You are an expert on neural network. Your need to provide implementation for the model pruning alg in python. Here are the whole project context:import argparse
import os
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from importlib.metadata import version

from lib.prune import prune_wanda, prune_magnitude, prune_sparsegpt, prune_ablate, check_sparsity, find_layers, prune_opposite_magnitude, prune_mama, prune_mama_mutation_1, prune_mama_mutation_2
from lib.eval import eval_ppl, eval_zero_shot
from lib.prune_one_shot_aigc import prune_aigc_technique1, prune_aigc_technique2, prune_aigc_technique3, prune_aigc_technique4, prune_aigc_technique5, prune_aigc_technique6, prune_aigc_technique7, prune_aigc_technique8, prune_aigc_technique9, prune_aigc_technique10

print('torch', version('torch'))
print('transformers', version('transformers'))
print('accelerate', version('accelerate'))
print('# of gpus: ', torch.cuda.device_count())

def get_llm(model_name, cache_dir="llm_weights"):
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
cache_dir=cache_dir,
low_cpu_mem_usage=True,
device_map="auto"
)

model.seqlen = model.config.max_position_embeddings
return model

def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, help='LLaMA model')
parser.add_argument('--seed', type=int, default=0, help='Seed for sampling the calibration data.')
parser.add_argument('--nsamples', type=int, default=128, help='Number of calibration samples.')
parser.add_argument('--sparsity_ratio', type=float, default=0, help='Sparsity level')
parser.add_argument("--sparsity_type", type=str, choices=["unstructured", "4:8", "2:4"])
parser.add_argument("--prune_method", type=str, choices=["magnitude", "wanda", "sparsegpt",
"ablate_mag_seq", "ablate_wanda_seq", "ablate_mag_iter", "ablate_wanda_iter", "search", "opposite_magnitude", "mama", "mama_mutation_1", "mama_mutation_2", "aigc_technique1", "aigc_technique2", "aigc_technique3", "aigc_technique4", "aigc_technique5", "aigc_technique6", "aigc_technique7", "aigc_technique8", "aigc_technique9", "aigc_technique10"])
parser.add_argument("--cache_dir", default="llm_weights", type=str )
parser.add_argument('--use_variant', action="store_true", help="whether to use the wanda variant described in the appendix")
parser.add_argument('--save', type=str, default=None, help='Path to save results.')
parser.add_argument('--save_model', type=str, default=None, help='Path to save the pruned model.')

parser.add_argument("--eval_zero_shot", action="store_true")
args = parser.parse_args()

# Setting seeds for reproducibility
np.random.seed(args.seed)
torch.random.manual_seed(args.seed)

# Handling n:m sparsity
prune_n, prune_m = 0, 0
if args.sparsity_type != "unstructured":
assert args.sparsity_ratio == 0.5, "sparsity ratio must be 0.5 for structured N:M sparsity"
prune_n, prune_m = map(int, args.sparsity_type.split(":"))

model_name = args.model.split("/")[-1]
print(f"loading llm model {args.model}")
model = get_llm(args.model, args.cache_dir)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=False)

device = torch.device("cuda:0")
if "30b" in args.model or "65b" in args.model: # for 30b and 65b we use device_map to load onto multiple A6000 GPUs, thus the processing here.
device = model.hf_device_map["lm_head"]
print("use device ", device)

if args.sparsity_ratio != 0:
print("pruning starts")
if args.prune_method == "wanda":
prune_wanda(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "magnitude":
prune_magnitude(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "sparsegpt":
prune_sparsegpt(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif "ablate" in args.prune_method:
prune_ablate(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "opposite_magnitude":
prune_opposite_magnitude(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "mama":
prune_mama(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "mama_mutation_1":
prune_mama_mutation_1(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "mama_mutation_2":
prune_mama_mutation_2(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique1":
prune_aigc_technique1(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique2":
prune_aigc_technique2(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique3":
prune_aigc_technique3(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique4":
prune_aigc_technique4(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique5":
prune_aigc_technique5(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique6":
prune_aigc_technique6(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique7":
prune_aigc_technique7(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique8":
prune_aigc_technique8(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique9":
prune_aigc_technique9(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)
elif args.prune_method == "aigc_technique10":
prune_aigc_technique10(args, model, tokenizer, device, prune_n=prune_n, prune_m=prune_m)

################################################################
print("*"*30)
sparsity_ratio = check_sparsity(model)
print(f"sparsity sanity check {sparsity_ratio:.4f}")
print("*"*30)
################################################################
ppl_test = eval_ppl(args, model, tokenizer, device)
print(f"wikitext perplexity {ppl_test}")

if not os.path.exists(args.save):
os.makedirs(args.save)
save_filepath = os.path.join(args.save, f"log_{args.prune_method}.txt")
with open(save_filepath, "w") as f:
print("method\tactual_sparsity\tppl_test", file=f, flush=True)
print(f"{args.prune_method}\t{sparsity_ratio:.4f}\t{ppl_test:.4f}", file=f, flush=True)

if args.eval_zero_shot:
accelerate=False
if "30b" in args.model or "65b" in args.model or "70b" in args.model:
accelerate=True

task_list = ["boolq", "rte","hellaswag","winogrande", "arc_easy","arc_challenge", "openbookqa"]
num_shot = 0
results = eval_zero_shot(args.model, model, tokenizer, task_list, num_shot, accelerate)
print("********************************")
print("zero_shot evaluation results")
print(results)

if args.save_model:
model.save_pretrained(args.save_model)
tokenizer.save_pretrained(args.save_model)

if __name__ == '__main__':
main()