Rename DPPy to DPPL in code.

In some cases it is better to use Dppl as prefix for class names.

Author: PokhodenkoSA

HowTo.rst

@@ -2,28 +2,28 @@
 Features
 ========
 
-DPPy is currently implemented using OpenCL 2.1. The features currently available
+DPPL is currently implemented using OpenCL 2.1. The features currently available
 are listed below with the help of sample code snippets. In this release we have
 the implementation of the OAK approach described in MS138 in section 4.3.2. The
 new decorator is described below.
 
-To access the features driver module have to be imported from numba.dppy.dppy_driver
+To access the features driver module have to be imported from numba.dppl.dppl_driver
 
 New Decorator
 =============
 
-The new decorator included in this release is *dppy.kernel*. Currently this decorator
+The new decorator included in this release is *dppl.kernel*. Currently this decorator
 takes only one option *access_types* which is explained below with the help of an example.
 Users can write OpenCL tpye kernels where they can identify the global id of the work item
 being executed. The supported methods inside a decorated function are:
 
-- dppy.get_global_id(dimidx)
-- dppy.get_local_id(dimidx)
-- dppy.get_group_num(dimidx)
-- dppy.get_num_groups(dimidx)
-- dppy.get_work_dim()
-- dppy.get_global_size(dimidx)
-- dppy.get_local_size(dimidx)
+- dppl.get_global_id(dimidx)
+- dppl.get_local_id(dimidx)
+- dppl.get_group_num(dimidx)
+- dppl.get_num_groups(dimidx)
+- dppl.get_work_dim()
+- dppl.get_global_size(dimidx)
+- dppl.get_local_size(dimidx)
 
 Currently no support is provided for local memory in the device and everything is in the
 global memory. Barrier and other memory fences will be provided once support for local
@@ -61,7 +61,7 @@ Primitive types are passed by value to the kernel, currently supported are int,
 Math Kernels
 ============
 
-This release has support for math kernels. See numba/dppy/tests/dppy/test_math_functions.py
+This release has support for math kernels. See numba/dppl/tests/dppl/test_math_functions.py
 for more details.
 
 
@@ -72,7 +72,7 @@ Examples
 Sum of two 1d arrays
 ====================
 
-Full example can be found at numba/dppy/examples/sum.py.
+Full example can be found at numba/dppl/examples/sum.py.
 
 To write a program that sums two 1d arrays we at first need a OpenCL device environment.
 We can get the environment by using *ocldrv.runtime.get_gpu_device()* for getting the
@@ -82,7 +82,7 @@ where *device_env.copy_array_to_device(data)* will read the ndarray and copy tha
 and *ocldrv.DeviceArray(device_env.get_env_ptr(), data)* will create a buffer in the device
 that has the same memory size as the ndarray being passed. The OpenCL Kernel in the
 folllowing example is *data_parallel_sum*. To get the id of the work item we are currently
-executing we need to use the  *dppy.get_global_id(0)*, since this example only 1 dimension
+executing we need to use the  *dppl.get_global_id(0)*, since this example only 1 dimension
 we only need to get the id in dimension 0.
 
 While invoking the kernel we need to pass the device environment and the global work size.
@@ -91,9 +91,9 @@ back to the host and we can use *device_env.copy_array_from_device(ddata)*.
 
 .. code-block:: python
 
-    @dppy.kernel
+    @dppl.kernel
     def data_parallel_sum(a, b, c):
-        i = dppy.get_global_id(0)
+        i = dppl.get_global_id(0)
         c[i] = a[i] + b[i]
 
     global_size = 10
@@ -126,7 +126,7 @@ ndArray Support
 
 Support for passing ndarray directly to kernels is also supported.
 
-Full example can be found at numba/dppy/examples/sum_ndarray.py
+Full example can be found at numba/dppl/examples/sum_ndarray.py
 
 For availing this feature instead of creating device buffers explicitly like the previous
 example, users can directly pass the ndarray to the kernel. Internally it will result in
@@ -148,7 +148,7 @@ Reduction
 
 This example will demonstrate a sum reduction of 1d array.
 
-Full example can be found at numba/dppy/examples/sum_reduction.py.
+Full example can be found at numba/dppl/examples/sum_reduction.py.
 
 In this example to sum the 1d array we invoke the Kernel multiple times.
 This can be implemented by invoking the kernel once, but that requires
@@ -161,15 +161,15 @@ ParFor Support
 
 *Parallel For* is supported in this release for upto 3 dimensions.
 
-Full examples can be found in numba/dppy/examples/pa_examples/
+Full examples can be found in numba/dppl/examples/pa_examples/
 
 
 =======
 Testing
 =======
 
-All examples can be found in numba/dppy/examples/
+All examples can be found in numba/dppl/examples/
 
-All tests can be found in numba/dppy/tests/dppy and can be triggered by the following command:
+All tests can be found in numba/dppl/tests/dppl and can be triggered by the following command:
 
-``python -m numba.runtests numba.dppy.tests`` 
+``python -m numba.runtests numba.dppl.tests``

README.rst

@@ -1,13 +1,13 @@
 
-DPPY
+DPPL
 ====
 
 ========
 1. What?
 ========
 
-DPPy proof-of-concept backend for NUMBA to support compilation for Intel CPU and
-GPU architectures. The present implementation of DPPy is based on OpenCL 2.1,
+DPPL proof-of-concept backend for NUMBA to support compilation for Intel CPU and
+GPU architectures. The present implementation of DPPL is based on OpenCL 2.1,
 but is likely to change in the future to rely on Sycl/DPC++ or Intel Level-0
 driver API.
 
@@ -22,57 +22,57 @@ driver API.
 - Cmake                : For managing build process of dependencies
 - Python3              : Version 3 is required
 - Conda or miniconda   : Can be found at https://docs.conda.io/en/latest/miniconda.html
-- OpenCL 2.1 driver    : DPPy currently works for both Intel GPUs and CPUs is a correct OpenCL driver version is found on the system. 
+- OpenCL 2.1 driver    : DPPL currently works for both Intel GPUs and CPUs is a correct OpenCL driver version is found on the system.
 Note. To use the GPU users should be added to "video" user group on Linux systems.
 
 
 The following requisites will need to be present in the system. Refer to next section for more details.
 *******************************************************************************************************
 
-- NUMBA v0.48          : The DPPy backend has only been tested for NUMBA v0.48. The included install script downloads and applies the DDPy patch to the correct NUMBA version.
+- NUMBA v0.48          : The DPPL backend has only been tested for NUMBA v0.48. The included install script downloads and applies the DDPy patch to the correct NUMBA version.
 
 - LLVM-SPIRV translator: Used for SPIRV generation from LLVM IR.
 
 - LLVMDEV              : To support LLVM IR generation.
 
-- Others               : All existing dependecies for NUMBA, such as llvmlite, also apply to DPPy.
+- Others               : All existing dependecies for NUMBA, such as llvmlite, also apply to DPPL.
 
 ==================
 3. How to install?
 ==================
 Install Pre-requisites
 *************************
-Make sure the dependencies of NUMBA-DPPY are installed in the system, for convenience
+Make sure the dependencies of NUMBA-DPPL are installed in the system, for convenience
 and to make sure the dependencies are installed with consistent version of LLVM we provide
 installation script that will create a CONDA environment and install LLVM-SPIRV translator,
 SPIRV-Tools and llvmlite in that environment. **To use this CONDA has to be available in the system**.
 
-The above mentioned installation script can be found `here <https://github.intel.com/SAT/numba-dppy-build-scripts>`_. Please follow the README to run the installation script. 
+The above mentioned installation script can be found `here <https://github.intel.com/SAT/numba-dppl-build-scripts>`_. Please follow the README to run the installation script.
 
 After successful installation the following message should be displayed:
 
     | #
     | # Use the following to activate the correct environment
     | #
-    | # `    $ ``conda activate numba-dppy-env`` `
+    | # `    $ ``conda activate numba-dppl-env`` `
     | #
     | #  Use the following to deactivate environment
     | #
     | # `    $ ``conda deactivate`` `
 
-The installer script creates a new conda environment called numba-dppy-env with
-all the needed dependencies already installed. **Please activate the numba-dppy-env before proceeding**.
+The installer script creates a new conda environment called numba-dppl-env with
+all the needed dependencies already installed. **Please activate the numba-dppl-env before proceeding**.
 
 
-Install DPPY backend
+Install DPPL backend
 ***********************
-NUMBA-DPPY also depend on DPPY backend. It can be found `here <https://github.intel.com/SAT/dppy>`_. Please run 
-`build_for_develop.sh` to install DPPY backend.
+NUMBA-DPPL also depend on DPPL backend. It can be found `here <https://github.com/IntelPython/PyDPPL>`_. Please run
+`build_for_develop.sh` to install DPPL backend.
 
-Install NUMBA-DPPY
+Install NUMBA-DPPL
 *********************
-After all the dependencies are installed please run ``build_for_develop.sh`` to get a local installation of NUMBA-DPPY. **Both step 2 and 3 assumes CONDA environment with
-the dependencies of NUMBA-DPPY installed in it, was activated**.
+After all the dependencies are installed please run ``build_for_develop.sh`` to get a local installation of NUMBA-DPPL. **Both step 2 and 3 assumes CONDA environment with
+the dependencies of NUMBA-DPPL installed in it, was activated**.
 
 ================
 4. Running tests
@@ -81,11 +81,11 @@ the dependencies of NUMBA-DPPY installed in it, was activated**.
 To make sure the installation was successful, try running the examples and the
 test suite:
 
-    $PATH_TO_NUMBA-DPPY/numba/dppy/examples/
+    $PATH_TO_NUMBA-DPPL/numba/dppl/examples/
 
 To run the test suite execute the following:
 
-    $ ``python -m numba.runtests numba.dppy.tests``
+    $ ``python -m numba.runtests numba.dppl.tests``
 
 ===========================
 5. How Tos and Known Issues
@@ -94,7 +94,7 @@ To run the test suite execute the following:
 Refer the HowTo.rst guide for an overview of the programming semantics,
 examples, supported functionalities, and known issues.
 
-*Installing while Intel OneAPI basekit is actvated have shown to throw error while installation of NUMBA-DPPY because of incompatible TBB interface, one way around that is to temporaily move env variable TBBROOT to something else*
+*Installing while Intel OneAPI basekit is actvated have shown to throw error while installation of NUMBA-DPPL because of incompatible TBB interface, one way around that is to temporaily move env variable TBBROOT to something else*
 
 
 ===================

buildscripts/condarecipe.dppy/build.sh renamed to buildscripts/condarecipe.pydppl/build.sh

@@ -35,14 +35,14 @@ requirements:
     - llvmlite >=0.31.*
     # TBB devel version is to match TBB libs
     - tbb-devel >=2019.5     # [not ((armv6l or armv7l or aarch64) or (win and py27))]
-    - dppy
+    - pydppl
   run:
     - python >=3.6
     - numpy >=1.17
     - setuptools
     # On channel https://anaconda.org/numba/
     - llvmlite >=0.31.*
-    - dppy
+    - pydppl
     - spirv-tools
     - llvm-spirv
   run_constrained:

buildscripts/condarecipe.dppy/meta.yaml renamed to buildscripts/condarecipe.pydppl/meta.yaml

@@ -218,8 +218,8 @@ def optional_str(x):
         DUMP_IR = _readenv("NUMBA_DUMP_IR", int,
                            DEBUG_FRONTEND or DEBUG_TYPEINFER)
 
-        # Save intermediate files being generated by DPPy
-        SAVE_DPPY_IR_FILES = _readenv("NUMBA_SAVE_DPPY_IR_FILES", int, 0)
+        # Save intermediate files being generated by DPPL
+        SAVE_DPPL_IR_FILES = _readenv("NUMBA_SAVE_DPPL_IR_FILES", int, 0)
 
         # Turn SPIRV-VALIDATION ON/OFF switch
         SPIRV_VAL = _readenv("NUMBA_SPIRV_VAL", int, 0)

buildscripts/condarecipe.dppy/run_test.sh renamed to buildscripts/condarecipe.pydppl/run_test.sh

@@ -8,11 +8,11 @@ class CPUDispatcher(dispatcher.Dispatcher):
     def __init__(self, py_func, locals={}, targetoptions={}, impl_kind='direct', pipeline_class=compiler.Compiler):
         if ('parallel' in targetoptions and isinstance(targetoptions['parallel'], dict) and
                 'offload' in targetoptions['parallel'] and  targetoptions['parallel']['offload'] == True):
-            import numba.dppy_config as dppy_config
-            if dppy_config.dppy_present:
-                from numba.dppy.compiler import DPPyCompiler
+            import numba.dppl_config as dppl_config
+            if dppl_config.dppl_present:
+                from numba.dppl.compiler import DPPLCompiler
                 dispatcher.Dispatcher.__init__(self, py_func, locals=locals,
-                        targetoptions=targetoptions, impl_kind=impl_kind, pipeline_class=DPPyCompiler)
+                        targetoptions=targetoptions, impl_kind=impl_kind, pipeline_class=DPPLCompiler)
             else:
                 print("---------------------------------------------------------------------------")
                 print("WARNING : offload=True option ignored. Ensure OpenCL drivers are installed.")

numba/core/config.py

@@ -184,9 +184,9 @@ def wrapper(func):
             return cuda.jit(func)
         if config.DISABLE_JIT and not target == 'npyufunc':
             return func
-        if target == 'dppy':
-            from . import dppy
-            return dppy.jit(func)
+        if target == 'dppl':
+            from . import dppl
+            return dppl.jit(func)
         disp = dispatcher(py_func=func, locals=locals,
                           targetoptions=targetoptions,
                           **dispatcher_args)

numba/core/cpu_dispatcher.py

@@ -172,10 +172,10 @@ def emit_environment_object(self):
         self.context.declare_env_global(self.module, envname)
 
     def lower(self):
-        # DRD : Add this hack for the time ebing. Having a global env pointer, 
-        # even if it is a null pointer, breaks spirv-val. 
+        # DRD : Add this hack for the time ebing. Having a global env pointer,
+        # even if it is a null pointer, breaks spirv-val.
         context_name = getattr(self.context, 'context_name', '')
-        if not context_name in ("dppy.jit",):
+        if not context_name in ("dppl.jit",):
                 # Emit the Env into the module
                 self.emit_environment_object()
 

numba/core/decorators.py

@@ -355,13 +355,13 @@ def resolve_argument_type(self, val):
         try:
             return typeof(val, Purpose.argument)
         except ValueError:
-            from numba.dppy_config import dppy_present, DeviceArray
-            if dppy_present:
+            from numba.dppl_config import dppl_present, DeviceArray
+            if dppl_present:
                 if(type(val) == DeviceArray):
                     return typeof(val._ndarray, Purpose.argument)
                 # DRD : Hmmm... is the assumption that this error is encountered
                 # when someone is using cuda, and already has done an import
-                # cuda? 
+                # cuda?
                 #elif numba.cuda.is_cuda_array(val):
                 #    return typeof(numba.cuda.as_cuda_array(val), Purpose.argument)
                 else: