Parfor lowering as kernels and dpnp ufunc compilation to kernels #957
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- DPEXRTQueue_CreateFromFilterString: to help create a
DPCTLSyclQueueRef object from a filter string.
- DpexrtQueue_SubmitRange: to submit a ranged kernel.
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The code is based on the existing DpexLowerer in
core/passes/lowerer, but refactored and cleaned up a bit.
- Add the ParforLoweringPass
- Utility modules kernel_builder and kernel_launcher
- Dpnp expressions that rely on the __array_ufunc__ method
of DpnpNdArray do not infer the usm_type and device type
of the LHS of the expression.
The pass is an incomplete implementation of an algorithm
that traverses all the basic blocks and checks CFD compliance
and fixes the LHS of all parfors created from dpnp array
expressions and pranges.
- The new DpjitPipeline uses the backported parfor passes
with the new parfor lowerer and parfor compute follows data
legalization pass.
- dpjit decorator now used DpjitCompiler.
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Added numba_dpex/core/typing/dpnpdecl.py. Added numba_dpex/dpnp_iface/dpnp_ufunc_db.py. Added numba_dpex/dpnp_iface/dpnpimpl.py.
- The commit makes expressions such as A*2, where A is a
dpnp.ndarray work.
- Adds support for dpnp.true_divide.
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Parfor lowering as kernels and dpnp ufunc compilation to kernels 5a2238b
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The PR is the initial set of changes to support dpnp functions and vector-style expressions in numba-dpex.
A large subset of Numba's Numpy ufuncs that can be lowered to a SPIR-V supporting device and are also supported by dpnp now can be compiled using numba-dpex.
Dpnp offloading follows the compute follows data programming model.
New infrastructure for lowering parfors to kernels
Have you added a test, reproducer or referred to an issue with a reproducer?
Have you tested your changes locally for CPU and GPU devices?
Have you made sure that new changes do not introduce compiler warnings?
If this PR is a work in progress, are you filing the PR as a draft?