from pathlib import Path from typing import Optional import pandas as pd -pd.set_option('display.max_rows', None) +pd.set_option("display.max_rows", None) def unmangle_helper(func_name: Optional[str]): from cpp_demangle import demangle + if func_name is None: return None if not func_name.startswith("_Z"): return func_name return demangle(func_name) def parse_elf(elf_path): from elftools.elf.elffile import ELFFile from elftools.elf.sections import SymbolTableSection + with open(elf_path, "rb") as f: elffile = ELFFile(f) section = elffile.get_section_by_name(".symtab")

return footprint_df def analyze_linker_map_helper(mapFile): from mapfile_parser import mapfile + ret = [] data = mapFile.toJson(humanReadable=False) # print("data", data) # input("!!!!") segments = data["segments"]

'mapfile_parser.mapfile' imported but unused (F401)

assert linker_map_file.is_file() mem_footprint_df = parse_elf(elf_file) from mapfile_parser import mapfile + mapFile = mapfile.MapFile() mapFile.readMapFile(linker_map_file) symbol_map = analyze_linker_map_helper(mapFile) symbol_map_df = pd.DataFrame(

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remote_program = workdir / program.name self.copy_to_remote(ssh, program, remote_program) args_str = " ".join(args) qemu = kwargs.get("qemu") pre = qemu if qemu is not None else "" - command = f"cd {workdir} && chmod +x {remote_program} && {pre} {remote_program} {args_str}; echo SSH EXIT=$?" + command = ( + f"cd {workdir} && chmod +x {remote_program} && {pre} {remote_program} {args_str}; echo SSH EXIT=$?" + ) stdin, stdout, stderr = ssh.exec_command(command) # print("stdin", stdin) # print("stdout", stdout) # print("stderr", stderr) output = stderr.read().strip() + stdout.read().strip()

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"""Called at the end of a run.""" platform = report.pre_df["Platform"] if (platform != "mlif").any(): return [] ret_artifacts = [] - elf_artifact = lookup_artifacts( - artifacts, name="generic_mlonmcu", fmt=ArtifactFormat.BIN, first_only=True - ) + elf_artifact = lookup_artifacts(artifacts, name="generic_mlonmcu", fmt=ArtifactFormat.BIN, first_only=True) assert len(elf_artifact) == 1, "ELF artifact not found!" elf_artifact = elf_artifact[0] - map_artifact = lookup_artifacts( - artifacts, name="generic_mlonmcu.map", fmt=ArtifactFormat.TEXT, first_only=True - ) + map_artifact = lookup_artifacts(artifacts, name="generic_mlonmcu.map", fmt=ArtifactFormat.TEXT, first_only=True) assert len(map_artifact) == 1, "Linker map artifact not found!" map_artifact = map_artifact[0] is_ld = "ld" in map_artifact.flags assert is_ld, "Non ld linker currently unsupported" mem_footprint_df = parse_elf(elf_artifact.path) from mapfile_parser import mapfile + mapFile = mapfile.MapFile() mapFile.readMapFile(map_artifact.path) symbol_map = analyze_linker_map_helper(mapFile) symbol_map_df = pd.DataFrame(

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if self.per_func: mem_footprint_per_func_data = generate_pie_data(mem_footprint_df, x="func", y="bytes", topk=topk) # print("per_func\n", mem_footprint_per_func_data, mem_footprint_per_func_data["bytes"].sum()) if self.to_file: - mem_footprint_per_func_artifact = Artifact("mem_footprint_per_func.csv", content=mem_footprint_per_func_data.to_csv(index=False), fmt=ArtifactFormat.TEXT) + mem_footprint_per_func_artifact = Artifact( + "mem_footprint_per_func.csv", + content=mem_footprint_per_func_data.to_csv(index=False), + fmt=ArtifactFormat.TEXT, + ) ret_artifacts.append(mem_footprint_per_func_artifact) if self.to_df: post_df = report.post_df.copy() post_df["ROM code (by func)"] = mem_footprint_per_func_data.to_dict() report.post_df = post_df

F-string is missing placeholders (F541)

if self.per_library: library_footprint_df = agg_library_footprint(mem_footprint_df, symbol_map_df, by="library", col="bytes") mem_footprint_per_library_data = generate_pie_data(library_footprint_df, x="library", y="bytes", topk=topk) # print("per_library\n", mem_footprint_per_library_data, mem_footprint_per_library_data["bytes"].sum()) if self.to_file: - mem_footprint_per_func_artifact = Artifact("mem_footprint_per_library.csv", content=mem_footprint_per_library_data.to_csv(index=False), fmt=ArtifactFormat.TEXT) + mem_footprint_per_func_artifact = Artifact( + "mem_footprint_per_library.csv", + content=mem_footprint_per_library_data.to_csv(index=False), + fmt=ArtifactFormat.TEXT, + ) ret_artifacts.append(mem_footprint_per_func_artifact) if self.to_df: post_df = report.post_df.copy() post_df["ROM code (by library)"] = mem_footprint_per_library_data.to_dict() report.post_df = post_df if True: # TODO: generalize # print("if1") if "libmuriscvnn.a" in mem_footprint_per_library_data["library"].unique(): # print("if2") - muriscvnn_bytes = mem_footprint_per_library_data[mem_footprint_per_library_data["library"] == "libmuriscvnn.a"]["bytes"].iloc[0] + muriscvnn_bytes = mem_footprint_per_library_data[ + mem_footprint_per_library_data["library"] == "libmuriscvnn.a" + ]["bytes"].iloc[0] # print("muriscvnn_bytes", muriscvnn_bytes) post_df = report.post_df.copy() post_df["ROM code (libmuriscvnn.a)"] = muriscvnn_bytes report.post_df = post_df if self.per_object: object_footprint_df = agg_library_footprint(mem_footprint_df, symbol_map_df, by="object", col="bytes") mem_footprint_per_object_data = generate_pie_data(object_footprint_df, x="object", y="bytes", topk=topk) # print("per_object\n", mem_footprint_per_object_data, mem_footprint_per_object_data["bytes"].sum()) if self.to_file: - mem_footprint_per_func_artifact = Artifact("mem_footprint_per_object.csv", content=mem_footprint_per_object_data.to_csv(index=False), fmt=ArtifactFormat.TEXT) + mem_footprint_per_func_artifact = Artifact( + "mem_footprint_per_object.csv", + content=mem_footprint_per_object_data.to_csv(index=False), + fmt=ArtifactFormat.TEXT, + ) ret_artifacts.append(mem_footprint_per_func_artifact) if self.to_df: post_df = report.post_df.copy() post_df["ROM code (by object)"] = mem_footprint_per_object_data.to_dict() report.post_df = post_df

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else ("Reference" if "tflm" in row.get("Backend") else "-") ) ) if not (row.get("feature_muriscvnn") or row.get("feature_muriscvnnbyoc")) else ( - "Vector" + (" (Portable)" if row.get("config_muriscvnn.use_portable") == 1 or row.get("config_muriscvnnbyoc.use_portable") == 1 else "") + "Vector" + + ( + " (Portable)" + if row.get("config_muriscvnn.use_portable") == 1 + or row.get("config_muriscvnnbyoc.use_portable") == 1 + else "" + ) if row.get("config_muriscvnn.use_vext") == 1 or row.get("config_muriscvnnbyoc.use_vext") == 1 else ( "Packed" if row.get("config_muriscvnn.use_pext") == 1 or row.get("config_muriscvnnbyoc.use_pext") == 1 else "Scalar"

MURISCVNN_PEXT = [["muriscvnn", "pext"] if enable_pext else []] CMSISNN_SCALAR = [["cmsisnn"] if enable_scalar else []] CMSISNN_DSP = [["cmsisnn", "arm_dsp"] if enable_dsp else []] CMSISNN_MVEI = [["cmsisnn", "arm_mvei", "arm_dsp"] if enable_mvei else []] TARGET_FEATURES = { - **{t: [ - *([*DEFAULT_SCALAR, *DEFAULT_VEXT, *DEFAULT_PEXT] if enable_default else []), - *([*MURISCVNN_SCALAR, *MURISCVNN_VEXT, *MURISCVNN_PEXT] if enable_muriscvnn else []), - *([*CMSISNN_SCALAR] if enable_cmsisnn else []), - ] for t in SPIKE_TARGETS}, + **{ + t: [ + *([*DEFAULT_SCALAR, *DEFAULT_VEXT, *DEFAULT_PEXT] if enable_default else []), + *([*MURISCVNN_SCALAR, *MURISCVNN_VEXT, *MURISCVNN_PEXT] if enable_muriscvnn else []), + *([*CMSISNN_SCALAR] if enable_cmsisnn else []), + ] + for t in SPIKE_TARGETS + }, "canmv_k230_ssh": [ *([*DEFAULT_SCALAR, *DEFAULT_VEXT] if enable_default else []), *([*MURISCVNN_SCALAR, *MURISCVNN_VEXT] if enable_muriscvnn else []), *([*CMSISNN_SCALAR] if enable_cmsisnn else []), ],

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BACKEND_DEFAULT_CONFIG = { "tflmi": {}, "tvmaot": {"usmp.algorithm": "hill_climb"}, "tvmaotplus": {"usmp.algorithm": "hill_climb"}, "tvmrt": {}, - "tvmllvm": {} + "tvmllvm": {}, } VLENS = [64, 128, 256, 512, 1024, 2048, 4096, 8192] DEFAULT_VLENS = [64, 128, 256, 512, 1024, 2048]

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run.add_model_by_name(model, context=context) run.add_backend_by_name(backend, context=context) run.add_target_by_name(target, context=context) if args.post: run.add_postprocesses_by_name(POSTPROCESSES_0) - run.add_postprocess(CustomPostprocess(), append=True) + run.add_postprocess( + CustomPostprocess(), append=True + ) run.add_postprocesses_by_name( POSTPROCESSES_1, append=True ) if args.baseline is not None: run.add_postprocess_by_name(

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if args.noop: stage = RunStage.LOAD else: stage = RunStage.COMPILE if MEM_ONLY else RunStage.RUN success = session.process_runs( - until=stage, num_workers=args.parallel, progress=args.progress, context=context, per_stage=args.runs_per_stage, + until=stage, + num_workers=args.parallel, + progress=args.progress, + context=context, + per_stage=args.runs_per_stage, ) report = session.get_reports() report_file = args.output report.export(report_file) print()

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ubuntu-latest pipelines will use ubuntu-24.04 soon. For more details, see https://github.com/actions/runner-images/issues/10636