mirror of https://github.com/python/cpython
946 lines
31 KiB
Python
946 lines
31 KiB
Python
"""Print a summary of specialization stats for all files in the
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default stats folders.
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"""
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# NOTE: Bytecode introspection modules (opcode, dis, etc.) should only
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# happen when loading a single dataset. When comparing datasets, it
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# could get it wrong, leading to subtle errors.
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import argparse
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import collections
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import json
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import os.path
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from datetime import date
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import itertools
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import sys
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import re
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if os.name == "nt":
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DEFAULT_DIR = "c:\\temp\\py_stats\\"
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else:
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DEFAULT_DIR = "/tmp/py_stats/"
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TOTAL = "specialization.hit", "specialization.miss", "execution_count"
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def format_ratio(num, den):
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"""
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Format a ratio as a percentage. When the denominator is 0, returns the empty
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string.
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"""
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if den == 0:
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return ""
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else:
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return f"{num/den:.01%}"
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def percentage_to_float(s):
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"""
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Converts a percentage string to a float. The empty string is returned as 0.0
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"""
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if s == "":
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return 0.0
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else:
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assert s[-1] == "%"
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return float(s[:-1])
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def join_rows(a_rows, b_rows):
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"""
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Joins two tables together, side-by-side, where the first column in each is a
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common key.
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"""
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if len(a_rows) == 0 and len(b_rows) == 0:
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return []
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if len(a_rows):
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a_ncols = list(set(len(x) for x in a_rows))
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if len(a_ncols) != 1:
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raise ValueError("Table a is ragged")
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if len(b_rows):
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b_ncols = list(set(len(x) for x in b_rows))
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if len(b_ncols) != 1:
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raise ValueError("Table b is ragged")
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if len(a_rows) and len(b_rows) and a_ncols[0] != b_ncols[0]:
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raise ValueError("Tables have different widths")
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if len(a_rows):
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ncols = a_ncols[0]
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else:
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ncols = b_ncols[0]
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default = [""] * (ncols - 1)
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a_data = {x[0]: x[1:] for x in a_rows}
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b_data = {x[0]: x[1:] for x in b_rows}
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if len(a_data) != len(a_rows) or len(b_data) != len(b_rows):
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raise ValueError("Duplicate keys")
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# To preserve ordering, use A's keys as is and then add any in B that aren't
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# in A
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keys = list(a_data.keys()) + [k for k in b_data.keys() if k not in a_data]
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return [(k, *a_data.get(k, default), *b_data.get(k, default)) for k in keys]
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def calculate_specialization_stats(family_stats, total):
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rows = []
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for key in sorted(family_stats):
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if key.startswith("specialization.failure_kinds"):
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continue
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if key in ("specialization.hit", "specialization.miss"):
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label = key[len("specialization.") :]
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elif key == "execution_count":
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continue
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elif key in (
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"specialization.success",
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"specialization.failure",
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"specializable",
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):
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continue
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elif key.startswith("pair"):
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continue
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else:
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label = key
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rows.append(
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(
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f"{label:>12}",
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f"{family_stats[key]:>12}",
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format_ratio(family_stats[key], total),
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)
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)
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return rows
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def calculate_specialization_success_failure(family_stats):
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total_attempts = 0
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for key in ("specialization.success", "specialization.failure"):
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total_attempts += family_stats.get(key, 0)
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rows = []
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if total_attempts:
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for key in ("specialization.success", "specialization.failure"):
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label = key[len("specialization.") :]
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label = label[0].upper() + label[1:]
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val = family_stats.get(key, 0)
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rows.append((label, val, format_ratio(val, total_attempts)))
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return rows
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def calculate_specialization_failure_kinds(name, family_stats, defines):
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total_failures = family_stats.get("specialization.failure", 0)
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failure_kinds = [0] * 40
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for key in family_stats:
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if not key.startswith("specialization.failure_kind"):
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continue
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_, index = key[:-1].split("[")
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index = int(index)
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failure_kinds[index] = family_stats[key]
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failures = [(value, index) for (index, value) in enumerate(failure_kinds)]
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failures.sort(reverse=True)
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rows = []
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for value, index in failures:
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if not value:
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continue
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rows.append(
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(
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kind_to_text(index, defines, name),
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value,
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format_ratio(value, total_failures),
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)
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)
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return rows
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def print_specialization_stats(name, family_stats, defines):
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if "specializable" not in family_stats:
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return
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total = sum(family_stats.get(kind, 0) for kind in TOTAL)
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if total == 0:
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return
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with Section(name, 3, f"specialization stats for {name} family"):
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rows = calculate_specialization_stats(family_stats, total)
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emit_table(("Kind", "Count", "Ratio"), rows)
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rows = calculate_specialization_success_failure(family_stats)
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if rows:
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print_title("Specialization attempts", 4)
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emit_table(("", "Count:", "Ratio:"), rows)
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rows = calculate_specialization_failure_kinds(name, family_stats, defines)
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emit_table(("Failure kind", "Count:", "Ratio:"), rows)
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def print_comparative_specialization_stats(
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name, base_family_stats, head_family_stats, defines
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):
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if "specializable" not in base_family_stats:
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return
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base_total = sum(base_family_stats.get(kind, 0) for kind in TOTAL)
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head_total = sum(head_family_stats.get(kind, 0) for kind in TOTAL)
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if base_total + head_total == 0:
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return
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with Section(name, 3, f"specialization stats for {name} family"):
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base_rows = calculate_specialization_stats(base_family_stats, base_total)
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head_rows = calculate_specialization_stats(head_family_stats, head_total)
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emit_table(
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("Kind", "Base Count", "Base Ratio", "Head Count", "Head Ratio"),
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join_rows(base_rows, head_rows),
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)
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base_rows = calculate_specialization_success_failure(base_family_stats)
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head_rows = calculate_specialization_success_failure(head_family_stats)
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rows = join_rows(base_rows, head_rows)
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if rows:
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print_title("Specialization attempts", 4)
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emit_table(
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("", "Base Count:", "Base Ratio:", "Head Count:", "Head Ratio:"), rows
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)
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base_rows = calculate_specialization_failure_kinds(
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name, base_family_stats, defines
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)
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head_rows = calculate_specialization_failure_kinds(
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name, head_family_stats, defines
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)
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emit_table(
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(
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"Failure kind",
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"Base Count:",
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"Base Ratio:",
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"Head Count:",
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"Head Ratio:",
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),
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join_rows(base_rows, head_rows),
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)
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def gather_stats(input):
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# Note the output of this function must be JSON-serializable
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if os.path.isfile(input):
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with open(input, "r") as fd:
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stats = json.load(fd)
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stats["_stats_defines"] = {
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int(k): v for k, v in stats["_stats_defines"].items()
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}
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stats["_defines"] = {int(k): v for k, v in stats["_defines"].items()}
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return stats
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elif os.path.isdir(input):
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stats = collections.Counter()
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for filename in os.listdir(input):
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with open(os.path.join(input, filename)) as fd:
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for line in fd:
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try:
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key, value = line.split(":")
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except ValueError:
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print(
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f"Unparsable line: '{line.strip()}' in {filename}",
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file=sys.stderr,
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)
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continue
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key = key.strip()
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value = int(value)
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stats[key] += value
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stats["__nfiles__"] += 1
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import opcode
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stats["_specialized_instructions"] = [
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op for op in opcode._specialized_opmap.keys() if "__" not in op
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]
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stats["_stats_defines"] = get_stats_defines()
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stats["_defines"] = get_defines()
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return stats
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else:
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raise ValueError(f"{input:r} is not a file or directory path")
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def extract_opcode_stats(stats, prefix):
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opcode_stats = collections.defaultdict(dict)
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for key, value in stats.items():
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if not key.startswith(prefix):
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continue
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name, _, rest = key[len(prefix) + 1 :].partition("]")
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opcode_stats[name][rest.strip(".")] = value
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return opcode_stats
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def parse_kinds(spec_src, prefix="SPEC_FAIL"):
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defines = collections.defaultdict(list)
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start = "#define " + prefix + "_"
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for line in spec_src:
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line = line.strip()
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if not line.startswith(start):
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continue
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line = line[len(start) :]
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name, val = line.split()
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defines[int(val.strip())].append(name.strip())
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return defines
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def pretty(defname):
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return defname.replace("_", " ").lower()
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def kind_to_text(kind, defines, opname):
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if kind <= 8:
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return pretty(defines[kind][0])
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if opname == "LOAD_SUPER_ATTR":
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opname = "SUPER"
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elif opname.endswith("ATTR"):
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opname = "ATTR"
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elif opname in ("FOR_ITER", "SEND"):
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opname = "ITER"
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elif opname.endswith("SUBSCR"):
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opname = "SUBSCR"
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for name in defines[kind]:
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if name.startswith(opname):
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return pretty(name[len(opname) + 1 :])
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return "kind " + str(kind)
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def categorized_counts(opcode_stats, specialized_instructions):
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basic = 0
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specialized = 0
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not_specialized = 0
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for name, opcode_stat in opcode_stats.items():
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if "execution_count" not in opcode_stat:
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continue
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count = opcode_stat["execution_count"]
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if "specializable" in opcode_stat:
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not_specialized += count
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elif name in specialized_instructions:
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miss = opcode_stat.get("specialization.miss", 0)
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not_specialized += miss
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specialized += count - miss
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else:
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basic += count
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return basic, not_specialized, specialized
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def print_title(name, level=2):
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print("#" * level, name)
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print()
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class Section:
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def __init__(self, title, level=2, summary=None):
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self.title = title
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self.level = level
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if summary is None:
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self.summary = title.lower()
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else:
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self.summary = summary
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def __enter__(self):
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print_title(self.title, self.level)
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print("<details>")
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print("<summary>", self.summary, "</summary>")
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print()
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return self
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def __exit__(*args):
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print()
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print("</details>")
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print()
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def to_str(x):
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if isinstance(x, int):
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return format(x, ",d")
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else:
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return str(x)
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def emit_table(header, rows):
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width = len(header)
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header_line = "|"
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under_line = "|"
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for item in header:
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under = "---"
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if item.endswith(":"):
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item = item[:-1]
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under += ":"
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header_line += item + " | "
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under_line += under + "|"
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print(header_line)
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print(under_line)
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for row in rows:
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if width is not None and len(row) != width:
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raise ValueError("Wrong number of elements in row '" + str(row) + "'")
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print("|", " | ".join(to_str(i) for i in row), "|")
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print()
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def emit_histogram(title, stats, key, total):
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rows = []
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for k, v in stats.items():
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if k.startswith(key):
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entry = int(re.match(r".+\[([0-9]+)\]", k).groups()[0])
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rows.append((f"<= {entry}", int(v), format_ratio(int(v), total)))
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# Don't include larger buckets with 0 entries
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for j in range(len(rows) - 1, -1, -1):
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if rows[j][1] != 0:
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break
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rows = rows[: j + 1]
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print(f"**{title}**\n")
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emit_table(("Range", "Count:", "Ratio:"), rows)
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def calculate_execution_counts(opcode_stats, total):
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counts = []
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for name, opcode_stat in opcode_stats.items():
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if "execution_count" in opcode_stat:
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count = opcode_stat["execution_count"]
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miss = 0
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if "specializable" not in opcode_stat:
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miss = opcode_stat.get("specialization.miss")
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counts.append((count, name, miss))
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counts.sort(reverse=True)
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cumulative = 0
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rows = []
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for count, name, miss in counts:
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cumulative += count
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if miss:
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miss = format_ratio(miss, count)
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else:
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miss = ""
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rows.append(
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(
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name,
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count,
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format_ratio(count, total),
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format_ratio(cumulative, total),
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miss,
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)
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)
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return rows
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def emit_execution_counts(opcode_stats, total):
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with Section("Execution counts", summary="execution counts for all instructions"):
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rows = calculate_execution_counts(opcode_stats, total)
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emit_table(("Name", "Count:", "Self:", "Cumulative:", "Miss ratio:"), rows)
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def _emit_comparative_execution_counts(base_rows, head_rows):
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base_data = {x[0]: x[1:] for x in base_rows}
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head_data = {x[0]: x[1:] for x in head_rows}
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opcodes = base_data.keys() | head_data.keys()
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rows = []
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default = [0, "0.0%", "0.0%", 0]
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for opcode in opcodes:
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base_entry = base_data.get(opcode, default)
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head_entry = head_data.get(opcode, default)
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if base_entry[0] == 0:
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change = 1
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else:
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change = (head_entry[0] - base_entry[0]) / base_entry[0]
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rows.append((opcode, base_entry[0], head_entry[0], f"{change:0.1%}"))
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rows.sort(key=lambda x: abs(percentage_to_float(x[-1])), reverse=True)
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emit_table(("Name", "Base Count:", "Head Count:", "Change:"), rows)
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|
|
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def emit_comparative_execution_counts(
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base_opcode_stats, base_total, head_opcode_stats, head_total, level=2
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):
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with Section(
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"Execution counts", summary="execution counts for all instructions", level=level
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):
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base_rows = calculate_execution_counts(base_opcode_stats, base_total)
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head_rows = calculate_execution_counts(head_opcode_stats, head_total)
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_emit_comparative_execution_counts(base_rows, head_rows)
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|
|
|
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def get_defines():
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spec_path = os.path.join(os.path.dirname(__file__), "../../Python/specialize.c")
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|
with open(spec_path) as spec_src:
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defines = parse_kinds(spec_src)
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return defines
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|
|
|
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def emit_specialization_stats(opcode_stats, defines):
|
|
with Section("Specialization stats", summary="specialization stats by family"):
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for name, opcode_stat in opcode_stats.items():
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print_specialization_stats(name, opcode_stat, defines)
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|
|
|
|
|
def emit_comparative_specialization_stats(
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base_opcode_stats, head_opcode_stats, defines
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):
|
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with Section("Specialization stats", summary="specialization stats by family"):
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opcodes = set(base_opcode_stats.keys()) & set(head_opcode_stats.keys())
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for opcode in opcodes:
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print_comparative_specialization_stats(
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opcode, base_opcode_stats[opcode], head_opcode_stats[opcode], defines
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)
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|
|
|
|
|
def calculate_specialization_effectiveness(
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opcode_stats, total, specialized_instructions
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|
):
|
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basic, not_specialized, specialized = categorized_counts(
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opcode_stats, specialized_instructions
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)
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return [
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("Basic", basic, format_ratio(basic, total)),
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("Not specialized", not_specialized, format_ratio(not_specialized, total)),
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("Specialized", specialized, format_ratio(specialized, total)),
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|
]
|
|
|
|
|
|
def emit_specialization_overview(opcode_stats, total, specialized_instructions):
|
|
with Section("Specialization effectiveness"):
|
|
rows = calculate_specialization_effectiveness(
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opcode_stats, total, specialized_instructions
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|
)
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|
emit_table(("Instructions", "Count:", "Ratio:"), rows)
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|
for title, field in (
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|
("Deferred", "specialization.deferred"),
|
|
("Misses", "specialization.miss"),
|
|
):
|
|
total = 0
|
|
counts = []
|
|
for name, opcode_stat in opcode_stats.items():
|
|
# Avoid double counting misses
|
|
if title == "Misses" and "specializable" in opcode_stat:
|
|
continue
|
|
value = opcode_stat.get(field, 0)
|
|
counts.append((value, name))
|
|
total += value
|
|
counts.sort(reverse=True)
|
|
if total:
|
|
with Section(f"{title} by instruction", 3):
|
|
rows = [
|
|
(name, count, format_ratio(count, total))
|
|
for (count, name) in counts[:10]
|
|
]
|
|
emit_table(("Name", "Count:", "Ratio:"), rows)
|
|
|
|
|
|
def emit_comparative_specialization_overview(
|
|
base_opcode_stats,
|
|
base_total,
|
|
head_opcode_stats,
|
|
head_total,
|
|
specialized_instructions,
|
|
):
|
|
with Section("Specialization effectiveness"):
|
|
base_rows = calculate_specialization_effectiveness(
|
|
base_opcode_stats, base_total, specialized_instructions
|
|
)
|
|
head_rows = calculate_specialization_effectiveness(
|
|
head_opcode_stats, head_total, specialized_instructions
|
|
)
|
|
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/cpython/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):
|
|
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, format_ratio(value, total)))
|
|
elif key.startswith("Calls "):
|
|
name, index = key[:-1].split("[")
|
|
index = int(index)
|
|
label = name + " (" + pretty(defines[index][0]) + ")"
|
|
rows.append((label, value, format_ratio(value, total)))
|
|
for key, value in stats.items():
|
|
if key.startswith("Frame"):
|
|
rows.append((key, value, format_ratio(value, total)))
|
|
return rows
|
|
|
|
|
|
def emit_call_stats(stats, defines):
|
|
with Section("Call stats", summary="Inlined calls and frame stats"):
|
|
rows = calculate_call_stats(stats, defines)
|
|
emit_table(("", "Count:", "Ratio:"), rows)
|
|
|
|
|
|
def emit_comparative_call_stats(base_stats, head_stats, defines):
|
|
with Section("Call stats", summary="Inlined calls and frame stats"):
|
|
base_rows = calculate_call_stats(base_stats, defines)
|
|
head_rows = calculate_call_stats(head_stats, defines)
|
|
rows = join_rows(base_rows, head_rows)
|
|
rows.sort(key=lambda x: -percentage_to_float(x[-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 = format_ratio(value, total_materializations)
|
|
elif "allocations" in key:
|
|
ratio = format_ratio(value, total_allocations)
|
|
elif "increfs" in key:
|
|
ratio = format_ratio(value, total_increfs)
|
|
elif "decrefs" in key:
|
|
ratio = format_ratio(value, total_decrefs)
|
|
else:
|
|
ratio = ""
|
|
label = key[6:].strip()
|
|
label = label[0].upper() + label[1:]
|
|
rows.append((label, value, ratio))
|
|
return rows
|
|
|
|
|
|
def calculate_gc_stats(stats):
|
|
gc_stats = []
|
|
for key, value in stats.items():
|
|
if not key.startswith("GC"):
|
|
continue
|
|
n, _, rest = key[3:].partition("]")
|
|
name = rest.strip()
|
|
gen_n = int(n)
|
|
while len(gc_stats) <= gen_n:
|
|
gc_stats.append({})
|
|
gc_stats[gen_n][name] = value
|
|
return [
|
|
(i, gen["collections"], gen["objects collected"], gen["object visits"])
|
|
for (i, gen) in enumerate(gc_stats)
|
|
]
|
|
|
|
|
|
def emit_object_stats(stats):
|
|
with Section("Object stats", summary="allocations, frees and dict materializatons"):
|
|
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 emit_gc_stats(stats):
|
|
with Section("GC stats", summary="GC collections and effectiveness"):
|
|
rows = calculate_gc_stats(stats)
|
|
emit_table(
|
|
("Generation:", "Collections:", "Objects collected:", "Object visits:"),
|
|
rows,
|
|
)
|
|
|
|
|
|
def emit_comparative_gc_stats(base_stats, head_stats):
|
|
with Section("GC stats", summary="GC collections and effectiveness"):
|
|
base_rows = calculate_gc_stats(base_stats)
|
|
head_rows = calculate_gc_stats(head_stats)
|
|
emit_table(
|
|
(
|
|
"Generation:",
|
|
"Base collections:",
|
|
"Head collections:",
|
|
"Base objects collected:",
|
|
"Head objects collected:",
|
|
"Base object visits:",
|
|
"Head object visits:",
|
|
),
|
|
join_rows(base_rows, head_rows),
|
|
)
|
|
|
|
|
|
def get_total(opcode_stats):
|
|
total = 0
|
|
for opcode_stat in opcode_stats.values():
|
|
if "execution_count" in opcode_stat:
|
|
total += opcode_stat["execution_count"]
|
|
return total
|
|
|
|
|
|
def emit_pair_counts(opcode_stats, total):
|
|
pair_counts = []
|
|
for name_i, opcode_stat in opcode_stats.items():
|
|
for key, value in opcode_stat.items():
|
|
if key.startswith("pair_count"):
|
|
name_j, _, _ = key[11:].partition("]")
|
|
if value:
|
|
pair_counts.append((value, (name_i, name_j)))
|
|
with Section("Pair counts", summary="Pair counts for top 100 pairs"):
|
|
pair_counts.sort(reverse=True)
|
|
cumulative = 0
|
|
rows = []
|
|
for count, pair in itertools.islice(pair_counts, 100):
|
|
name_i, name_j = pair
|
|
cumulative += count
|
|
rows.append(
|
|
(
|
|
f"{name_i} {name_j}",
|
|
count,
|
|
format_ratio(count, total),
|
|
format_ratio(cumulative, total),
|
|
)
|
|
)
|
|
emit_table(("Pair", "Count:", "Self:", "Cumulative:"), rows)
|
|
with Section(
|
|
"Predecessor/Successor Pairs",
|
|
summary="Top 5 predecessors and successors of each opcode",
|
|
):
|
|
predecessors = collections.defaultdict(collections.Counter)
|
|
successors = collections.defaultdict(collections.Counter)
|
|
total_predecessors = collections.Counter()
|
|
total_successors = collections.Counter()
|
|
for count, (first, second) in pair_counts:
|
|
if count:
|
|
predecessors[second][first] = count
|
|
successors[first][second] = count
|
|
total_predecessors[second] += count
|
|
total_successors[first] += count
|
|
for name in opcode_stats.keys():
|
|
total1 = total_predecessors[name]
|
|
total2 = total_successors[name]
|
|
if total1 == 0 and total2 == 0:
|
|
continue
|
|
pred_rows = succ_rows = ()
|
|
if total1:
|
|
pred_rows = [
|
|
(pred, count, f"{count/total1:.1%}")
|
|
for (pred, count) in predecessors[name].most_common(5)
|
|
]
|
|
if total2:
|
|
succ_rows = [
|
|
(succ, count, f"{count/total2:.1%}")
|
|
for (succ, count) in successors[name].most_common(5)
|
|
]
|
|
with Section(name, 3, f"Successors and predecessors for {name}"):
|
|
emit_table(("Predecessors", "Count:", "Percentage:"), pred_rows)
|
|
emit_table(("Successors", "Count:", "Percentage:"), succ_rows)
|
|
|
|
|
|
def calculate_optimization_stats(stats):
|
|
attempts = stats["Optimization attempts"]
|
|
created = stats["Optimization traces created"]
|
|
executed = stats["Optimization traces executed"]
|
|
uops = stats["Optimization uops executed"]
|
|
trace_stack_overflow = stats["Optimization trace stack overflow"]
|
|
trace_stack_underflow = stats["Optimization trace stack underflow"]
|
|
trace_too_long = stats["Optimization trace too long"]
|
|
trace_too_short = stats["Optimiztion trace too short"]
|
|
inner_loop = stats["Optimization inner loop"]
|
|
recursive_call = stats["Optimization recursive call"]
|
|
|
|
return [
|
|
("Optimization attempts", attempts, ""),
|
|
("Traces created", created, format_ratio(created, attempts)),
|
|
("Traces executed", executed, ""),
|
|
("Uops executed", uops, int(uops / (executed or 1))),
|
|
("Trace stack overflow", trace_stack_overflow, ""),
|
|
("Trace stack underflow", trace_stack_underflow, ""),
|
|
("Trace too long", trace_too_long, ""),
|
|
("Trace too short", trace_too_short, ""),
|
|
("Inner loop found", inner_loop, ""),
|
|
("Recursive call", recursive_call, ""),
|
|
]
|
|
|
|
|
|
def calculate_uop_execution_counts(opcode_stats):
|
|
total = 0
|
|
counts = []
|
|
for name, opcode_stat in opcode_stats.items():
|
|
if "execution_count" in opcode_stat:
|
|
count = opcode_stat["execution_count"]
|
|
counts.append((count, name))
|
|
total += count
|
|
counts.sort(reverse=True)
|
|
cumulative = 0
|
|
rows = []
|
|
for count, name in counts:
|
|
cumulative += count
|
|
rows.append(
|
|
(name, count, format_ratio(count, total), format_ratio(cumulative, total))
|
|
)
|
|
return rows
|
|
|
|
|
|
def emit_optimization_stats(stats):
|
|
if "Optimization attempts" not in stats:
|
|
return
|
|
|
|
uop_stats = extract_opcode_stats(stats, "uops")
|
|
|
|
with Section(
|
|
"Optimization (Tier 2) stats", summary="statistics about the Tier 2 optimizer"
|
|
):
|
|
with Section("Overall stats", level=3):
|
|
rows = calculate_optimization_stats(stats)
|
|
emit_table(("", "Count:", "Ratio:"), rows)
|
|
|
|
emit_histogram(
|
|
"Trace length histogram",
|
|
stats,
|
|
"Trace length",
|
|
stats["Optimization traces created"],
|
|
)
|
|
emit_histogram(
|
|
"Optimized trace length histogram",
|
|
stats,
|
|
"Optimized trace length",
|
|
stats["Optimization traces created"],
|
|
)
|
|
emit_histogram(
|
|
"Trace run length histogram",
|
|
stats,
|
|
"Trace run length",
|
|
stats["Optimization traces executed"],
|
|
)
|
|
|
|
with Section("Uop stats", level=3):
|
|
rows = calculate_uop_execution_counts(uop_stats)
|
|
emit_table(("Uop", "Count:", "Self:", "Cumulative:"), rows)
|
|
|
|
with Section("Unsupported opcodes", level=3):
|
|
unsupported_opcodes = extract_opcode_stats(stats, "unsupported_opcode")
|
|
data = []
|
|
for opcode, entry in unsupported_opcodes.items():
|
|
data.append((entry["count"], opcode))
|
|
data.sort(reverse=True)
|
|
rows = [(x[1], x[0]) for x in data]
|
|
emit_table(("Opcode", "Count"), rows)
|
|
|
|
|
|
def emit_comparative_optimization_stats(base_stats, head_stats):
|
|
print("## Comparative optimization stats not implemented\n\n")
|
|
|
|
|
|
def output_single_stats(stats):
|
|
opcode_stats = extract_opcode_stats(stats, "opcode")
|
|
total = get_total(opcode_stats)
|
|
emit_execution_counts(opcode_stats, total)
|
|
emit_pair_counts(opcode_stats, total)
|
|
emit_specialization_stats(opcode_stats, stats["_defines"])
|
|
emit_specialization_overview(
|
|
opcode_stats, total, stats["_specialized_instructions"]
|
|
)
|
|
emit_call_stats(stats, stats["_stats_defines"])
|
|
emit_object_stats(stats)
|
|
emit_gc_stats(stats)
|
|
emit_optimization_stats(stats)
|
|
with Section("Meta stats", summary="Meta statistics"):
|
|
emit_table(("", "Count:"), [("Number of data files", stats["__nfiles__"])])
|
|
|
|
|
|
def output_comparative_stats(base_stats, head_stats):
|
|
base_opcode_stats = extract_opcode_stats(base_stats, "opcode")
|
|
base_total = get_total(base_opcode_stats)
|
|
|
|
head_opcode_stats = extract_opcode_stats(head_stats, "opcode")
|
|
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, head_stats["_defines"]
|
|
)
|
|
emit_comparative_specialization_overview(
|
|
base_opcode_stats,
|
|
base_total,
|
|
head_opcode_stats,
|
|
head_total,
|
|
head_stats["_specialized_instructions"],
|
|
)
|
|
emit_comparative_call_stats(base_stats, head_stats, head_stats["_stats_defines"])
|
|
emit_comparative_object_stats(base_stats, head_stats)
|
|
emit_comparative_gc_stats(base_stats, head_stats)
|
|
emit_comparative_optimization_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()
|