[GitHub] [tvm] zxybazh commented on pull request #12648: [MetaSchedule] Complete NCHW Conv2D Winograd Kernel Scheduling

[GitBox <gi...@apache.org>]

zxybazh commented on PR #12648:
URL: https://github.com/apache/tvm/pull/12648#issuecomment-1231920134

   Follow up with CUDA benchmarking results on Geforce RTX 3070, all data layouts are `NCHW`, padding `(1, 1)`, kernel size `(3, 3)`. Workload is a single conv2d function, dispatched to `nn.contrib_conv2d_winograd_without_weight_transform` for both AutoTVM and MetaSchedule.
   
   <table class="table table-bordered table-hover table-condensed">
   <thead><tr><th title="Field #1">Data Shape</th>
   <th title="Field #2">Kernel Layout</th>
   <th title="Field #3">Kernel Shape</th>
   <th title="Field #4">Winograd</th>
   <th title="Field #5">MS trials</th>
   <th title="Field #6">AutoTVM trials</th>
   <th title="Field #7">MS Perf(ms)</th>
   <th title="Field #8">AutoTVM Perf(ms)</th>
   <th title="Field #9">Perf Compare</th>
   </tr></thead>
   <tbody><tr>
   <td>(1, 512, 7, 7)</td>
   <td>OIHW</td>
   <td>(512, 512, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.05053524796891558</td>
   <td>0.05030714765801846</td>
   <td align="right">-0.4513687378%</td>
   </tr>
   <tr>
   <td>(2, 64, 56, 56)</td>
   <td>OIHW</td>
   <td>(64, 64, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.040951505223880594</td>
   <td>0.04111647433704021</td>
   <td align="right">0.4028401698%</td>
   </tr>
   <tr>
   <td>(2, 48, 56, 56)</td>
   <td>OIHW</td>
   <td>(48, 48, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.029897891745708238</td>
   <td>0.030240458663465385</td>
   <td align="right">1.1457895449%</td>
   </tr>
   <tr>
   <td>(1, 64, 28, 28)</td>
   <td>OIHW</td>
   <td>(64, 64, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.010250015296394941</td>
   <td>0.010370220173567484</td>
   <td align="right">1.1727287589%</td>
   </tr>
   <tr>
   <td>(1, 128, 28, 28)</td>
   <td>OIHW</td>
   <td>(128, 128, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.026061056047912482</td>
   <td>0.02657971123398702</td>
   <td align="right">1.9901541408%</td>
   </tr>
   <tr>
   <td>(1, 64, 56, 56)</td>
   <td>OIHW</td>
   <td>(64, 64, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.022871235249414843</td>
   <td>0.023843754776970795</td>
   <td align="right">4.2521513025%</td>
   </tr>
   <tr>
   <td>(1, 128, 14, 14)</td>
   <td>OIHW</td>
   <td>(128, 128, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.011837556120361336</td>
   <td>0.012637039015519788</td>
   <td align="right">6.7537833572%</td>
   </tr>
   <tr>
   <td>(1, 256, 14, 14)</td>
   <td>OIHW</td>
   <td>(256, 256, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.028101132421289355</td>
   <td>0.03071125413188647</td>
   <td align="right">9.2883150453%</td>
   </tr>
   <tr>
   <td>(1, 80, 73, 73)</td>
   <td>OIHW</td>
   <td>(192, 80, 3, 3)</td>
   <td>Yes</td>
   <td align="right">2048</td>
   <td align="right">1024</td>
   <td>0.2016123825065274</td>
   <td>0.22382275871015564</td>
   <td align="right">11.0163750497%</td>
   </tr>
   </tbody></table>


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