[BugFix][VTA] Fix vta_conv2d crash issue after change vta_config.json configuration. (#3213)
Issue:
Once change LOG_BLOCK_IN or LOG_BLOCK_OUT into > 4 value, when run vta
“Simple Matrix Multiply” or load vta, vta would crash at vta_conv2d.py.
Analysis:
This issue caused by resnet18 logic of vta_conv2d.py which have
in_filter minmum size that is 16. > 4 value would cause such in_filter
check failed then make xfer_size be empty and find_schedules function
return a empty list finally cause crash.
Solution:
add the empty list check.
[VTA][TSIM] Use Module instead of RawModule for testbench by creating an empty bundle for the IO (#3242)
* use Module instead of RawModule for testbench by creating an empty bundle for the IO
* change default back to verilog
Modified pick best to accumulate the best configurations from both the input and output file. (#3225)
Add packing for int8 1x1 convolution and support the int8 group convolution on X86 (#2991)
* Support the 1x1 int8 conv with NHWC layout and weight packing
fix linter
* fix the memoize issue
* fix the failed nhwc test
* add the schedule for pack to unbreak other tests
* skip avx512 compile
* Support the 1x1 int8 conv with NHWC layout and weight packing
fix linter
* fix the memoize issue
* fix the failed nhwc test
* add the schedule for pack to unbreak other tests
* skip avx512 compile
* Unify the data_layout and kernel_layout relation
* add asf header
* fix the comment
* retrigger the build/test
[Relay][ONNX] fix #3134 converter where initializers were not registered as nodes (#3143)
[Datatypes] Custom datatypes (#2900)
* Register and use custom datatypes in TVM
This patch adds the ability to register and use a custom datatype from Python,
using the `register_datatype` call. The datatype can then be passed as the
`dtype` parameter using the syntax `dtype="custom[<type_name>]bitsxlanes"`.
* Removes extra file
* Register custom datatypes with TVM; specify Cast and Add lowering
This commit adds functionality for registering custom datatypes with TVM, and
furthermore adding custom lowering functions to lower those custom datatypes.
This commit only adds lowering for the Cast and Add ops; more ops will be added
soon.
Check out some custom datatype samples in my repository of samples:
https://github.com/gussmith23/tvm-custom-datatype-samples
* Register and lower casts from Python
* Formatting
* Fix include; was including too much
* Add comment
* Add DatatypeRegistered
* Add storage size field to custom datatypes
This field indicates the bitwidth of the opaque block of data into which
instances of the datatype will be stored, when TVM compiles. For example, if I
create a datatype with a storage size of 16, then
- Constants of that datatype will be created as unsigned 16-bit ints
- Calls to external functions taking that datatype will pass the data as
unsigned 16-bit ints
- External functions returning that datatype will be assumed to return unsigned
16-bit ints.
* Change how lowering funcs (Cast and other ops) are named in registry
tvm.datatypes.lower.<target>.cast.<dst-type>.<src-type>
becomes
tvm.datatypes.lower.<target>.Cast.<dst-type>.<src-type>
And fixes some sloppy code around how the other ops were being formatted.
* Update Python register_datatype to accept storage size
* Oops, left out one cast->Cast change
* Look up storage size when parsing `custom[typename]`
When we encounter this type string in Python, it will be parsed into a Halide
type object in C++. Some of my original code supported this parsing, but we now
have to attach the storage type to the type (by setting the bits field).
* Change how external calls for casting/other ops are done
Firstly, we now use the storage size of the custom type when determining
input/output types; e.g. a cast to a custom type with storage size 16 is seen as
a call to an external function returning an opaque uint of size 16.
Secondly, write a macro to handle the other ops. Originally I thought I could
handle these at runtime, with a single `_register_op` global. I transitioned
instead to using individual `_register_Add` etc. calls generated with a macro,
but I don't remember why.
* When encountering a custom type immediate, generate UIntImm
* Translate custom types to LLVM type
* Generate correct return type in Casts
Originally I was assuming that the result type from casts was always a custom
datatype, and so I was making the Call return a UInt type.
* Use TVM-idiomatic recursion style in DatatypesLowerer
This was actually a bug, I'm pretty sure; we wouldn't have recursed deep on any
complex programs. As a result of making this change, I also uncovered another
potential bug, where the datatypes lowering pass would attempt to lower a Load
of a custom type. By commenting out the `Mutate_` for Load, I was able to stop
the error from cropping up, but frankly, I'm not satisfied with the solution;
how is it that we are able to run codegen when Loads of custom datatypes are
present in the IR? I have not written any code, to my knowledge, that will
support this. Perhaps Load does not care about the underlying datatype?
* Use CHECK
* Add comment about which Mutate_s are needed
* Add comments
* Add GetCustomDatatypeRegistered as an extern C function
* Formatting, comments, casting
* Change how datatype string is formatted
* Use bits() instead of GetStorageSize
Use bits() instead of GetStorageSize
* Change comment
* Add datatype.py
* Change registered function name (datatypes->datatype)
* Remove GetStorageSize
* Format custom datatypes like any other datatype
Specifically, we now print the bits and lanes after the `custom[...]` string.
* Correctly implement datatype lowering in Python
* Remove unneeded include
* Make function naming consistent
* Use CHECK instead of internal_assert
* Rename macro
* Formatting
* Rename functions
* Implement Cast lowering
`_datatype_register_op` is now able to lower both binary ops and Casts.
* Formatting
* Formatting
* Clang format, google style
* Fix std::string/extern "C" warnings
* Formatting
* Formatting
* Lower Allocates and Loads during datatype lowering
This should ensure that there are no custom datatypes remaining once datatype
lowering is done. This will allow us to remove the code in the LLVM codegen
which deals with custom datatypes.
* Revert additions to codegen_llvm.cc which are now unneeded
* Pass cpplint on lower_datatypes.cc
* Add clarifying comment
* Remove datatype lowering registration funcs from C++
* Add CHECKs
* Remove TODO
* Remove all references to storage size
* Move and rename function
* Rename function
* Remove done TODOs and other handled comments
* Remove irrelevant Load code and comments
* Comment out the IR node types I'm not sure about yet
* Add bfloat16 datatype unittest
* Fix MakeConstScalar
MakeConstScalar for a custom datatype will now call out to a function which can
be registered on a per-datatype basis. The function will take a double and
return the equivalent value in the custom datatype format.
Note that these code paths are not actually used or tested at the moment. I have
not yet written an example which uses const scalars of a custom datatype.
* Formatting
* Change pass name
* Allow users to register whatever lowering function they want
Tianqi pointed out that users should be able to register whatever lowering
function they want, and should not be constrained to registering lowering
functions which just call out to external libraries.
I still provide a function for making lowering functions which call out to
external libraries, for convenience.
* Add clarifying comment
* Remove unneeded comment
* Remove unneeded function
* Rename file
* Undo unnecessary change
* Undo unnecessary change
* Make naming consistent
Rename "datatypes" to "custom datatypes" in most contexts.
* Revert an artifact of old code
* Fix build warnings, add TODO
* Lint
* Remove unnecessary use of extern C by separating decl and impl
* Error checking
* Remove TODO
* Missed a name change
* Lint
* Python lint
* Correctly format datatype
* Move bfloat16 to 3rdparty
* "custom_datatypes" --> "datatype" in most places
I left the pass as "LowerCustomDatatypes" to indicate that we're not lowering
anything other than custom datatypes. Otherwise, everything else has been
changed.
* Upgrade datatype unittest
I used a float calculator to generate some real testcases for the unittest.
* Separate public includes and private implementation
Specifically, create cleaner decoupling between datatypes stuff in packed_func
and the datatype registry implementation.
* Formatting
* Limit custom datatype codes to >128
* Add TODOs
* Fix comment
* Formatting
* Clean up datatype unittest
* Remove un-exported functions in public headers; UIntImm->FloatImm
More places where I accidentally was using implementation-only functions in
public headers.
Additionally, store custom datatype immediates as FloatImms. A later change will
add new lowering logic to lower these FloatImms to UIntImms.
Plus formatting change.
* Lint
* Use FloatImm (not UIntImm) to hold immediates of custom datatypes
This change switches from using UIntImm to FloatImm for storing immediates of
custom datatypes. The value of the number is stored in a double, which should be
enough precision for now, for most custom types we will explore in the immediate
future.
In line with this change, we change the datatype lowering so that FloatImms are
lowered to UInts of the appropriate size. Originally, this was going to be done
by allowing the user to register a double->uint_<storage size>_t conversion
which would be called at compile time to convert the value from the FloatImm to
a UInt and store it in a UIntImm. After discussions with Tianqi, we decided to
take the simpler route, and lower FloatImms just as we lower all other ops: by
replacing them with Call nodes. In this case, presumably the user will Call out
to a conversion function in their datatype library.
The justification for this decision is due to the functionality added in #1486.
This pull request adds the ability to load LLVM bytecode in at compile time.
This applies in our case as follows:
1. The user writes their custom datatype programs and registers their lowering
functions in the same way we've been doing it so far. All operations over
custom datatypes are lowered to Calls to the datatype library.
2. The user compiles their datatype library to LLVM bytecode.
3. At TVM compile time, the user loads the LLVM bytecode. Depending on how the
datatype library is written, Clang should be able to perform constant
folding over the custom datatype immediates, even if their conversions are
done with calls to the library.
Additionally adds test to test the FloatImm codepath.
* Re-add a change I removed accidentally during rebase
* Cleanup
* Remove unnecessary TVM_DLLs
* Add custom datatype utilities source file to Go runtime pack
* Revert "Remove unnecessary TVM_DLLs"
This reverts commit
4b742b99557fd3bf0ce6617f033c8b444b74eda4.
* Mark bfloat code as TVM_DLL
* Moves custom datatype runtime utilities to c_runtime_api.cc
* Revert "Add custom datatype utilities source file to Go runtime pack"
This reverts commit
aecbcde0b2cc09a2693955b77037fe20f93b5bfd.
* Move datatype parsing to its own function
* Change comments
* Remove unneeded function
* Formatting
* Formatting
* Documentation
* Add kCustomBegin, use it for checking for custom types
* Documentation
* Formatting
* Move static definition to implementation
* Remove comment
* Decide toBeLowered before lowering arguments of Expr
In the past, e.g. when lowering custom datatypes for an Add, we would lower a
and b first, and then decide whether the resulting new Add needed to be lowered
based on the (new) types of a and b. Now, instead, we need to check the types of
a and b first (to see if they're custom types), and then lower them (so they'll
become non-custom types), and then lower the new Add.
* Revert "Move datatype parsing to its own function"
This reverts commit
d554a5881afcf69af1c070d882a7651022703a09.
This broke parsing. Will figure this out later. There isn't a really clean way
to separate this out given how the rest of the function is written.
* Replace comment
* Documentation
* Remove comment and TVM_DLL
* Better error messages
* Remove artifact of rebase
* Separate datatypes parsing to its own function
* Add \returns
* Comment changes; add TODO
* Refactor tests
cleanup: removed a piece of code that is redundant now given updates to HalideIR submodule (#3169)
Register all operators' Python attributes in Python so they can be easily accessed from Python code (#3175)
[RFC] [VTA] [TSIM] Enabling Cycle-Accurate Hardware Simulation for VTA #3009 (#3010)
* merge files
* move verilator to the right place
* change name to tsim
* add default rule to be build and run
* add README for tsim
* Update README.md
* add some structural feedback
* change name of VTASim to VTADPISim
* more renaming
* update comment
* add license
* fix indentation
* add switch for vta-tsim
* add more licenses
* update readme
* address some of the new feedback
* add some feedback from cpplint
* add one more whitespace
* pass pointer so linter is happy
* pass pointer so linter is happy
* README moved to vta documentation
* create types for dpi functions, so they can be handle easily
* fix pointer style
* add feedback from docs
* parametrize width data and pointers
* fix comments
* fix comment
* add comment to class
* add missing parameters
* move README back to tsim example
* add feedback
* add more comments and remove un-necessary argument in finish
* update comments
* fix cpplint
* fix doc
[FRONTEND][TFLITE] Add FULLY_CONNECTED op into tflite frontend, support Inception V4 (#3019)
* Add FULLY_CONNECTED op into tflite frontend, support Inception V4
* Fix comment style in TF Lite tests.
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