340,7 @@
338340
"outputs": [
339341
{
340342
"data": {
341-
"image/png": 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\n",
343+
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\n",
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"text/plain": [
343345
"<Figure size 432x288 with 1 Axes>"
344346
]
@@ -350,7 +352,7 @@
350352
},
351353
{
352354
"data": {
353-
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\n",
355+
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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]

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