Contents

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.

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 v3.1.0 (commit 0091797c30f16250dec7a40f9ee1a8a33bcfd65e)
onednn_verbose,info,cpu,runtime:OpenMP,nthr:56
onednn_verbose,info,cpu,isa:Intel AVX-512 with AVX512BW, AVX512VL, and AVX512DQ extensions
onednn_verbose,info,gpu,runtime:none
onednn_verbose,info,prim_template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time
onednn_verbose,create:check,matmul,dimension src:1 is inconsistent with weights:0,src/common/matmul.cpp:69

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 v3.1.0 (commit 0091797c30f16250dec7a40f9ee1a8a33bcfd65e)
onednn_verbose,info,cpu,runtime:OpenMP,nthr:56
onednn_verbose,info,cpu,isa:Intel AVX-512 with AVX512BW, AVX512VL, and AVX512DQ extensions
onednn_verbose,info,gpu,runtime:none
onednn_verbose,info,prim_template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time
onednn_verbose,create:dispatch,matmul,cpu,matmul,brg:avx512_core_amx_fp16,undef,src_u8::any::f0 wei_s8::any::f0 dst_f32::any::f0,,,256x256:256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,create:dispatch,matmul,cpu,matmul,brg:avx512_core_amx,undef,src_u8::any::f0 wei_s8::any::f0 dst_f32::any::f0,,,256x256:256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,create:dispatch,brgemm_matmul,datatype configuration not supported on this isa,src/cpu/x64/matmul/brgemm_matmul_utils.cpp:931
onednn_verbose,create:dispatch,matmul,cpu,matmul,brg:avx512_core_bf16,undef,src_u8::any::f0 wei_s8::any::f0 dst_f32::any::f0,,,256x256:256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,create:dispatch,matmul,cpu,matmul,brg:avx512_core_vnni,undef,src_u8::any::f0 wei_s8::any::f0 dst_f32::any::f0,,,256x256:256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,create:dispatch,matmul,cpu,matmul,brg:avx2_vnni_2,undef,src_u8::any::f0 wei_s8::any::f0 dst_f32::any::f0,,,256x256:256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101
onednn_verbose,create:dispatch,matmul,cpu,matmul,brg:avx2_vnni,undef,src_u8::any::f0 wei_s8::any::f0 dst_f32::any::f0,,,256x256:256x256:256x256,unsupported isa,src/cpu/x64/matmul/brgemm_matmul.cpp:101

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 v3.1.0 (commit 2cdd9ee1364b6c5b107aff8738af352a746d0434)
onednn_verbose,info,cpu,runtime:OpenMP,nthr:56
onednn_verbose,info,cpu,isa:Intel AVX-512 with AVX512BW, AVX512VL, and AVX512DQ extensions
onednn_verbose,info,gpu,runtime:none
onednn_verbose,info,prim_template:timestamp,operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time
onednn_verbose,1681823859527.679932,create:cache_miss,cpu,convolution,jit:avx512_core,forward_training,src_f32::blocked:aBcd16b:f0 wei_f32::blocked:ABcd16b16a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:aBcd16b:f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,13.313
onednn_verbose,1681823859541.047119,create:cache_hit,cpu,convolution,jit:avx512_core,forward_training,src_f32::blocked:aBcd16b:f0 wei_f32::blocked:ABcd16b16a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:aBcd16b:f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,0.00292969
onednn_verbose,1681823859567.496094,create:cache_miss,cpu,reorder,jit:uni,undef,src_f32::blocked:a:f0 dst_f32::blocked:a:f0,,,16,0.0759277
onednn_verbose,1681823859567.612061,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:a:f0 dst_f32::blocked:a:f0,,,16,0.00195312
onednn_verbose,1681823859567.902100,create:cache_miss,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd16b16a:f0,,,16x16x5x5,0.0720215
onednn_verbose,1681823859567.996094,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:ABcd16b16a:f0,,,16x16x5x5,0.201904
onednn_verbose,1681823859568.535889,create:cache_miss,cpu,reorder,jit:blk,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd16b:f0,,,2x16x7x7,0.0432129
onednn_verbose,1681823859568.597900,exec,cpu,reorder,jit:blk,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:aBcd16b:f0,,,2x16x7x7,0.258057
onednn_verbose,1681823859568.868896,exec,cpu,convolution,jit:avx512_core,forward_training,src_f32::blocked:aBcd16b:f0 wei_f32::blocked:ABcd16b16a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:aBcd16b:f0,,alg:convolution_direct,mb2_ic16oc16_ih7oh7kh5sh1dh0ph2_iw7ow7kw5sw1dw0pw2,40.6201
onednn_verbose,1681823859610.262939,create:cache_miss,cpu,reorder,jit:blk,undef,src_f32::blocked:aBcd16b:f0 dst_f32::blocked:abcd:f0,,,2x16x7x7,0.052002
onednn_verbose,1681823859610.383057,exec,cpu,reorder,jit:blk,undef,src_f32::blocked:aBcd16b:f0 dst_f32::blocked:abcd:f0,,,2x16x7x7,0.189941

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. This verbose header is printed upon first logged information.

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:

  • 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.

  • 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:

  1. tensor_name is one of the tensors names listed in the Naming Conventions, and denotes a tensor supported by the corresponding primitive.

  2. properties denotes if a tensor was created with format_kind::any and has padded area or an offset from original memory.

  3. 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 \(\weights\) tensor for the Winograd convolution.

  4. strides denotes stride values in case the memory is not dense. If the memory is dense, the field will be empty.

  5. extra_flags is unspecified information that is intended for development purposes.

Note

When oneDNN verbose mode is enabled with GPU engines, oneDNN adds extra stream synchronization on entry and on exit in the dnnl::primitive::execute() call. The execution time is calculated based on wall time measured before and after primitive execution.

Note

When oneDNN verbose mode is enabled for builds with Compute Library for the Arm architecture, any failures in the validation of Compute Library primitives will be detailed in the verbose output.

Warning

Verbose mode has non-negligible performance impact especially on GPU or if the output rate is high.

Performance Profiling and Inspection Configuring oneDNN for Benchmarking