Visible to Intel only — GUID: GUID-1C7DFDDF-2EEF-4E50-B3E1-F7DA62A9702D
Abs
AbsBackward
Add
AvgPool
AvgPoolBackward
BatchNormForwardTraining
BatchNormInference
BatchNormTrainingBackward
BiasAdd
BiasAddBackward
Clamp
ClampBackward
Concat
Convolution
ConvolutionBackwardData
ConvolutionBackwardWeights
ConvTranspose
ConvTransposeBackwardData
ConvTransposeBackwardWeights
Dequantize
Divide
DynamicDequantize
DynamicQuantize
Elu
EluBackward
End
Exp
GELU
GELUBackward
HardSigmoid
HardSigmoidBackward
HardSwish
HardSwishBackward
Interpolate
InterpolateBackward
LayerNorm
LayerNormBackward
LeakyReLU
Log
LogSoftmax
LogSoftmaxBackward
MatMul
Maximum
MaxPool
MaxPoolBackward
Minimum
Mish
MishBackward
Multiply
Pow
PReLU
PReLUBackward
Quantize
Reciprocal
ReduceL1
ReduceL2
ReduceMax
ReduceMean
ReduceMin
ReduceProd
ReduceSum
ReLU
ReLUBackward
Reorder
Round
Select
Sigmoid
SigmoidBackward
SoftMax
SoftMaxBackward
SoftPlus
SoftPlusBackward
Sqrt
SqrtBackward
Square
SquaredDifference
StaticReshape
StaticTranspose
Subtract
Tanh
TanhBackward
TypeCast
Wildcard
enum dnnl_alg_kind_t
enum dnnl_normalization_flags_t
enum dnnl_primitive_kind_t
enum dnnl_prop_kind_t
enum dnnl_query_t
enum dnnl::normalization_flags
enum dnnl::query
struct dnnl_exec_arg_t
struct dnnl_primitive
struct dnnl_primitive_desc
struct dnnl::primitive
struct dnnl::primitive_desc
struct dnnl::primitive_desc_base
enum dnnl_rnn_direction_t
enum dnnl_rnn_flags_t
enum dnnl::rnn_direction
enum dnnl::rnn_flags
struct dnnl::augru_backward
struct dnnl::augru_forward
struct dnnl::gru_backward
struct dnnl::gru_forward
struct dnnl::lbr_augru_backward
struct dnnl::lbr_augru_forward
struct dnnl::lbr_gru_backward
struct dnnl::lbr_gru_forward
struct dnnl::lstm_backward
struct dnnl::lstm_forward
struct dnnl::rnn_primitive_desc_base
struct dnnl::vanilla_rnn_backward
struct dnnl::vanilla_rnn_forward
Visible to Intel only — GUID: GUID-1C7DFDDF-2EEF-4E50-B3E1-F7DA62A9702D
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 256x256:25x256This 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:144The 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 256x256:256x256This 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:97Above, 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 ic16ih7oc16oh7kh5ph2nwipDecrypting 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.
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.