import re from functools import cached_property import llvmlite.binding as ll from llvmlite import ir from numba.core import (cgutils, config, debuginfo, itanium_mangler, types, typing, utils) from numba.core.dispatcher import Dispatcher from numba.core.base import BaseContext from numba.core.callconv import BaseCallConv, MinimalCallConv from numba.core.typing import cmathdecl from numba.core import datamodel from .cudadrv import nvvm from numba.cuda import codegen, nvvmutils, ufuncs from numba.cuda.models import cuda_data_manager # ----------------------------------------------------------------------------- # Typing class CUDATypingContext(typing.BaseContext): def load_additional_registries(self): from . import cudadecl, cudamath, libdevicedecl, vector_types from numba.core.typing import enumdecl, cffi_utils self.install_registry(cudadecl.registry) self.install_registry(cffi_utils.registry) self.install_registry(cudamath.registry) self.install_registry(cmathdecl.registry) self.install_registry(libdevicedecl.registry) self.install_registry(enumdecl.registry) self.install_registry(vector_types.typing_registry) def resolve_value_type(self, val): # treat other dispatcher object as another device function from numba.cuda.dispatcher import CUDADispatcher if (isinstance(val, Dispatcher) and not isinstance(val, CUDADispatcher)): try: # use cached device function val = val.__dispatcher except AttributeError: if not val._can_compile: raise ValueError('using cpu function on device ' 'but its compilation is disabled') targetoptions = val.targetoptions.copy() targetoptions['device'] = True targetoptions['debug'] = targetoptions.get('debug', False) targetoptions['opt'] = targetoptions.get('opt', True) disp = CUDADispatcher(val.py_func, targetoptions) # cache the device function for future use and to avoid # duplicated copy of the same function. val.__dispatcher = disp val = disp # continue with parent logic return super(CUDATypingContext, self).resolve_value_type(val) # ----------------------------------------------------------------------------- # Implementation VALID_CHARS = re.compile(r'[^a-z0-9]', re.I) class CUDATargetContext(BaseContext): implement_powi_as_math_call = True strict_alignment = True def __init__(self, typingctx, target='cuda'): super().__init__(typingctx, target) self.data_model_manager = cuda_data_manager.chain( datamodel.default_manager ) @property def DIBuilder(self): return debuginfo.DIBuilder @property def enable_boundscheck(self): # Unconditionally disabled return False # Overrides def create_module(self, name): return self._internal_codegen._create_empty_module(name) def init(self): self._internal_codegen = codegen.JITCUDACodegen("numba.cuda.jit") self._target_data = None def load_additional_registries(self): # side effect of import needed for numba.cpython.*, the builtins # registry is updated at import time. from numba.cpython import numbers, tupleobj, slicing # noqa: F401 from numba.cpython import rangeobj, iterators, enumimpl # noqa: F401 from numba.cpython import unicode, charseq # noqa: F401 from numba.cpython import cmathimpl from numba.misc import cffiimpl from numba.np import arrayobj # noqa: F401 from numba.np import npdatetime # noqa: F401 from . import ( cudaimpl, printimpl, libdeviceimpl, mathimpl, vector_types ) # fix for #8940 from numba.np.unsafe import ndarray # noqa F401 self.install_registry(cudaimpl.registry) self.install_registry(cffiimpl.registry) self.install_registry(printimpl.registry) self.install_registry(libdeviceimpl.registry) self.install_registry(cmathimpl.registry) self.install_registry(mathimpl.registry) self.install_registry(vector_types.impl_registry) def codegen(self): return self._internal_codegen @property def target_data(self): if self._target_data is None: self._target_data = ll.create_target_data(nvvm.NVVM().data_layout) return self._target_data @cached_property def nonconst_module_attrs(self): """ Some CUDA intrinsics are at the module level, but cannot be treated as constants, because they are loaded from a special register in the PTX. These include threadIdx, blockDim, etc. """ from numba import cuda nonconsts = ('threadIdx', 'blockDim', 'blockIdx', 'gridDim', 'laneid', 'warpsize') nonconsts_with_mod = tuple([(types.Module(cuda), nc) for nc in nonconsts]) return nonconsts_with_mod @cached_property def call_conv(self): return CUDACallConv(self) def mangler(self, name, argtypes, *, abi_tags=(), uid=None): return itanium_mangler.mangle(name, argtypes, abi_tags=abi_tags, uid=uid) def prepare_cuda_kernel(self, codelib, fndesc, debug, lineinfo, nvvm_options, filename, linenum, max_registers=None): """ Adapt a code library ``codelib`` with the numba compiled CUDA kernel with name ``fname`` and arguments ``argtypes`` for NVVM. A new library is created with a wrapper function that can be used as the kernel entry point for the given kernel. Returns the new code library and the wrapper function. Parameters: codelib: The CodeLibrary containing the device function to wrap in a kernel call. fndesc: The FunctionDescriptor of the source function. debug: Whether to compile with debug. lineinfo: Whether to emit line info. nvvm_options: Dict of NVVM options used when compiling the new library. filename: The source filename that the function is contained in. linenum: The source line that the function is on. max_registers: The max_registers argument for the code library. """ kernel_name = itanium_mangler.prepend_namespace( fndesc.llvm_func_name, ns='cudapy', ) library = self.codegen().create_library(f'{codelib.name}_kernel_', entry_name=kernel_name, nvvm_options=nvvm_options, max_registers=max_registers) library.add_linking_library(codelib) wrapper = self.generate_kernel_wrapper(library, fndesc, kernel_name, debug, lineinfo, filename, linenum) return library, wrapper def generate_kernel_wrapper(self, library, fndesc, kernel_name, debug, lineinfo, filename, linenum): """ Generate the kernel wrapper in the given ``library``. The function being wrapped is described by ``fndesc``. The wrapper function is returned. """ argtypes = fndesc.argtypes arginfo = self.get_arg_packer(argtypes) argtys = list(arginfo.argument_types) wrapfnty = ir.FunctionType(ir.VoidType(), argtys) wrapper_module = self.create_module("cuda.kernel.wrapper") fnty = ir.FunctionType(ir.IntType(32), [self.call_conv.get_return_type(types.pyobject)] + argtys) func = ir.Function(wrapper_module, fnty, fndesc.llvm_func_name) prefixed = itanium_mangler.prepend_namespace(func.name, ns='cudapy') wrapfn = ir.Function(wrapper_module, wrapfnty, prefixed) builder = ir.IRBuilder(wrapfn.append_basic_block('')) if debug or lineinfo: directives_only = lineinfo and not debug debuginfo = self.DIBuilder(module=wrapper_module, filepath=filename, cgctx=self, directives_only=directives_only) debuginfo.mark_subprogram( wrapfn, kernel_name, fndesc.args, argtypes, linenum, ) debuginfo.mark_location(builder, linenum) # Define error handling variable def define_error_gv(postfix): name = wrapfn.name + postfix gv = cgutils.add_global_variable(wrapper_module, ir.IntType(32), name) gv.initializer = ir.Constant(gv.type.pointee, None) return gv gv_exc = define_error_gv("__errcode__") gv_tid = [] gv_ctaid = [] for i in 'xyz': gv_tid.append(define_error_gv("__tid%s__" % i)) gv_ctaid.append(define_error_gv("__ctaid%s__" % i)) callargs = arginfo.from_arguments(builder, wrapfn.args) status, _ = self.call_conv.call_function( builder, func, types.void, argtypes, callargs) if debug: # Check error status with cgutils.if_likely(builder, status.is_ok): builder.ret_void() with builder.if_then(builder.not_(status.is_python_exc)): # User exception raised old = ir.Constant(gv_exc.type.pointee, None) # Use atomic cmpxchg to prevent rewriting the error status # Only the first error is recorded xchg = builder.cmpxchg(gv_exc, old, status.code, 'monotonic', 'monotonic') changed = builder.extract_value(xchg, 1) # If the xchange is successful, save the thread ID. sreg = nvvmutils.SRegBuilder(builder) with builder.if_then(changed): for dim, ptr, in zip("xyz", gv_tid): val = sreg.tid(dim) builder.store(val, ptr) for dim, ptr, in zip("xyz", gv_ctaid): val = sreg.ctaid(dim) builder.store(val, ptr) builder.ret_void() nvvm.set_cuda_kernel(wrapfn) library.add_ir_module(wrapper_module) if debug or lineinfo: debuginfo.finalize() library.finalize() if config.DUMP_LLVM: utils.dump_llvm(fndesc, wrapper_module) return library.get_function(wrapfn.name) def make_constant_array(self, builder, aryty, arr): """ Unlike the parent version. This returns a a pointer in the constant addrspace. """ lmod = builder.module constvals = [ self.get_constant(types.byte, i) for i in iter(arr.tobytes(order='A')) ] constaryty = ir.ArrayType(ir.IntType(8), len(constvals)) constary = ir.Constant(constaryty, constvals) addrspace = nvvm.ADDRSPACE_CONSTANT gv = cgutils.add_global_variable(lmod, constary.type, "_cudapy_cmem", addrspace=addrspace) gv.linkage = 'internal' gv.global_constant = True gv.initializer = constary # Preserve the underlying alignment lldtype = self.get_data_type(aryty.dtype) align = self.get_abi_sizeof(lldtype) gv.align = 2 ** (align - 1).bit_length() # Convert to generic address-space ptrty = ir.PointerType(ir.IntType(8)) genptr = builder.addrspacecast(gv, ptrty, 'generic') # Create array object ary = self.make_array(aryty)(self, builder) kshape = [self.get_constant(types.intp, s) for s in arr.shape] kstrides = [self.get_constant(types.intp, s) for s in arr.strides] self.populate_array(ary, data=builder.bitcast(genptr, ary.data.type), shape=kshape, strides=kstrides, itemsize=ary.itemsize, parent=ary.parent, meminfo=None) return ary._getvalue() def insert_const_string(self, mod, string): """ Unlike the parent version. This returns a a pointer in the constant addrspace. """ text = cgutils.make_bytearray(string.encode("utf-8") + b"\x00") name = '$'.join(["__conststring__", itanium_mangler.mangle_identifier(string)]) # Try to reuse existing global gv = mod.globals.get(name) if gv is None: # Not defined yet gv = cgutils.add_global_variable(mod, text.type, name, addrspace=nvvm.ADDRSPACE_CONSTANT) gv.linkage = 'internal' gv.global_constant = True gv.initializer = text # Cast to a i8* pointer charty = gv.type.pointee.element return gv.bitcast(charty.as_pointer(nvvm.ADDRSPACE_CONSTANT)) def insert_string_const_addrspace(self, builder, string): """ Insert a constant string in the constant addresspace and return a generic i8 pointer to the data. This function attempts to deduplicate. """ lmod = builder.module gv = self.insert_const_string(lmod, string) charptrty = ir.PointerType(ir.IntType(8)) return builder.addrspacecast(gv, charptrty, 'generic') def optimize_function(self, func): """Run O1 function passes """ pass ## XXX skipped for now # fpm = lp.FunctionPassManager.new(func.module) # # lp.PassManagerBuilder.new().populate(fpm) # # fpm.initialize() # fpm.run(func) # fpm.finalize() def get_ufunc_info(self, ufunc_key): return ufuncs.get_ufunc_info(ufunc_key) class CUDACallConv(MinimalCallConv): pass class CUDACABICallConv(BaseCallConv): """ Calling convention aimed at matching the CUDA C/C++ ABI. The implemented function signature is: () Exceptions are unsupported in this convention. """ def _make_call_helper(self, builder): # Call helpers are used to help report exceptions back to Python, so # none is required here. return None def return_value(self, builder, retval): return builder.ret(retval) def return_user_exc(self, builder, exc, exc_args=None, loc=None, func_name=None): msg = "Python exceptions are unsupported in the CUDA C/C++ ABI" raise NotImplementedError(msg) def return_status_propagate(self, builder, status): msg = "Return status is unsupported in the CUDA C/C++ ABI" raise NotImplementedError(msg) def get_function_type(self, restype, argtypes): """ Get the LLVM IR Function type for *restype* and *argtypes*. """ arginfo = self._get_arg_packer(argtypes) argtypes = list(arginfo.argument_types) fnty = ir.FunctionType(self.get_return_type(restype), argtypes) return fnty def decorate_function(self, fn, args, fe_argtypes, noalias=False): """ Set names and attributes of function arguments. """ assert not noalias arginfo = self._get_arg_packer(fe_argtypes) arginfo.assign_names(self.get_arguments(fn), ['arg.' + a for a in args]) def get_arguments(self, func): """ Get the Python-level arguments of LLVM *func*. """ return func.args def call_function(self, builder, callee, resty, argtys, args): """ Call the Numba-compiled *callee*. """ arginfo = self._get_arg_packer(argtys) realargs = arginfo.as_arguments(builder, args) code = builder.call(callee, realargs) # No status required as we don't support exceptions or a distinct None # value in a C ABI. status = None out = self.context.get_returned_value(builder, resty, code) return status, out def get_return_type(self, ty): return self.context.data_model_manager[ty].get_return_type()