improve mkldnn doc (apache#17198)

docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md

@@ -214,13 +214,17 @@ export MKLDNN_VERBOSE=1
 ```
 For example, by running above code snippet, the following debugging logs providing more insights on MKL-DNN primitives `convolution` and `reorder`. That includes: Memory layout, infer shape and the time cost of primitive execution.
 ```
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
-mkldnn_verbose,exec,convolution,jit:avx512_common,forward_inference,fsrc:nChw16c fwei:OIhw16i16o fbia:undef fdst:nChw16c,alg:convolution_direct,mb32_g1ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,9.98193
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
+dnnl_verbose,info,DNNL v1.1.2 (commit cb2cc7ac17ff4e2ef50805c7048d33256d82be4d)
+dnnl_verbose,info,Detected ISA is Intel AVX-512 with Intel DL Boost
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd16b:f0,,,32x32x256x256,7.43701
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd16b16a:f0,,,32x32x3x3,0.202148
+dnnl_verbose,exec,cpu,convolution,jit:avx512_common,forward_inference,src_f32::blocked:aBcd16b:f0 wei_f32::blocked:ABcd16b16a:f0 bia_undef::undef::f0 dst_f32::blocked:aBcd16b:f0,,alg:convolution_direct,mb32_ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,20.7539
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd16b16a:f0,,,32x32x3x3,1.86694
+dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:aBcd16b:f0 dst_f32::blocked:abcd:f0,,,32x32x256x256,35.9771
 ```
 
+You can find step-by-step guidance to do profiling for MKLDNN primitives in [Profiling MKLDNN Operators](https://mxnet.apache.org/api/python/docs/tutorials/performance/backend/profiler.html#Profiling-MKLDNN-Operators).
+
 <h2 id="5">Enable MKL BLAS</h2>
 
 With MKL BLAS, the performace is expected to furtherly improved with variable range depending on the computation load of the models.

docs/python_docs/python/tutorials/performance/backend/profiler.md

@@ -211,6 +211,30 @@ Let's zoom in to check the time taken by operators
 
 The above picture visualizes the sequence in which the operators were executed and the time taken by each operator.
 
+### Profiling MKLDNN Operators
+Reagrding MKLDNN operators, the library has already provided the internal profiling tool. Firstly, you need set `MKLDNN_VERBOSE=1` to enable internal profiler.
+
+`$ MKLDNN_VERBOSE=1 python my_script.py > mkldnn_verbose.log`
+
+Now, the detailed profiling insights of each mkldnn prmitive are saved into `mkldnn_verbose.log` (like below).
+
+```
+dnnl_verbose,info,DNNL v1.1.2 (commit cb2cc7ac17ff4e2ef50805c7048d33256d82be4d)
+dnnl_verbose,info,Detected ISA is Intel AVX-512 with Intel DL Boost
+dnnl_verbose,exec,cpu,convolution,jit:avx512_common,forward_inference,src_f32::blocked:aBcd16b:f0 wei_f32::blocked:ABcd16b16a:f0 bia_undef::undef::f0 dst_f32::blocked:aBcd16b:f0,,alg:convolution_direct,mb32_ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,20.7539
+```
+
+For example, if you want to calculate the total executing time of `convolution` primitive, you can just run:
+
+`$ cat mkldnn_verbose.log | grep "exec,cpu,convolution" | awk 'BEGIN{FS=","} {SUM+=$11} END {print SUM}'`
+
+Moreover, you can set `MKLDNN_VERBOSE=2` to collect both creating and executing time of each primitive.
+
+`$ cat mkldnn_verbose.log | grep "create,cpu,convolution" | awk 'BEGIN{FS=","} {SUM+=$11} END {print SUM}'`
+
+`$ cat mkldnn_verbose.log | grep "exec,cpu,convolution" | awk 'BEGIN{FS=","} {SUM+=$11} END {print SUM}'`
+
+
 ### Profiling Custom Operators
 Should the existing NDArray operators fail to meet all your model's needs, MXNet supports [Custom Operators](/api/python/docs/tutorials/extend/customop.html) that you can define in Python. In `forward()` and `backward()` of a custom operator, there are two kinds of code: "pure Python" code (NumPy operators included) and "sub-operators" (NDArray operators called within `forward()` and `backward()`). With that said, MXNet can profile the execution time of both kinds without additional setup. Specifically, the MXNet profiler will break a single custom operator call into a pure Python event and several sub-operator events if there are any. Furthermore, all of those events will have a prefix in their names, which is, conveniently, the name of the custom operator you called.