Why is the performance of the mkl_conv in the torch test worse than that of the benchdnn test?

[Serenagirl]

I tested aten::mkldnn_convolutioin in pytorch:

import torch
import torch.nn.functional as F
from torch.profiler import profile, record_function, ProfilerActivity

input_tensor = torch.randn(1, 64, 170, 256)  # [batch_size, in_channels, height, width]
weight_tensor = torch.randn(64, 64, 3, 3)    # [out_channels, in_channels, kernel_height, kernel_width]
bias_tensor = torch.randn(64)               # [out_channels]

stride = (1, 1)
padding = (1, 1)
dilation = (1, 1)
groups = 1

with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
    with record_function("aten::mkldnn_convolution"):
        output = F.conv2d(input_tensor, weight_tensor, bias_tensor, stride, padding, dilation, groups)
print(prof.key_averages().table(sort_by="self_cpu_time_total"))

and got time total:144ms,but I tested the same convolution in benchdnn:
OMP_NUM_THREADS=1 taskset -c 0 ./benchdnn --conv --mode=P --dt=f32 --dir=FWD_B --mb=1 ic64ih170iw256oc64oh168ow254kh3kw3sh1sw1dh1dw1ph1pw1

total perf: min(ms):349.276 avg(ms):349.385
total: 3.54s; fill: 0.05s (1%);

why the difference

[rupakroyintel]

@Serenagirl Thanks for reaching out.
Can you share the following information:

• oneDNN Verbose log for the PyTorch code execution?
• Platform information
  Also, is there any particular reason for setting OMP_NUM_THREADS=1 for the benchdnn test?

Here are some best practices for Configuring oneDNN for Benchmarking.