# Sparse benchmarks

# This benchmark is for  sparse matmul performance test.
# They exist for comparing the performance of sparse matrix routines
# `sparse @ vector`, `sparse @ sparse` and `sparse @ dense` with different backends (CPU/CUDA)
# and with other frameworks such as scipy.

import sys
import argparse
import torch
import torch.utils.benchmark as benchmark_utils
from .utils import load_dlmc_dataset
from scipy.sparse import isspmatrix
import os


def scipy_matmul(mat1, mat2):
    if isspmatrix(mat1) and isspmatrix(mat2):
        return mat1.dot(mat2).tocoo()
    return mat1.dot(mat2)

def matmul_backward(a_dense, b_dense, grad_output):
    r1 = a_dense.matmul(b_dense)
    r1.backward(grad_output)


def sparse_matmul_backward(a, b, grad_output):
    c = torch.sparse.mm(a, b)
    c.backward(grad_output)


OPS_MAP = {
    "sparse@sparse": "torch.sparse.mm",
    "sparse@dense": "torch.matmul",
    "sparse@vector": "torch.matmul",
}


# also get the arguments as input from the user using `argparse`
def parse_args():
    parser = argparse.ArgumentParser(description='matmul benchmark')
    parser.add_argument('--path', type=str, help='DLMC dataset path')
    parser.add_argument('--dataset', type=str, default='magnitude_pruning')
    parser.add_argument('--hidden-size', '--hidden_size', default=2048, type=int)
    parser.add_argument('--backward-test', '--backward_test', action="store_true")
    parser.add_argument('--operation', type=str, help="|".join(OPS_MAP.keys()), default=next(iter(OPS_MAP)))
    parser.add_argument('--with-cuda', '--with_cuda', action='store_true')
    parser.add_argument('--timer-min-run-time', '--timer_min_run_time', default=1, type=float)
    return parser


def get_tasks(op, backward_test, device):
    def filter_ops(operation):
        if backward_test:
            test_name = device + ":matmul-backward"
            return [
                (test_name, device, "torch:" + operation.replace("sparse", "dense"),
                 "matmul_backward(dx, dy, grad_output)"),
                (test_name, device, "torch:" + operation, "sparse_matmul_backward(x, y, sparse_grad_output)")
            ]
        else:
            test_name = device + ":matmul-forward"
            return list(filter(None, [
                (test_name, device, "torch:" + operation.replace("sparse", "dense"),
                 "{}(dx, dy)".format(OPS_MAP[operation])),
                (test_name, device, "torch:" + operation, "{}(x, y)".format(OPS_MAP[operation])),
                (test_name, device, "scipy:" + operation, "scipy_matmul(sx, sy)") if device == "cpu" else None
            ]))

    all_operations = {
        "sparse@sparse": filter_ops("sparse@sparse"),
        "sparse@dense": filter_ops("sparse@dense"),
        "sparse@vector": filter_ops("sparse@vector"),
    }
    return all_operations[op]


if __name__ == '__main__':
    parser = parse_args()
    args = parser.parse_args()

    if args.with_cuda and not torch.cuda.is_available():
        raise RuntimeError("No CUDA available")

    dataset_path = args.path
    dataset_name = args.dataset
    dataset_path = os.path.join(dataset_path, dataset_name)
    device = 'cuda' if args.with_cuda else 'cpu'

    tasks = get_tasks(args.operation, args.backward_test, device)
    repeats = 3
    timers = [
        benchmark_utils.Timer(
            stmt=stmt,
            globals={
                "scipy_matmul": scipy_matmul,
                "matmul_backward": matmul_backward,
                "sparse_matmul_backward": sparse_matmul_backward,
                **variables
            },
            label=label,
            sub_label=sub_label,
            description=f"{sparsity}",
            env=device,
        )
        for sparsity in [0.5, 0.7, 0.8, 0.9, 0.95, 0.98]
        for label, device, sub_label, stmt in tasks
        for variables in
        load_dlmc_dataset(dataset_path, args.operation, args.hidden_size, sparsity, device, args.backward_test)
    ]
    measurements = []

    for i, timer in enumerate(timers * repeats):
        m = timer.blocked_autorange(min_run_time=args.timer_min_run_time)
        m.metadata = {
            "device": 'cuda' if m.task_spec.env.find("cuda") >= 0 else 'cpu'
        }
        measurements.append(m)
        print(f"\r{i + 1} / {len(timers) * repeats}", end="")
        sys.stdout.flush()
    print()

    comparison = benchmark_utils.Compare(measurements)

    print("== Results " + "=" * 80 + "\n" + "/" * 95 + "\n")
    comparison.print()
