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122 lines
5.2 KiB
122 lines
5.2 KiB
#!/usr/bin/env python3
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# compare kernels created by HEAD against master
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from collections import defaultdict
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import os, multiprocessing, logging, pickle, sqlite3, difflib, functools, warnings
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from typing import Callable, List, Set, Tuple, Union, cast
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from tinygrad.helpers import VERSION, Context, ContextVar, colored, db_connection, getenv, tqdm
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from tinygrad.engine.schedule import ScheduleContext, schedule_uop
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from tinygrad.codegen.kernel import Kernel, Opt
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from tinygrad.renderer import Renderer
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from tinygrad.ops import UOp
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from test.helpers import print_diff
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# *** process replay settings
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# internal
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PAGE_SIZE = getenv("PAGE_SIZE", 100)
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REF = os.getenv("GITHUB_REF_NAME", "")
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MAX_DIFF_PCT = getenv("PROCESS_REPLAY_MAX_DIFF_PCT", 20)
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TABLE_NAME = f"process_replay_{VERSION}"
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os.environ["RUN_PROCESS_REPLAY"] = "0"
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early_stop = multiprocessing.Event()
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logging.basicConfig(level=logging.INFO, format="%(message)s")
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# user config
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ASSERT_DIFF = int((flag:="[pr]") in os.getenv("COMMIT_MESSAGE", flag) or flag in os.getenv("PR_TITLE", flag))
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if not getenv("ASSERT_PROCESS_REPLAY", 1): ASSERT_DIFF = 0
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SKIP_PROCESS_REPLAY = (k:="[skip_process_replay]") in os.getenv("COMMIT_MESSAGE", "") or k in os.getenv("PR_TITLE", "")
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if REF == "master": SKIP_PROCESS_REPLAY = True
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class ProcessReplayWarning(Warning): pass
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# *** recreators
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def recreate_sched(ast:UOp, assigns:Set[UOp]) -> UOp:
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# NOTE: process replay isn't meant to actually schedule anything
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return schedule_uop(ast, ScheduleContext(assigns=assigns, tensor_uops=defaultdict(list))).ast
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def recreate_kernel(ast:UOp, opts:Renderer, applied_opts:List[Opt], name:str, _) -> str:
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k = Kernel(ast, opts=opts)
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for opt in applied_opts: k.apply_opt(opt)
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# NOTE: replay with the captured renderer, not the one in master
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return k.opts.render(name, cast(List,k.to_program().uops))
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# *** diff a "good" recreation against the generated version
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def diff(offset:int, name:str, fxn:Callable) -> Union[Tuple[int, int], bool]:
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if early_stop.is_set(): return True
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conn = db_connection()
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cur = conn.cursor()
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cur.execute(f"SELECT val FROM '{name}_{TABLE_NAME}' LIMIT ? OFFSET ?", (PAGE_SIZE, offset))
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additions, deletions, changed = 0, 0, 0
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for row in cur.fetchall():
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if changed > MAX_DIFF_PCT:
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warnings.warn(f"detected changes in over {MAX_DIFF_PCT}% of {name}s. skipping further diff generation.")
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early_stop.set()
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break
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# try unpickle
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try: args = pickle.loads(row[0])
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except Exception as e:
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changed += 1
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warnings.warn(f"FAILED TO UNPICKLE OBJECTS {e}", ProcessReplayWarning)
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continue
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# try recreate
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try:
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with Context(**{k:v for k,v in args[-2].items() if k in ContextVar._cache and k != "DEBUG"}): good = fxn(*args[:-2])
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if good is None: continue
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except Exception as e:
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changed += 1
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warnings.warn(f"FAILED TO RECREATE KERNEL {e}", ProcessReplayWarning)
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for x in args[:-1]: logging.info(x)
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continue
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# diff kernels
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try: assert args[-1] == good
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except AssertionError:
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changed += 1
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logging.info("PROCESS REPLAY DETECTED CHANGE")
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for x in args[:-1]: logging.info(x)
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print_diff(good, args[-1])
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changes = list(difflib.unified_diff(str(good).splitlines(), str(args[-1]).splitlines()))
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additions += len([x for x in changes if x.startswith("+")])
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deletions += len([x for x in changes if x.startswith("-")])
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if ASSERT_DIFF: return additions, deletions
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conn.commit()
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cur.close()
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return additions, deletions
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# *** generic runner for executing fxn across all rows of a table in parallel
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def _pmap(name:str, fxn:Callable, maxtasksperchild:int=16) -> None:
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conn = db_connection()
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cur = conn.cursor()
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try: row_count = cur.execute(f"select count(*) from '{name}_{TABLE_NAME}'").fetchone()[0]
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except sqlite3.OperationalError:
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warnings.warn(f"{name}_{TABLE_NAME} isn't accessible in master, did DB_VERSION change?", ProcessReplayWarning)
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return None
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conn.commit()
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cur.close()
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with multiprocessing.get_context("spawn").Pool(multiprocessing.cpu_count(), maxtasksperchild=maxtasksperchild) as pool:
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inputs = list(range(0, row_count, PAGE_SIZE))
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ret: List[Union[bool, Tuple[int, int]]] = list(tqdm(pool.imap_unordered(functools.partial(diff, name=name, fxn=fxn), inputs), total=len(inputs)))
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pool.close()
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pool.join()
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pool.terminate()
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changed = [bool(x[0] or x[1]) if isinstance(x, tuple) else x for x in ret]
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insertion, deletions = [x[0] for x in ret if isinstance(x, tuple)], [x[1] for x in ret if isinstance(x, tuple)]
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logging.info(f"{sum(changed)} kernels changed")
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if sum(insertion) != 0: logging.info(colored(f"{sum(insertion)} insertions(+)", "green"))
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if sum(deletions) != 0: logging.info(colored(f"{sum(deletions)} deletions(-)", "red"))
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if any(changed): warnings.warn("process replay detected changes", ProcessReplayWarning)
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# *** main loop
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if __name__ == "__main__":
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if SKIP_PROCESS_REPLAY:
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logging.info("skipping process replay.")
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exit(0)
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if ASSERT_DIFF: warnings.filterwarnings("error", category=ProcessReplayWarning)
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for name,fxn in [("schedule", recreate_sched), ("kernel", recreate_kernel)]:
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logging.info(f"***** {name} diff")
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try: _pmap(name, fxn)
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except Exception as e:
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if ASSERT_DIFF: raise e
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logging.error(f"{name} diff err {e}")
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