3 ms·

The TVM results on resnet50 and mobilenet seem a bit off. On GTX 1070 Ti, with an input of size (1, 3, 224, 224) TVM result Resnet50 : 100 inference/sec (0.00

by masahi 8y ago

The TVM results on resnet50 and mobilenet seem a bit off. On GTX 1070 Ti, with an input of size (1, 3, 224, 224) TVM result Resnet50 : 100 inference/sec (0.009983 sec per each run) Mobilenet: 450 inference/sec (0.002220 sec per each run) PlaidML result Resnet50 : 107 inference/sec (0.009302 sec per each run) Mobilenet: 473 inference/sec (0.002112 sec per each run) My benchmark script for tvm is here https://gist.github.com/masahi/a386c2ce5b5f8c2d9f7af5e09a8d880b https://gist.github.com/masahi/a386c2ce5b5f8c2d9f7af5e09a8d8...

hedgehog 8y ago
Thanks, I've shared this with our team to have a look. There was some subtlety to timing the right thing and it's possible we missed something.
b33pr 8y ago
Thank you so much for pointing this out. We'll get updated numbers out soon. How did you benchmark plaid, out of curiosity? The error which I correct here (https://github.com/brianretford/nnvm-rocm/blob/master/mxnet_imagenet_inference.py https://github.com/brianretford/nnvm-rocm/blob/master/mxnet_...) was caused by a desire to roughly approximate how keras does things, and plaidbench w/ keras is the easiest way for us to evaluate things, though it definitely adds in a lot of overhead. My script roughly matches the numbers I get out of your script, though I will say that I think the TVM time_evaluator should be calling Sync on the inside of its loop, to be fair (which I patched it to do to compare against your methodology). It doesn't make a huge difference, but it does exist. If I just pull the overall kernel runtime from our logs, I get ~525 inferences/sec.
- masahi 8y ago
  for plaid, I used plaidbench keras mobilenet plaidbench keras resnet50 time_evaluator is what tvm/nnvm folks use for benchmark. See their benchmark script here https://github.com/dmlc/nnvm/blob/master/examples/benchmark/gpu_imagenet_bench.py https://github.com/dmlc/nnvm/blob/master/examples/benchmark/...

by masahi 8y ago

hedgehog 8y ago

Thanks, I've shared this with our team to have a look. There was some subtlety to timing the right thing and it's possible we missed something.

b33pr 8y ago

Thank you so much for pointing this out. We'll get updated numbers out soon. How did you benchmark plaid, out of curiosity? The error which I correct here (https://github.com/brianretford/nnvm-rocm/blob/master/mxnet_imagenet_inference.py https://github.com/brianretford/nnvm-rocm/blob/master/mxnet_...) was caused by a desire to roughly approximate how keras does things, and plaidbench w/ keras is the easiest way for us to evaluate things, though it definitely adds in a lot of overhead. My script roughly matches the numbers I get out of your script, though I will say that I think the TVM time_evaluator should be calling Sync on the inside of its loop, to be fair (which I patched it to do to compare against your methodology). It doesn't make a huge difference, but it does exist. If I just pull the overall kernel runtime from our logs, I get ~525 inferences/sec.

masahi 8y ago
for plaid, I used plaidbench keras mobilenet plaidbench keras resnet50 time_evaluator is what tvm/nnvm folks use for benchmark. See their benchmark script here https://github.com/dmlc/nnvm/blob/master/examples/benchmark/gpu_imagenet_bench.py https://github.com/dmlc/nnvm/blob/master/examples/benchmark/...