release v0.8.0

Former-commit-id: 004db680b9e3996ec511ee818df6c0c02bf13603

tests/data/test_supervised.py

@@ -0,0 +1,44 @@
+import os
+
+import pytest
+from datasets import load_dataset
+
+from llamafactory.data import get_dataset
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_tokenizer
+
+
+TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-LlamaForCausalLM")
+
+TRAINING_ARGS = {
+    "model_name_or_path": TINY_LLAMA,
+    "stage": "sft",
+    "do_train": True,
+    "finetuning_type": "full",
+    "dataset": "llamafactory/tiny_dataset",
+    "dataset_dir": "ONLINE",
+    "template": "llama3",
+    "cutoff_len": 1024,
+    "overwrite_cache": True,
+    "output_dir": "dummy_dir",
+    "overwrite_output_dir": True,
+    "fp16": True,
+}
+
+
+@pytest.mark.parametrize("test_num", [5])
+def test_supervised(test_num: int):
+    model_args, data_args, training_args, _, _ = get_train_args(TRAINING_ARGS)
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    tokenized_data = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
+
+    original_data = load_dataset(TRAINING_ARGS["dataset"], split="train")
+    for test_idx in range(test_num):
+        decode_result = tokenizer.decode(tokenized_data["input_ids"][test_idx])
+        messages = [
+            {"role": "user", "content": original_data[test_idx]["instruction"]},
+            {"role": "assistant", "content": original_data[test_idx]["output"]},
+        ]
+        templated_result = tokenizer.apply_chat_template(messages, tokenize=False)
+        assert decode_result == templated_result