* Migrate on OneDNN 2.7 * [CPU] Enabled brconv implementation * Post ops optimizations * [CPU] Enabled I8 precision on activations for Convolution node * [CPU][WA] Disabled Deconvolution + post ops fusing optimization * Fixed FQ post op optimization * [CPU] Optimize post ops processing * [WA] Add node name if tensor names are empty * [WA] remove layout compatibility chheck that leads to the fase-positive exceptions * [CPU] Optimize processing for FQ + Sum + FQ post ops pattern * [CPU][WA] Enabled ReduceSum -> AvgPool transformation due to perf issues * fix compiler error * rebase onednn master * cherry pick from 2.7 to 2.6 * [WA] make cpu case to run completed * fix xmm zero check * reopen 'FuseDeconvolutionAndSimpleOperation' Transform to fix CPU 'ConvolutionBackpropDataLayerTest' fail issue * [WR] Removed failed the ReduceMean tests caused by |
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| .. | ||
| README.md | ||
| cpu_dump_check.py | ||
| requirements.txt | ||
README.md
CPU Dump Check Tool
Compile CPU plugin with -DENABLE_DEBUG_CAPS=ON, then this tool allows:
- dump each output tensors from CPU plugin:
python3 cpu_dump_check.py -m=/path/to/model dump1
- comparing two dumps and analyze differences:
python3 cpu_dump_check.py -m=/path/to/model dump1 dump2
- visualize first error map:
python3 cpu_dump_check.py -m=/path/to/model dump1 dump2 -v