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# SPDX-License-Identifier: Apache-2.0
"""Summary of a device profile of ``profile_ops.py`` (CPU only, standard library): the OPT_BASELINE.md tables.
python code/scripts/profile_summary.py <ops_perf_results_*.csv> <out.json> [--md out.md]
Reads the Tracy ops CSV (``python -m tracy -r ...``) and its sections ``eager`` .. ``eager_end`` and ``trace`` ..
``trace_end``. The eager section carries per-module (``m:<module>``) and per-conv (``c:<layer>``) signposts; the
replay has the same op sequence (checked op code by op code), so every replayed op inherits the module / layer of
the eager op at the same position. Per section: program count, device kernel sum, FW sum, op-to-op gap sum and
median, span (first FW start -> last FW end at the CSV's chip frequency), cores per op, math fidelities; tables per
category, module, op code and layer; the top single ops; the largest gaps. If the trace section holds several
replays (``profile_ops.py --replays N``), the last replay session is summarised. Input shapes of the CSV are those of
the first op that built a cached program (``--no-op-info-cache`` in tracy shows every op's own), so read shapes of
later ops of the same program with care; durations are per op.
"""
from __future__ import annotations
import argparse
import csv
import json
import re
import statistics
from collections import OrderedDict, defaultdict
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
KERNEL = "DEVICE KERNEL DURATION [ns]"
FW = "DEVICE FW DURATION [ns]"
GAP = "OP TO OP LATENCY [ns]"
START = "DEVICE FW START CYCLE"
END = "DEVICE FW END CYCLE"
CATEGORIES = [ # (category, regex on the op code), first match wins
("segment_reduce (K1 generic_op)", r"GenericOp|generic_op|Generic"),
("conv2d", r"Conv2d|conv2d|Conv2D"),
("halo", r"Halo|halo"),
("matmul (point linears, 1x1 convs, resize matmuls)", r"Matmul|matmul|Linear"),
("slice / DRAM-slicing copies", r"Slice|slice"),
("ternary (addcmul)", r"Ternary|ternary|Addcmul|addcmul"),
("binary (adds, two-term / K-split sums)", r"Binary|binary"),
("softmax", r"Softmax|softmax"),
("reduction", r"Reduce|reduce|Max\b|MaxDevice"),
("embedding (gathers)", r"Embedding|embedding"),
("tilize / untilize / layout", r"Tilize|tilize|Untilize|untilize|ToLayout|Layout"),
("reshape / transpose / permute / concat", r"Reshape|reshape|Transpose|transpose|Permute|permute|View|Concat|concat"),
("move / copy / reshard", r"Move|move|Copy|copy|Shard|shard|Interleaved|interleaved|Reallocate"),
("pad / fill", r"Pad|pad|Fill|fill"),
("unary / typecast (hardswish, relu, sigmoid)", r"Unary|unary|Typecast|typecast"),
]
COMPUTE = ("Conv2dDeviceOperation", "MatmulDeviceOperation")
def category(op: str) -> str:
for name, pat in CATEGORIES:
if re.search(pat, op):
return name
return "other"
def num(row: Dict[str, str], key: str) -> float:
v = (row.get(key) or "").strip()
try:
return float(v) if v else 0.0
except ValueError:
return 0.0
def logical(v: str) -> str:
"""'32[16]' (padded[logical]) -> '16'; '7' -> '7'."""
m = re.match(r"\s*([0-9]+)(?:\[([0-9]+)\])?", v or "")
return (m.group(2) or m.group(1)) if m else "?"
def chip_freq_mhz(csv_path: Path) -> Optional[float]:
for cand in [csv_path.parent / "profile_log_device.csv", csv_path.parent.parent / "profile_log_device.csv",
csv_path.parent / ".logs" / "profile_log_device.csv",
csv_path.parent.parent / ".logs" / "profile_log_device.csv"]:
try:
head = cand.read_text(errors="ignore").splitlines()[0]
except (OSError, IndexError):
continue
m = re.search(r"CHIP_FREQ\[MHz\]:\s*([0-9.]+)", head)
if m:
return float(m.group(1))
return None
def sections(rows: List[Dict[str, str]], name_key: str) -> Dict[str, List[Tuple[Dict[str, str], str, str]]]:
"""{"eager": [(row, module, layer)], "trace": [...]} from the signposts."""
out: Dict[str, List[Tuple[Dict[str, str], str, str]]] = {"eager": [], "trace": []}
cur: Optional[str] = None
module, layer = "", ""
for r in rows:
if (r.get("OP TYPE") or "") == "signpost":
code = r.get(name_key, "")
if code in ("eager", "trace"):
cur, module, layer = code, "", ""
elif code in ("eager_end", "trace_end"):
cur = None
elif code.startswith("m:"):
module, layer = code[2:], ""
elif code.startswith("c:"):
layer = code[2:]
continue
if cur is not None:
out[cur].append((r, module, layer))
return out
def last_replay(sec: List[Tuple[Dict[str, str], str, str]]) -> List[Tuple[Dict[str, str], str, str]]:
"""The ops of the last trace replay session (all of them when the CSV has no session ids)."""
ids = [(r.get("METAL TRACE REPLAY SESSION ID") or "").strip() for r, _, _ in sec]
if not any(ids):
return sec
last = [i for i in ids if i][-1]
return [x for x, i in zip(sec, ids) if i == last]
def summarize(sec: List[Tuple[Dict[str, str], str, str]], name_key: str, freq: float, top: int = 10) -> Dict[str, Any]:
rows = [r for r, _, _ in sec]
kern = [num(r, KERNEL) / 1e3 for r in rows]
fw = [num(r, FW) / 1e3 for r in rows]
gaps = [num(r, GAP) / 1e3 for r in rows]
if gaps:
gaps[0] = 0.0 # the first op's gap reaches back across the section boundary
starts = [num(r, START) for r in rows if num(r, START) > 0]
ends = [num(r, END) for r in rows if num(r, END) > 0]
span = (max(ends) - min(starts)) / freq if starts and ends else None
total = sum(kern) or 1.0
def group(keyfn) -> List[Dict[str, Any]]:
agg: Dict[str, List[float]] = OrderedDict()
for (r, m, l), k, g in zip(sec, kern, gaps):
key = keyfn(r, m, l)
a = agg.setdefault(key, [0, 0.0, 0.0, 0.0])
a[0] += 1
a[1] += k
a[2] += g
if r.get(name_key, "") in COMPUTE:
a[3] += k
return [{"key": key, "ops": int(c), "kernel_us": round(t, 1), "share": round(t / total, 4),
"gap_us": round(gs, 1), "compute_us": round(cu, 1)}
for key, (c, t, gs, cu) in sorted(agg.items(), key=lambda kv: -kv[1][1])]
fid: Dict[str, int] = defaultdict(int)
cores = []
for r in rows:
f = (r.get("MATH FIDELITY") or "").strip()
if f:
fid[f] += 1
cores.append(num(r, "CORE COUNT"))
order = sorted(range(len(rows)), key=lambda i: -kern[i])[:top]
top_ops = []
for i in order:
r, m, l = sec[i]
shape = "x".join(logical(r.get(f"INPUT_0_{d}_PAD[LOGICAL]") or r.get(f"INPUT_0_{d}") or "") for d in "WZYX")
oshape = "x".join(logical(r.get(f"OUTPUT_0_{d}_PAD[LOGICAL]") or r.get(f"OUTPUT_0_{d}") or "") for d in "WZYX")
top_ops.append({"index": i, "op": r.get(name_key), "module": m, "layer": l, "kernel_us": round(kern[i], 1),
"fw_us": round(fw[i], 1), "cores": int(num(r, "CORE COUNT")),
"fidelity": (r.get("MATH FIDELITY") or "").strip(), "input0": shape, "output0": oshape,
"in0_dtype": (r.get("INPUT_0_DATATYPE") or "").strip(),
"in0_mem": (r.get("INPUT_0_MEMORY") or "").strip(),
"out0_mem": (r.get("OUTPUT_0_MEMORY") or "").strip()})
gap_order = sorted(range(1, len(rows)), key=lambda i: -gaps[i])[:top]
top_gaps = [{"index": i, "op": rows[i].get(name_key), "after": rows[i - 1].get(name_key),
"gap_us": round(gaps[i], 1), "module": sec[i][1], "layer": sec[i][2]} for i in gap_order]
nz_gaps = [g for g in gaps[1:] if g > 0]
ops_list = [{"index": i, "op": r.get(name_key), "module": m, "layer": l, "kernel_us": round(k, 2),
"gap_us": round(g, 2), "cores": int(num(r, "CORE COUNT")),
"fidelity": (r.get("MATH FIDELITY") or "").strip(),
"input0": "x".join(logical(r.get(f"INPUT_0_{d}_PAD[LOGICAL]") or r.get(f"INPUT_0_{d}") or "")
for d in "WZYX"),
"in0_mem": (r.get("INPUT_0_MEMORY") or "").strip(),
"out0_mem": (r.get("OUTPUT_0_MEMORY") or "").strip()}
for i, ((r, m, l), k, g) in enumerate(zip(sec, kern, gaps))]
return {
"ops": len(rows), "kernel_sum_us": round(sum(kern), 1), "fw_sum_us": round(sum(fw), 1),
"op2op_sum_us": round(sum(gaps), 1),
"op2op_median_us": round(statistics.median(nz_gaps), 2) if nz_gaps else None,
"span_us": round(span, 1) if span else None, "freq_mhz": freq,
"cores_median": statistics.median(cores) if cores else None,
"ops_below_120_cores": sum(1 for c in cores if 0 < c < 120),
"compute_us": round(sum(k for r, k in zip(rows, kern) if r.get(name_key, "") in COMPUTE), 1),
"fidelity": dict(fid),
"by_op": group(lambda r, m, l: r.get(name_key, "?")),
"by_category": group(lambda r, m, l: category(r.get(name_key, "?"))),
"by_module": group(lambda r, m, l: m or "?"),
"by_layer": group(lambda r, m, l: l or f"[{m or '?'}]"),
"top_ops": top_ops, "top_gaps": top_gaps, "ops_list": ops_list,
}
def md_table(rows: List[Dict[str, Any]], n: int, label: str) -> str:
out = [f"| {label} | ops | kernel us | share | compute us | op-to-op us |", "|---|---:|---:|---:|---:|---:|"]
for r in rows[:n]:
out.append(f"| {r['key']} | {r['ops']} | {r['kernel_us']:.1f} | {100 * r['share']:.1f} % | "
f"{r['compute_us']:.1f} | {r['gap_us']:.1f} |")
return "\n".join(out)
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("csv")
ap.add_argument("out_json")
ap.add_argument("--md")
ap.add_argument("--freq-mhz", type=float, default=None)
a = ap.parse_args()
path = Path(a.csv)
with open(path, newline="") as f:
rows = list(csv.DictReader(f))
name_key = "OP CODE" if rows and "OP CODE" in rows[0] else "OP NAME"
freq = a.freq_mhz or chip_freq_mhz(path) or 1350.0
secs = sections(rows, name_key)
eager, trace_all = secs["eager"], secs["trace"]
trace = last_replay(trace_all)
# the replay inherits the module / layer of the eager op at the same position (identical op sequence)
same = len(eager) == len(trace) and all(e[0].get(name_key) == t[0].get(name_key) for e, t in zip(eager, trace))
if same:
trace = [(r, e[1], e[2]) for (r, _, _), e in zip(trace, eager)]
res = {"csv": str(path), "columns": list(rows[0].keys()) if rows else [], "freq_mhz": freq,
"trace_matches_eager_op_sequence": same, "trace_section_ops": len(trace_all),
"trace_ids": sorted({(r.get("METAL TRACE ID") or "").strip() for r, _, _ in trace_all}),
"replay_sessions": sorted({(r.get("METAL TRACE REPLAY SESSION ID") or "").strip() for r, _, _ in trace_all}),
"eager": summarize(eager, name_key, freq), "trace": summarize(trace, name_key, freq)}
Path(a.out_json).write_text(json.dumps(res, indent=1) + "\n")
t = res["trace"]
lines = [f"csv: {path}", f"freq {freq} MHz; trace op sequence == eager: {same}; trace ids {res['trace_ids']}; "
f"replay sessions {res['replay_sessions']} ({len(trace_all)} ops in the trace section)"]
for s in ("eager", "trace"):
x = res[s]
lines.append(f"{s}: ops {x['ops']} kernel {x['kernel_sum_us']:.1f} us (conv/matmul {x['compute_us']:.1f}) "
f"fw {x['fw_sum_us']:.1f} us op2op {x['op2op_sum_us']:.1f} us (median {x['op2op_median_us']}) "
f"span {x['span_us']} us cores median {x['cores_median']} <120 cores: "
f"{x['ops_below_120_cores']} fidelity {x['fidelity']}")
lines += ["", md_table(t["by_category"], 20, "category (trace)"), "", md_table(t["by_module"], 20, "module (trace)"),
"", md_table(t["by_op"], 25, "op code (trace)"), "", md_table(t["by_layer"], 40, "layer (trace)"), "",
"| # | op | module | layer | kernel us | cores | fidelity | input0 | output0 | in mem | out mem |",
"|---:|---|---|---|---:|---:|---|---|---|---|---|"]
for k, o in enumerate(t["top_ops"], 1):
lines.append(f"| {k} | {o['op']} | {o['module']} | {o['layer']} | {o['kernel_us']:.1f} | {o['cores']} | "
f"{o['fidelity']} | {o['input0']} | {o['output0']} | {o['in0_mem']} | {o['out0_mem']} |")
lines += ["", "largest op-to-op gaps (trace): " + json.dumps(t["top_gaps"])]
text = "\n".join(lines)
print(text)
if a.md:
Path(a.md).write_text(text + "\n")
if __name__ == "__main__":
main()
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