forked from jiuyuan/InfiniTensor
添加 MLU 平台分布式验收脚本 (#223)
* 添加 MLU 平台分布式验收脚本 * add fp16 test, fix cast * fix * add onnxsim for llama * add matmul tf32 for mlu * add submodule: onnxsim_large_model * fix * modified bang_launch.py, start_single * add test for albert/opt * change file path --------- Co-authored-by: xgqdut2016 <kenan_gewei@163.com>
This commit is contained in:
Bolun Zhang
GitHub
parent
985d0dee5f
commit
fac28c25f6
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@ -13,3 +13,6 @@
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[submodule "example"]
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path = examples/NNmodel
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url = git@github.com:wanghailu0717/NNmodel.git
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[submodule "examples/distributed/onnxsim_large_model"]
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path = examples/distributed/onnxsim_large_model
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url = git@github.com:luchangli03/onnxsim_large_model.git
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@ -1,5 +1,7 @@
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# 分布式脚本
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## 英伟达平台运行方式
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#### 1. 运行pytorch模型并生成输入和标准输出，可选择导出onnx
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使用 `--export_onnx` 设置导出onnx的目录，默认为当前路径 `./`，不使用这个flag则只进行计算和生成输入输出。
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@ -15,3 +17,23 @@ python run_pytorch.py --model gpt2 --batch_size 1 --length 1 --export_onnx ./
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```bash
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python cuda_launch.py --model "/XXX/XXX.onnx" --nproc_per_node 4
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```
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## 寒武纪平台运行方式
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**将上述运行脚本 `run_pytorch.py` 以及 `cuda_launch.py` 针对寒武纪平台做了相应的适配，具体见 `run_pytorch_mlu.py` 以及 `bang_launch.py`。**
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#### 1. 运行pytorch模型并生成输入和标准输出，可选择导出onnx
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使用 `--export_onnx` 设置导出onnx的目录，默认为当前路径 `./`，不使用这个flag则只进行计算和生成输入输出。
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```bash
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python run_pytorch_mlu.py --model gpt2 --batch_size 1 --length 1 --export_onnx ./
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```
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会在当前目录下生成输入输出文件`test_inputs.npy` 和 `test_results.npy`，目前只支持单一输入输出。
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#### 2. 运行InfiniTensor分布式脚本
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```bash
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python bang_launch.py --model "/XXX/XXX.onnx" --nproc_per_node 4
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```
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@ -1,35 +1,39 @@
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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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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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"--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, default="/data/onnx_models/llama2/llama_bs1_seq1024.onnx",
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help="path to the ONNX model file."
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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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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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"--type", type=str, choices=["fp32", "fp16", "tf32"], default="fp32", help="data type"
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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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@ -40,39 +44,46 @@ def parse_args():
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args.batch_size,
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args.length,
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args.gen_std,
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args.type,
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)
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def run_model(model, runtime, world_size=1, rank=0, n=10):
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stub = OnnxStub(model, runtime)
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def run_model(model, runtime, world_size=1, rank=0, n=10, data_type="default"):
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stub = OnnxStub(model, runtime, matmul_compute_type=data_type)
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load_inputs(stub, world_size, rank)
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# stub.tune()
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stub.run()
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# get outputs
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time.sleep(0.01)
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outputs = next(stub.outputs.values().__iter__()).copyout_numpy()
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# bench
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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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begin = time.time()
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for _ in range(n * 2):
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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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avg_time = (end - begin) / (n * 2)
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print(f"average time: {avg_time}")
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return outputs
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def load_inputs(stub, world_size=1, rank=0):
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input = np.load(f"./data/input_{i}.npy")
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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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def run_and_compare(name, model, runtime, world_size=1, rank = 0):
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def run_and_compare(name, model, runtime, world_size=1, rank=0, data_type="default"):
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results = np.load(f"./data/output.npy")
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outputs = run_model(model, runtime, world_size, rank)
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print("answer argmax:", np.argmax(results))
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print("output argmax:", np.argmax(outputs))
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#np.testing.assert_allclose(outputs, results, rtol=1e-3, atol=1e-3)
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getDiff(results, outputs)
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outputs = run_model(model, runtime, world_size, rank, data_type=data_type)
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print("outputs abs mean:", abs(outputs).mean())
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print("max abs diff:", abs(outputs - results).max())
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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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name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto, data_type: str
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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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@ -85,7 +96,7 @@ def start_worker(
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save_as_external_data=True,
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location=extern_path,
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)
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infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
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#infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
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runtime = backend.BangRuntime(local_rank)
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# print("init comm")
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runtime.init_comm(
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@ -93,13 +104,12 @@ def start_worker(
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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, world_size, rank)
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run_and_compare(name, model, runtime, world_size, rank, data_type)
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def start_single(name, model):
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def start_single(name, model, data_type):
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runtime = backend.BangRuntime(0)
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run_and_compare(name, model, runtime)
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run_and_compare(name, model, runtime, data_type=data_type)
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def generate_input_output(model):
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os.makedirs(os.path.dirname("./data/"), exist_ok=True)
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@ -132,55 +142,36 @@ def generate_input_output(model):
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np.save(f"./data/output", output)
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def load_inputs(stub, world_size=1, rank=0):
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input = np.load(f"./data/input_{i}.npy")
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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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def getDiff(base, test):
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absolute_diff = np.abs(np.subtract(base, test))
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max_absolute_diff = np.max(absolute_diff)
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baseCopy = base.astype(np.float64).ravel()
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testCopy = test.astype(np.float64).ravel()
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upValue = np.sum(np.abs(baseCopy - testCopy))
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downValue = np.sum(np.abs(baseCopy)) + np.float64(1e-9)
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max_relative_diff = upValue / downValue
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print(f"Max absolute difference: {max_absolute_diff}\n"
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f"Max relative difference: {max_relative_diff}")
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return max_absolute_diff, max_relative_diff
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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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nnodes, nproc_per_node, name, model_path, bs, length, gen_std, data_type = parse_args()
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data_type = "default" if data_type == "fp32" else data_type
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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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p = mp.Process(target=generate_input_output, args=[model])
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p.start()
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p.join()
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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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generate_input_output(model)
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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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print("run model by single MLU.")
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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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if nproc_per_node == 1:
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# run single process.
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# use standalone process to isolate bang.
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print("run model by single MLU.")
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# p = mp.Process(target=start_single, args=(name, model, data_type))
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# p.start()
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# p.join()
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start_single(name, model, data_type)
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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} MLUs in parallel.")
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print(f"run model by {world_size} MLU 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=(name, world_size, rank, rank % nproc_per_node, model),
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args=(name, world_size, rank, rank % nproc_per_node, model, data_type),
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)
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for rank in range(world_size)
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]
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@ -0,0 +1,249 @@
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import argparse
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import torch
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import torch_mlu
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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 AlbertModel, AlbertConfig
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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 sys
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import os
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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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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", "albert"], 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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"--type", type=str, choices=["fp32", "fp16", "tf32"], required=True, help="model data type"
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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.type
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)
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def get_model(modelname):
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match modelname:
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case "albert":
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model = AlbertModel.from_pretrained("albert/albert-base-v2")
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voc_size = AlbertConfig().vocab_size
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case "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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case "gpt2":
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model = GPT2Model.from_pretrained("GPT2")
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voc_size = GPT2Config().vocab_size
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case "opt":
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model = OPTModel.from_pretrained("facebook/opt-125m")
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voc_size = OPTConfig().vocab_size
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case "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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case _:
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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, dtype="fp32"):
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data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
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os.makedirs(os.path.dirname("./data/"), exist_ok=True)
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np.save("./data/input_0", data)
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inputs = torch.from_numpy(data).to("mlu")
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torch_model = torch_model.to("mlu")
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if dtype == "fp16":
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torch_model = torch_model.half()
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n_iter = 20
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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.mlu.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.mlu.synchronize()
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outputs = torch_model(inputs)
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torch.mlu.synchronize()
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torch.mlu.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.mlu.memory.empty_cache()
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np.save("./data/output", np.array(outputs))
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print("Save input & output into ./data.")
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def export_onnx(modelname, model, data, path, extern=False, dtype="fp32"):
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data = data.to("mlu")
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model = model.to("mlu")
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if dtype == "fp16":
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model = model.half()
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torch.onnx.export(model, data, path, verbose=False, do_constant_folding=True)
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if modelname != "llama":
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# use onnxsim to simplify
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onnx_model = onnx.load(path)
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onnx_model, check = simplify(onnx_model, skipped_optimizers=['eliminate_duplicate_initializer'])
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# onnx_model, check = simplify(onnx_model, skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
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assert check
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add_value_info_for_constants(onnx_model)
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onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
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if extern:
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extern_path = path.replace('.onnx', '.pb')
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if os.path.exists(extern_path):
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os.remove(extern_path)
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extern_path = extern_path.split("/")[-1]
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convert_model_to_external_data(
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onnx_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(onnx_model, path)
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else:
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# use third party tool to simplify llama
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# reference: https://github.com/luchangli03/onnxsim_large_model/
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sys.path.append("onnxsim_large_model")
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from onnx_utils import set_onnx_input_shape
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from compress_model import SIZE_1MB, compress_onnx_model, uncompress_onnx_model
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in_model_path = path
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out_model_path = path
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if not out_model_path:
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out_model_path = in_model_path[:-5] + ".sim.onnx"
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if os.path.isdir(out_model_path):
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out_model_path = os.path.join(out_model_path, os.path.basename(in_model_path))
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onnx_model = onnx.load(in_model_path)
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print(f"load model from {in_model_path} success")
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size_th_bytes = 1024 * 1024
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onnx_model, removed_inits = compress_onnx_model(onnx_model, size_th_bytes=size_th_bytes)
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print(f"compress model success")
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onnx_model = set_onnx_input_shape(onnx_model, "")
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tensor_size_threshold = f"1024KB"
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skipped_optimizers = []
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skipped_optimizers.append("eliminate_duplicate_initializer")
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onnx_model, check = simplify(onnx_model, skipped_optimizers=skipped_optimizers,
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tensor_size_threshold=tensor_size_threshold)
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if not check:
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raise ValueError(f"simplify compressed model {in_model_path} failed")
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print(f"simplify model success")
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onnx_model = uncompress_onnx_model(onnx_model, removed_inits)
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print(f"uncompress model success")
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|
||||
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():
|
||||
torch.backends.mlu.matmul.allow_tf32 = False
|
||||
torch.backends.cnnl.allow_tf32 = False
|
||||
modelname, batchsize, seqlen, export_path, dtype = parse_args()
|
||||
if dtype == "tf32":
|
||||
torch.backends.mlu.matmul.allow_tf32 = True
|
||||
else:
|
||||
os.environ["CAMBRICON_TF32_OVERRIDE"] = "0"
|
||||
|
||||
model, voc_size = get_model(modelname)
|
||||
if export_path is not None:
|
||||
filename = "{}_{}_{}_{}.onnx".format(modelname, batchsize, seqlen, dtype)
|
||||
path = os.path.join(export_path, filename)
|
||||
if not os.path.exists(path):
|
||||
param = torch.zeros((batchsize, seqlen), dtype=torch.int)
|
||||
export_onnx(modelname, model, param, path, True, dtype)
|
||||
else:
|
||||
print("Onnx path exists, skipping export.")
|
||||
|
||||
run_pytorch(model, voc_size, batchsize, seqlen, dtype)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1 @@
|
|||
Subproject commit cbcf3fbf985a00494b0f136c92eaccd42031bf65
|
||||
|
|
@ -199,6 +199,24 @@ class CastCnnl : public BangKernelWithoutConfig {
|
|||
dim.data()));
|
||||
NlCastType = CNNL_CAST_UINT32_TO_INT64;
|
||||
break;
|
||||
case CastType::Float162Float:
|
||||
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
|
||||
CNNL_DTYPE_HALF, dim.size(),
|
||||
dim.data()));
|
||||
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
|
||||
CNNL_DTYPE_FLOAT, dim.size(),
|
||||
dim.data()));
|
||||
NlCastType = CNNL_CAST_HALF_TO_FLOAT;
|
||||
break;
|
||||
case CastType::Float2Float16:
|
||||
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
|
||||
CNNL_DTYPE_FLOAT, dim.size(),
|
||||
dim.data()));
|
||||
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
|
||||
CNNL_DTYPE_HALF, dim.size(),
|
||||
dim.data()));
|
||||
NlCastType = CNNL_CAST_FLOAT_TO_HALF;
|
||||
break;
|
||||
default:
|
||||
IT_TODO_HALT();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -19,14 +19,16 @@ class LayerNormCnnl : public BangKernelWithoutConfig {
|
|||
void *const outputData = (op->getOutput()->getRawDataPtr<void *>());
|
||||
|
||||
auto inDims = op->getInputs(0)->getDims();
|
||||
auto fiterDims = op->getInputs(1)->getDims();
|
||||
auto outDims = op->getOutput()->getDims();
|
||||
auto fiterDims = op->getOutput(1)->getDims();
|
||||
|
||||
float eps = op->getEps();
|
||||
const int axis = op->getAxis();
|
||||
|
||||
cnnlTensorDescriptor_t inDesc, fiterDesc, outDesc;
|
||||
Shape outMeanDims(outDims);
|
||||
outMeanDims.erase(outMeanDims.begin() + axis);
|
||||
|
||||
cnnlTensorDescriptor_t inDesc, fiterDesc, outDesc, outMeanDesc;
|
||||
checkCnnlError(cnnlCreateTensorDescriptor(&inDesc));
|
||||
checkCnnlError(cnnlSetTensorDescriptor(
|
||||
inDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
|
||||
|
|
@ -39,15 +41,23 @@ class LayerNormCnnl : public BangKernelWithoutConfig {
|
|||
checkCnnlError(cnnlSetTensorDescriptor(
|
||||
outDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
|
||||
outDims.size(), outDims.data()));
|
||||
checkCnnlError(cnnlCreateTensorDescriptor(&outMeanDesc));
|
||||
checkCnnlError(cnnlSetTensorDescriptor(
|
||||
outMeanDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
|
||||
outMeanDims.size(), outMeanDims.data()));
|
||||
size_t wsSize;
|
||||
cnnlGetLayerNormOpWorkspaceSize(context->cnnlHandle(), axis, inDesc,
|
||||
&wsSize);
|
||||
BangPtr wsData = context->getWorkspace(wsSize);
|
||||
size_t meanSize =
|
||||
cnnlGetTensorElementNum(outMeanDesc) * op->getDType().getSize();
|
||||
BangPtr meanData = context->getWorkspace(meanSize);
|
||||
BangPtr rstdData = context->getWorkspace(meanSize);
|
||||
|
||||
cnnlStatus_t stat = cnnlLayerNormForward(
|
||||
context->cnnlHandle(), inDesc, inputData, axis, fiterDesc,
|
||||
scaleData, biasData, eps, wsData, wsSize, outDesc, outputData,
|
||||
inDesc, NULL, NULL);
|
||||
outMeanDesc, meanData, rstdData);
|
||||
|
||||
if (stat != CNNL_STATUS_SUCCESS)
|
||||
return;
|
||||
|
|
|
|||
|
|
@ -66,6 +66,13 @@ class MatmulCnnl : public BangKernelWithoutConfig {
|
|||
cnnlSetMatMulDescAttr(bmm_desc, CNNL_MATMUL_DESC_TRANSB, &transB,
|
||||
sizeof(int32_t));
|
||||
|
||||
std::string computeTypeStr = op->getComputeType();
|
||||
if (computeTypeStr == "tf32") {
|
||||
int32_t tf32 = 1;
|
||||
cnnlSetMatMulDescAttr(bmm_desc, CNNL_MATMUL_ALLOW_TF32, &tf32,
|
||||
sizeof(int32_t));
|
||||
}
|
||||
|
||||
cnnlMatMulAlgo_t bmm_algo;
|
||||
cnnlMatMulAlgoCreate(&bmm_algo);
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue