forked from jiuyuan/InfiniTensor
154 lines
4.3 KiB
Python
154 lines
4.3 KiB
Python
import argparse
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import os
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import time
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import multiprocessing as mp
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from pyinfinitensor.onnx import OnnxStub, backend
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import onnx
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from onnx.external_data_helper import convert_model_to_external_data
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import numpy as np
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from parallel_opt import parallel_model
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os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
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def parse_args():
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parser = argparse.ArgumentParser(description="launch distributed infinitensor")
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parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
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parser.add_argument(
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"--nproc_per_node", type=int, default=1, help="number of processes per node"
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)
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parser.add_argument(
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"--name", type=str, default="test", help="name of this instance."
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)
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parser.add_argument(
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"--model", type=str, required=True, help="path to the ONNX model file."
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)
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parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
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parser.add_argument("--length", type=int, default=1, help="sequence length.")
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parser.add_argument(
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"--gen_std",
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action="store_true",
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help="whether to generate the standard results.",
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)
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args = parser.parse_args()
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print("arg setting: ", args)
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return (
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args.num_nodes,
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args.nproc_per_node,
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args.name,
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args.model,
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args.batch_size,
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args.length,
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args.gen_std,
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)
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def run_model(model, runtime, inputs: np.array, n=20):
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stub = OnnxStub(model, runtime)
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next(stub.inputs.items().__iter__())[1].copyin_numpy(inputs)
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stub.tune()
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stub.run()
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# get outputs
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outputs = np.array(next(stub.outputs.items().__iter__())[1].copyout_float())
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# bench
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next(stub.inputs.items().__iter__())[1].copyin_numpy(inputs)
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begin = time.time()
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for _ in range(n):
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stub.run()
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end = time.time()
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avg_time = (end - begin) / n
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print(f"average time: {avg_time}")
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return outputs
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def run_and_compare(name, model, runtime):
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data = np.load(f"{name}_inputs.npy")
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results = np.load(f"{name}_results.npy")
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outputs = run_model(model, runtime, data)
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print("outputs sum:", outputs.sum())
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print("max abs diff:", abs(outputs - results).max())
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print("max rel diff:", abs((outputs - results) / results).max())
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# assert np.allclose(outputs, results, rtol=1e-3, atol=1e-6)
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def start_worker(
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name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto
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):
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dist_name = name + "_dist"
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model = parallel_model(model, world_size, rank)
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extern_path = f"./{dist_name}_rank{rank}.pb"
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if os.path.exists(extern_path):
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os.remove(extern_path)
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convert_model_to_external_data(
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model,
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all_tensors_to_one_file=True,
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location=extern_path,
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size_threshold=1024,
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convert_attribute=False,
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)
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onnx.save(model, f"./{dist_name}_rank{rank}.onnx")
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runtime = backend.CudaRuntime(local_rank)
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# print("init comm")
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runtime.init_comm(
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dist_name,
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world_size,
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rank,
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)
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run_and_compare(name, model, runtime)
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def start_single(name, model):
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runtime = backend.CudaRuntime(0)
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run_and_compare(name, model, runtime)
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def gen_standard(name, model, voc_size, bs, len):
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# generate standard results
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data = np.random.randint(0, voc_size, (bs, len), dtype=np.int32)
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np.save(f"{name}_inputs", data)
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runtime = backend.CudaRuntime(0)
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outputs = run_model(model, runtime, data, 1)
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np.save(f"{name}_results", outputs)
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def main():
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nnodes, nproc_per_node, name, model_path, bs, length, gen_std = parse_args()
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model = onnx.load(model_path)
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# generate standart output
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if gen_std:
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print(f"generate standard data for {name}.")
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# a small vocabulary size to fit all LLM.
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voc_size = 1000
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gen_standard(name, model, voc_size, bs, length)
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return
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# run single process.
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# use standalone process to isolate cuda.
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p = mp.Process(target=start_single, args=(name, model))
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p.start()
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p.join()
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# run distributed parallel.
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world_size = nnodes * nproc_per_node
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workers = [
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mp.Process(
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target=start_worker,
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args=(name, world_size, rank, rank % nproc_per_node, model),
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)
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for rank in range(world_size)
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]
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for w in workers:
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w.start()
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for w in workers:
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w.join()
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if __name__ == "__main__":
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main()
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