mkl-dnn-0.14.zip_DNN_matlab_mkl

# benchdnn **benchdnn** is a standalone correctness and performance benchmark for [Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)](/intel/mkl-dnn) library. The purpose of the benchmark is extended and robust correctness verification of the primitives provided by MKL-DNN. So far **benchdnn** supports convolutions and inner products of different data types. It also implicitly tests reorders. ## License **benchdnn** is licensed under [Apache License Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). ## Usage (main driver) **benchdnn** itself is a driver for different implementation specific harnesses. So far it has harness for Intel MKL-DNN convolution, inner product, reorder, batch normalization, and harness for testing itself. The usage: ``` $ ./benchdnn: [--HARNESS] [--mode=MODE] [-vN|--verbose=N] HARNESS-OPTS ``` where: - `HARNESS` is either `conv` [default], `ip`, `reorder`, `bnorm`, `rnn` or `self` - `MODE` -- string that contains flags for benchmark mode. Use `C` or `c` for correctness (used by default), and `P` or `p` for performance - `N` -- verbose level (integer from 0 [default] to ...) - `HARNESS-OPTS` are passed to the chosen harness Returns `0` on success (all tests passed), and non-zero in case of any error happened. ## Usage (convolution harness) The usage: ``` [harness-knobs] [conv-desc] ... ``` where *harness-knobs* are: - `--cfg={f32, u8s8u8s32, ...}` configuration (see below), default `f32` - `--dir={FWD_D (forward data), FWD_B (forward data + bias), BWD_D (backward data), BWD_W (backward weights), BWD_WB (backward weights + bias)}` direction, default `FWD_B` - `--alg={DIRECT, WINO}` convolution algorithm, default DIRECT - `--merge={NONE, RELU}` merged primitive, default NONE (nothing merged) - `--attr="attr_str"` convolution attributes (see in the section below), default `""` (no attributes set) - `--mb=N` override minibatch that is specified in convolution description, default `0` (use mb specified in conv desc) - `--match=regex` check only convolutions that match with regex, default is `".*"`. Notice: Windows may only interpret string arguments surrounded by double quotation marks. - `--skip-impl="str1[:str2]..."` skip implementation (see mkldnn_query_impl_info_str), default `""` - `--allow-unimpl=true|false` do not treat unimplemented configuration as an error, default `false` - `--perf-template=template-str` set template for performance report (see section *Performance measurements*) - `--reset` reset all the parameters set before to default one - `-vN|--verbose=N` verbose level, default `0` - `--batch=file` use options from the given file (see in subdirectory) and *conv-desc* is convolution description. The canonical form is: ``` gXmbXicXihXiwXocXohXowXkhXkwXshXswXphXpwXdhXdwXnS ``` Here X is a number and S is string (n stands for name). Some of the parameters might be omitted if there is either default one (e.g. if g is not specified **benchdnn** uses 1) or if the can be computed automatically (e.g. output shape can be derived from the input one and kernel). Also if either width or height is not specified than it is assumed height == width. Special symbol `_` is ignored, hence maybe used as delimiter. See `str2desc()` in conv/conv_aux.cpp for more details and implicit rules :^) The attribute string *attr_str* is defined as (new lines for readability): ``` [irmode={nearest,down};] [oscale={none,common,per_oc}[:scale];] [post_ops='[{relu,sum[:sum_scale]};]...';] ``` Here `irmode` defines the rounding mode for integer output (default is nearest). Next, `oscale` stands for output_scales. The first parameter is the policy that is defined below. The second optional parameter is a scale that specifies either the one common output scale (for `none` and `common` polices) or a starting point for `per_oc` policy, which uses many scales. The default scale is 1.0. Known policies are: - `none` (default) means no output scales set (i.e. scale = 1.) - `common` corresponds to `mask=0` with common scale factor - `per_oc` corresponds to `mask=1<<1` (i.e. output channels) with different scale factors Next, `post_ops` stands for post operation sequence. Currently supported post ops are: - `relu` with no parameters (i.e. corresponding scale is 1., alg = eltwise_relu, alpha = beta = 0.) - `sum` with optional parameter scale (default 1.) ### convolution configurations (aka precision specification) `--cfg` option specifies what convolution would be used in terms of data type. Also it defines all the magic with data filling inside. For integer type saturation is implicitly implied. Finally configuration defines threshold for computation errors (ideally we want keep it 0 and it seems to work for now). The table below shows cases supported by Intel MKL-DNN and corresponding configurations for **benchdnn**: |src type | wei type | dst type | acc type | cfg | notes |:--- |:--- |:--- |:--- |:--- |:--- | f32 | f32 | f32 | f32 | f32 | inference optimized for sse4.2+, training avx2+ | s16 | s16 | s32 | s32 | s16s16s32s32 | optimized for processors with support of 4vnni, forward pass only (aka FWD_D, FWD_B) | s32 | s16 | s16 | s32 | s32s16s16s32 | optimized for processors with support of 4vnni, backward wrt data only (aka BWD_D) | s16 | s32 | s16 | s32 | s16s32s16s32 | optimized for processors with support of 4vnni, backward wrt weights (aka BWD_W, BWD_WB) | u8 | s8 | f32 | s32 | u8s8f32s32 | optimized for processors with support of avx512vl, forward pass only (aka FWD_D, FWD_B) | u8 | s8 | s32 | s32 | u8s8s32s32 | same notes as for u8s8s32s32 | u8 | s8 | s8 | s32 | u8s8s8s32 | same notes as for u8s8s32s32 | u8 | s8 | u8 | s32 | u8s8u8s32 | same notes as for u8s8s32s32 ## Performance measurements **benchdnn** supports custom performance report. Template is passed via command line and consists of terminal and nonterminal symbols. Nonterminal symbols are printed as is. Description of terminal symbols is given below. There is also a notion of modifiers (marked as @) that change meaning of terminal symbols, e.g. sign '-' means minimum of (in terms of time). See table of modifiers below. > **caution:** threads have to be pinned in order to get consistent frequency | abbreviation | description |:------------ |:----------- | %d | problem descriptor | %D | expanded problem descriptor (conv parameters in csv format) | %n | problem name | %z | direction | %@F | effective cpu frequency computed as clocks[@] / time[@] | %O | number of ops required (padding is not taken into account) | %@t | time in ms | %@c | time in clocks | %@p | ops per second | modifier | description |:-------- |:----------- | | default | - | min (time) -- default | 0 | avg (time) | + | max (time) | | | K | Kilo (1e3) | M | Mega (1e6) | G | Giga (1e9) The definition of expanded problem descriptor is: `g,mb,ic,ih,iw,oc,oh,ow,kh,kw,sh,sw,ph,pw`. The default template can be found in conv/bench_conv.cpp that is defined as `perf,%n,%d,%GO,%GF,%-t,%-Gp,%0t,%0Gp`. That will produce the following output in CSV format: ``` string: perf convolution name full conv-desc number of giga ops calculated effective cpu frequency in GHz (amb clocks[min] / time[min]) minimum time spent in ms best gigaops (since it corresponds to mimimum time) average time spent in ms average gigaops (since it corresponds to average time) ``` ## Examples Run the set of f32 forward convolutions from inputs/conv_all file w/ bias and default minibatch: ``` $ ./benchdnn --conv \ --cfg=f32 --dir=FWD_B --batch=inputs/conv_all ``` Run t