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* Add slangpy doc, fix cuda prelude.
* more bug fix.
* fix.
* fix.
* More fix.
* fix.
* f
* fix prelude.
* update prelude.
* update doc
* Update prelude.
* add zeros_like
* update doc.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Small fixes and cleanups on CUDA/CPP codegen.
* Disable `legalizeEmptyTypes` for now.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Add PyTorch C++ binding generation.
* fix
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Added higher-order differentiability decorators for built-ins + preliminary tests
* Update diff.meta.slang
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* Bug fixes.
* Fix.
* Only perform autodiff for functions whose derivative is actually used.
* Fix loop optimize bug.
* Fix high order diff.
* Fix trivial diff func generation.
* Fixes.
* Cleanup.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Fix Phi simplification bug.
* Fix up.
* Fix.
* Fix.
* Fix.
* Fix.
* Fix.
* Fix test.
* Fix test.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Support high order diff pattern: `bwd_diff(fwd_diff(f))`.
* Fix.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Add support for `[PrimalSubstitute]` and `[PrimalSubstituteOf]`.
* Fix
* Fix.
* Cleanup.
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Co-authored-by: Yong He <yhe@nvidia.com>
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`[*DerivativeOf]` attribs. (#2688)
* Reuse higher-order `ResolveInvoke` logic to resolve func refs in [*DerivativeOf] attribs.
* Add diff implementation matrix versions of binary and ternary intrinsics.
* Add diff impl for legacy intrinsics.
* Fix diagnostics of using non-differentiable function in a diff operator.
* Add diff implementation for `determinant`.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Implement derivatives for HLSL intrinsics.
* Vector intrinsics.
* Add all intrinsics.
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Co-authored-by: Yong He <yhe@nvidia.com>
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(#2681)
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* Fix differentiable type registration
* Fix use of non-differentiable return value in a differentiable func.
* Fix use of primal inst that does not dominate the diff block.
* Fix primal inst hoisting, and add missing type legalization logic.
* Make `detach` defined on all differentiable T.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* WIP: start adding backward derivatives
* Overhaul `transposeParameterBlock` to support `inout` params.
* Small bug fixes.
* Bug fix on differentiable intrinsic specialization.
* Fixes.
* Run autodiff tests on CPU.
* Clean up.
* Overhaul `transposeParameterBlock` to support `inout` params.
* Small bug fixes.
* Bug fix on differentiable intrinsic specialization.
* Fixes.
* Run autodiff tests on CPU.
* Clean up.
* More bug fixes.,
* WIP: working on detach
* Arithmetic simplifications and more IR clean up logic.
* WIP: adding detach and abs
* Fix detach and abs
* Fix.
* Add IR transform pass for cleaner code emit.
* Fix test cases.
* Fix type system logic for reference type.
* Add backward derivatives for functions that already have forward derivatives
* Fix changes
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Co-authored-by: Yong He <yhe@nvidia.com>
Co-authored-by: Lifan Wu <lifanw@nvidia.com>
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* Overhaul `transposeParameterBlock` to support `inout` params.
* Small bug fixes.
* Bug fix on differentiable intrinsic specialization.
* Fixes.
* Run autodiff tests on CPU.
* Clean up.
* More bug fixes.,
* Add test coverage on inout param.
* Fix language server hinting for transcribed mutable params.
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Co-authored-by: Yong He <yhe@nvidia.com>
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stdlib. (#2615)
* Allow array parameters in forward diff.
* Use type canonicalization instead of coersion.
* Reimplement array type.
* Fix.
* Update test case.
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Frontend work for `[BackwardDerivative]` and `[BackwardDerivativeOf]`.
* Fix clang issue.
* Fix.
* fix gcc issue
* fix formatting.
Co-authored-by: Yong He <yhe@nvidia.com>
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Initial plumbing of backward autodiff in the frontend.
* More plumbing.
* Initial reverse autodiff working.
* Bug fixes.
* Misc.
* Remove redundant code.
* More clean up.
* Misc.
* Rebase and add backward diff test.
* Disable test.
* Clean up.
* Minor fix.
Co-authored-by: Yong He <yhe@nvidia.com>
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* Add [ForwardDerivativeOf] attribute.
* Fix handling around phi nodes.
* Fixes.
* Remove IR opcode for ForwardDerivativeOfDecoration.
Co-authored-by: Yong He <yhe@nvidia.com>
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Co-authored-by: Yong He <yhe@nvidia.com>
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vector intrinsics) (#2481)
* Added vector exp definition
* Naming
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Rename `[__custom_jvp]` -> `[ForwardDerivative]`.
* Rename the classes.
* More renaming.
Co-authored-by: Yong He <yhe@nvidia.com>
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Co-authored-by: Yong He <yhe@nvidia.com>
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* Auto synthesis of IDifferntial interface methods.
* Add comments.
Co-authored-by: Yong He <yhe@nvidia.com>
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* Modified the new type system to support generic differentiable types and added support for differentiating overloaded functions.
* Changed a few asserts to release asserts to avoid unreferenced variable errors
* Fixed a naming issue with TypeWitnessBreadcumb::Flavor::Decl
* Added logic to avoid tracking differentiable types if the module does not use auto-diff or define differentiable types.
* Moved the auto-diff passes to after the specialization step, added a more complex generics test
* Added a generics stress test and fixed AST-side logic. IR side needs some more work
* Added differential getter and setter logic, fixed multiple issues with DifferentiableTypeDictionary, added support for loops and conditions
* Changed differential getters to use pointer types, added getter type checking
* Fixed some bugs related to diff type registration and differential getters
* Removed some superfluous code
* Removed some more unused code.
* Fixed an issue with witness substitution
* Minor fix
Co-authored-by: Yong He <yonghe@outlook.com>
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* Merge slang-ir-diff-jvp.cpp
* Added support and tests for other float vector types
* Added swizzle test and code to handle it (tests failing currently)
* Fixed one test, the other is still pending
* Fixed instruction cloning logic to avoid modifying original function
* Fixed an issue with custom 'pow_jvp' and added support for vector contructor
* Minor update to comments
* Fixed support for division
* Fixed an issue with uninitialized diagnostic sink
* Moved derivative processing to after mandatory inlining.
Skip instructions that don't have side-effects and aren't used by anything.
* WIP: Handling unconditional control flow and multi-block functions
* Support for unconditional multi-block functions
* Added a dead code elimination step to the derivative pass
* Changed name of 'hasNoSideEffects()'
* Refactored variable names
* Added initial IR defs for new type system
* Added necessary logic for semantic checking
* Overhauled type system to use builtin pair types and conform to the IDifferentiable interface
* Automatically replace IRDifferentiablePairType to a custom IRStructType
* Added generics handling by expanding the conformance context functionality and allowing for type parameters
* Minor fix: early return in processPairTypes()
* Minor fixes to differentiable resolution on generic types
* Added new instructions for differential pairs. Basic tests work now.
Looking into generic types.
* Adjusted most tests to the new type system. OutType and InOutType are still not properly working.
* Updated __jvp to produce both primal and differential output
* Moved autodiff related declarations to diff.meta.slang
* Refactored variable names
* Added initial IR defs for new type system
* Added necessary logic for semantic checking
* Overhauled type system to use builtin pair types and conform to the IDifferentiable interface
* Automatically replace IRDifferentiablePairType to a custom IRStructType
* Added generics handling by expanding the conformance context functionality and allowing for type parameters
* Minor fix: early return in processPairTypes()
* Minor fixes to differentiable resolution on generic types
* Added new instructions for differential pairs. Basic tests work now.
Looking into generic types.
* Adjusted most tests to the new type system. OutType and InOutType are still not properly working.
* Updated __jvp to produce both primal and differential output
* Moved autodiff related declarations to diff.meta.slang
* Removed external changes
* Cleanup the transcription logic: each case returns a pair of insts for the primal and differential computation.
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