Intel® oneAPI Deep Neural Network Developer Guide and Reference
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Visible to Intel only — GUID: GUID-0B1FAF98-9C8E-4160-856E-DA75B0B48200
Verbose Mode
oneDNN verbose mode enables tracing the execution of oneDNN API calls. This is a useful feature for collecting statistics to profile an application or for troubleshooting API usage errors. 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 type of tracing information to display.
Environment variable |
Value |
Description |
|---|---|---|
ONEDNN_VERBOSE |
none |
no messages printed |
** error ** |
error messages (default) |
|
check |
primitive creation parameter checking information |
|
profile_create |
primitive creation timings |
|
profile_exec |
primitive execution timings |
|
profile |
primitive creation and execution timings |
|
dispatch |
primitive dispatching information |
|
all |
enables all above flags but none |
|
debuginfo=<level> |
enables internal debug printing (for developers) |
|
ONEDNN_VERBOSE_TIMESTAMP |
0 |
display timestamps disabled (default) |
1 |
display timestamps enabled |
The verbose flags can be combined, e.g. ONEDNN_VERBOSE=profile,dispatch will enable printing both performance profiling information, and information relative to why a given oneDNN primitive implementation was dispatched. In general, we recommend using ONEDNN_VERBOSE=all, unless message printing overhead becomes noticeable.
debuginfo information is available only if the library is built with ONEDNN_DEV_MODE=ON.
oneDNN verbose also provides a filter option, which takes a regular expression and applies the verbose output to matching components. Currently, the supported components are primitive, graph, gemm_api and primitive kind names. Here are some examples of usage:
ONEDNN_VERBOSE=profile_exec,filter=graph will print verbose of compiled_partition execution profiling from graph API
ONEDNN_VERBOSE=profile_exec,filter=prim will print verbose of primitive execution profiling from primitive API
ONEDNN_VERBOSE=profile_exec,filter=conv\|matmul will print execution profiling verbose of (de)convolution and matmul primitive
Filter won’t work if the regular expression is invalid
Only the last one will take effect if multiple filters are specified
oneDNN supports the following legacy settings:
Environment variable |
Value |
Description |
|---|---|---|
ONEDNN_VERBOSE |
0 |
no verbose output, replaced by none |
1 |
primitive execution profiling timings, replaced by profile_exec |
|
2 |
primitive creation and execution timings, replaced by profile |
The oneDNN verbose can also be managed at run-time with the following functions:
The function setting takes precedence over the environment variable.
Example
Troubleshooting primitive creation issues
When facing functional issues, we recommend using ONEDNN_VERBOSE=all as it will provide insights on why a given primitive cannot be created. Here is an example of output one can get when providing incorrect dimensions to a matmul primitive.
ONEDNN_VERBOSE=all ./benchdnn --matmul 256x256:25x256
This produces the following output:
onednn_verbose,info,oneDNN v3.2.0 (commit 6afab8e57f65a8995685d97ba6f80fa6c24b87a0) onednn_verbose,info,cpu,runtime:OpenMP,nthr:128 onednn_verbose,info,cpu,isa:Intel AVX-512 with Intel DL Boost onednn_verbose,info,gpu,runtime:none onednn_verbose,info,graph,backend,0:dnnl_backend onednn_verbose,primitive,info,template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time onednn_verbose,graph,info,template:operation,engine,partition_id,partition_kind,op_names,data_formats,logical_tensors,fpmath_mode,backend,exec_time onednn_verbose,primitive,create:check,matmul,dimension src:1 is inconsistent with weights:0,src/common/matmul.cpp:144
The last line here shows that the matmul primitive failed to be created because of a dimension mismatch between its two operands.
Profiling a workload
To understand a full application performance, it is useful to break down performance bottlenecks. ONEDNN_VERBOSE=profile does just that and shows
how much time is spent in primitive creation
how much time is spent in each primitive execution
how often a given primitive is called.
Please see the profiling example here, as it uses ONEDNN_VERBOSE output to tune oneDNN code to align with best practices.
Understanding why a given implementation is dispatched
When performance is lower than expected, it is usually likely due to the dispatching of a lower performing implementation. Hence it can be useful to understand what circumstance led oneDNN to dispatch a lower performance implementation. This can be observed by using ONEDNN_VERBOSE=dispatch.
ONEDNN_VERBOSE=dispatch ./benchdnn --matmul --dt=u8:s8:f32 256x256:256x256
This produces the following log (shortened for brevity).
onednn_verbose,info,oneDNN v3.2.0 (commit 6afab8e57f65a8995685d97ba6f80fa6c24b87a0) onednn_verbose,info,cpu,runtime:OpenMP,nthr:128 onednn_verbose,info,cpu,isa:Intel AVX-512 with Intel DL Boost onednn_verbose,info,gpu,runtime:none onednn_verbose,info,graph,backend,0:dnnl_backend onednn_verbose,primitive,info,template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time onednn_verbose,graph,info,template:operation,engine,partition_id,partition_kind,op_names,data_formats,logical_tensors,fpmath_mode,backend,exec_time onednn_verbose,primitive,create:dispatch,matmul,cpu,matmul,brg:avx512_core_amx_fp16,undef,src_u8:a:any:any::f0 wei_s8:a:any:any::f0 dst_f32:a:any:any::f0,,,256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:97 onednn_verbose,primitive,create:dispatch,matmul,cpu,matmul,brg:avx512_core_amx,undef,src_u8:a:any:any::f0 wei_s8:a:any:any::f0 dst_f32:a:any:any::f0,,,256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:97 onednn_verbose,primitive,create:dispatch,matmul,cpu,matmul,brg:avx512_core_fp16,undef,src_u8:a:any:any::f0 wei_s8:a:any:any::f0 dst_f32:a:any:any::f0,,,256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:97 onednn_verbose,primitive,create:dispatch,matmul,cpu,matmul,brg:avx512_core_bf16,undef,src_u8:a:any:any::f0 wei_s8:a:any:any::f0 dst_f32:a:any:any::f0,,,256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:97
Above, we can see that the highest performance implementations were not dispatched either because they required a higher ISA, or because they did not support that datatype configuration.
Enable ONEDNN_VERBOSE with timestamps
ONEDNN_VERBOSE=profile ONEDNN_VERBOSE_TIMESTAMP=1 ./benchdnn --conv ic16ih7oc16oh7kh5ph2n"wip"
This produces the following output:
onednn_verbose,info,oneDNN v3.2.0 (commit 6afab8e57f65a8995685d97ba6f80fa6c24b87a0) onednn_verbose,info,cpu,runtime:OpenMP,nthr:128 onednn_verbose,info,cpu,isa:Intel AVX-512 with Intel DL Boost onednn_verbose,info,gpu,runtime:none onednn_verbose,info,graph,backend,0:dnnl_backend onednn_verbose,primitive,info,template:timestamp,operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time onednn_verbose,graph,info,template:timestamp,operation,engine,partition_id,partition_kind,op_names,data_formats,logical_tensors,fpmath_mode,backend,exec_time onednn_verbose,1693533460193.346924,primitive,create:cache_miss,cpu,convolution,jit:avx512_core,forward_training,src_f32:a:blocked:aBcd16b::f0 wei_f32:a:blocked:ABcd16b16a::f0 bia_f32:a:blocked:a::f0 dst_f32:a:blocked:aBcd16b::f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,0.709961 onednn_verbose,1693533460194.199951,primitive,create:cache_hit,cpu,convolution,jit:avx512_core,forward_training,src_f32:a:blocked:aBcd16b::f0 wei_f32:a:blocked:ABcd16b16a::f0 bia_f32:a:blocked:a::f0 dst_f32:a:blocked:aBcd16b::f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,0.0161133 onednn_verbose,1693533460228.559082,primitive,create:cache_miss,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd::f0 dst_f32::blocked:ABcd16b16a::f0,,,16x16x5x5,0.724854 onednn_verbose,1693533460229.437012,primitive,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd::f0 dst_f32::blocked:ABcd16b16a::f0,,,16x16x5x5,16.481 onednn_verbose,1693533460259.165039,primitive,create:cache_miss,cpu,reorder,jit:blk,undef,src_f32::blocked:abcd::f0 dst_f32::blocked:aBcd16b::f0,,,2x16x7x7,0.349854 onednn_verbose,1693533460259.586914,primitive,exec,cpu,reorder,jit:blk,undef,src_f32::blocked:abcd::f0 dst_f32::blocked:aBcd16b::f0,,,2x16x7x7,12.604 onednn_verbose,1693533460272.332031,primitive,create:cache_miss,cpu,reorder,simple:any,undef,src_f32::blocked:a::f0 dst_f32::blocked:a::f0,,,16,0.0358887 onednn_verbose,1693533460272.416992,primitive,exec,cpu,reorder,simple:any,undef,src_f32::blocked:a::f0 dst_f32::blocked:a::f0,,,16,0.052002 onednn_verbose,1693533460272.561035,primitive,exec,cpu,convolution,jit:avx512_core,forward_training,src_f32:a:blocked:aBcd16b::f0 wei_f32:a:blocked:ABcd16b16a::f0 bia_f32:a:blocked:a::f0 dst_f32:a:blocked:aBcd16b::f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,0.0878906 onednn_verbose,1693533460313.719971,primitive,create:cache_miss,cpu,reorder,jit:blk,undef,src_f32::blocked:aBcd16b::f0 dst_f32::blocked:abcd::f0,,,2x16x7x7,0.275146 onednn_verbose,1693533460314.072021,primitive,exec,cpu,reorder,jit:blk,undef,src_f32::blocked:aBcd16b::f0 dst_f32::blocked:abcd::f0,,,2x16x7x7,18.8389 0:PASSED __REPRO: --conv ic16ih7oc16oh7kh5ph2nwip
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. It also includes graph API backends, the supported instruction set architecture, and the verbose output format template since the amount of fields may vary depending on the set of enabled environment variables. This verbose header is printed when information is first logged.
Each subsequent line of primitive verbose information is formatted as a comma-separated list and contains the following, in order of appearance in the line from left to right:
onednn_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.
API kind: primitive|graph|common for API information
operation: exec|create:<cache_hit|cache_miss|from_cache_blob> for profiling information, error|check|dispatch for other information.
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 : properties : format_kind : format_tag : strides : 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.
properties denotes if a tensor was created with format_kind::any and has padded area or an offset from original memory.
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.strides denotes stride values in case the memory is not dense. If the memory is dense, the field will be empty.
extra_flags is unspecified information that is intended for development purposes.