Support comparing two sets of pystats (GH-98816)

This adds support for comparing pystats collected from two different builds.

- The `--json-output` can be used to load in a set of raw stats and output a
  JSON file.
- Two of these JSON files can be provided on the next run, and then comparative
  results between the two are output.
This commit is contained in:
Michael Droettboom 2022-11-04 06:15:54 -04:00 committed by GitHub
parent 044bcc1771
commit 2844aa6a8e
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
2 changed files with 365 additions and 122 deletions

View file

@ -131,7 +131,8 @@ General Options
Turn on internal statistics gathering.
The statistics will be dumped to a arbitrary (probably unique) file in
``/tmp/py_stats/``, or ``C:\temp\py_stats\`` on Windows.
``/tmp/py_stats/``, or ``C:\temp\py_stats\`` on Windows. If that directory
does not exist, results will be printed on stdout.
Use ``Tools/scripts/summarize_stats.py`` to read the stats.

View file

@ -2,7 +2,9 @@
default stats folders.
"""
import argparse
import collections
import json
import os.path
import opcode
from datetime import date
@ -32,6 +34,93 @@ opmap = dict(sorted(opmap.items()))
TOTAL = "specialization.deferred", "specialization.hit", "specialization.miss", "execution_count"
def join_rows(a_rows, b_rows):
"""
Joins two tables together, side-by-side, where the first column in each is a
common key.
"""
if len(a_rows) == 0 and len(b_rows) == 0:
return []
if len(a_rows):
a_ncols = list(set(len(x) for x in a_rows))
if len(a_ncols) != 1:
raise ValueError("Table a is ragged")
if len(b_rows):
b_ncols = list(set(len(x) for x in b_rows))
if len(b_ncols) != 1:
raise ValueError("Table b is ragged")
if len(a_rows) and len(b_rows) and a_ncols[0] != b_ncols[0]:
raise ValueError("Tables have different widths")
if len(a_rows):
ncols = a_ncols[0]
else:
ncols = b_ncols[0]
default = [""] * (ncols - 1)
a_data = {x[0]: x[1:] for x in a_rows}
b_data = {x[0]: x[1:] for x in b_rows}
if len(a_data) != len(a_rows) or len(b_data) != len(b_rows):
raise ValueError("Duplicate keys")
# To preserve ordering, use A's keys as is and then add any in B that aren't
# in A
keys = list(a_data.keys()) + [k for k in b_data.keys() if k not in a_data]
return [(k, *a_data.get(k, default), *b_data.get(k, default)) for k in keys]
def calculate_specialization_stats(family_stats, total):
rows = []
for key in sorted(family_stats):
if key.startswith("specialization.failure_kinds"):
continue
if key in ("specialization.hit", "specialization.miss"):
label = key[len("specialization."):]
elif key == "execution_count":
label = "unquickened"
elif key in ("specialization.success", "specialization.failure", "specializable"):
continue
elif key.startswith("pair"):
continue
else:
label = key
rows.append((f"{label:>12}", f"{family_stats[key]:>12}", f"{100*family_stats[key]/total:0.1f}%"))
return rows
def calculate_specialization_success_failure(family_stats):
total_attempts = 0
for key in ("specialization.success", "specialization.failure"):
total_attempts += family_stats.get(key, 0)
rows = []
if total_attempts:
for key in ("specialization.success", "specialization.failure"):
label = key[len("specialization."):]
label = label[0].upper() + label[1:]
val = family_stats.get(key, 0)
rows.append((label, val, f"{100*val/total_attempts:0.1f}%"))
return rows
def calculate_specialization_failure_kinds(name, family_stats, defines):
total_failures = family_stats.get("specialization.failure", 0)
failure_kinds = [ 0 ] * 30
for key in family_stats:
if not key.startswith("specialization.failure_kind"):
continue
_, index = key[:-1].split("[")
index = int(index)
failure_kinds[index] = family_stats[key]
failures = [(value, index) for (index, value) in enumerate(failure_kinds)]
failures.sort(reverse=True)
rows = []
for value, index in failures:
if not value:
continue
rows.append((kind_to_text(index, defines, name), value, f"{100*value/total_failures:0.1f}%"))
return rows
def print_specialization_stats(name, family_stats, defines):
if "specializable" not in family_stats:
return
@ -39,65 +128,65 @@ def print_specialization_stats(name, family_stats, defines):
if total == 0:
return
with Section(name, 3, f"specialization stats for {name} family"):
rows = []
for key in sorted(family_stats):
if key.startswith("specialization.failure_kinds"):
continue
if key in ("specialization.hit", "specialization.miss"):
label = key[len("specialization."):]
elif key == "execution_count":
label = "unquickened"
elif key in ("specialization.success", "specialization.failure", "specializable"):
continue
elif key.startswith("pair"):
continue
else:
label = key
rows.append((f"{label:>12}", f"{family_stats[key]:>12}", f"{100*family_stats[key]/total:0.1f}%"))
rows = calculate_specialization_stats(family_stats, total)
emit_table(("Kind", "Count", "Ratio"), rows)
print_title("Specialization attempts", 4)
total_attempts = 0
for key in ("specialization.success", "specialization.failure"):
total_attempts += family_stats.get(key, 0)
rows = []
if total_attempts:
for key in ("specialization.success", "specialization.failure"):
label = key[len("specialization."):]
label = label[0].upper() + label[1:]
val = family_stats.get(key, 0)
rows.append((label, val, f"{100*val/total_attempts:0.1f}%"))
rows = calculate_specialization_success_failure(family_stats)
if rows:
print_title("Specialization attempts", 4)
emit_table(("", "Count:", "Ratio:"), rows)
total_failures = family_stats.get("specialization.failure", 0)
failure_kinds = [ 0 ] * 30
for key in family_stats:
if not key.startswith("specialization.failure_kind"):
continue
_, index = key[:-1].split("[")
index = int(index)
failure_kinds[index] = family_stats[key]
failures = [(value, index) for (index, value) in enumerate(failure_kinds)]
failures.sort(reverse=True)
rows = []
for value, index in failures:
if not value:
continue
rows.append((kind_to_text(index, defines, name), value, f"{100*value/total_failures:0.1f}%"))
emit_table(("Failure kind", "Count:", "Ratio:"), rows)
rows = calculate_specialization_failure_kinds(name, family_stats, defines)
emit_table(("Failure kind", "Count:", "Ratio:"), rows)
def gather_stats():
stats = collections.Counter()
for filename in os.listdir(DEFAULT_DIR):
with open(os.path.join(DEFAULT_DIR, filename)) as fd:
for line in fd:
try:
key, value = line.split(":")
except ValueError:
print (f"Unparsable line: '{line.strip()}' in {filename}", file=sys.stderr)
continue
key = key.strip()
value = int(value)
stats[key] += value
return stats
def print_comparative_specialization_stats(name, base_family_stats, head_family_stats, defines):
if "specializable" not in base_family_stats:
return
base_total = sum(base_family_stats.get(kind, 0) for kind in TOTAL)
head_total = sum(head_family_stats.get(kind, 0) for kind in TOTAL)
if base_total + head_total == 0:
return
with Section(name, 3, f"specialization stats for {name} family"):
base_rows = calculate_specialization_stats(base_family_stats, base_total)
head_rows = calculate_specialization_stats(head_family_stats, head_total)
emit_table(
("Kind", "Base Count", "Base Ratio", "Head Count", "Head Ratio"),
join_rows(base_rows, head_rows)
)
base_rows = calculate_specialization_success_failure(base_family_stats)
head_rows = calculate_specialization_success_failure(head_family_stats)
rows = join_rows(base_rows, head_rows)
if rows:
print_title("Specialization attempts", 4)
emit_table(("", "Base Count:", "Base Ratio:", "Head Count:", "Head Ratio:"), rows)
base_rows = calculate_specialization_failure_kinds(name, base_family_stats, defines)
head_rows = calculate_specialization_failure_kinds(name, head_family_stats, defines)
emit_table(
("Failure kind", "Base Count:", "Base Ratio:", "Head Count:", "Head Ratio:"),
join_rows(base_rows, head_rows)
)
def gather_stats(input):
# Note the output of this function must be JSON-serializable
if os.path.isfile(input):
with open(input, "r") as fd:
return json.load(fd)
elif os.path.isdir(input):
stats = collections.Counter()
for filename in os.listdir(input):
with open(os.path.join(input, filename)) as fd:
for line in fd:
try:
key, value = line.split(":")
except ValueError:
print(f"Unparsable line: '{line.strip()}' in {filename}", file=sys.stderr)
continue
key = key.strip()
value = int(value)
stats[key] += value
return stats
else:
raise ValueError(f"{input:r} is not a file or directory path")
def extract_opcode_stats(stats):
opcode_stats = [ {} for _ in range(256) ]
@ -213,50 +302,98 @@ def emit_table(header, rows):
print("|", " | ".join(to_str(i) for i in row), "|")
print()
def calculate_execution_counts(opcode_stats, total):
counts = []
for i, opcode_stat in enumerate(opcode_stats):
if "execution_count" in opcode_stat:
count = opcode_stat['execution_count']
miss = 0
if "specializable" not in opcode_stat:
miss = opcode_stat.get("specialization.miss")
counts.append((count, opname[i], miss))
counts.sort(reverse=True)
cumulative = 0
rows = []
for (count, name, miss) in counts:
cumulative += count
if miss:
miss = f"{100*miss/count:0.1f}%"
else:
miss = ""
rows.append((name, count, f"{100*count/total:0.1f}%",
f"{100*cumulative/total:0.1f}%", miss))
return rows
def emit_execution_counts(opcode_stats, total):
with Section("Execution counts", summary="execution counts for all instructions"):
counts = []
for i, opcode_stat in enumerate(opcode_stats):
if "execution_count" in opcode_stat:
count = opcode_stat['execution_count']
miss = 0
if "specializable" not in opcode_stat:
miss = opcode_stat.get("specialization.miss")
counts.append((count, opname[i], miss))
counts.sort(reverse=True)
cumulative = 0
rows = []
for (count, name, miss) in counts:
cumulative += count
if miss:
miss = f"{100*miss/count:0.1f}%"
else:
miss = ""
rows.append((name, count, f"{100*count/total:0.1f}%",
f"{100*cumulative/total:0.1f}%", miss))
rows = calculate_execution_counts(opcode_stats, total)
emit_table(
("Name", "Count:", "Self:", "Cumulative:", "Miss ratio:"),
rows
)
def emit_comparative_execution_counts(
base_opcode_stats, base_total, head_opcode_stats, head_total
):
with Section("Execution counts", summary="execution counts for all instructions"):
base_rows = calculate_execution_counts(base_opcode_stats, base_total)
head_rows = calculate_execution_counts(head_opcode_stats, head_total)
base_data = dict((x[0], x[1:]) for x in base_rows)
head_data = dict((x[0], x[1:]) for x in head_rows)
opcodes = set(base_data.keys()) | set(head_data.keys())
def emit_specialization_stats(opcode_stats):
rows = []
default = [0, "0.0%", "0.0%", 0]
for opcode in opcodes:
base_entry = base_data.get(opcode, default)
head_entry = head_data.get(opcode, default)
if base_entry[0] == 0:
change = 1
else:
change = (head_entry[0] - base_entry[0]) / base_entry[0]
rows.append(
(opcode, base_entry[0], head_entry[0],
f"{100*change:0.1f}%"))
rows.sort(key=lambda x: -abs(float(x[-1][:-1])))
emit_table(
("Name", "Base Count:", "Head Count:", "Change:"),
rows
)
def get_defines():
spec_path = os.path.join(os.path.dirname(__file__), "../../Python/specialize.c")
with open(spec_path) as spec_src:
defines = parse_kinds(spec_src)
return defines
def emit_specialization_stats(opcode_stats):
defines = get_defines()
with Section("Specialization stats", summary="specialization stats by family"):
for i, opcode_stat in enumerate(opcode_stats):
name = opname[i]
print_specialization_stats(name, opcode_stat, defines)
def emit_specialization_overview(opcode_stats, total):
def emit_comparative_specialization_stats(base_opcode_stats, head_opcode_stats):
defines = get_defines()
with Section("Specialization stats", summary="specialization stats by family"):
for i, (base_opcode_stat, head_opcode_stat) in enumerate(zip(base_opcode_stats, head_opcode_stats)):
name = opname[i]
print_comparative_specialization_stats(name, base_opcode_stat, head_opcode_stat, defines)
def calculate_specialization_effectiveness(opcode_stats, total):
basic, not_specialized, specialized = categorized_counts(opcode_stats)
return [
("Basic", basic, f"{basic*100/total:0.1f}%"),
("Not specialized", not_specialized, f"{not_specialized*100/total:0.1f}%"),
("Specialized", specialized, f"{specialized*100/total:0.1f}%"),
]
def emit_specialization_overview(opcode_stats, total):
with Section("Specialization effectiveness"):
emit_table(("Instructions", "Count:", "Ratio:"), (
("Basic", basic, f"{basic*100/total:0.1f}%"),
("Not specialized", not_specialized, f"{not_specialized*100/total:0.1f}%"),
("Specialized", specialized, f"{specialized*100/total:0.1f}%"),
))
rows = calculate_specialization_effectiveness(opcode_stats, total)
emit_table(("Instructions", "Count:", "Ratio:"), rows)
for title, field in (("Deferred", "specialization.deferred"), ("Misses", "specialization.miss")):
total = 0
counts = []
@ -270,53 +407,91 @@ def emit_specialization_overview(opcode_stats, total):
rows = [ (name, count, f"{100*count/total:0.1f}%") for (count, name) in counts[:10] ]
emit_table(("Name", "Count:", "Ratio:"), rows)
def emit_call_stats(stats):
def emit_comparative_specialization_overview(base_opcode_stats, base_total, head_opcode_stats, head_total):
with Section("Specialization effectiveness"):
base_rows = calculate_specialization_effectiveness(base_opcode_stats, base_total)
head_rows = calculate_specialization_effectiveness(head_opcode_stats, head_total)
emit_table(
("Instructions", "Base Count:", "Base Ratio:", "Head Count:", "Head Ratio:"),
join_rows(base_rows, head_rows)
)
def get_stats_defines():
stats_path = os.path.join(os.path.dirname(__file__), "../../Include/pystats.h")
with open(stats_path) as stats_src:
defines = parse_kinds(stats_src, prefix="EVAL_CALL")
return defines
def calculate_call_stats(stats):
defines = get_stats_defines()
total = 0
for key, value in stats.items():
if "Calls to" in key:
total += value
rows = []
for key, value in stats.items():
if "Calls to" in key:
rows.append((key, value, f"{100*value/total:0.1f}%"))
elif key.startswith("Calls "):
name, index = key[:-1].split("[")
index = int(index)
label = name + " (" + pretty(defines[index][0]) + ")"
rows.append((label, value, f"{100*value/total:0.1f}%"))
for key, value in stats.items():
if key.startswith("Frame"):
rows.append((key, value, f"{100*value/total:0.1f}%"))
return rows
def emit_call_stats(stats):
with Section("Call stats", summary="Inlined calls and frame stats"):
total = 0
for key, value in stats.items():
if "Calls to" in key:
total += value
rows = []
for key, value in stats.items():
if "Calls to" in key:
rows.append((key, value, f"{100*value/total:0.1f}%"))
elif key.startswith("Calls "):
name, index = key[:-1].split("[")
index = int(index)
label = name + " (" + pretty(defines[index][0]) + ")"
rows.append((label, value, f"{100*value/total:0.1f}%"))
for key, value in stats.items():
if key.startswith("Frame"):
rows.append((key, value, f"{100*value/total:0.1f}%"))
rows = calculate_call_stats(stats)
emit_table(("", "Count:", "Ratio:"), rows)
def emit_comparative_call_stats(base_stats, head_stats):
with Section("Call stats", summary="Inlined calls and frame stats"):
base_rows = calculate_call_stats(base_stats)
head_rows = calculate_call_stats(head_stats)
rows = join_rows(base_rows, head_rows)
rows.sort(key=lambda x: -float(x[-1][:-1]))
emit_table(
("", "Base Count:", "Base Ratio:", "Head Count:", "Head Ratio:"),
rows
)
def calculate_object_stats(stats):
total_materializations = stats.get("Object new values")
total_allocations = stats.get("Object allocations") + stats.get("Object allocations from freelist")
total_increfs = stats.get("Object interpreter increfs") + stats.get("Object increfs")
total_decrefs = stats.get("Object interpreter decrefs") + stats.get("Object decrefs")
rows = []
for key, value in stats.items():
if key.startswith("Object"):
if "materialize" in key:
ratio = f"{100*value/total_materializations:0.1f}%"
elif "allocations" in key:
ratio = f"{100*value/total_allocations:0.1f}%"
elif "increfs" in key:
ratio = f"{100*value/total_increfs:0.1f}%"
elif "decrefs" in key:
ratio = f"{100*value/total_decrefs:0.1f}%"
else:
ratio = ""
label = key[6:].strip()
label = label[0].upper() + label[1:]
rows.append((label, value, ratio))
return rows
def emit_object_stats(stats):
with Section("Object stats", summary="allocations, frees and dict materializatons"):
total_materializations = stats.get("Object new values")
total_allocations = stats.get("Object allocations") + stats.get("Object allocations from freelist")
total_increfs = stats.get("Object interpreter increfs") + stats.get("Object increfs")
total_decrefs = stats.get("Object interpreter decrefs") + stats.get("Object decrefs")
rows = []
for key, value in stats.items():
if key.startswith("Object"):
if "materialize" in key:
ratio = f"{100*value/total_materializations:0.1f}%"
elif "allocations" in key:
ratio = f"{100*value/total_allocations:0.1f}%"
elif "increfs" in key:
ratio = f"{100*value/total_increfs:0.1f}%"
elif "decrefs" in key:
ratio = f"{100*value/total_decrefs:0.1f}%"
else:
ratio = ""
label = key[6:].strip()
label = label[0].upper() + label[1:]
rows.append((label, value, ratio))
rows = calculate_object_stats(stats)
emit_table(("", "Count:", "Ratio:"), rows)
def emit_comparative_object_stats(base_stats, head_stats):
with Section("Object stats", summary="allocations, frees and dict materializatons"):
base_rows = calculate_object_stats(base_stats)
head_rows = calculate_object_stats(head_stats)
emit_table(("", "Base Count:", "Base Ratio:", "Head Count:", "Head Ratio:"), join_rows(base_rows, head_rows))
def get_total(opcode_stats):
total = 0
for opcode_stat in opcode_stats:
@ -377,8 +552,7 @@ def emit_pair_counts(opcode_stats, total):
succ_rows
)
def main():
stats = gather_stats()
def output_single_stats(stats):
opcode_stats = extract_opcode_stats(stats)
total = get_total(opcode_stats)
emit_execution_counts(opcode_stats, total)
@ -387,8 +561,76 @@ def main():
emit_specialization_overview(opcode_stats, total)
emit_call_stats(stats)
emit_object_stats(stats)
def output_comparative_stats(base_stats, head_stats):
base_opcode_stats = extract_opcode_stats(base_stats)
base_total = get_total(base_opcode_stats)
head_opcode_stats = extract_opcode_stats(head_stats)
head_total = get_total(head_opcode_stats)
emit_comparative_execution_counts(
base_opcode_stats, base_total, head_opcode_stats, head_total
)
emit_comparative_specialization_stats(
base_opcode_stats, head_opcode_stats
)
emit_comparative_specialization_overview(
base_opcode_stats, base_total, head_opcode_stats, head_total
)
emit_comparative_call_stats(base_stats, head_stats)
emit_comparative_object_stats(base_stats, head_stats)
def output_stats(inputs, json_output=None):
if len(inputs) == 1:
stats = gather_stats(inputs[0])
if json_output is not None:
json.dump(stats, json_output)
output_single_stats(stats)
elif len(inputs) == 2:
if json_output is not None:
raise ValueError(
"Can not output to JSON when there are multiple inputs"
)
base_stats = gather_stats(inputs[0])
head_stats = gather_stats(inputs[1])
output_comparative_stats(base_stats, head_stats)
print("---")
print("Stats gathered on:", date.today())
def main():
parser = argparse.ArgumentParser(description="Summarize pystats results")
parser.add_argument(
"inputs",
nargs="*",
type=str,
default=[DEFAULT_DIR],
help=f"""
Input source(s).
For each entry, if a .json file, the output provided by --json-output from a previous run;
if a directory, a directory containing raw pystats .txt files.
If one source is provided, its stats are printed.
If two sources are provided, comparative stats are printed.
Default is {DEFAULT_DIR}.
"""
)
parser.add_argument(
"--json-output",
nargs="?",
type=argparse.FileType("w"),
help="Output complete raw results to the given JSON file."
)
args = parser.parse_args()
if len(args.inputs) > 2:
raise ValueError("0-2 arguments may be provided.")
output_stats(args.inputs, json_output=args.json_output)
if __name__ == "__main__":
main()