hlsfactory.harp.harp_graph

Module Contents

Classes

Node
Edge

Functions

create_dir_if_not_exists
atoi
natural_keys
get_root_path	Root directory for the upstream batch driver’s output tree.
create_pseudo_node_block
add_to_graph
copy_files	copy the generated files to the project directory
read_json_graph	reads a graph in json format as a netwrokx graph
get_harp_clang_bin	Resolve the clang used to emit ProGraML-compatible LLVM IR.
emit_llvm_ir	Compile {path}/{name}.{c,cpp,cc} to {path}/{name}.ll.
llvm_to_nx	reads a LLVM IR and converts it to a netwrokx graph
make_json_readable	gets a json file and beautifies it to make it readable
extract_function_names	extract the names of the function in c code along with their line number
get_tc_for_loop	get trip count of the for loop
get_icmp	gets an llvm file and returns the icmp instructions of each for loop
get_pragmas_loops	gets a c kernel and returns the pragmas of each for loop
create_pragma_nodes	creates nodes for each pragma to be added to the graph
prune_redundant_nodes
process_graph	adjusts the node/edge attributes, removes redundant nodes, and writes the final graph to be used by GNN-DSE args: name: kernel name dest: where to store the graph
graph_generator	runs ProGraML [ICML’21] to generate the graph, adds the pragma nodes, processes the final graph to be accepted by GNN-DSE args: name: kernel name path: path to parent directory of the kernel file benchmark: [machsuite|poly] None: simple program output_dir: destination for the processed graph, defaulting to the upstream batch-driver tree
get_for_blocks_info
augment_graph_hierarchy
add_auxiliary_nodes
remove_extra_header
write_csv_file
run_graph_gen

Data

PRAGMA_POSITION
BENCHMARK
type_graph
MACHSUITE_KERNEL
poly_KERNEL
ALL_KERNEL
HLSFACTORY_HARP_CLANG_ENV_VAR
DEFAULT_HARP_CLANG
SOURCE_EXTENSIONS
C_CONTROL_KEYWORDS

API

hlsfactory.harp.harp_graph.create_dir_if_not_exists(dir_path) → None

hlsfactory.harp.harp_graph.atoi(text)

hlsfactory.harp.harp_graph.natural_keys(text)

hlsfactory.harp.harp_graph.get_root_path() → str

Root directory for the upstream batch driver’s output tree.

Upstream resolves this to the HARP repository checkout. Only run_graph_gen and its helpers use it; the per-design entry point graph_generator does not. HLSFACTORY_HARP_ROOT overrides it.

hlsfactory.harp.harp_graph.PRAGMA_POSITION = None

hlsfactory.harp.harp_graph.BENCHMARK = 'poly'

hlsfactory.harp.harp_graph.type_graph = 'harp'

hlsfactory.harp.harp_graph.MACHSUITE_KERNEL = ['aes', 'gemm-blocked', 'gemm-ncubed', 'spmv-crs', 'spmv-ellpack', 'stencil_stencil2d', 'nw', 'md', ...

hlsfactory.harp.harp_graph.poly_KERNEL = ['2mm', '3mm', 'adi', 'atax', 'bicg', 'bicg-large', 'covariance', 'doitgen', 'doitgen-red', 'fdtd-2d...

hlsfactory.harp.harp_graph.ALL_KERNEL = None

class hlsfactory.harp.harp_graph.Node(block, function, text, type_n, features=None)

Initialization

get_attr(after_process=True)

args:

after_processTrue if nodes are added to existing GNN-DSE graphs

False for initial graph generation in GNN-DSE

class hlsfactory.harp.harp_graph.Edge(src, dst, flow, position)

Initialization

get_attr()

hlsfactory.harp.harp_graph.create_pseudo_node_block(block, function)

hlsfactory.harp.harp_graph.add_to_graph(g_nx, nodes, edges) → None

hlsfactory.harp.harp_graph.copy_files(name, src, dest) → None

copy the generated files to the project directory

args:

name: the kernel name src: the path to the files dest: where you want to copy the files

hlsfactory.harp.harp_graph.read_json_graph(name, readable=True)

reads a graph in json format as a netwrokx graph

args:

name: name of the json file/ kernel’s name reaable: whether to store a readable format of the json file

returns:

g_nx: graph in networkx format

hlsfactory.harp.harp_graph.HLSFACTORY_HARP_CLANG_ENV_VAR = 'HLSFACTORY_HARP_CLANG'

hlsfactory.harp.harp_graph.DEFAULT_HARP_CLANG = 'clang-14'

hlsfactory.harp.harp_graph.SOURCE_EXTENSIONS = ('.c', '.cpp', '.cc')

hlsfactory.harp.harp_graph.get_harp_clang_bin(clang_bin=None) → str

Resolve the clang used to emit ProGraML-compatible LLVM IR.

The llvm2graph binary bundled with programl is an LLVM 10 parser. It cannot read opaque pointers (clang >= 15) and rejects the noundef attribute (clang >= 12), so a clang no newer than 14 is required.

hlsfactory.harp.harp_graph.emit_llvm_ir(name, path, clang_bin=None, extra_args=None) → str

Compile {path}/{name}.{c,cpp,cc} to {path}/{name}.ll.

Replaces upstream HARP’s clang_script.sh, which is not vendored here. Two flags are required:

• -disable-noundef-analysis keeps the IR parseable by the LLVM 10 based llvm2graph bundled with programl.

• -fno-discard-value-names keeps basic blocks named (for.cond, for.body, …) instead of numbered. get_icmp locates loops by those names, and silently finds none without it.

Returns:

str: Path to the generated .ll file.

Raises:

FileNotFoundError: If no source file for name exists under path. RuntimeError: If clang fails.

hlsfactory.harp.harp_graph.llvm_to_nx(name)

reads a LLVM IR and converts it to a netwrokx graph

args:

name: name of the LLVM file/ kernel’s name

returns:

g_nx: graph in networkx format

hlsfactory.harp.harp_graph.make_json_readable(name, js_graph) → None

gets a json file and beautifies it to make it readable

args:

name: kernel name js_graph: the graph in networkx format read from the json file

writes:

a readable json file with name {name}_pretty.json

hlsfactory.harp.harp_graph.C_CONTROL_KEYWORDS = 'frozenset(...)'

hlsfactory.harp.harp_graph.extract_function_names(c_code)

extract the names of the function in c code along with their line number

args:

c_code: the c_code read with code.read()

return:

a list of tuples of (function name, line number)

hlsfactory.harp.harp_graph.get_tc_for_loop(for_loop_text)

get trip count of the for loop

hlsfactory.harp.harp_graph.get_icmp(path, name, log=False)

gets an llvm file and returns the icmp instructions of each for loop

args:

path: parent directory of the llvm file name: kernel name

returns:

a dictionary corresponding to the icmp instructions:

{for loop id: [icmp instruction, for.cond line number, icmp line number]}

number of for loops

hlsfactory.harp.harp_graph.get_pragmas_loops(path, name, EXT='c', log=False)

gets a c kernel and returns the pragmas of each for loop

args:

path: parent directory of the kernel file name: kernel name

returns:

a dictionary with each entry showing the for loop and its pragmas

{for loop id: [for loop source code, [list of pragmas]]}

number of for loops

hlsfactory.harp.harp_graph.create_pragma_nodes(g_nx, g_nx_nodes, for_dict_source, for_dict_llvm, log=True)

creates nodes for each pragma to be added to the graph

args:

g_nx: the graph object g_nx_nodes: number of nodes of the graph object for_dict_source: the for loops along with their pragmas for_dict_llvm: the for loops along with their icmp instruction in llvm

returns:

a list of nodes and a list of edges to be added to the graph

hlsfactory.harp.harp_graph.prune_redundant_nodes(g_new) → None

hlsfactory.harp.harp_graph.process_graph(name, g, csv_dict=None, output_dir=None) → None

adjusts the node/edge attributes, removes redundant nodes,

and writes the final graph to be used by GNN-DSE

args:

name: kernel name dest: where to store the graph

hlsfactory.harp.harp_graph.graph_generator(name, path, benchmark, generate_programl=False, csv_dict=None, output_dir=None) → None

runs ProGraML [ICML’21] to generate the graph, adds the pragma nodes,

processes the final graph to be accepted by GNN-DSE

args:

name: kernel name path: path to parent directory of the kernel file benchmark: [machsuite|poly] None: simple program output_dir: destination for the processed graph, defaulting to the upstream batch-driver tree

hlsfactory.harp.harp_graph.get_for_blocks_info(name, path)

hlsfactory.harp.harp_graph.augment_graph_hierarchy(name, for_blocks_info, src_path, dst_path, csv_dict=None, node_type='block') → None

hlsfactory.harp.harp_graph.add_auxiliary_nodes(name, path, processed_path, csv_dict, node_type='block', connected=False) → None

hlsfactory.harp.harp_graph.remove_extra_header(src_dir, kernel_name) → None

hlsfactory.harp.harp_graph.write_csv_file(csv_dict, csv_header, file_path) → None

hlsfactory.harp.harp_graph.run_graph_gen(mode='initial', connected=True, target=None, ALL_KERNEL=ALL_KERNEL) → None