qibotn/tools/inspect_contraction_tree.py

"""Inspect cotengra contraction trees for dominant torch matmul shapes."""

from __future__ import annotations

import argparse
import importlib
import math
import pickle
from collections import Counter, defaultdict
from pathlib import Path


def _prod(values):
    out = 1
    for value in values:
        out *= int(value)
    return out


def _broadcast_batch(a_batch, b_batch):
    if a_batch == b_batch:
        return _prod(a_batch)
    if not a_batch:
        return _prod(b_batch)
    if not b_batch:
        return _prod(a_batch)

    ndim = max(len(a_batch), len(b_batch))
    a_batch = (1,) * (ndim - len(a_batch)) + tuple(a_batch)
    b_batch = (1,) * (ndim - len(b_batch)) + tuple(b_batch)
    return _prod(max(a, b) for a, b in zip(a_batch, b_batch))


def _load_tree(path, index):
    with Path(path).open("rb") as f:
        payload = pickle.load(f)
    trees = payload["trees"] if isinstance(payload, dict) else payload
    if not isinstance(trees, (list, tuple)):
        trees = [trees]
    return trees[index]


def _analyze_tree(tree):
    contract_mod = importlib.import_module("cotengra.contract")
    contractions = contract_mod.extract_contractions(tree)
    size_dict = tree.size_dict
    ops = []
    counts = Counter()

    for op_index, (parent, left, right, tdot, arg, perm) in enumerate(contractions):
        if left is None and right is None:
            counts["preprocess"] += 1
            continue

        left_inds = tree.get_inds(left)
        right_inds = tree.get_inds(right)
        parent_inds = tree.get_inds(parent)
        left_shape = tuple(size_dict[ix] for ix in left_inds)
        right_shape = tuple(size_dict[ix] for ix in right_inds)

        if tdot:
            parsed = contract_mod._parse_tensordot_axes_to_matmul(
                arg,
                left_shape,
                right_shape,
            )
        else:
            parsed = contract_mod._parse_eq_to_batch_matmul(
                arg,
                left_shape,
                right_shape,
            )

        (
            _eq_a,
            _eq_b,
            new_shape_a,
            new_shape_b,
            _new_shape_ab,
            _perm_ab,
            pure_multiplication,
        ) = parsed

        matmul_shape = None
        matmul_flops = 0
        if pure_multiplication:
            kind = "mul"
        else:
            a_shape = tuple(new_shape_a or left_shape)
            b_shape = tuple(new_shape_b or right_shape)
            batch = _broadcast_batch(a_shape[:-2], b_shape[:-2])
            m, k, n = int(a_shape[-2]), int(a_shape[-1]), int(b_shape[-1])
            kind = "mm" if batch == 1 else "bmm"
            matmul_shape = (batch, m, k, n)
            matmul_flops = batch * m * k * n

        tree_flops = int(tree.get_flops(parent))
        out_size = int(tree.get_size(parent))
        ops.append(
            {
                "index": op_index,
                "kind": kind,
                "matmul_shape": matmul_shape,
                "matmul_flops": matmul_flops,
                "tree_flops": tree_flops,
                "out_size": out_size,
                "left_shape": left_shape,
                "right_shape": right_shape,
                "left_rank": len(left_inds),
                "right_rank": len(right_inds),
                "out_rank": len(parent_inds),
                "perm": perm,
            }
        )
        counts[kind] += 1

    return contractions, ops, counts


def _format_log(value, base):
    return "-inf" if value <= 0 else f"{math.log(value, base):.3f}"


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("tree", help="Pickle file containing one tree or {'trees': [...]}.")
    parser.add_argument("--index", type=int, default=0, help="Tree index in the file.")
    parser.add_argument("--top", type=int, default=20, help="Number of top ops to print.")
    parser.add_argument(
        "--dtype-bytes",
        type=int,
        default=8,
        help="Bytes per element for memory estimates, for example 8 for complex64.",
    )
    args = parser.parse_args()

    tree = _load_tree(args.tree, args.index)
    contractions, ops, counts = _analyze_tree(tree)
    nslices = int(getattr(tree, "multiplicity", 1))
    per_slice_flops = sum(op["tree_flops"] for op in ops)
    per_slice_write = sum(op["out_size"] for op in ops)
    max_out = max((op["out_size"] for op in ops), default=0)
    all_flops = per_slice_flops * nslices
    all_write = per_slice_write * nslices

    print(f"tree={args.tree} index={args.index}")
    print(
        "summary "
        f"slices={nslices} contractions={len(contractions)} "
        f"counts={dict(counts)}"
    )
    print(
        "per_slice "
        f"log10_flops={_format_log(per_slice_flops, 10)} "
        f"log10_write={_format_log(per_slice_write, 10)} "
        f"log2_max_output={_format_log(max_out, 2)} "
        f"max_output_gib={max_out * args.dtype_bytes / 1024**3:.6g}"
    )
    print(
        "all_slices "
        f"log10_flops={_format_log(all_flops, 10)} "
        f"log10_write={_format_log(all_write, 10)}"
    )

    print(f"\ntop_{args.top}_ops_by_flops")
    for op in sorted(ops, key=lambda item: item["tree_flops"], reverse=True)[: args.top]:
        print(
            f"op={op['index']} kind={op['kind']} "
            f"flops={op['tree_flops']:.6e} out={op['out_size']:.6e} "
            f"matmul={op['matmul_shape']} "
            f"ranks=({op['left_rank']},{op['right_rank']}->{op['out_rank']}) "
            f"lhs={op['left_shape']} rhs={op['right_shape']}"
        )

    by_shape = defaultdict(lambda: [0, 0, 0])
    for op in ops:
        shape = op["matmul_shape"]
        if shape is None:
            continue
        by_shape[shape][0] += 1
        by_shape[shape][1] += op["tree_flops"]
        by_shape[shape][2] += op["out_size"]

    print(f"\ntop_{args.top}_matmul_shapes_by_flops")
    for shape, (count, flops, out_size) in sorted(
        by_shape.items(),
        key=lambda item: item[1][1],
        reverse=True,
    )[: args.top]:
        print(
            f"shape={shape} count={count} "
            f"flops={flops:.6e} output={out_size:.6e}"
        )

    print(f"\ntop_{args.top}_matmul_shapes_by_count")
    for shape, (count, flops, out_size) in sorted(
        by_shape.items(),
        key=lambda item: item[1][0],
        reverse=True,
    )[: args.top]:
        print(
            f"shape={shape} count={count} "
            f"flops={flops:.6e} output={out_size:.6e}"
        )


if __name__ == "__main__":
    main()