#!/usr/bin/env python3
# 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_<t>`` .. ``eager_<t>_end`` and
``trace_<t>`` .. ``trace_<t>_end`` for each trace ``<t>`` (``a`` = ``encseg``, ``b`` = ``det``). The eager sections
carry per-module (``m:<module>``) and per-layer (``c:<layer>``) signposts; each replay has the same op sequence as its
eager forward (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, layer (module / layer) and layer kind (the layer name summed over all blocks); the top single ops; the largest
gaps. If a trace section holds several replays, 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
    ("conv2d", r"Conv2d|conv2d|Conv2D"),
    ("conv_transpose2d", r"ConvTranspose|conv_transpose"),
    ("halo", r"Halo|halo"),
    ("attention (SDPA)", r"SDPA|Sdpa|ScaledDotProduct|sdpa"),
    ("top-k", r"TopK|Topk|topk"),
    ("softmax", r"Softmax|softmax"),
    ("layer norm", r"LayerNorm|layernorm"),
    ("custom kernel (K1 segment max)", r"Generic|generic"),
    ("pool (local max)", r"Pool|pool"),
    ("matmul (linear, 1x1 conv)", r"Matmul|matmul|Linear"),
    ("slice / DRAM-slicing copies", r"Slice|slice"),
    ("binary (residual / two-term sums, bias adds, masks)", r"Binary|binary"),
    ("reduction (max / sum)", r"Reduce|reduce|Max\b|MaxDevice"),
    ("embedding (gathers: CPE im2col, attention, inverse, unpool, BEV)", r"Embedding|embedding"),
    ("tilize / untilize / layout", r"Tilize|tilize|Untilize|untilize|ToLayout|Layout"),
    ("reshape / transpose / permute", r"Reshape|reshape|Transpose|transpose|Permute|permute|View"),
    ("move / copy / reshard", r"Move|move|Copy|copy|Shard|shard|Interleaved|interleaved|Reallocate"),
    ("pad / fill", r"Pad|pad|Fill|fill"),
    ("unary / typecast", r"Unary|unary|Typecast|typecast"),
]
COMPUTE = ("Conv2dDeviceOperation", "MatmulDeviceOperation", "ConvTranspose2dDeviceOperation")


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_a": [(row, module, layer)], "trace_a": [...], ...} from the signposts."""
    out: Dict[str, List[Tuple[Dict[str, str], str, str]]] = {}
    cur: Optional[str] = None
    module, layer = "", ""
    for r in rows:
        if (r.get("OP TYPE") or "") == "signpost":
            code = r.get(name_key, "")
            if re.fullmatch(r"(eager|trace)_[a-z]+", code):
                cur, module, layer = code, "", ""
                out.setdefault(cur, [])
            elif re.fullmatch(r"(eager|trace)_[a-z]+_end", code):
                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 = 25) -> 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: f"{m or '?'}/{l}" if l else f"{m or '?'}"),
        "by_layer_kind": group(lambda r, m, l: l if l else 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)
    tags = sorted({k.split("_", 1)[1] for k in secs if k.startswith("trace_")})
    res: Dict[str, Any] = {"csv": str(path), "columns": list(rows[0].keys()) if rows else [], "freq_mhz": freq,
                           "traces": {}}
    lines = [f"csv: {path}", f"freq {freq} MHz"]
    for tag in tags:
        eager, trace_all = secs.get(f"eager_{tag}", []), secs[f"trace_{tag}"]
        trace = last_replay(trace_all)
        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["traces"][tag] = {
            "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) if eager else None, "trace": summarize(trace, name_key, freq)}
        x = res["traces"][tag]
        lines.append(f"\n## trace {tag}: op sequence == eager: {same}; trace ids {x['trace_ids']}; replay sessions "
                     f"{x['replay_sessions']} ({len(trace_all)} ops in the trace section)")
        for s in ("eager", "trace"):
            y = x[s]
            if y is None:
                continue
            lines.append(f"{s}: ops {y['ops']}  kernel {y['kernel_sum_us']:.1f} us (conv/matmul {y['compute_us']:.1f})  "
                         f"fw {y['fw_sum_us']:.1f} us  op2op {y['op2op_sum_us']:.1f} us (median {y['op2op_median_us']})  "
                         f"span {y['span_us']} us  cores median {y['cores_median']}  <120 cores: "
                         f"{y['ops_below_120_cores']}  fidelity {y['fidelity']}")
        t = x["trace"]
        lines += ["", md_table(t["by_category"], 25, f"category (trace {tag})"), "",
                  md_table(t["by_module"], 40, f"module (trace {tag})"), "",
                  md_table(t["by_layer_kind"], 30, f"layer kind (trace {tag})"), "",
                  md_table(t["by_op"], 30, f"op code (trace {tag})"), "",
                  md_table(t["by_layer"], 60, f"module/layer (trace {tag})"), "",
                  "| # | 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 += ["", f"largest op-to-op gaps (trace {tag}): " + json.dumps(t["top_gaps"])]
    Path(a.out_json).write_text(json.dumps(res, indent=1) + "\n")
    text = "\n".join(lines)
    print(text)
    if a.md:
        Path(a.md).write_text(text + "\n")


if __name__ == "__main__":
    main()