forked from jiuyuan/InfiniTensor
Kunlun dist op (#225)
* kunlun dist inference fix * kunlun distributed * 添加昆仑芯分布式脚本以及解决运行llama遇到的问题 * set -j8 * format * move run_pytorch.py int o cuda/ * update notes --------- Co-authored-by: weijie01 <weijie01@baidu.com> Co-authored-by: wanghailu <wanghailu0717@163.com> Co-authored-by: Haojie Wang <haojie0429@gmail.com>
This commit is contained in:
zhangyue
GitHub
parent
d1de3ab5c2
commit
985d0dee5f
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export HF_ENDPOINT=https://hf-mirror.com
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models=("bert" "gpt2" "llama")
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batch_size=(1 32)
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seq_len=(100 500)
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nproc=(1 2 4)
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for model in "${models[@]}"; do
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for bs in "${batch_size[@]}"; do
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for len in "${seq_len[@]}"; do
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python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len" --export_onnx ../models/"$model" --export_only
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done
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done
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done
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import sys
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sys.path.append('../')
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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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from onnx.shape_inference import infer_shapes_path
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import numpy as np
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from parallel_opt import parallel_model
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from functools import wraps
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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=2, help="number of processes per node"
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)
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parser.add_argument(
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"--name", type=str, choices=["gpt2", "bert", "llama"], help="name of model."
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)
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parser.add_argument(
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"--model", type=str, default="", help="path to the ONNX model file."
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)
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parser.add_argument(
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"--gen_std",
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default=False,
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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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parser.add_argument(
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"--run_single",
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default=False,
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action="store_true",
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help="whether run model with single process with standard inputs"
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)
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parser.add_argument(
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"--input_dir",
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default="./",
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help="path to save model input data"
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)
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parser.add_argument(
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"--result_dir",
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default="./",
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help="path to save model standard output"
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)
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parser.add_argument(
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"--internal_model_dir",
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default="./",
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help="path to save internal onnx model for parallel run"
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)
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args = parser.parse_args()
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# check path, mkdir if not exist
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check_exists(args.input_dir)
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check_exists(args.result_dir)
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check_exists(args.internal_model_dir)
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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.gen_std,
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args.run_single,
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args.input_dir,
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args.result_dir,
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args.internal_model_dir
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)
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"""
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utils function for this scripts
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"""
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def check_exists(path: str):
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if not os.path.exists(path):
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os.makedirs(path)
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def np_assert(base, test, rtol=1e-2, atol=1e-1):
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# np.testing.assert_allclose(test, base, rtol, atol)
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print("max abs diff:", abs(base - test).max())
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"""
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Perf wrapper, run function n times
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then average
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"""
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def perf_it(n):
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def decorator(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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# warmup
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for _ in range(n):
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func(*args, **kwargs)
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t_total = 0
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for _ in range(n):
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t0 = time.time()
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func(*args, **kwargs)
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t1 = time.time()
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t_total += t1 - t0
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avg_time = (t_total) / n
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print(f"Avg runtime of {n} time is {avg_time:.6f} seconds")
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return avg_time
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return wrapper
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return decorator
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"""
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Run InfiniTensor model with Standard input
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check=True: check with standard output gen by pytorch
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perf=True: run n times to get avg time
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"""
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def run_model(task_name,
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model,
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runtime,
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world_size=1,
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rank=0,
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n=10,
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check=True,
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perf=True):
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stub = OnnxStub(model, runtime,
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use_naive_allocator=True \
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if task_name == "llama" else False)
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# load in Onnx model inputs
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def load_inputs(stub: OnnxStub):
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# check exists
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inputs = []
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input_path = os.path.join(input_dir, \
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f"{task_name}_input_{i}.npy")
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print(input_path)
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if os.path.exists(input_path):
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input = np.load(input_path)
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else :
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raise KeyError(f"{i} th input of model not exists")
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# check shape
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if all(x == y for x,y in zip(input.shape, tensor.shape())):
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tensor.copyin_numpy(input)
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else:
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tensor.copyin_numpy(np.hsplit(input, world_size)[rank])
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load_inputs(stub)
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# stub.tune()
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stub.run()
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time.sleep(0.01)
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output = next(stub.outputs.values().__iter__()).copyout_numpy()
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# check output results with standard output
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if check:
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st_output_path = os.path.join(result_dir, \
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f"{task_name}_output.npy")
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assert os.path.exists(st_output_path) , \
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"standard output not exists"
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st_output = np.load(st_output_path)
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if np.isnan(output).any():
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print("Nan in output")
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exit()
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np_assert(st_output, output)
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# perf
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if perf:
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@perf_it(n)
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def perf_infinitensor(stub: OnnxStub):
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stub.run()
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perf_infinitensor(stub)
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return output
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"""
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Start a worker in Parallel
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"""
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def start_worker(name: str,
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world_size: int,
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rank: int,
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local_rank: int,
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model: onnx.ModelProto):
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dist_name = name + "_dist"
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# partial a onnx model to world_size part
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model = parallel_model(model, world_size, rank)
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onnx.save(model, os.path.join(internal_model_dir, \
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f"{dist_name}_rank{rank}.onnx"), save_as_external_data=True)
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runtime = backend.KUNLUNRuntime(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_model(name, model, runtime, world_size, rank)
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"""
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generate standard input/output with
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sigle card run
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"""
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def gen_standard(task_name: str, model: onnx.ModelProto):
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runtime = backend.KUNLUNRuntime(0)
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stub = OnnxStub(model, runtime)
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position_id = 0
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# generate random input for model
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input = tensor.copyout_numpy()
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if np.issubdtype(input.dtype, np.integer):
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if input.size == 1:
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input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
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else:
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input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
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elif input.dtype == np.bool_:
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input = np.random.randint(0,2,size=input.shape) > 0
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else:
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if i == 0:
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input = np.ones(input.shape).astype(input.dtype)
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position_id = input.shape[-1] - 1
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else:
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input = np.random.rand(*input.shape).astype(input.dtype)
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tensor.copyin_numpy(input)
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np.save(os.path.join(input_dir, \
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f"{task_name}_input_{i}.npy"), input)
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stub.run()
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# print(stub.outputs)
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output = next(stub.outputs.values().__iter__()).copyout_numpy()
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if np.isnan(output).any():
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print("Nan in output")
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exit()
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np.save(os.path.join(result_dir, f"{task_name}_output.npy"), output)
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def main():
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global input_dir, result_dir, internal_model_dir
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nnodes, nproc_per_node, task_name, \
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model_path, gen_std, run_single, \
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input_dir, result_dir, internal_model_dir = parse_args()
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# load input onnx model
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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("Generate inputs and outputs.")
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gen_standard(task_name, model)
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return
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if run_single:
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print("Run model by one GPU card.")
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runtime = backend.KUNLUNRuntime(0)
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run_model(task_name, model, runtime)
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return
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# run distributed parallel.
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world_size = nnodes * nproc_per_node
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print(f"Run model by {world_size} GPU in parallel.")
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workers = [
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mp.Process(
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target=start_worker,
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args=(task_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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@ -0,0 +1,36 @@
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export HF_ENDPOINT=https://hf-mirror.com
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# models=("bert" "gpt2" "llama")
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models=("bert" "gpt2")
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batch_size=(1 32)
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seq_len=(100 500)
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nproc=(1 2 4)
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results_dir="results"
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if [ -d "$results_dir" ]; then
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echo "directory ./$results_dir exists"
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else
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mkdir -p "$results_dir"
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echo "mkdir $results_dir, logs saved there"
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fi
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for model in "${models[@]}"; do
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for bs in "${batch_size[@]}"; do
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for len in "${seq_len[@]}"; do
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# run pytorch model
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echo "Run pytorch $model with batch_size=$bs length=$len ."
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python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len" #> results/"$model"_"$bs"_"$len"_pytorch
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for n in "${nproc[@]}"; do
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# run infinitensor
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echo "Run $n parallel infinitensor "$model" with batch_size=$bs and length=$len ."
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python kunlun_launch.py --name "$model" --model ../models/"$model"/"$model"_"$bs"_"$len".onnx --nproc_per_node=$n # >> results/"$model"_"$bs"_"$len"_infini
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# delete internal files
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find ./ -type f -name "*.onnx" -delete
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find ./ -type f -name "*.pb" -delete
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done
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find ./ -type f -name "*.npy" -delete
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done
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done
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done
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export HF_ENDPOINT=https://hf-mirror.com
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# models=("bert" "gpt2" "llama")
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models=("llama")
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batch_size=(1 )
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seq_len=(100 500)
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nproc=(1 2 4)
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results_dir="results"
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if [ -d "$results_dir" ]; then
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echo "directory ./$results_dir exists"
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else
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mkdir -p "$results_dir"
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echo "mkdir $results_dir, logs saved there"
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fi
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for model in "${models[@]}"; do
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for bs in "${batch_size[@]}"; do
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for len in "${seq_len[@]}"; do
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echo "Run pytorch llama with batch_size="$bs" and length="$len""
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python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len"
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for n in "${nproc[@]}"; do
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# run pytorch model
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echo "Run infinitensor llama with batch_size="$bs" and length="$len" and nproc="$n"."
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python kunlun_launch.py --name llama --model ../models/llama/llama_"$bs"_"$len"_fp32.onnx --nproc_per_node=$n
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# delete internal files
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find ./ -type f -name "*.onnx" -delete
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find ./ -type f -name "*0c" -delete
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done
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find ./ -type f -name "*.npy" -delete
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done
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done
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done
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import argparse
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import torch
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from transformers import BertModel, BertConfig
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from transformers import GPT2Model, GPT2Config
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from transformers import OPTModel, OPTConfig
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from transformers import LlamaModel, LlamaConfig
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import time
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import numpy as np
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import onnx
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import os
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import sys
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from onnx.external_data_helper import convert_model_to_external_data
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from onnxsim import simplify
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.allow_tf32 = False
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def parse_args():
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parser = argparse.ArgumentParser(description="Run pytorch gpt2/bert/opt and optionally export onnx.")
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parser.add_argument(
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"--model", type=str, choices=["gpt2", "bert", "opt", "llama"], required=True, help="model type"
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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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"--export_onnx",
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type=str,
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nargs="?",
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default=None,
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const="./",
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help="whether and where to export onnx file",
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)
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parser.add_argument(
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"--input_dir",
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type=str,
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default="./",
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help="path to save pytorch model input data"
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)
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parser.add_argument(
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"--result_dir",
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type=str,
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default="./",
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help="path to save pytorch model output data"
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)
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parser.add_argument(
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"--export_only",
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action="store_true"
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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.model,
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args.batch_size,
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args.length,
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args.export_onnx,
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args.input_dir,
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args.result_dir,
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args.export_only
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)
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def get_model(modelname):
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if modelname == "bert":
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model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, hidden_act="gelu_new") # erf is not impl by infini
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voc_size = BertConfig().vocab_size
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elif modelname == "gpt2":
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model = GPT2Model.from_pretrained("gpt2")
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voc_size = GPT2Config().vocab_size
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elif modelname == "opt":
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model = OPTModel.from_pretrained("./opt-125m")
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voc_size = OPTConfig().vocab_size
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elif modelname == "llama":
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model = LlamaModel.from_pretrained("meta-llama/Llama-2-7b-hf")
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voc_size = LlamaConfig().vocab_size
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else :
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raise KeyError(modelname)
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model = model.eval()
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return model, voc_size
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def run_pytorch(torch_model, voc_size, batchsize, len, model_name):
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data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
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np.save(os.path.join(input_dir, f"{model_name}_input_0.npy"), data)
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inputs = torch.from_numpy(data).to("cuda")
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torch_model = torch_model.to("cuda")
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n_iter = 10
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with torch.no_grad():
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for _ in range(10):
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outputs = torch_model(inputs)
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torch.cuda.synchronize()
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begin = time.time()
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with torch.no_grad():
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for _ in range(n_iter):
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torch.cuda.synchronize()
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outputs = torch_model(inputs)
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#
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torch.cuda.synchronize()
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torch.cuda.synchronize()
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end = time.time()
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avg_time = (end - begin) / n_iter
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outputs = outputs.last_hidden_state.to("cpu")
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print("outputs abs mean:", abs(np.array(outputs)).mean())
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print(f"average time: {avg_time}")
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||||
torch.cuda.memory.empty_cache()
|
||||
np.save(os.path.join(result_dir, f"{model_name}_output.npy"), \
|
||||
np.array(outputs))
|
||||
print(f"Save input & output as {model_name}_input_0.npy and {model_name}_output.npy")
|
||||
|
||||
|
||||
def export_onnx(model_name, model, data, path, extern=False):
|
||||
# torch.onnx.export(model, data, path, verbose=False, do_constant_folding=True)
|
||||
|
||||
if model_name != "llama":
|
||||
onnx_model = onnx.load(path)
|
||||
onnx_model, check = simplify(onnx_model,
|
||||
skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
|
||||
# skipped_optimizers=['fuse_qkv'])
|
||||
assert check
|
||||
add_value_info_for_constants(onnx_model)
|
||||
onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
|
||||
if extern:
|
||||
extern_path = path.replace('.onnx', '.pb')
|
||||
if os.path.exists(extern_path):
|
||||
os.remove(extern_path)
|
||||
convert_model_to_external_data(
|
||||
onnx_model,
|
||||
all_tensors_to_one_file=True,
|
||||
location=extern_path.split("/")[-1],
|
||||
size_threshold=1024,
|
||||
convert_attribute=False,
|
||||
)
|
||||
onnx.save(onnx_model, path)
|
||||
else:
|
||||
sys.path.append("onnxsim_large_model")
|
||||
from onnx_utils import set_onnx_input_shape
|
||||
from compress_model import SIZE_1MB, compress_onnx_model, uncompress_onnx_model
|
||||
|
||||
in_model_path = path
|
||||
out_model_path = in_model_path[:-5] + ".sim.onnx"
|
||||
|
||||
onnx_model = onnx.load(in_model_path)
|
||||
print(f"load model from {in_model_path} success")
|
||||
|
||||
size_th_bytes = 1024 * 1024
|
||||
onnx_model, removed_inits = compress_onnx_model(onnx_model, size_th_bytes=size_th_bytes)
|
||||
print("compress model success")
|
||||
|
||||
onnx_model = set_onnx_input_shape(onnx_model, "")
|
||||
tensor_size_threshold = f"1024KB"
|
||||
skipped_optimizers = []
|
||||
skipped_optimizers.append("eliminate_duplicate_initializer")
|
||||
onnx_model, check = simplify(onnx_model, skipped_optimizers=skipped_optimizers,
|
||||
tensor_size_threshold=tensor_size_threshold)
|
||||
if not check:
|
||||
raise ValueError(f"simplify compressed model {in_model_path} failed")
|
||||
|
||||
print(f"simplify model success")
|
||||
|
||||
onnx_model = uncompress_onnx_model(onnx_model, removed_inits)
|
||||
print(f"uncompress model success")
|
||||
|
||||
add_value_info_for_constants(onnx_model)
|
||||
|
||||
onnx.save(onnx_model, out_model_path, save_as_external_data=True)
|
||||
|
||||
|
||||
def add_value_info_for_constants(model : onnx.ModelProto):
|
||||
"""
|
||||
Currently onnx.shape_inference doesn't use the shape of initializers, so add
|
||||
that info explicitly as ValueInfoProtos.
|
||||
Mutates the model.
|
||||
Args:
|
||||
model: The ModelProto to update.
|
||||
"""
|
||||
# All (top-level) constants will have ValueInfos before IRv4 as they are all inputs
|
||||
if model.ir_version < 4:
|
||||
return
|
||||
|
||||
def add_const_value_infos_to_graph(graph : onnx.GraphProto):
|
||||
inputs = {i.name for i in graph.input}
|
||||
existing_info = {vi.name: vi for vi in graph.value_info}
|
||||
for init in graph.initializer:
|
||||
# Check it really is a constant, not an input
|
||||
if init.name in inputs:
|
||||
continue
|
||||
|
||||
# The details we want to add
|
||||
elem_type = init.data_type
|
||||
shape = init.dims
|
||||
|
||||
# Get existing or create new value info for this constant
|
||||
vi = existing_info.get(init.name)
|
||||
if vi is None:
|
||||
vi = graph.value_info.add()
|
||||
vi.name = init.name
|
||||
|
||||
# Even though it would be weird, we will not overwrite info even if it doesn't match
|
||||
tt = vi.type.tensor_type
|
||||
if tt.elem_type == onnx.TensorProto.UNDEFINED:
|
||||
tt.elem_type = elem_type
|
||||
if not tt.HasField("shape"):
|
||||
# Ensure we set an empty list if the const is scalar (zero dims)
|
||||
tt.shape.dim.extend([])
|
||||
for dim in shape:
|
||||
tt.shape.dim.add().dim_value = dim
|
||||
|
||||
# Handle subgraphs
|
||||
for node in graph.node:
|
||||
for attr in node.attribute:
|
||||
# Ref attrs refer to other attrs, so we don't need to do anything
|
||||
if attr.ref_attr_name != "":
|
||||
continue
|
||||
|
||||
if attr.type == onnx.AttributeProto.GRAPH:
|
||||
add_const_value_infos_to_graph(attr.g)
|
||||
if attr.type == onnx.AttributeProto.GRAPHS:
|
||||
for g in attr.graphs:
|
||||
add_const_value_infos_to_graph(g)
|
||||
|
||||
|
||||
return add_const_value_infos_to_graph(model.graph)
|
||||
|
||||
|
||||
def main():
|
||||
global input_dir, result_dir
|
||||
|
||||
modelname, batchsize, seqlen, \
|
||||
export_path, input_dir, result_dir, export_only = parse_args()
|
||||
|
||||
model, voc_size = get_model(modelname) # pytorch model
|
||||
|
||||
if export_path is not None:
|
||||
os.makedirs(export_path, exist_ok=True)
|
||||
filename = "{}_{}_{}.onnx".format(modelname, batchsize, seqlen)
|
||||
path = os.path.join(export_path, filename)
|
||||
param = torch.zeros((batchsize, seqlen), dtype=torch.int)
|
||||
export_onnx(modelname, model, param, path, True) # export pytorch model to onnx model
|
||||
if export_only:
|
||||
return
|
||||
|
||||
run_pytorch(model, voc_size, batchsize, seqlen, modelname)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,213 +0,0 @@
|
|||
import argparse
|
||||
import os
|
||||
import time
|
||||
import multiprocessing as mp
|
||||
from pyinfinitensor.onnx import OnnxStub, backend
|
||||
import onnx
|
||||
from onnx.external_data_helper import convert_model_to_external_data
|
||||
from onnx.shape_inference import infer_shapes_path
|
||||
import numpy as np
|
||||
from parallel_opt import parallel_model
|
||||
|
||||
st_input_dir = "standard/inputs/"
|
||||
st_output_dir = "standard/outputs/"
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="launch distributed infinitensor")
|
||||
parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
|
||||
parser.add_argument(
|
||||
"--nproc_per_node", type=int, default=2, help="number of processes per node"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--name", type=str, default="test", help="name of this instance."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model", type=str, default="/data1/shared/panzezhong/llama/fp32/my_llama_fp32.sim.onnx", help="path to the ONNX model file."
|
||||
)
|
||||
parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
|
||||
parser.add_argument("--length", type=int, default=1, help="sequence length.")
|
||||
parser.add_argument(
|
||||
"--gen_std",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="whether to generate the standard results.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--run_single",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="whether run model with single process with standard inputs"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
print("arg setting: ", args)
|
||||
return (
|
||||
args.num_nodes,
|
||||
args.nproc_per_node,
|
||||
args.name,
|
||||
args.model,
|
||||
args.batch_size,
|
||||
args.length,
|
||||
args.gen_std,
|
||||
args.run_single
|
||||
)
|
||||
|
||||
|
||||
def run_model(model, runtime, world_size=1, rank=0, n=10):
|
||||
stub = OnnxStub(model, runtime)
|
||||
load_inputs(stub, world_size, rank)
|
||||
# stub.tune()
|
||||
stub.run()
|
||||
# get outputs
|
||||
time.sleep(0.01)
|
||||
outputs = next(stub.outputs.values().__iter__()).copyout_numpy()
|
||||
|
||||
# bench
|
||||
begin = time.time()
|
||||
for _ in range(n):
|
||||
stub.run()
|
||||
end = time.time()
|
||||
avg_time = (end - begin) / n
|
||||
print(f"average time: {avg_time}")
|
||||
return outputs
|
||||
|
||||
|
||||
|
||||
def run_and_compare(name, model, runtime, world_size=1, rank = 0):
|
||||
results = np.load(os.path.join(st_output_dir,f"output.npy"))
|
||||
outputs = run_model(model, runtime, world_size, rank)
|
||||
print(outputs[:100])
|
||||
if np.isnan(outputs).any():
|
||||
print("Nan in output")
|
||||
print("answer argmax:", np.argmax(results))
|
||||
print("output argmax:", np.argmax(outputs))
|
||||
#np.testing.assert_allclose(outputs, results, rtol=1e-3, atol=1e-3)
|
||||
getDiff(results, outputs)
|
||||
|
||||
|
||||
def start_worker(
|
||||
name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto
|
||||
):
|
||||
dist_name = name + "_dist"
|
||||
model = parallel_model(model, world_size, rank)
|
||||
extern_path = f"./{dist_name}_rank{rank}.pb"
|
||||
if os.path.exists(extern_path):
|
||||
os.remove(extern_path)
|
||||
onnx.save_model(
|
||||
model,
|
||||
f"./{dist_name}_rank{rank}.onnx",
|
||||
save_as_external_data=True,
|
||||
location=extern_path,
|
||||
)
|
||||
infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
|
||||
runtime = backend.KUNLUNRuntime(local_rank)
|
||||
# print("init comm")
|
||||
runtime.init_comm(
|
||||
dist_name,
|
||||
world_size,
|
||||
rank,
|
||||
)
|
||||
run_and_compare(name, model, runtime, world_size, rank)
|
||||
|
||||
|
||||
def start_single(name, model):
|
||||
runtime = backend.KUNLUNRuntime(0)
|
||||
run_and_compare(name, model, runtime)
|
||||
|
||||
|
||||
def generate_input_output(model):
|
||||
runtime = backend.KUNLUNRuntime(0)
|
||||
stub = OnnxStub(model, runtime)
|
||||
position_id = 0
|
||||
for i, (name, tensor) in enumerate(stub.inputs.items()):
|
||||
input = tensor.copyout_numpy()
|
||||
if np.issubdtype(input.dtype, np.integer):
|
||||
if input.size == 1:
|
||||
# input = np.array([position_id])
|
||||
input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
|
||||
else:
|
||||
input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
|
||||
elif input.dtype == np.bool_:
|
||||
input = np.random.randint(0,2,size=input.shape) > 0
|
||||
else:
|
||||
if i == 0:
|
||||
input = np.ones(input.shape).astype(input.dtype)
|
||||
position_id = input.shape[-1] - 1
|
||||
else:
|
||||
input = np.random.rand(*input.shape).astype(input.dtype)
|
||||
tensor.copyin_numpy(input)
|
||||
np.save(os.path.join(st_input_dir, f"input_{i}"), input)
|
||||
stub.run()
|
||||
# print(stub.outputs)
|
||||
time.sleep(0.01)
|
||||
output = next(stub.outputs.values().__iter__()).copyout_numpy()
|
||||
print(output[:100])
|
||||
if np.isnan(output).any():
|
||||
print("Nan in output")
|
||||
np.save(os.path.join(st_output_dir, f"output"), output)
|
||||
|
||||
|
||||
def load_inputs(stub, world_size=1, rank=0):
|
||||
for i, (name, tensor) in enumerate(stub.inputs.items()):
|
||||
input = np.load(os.path.join(st_input_dir, f"input_{i}.npy"))
|
||||
if all(x == y for x,y in zip(input.shape,tensor.shape())):
|
||||
tensor.copyin_numpy(input)
|
||||
else:
|
||||
tensor.copyin_numpy(np.hsplit(input, world_size)[rank])
|
||||
|
||||
|
||||
def getDiff(base, test):
|
||||
absolute_diff = np.abs(np.subtract(base, test))
|
||||
max_absolute_diff = np.max(absolute_diff)
|
||||
|
||||
baseCopy = base.astype(np.float64).ravel()
|
||||
testCopy = test.astype(np.float64).ravel()
|
||||
upValue = np.sum(np.abs(baseCopy - testCopy))
|
||||
downValue = np.sum(np.abs(baseCopy)) + np.float64(1e-9)
|
||||
max_relative_diff = upValue / downValue
|
||||
print(f"Max absolute difference: {max_absolute_diff}\nMax relative difference: {max_relative_diff}")
|
||||
|
||||
return max_absolute_diff, max_relative_diff
|
||||
|
||||
|
||||
def main():
|
||||
nnodes, nproc_per_node, name, model_path, bs, length, gen_std, run_single = parse_args()
|
||||
|
||||
model = onnx.load(model_path)
|
||||
|
||||
# generate standart output
|
||||
if gen_std:
|
||||
print("Generate inputs and outputs.")
|
||||
p = mp.Process(target=generate_input_output, args=[model])
|
||||
p.start()
|
||||
p.join()
|
||||
return
|
||||
|
||||
# # run single process.
|
||||
# # use standalone process to isolate cuda.
|
||||
if run_single:
|
||||
print("run model by single GPU.")
|
||||
p = mp.Process(target=start_single, args=(name, model))
|
||||
p.start()
|
||||
p.join()
|
||||
return
|
||||
|
||||
# run distributed parallel.
|
||||
world_size = nnodes * nproc_per_node
|
||||
print(f"run model by {world_size} GPU in parallel.")
|
||||
workers = [
|
||||
mp.Process(
|
||||
target=start_worker,
|
||||
args=(name, world_size, rank, rank % nproc_per_node, model),
|
||||
)
|
||||
for rank in range(world_size)
|
||||
]
|
||||
|
||||
for w in workers:
|
||||
w.start()
|
||||
|
||||
for w in workers:
|
||||
w.join()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -110,7 +110,6 @@ def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0):
|
|||
s_dim = 0
|
||||
elif in_plc.dim == 2:
|
||||
s_dim = 1
|
||||
|
||||
assert s_dim != -1
|
||||
assert out_dims[s_dim] % tp_world_size == 0, out_dims
|
||||
out_dims[s_dim] //= tp_world_size
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ class KUNLUNRuntimeObj : public RuntimeObj {
|
|||
ctx = xdnn::create_context();
|
||||
// 10GB for Longformer
|
||||
// size_t longformerNum = 3lu * (1 << 30);
|
||||
size_t workspaceSize = 3llu << 30; // 3 GB
|
||||
size_t workspaceSize = 2llu << 30; // 2 GB
|
||||
KUNLUNPtr wkspacePtr = alloc(workspaceSize);
|
||||
workspace =
|
||||
make_ref<WorkspaceObj<KUNLUNPtr>>(wkspacePtr, workspaceSize);
|
||||
|
|
@ -42,7 +42,7 @@ class KUNLUNRuntimeObj : public RuntimeObj {
|
|||
KUNLUNPtr alloc(size_t size) override {
|
||||
void *ptr;
|
||||
checkKUNLUNError(
|
||||
xpu_malloc_ex((void **)&ptr, size, XPUMemoryKind::XPU_MEM_MAIN));
|
||||
xpu_malloc((void **)&ptr, size, XPUMemoryKind::XPU_MEM_HBM));
|
||||
return ptr;
|
||||
}
|
||||
void dealloc(void *ptr) override { xpu_free(ptr); }
|
||||
|
|
|
|||
|
|
@ -34,8 +34,8 @@ class XcclCommunicatorObj final : public CommunicatorObj {
|
|||
auto begin = std::chrono::steady_clock::now();
|
||||
while (!std::filesystem::exists(filePath)) {
|
||||
auto now = std::chrono::steady_clock::now();
|
||||
_IT_ASSERT_2(now < begin + std::chrono::seconds(10),
|
||||
"time limit (10s) exceeded.");
|
||||
_IT_ASSERT_2(now < begin + std::chrono::seconds(100),
|
||||
"time limit (100s) exceeded.");
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(100));
|
||||
}
|
||||
std::ifstream ifs(filePath, std::ios::binary);
|
||||
|
|
|
|||
|
|
@ -967,7 +967,7 @@ class OnnxStub:
|
|||
tensors[node.input[0]],
|
||||
tensors.get(node.output[0]),
|
||||
)
|
||||
elif node.op_type == "Constant":
|
||||
elif node.op_type in ["Constant", "ConstantOfShape"]:
|
||||
output_name = node.output[0]
|
||||
attributes = _parse_attribute(node)
|
||||
tensor = attributes["value"]
|
||||
|
|
|
|||
|
|
@ -97,11 +97,14 @@ class DivXdnn : public KUNLUNKernelWithoutConfig {
|
|||
auto aDim = op->getInputs(0)->getDims();
|
||||
auto bSize = op->getInputs(1)->size();
|
||||
auto bDim = op->getInputs(1)->getDims();
|
||||
auto dtype = op->getDType();
|
||||
|
||||
// op input a, b is scalar while aDim and b Dim is empty
|
||||
if (bDim.size() == 0) {
|
||||
bDim.push_back(1);
|
||||
}
|
||||
if (aDim.size() == 0) {
|
||||
aDim.push_back(1);
|
||||
}
|
||||
|
||||
if (aSize == bSize) {
|
||||
// Do ElementWise Sub with no broadcast
|
||||
|
|
@ -109,23 +112,9 @@ class DivXdnn : public KUNLUNKernelWithoutConfig {
|
|||
(float *)aData, (float *)bData,
|
||||
(float *)cData, aSize));
|
||||
} else {
|
||||
// Do broadcast div
|
||||
Shape aligned = infer_broadcast(aDim, bDim);
|
||||
if (aligned == aDim) {
|
||||
// BData need to be broadcasted
|
||||
checkKUNLUNError(xdnn::broadcast_div<float>(
|
||||
context->KUNLUNHandle(), (float *)aData, (float *)bData,
|
||||
(float *)cData, aDim, bDim));
|
||||
} else {
|
||||
// Use workspace to broadcast aData
|
||||
KUNLUNPtr wks = context->getWorkspace(bSize * dtype.getSize());
|
||||
checkKUNLUNError(xdnn::broadcast<float>(
|
||||
context->KUNLUNHandle(), (float *)aData, (float *)wks, aDim,
|
||||
bDim));
|
||||
checkKUNLUNError(xdnn::div<float>(context->KUNLUNHandle(),
|
||||
(float *)wks, (float *)bData,
|
||||
(float *)cData, bSize));
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue