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* Add mlir c api

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* Create a type attribute

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* Parse module

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* Add mlir dump function

* Add test case

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* Fix tidy issues

* Update mlit version

* Update to newer mlir

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* Move mlir to the gpu and update the test

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* Fix bug when appending module

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* Remove old cmake flag

* Update message

* Add return

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* Add mlir_compile

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* Register dialect

* Handle unsinged integers

* Dont provide output for return instruction

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* Add code to insert memrefs

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* Add mlir verification

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* Enable pointwise_fusion

* Disable eliminate_data_type

* Set kernal name

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* Fix device name

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* Fix output arg

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* Updates

* Upate hash

* Add fuse_mlir pass

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* Add fuse mlir

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* Update mlir

* Sort parameter names

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* Reenable disabled passes

* Remove old mlir conv

* Remove asym default padding

* Add more verbose tracing

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* Fix compilation errors

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* Whitelist operators

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* Add namespace

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* Update triple

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* Use func dialect

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* Use func.return

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* Upgrade mlir version

* Add comment

* Handle symetrical padding

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* Cleanup debug output

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* List failed tests

* Move mlir compile to jit pipeline

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* Update version

* Add source locations

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* Correctly add module

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* Update failed tests

* Fix failures when mlir is disabled

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* Update mlir version

* Check type for fp32

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* Remove failed test

* Update mlir in driver

* Tidy fixes

* Foramt

* Tidy fixes

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* Fix const

* Remove from requirements

* Fix cmake version

* Fix tidy warning

* Use another ifdef

* Fix tidy

* Other tidy fix

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* Update hash

* Add missing license files

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* Fix fnction name

This is an extension to insert_module_instructions, but instead of just inserting from a module, it can insert a range or a vector of instructions.

should be --fp16 , not --fp16ref

Allows PyTorch converted version of SSD-resnet34 to work

 Compiles significantly faster than constructing all the objects. It also reduces recompiles as well.

This PR only adds an example using MIOpen Calls.

Add an example using rocBLAS Calls

This PR only adds an example using HIP kernel.

* Add function to get a module tree
* Get parent module in the pass manager

One-line fix to register the op miopen_fusion. This error was causing loading of compiled model files (*.mxr) to fail.

* Jit contiguous

Used to determine what files contain a license and are stamped. If not we exit and return an error code that can be later ingested by another script, as well as a list of the outstanding files in questions.

Currently baked in the list of files we should support or not support with licenses in them a well as some stuff to quickly ignore

Adds compute_method for the experimental custom ops.
Adds a test for the same using HIP APIs.
Depends on #1183
Solves #1101

* remove eliminate workspace
* remove sync device and other tags

Regenerate notebook header for licensing

Updated each source file in the repo with the existing license.

* Fixing misspelled macro
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

* remove code for allocation of C param in dot lowering

* formatting
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

* [#935] Update tf_parser to have add_common_op() for parse_relu6

Similar to that of the onnx_parser.cpp add a add_common_op template and functionality to support clip based operations. This is done so clip operations can be guarenteed to have the same dimensions.

* fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* fixup! fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* fixup! fixup! fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* fixup! fixup! fixup! fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* Formatting

* fixup! Formatting
Co-authored-by: Umang Yadav <29876643+umangyadav@users.noreply.github.com>
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

* add allocate op header

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* add replace_allocate pass

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* move output param to remove_allocate pass

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* fix bugs in replace_allocate pass

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* fix verify if tests

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* move if op logic

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* cleanup lowering

* cleanup lowering

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* fix tidy

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* fix tidy

* add cpu allocate check

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* change cpu allocate in pass

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* add some tests for replace_allocate pass

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* pass by ref

* fix run_pass

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* update variable name for module

* update dce to use contains() and fix tidy

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* update cppcheck

* add if test

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* add if test

* rename var to mod_output_names

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* remove conditional

* update allocate op and tests

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* update replace_allocate tests

* update create_output_names() and conditional in replace_allocate

* formatting

* remove extra variable in replace_allocate

* update tools script for allocation_model
Co-authored-by: Umang Yadav <29876643+umangyadav@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

* Use custom distance function

* Pass module, skip order check if other module

* Change other valid()

* Remove unnecessary declaration

* test multiple module dependency

* Refactor to make more clear

* Code cleanup

* Simplify fix

* Test EXPECT
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

Consolidate the vectorize and preload
Add vectorization to reduction
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>

prioritizing int8 over int8x4 when it is applicable
Amend return to continue in apply loop
Adding error handling in case int8x4 compilation failed
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

Break up the gpu::code_object  print to show the actual kernels...

gpu::code_object::add_kernel: 0.646121ms, 5%
gpu::code_object::mul_kernel: 0.623822ms, 5%
gpu::code_object::add_mul_erf_add_mul_mul_kernel: 0.498902ms, 4%
gpu::code_object::mul_add_kernel: 0.478352ms, 4%

clang++ complained about not knowing of std::string for a file
Authored-by: Yves Renier <102358016+yves-renier@users.noreply.github.com>

update protobuf version

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.6.4 to 2.7.2.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.6.4...v2.7.2

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

Following up on issue #1166 and PR #1220. Using the same approach as in #1220 for parallelizing the eval calls, we can significantly reduce the time spent on eliminate_contiguous pass.

Addressing issue #1166 - propagate_constant pass currently uses a recursive approach to find all instructions in a module that can be evaluated to a literal and performs the replacement in the same call.

New approach:

Perform single pass though instructions in the module to determine which instructions can be evaluated
Evaluate selected instructions in parallel
Replace the selected instructions with the corresponding literal

* Upgrade to cppcheck 2.8

raw is the download for the file, blob is the url for the github page.

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.3 to 2.6.4.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.5.3...v2.6.4

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

* Improve applicable batched gemms for bert

Remove std references in runtime compilation since these are not available when using hiprtc and the headers may not be available on the system

* Fuse gemm add with pointwise fusions

As described in #1196, the ONNX mean parser does not work correctly for integral types. This update fixes the issue by handling integral types separately, where summation is performed before division. Additional test cases have also been added for handling integral types.

For clarity on kernel names found when profiling. The new names are set to the order of the ops being compiled. For example: add + relu = add_relu_kernel.