Intel® oneAPI Deep Neural Network Developer Guide and Reference
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Visible to Intel only — GUID: GUID-BC65C02B-3991-46AA-B64A-CE4C39266442
Verbose Mode
It is often useful to collect information about how much of an application runtime is spent executing oneDNN primitives and which of those take the most time. oneDNN verbose mode enables tracing execution of oneDNN primitives and collection of basic statistics like execution time and primitive parameters. When verbose mode is enabled oneDNN will print out information to stdout.
Build-Time Controls
At build-time, support for this feature is controlled via cmake option ONEDNN_VERBOSE.
CMake Option |
Supported values (defaults in bold) |
Description |
|---|---|---|
ONEDNN_VERBOSE |
ON , OFF |
Enables verbose mode |
Run-Time Controls
When the feature is enabled at build-time, the ONEDNN_VERBOSE environment variable can be used to turn verbose mode on and control the level of verbosity.
Environment variable |
Value |
Description |
|---|---|---|
ONEDNN_VERBOSE |
0 1 2 |
no verbose output (default) primitive information at execution primitive information at creation and execution |
ONEDNN_VERBOSE_TIMESTAMP |
0 1 |
display timestamps disabled (default) display timestamps enabled |
This feature can also be managed at run-time with the following functions:
The function setting takes precedence over the environment variable.
Example
Enable ONEDNN_VERBOSE
ONEDNN_VERBOSE=1 ./benchdnn --conv ic16ih7oc16oh7kh5ph2n"wip"
This produces the following output (the line breaks were added to fit the page width):
dnnl_verbose,info,DNNL v1.3.0 (commit d0fc158e98590dfad0165e568ca466876a794597) dnnl_verbose,info,cpu,runtime:OpenMP dnnl_verbose,info,cpu,isa:Intel AVX2 dnnl_verbose,info,gpu,runtime:none dnnl_verbose,info,prim_template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd8b:f0,,,2x16x7x7,0.0200195 dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd8b8a:f0,,,16x16x5x5,0.0251465 dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd8b:f0,,,2x16x7x7,0.0180664 dnnl_verbose,exec,cpu,reorder,simple:any,undef,src_f32::blocked:a:f0 dst_f32::blocked:a:f0,,,16,0.0229492 dnnl_verbose,exec,cpu,convolution,jit:avx2,forward_training,src_f32::blocked:aBcd8b:f0 wei_f32::blocked:ABcd8b8a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:aBcd8b:f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,0.0390625 dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:aBcd8b:f0 dst_f32::blocked:abcd:f0,,,2x16x7x7,0.173096
Enable ONEDNN_VERBOSE with timestamps
ONEDNN_VERBOSE=1 ONEDNN_VERBOSE_TIMESTAMP=1 ./benchdnn --conv ic16ih7oc16oh7kh5ph2n"wip"
This produces the following output (the line breaks were added to fit the page width):
dnnl_verbose,info,oneDNN v2.0.0 (commit N/A) dnnl_verbose,info,cpu,runtime:OpenMP dnnl_verbose,info,cpu,isa:Intel AVX2 dnnl_verbose,info,gpu,runtime:none dnnl_verbose,info,prim_template:timestamp,operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time dnnl_verbose,1607393146348.667969,exec,cpu,reorder,jit:blk,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd8b:f0,,,2x16x7x7,3.58594 dnnl_verbose,1607393146356.743896,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd8b8a:f0,,,16x16x5x5,3.63916 dnnl_verbose,1607393146364.541992,exec,cpu,reorder,jit:blk,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd8b:f0,,,2x16x7x7,2.35693 dnnl_verbose,1607393146367.198975,exec,cpu,reorder,simple:any,undef,src_f32::blocked:a:f0 dst_f32::blocked:a:f0,,,16,3.71191 dnnl_verbose,1607393146371.002930,exec,cpu,convolution,jit:avx2,forward_training,src_f32::blocked:aBcd8b:f0 wei_f32::blocked:ABcd8b8a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:aBcd8b:f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,3.93018 dnnl_verbose,1607393146380.145020,exec,cpu,reorder,jit:blk,undef,src_f32::blocked:aBcd8b:f0 dst_f32::blocked:abcd:f0,,,2x16x7x7,1.75708
Decrypting the Output
The first lines of verbose information, which are denoted with info, contain the build version and git hash, if available, as well as CPU and GPU runtimes, the supported instruction set architecture and the verbose output format template since amount of fields may vary depeding on set of enviroment variables enabled.
Each subsequent line of verbose information is formatted as a comma-separated list contains, in order of appearance in the line from left to right:
dnnl_verbose marker string
if ONEDNN_VERBOSE_TIMESTAMP=1 is specified, start time of the call. On Linux this number represents amount of milliseconds since Unix epoch. On Windows this number represents amount of milliseconds since the last system start.
operation: create:<cache_hit|cache_miss|from_cache_blob> or exec
engine kind: cpu or gpu (cpu2gpu or gpu2cpu for cross-engine reorder)
primitive name: convolution, reorder, sum, etc
primitive implementation
propagation kind: forward_training, forward_inference, backward, etc
information about all operation tensors (separated by space)
primitive attributes
auxiliary information like algorithm name or number of inputs
a problem description in benchdnn format
execution time in milliseconds
The information about a particular operation tensors has the following format: tensor_name _ data_type :: format_kind : format_tag : extra_flags, where:
tensor_name is one of the tensors names listed in the Naming Conventions, and denotes a tensor supported by the corresponding primitive
data_type, format_kind and format_tag denote values from dnnl::memory::data_type, dnnl::memory::format_kind and dnnl::memory::format_tag respectively. Note, that certain markers may be missing in some cases, such as format_tag for the
tensor for the Winograd convolution.extra_flags is unspecified information that is intended for development purposes
Please see the profiling example here, as it uses ONEDNN_VERBOSE output to tune oneDNN code to align with best practices.