GitLab

* Use gemm_multiple_D instead

* Add gemm bias relu quantization example

* Add pure gemm quantization example

* Add quantization of perchannel conv + bias + relu example

* Refine the code

* Rename multiplier to requant_scale

* Rename the folder

* Remove redundant comment

* Rename the file. Prepare to add perchannel

* Add conv perchannel instance

* Move to quantization folder

* Add conv perchannel client example

* Apply Rangify constructor of HostTensorDescriptor & Tensor<>

* Fix merge error

* Refine the device batchnorm-backward base API templates and data type assignments

* Remove duplicated kernel file

* Add batchnorm backward instances and external API

* Add batchnorm-backward profiler and tests

* Add client example which uses batchnorm backward external API

* Merge test/batchnorm_fwd and test/batchnorm_bwd into one directory

* Loose the threshold for batchnorm-backward check_err()

* Implemented batchnorm-backward Blockwise and Multiblock kernels

* Add batchnorm-backward device op

* Add batchnorm-backward host-reference op

* Add batchnorm-backward example

* Parameters renaming in batchnorm backward kernels and device op

* Change in the example to loose the threshold for ScaleDiff checking

* Add comments to explain the implementation of batchnorm-backward

* Parameters renaming again in batchnorm backward kernels

* Improve the expression calculation for performance

* Add batchnorm backward to README

* Add comments to explain inv-variance in batchnorm forward and backward

* Renaming the batchnorm forward training and inferring examples

* Add/update the comments for batchnorm-backward kernels

* Renaming again

* Add block_sync_lds between two consecutive blockwise reductions

* Move common expression 1/N out of the static_for loops

* Add dy_elementwise_op

* Renaming in backward example again

* Add checking for reduceDims in reference_batchnorm_backward

* Update to comments and codes format

* Rename in the comments

* Remove common expression out of the loop in reference_batchnorm_backward_nhwc_c

* Add block_sync_lds() between blockwise reduction again

* Fix comments again

* Remove int8 from batchnorm-forward instances since it is not needed for forward training and could fail test

* Update to device_batchnorm_forward base class to include all template parameters for problem description

* Add batchnorm forward instances and external api

* Add batchnorm forward profiler module which uses the external api

* Add some comments in batchnorm_forward example to explain the dimensions in lengths[]

* Replace the reference_batchnorm_forward_nhwc_c by generic reference_batchnorm_forward

* Improvement to the batchnorm infer base API

* Add batchnorm forward client example which shows using the batchnorm forward external API

* Add test for batchnorm forward

* Tuning the batchnorm profiler initialized values and error threshold

* Add support for bhalf_t in instances/external api/tests

* Add support for int8_t in instances/external api/tests

* Add support for double in instances/external api/tests

* Let ScaleDataType and BiasDataType be same as XDataType and YDataType when creating instances

* Checking before running best instance in batchnorm_fwd_nhwc client example

* Add checking for YElementwiseOp in batchnorm_forward external API

* Add more types in batchnorm forward profiler

* Add more test lengths
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>

* FastGelu support for more data types.

* AddFastGelu & FastGelu instances.

* Client example.

* clang-format

* Remove unused stride variable.

* Add new line at EOF.
Co-authored-by: Adam Osewski <aosewski@amd.com>

* workaround bf16 atten fwd issue on gfx908

* typo

* fixed bug in softmax reference & add bf16 examples for batched_gemm_scale_softmax_gemm

* added bf16 tests for batched_gemm_softmax_gemm_permute

* changed format of device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instance.cpp

* changed format device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instance.cpp

* aligned annotations

* modified CMakeLists for examples

* add common example code of fp16/bf16 version for batched_gemm_scale_softmax_gemm_xdl

* use macro to control the instances

* added macro control into instances

* clang-format some files

* changed error tolerance for bf16

* changed index for 10_elementwise_normalization

* fixed xdlops code bug in amd_xdlops.hpp
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>

* start add example

* add device dl

* change launch kernel

* change init data method

* change example config

* add config valid check

* add instance for dl bwd

* add instance to ckProfiler

* reserver to profiler and cmakelist

* add instance to ckProfiler2

* change instance f32 config

* fix example return value
Co-authored-by: letaoqin <letaoqin@amd.com>
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>

We can use this template to eliminate duplicated iterator computing
logics. By providing return type to ck::accumulate_n(), we can avoid
type conversion operations.

* Add missing ignore expression

* Add missing include directive

* add client example for elementwise_normalization

* clang format elementwise_layernorm2d.cpp

* changed some naming to make it more understandable

* changed naming of input into ab_input

* fixed bug for threadwise_x_store

* add elementwise operation to reference

* Rename example folder for GroupedConvFwdMultipleD

* Unify example codes

* Change target names

* Add fp16 example for multiple d instance

* Re-format common.hpp

* Add interface 'DeviceGroupedConvFwd'

* Use simpler interface

* Move common conv params out

* Rename conv fwd client example folder

* Add missing include directive

* Update grouped conv instance implementations

* Simplify ckProfiler (grouped conv forward)

* Use GroupedConvFwd to implement client example

* Use greater groupe count in example

* Add custom target to group examples

* Add extra tag param to instance factory function

* Use tag to differentiate factory functions

* Add missing tag argument for factory function

* Remove inheritance relationship

* Remove no-longer used include directive

* Add license in front of file

* Remove redundant CMake setting

* Extract common code from files

* Rename folder 'convnd' to 'conv'

* Use std::array<> to accept compile-time kwnown # of arguments

* Fix compilation error of tuning parameter

* In example, use same setting as unit-test

* Remove no-longer used include directive

* Add interface for grouped conv bwd weight

* Add group support for conv bwd weight

* Add grouped conv bwd weight example

* Use group parameter in example

* Rename example folder

* Remove non-grouped version example source files

* Rename device op template

* Add group support to convolution backward weight

* Remove debug messages

* Use smaller group size in example

* Use named variable as loop terminate condition

* Prettify example output message

* Enlarge used grid size

* Allow real grid size exceeds expected grid size

* Rename interface file

* Add client example for group...

* Remove interface 'DeviceGroupedConvBwdData'

* Remove no-longer needed include directive

* Rename client example folder

* add fused addition lyernorm

* add fused addition lyernorm

* changed CMakelist

* removed annotates

* modified descriptor of C

* fixed bug in gridwise add layernorm

* format the files

* modified name from add&layernorm into elementwise&layernorm

* created fused elementwise layernorm branch

* change input into tuple type

* add sweep once to reduce load & read of C from global memory

* modified Argument api

* modified way to malloc c in global memory

* changed gamma and beta to m_k_desc

* fixed bug when sweep once and move CDataType when define device level struct

* add src dim for gamma and beta

* implement optimization for coalesced

* delete a annotation line

* fixed some bug to meet the requirements of ck

* add bandwidth computing in example, and fixed the time unit

* move device_elementwise_layernorm_impl.hpp into device/impl

* fixed bug in device_elementwise_layernorm_impl.hpp

* changed name from layernorm into normalization

* clang-format the changed files

* changed the names

* moved immidiate results into lds, it become faster in non-sweeponce cases

* changed naming of C into X to make the defination more clear

* changed naming in example

* add tests for elementwise normalization

* move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization

* move test_elementwise_layernorm_fp16 into new folder

* move elementwise_normalization_instances into a new folder

* add more tests in test_elementwise_layernorm_fp16.cpp

* added some corner cases in test

* fixed method to compute lds size for matrix X

* changed name of 44_elementwise_normalization into 45_elementwise_normalization

* modified some comments

* modified some other confused comments

* reduce redundant tests in test_elementwise_layernorm_fp16.cpp

* Sync the naming

* Sync the test of layernorm with groupnorm

* Sync the naming

* Minor change for comment and log

* [What] Add saveMean and SaveInvVariance in the interface.
[Why] These can optimize the backward

* Improve example reusability

* Remove no-longer used file

* Rename folder of grouped_conv_bwd_data example

* Add normal grouped conv bwd example

* Add interface 'DeviceGroupedConvBwdData'

* Prettify comment of device op type arguments

* Add grouped conv2d/conv3d backward data fp16 instances

* Fix wrong template argument

* Add grouped_conv2d_bwd_data client example

* Use simpler expression to calculate memory size

* Fix formating

* Remove grouped_conv3d_bw_data instances

Underlying device operator is not ready to handle 3D input

* Remove no-longer necessary include directive

* Add missing include directive

* Use more realistic conv param in example

* Add gridwise gemm pipeline v1/v2 selector

* Pipeline selector working, test-wise add pipeline options to one instance

* Add gemm instances

* Add debug info to DeviceGemmXdl

* Add debug info to DeviceGemmXdl_CShuffle

* Add debug info to DeviceGemmXdl_CShuffle and instances to gemm_add_add_fastgelu

* Minor fix

* Add debug info to DeviceBatchedGemmXdl and instances to batched_gemm

* set up inter-wave configuration

* use defualt loop scheduling for supported gemm ops

for blanket-applying interwave scheduling for all supported gemm ops, define macro CK_EXPERIMENTAL_DEFAULT_TO_INTER_WAVE_SCHEDULING=1. this should be discouraged though as it is not covered by CI

* Add enum PipelineVersion

* Update instances

* Format

* Fix the merge conflict

* Add flags to disable added instances

* Test disable flag check

* Disable flag check

* Enable the instances
Co-authored-by: Anthony Chang <ac.chang@outlook.com>

* Add reduction across all dims cases.

* host softmax: handle all reduce

* Test cases when reduced dim is not innermost axis.

* Fix syntax.

* Test non innermost dim for fp32 and int8

* Group test suites wrt NumReduceDim.

* Additionally test failing cases.

* Throw error when Rank or NumReduceDims doesn't match arguments.

* Check reducedDims has correct values

* Move don't reuse DeviceReduceMultiblock IsSupportedArgument method.
Instead implement own. (in fact just get rid of one check to enable
reduction across inner dimensions).

* Reorganize unit tests to better cover use scenarios.

* Test input validation
* Test reduction of inner dimensions with custom op instances.

* Refactor fp32 and int8 unit tests.

* Fix FP32 instance template parameters.

* Add more instances.

* Instances with InSrcVectorDim=0.

* Do not initialize and copy data when arg not supported.

* ckProfiler Softmax use instance factory.

* Refactor device softmax IsSupported.

* Additionally add non-polymorphic api functions

* Split softmax instances into multiple files.

* Fix profiler.

* Reorganize tests to reuse profiler and cover edge cases.

* Clang-format

* I8 Softmax instances along with UT.

* Reuse type alias definitions from instance factory header.

* Clean included headers

* Fix variable names.

* Add missing checks in Argument constructor.
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Anthony Chang <ac.chang@outlook.com>

* Add conv2d requant example

* Fix bash error

* Rename example

* 1. Rename gemm quantization
2. shares the requantization lambda function with conv

* Refine declare type

* Add conv bias relu quantization exmaple

* clang format

* Fix compile error due to merge develop

* Fix CI error

* Extract quantization post operation into another file

* Support quantization for non piecewise linear function

* Add instance for conv quantization

* Add convolution quantization factory

* Add convolution quantization client example

* Add more instances with different template parameters

* clang format

* Sync the naming with the develop

* add device of dl

* fix k1 of GridwiseGemmDl_km_kn_mn_v1r3

* init version for dl conv

* add example(init)

* result right

* disable elementwise operation

* check parameters

* add fp32,int8 example and change check code

* change deive file and class name

* add check vector access of C

* add instance

* add to ckProfiler

* add Filter1x1Pad0 instances

* fix ignore error

* fix for CI
Co-authored-by: letaoqin <letaoqin@amd.com>

* Update to the batchnorm-forward API and base class

* Fix leeked header including in gridwise_set_buffer_value.hpp

* Add kernels and device file for batchnorm-forward welford supporting both blockwise and multi-block reduction

* Update to the batchnorm-forward example to use the new batchnorm-forward device interface

* Change the batchnorm-forward reference to use sequential welford method

* Change to assign the workspace into four buffers in the host layer

* Use GetReduceCountPerThread functor to replace the initial count for Blockwise and Multiblock welford

* Tiny correction and remove un-used file under example/34_batchnorm

* Renaming in the kernel arguments

* Explicitly use ck::math::sqrt in batchnorm-forward kernels

* Add some comments to some kernels

* Tiny fix

* Generalize the data types in reference_batchnorm_forward_nhwc_c

* Use ck::ignore to mark un-used parameters

* Move GetReduceCountPerThread functor codes from kernel to device

* Remove some un-used codes in device_batchnorm_forward_impl.hpp

* Tiny fix in batchnorm_forward example

* Move GetReduceCountPerThread() to welford_helper.hpp

* Use seperate data type for Scale and Bias

* Renaming in device Op

* Tiny fix in forward example

* Updata to batchnorm-infer (type spliting, renaming)

* Add time and bandwidth measurement to the batchnorm-forward example

* Add support of elementwise operation for batchnorm forward output

* Reduce object copying by passing object as reference type

* Tiny change for performance

* Updates for performance again

* Some Renamings

* Add GetActualVariance template parameter for ThreadwiseWelfordMerge

* Tiny update in reference batchnorm forward nhwc/c

* Move batchnorm multiblock kernel files to grid/batchnorm_multiblock sub-directory

* Fuse mean and bias in the normalization calculation
Co-authored-by: root <root@dc-smc-18.amd.com>
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>

* reopen masking att instance due to CI is upgraded

* re-enable instances previously failed on 9110

* enable ksize-kpadding pair validity test

* add non-masked attention+permute test; expose masking boolean to attention kernel handles

* disable bench

* fix test

* move files

* bulk rename batched_gemm_masking_scale_softmax_gemm_permute to batched_gemm_softmax_gemm_permute

* format

* amend rename

* disable bench in test

* add mask/no-mask test for non-permute attention kernels

* disable broken kernel instance

* example working

add non-permuted problem statement

evaluating whether overhead comes from permutation or the extra kernel arg

* interface for bias addition without implementing it

* test and profiler running

* tidy

* mask type determined by enum class

* unify example code

* move masking specialization to its own header

* align formats

* extract helper functions

* experiment merging dims for attn w/ permute; shows perf parity with attn wo/ permute

* add tensor specialization to template args

since tensor spec packed shows perf parity when permutation isn't needed

remove redundant template args

comment on 'packed' tensor specialization

* grouped attention with input/output permute example

* format

* clean up

* refactor acc0 tile visitor
Co-authored-by: shaojiewang <wsjmessi@163.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Simplify the macros for declaring and defining the add_device_reduce_instance_xxxx() instances

* Change the types of lengths and strides from std::vector to std::array for the reduction device interfaces

* Remove DeviceSoftmaxImpl's depending on DeviceReduceMultiblock

* Split the cpp and hpp files for reduction instances to enable more parallel compiling

* Remove the using of macros for declaring reduction instances and instance references

* Update to add_device_reduce_instance_xxxx templated functions

* Use ReduceOperation+InElementwiseOp+AccElementwiseOp to repace the ReduceOpId in defining add_reduce_instance_xxxx() templates

* Change return format

This reverts commit efbcc6ed.

* add fused addition lyernorm

* add fused addition lyernorm

* changed CMakelist

* removed annotates

* modified descriptor of C

* fixed bug in gridwise add layernorm

* format the files

* modified name from add&layernorm into elementwise&layernorm

* created fused elementwise layernorm branch

* change input into tuple type

* add sweep once to reduce load & read of C from global memory

* modified Argument api

* modified way to malloc c in global memory

* changed gamma and beta to m_k_desc

* fixed bug when sweep once and move CDataType when define device level struct

* add src dim for gamma and beta

* implement optimization for coalesced

* delete a annotation line

* fixed some bug to meet the requirements of ck

* add bandwidth computing in example, and fixed the time unit

* move device_elementwise_layernorm_impl.hpp into device/impl

* fixed bug in device_elementwise_layernorm_impl.hpp

* changed name from layernorm into normalization

* clang-format the changed files

* changed the names

* moved immidiate results into lds, it become faster in non-sweeponce cases

* changed naming of C into X to make the defination more clear

* changed naming in example

* add tests for elementwise normalization

* move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization

* move test_elementwise_layernorm_fp16 into new folder

* move elementwise_normalization_instances into a new folder

* add more tests in test_elementwise_layernorm_fp16.cpp

* added some corner cases in test

* fixed method to compute lds size for matrix X

* changed name of 44_elementwise_normalization into 45_elementwise_normalization

* modified some comments

* modified some other confused comments

* reduce redundant tests in test_elementwise_layernorm_fp16.cpp

* Move kernel implementation files under impl directory.

* Update examples paths.

* Update device kernel impl include paths.

* Update tensor operation instances include paths.

* Update profiler and tests include paths.

* Clang-format

* Update include paths for batched gemm reduce

* Refactor UnitTest ConvNDBwdWeight.

* Refactor fwd and bwd data convND UT.

* Fix used test macro.

* Fix include path.

* Fix include paths.

* Fix include paths in profiler and tests.

* Fix include paths.
Co-authored-by: Adam Osewski <aosewski@amd.com>

* Fix bug of profiler for layernorm

* 1. Rename layernorm into normalization
2. Decouple softmax from normalization

* clang-format

* start split k

* add base device class

* add example after merge develop

* add gridwise gemm

* add b matrix split k

* split=1

* change name for kb

* not bias result right

* bias only add once

* fix register spill

* regular code

* add fp32 example

* fix for 64bit index

* fix CheckValidity of gridwise

* use another instance to check the efficiency

* optimize group layer norm

* 1. coalesce load/store data for gridwise layer norm welford. 2. move a sqrt and divison into a outer static loop

* add more instances to layernorm

* add 2 more test cases

* remove ignore in generating tuple of vector
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* fix

* fix

* add instance

* fix build

* fix build

* add lower triangle bmm

* init code for tile skipping

* functionality right with lower triangle mask

* add decoder lower triangular mask calculation

* use 7*13 group

* fix n2 compute error

* attention with lower triangle mask with tile skipping

* add template to distinguish masking kernel

* rename template and remove default template value

* remove lower triangle gemm reference struct

* add some comments on example

* add 10 instance for masking bmm + scale + softmax + bmm + permute kernels

* add test

* add test file

* add gtest for bmm masking scale softmax bmm permute

* clang-format

* fix compile error

* check lef bottom corner for tile skipping

* fix error: check left bottom corner for tile skipping

* add k padding

* add test and instance for MNK padding

* passing a mask struct

* fix instances

* delete used comments

* format
Co-authored-by: danyao12 <yaodan@dc-smc-13.amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Add groupnorm example by layernorm
1.  Reference is not ready
2. shape of gamma and beta need to be fix

* Let shape of gamma and beta can be same as x

* Modify test, instance and client example

* [What] Fix bug of layernorm for greater than 2 dimension.
[Why] We need to get upper length from merge transform instead of embed transform.

* Add reference for groupnorm

* Fuse sigmoid after groupnorm

* [What] Rename original layernorm into layernorm2d
[Why] Prepare to add groupnorm using layernorm5d

* clang-format

* Add groupnorm test

* Refine error message

* Add groupnorm ckProfiler

* Test groupnorm kernel from device_instance

* update example

* upadte profiler

* Fix test naming

* Fix argc number

* Move descriptor and sweeponce to argument for quick debugging
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Add example folder for 'DeviceElementwise'

* Re-structure example files

* Move common parts into common.hpp

* Use more strict input

* Add more helper methods in 'DeviceElementwise'

* Use more specific method to write example

* Allow specify problem through command line argument

* Allow specify problem 'axes' through command line argument

* Add check to template type argument

* Add transpose_shape() to generalize shape permute

* Generalize transpose utility functions

* Use better name for tensor indices

* Add checks in helper functions

* Remove debug messages

* Refine error message for check_err()

* Generalize variable naming in example code

* Add device op 'DevicePermute'

This device op is clone of 'DeviceElementwise'

* Use 'DevicePermute' device op in example

* Remove 'elementwise' from identifiers

* Remove 'elementwise' from file paths

* Remove base class of 'DevicePermute'

* Let 'DevicePermute' inherit from 'BaseOperator'

* Add simple type traits to validate device op type

* Add static_assert() to check type constraints

* Create 'DevicePermuteBase' to generate methods

* Use indirect base type to generate methods

* Remove 'is_device_op<>' type traits

* Only accept single-input-single-output for 'DervicePermute'

* Simplify 'DevicePermute' interface

* Re-format 'DeviceElementwise'

* Use CRTP to generate overridden virtual method

* Remove unnecessary include directives

* Distinguish input & output shape in 'DevicePermute'

* Passing 'axes' to 'DevicePermute'

* Use more reasonable return value for Invoker::Run()

* Add 'GridwisePermute' kernel

This kernel is a clone of 'GridwiseElementwise_1D'

* Remove no-longer used type argument

* Check if input/output shape meet the requirement

* Remove no-longer used method

* Remove never-entered-if-clause

* Change problem description for 'DevicePermute'

* Transform descriptor into 3 dimensions

* Add debug code the verify result

* Add comment to indicate template argument location

* Add N/H/WPerBlock template parameter to 'DevicePermute'

* Rename 'GridwisePermute' to 'GridwiseCopy'

* Check tensor descriptor dimensions in 'GridwiseElementwise_1D'

* Add missing include directive

* Add 'BlockSize' parameter to 'DevicePermute'

* Remove no-longer used method

* Add 'BlockToTileMap' for 'GridwiseCopy'

* Use the normal Block2TileMap convention

* Rename 'BlockToTileMap' as 'Block2TileMap'

* Fix most of compilation errors

* Let 'Block2TileMap' map block to 2d coordinate

* Allow data transfer in 'GridwiseCopy'

* Fix wrong output descriptor for 2nd blockwise copy

* Rename 'GridwiseCopy' as 'GridwisePermute'

* Remove '1d' in identifiers

* Remove commented-out codes

* Remove 'MPerThread' template parameter

* Seperate template parameters

* Unify variable namming convention

* Use more verbose way to create expressions

* Add template parameter 'InBlockLdsExtraW'

* Release the constraint on In/OutGridDesc

* Use date type directly as template argument

* Re-arrange template arguments for blockwise copy

* Remove no-longer used template parameters

* Embed layout in the variable names

* Add GridwisePermute::CheckValidity()

* Extract local types as template parameters

* Rename local type alias

* Add more template parameters (vector width related)

* Calculate new SrcVectorDim/DstVectorDim after merge descriptor dimensions

* Fill tensor values start from 1

* Re-formate example code

* Avoid too-large block id

* Add comment

* Make sure 'SrcVectorDim' is not same as 'DstVectorDim'

* Add check for the 'VectorDim' & 'ScalarPerVector' template params

* Let 'DstVectorDim' equals 'SrcVectorDim' after transpose out grid desc

* Remove no-longer used template parameter 'NPerBlock'

* Fix wrong descriptor creation logics

* Specify problem in each examples

* Use better example name

* Add new example 'example_permute_NxHxW_fp32'

* Add example for demonstrating bundle multiple elems in tensor

* Add support to permute multiple elements together

* Change the default problem size

* Add span<> class template

* Use span<> to generalize check_err() interface

* Fix ambiguous ctor call

* Avoid create necessary objects

* Use helper functions to simplify example code

* Add example for 4xfp16 permute

* Disable failed-to-compile example

* Add check for the NUM_ELEMS_IN_BUNDLE

* Remove redundant parameter in helper lambda function

* Add check for the input tensor type's byte-size

* Check scalar-per-vector with padded length

* Use more verbose name to avoid name collision

* Use fixed 'VectorDim' & 'ScalarPerVector' for LDS

* Embed shape info in name of descriptor constructor

* Rename example folder '36_permute' into '37_permute'

* Avoid using too-large LDS in kernel code

* Remove redundant example

* Usw switch() to group similar codes

* Add const to the span<> type arguement

* Simply initialize tensor with floating point values

* Use fp16 as data type in all examples

* Enlarge tensor size in example

* Enalrge N-dim in example

* Add check for the bundled type in example

* Use more stricter error threshold

* Remove global load/store loop in kernel code

* Measure execution time by default

* Use faster device op config for example 'NxHxW_fp16'

* Use faster device op config for example '1xHxW_fp16'

* Use faster device op config for example 'HxWx4_fp16'

* Remove cmd arg parsing logics

* Rename functions

* Extract bundle permutation logic out

* Simplify permute bundle example

* Add Tensor<>::GetElementSpaceSizeInBytes()

* Add Tensor<>::data()

* Use new methods to simplify code

* Use type alias to replace duplicated code

* Use existing method to shorten code

* Allow FillUniformDistribution accept range arugment

* Intialize random values in range

* Add Tensor<>::size()

* Use more meaningful names in permute bundle example

* Use more meaningful names in permute element examples

* Use rangified copy() to copy elements

* Use function return value directly to eliminate variables

* Add to_array() conversion tool to eliminate more variables

* Add Tensor<>::AsSpan<>() to create view of tensor values

* Use AsSpan() to shorten check_err() calls

* Remove no-longer-used 'using' directives

* Move 'using' directive to proper code position

* Remove redudant variables

* Remove useless static_assert()

* Add check for range types

* Declare variable right before first use

* Move long return type as tailing return type

* Add BaseInvokerCRTP<> class template to generate method

* Create new base type for 'DervicePermute' implementations

* Move 'NumDim' template param to the first

* Rename 'DevicePermute' to 'DevicePermuteImpl'

* Add 'noexcept' specifier to CRTP generated method

* Move 'Block2TileMap' definition into 'GridwisePermute'

* Use type alias to reduce code

* Unify naming style in 'DevicePermute'

* Add comments in 'GridwisePermute'

* Rename permute example folder

* Use std::cerr to report error

* Use larger shape in examples

* Rename '38_permute' to '39_permute'

* Make sure we use unsigned type for shape & indices

* Remove opt-ed out assertion

* Remove template BaseInvokerCRTP<>

* sanity check

* add attribution

* add irrgular k tile size for batched attention

* format