[vulkan] Add new VMA vulkan functions. #4893
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* Add new VMA vulkan functions. * fix
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* Add new VMA vulkan functions. * fix
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* [Build] Switch to scikit-build as the build backend (#4624)
* switch to skbuild
* Switch the build system to scikit-build
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* [build] Install export core library to build dir (#4866)
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* [Doc] Add limitation about TLS optimization (#4877)
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* [ci] Use the updated docker image for libtaichi_export_core (#4881)
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* [build] Change the library output dir for export core (#4880)
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* [metal] Complete Device API (#4862)
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* [bug] Fixed type promotion rule for bit-shift operations (#4884)
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* [vulkan] Fix typo for waitSemaphoreCount (#4892)
* [vulkan] Add new VMA vulkan functions. (#4893)
* Add new VMA vulkan functions.
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* [refactor] Create MatrixImpl to differentiate Taichi and Python scopes (#4853)
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* [Doc] Update trouble shooting URL in bug report template (#4988)
* [Lang] [type] Refactor quantized_types module and make quant APIs public (#4985)
* [Type] Refactor quantized_types module and make quant APIs public
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* Fix pylint
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* Update README.md
Tests if netlify is still working
* [test] Fix a few mis-configured ndarray tests (#5000)
* [Doc] Fix netlify cache & sync doc without pr content (#5003)
* [refactor] Program owns allocated ndarrays.
The end goal of this refactor is let Ndarray be a simple wrapper around
(DeviceAllocation, dtype, shape) without having to worry about memory
allocation/deallocation. But its current implementation heavily couples
with Program*, so an intermediate state would be:
- If created from Program, Ndarray handles deviceallocation in
ctor/dtor.
- We'll add another ctor simply constructing Ndarray from
(DeviceAllocation, dtype, shape) and update the codebase to it.
ghstack-source-id: bdfd24154428a3fd92ca05688333509d0402e53a
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/4996
* [test] Add test for Ndarray from DeviceAllocation
ghstack-source-id: 7d7c5486bce0a491170b52ba3ae809b4853d5447
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/4997
* [refactor] Construct ndarray from existing DeviceAllocation.
The end goal is make this the only ctor for Ndarray class.
ghstack-source-id: a7294096285b3879a84ffbb41196a136b2b605a8
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/4998
* [refactor] Free ndarray's memory when python GC triggers
Previously Program manages lifetime of all allocated ndarrays. So when
you call `del ndarray` in python, its memory was not freed. This PR
changes the behavior that `ndarray` memory gets deallocated when python
GC triggers, or its containing `Program` gets destructed, whichever
happens first. There're some quirks around how we handle the async
python GC and manual `ti.reset()`. Thanks to k-ye, we now added a
`generation` number to track the containing program instance of ndarrays
so that memory deallocation happens correctly.
ghstack-source-id: 4fdef9c8285e2188c4afaffe6febdd45e5164b15
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/4999
* [refactor] Move ndarray fast fill methods to Program
This PR gets rid of `LlvmProgramImpl*` member inside `Ndarray` class,
which is a step closer towards decoupling `Ndarray` and memory
management.
ghstack-source-id: 181d28eba3ded5c95d8c70c95a293505bbfebf01
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5002
* [refactor] Get rid of data_ptr_ in Ndarray
ghstack-source-id: d795592d21f4a3da4a6c8ccffdce1dbc40ad99aa
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5004
* [Doc] Branding updates. Also tests netlify. (#4994)
* Branding updates. Also tests netlify.
* Minor editorial updates to trigger netlify preview.
* Minor updates to re-trigger CI/CD
* [AOT] Supported inclusion of taichi as subdirectory for AOT modules (#5007)
* Support building taichi as CMake subdirectory
* Fixes for export-less integration on Android
* [misc] Version bump: v1.0.2 -> v1.0.3 (#5008)
* [Lang] [type] Fix parameter name 'range' for ti.types.quant.fixed (#5006)
* [SIMT] Add match_any warp intrinsics (#4921)
* add match_any warp intrinsic
del f32
reset
* alter predicate to value
* update warp.py to sync with PR4957
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* [doc] Update community section (#4943)
* [doc] Update community section
add active events and communication
* fix typo
* refine docs
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* Update README.md
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* [llvm] Add serializable LlvmLaunchArgInfo (#4992)
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* [bug] Fixed numerical error for Atomic-Sub between unsigned values with different number of bits (#5011)
* [refactor] Move get ndarray data ptr to program (#5012)
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* [ci] [build] Enable ccache for windows docker (#5001)
* Enable ccache for windows docker
* only run windows docker job
* copy ccache_folder
* trigger CI
* Re-enable all jobs
* remove dumb text
* [test] Unify kernel setup for ndarray related tests
We'll reuse these two kernels for cgraph tests as well so let's clean it
up first.
ghstack-source-id: acd772f092ac9044197ac2e1f16100ba4ba9005d
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5014
* [aot] Build and run graph without serialization
This PR servces as the base PR with a minimal example of building and
running a Graph. Runtime values for graph arguments can be either
scalars or ndarrays.
For detailed proposal please see #4786.
Things handled in this PR:
- Maximize common code/runtime shared by the two workflows below:
1. build -> compile -> run
2. build -> compile -> serialize -> deserilize -> run
- Graph arguments are annotated with dtype and element shape for ndarray (temporary
until we have vec3 types in C++)
Things that we've discussed but not included in this PR:
- C API: I'll leave that for a unified C API PR in the future.
- bind IValues to graph: easy, will add later.
ghstack-source-id: f459afccdde56b59ab0ecc860ed11d761a20fe0a
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5015
* [Llvm] Add AOT builder and loader (#5013)
* [Llvm] Add AOT builder and loader
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* check nullptr
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* [ci] Fix nightly macos (#5018)
* [bug] Revert freeing ndarray memory when python GC triggers (#5019)
* [SIMT] Add match_all warp intrinsics (#4961)
* add match_all warp intrinsic by ptx
* add args to match_all in warp.py
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* update warp.py to sync with PR4957
* update llvm_context.cpp: add more details about match_all_sync intrinsic
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* Update test_simt.py
Initialize a with1
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* [AOT] Support importing external Vulkan buffers (#5020)
* [Bug] [type] Fix frontend type check for reading a whole bit_struct (#5027)
* fix fast_gui rgba bug (#5031)
* [doc] Update OS names (#5030)
* [ci] Disable win cpu docker job test (#5033)
* Disable win cpu docker job test
* Revert changes on naming
* [aot] Serialize built graph, deserialize and run.
related: #4786
[Update]: based on an offline discussion with k-ye, I've split the
original `Graph` class into `GraphBuilder` and `CompiledGraph` classes
in C++. Note that the implementation didn't follow exactly the builder
design pattern as our builder is slightly simpler as shown below.
The complexity in our problem is more in the need of serialization and
deserialization for the same graph representation intead of its
construction process. So IMHO it's good enough to separate the
GraphBuilder and Runner(`CompiledGraph`) as we discussed. Please feel
free to correct me if I'm wrong!
```
GraphBuilder
|
compile()
|
|
CompiledGraph <---- serialize/deserialize ----> file
|
|
run()
```
This PR demonstrates a minimal example of serializing a built graph,
deserializing and running it.
ghstack-source-id: 7dda7cc11ef3a946f31d75783a8cfd1836e47ba5
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5024
* [aot] Move ArgKind as first argument in Arg class
Thought this might be more intuitive for users.
ghstack-source-id: 865062f0982db4d69a41ba345a1d254c2054a12f
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5025
* [aot] Bind graph APIs to python and add mpm88 example (#5034)
This PR supports graph builder and runner APIs in python. Note for
simplicity I've merged builder and runner in the same Python class.
Please feel free to comment if you have any suggestions.
This PR also adds a test of saving mpm88 graph in aot module, as well
as an example script to demonstrate the speed improvement (15fps ->
45fps) compared to the current taichi.
ghstack-source-id: 600e604b141f9e534045f930d8424125c38ed875
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5026
* [Lang] [type] Refactor quant type definition APIs (#5036)
* [Lang] [type] Refactor quant type definition APIs
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* [Metal] Support Ndarray (#4720)
* [Metal] Support Ndarray
* simple work
* fix copying
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* wip
* fixes
* fix devalloc id bug, enable tests
* fix extra_arg offset
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* remove size
* rm size
* ref
* fix for ret matrix type
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* fix test
* fix zero
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* [Lang] Fix potential precision bug when using math vector and matrix types (#5032)
* fix fast_gui rgba bug
* fix floating precision problem for vector types
* add more flexible initialization methods for matrix type
* add more flexible initialization methods for matrix type
* add glsl determinant and inverse function support
* add glsl determinant and inverse function support
* add glsl determinant and inverse function support
* add glsl determinant and inverse function support
* add glsl determinant and inverse function support
* fix matrix precision type bug and use matrix-member inverse
* fix matrix precision type bug and use matrix-member inverse
* [Vulkan] Fixed vulkan backend crash on AOT examples (#5047)
* Exit CI builds when download of prebuilt packages fails (#5043)
* [ci] Run cpp tests via run_tests.py (#5035)
* [ci] Run cpp tests via run_tests.py
* default to False
* enable cpp on win
* Set host_write to false for opengl ndarray (#5038)
As discussed ndarrays can be written through calling write kernels, but
it shouldn't support directly map on host and write to it.
* [Lang] Build sparse matrix from ndarray (#4841)
* build sparse matrix from ndarray
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* add shape property
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* fix errors
* fix pylint
* fix failed test
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* improve
* improve
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* fix ndarray data ptr not found
* pylint
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* check ndarray dimension when build sparse matrix
* improve
* add example docstring
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* [bug] Added type promotion support for atan2 (#5037)
* [bug] Added type promotion rule for atan2
* Fixed minor issue
* Modified type promotion rule
* [Doc] Updated type system (#5054)
* Editorial updates
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* [bug] Ndarray type should include primitive dtype as well (#5052)
This PR does three things:
- Switch cgraph `Arg` to take in `ti.f32/i32` instead of string
`f32/i32` as inputs
- Fix a bug that when we produce injected ndarray args for compilation
we only produced f32 ndarrays, which won't work for ndarray of other
primitive dtypes.
- No need to specify `element_shape` if it's scalar arg or scalar
ndarray arg.
* [Lang] [ir] Add short-circuit if-then-else operator (#5022)
* [Lang] [ir] Add a short-circuit if-then-else operator and use it to implement IfExp
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* [Lang] Struct Classes implementation (#4989)
* Initial Struct Classes implementation
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* Update method translation to mark as taichi funcs
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* Revert unwanted changes to impl.py
* Update test_api.py
* Update struct.py
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* Update class decorator docstring
* Update func marking and add tests
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* [Lang] [type] Disallow reading a whole bit_struct (#5061)
* [bug] Remove operator ! for Expr (#5062)
* [build] [bug] Ensure the assets folder is copied to the project directory (#5063)
* Bugfix: ensure the assets folder are copied
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* call copy_assets before setup()
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* [refactor] Split GraphBuilder out of Graph class (#5064)
* [aot] [CUDA-AOT PR #0] Refactored compile_module_to_executable() to CUDAModuleToFunctionConverter (#5070)
* [refactor] Specialized Ndarray Type is (element_type, shape, layout)
ghstack-source-id: 977cd453359b8ccc09deccacc62a915abcd42734
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5065
* [refactor] Pass element_shape and layout to C++ Ndarray
Note we still flatten element_shape in the C++ Ndarray, which is blocked by the accessors and will be fixed
in the following PRs.
ghstack-source-id: 0cb5c05f0ad4c188546d7174a1d82f398bc717c2
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5066
* [Lang] Support constructing vector and matrix ndarray from ti.ndarray()
ghstack-source-id: 3055ba79c35aecea61c449038b4f8c07e87571b9
Pull Request resolved: https://github.com/taichi-dev/taichi/pull/5073
* [refactor] Resolve comments from #5065 (#5074)
* [Example] Update mass_spring_3d_ggui.py to v2 (#3879)
* cleaner mass_spring_3d_ggui.py
* fix collision
* Fix penetration
* No capitalized globals
* fix compute_force
* parameter tweaks
* Looks good now
* Update TaichiCore.cmake
* Update mass_spring_3d_ggui.py
* Update mass_spring_3d_ggui.py
Change variable name `allow_bending` to `bending_springs`.
* [doc] Fix broken link for github action status badge (#5076)
* [doc] Fix link for github action status badge
* Update README.md
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* Update README.md
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* [llvm] Specialize element shape for LLVM backend (#5071)
* Specialize element shape for LLVM backend
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* [spirv] Specialize element shape for spirv codegen. (#5068)
* Specialize element shape for spirv codegen.
* Fix index for size_var_names
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* Slight changes for better code style.
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* [Lang] Add more initialization routines for glsl matrix types (#5069)
* add more initialization routines for glsl matrix types
* add more initialization routines for glsl matrix types
* [cuda] [simt] Add assertions for warp intrinsics on old GPUs (#5077)
* Add guard for cc smaller than 70
* Fix
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* Fix pylint
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* [refactor] Correctly set ndarray element_size and nelement (#5080)
* [infra] Refactor Vulkan runtime into true Common Runtime (#5058)
* Remove all references to Vulkan in common runtime & fix device API for OpenGL (bindings) and DirectX 11 (memory leaks)
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* fix tests
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* fix cpp test
* update
* update
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* [llvm] [aot] CUDA-AOT PR #1: Extracted common logics from CPUAotModuleImpl into LLVMAotModule (#5072)
* [llvm] [aot] CUDA-AOT PR #1: Extracted common logics from CPUAotModuleImpl into LLVMAotModule
* Renamed LLVMAotModule
* Fixed minor issue
* [llvm] [refactor] Merge AtomicOpStmt codegen in CPU and CUDA backends (#5086)
* [llvm] [refactor] Merge AtomicOpStmt codegen in CodeGenLLVMCUDA and CodeGenLLVM
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* [refactor] Make sure Ndarray shape is field shape (#5085)
* [autodiff] Allocate dual and adjoint snode (#5083)
* allocate dual and decouple grad and adjoint
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* update
* update
* update the adjoint name
* fix matrix
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* recover the grad name
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* [build] [refactor] Change CMake global include_directories to target based function (#5082)
* Change to target_include_directories
* Update runtime cmake
* Pre-commit format
* [Doc] Add documentation of Taichi Struct Classes. (#5075)
* Add documentation of Taichi Struct Classes.
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* edits
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* Update docs/lang/articles/advanced/odop.md
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* Fix capitalization of Python
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* [llvm] [aot] Add LLVM-CPU AOT tests (#5079)
* [llvm] [aot] Add LLVM-CPU AOT tests
* Refactored AOT test framework
* Fixed minor issue
* Enabled LLVM CPU-AOT for arm64 architecture
* Added aot unit tests programming guide
* Fixed typo
* Refactored AOT test framework
* [autodiff] Extract shared components for reverse and forward mode (#5088)
extract shared components for reverse and forward mode
* [llvm] [refactor] Use LLVM native atomic ops if possible (#5091)
* [llvm] [refactor] Use LLVM native atomic ops if possible
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* [bug] Minor fix for ndarray element_shape in graph mode (#5093)
Now that ndarray's element_shape is separated from shape, this hack can
be removed.
* Use pre-calculated runtime size array for gfx runtime. (#5094)
* [Doc] Improve ODOP doc structure (#5089)
* [llvm] [aot] CUDA-AOT PR #2: Implemented AOTModuleLoader & AOTModuleBuilder for LLVM-CUDA backend (#5087)
* [llvm] [aot] Add LLVM-CPU AOT tests
* Refactored AOT test framework
* Fixed minor issue
* Enabled LLVM CPU-AOT for arm64 architecture
* Added aot unit tests programming guide
* [llvm] [aot] CUDA-AOT PR #2: Implemented AOT Module Loader for LLVM-CUDA backend
* Fixed typo
* Fixed minor issue
* Refactored AOT test framework
* [llvm] [aot] Add LLVM-CUDA AOT tests
* Added cuda device availability check
* clean hidden override functions (#5097)
* [refactor] Update Ndarray constructor used in AOT runtime. (#5095)
This constructor is mainly used to construct an Ndarray out of an
existing device allocation. This PR updates the behavior of this
constructor to seprate element_shape out of shape.
* [refactor] Remove ndarray element shape from extra arg buffer (#5100)
* Remove element shape from extra args.
* [llvm] [refactor] Move load_bit_pointer() to CodeGenLLVM (#5099)
* [llvm] [refactor] Move load_bit_pointer() to CodeGenLLVM
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* [test] Save mpm88 graph in python and load in C++ test. (#5104)
This is a simplified version of
https://github.com/ailzhang/taichi-aot-demo/tree/mpm88_cgraph_demo which
strips the GGUI rendering part. Let's add this as a test (as well as
demo ;) ) in the codebase. We used to test the saving part of mpm88
btw and it was replaced with this e2e test.
Huge thanks to @k-ye for help debugging the GGUI rendering issue!
* [Example] Update visual effects of mass_spring_3d_ggui.py (#5081)
* update scene for mass_spring simulation
* update scene for mass_spring simulation
* update scene for mass_spring simulation
* [type] [refactor] Remove redundant promotion for custom int in type_check (#5102)
* [llvm] [refactor] Replace cast_int() with LLVM native integer cast (#5110)
* [llvm] [refactor] Use LLVM native integer cast
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* [type] [llvm] [refactor] Fix function names in codegen_llvm_quant (#5115)
* [type] [llvm] [refactor] Fix function names in codegen_llvm_quant
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* [bug] Fix build without llvm backend crash (#5113)
* [bug] Fix build without llvm backend crash
* Update taichi/python/export_lang.cpp
Co-authored-by: yekuang <k-ye@users.noreply.github.com>
Co-authored-by: yekuang <k-ye@users.noreply.github.com>
* [build] [refactor] Move Vulkan runtime out of backends dir (#5106)
* Precommit fix
* Add spirv source
* Move device code back to backends
* Expose glfw include in vulkan rhi
* Fix llvm include
* Fix include for test
* [autodiff] Add forward mode pipeline for autodiff pass (#5098)
* Add forward mode pipeline for autodiff pass
* Replace the grad parameter with AutodiffMode to distinguish three kinds of kernels primal, forward ad and reverse ad
* [aot] [llvm] LLVM AOT Field #0: Implemented FieldCacheData & refactored initialize_llvm_runtime_snodes() (#5108)
* [aot] [llvm] Implemented FieldCacheData and refactored initialize_llvm_runtime_snodes()
* Addressed compilation erros
* Added initialization for struct members
* Minor fix
* [aot][bug] Use cached compiled kernel pointer when it's added to graph (#5122)
multiple times
This bug was triggered when we tried to port stable_fluid demo so this
PR also added a cgraph based stable fluid demo.
```
ti example stable_fluid_graph
```
Note it's not ideal to save both `FunctionType compiled_` as well as
`aot::Kernel compiled_aot_kernel_` inside C++ `Kernel` class. But we plan to clean that
up (likely by getting rid of `FunctionType compiled_`) in #5114.
* [aot] [llvm] LLVM AOT Field #1: Adjust serialization/deserialization logics for FieldCacheData (#5111)
* [aot] [llvm] Implemented FieldCacheData and refactored initialize_llvm_runtime_snodes()
* Addressed compilation erros
* [aot] [llvm] LLVM AOT Field #1: Adjust serialization/deserialization logics for FieldCacheData
* Editorial update (#5119)
* [lang] Texture support 0/n: IR changes (#5134)
* fix mass_spring_3d_ggui backend (#5127)
* [Example] Fix block_dim warning in ggui (#5128)
* fix block dim warning in ggui
* fix block dim warning in ggui
* fix block dim warning in ggui
* [ci] Enable yapf and isort on example files (#5140)
Note we explicitly exclude running pylint on them as it requires a bunch
of manual fixes first.
* [type] [refactor] Misc improvements to quant codegen (#5129)
* Replace is_custom_type() with is_quant()
* Rename two functions
* Use get_constant() if possible
* Rename two metal functions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [aot] [llvm] LLVM AOT Field #2: Updated LLVM AOTModuleLoader & AOTModuleBuilder to support Fields (#5120)
* [aot] [llvm] Implemented FieldCacheData and refactored initialize_llvm_runtime_snodes()
* Addressed compilation erros
* [aot] [llvm] LLVM AOT Field #1: Adjust serialization/deserialization logics for FieldCacheData
* [llvm] [aot] Added Field support for LLVM AOT
* [aot] [llvm] LLVM AOT Field #2: Updated LLVM AOTModuleLoader & AOTModuleBuilder to support Fields
* Fixed merge issues
* Stopped abusing Program*
Co-authored-by: Frost Ming <mianghong@gmail.com>
Co-authored-by: Taichi Gardener <taichigardener@gmail.com>
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Co-authored-by: Zhanlue Yang <zy2284@columbia.edu>
Co-authored-by: Bo Qiao <boqiao@taichi.graphics>
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Co-authored-by: Chang Yu <g1n0st@live.com>
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Fixes #4891