def test1(self): typingctx = typing.Context() targetctx = cpu.CPUContext(typingctx) test_ir = compiler.run_frontend(test_will_propagate) #print("Num blocks = ", len(test_ir.blocks)) #print(test_ir.dump()) with cpu_target.nested_context(typingctx, targetctx): typingctx.refresh() targetctx.refresh() args = (types.int64, types.int64, types.int64) typemap, return_type, calltypes = compiler.type_inference_stage(typingctx, test_ir, args, None) #print("typemap = ", typemap) #print("return_type = ", return_type) type_annotation = type_annotations.TypeAnnotation( func_ir=test_ir, typemap=typemap, calltypes=calltypes, lifted=(), lifted_from=None, args=args, return_type=return_type, html_output=config.HTML) remove_dels(test_ir.blocks) in_cps, out_cps = copy_propagate(test_ir.blocks, typemap) apply_copy_propagate(test_ir.blocks, in_cps, get_name_var_table(test_ir.blocks), typemap, calltypes) remove_dead(test_ir.blocks, test_ir.arg_names, test_ir) self.assertFalse(findLhsAssign(test_ir, "x"))
def _stencil_wrapper(self, result, sigret, return_type, typemap, calltypes, *args): # Overall approach: # 1) Construct a string containing a function definition for the stencil function # that will execute the stencil kernel. This function definition includes a # unique stencil function name, the parameters to the stencil kernel, loop # nests across the dimenions of the input array. Those loop nests use the # computed stencil kernel size so as not to try to compute elements where # elements outside the bounds of the input array would be needed. # 2) The but of the loop nest in this new function is a special sentinel # assignment. # 3) Get the IR of this new function. # 4) Split the block containing the sentinel assignment and remove the sentinel # assignment. Insert the stencil kernel IR into the stencil function IR # after label and variable renaming of the stencil kernel IR to prevent # conflicts with the stencil function IR. # 5) Compile the combined stencil function IR + stencil kernel IR into existence. # Copy the kernel so that our changes for this callsite # won't effect other callsites. (kernel_copy, copy_calltypes) = self.copy_ir_with_calltypes(self.kernel_ir, calltypes) # The stencil kernel body becomes the body of a loop, for which args aren't needed. ir_utils.remove_args(kernel_copy.blocks) first_arg = kernel_copy.arg_names[0] in_cps, out_cps = ir_utils.copy_propagate(kernel_copy.blocks, typemap) name_var_table = ir_utils.get_name_var_table(kernel_copy.blocks) ir_utils.apply_copy_propagate(kernel_copy.blocks, in_cps, name_var_table, typemap, copy_calltypes) if "out" in name_var_table: raise ValueError( "Cannot use the reserved word 'out' in stencil kernels.") sentinel_name = ir_utils.get_unused_var_name("__sentinel__", name_var_table) if config.DEBUG_ARRAY_OPT == 1: print("name_var_table", name_var_table, sentinel_name) the_array = args[0] if config.DEBUG_ARRAY_OPT == 1: print("_stencil_wrapper", return_type, return_type.dtype, type(return_type.dtype), args) ir_utils.dump_blocks(kernel_copy.blocks) # We generate a Numba function to execute this stencil and here # create the unique name of this function. stencil_func_name = "__numba_stencil_%s_%s" % (hex( id(the_array)).replace("-", "_"), self.id) # We will put a loop nest in the generated function for each # dimension in the input array. Here we create the name for # the index variable for each dimension. index0, index1, ... index_vars = [] for i in range(the_array.ndim): index_var_name = ir_utils.get_unused_var_name( "index" + str(i), name_var_table) index_vars += [index_var_name] # Create extra signature for out and neighborhood. out_name = ir_utils.get_unused_var_name("out", name_var_table) neighborhood_name = ir_utils.get_unused_var_name( "neighborhood", name_var_table) sig_extra = "" if result is not None: sig_extra += ", {}=None".format(out_name) if "neighborhood" in dict(self.kws): sig_extra += ", {}=None".format(neighborhood_name) # Get a list of the standard indexed array names. standard_indexed = self.options.get("standard_indexing", []) if first_arg in standard_indexed: raise ValueError("The first argument to a stencil kernel must " "use relative indexing, not standard indexing.") if len(set(standard_indexed) - set(kernel_copy.arg_names)) != 0: raise ValueError("Standard indexing requested for an array name " "not present in the stencil kernel definition.") # Add index variables to getitems in the IR to transition the accesses # in the kernel from relative to regular Python indexing. Returns the # computed size of the stencil kernel and a list of the relatively indexed # arrays. kernel_size, relatively_indexed = self.add_indices_to_kernel( kernel_copy, index_vars, the_array.ndim, self.neighborhood, standard_indexed) if self.neighborhood is None: self.neighborhood = kernel_size if config.DEBUG_ARRAY_OPT == 1: print("After add_indices_to_kernel") ir_utils.dump_blocks(kernel_copy.blocks) # The return in the stencil kernel becomes a setitem for that # particular point in the iteration space. ret_blocks = self.replace_return_with_setitem(kernel_copy.blocks, index_vars, out_name) if config.DEBUG_ARRAY_OPT == 1: print("After replace_return_with_setitem", ret_blocks) ir_utils.dump_blocks(kernel_copy.blocks) # Start to form the new function to execute the stencil kernel. func_text = "def {}({}{}):\n".format(stencil_func_name, ",".join(kernel_copy.arg_names), sig_extra) # Get loop ranges for each dimension, which could be either int # or variable. In the latter case we'll use the extra neighborhood # argument to the function. ranges = [] for i in range(the_array.ndim): if isinstance(kernel_size[i][0], int): lo = kernel_size[i][0] hi = kernel_size[i][1] else: lo = "{}[{}][0]".format(neighborhood_name, i) hi = "{}[{}][1]".format(neighborhood_name, i) ranges.append((lo, hi)) # If there are more than one relatively indexed arrays, add a call to # a function that will raise an error if any of the relatively indexed # arrays are of different size than the first input array. if len(relatively_indexed) > 1: func_text += " raise_if_incompatible_array_sizes(" + first_arg for other_array in relatively_indexed: if other_array != first_arg: func_text += "," + other_array func_text += ")\n" # Get the shape of the first input array. shape_name = ir_utils.get_unused_var_name("full_shape", name_var_table) func_text += " {} = {}.shape\n".format(shape_name, first_arg) # If we have to allocate the output array (the out argument was not used) # then us numpy.full if the user specified a cval stencil decorator option # or np.zeros if they didn't to allocate the array. if result is None: return_type_name = numpy_support.as_dtype( return_type.dtype).type.__name__ if "cval" in self.options: cval = self.options["cval"] if return_type.dtype != typing.typeof.typeof(cval): raise ValueError( "cval type does not match stencil return type.") out_init = "{} = np.full({}, {}, dtype=np.{})\n".format( out_name, shape_name, cval, return_type_name) else: out_init = "{} = np.zeros({}, dtype=np.{})\n".format( out_name, shape_name, return_type_name) func_text += " " + out_init offset = 1 # Add the loop nests to the new function. for i in range(the_array.ndim): for j in range(offset): func_text += " " # ranges[i][0] is the minimum index used in the i'th dimension # but minimum's greater than 0 don't preclude any entry in the array. # So, take the minimum of 0 and the minimum index found in the kernel # and this will be a negative number (potentially -0). Then, we do # unary - on that to get the positive offset in this dimension whose # use is precluded. # ranges[i][1] is the maximum of 0 and the observed maximum index # in this dimension because negative maximums would not cause us to # preclude any entry in the array from being used. func_text += ("for {} in range(-min(0,{})," "{}[{}]-max(0,{})):\n").format( index_vars[i], ranges[i][0], shape_name, i, ranges[i][1]) offset += 1 for j in range(offset): func_text += " " # Put a sentinel in the code so we can locate it in the IR. We will # remove this sentinel assignment and replace it with the IR for the # stencil kernel body. func_text += "{} = 0\n".format(sentinel_name) func_text += " return {}\n".format(out_name) if config.DEBUG_ARRAY_OPT == 1: print("new stencil func text") print(func_text) # Force the new stencil function into existence. exec_(func_text) in globals(), locals() stencil_func = eval(stencil_func_name) if sigret is not None: pysig = utils.pysignature(stencil_func) sigret.pysig = pysig # Get the IR for the newly created stencil function. stencil_ir = compiler.run_frontend(stencil_func) ir_utils.remove_dels(stencil_ir.blocks) # rename all variables in stencil_ir afresh var_table = ir_utils.get_name_var_table(stencil_ir.blocks) new_var_dict = {} reserved_names = ( [sentinel_name, out_name, neighborhood_name, shape_name] + kernel_copy.arg_names + index_vars) for name, var in var_table.items(): if not name in reserved_names: new_var_dict[name] = ir_utils.mk_unique_var(name) ir_utils.replace_var_names(stencil_ir.blocks, new_var_dict) stencil_stub_last_label = max(stencil_ir.blocks.keys()) + 1 # Shift lables in the kernel copy so they are guaranteed unique # and don't conflict with any labels in the stencil_ir. kernel_copy.blocks = ir_utils.add_offset_to_labels( kernel_copy.blocks, stencil_stub_last_label) new_label = max(kernel_copy.blocks.keys()) + 1 # Adjust ret_blocks to account for addition of the offset. ret_blocks = [x + stencil_stub_last_label for x in ret_blocks] if config.DEBUG_ARRAY_OPT == 1: print("ret_blocks w/ offsets", ret_blocks, stencil_stub_last_label) print("before replace sentinel stencil_ir") ir_utils.dump_blocks(stencil_ir.blocks) print("before replace sentinel kernel_copy") ir_utils.dump_blocks(kernel_copy.blocks) # Search all the block in the stencil outline for the sentinel. for label, block in stencil_ir.blocks.items(): for i, inst in enumerate(block.body): if (isinstance(inst, ir.Assign) and inst.target.name == sentinel_name): # We found the sentinel assignment. loc = inst.loc scope = block.scope # split block across __sentinel__ # A new block is allocated for the statements prior to the # sentinel but the new block maintains the current block # label. prev_block = ir.Block(scope, loc) prev_block.body = block.body[:i] # The current block is used for statements after sentinel. block.body = block.body[i + 1:] # But the current block gets a new label. body_first_label = min(kernel_copy.blocks.keys()) # The previous block jumps to the minimum labelled block of # the parfor body. prev_block.append(ir.Jump(body_first_label, loc)) # Add all the parfor loop body blocks to the gufunc # function's IR. for (l, b) in kernel_copy.blocks.items(): stencil_ir.blocks[l] = b stencil_ir.blocks[new_label] = block stencil_ir.blocks[label] = prev_block # Add a jump from all the blocks that previously contained # a return in the stencil kernel to the block # containing statements after the sentinel. for ret_block in ret_blocks: stencil_ir.blocks[ret_block].append( ir.Jump(new_label, loc)) break else: continue break stencil_ir.blocks = ir_utils.rename_labels(stencil_ir.blocks) ir_utils.remove_dels(stencil_ir.blocks) assert (isinstance(the_array, types.Type)) array_types = args new_stencil_param_types = list(array_types) if config.DEBUG_ARRAY_OPT == 1: print("new_stencil_param_types", new_stencil_param_types) ir_utils.dump_blocks(stencil_ir.blocks) # Compile the combined stencil function with the replaced loop # body in it. new_func = compiler.compile_ir(self._typingctx, self._targetctx, stencil_ir, new_stencil_param_types, None, compiler.DEFAULT_FLAGS, {}) return new_func
def _mk_stencil_parfor(self, label, in_args, out_arr, stencil_ir, index_offsets, target, return_type, stencil_func, arg_to_arr_dict): """ Converts a set of stencil kernel blocks to a parfor. """ gen_nodes = [] stencil_blocks = stencil_ir.blocks if config.DEBUG_ARRAY_OPT == 1: print("_mk_stencil_parfor", label, in_args, out_arr, index_offsets, return_type, stencil_func, stencil_blocks) ir_utils.dump_blocks(stencil_blocks) in_arr = in_args[0] # run copy propagate to replace in_args copies (e.g. a = A) in_arr_typ = self.typemap[in_arr.name] in_cps, out_cps = ir_utils.copy_propagate(stencil_blocks, self.typemap) name_var_table = ir_utils.get_name_var_table(stencil_blocks) ir_utils.apply_copy_propagate( stencil_blocks, in_cps, name_var_table, self.typemap, self.calltypes) if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after copy_propagate") ir_utils.dump_blocks(stencil_blocks) ir_utils.remove_dead(stencil_blocks, self.func_ir.arg_names, stencil_ir, self.typemap) if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after removing dead code") ir_utils.dump_blocks(stencil_blocks) # create parfor vars ndims = self.typemap[in_arr.name].ndim scope = in_arr.scope loc = in_arr.loc parfor_vars = [] for i in range(ndims): parfor_var = ir.Var(scope, mk_unique_var( "$parfor_index_var"), loc) self.typemap[parfor_var.name] = types.intp parfor_vars.append(parfor_var) start_lengths, end_lengths = self._replace_stencil_accesses( stencil_blocks, parfor_vars, in_args, index_offsets, stencil_func, arg_to_arr_dict) if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after replace stencil accesses") ir_utils.dump_blocks(stencil_blocks) # create parfor loop nests loopnests = [] equiv_set = self.array_analysis.get_equiv_set(label) in_arr_dim_sizes = equiv_set.get_shape(in_arr) assert ndims == len(in_arr_dim_sizes) for i in range(ndims): last_ind = self._get_stencil_last_ind(in_arr_dim_sizes[i], end_lengths[i], gen_nodes, scope, loc) start_ind = self._get_stencil_start_ind( start_lengths[i], gen_nodes, scope, loc) # start from stencil size to avoid invalid array access loopnests.append(numba.parfor.LoopNest(parfor_vars[i], start_ind, last_ind, 1)) # We have to guarantee that the exit block has maximum label and that # there's only one exit block for the parfor body. # So, all return statements will change to jump to the parfor exit block. parfor_body_exit_label = max(stencil_blocks.keys()) + 1 stencil_blocks[parfor_body_exit_label] = ir.Block(scope, loc) exit_value_var = ir.Var(scope, mk_unique_var("$parfor_exit_value"), loc) self.typemap[exit_value_var.name] = return_type.dtype # create parfor index var for_replacing_ret = [] if ndims == 1: parfor_ind_var = parfor_vars[0] else: parfor_ind_var = ir.Var(scope, mk_unique_var( "$parfor_index_tuple_var"), loc) self.typemap[parfor_ind_var.name] = types.containers.UniTuple( types.intp, ndims) tuple_call = ir.Expr.build_tuple(parfor_vars, loc) tuple_assign = ir.Assign(tuple_call, parfor_ind_var, loc) for_replacing_ret.append(tuple_assign) if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after creating parfor index var") ir_utils.dump_blocks(stencil_blocks) # empty init block init_block = ir.Block(scope, loc) if out_arr == None: in_arr_typ = self.typemap[in_arr.name] shape_name = ir_utils.mk_unique_var("in_arr_shape") shape_var = ir.Var(scope, shape_name, loc) shape_getattr = ir.Expr.getattr(in_arr, "shape", loc) self.typemap[shape_name] = types.containers.UniTuple(types.intp, in_arr_typ.ndim) init_block.body.extend([ir.Assign(shape_getattr, shape_var, loc)]) zero_name = ir_utils.mk_unique_var("zero_val") zero_var = ir.Var(scope, zero_name, loc) if "cval" in stencil_func.options: cval = stencil_func.options["cval"] # TODO: Loosen this restriction to adhere to casting rules. if return_type.dtype != typing.typeof.typeof(cval): raise ValueError("cval type does not match stencil return type.") temp2 = return_type.dtype(cval) else: temp2 = return_type.dtype(0) full_const = ir.Const(temp2, loc) self.typemap[zero_name] = return_type.dtype init_block.body.extend([ir.Assign(full_const, zero_var, loc)]) so_name = ir_utils.mk_unique_var("stencil_output") out_arr = ir.Var(scope, so_name, loc) self.typemap[out_arr.name] = numba.types.npytypes.Array( return_type.dtype, in_arr_typ.ndim, in_arr_typ.layout) dtype_g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc) self.typemap[dtype_g_np_var.name] = types.misc.Module(np) dtype_g_np = ir.Global('np', np, loc) dtype_g_np_assign = ir.Assign(dtype_g_np, dtype_g_np_var, loc) init_block.body.append(dtype_g_np_assign) dtype_np_attr_call = ir.Expr.getattr(dtype_g_np_var, return_type.dtype.name, loc) dtype_attr_var = ir.Var(scope, mk_unique_var("$np_attr_attr"), loc) self.typemap[dtype_attr_var.name] = types.functions.NumberClass(return_type.dtype) dtype_attr_assign = ir.Assign(dtype_np_attr_call, dtype_attr_var, loc) init_block.body.append(dtype_attr_assign) stmts = ir_utils.gen_np_call("full", np.full, out_arr, [shape_var, zero_var, dtype_attr_var], self.typingctx, self.typemap, self.calltypes) equiv_set.insert_equiv(out_arr, in_arr_dim_sizes) init_block.body.extend(stmts) self.replace_return_with_setitem(stencil_blocks, exit_value_var, parfor_body_exit_label) if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after replacing return") ir_utils.dump_blocks(stencil_blocks) setitem_call = ir.SetItem(out_arr, parfor_ind_var, exit_value_var, loc) self.calltypes[setitem_call] = signature( types.none, self.typemap[out_arr.name], self.typemap[parfor_ind_var.name], self.typemap[out_arr.name].dtype ) stencil_blocks[parfor_body_exit_label].body.extend(for_replacing_ret) stencil_blocks[parfor_body_exit_label].body.append(setitem_call) # simplify CFG of parfor body (exit block could be simplified often) # add dummy return to enable CFG stencil_blocks[parfor_body_exit_label].body.append(ir.Return(0, ir.Loc("stencilparfor_dummy", -1))) stencil_blocks = ir_utils.simplify_CFG(stencil_blocks) stencil_blocks[max(stencil_blocks.keys())].body.pop() if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after adding SetItem") ir_utils.dump_blocks(stencil_blocks) pattern = ('stencil', [start_lengths, end_lengths]) parfor = numba.parfor.Parfor(loopnests, init_block, stencil_blocks, loc, parfor_ind_var, equiv_set, pattern, self.flags) gen_nodes.append(parfor) gen_nodes.append(ir.Assign(out_arr, target, loc)) return gen_nodes
def _mk_stencil_parfor(self, label, in_args, out_arr, stencil_blocks, index_offsets, target, return_type, stencil_func, arg_to_arr_dict): """ Converts a set of stencil kernel blocks to a parfor. """ gen_nodes = [] if config.DEBUG_ARRAY_OPT == 1: print("_mk_stencil_parfor", label, in_args, out_arr, index_offsets, return_type, stencil_func, stencil_blocks) ir_utils.dump_blocks(stencil_blocks) in_arr = in_args[0] # run copy propagate to replace in_args copies (e.g. a = A) in_arr_typ = self.typemap[in_arr.name] in_cps, out_cps = ir_utils.copy_propagate(stencil_blocks, self.typemap) name_var_table = ir_utils.get_name_var_table(stencil_blocks) ir_utils.apply_copy_propagate(stencil_blocks, in_cps, name_var_table, self.typemap, self.calltypes) if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after copy_propagate") ir_utils.dump_blocks(stencil_blocks) ir_utils.remove_dead(stencil_blocks, self.func_ir.arg_names, self.typemap) if config.DEBUG_ARRAY_OPT == 1: print("stencil_blocks after removing dead code") ir_utils.dump_blocks(stencil_blocks) # create parfor vars ndims = self.typemap[in_arr.name].ndim scope = in_arr.scope loc = in_arr.loc parfor_vars = [] for i in range(ndims): parfor_var = ir.Var(scope, mk_unique_var("$parfor_index_var"), loc) self.typemap[parfor_var.name] = types.intp parfor_vars.append(parfor_var) start_lengths, end_lengths = self._replace_stencil_accesses( stencil_blocks, parfor_vars, in_args, index_offsets, stencil_func, arg_to_arr_dict) # create parfor loop nests loopnests = [] equiv_set = self.array_analysis.get_equiv_set(label) in_arr_dim_sizes = equiv_set.get_shape(in_arr.name) assert ndims == len(in_arr_dim_sizes) for i in range(ndims): last_ind = self._get_stencil_last_ind(in_arr_dim_sizes[i], end_lengths[i], gen_nodes, scope, loc) start_ind = self._get_stencil_start_ind(start_lengths[i], gen_nodes, scope, loc) # start from stencil size to avoid invalid array access loopnests.append( numba.parfor.LoopNest(parfor_vars[i], start_ind, last_ind, 1)) # replace return value to setitem to output array return_node = stencil_blocks[max(stencil_blocks.keys())].body.pop() assert isinstance(return_node, ir.Return) last_node = stencil_blocks[max(stencil_blocks.keys())].body.pop() while not isinstance(last_node, ir.Assign) or not isinstance( last_node.value, ir.Expr) or not last_node.value.op == 'cast': last_node = stencil_blocks[max(stencil_blocks.keys())].body.pop() assert isinstance(last_node, ir.Assign) assert isinstance(last_node.value, ir.Expr) assert last_node.value.op == 'cast' return_val = last_node.value.value # create parfor index var if ndims == 1: parfor_ind_var = parfor_vars[0] else: parfor_ind_var = ir.Var(scope, mk_unique_var("$parfor_index_tuple_var"), loc) self.typemap[parfor_ind_var.name] = types.containers.UniTuple( types.intp, ndims) tuple_call = ir.Expr.build_tuple(parfor_vars, loc) tuple_assign = ir.Assign(tuple_call, parfor_ind_var, loc) stencil_blocks[max( stencil_blocks.keys())].body.append(tuple_assign) # empty init block init_block = ir.Block(scope, loc) if out_arr == None: in_arr_typ = self.typemap[in_arr.name] shape_name = ir_utils.mk_unique_var("in_arr_shape") shape_var = ir.Var(scope, shape_name, loc) shape_getattr = ir.Expr.getattr(in_arr, "shape", loc) self.typemap[shape_name] = types.containers.UniTuple( types.intp, in_arr_typ.ndim) init_block.body.extend([ir.Assign(shape_getattr, shape_var, loc)]) zero_name = ir_utils.mk_unique_var("zero_val") zero_var = ir.Var(scope, zero_name, loc) if "cval" in stencil_func.options: cval = stencil_func.options["cval"] # TODO: Loosen this restriction to adhere to casting rules. if return_type.dtype != typing.typeof.typeof(cval): raise ValueError( "cval type does not match stencil return type.") temp2 = return_type.dtype(cval) else: temp2 = return_type.dtype(0) full_const = ir.Const(temp2, loc) self.typemap[zero_name] = return_type.dtype init_block.body.extend([ir.Assign(full_const, zero_var, loc)]) so_name = ir_utils.mk_unique_var("stencil_output") out_arr = ir.Var(scope, so_name, loc) self.typemap[out_arr.name] = numba.types.npytypes.Array( return_type.dtype, in_arr_typ.ndim, in_arr_typ.layout) dtype_g_np_var = ir.Var(scope, mk_unique_var("$np_g_var"), loc) self.typemap[dtype_g_np_var.name] = types.misc.Module(np) dtype_g_np = ir.Global('np', np, loc) dtype_g_np_assign = ir.Assign(dtype_g_np, dtype_g_np_var, loc) init_block.body.append(dtype_g_np_assign) dtype_np_attr_call = ir.Expr.getattr(dtype_g_np_var, return_type.dtype.name, loc) dtype_attr_var = ir.Var(scope, mk_unique_var("$np_attr_attr"), loc) self.typemap[dtype_attr_var.name] = types.functions.NumberClass( return_type.dtype) dtype_attr_assign = ir.Assign(dtype_np_attr_call, dtype_attr_var, loc) init_block.body.append(dtype_attr_assign) stmts = ir_utils.gen_np_call("full", np.full, out_arr, [shape_var, zero_var, dtype_attr_var], self.typingctx, self.typemap, self.calltypes) equiv_set.insert_equiv(out_arr, in_arr_dim_sizes) init_block.body.extend(stmts) setitem_call = ir.SetItem(out_arr, parfor_ind_var, return_val, loc) self.calltypes[setitem_call] = signature( types.none, self.typemap[out_arr.name], self.typemap[parfor_ind_var.name], self.typemap[out_arr.name].dtype) stencil_blocks[max(stencil_blocks.keys())].body.append(setitem_call) parfor = numba.parfor.Parfor(loopnests, init_block, stencil_blocks, loc, parfor_ind_var, equiv_set) parfor.patterns = [('stencil', [start_lengths, end_lengths])] gen_nodes.append(parfor) gen_nodes.append(ir.Assign(out_arr, target, loc)) return gen_nodes
def _stencil_wrapper(self, result, sigret, return_type, typemap, calltypes, *args): # Overall approach: # 1) Construct a string containing a function definition for the stencil function # that will execute the stencil kernel. This function definition includes a # unique stencil function name, the parameters to the stencil kernel, loop # nests across the dimenions of the input array. Those loop nests use the # computed stencil kernel size so as not to try to compute elements where # elements outside the bounds of the input array would be needed. # 2) The but of the loop nest in this new function is a special sentinel # assignment. # 3) Get the IR of this new function. # 4) Split the block containing the sentinel assignment and remove the sentinel # assignment. Insert the stencil kernel IR into the stencil function IR # after label and variable renaming of the stencil kernel IR to prevent # conflicts with the stencil function IR. # 5) Compile the combined stencil function IR + stencil kernel IR into existence. # Copy the kernel so that our changes for this callsite # won't effect other callsites. (kernel_copy, copy_calltypes) = self.copy_ir_with_calltypes( self.kernel_ir, calltypes) # The stencil kernel body becomes the body of a loop, for which args aren't needed. ir_utils.remove_args(kernel_copy.blocks) first_arg = kernel_copy.arg_names[0] in_cps, out_cps = ir_utils.copy_propagate(kernel_copy.blocks, typemap) name_var_table = ir_utils.get_name_var_table(kernel_copy.blocks) ir_utils.apply_copy_propagate( kernel_copy.blocks, in_cps, name_var_table, typemap, copy_calltypes) if "out" in name_var_table: raise ValueError("Cannot use the reserved word 'out' in stencil kernels.") sentinel_name = ir_utils.get_unused_var_name("__sentinel__", name_var_table) if config.DEBUG_ARRAY_OPT == 1: print("name_var_table", name_var_table, sentinel_name) the_array = args[0] if config.DEBUG_ARRAY_OPT == 1: print("_stencil_wrapper", return_type, return_type.dtype, type(return_type.dtype), args) ir_utils.dump_blocks(kernel_copy.blocks) # We generate a Numba function to execute this stencil and here # create the unique name of this function. stencil_func_name = "__numba_stencil_%s_%s" % ( hex(id(the_array)).replace("-", "_"), self.id) # We will put a loop nest in the generated function for each # dimension in the input array. Here we create the name for # the index variable for each dimension. index0, index1, ... index_vars = [] for i in range(the_array.ndim): index_var_name = ir_utils.get_unused_var_name("index" + str(i), name_var_table) index_vars += [index_var_name] # Create extra signature for out and neighborhood. out_name = ir_utils.get_unused_var_name("out", name_var_table) neighborhood_name = ir_utils.get_unused_var_name("neighborhood", name_var_table) sig_extra = "" if result is not None: sig_extra += ", {}=None".format(out_name) if "neighborhood" in dict(self.kws): sig_extra += ", {}=None".format(neighborhood_name) # Get a list of the standard indexed array names. standard_indexed = self.options.get("standard_indexing", []) if first_arg in standard_indexed: raise ValueError("The first argument to a stencil kernel must " "use relative indexing, not standard indexing.") if len(set(standard_indexed) - set(kernel_copy.arg_names)) != 0: raise ValueError("Standard indexing requested for an array name " "not present in the stencil kernel definition.") # Add index variables to getitems in the IR to transition the accesses # in the kernel from relative to regular Python indexing. Returns the # computed size of the stencil kernel and a list of the relatively indexed # arrays. kernel_size, relatively_indexed = self.add_indices_to_kernel( kernel_copy, index_vars, the_array.ndim, self.neighborhood, standard_indexed) if self.neighborhood is None: self.neighborhood = kernel_size if config.DEBUG_ARRAY_OPT == 1: print("After add_indices_to_kernel") ir_utils.dump_blocks(kernel_copy.blocks) # The return in the stencil kernel becomes a setitem for that # particular point in the iteration space. ret_blocks = self.replace_return_with_setitem(kernel_copy.blocks, index_vars, out_name) if config.DEBUG_ARRAY_OPT == 1: print("After replace_return_with_setitem", ret_blocks) ir_utils.dump_blocks(kernel_copy.blocks) # Start to form the new function to execute the stencil kernel. func_text = "def {}({}{}):\n".format(stencil_func_name, ",".join(kernel_copy.arg_names), sig_extra) # Get loop ranges for each dimension, which could be either int # or variable. In the latter case we'll use the extra neighborhood # argument to the function. ranges = [] for i in range(the_array.ndim): if isinstance(kernel_size[i][0], int): lo = kernel_size[i][0] hi = kernel_size[i][1] else: lo = "{}[{}][0]".format(neighborhood_name, i) hi = "{}[{}][1]".format(neighborhood_name, i) ranges.append((lo, hi)) # If there are more than one relatively indexed arrays, add a call to # a function that will raise an error if any of the relatively indexed # arrays are of different size than the first input array. if len(relatively_indexed) > 1: func_text += " raise_if_incompatible_array_sizes(" + first_arg for other_array in relatively_indexed: if other_array != first_arg: func_text += "," + other_array func_text += ")\n" # Get the shape of the first input array. shape_name = ir_utils.get_unused_var_name("full_shape", name_var_table) func_text += " {} = {}.shape\n".format(shape_name, first_arg) # If we have to allocate the output array (the out argument was not used) # then us numpy.full if the user specified a cval stencil decorator option # or np.zeros if they didn't to allocate the array. if result is None: if "cval" in self.options: cval = self.options["cval"] if return_type.dtype != typing.typeof.typeof(cval): raise ValueError( "cval type does not match stencil return type.") out_init ="{} = np.full({}, {}, dtype=np.{})\n".format( out_name, shape_name, cval, return_type.dtype) else: out_init ="{} = np.zeros({}, dtype=np.{})\n".format( out_name, shape_name, return_type.dtype) func_text += " " + out_init offset = 1 # Add the loop nests to the new function. for i in range(the_array.ndim): for j in range(offset): func_text += " " # ranges[i][0] is the minimum index used in the i'th dimension # but minimum's greater than 0 don't preclude any entry in the array. # So, take the minimum of 0 and the minimum index found in the kernel # and this will be a negative number (potentially -0). Then, we do # unary - on that to get the positive offset in this dimension whose # use is precluded. # ranges[i][1] is the maximum of 0 and the observed maximum index # in this dimension because negative maximums would not cause us to # preclude any entry in the array from being used. func_text += ("for {} in range(-min(0,{})," "{}[{}]-max(0,{})):\n").format( index_vars[i], ranges[i][0], shape_name, i, ranges[i][1]) offset += 1 for j in range(offset): func_text += " " # Put a sentinel in the code so we can locate it in the IR. We will # remove this sentinel assignment and replace it with the IR for the # stencil kernel body. func_text += "{} = 0\n".format(sentinel_name) func_text += " return {}\n".format(out_name) if config.DEBUG_ARRAY_OPT == 1: print("new stencil func text") print(func_text) # Force the new stencil function into existence. exec_(func_text) in globals(), locals() stencil_func = eval(stencil_func_name) if sigret is not None: pysig = utils.pysignature(stencil_func) sigret.pysig = pysig # Get the IR for the newly created stencil function. stencil_ir = compiler.run_frontend(stencil_func) ir_utils.remove_dels(stencil_ir.blocks) # rename all variables in stencil_ir afresh var_table = ir_utils.get_name_var_table(stencil_ir.blocks) new_var_dict = {} reserved_names = ([sentinel_name, out_name, neighborhood_name, shape_name] + kernel_copy.arg_names + index_vars) for name, var in var_table.items(): if not name in reserved_names: new_var_dict[name] = ir_utils.mk_unique_var(name) ir_utils.replace_var_names(stencil_ir.blocks, new_var_dict) stencil_stub_last_label = max(stencil_ir.blocks.keys()) + 1 # Shift lables in the kernel copy so they are guaranteed unique # and don't conflict with any labels in the stencil_ir. kernel_copy.blocks = ir_utils.add_offset_to_labels( kernel_copy.blocks, stencil_stub_last_label) new_label = max(kernel_copy.blocks.keys()) + 1 # Adjust ret_blocks to account for addition of the offset. ret_blocks = [x + stencil_stub_last_label for x in ret_blocks] if config.DEBUG_ARRAY_OPT == 1: print("ret_blocks w/ offsets", ret_blocks, stencil_stub_last_label) print("before replace sentinel stencil_ir") ir_utils.dump_blocks(stencil_ir.blocks) print("before replace sentinel kernel_copy") ir_utils.dump_blocks(kernel_copy.blocks) # Search all the block in the stencil outline for the sentinel. for label, block in stencil_ir.blocks.items(): for i, inst in enumerate(block.body): if (isinstance( inst, ir.Assign) and inst.target.name == sentinel_name): # We found the sentinel assignment. loc = inst.loc scope = block.scope # split block across __sentinel__ # A new block is allocated for the statements prior to the # sentinel but the new block maintains the current block # label. prev_block = ir.Block(scope, loc) prev_block.body = block.body[:i] # The current block is used for statements after sentinel. block.body = block.body[i + 1:] # But the current block gets a new label. body_first_label = min(kernel_copy.blocks.keys()) # The previous block jumps to the minimum labelled block of # the parfor body. prev_block.append(ir.Jump(body_first_label, loc)) # Add all the parfor loop body blocks to the gufunc # function's IR. for (l, b) in kernel_copy.blocks.items(): stencil_ir.blocks[l] = b stencil_ir.blocks[new_label] = block stencil_ir.blocks[label] = prev_block # Add a jump from all the blocks that previously contained # a return in the stencil kernel to the block # containing statements after the sentinel. for ret_block in ret_blocks: stencil_ir.blocks[ret_block].append( ir.Jump(new_label, loc)) break else: continue break stencil_ir.blocks = ir_utils.rename_labels(stencil_ir.blocks) ir_utils.remove_dels(stencil_ir.blocks) assert(isinstance(the_array, types.Type)) array_types = args new_stencil_param_types = list(array_types) if config.DEBUG_ARRAY_OPT == 1: print("new_stencil_param_types", new_stencil_param_types) ir_utils.dump_blocks(stencil_ir.blocks) # Compile the combined stencil function with the replaced loop # body in it. new_func = compiler.compile_ir( self._typingctx, self._targetctx, stencil_ir, new_stencil_param_types, None, compiler.DEFAULT_FLAGS, {}) return new_func