OPUS-MT-SYNTHETIC-EN-EU Ascend NPU 部署指南

opus-mt-synthetic-en-eu-ascend/
├── inference.py          # 推理测试脚本
├── log.txt               # 测试日志
├── README.md             # 本文档
├── test_sample.txt       # 测试样本
├── inference_result.json # 推理结果
└── precision_result.json # 精度测试结果

docker exec -it test-modelagent bash

source /usr/local/Ascend/ascend-toolkit/set_env.sh

pip install transformers torch_npu

cd /data/ysws/agentsp/5-18/opus-mt-synthetic-en-eu-ascend/

# 使用默认测试句子
python3 inference.py

# 指定设备
python3 inference.py npu:0

cd /data/ysws/agentsp/5-18/opus-mt-synthetic-en-eu-ascend/

# 运行完整精度测试
python3 inference.py precision_test

import torch
from transformers import MarianTokenizer, MarianMTModel

MODEL_DIR = "/data/ysws/agentsp/5-18/opus-mt-synthetic-en-eu/Helsinki-NLP/opus-mt-synthetic-en-eu"

tokenizer = MarianTokenizer.from_pretrained(MODEL_DIR)
model = MarianMTModel.from_pretrained(MODEL_DIR)
model = model.to("npu:0")
model.eval()

src_texts = ["Hello, how are you today?"]
inputs = tokenizer(src_texts, return_tensors="pt", padding=True)
inputs = {k: v.to("npu:0") for k, v in inputs.items()}

with torch.no_grad():
    outputs = model.generate(inputs['input_ids'], max_new_tokens=50)

translations = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(translations)  # ["Hello, nola zaude gaur?"]

src_texts = [
    "Hello, how are you today?",
    "I am very happy to see you.",
    "The weather is nice today."
]

inputs = tokenizer(src_texts, return_tensors="pt", padding=True)
inputs = {k: v.to("npu:0") for k, v in inputs.items()}

with torch.no_grad():
    outputs = model.generate(inputs['input_ids'], max_new_tokens=50)

translations = tokenizer.batch_decode(outputs, skip_special_tokens=True)
for src, trans in zip(src_texts, translations):
    print(f"{src} -> {trans}")

{
  "hidden_size": 768,
  "encoder_layers": 6,
  "decoder_layers": 6,
  "encoder_attention_heads": 12,
  "decoder_attention_heads": 12,
  "d_model": 768
}