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add new benchmark suite
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‎benchmarks/scripts_v2/benchmark_cli.py‎

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import uuid
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import os
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import json
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import argparse
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import time
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import numpy as np
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import cpuinfo
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import sys
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import datetime
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import tensorcircuit as tc
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from benchmark_core import benchmark_mega_function
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def arg():
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parser = argparse.ArgumentParser(description="TensorCircuit Benchmark Parameters.")
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parser.add_argument(
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"-n", dest="n", type=int, nargs=1, help="# of Qubits", default=[12]
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)
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parser.add_argument(
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"-nlayer", dest="nlayer", type=int, nargs=1, help="# of layers", default=[3]
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)
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parser.add_argument(
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"-nitrs", dest="nitrs", type=int, nargs=1, help="# of iterations", default=[10]
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)
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parser.add_argument(
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"-t", dest="timeLimit", type=int, nargs=1, help="Time limit(s)", default=[600]
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)
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parser.add_argument(
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"-gpu", dest="isgpu", type=int, nargs=1, help="GPU available", default=[0]
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)
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parser.add_argument(
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"-lx", dest="lx", type=int, nargs=1, help="Lattice size x (for 2D)", default=[3]
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)
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parser.add_argument(
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"-ly", dest="ly", type=int, nargs=1, help="Lattice size y (for 2D)", default=[4]
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)
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parser.add_argument(
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"-path",
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dest="path",
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type=str,
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nargs=1,
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help="output json dir path ended with /",
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default=[None],
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)
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parser.add_argument(
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"-circuit_type",
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dest="circuit_type",
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type=str,
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nargs=1,
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help="Type of circuit (circuit, dmcircuit, mpscircuit)",
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default=["circuit"],
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)
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parser.add_argument(
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"-layout_type",
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dest="layout_type",
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type=str,
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nargs=1,
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help="Circuit layout (1d, 2d)",
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default=["1d"],
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)
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parser.add_argument(
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"-operation",
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dest="operation",
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type=str,
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nargs=1,
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help="Operation to perform (state, sample, exps)",
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default=["state"],
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)
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parser.add_argument(
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"-noisy",
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dest="noisy",
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type=int,
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nargs=1,
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help="Whether to add noise (0 or 1)",
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default=[0],
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)
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parser.add_argument(
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"-noisy_type",
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dest="noisy_type",
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type=str,
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nargs=1,
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help="Type of noise channel (depolarizing, amplitudedamping)",
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default=["depolarizing"],
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)
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parser.add_argument(
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"-use_grad",
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dest="use_grad",
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type=int,
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nargs=1,
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help="Whether to compute gradient (0 or 1)",
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default=[0],
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)
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parser.add_argument(
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"-use_vmap",
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dest="use_vmap",
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type=int,
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nargs=1,
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help="Whether to use vectorized operations (0 or 1)",
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default=[0],
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)
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parser.add_argument(
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"-batch_size",
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dest="batch_size",
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type=int,
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nargs=1,
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help="Batch size for vmap operations",
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default=[5],
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)
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parser.add_argument(
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"-backend",
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dest="backend",
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type=str,
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nargs=1,
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help="Backend to use (tensorflow, jax, pytorch)",
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default=["tensorflow"],
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)
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parser.add_argument(
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"-dtype",
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dest="dtype",
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type=str,
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nargs=1,
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help="Data type (complex64, complex128)",
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default=["complex64"],
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)
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parser.add_argument(
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"-contractor",
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dest="contractor",
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type=str,
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nargs=1,
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help="Contractor setting (e.g., cotengra-16-128)",
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default=[None],
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)
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args = parser.parse_args()
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return [
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args.n[0],
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args.nlayer[0],
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args.nitrs[0],
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args.timeLimit[0],
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args.isgpu[0],
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args.lx[0],
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args.ly[0],
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args.path[0],
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args.circuit_type[0],
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args.layout_type[0],
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args.operation[0],
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args.noisy[0],
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args.noisy_type[0],
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args.use_grad[0],
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args.use_vmap[0],
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args.batch_size[0],
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args.backend[0],
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args.dtype[0],
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args.contractor[0],
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]
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def timing(f, nitrs, timeLimit, params):
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t0 = time.time()
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a = f(params)
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if hasattr(a, "block_until_ready"):
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a.block_until_ready()
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t1 = time.time()
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Nitrs = 1e-8
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for _ in range(nitrs):
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a = f(params)
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if hasattr(a, "block_until_ready"):
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a.block_until_ready()
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Nitrs += 1
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if time.time() - t1 > timeLimit:
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break
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t2 = time.time()
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return t1 - t0, (t2 - t1) / Nitrs, int(Nitrs)
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def save(data, _uuid, path):
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if path is None:
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return
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with open(path + _uuid + ".json", "w") as f:
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json.dump(
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data,
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f,
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indent=4,
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)
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def benchmark_cli(
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uuid,
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n,
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nlayer,
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nitrs,
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timeLimit,
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isgpu,
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lx,
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ly,
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circuit_type,
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layout_type,
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operation,
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noisy,
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noisy_type,
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use_grad,
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use_vmap,
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batch_size,
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backend,
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dtype,
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contractor,
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path,
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):
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meta = {}
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# Setup GPU
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if isgpu == 0:
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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meta["isgpu"] = "off"
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else:
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meta["isgpu"] = "on"
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# Setup backend and dtype
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tc.set_backend(backend)
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tc.set_dtype(dtype)
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meta["Software"] = "tensorcircuit-ng"
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meta["Cpuinfo"] = cpuinfo.get_cpu_info()["brand_raw"]
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meta["Version"] = {
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"sys": sys.version,
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"tensorcircuit": tc.__version__,
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"numpy": np.__version__,
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}
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meta["Benchmark test parameters"] = {
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"nQubits": n,
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"nlayer": nlayer,
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"nitrs": nitrs,
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"timeLimit": timeLimit,
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"lx": lx,
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"ly": ly,
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"circuit_type": circuit_type,
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"layout_type": layout_type,
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"operation": operation,
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"noisy": noisy,
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"noisy_type": noisy_type,
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"use_grad": use_grad,
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"use_vmap": use_vmap,
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"batch_size": batch_size,
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"backend": backend,
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"dtype": dtype,
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"contractor": contractor,
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}
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meta["UUID"] = uuid
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meta["Benchmark Time"] = (
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datetime.datetime.now().astimezone().strftime("%Y-%m-%d %H:%M %Z")
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)
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meta["Results"] = {}
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# Create benchmark function using the mega function
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benchmark_func = benchmark_mega_function(
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nqubits=n,
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nlayers=nlayer,
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lx=lx,
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ly=ly,
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circuit_type=circuit_type,
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layout_type=layout_type,
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operation=operation,
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noisy=bool(noisy),
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noisy_type=noisy_type,
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use_grad=bool(use_grad),
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use_vmap=bool(use_vmap),
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contractor=contractor,
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)
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# Create parameters for testing
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params_shape = (nlayer, n) # Match the format in generate_1d_circuit
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if use_vmap:
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params_shape = (batch_size, nlayer, n)
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params = tc.backend.convert_to_tensor(
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np.random.uniform(0, 2 * np.pi, size=params_shape).astype(
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dtype.replace("complex", "float")
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)
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)
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# Run benchmark
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ct, it, Nitrs = timing(benchmark_func, nitrs, timeLimit, params)
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meta["Results"] = {
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"Construction time": ct,
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"Iteration time": it,
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"# of actual iterations": Nitrs,
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}
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print(meta)
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return meta
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if __name__ == "__main__":
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_uuid = str(uuid.uuid4())
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(
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n,
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nlayer,
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nitrs,
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timeLimit,
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isgpu,
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lx,
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ly,
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path,
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circuit_type,
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layout_type,
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operation,
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noisy,
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noisy_type,
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use_grad,
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use_vmap,
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batch_size,
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backend,
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dtype,
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contractor,
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) = arg()
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results = benchmark_cli(
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_uuid,
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n,
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nlayer,
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nitrs,
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timeLimit,
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isgpu,
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lx,
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ly,
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circuit_type,
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layout_type,
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operation,
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noisy,
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noisy_type,
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use_grad,
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use_vmap,
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batch_size,
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backend,
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dtype,
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contractor,
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path,
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)
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save(results, _uuid, path)

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