release v0.8.0

src/llamafactory/data/template.py

@@ -700,17 +700,8 @@ def get_template_and_fix_tokenizer(
 _register_template(
     name="llama2",
     format_user=StringFormatter(slots=[{"bos_token"}, "[INST] {{content}} [/INST]"]),
+    format_assistant=StringFormatter(slots=[" {{content}} ", {"eos_token"}]),
     format_system=StringFormatter(slots=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
-    default_system=(
-        "You are a helpful, respectful and honest assistant. "
-        "Always answer as helpfully as possible, while being safe. "
-        "Your answers should not include any harmful, unethical, "
-        "racist, sexist, toxic, dangerous, or illegal content. "
-        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
-        "If a question does not make any sense, or is not factually coherent, "
-        "explain why instead of answering something not correct. "
-        "If you don't know the answer to a question, please don't share false information."
-    ),
 )
 
 

src/llamafactory/extras/env.py

@@ -12,7 +12,7 @@
 from .packages import is_vllm_available
 
 
-VERSION = "0.7.2.dev0"
+VERSION = "0.8.0"
 
 
 def print_env() -> None:

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

tests/model/test_attention.py → tests/model/model_utils/test_attention.py

@@ -30,8 +30,8 @@ def test_attention():
                 "flash_attn": requested_attention,
             }
         )
-        tokenizer = load_tokenizer(model_args)
-        model = load_model(tokenizer["tokenizer"], model_args, finetuning_args)
+        tokenizer_module = load_tokenizer(model_args)
+        model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args)
         for module in model.modules():
             if "Attention" in module.__class__.__name__:
                 assert module.__class__.__name__ == llama_attention_classes[requested_attention]

tests/model/test_freeze.py

@@ -0,0 +1,61 @@
+import os
+
+import torch
+
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_model, 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": "freeze",
+    "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,
+}
+
+
+def test_freeze_all_modules():
+    model_args, _, _, finetuning_args, _ = get_train_args(
+        {
+            "freeze_trainable_layers": 1,
+            **TRAINING_ARGS,
+        }
+    )
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+    for name, param in model.named_parameters():
+        if name.startswith("model.layers.1."):
+            assert param.requires_grad is True
+            assert param.dtype == torch.float32
+        else:
+            assert param.requires_grad is False
+            assert param.dtype == torch.float16
+
+
+def test_freeze_extra_modules():
+    model_args, _, _, finetuning_args, _ = get_train_args(
+        {
+            "freeze_trainable_layers": 1,
+            "freeze_extra_modules": "embed_tokens,lm_head",
+            **TRAINING_ARGS,
+        }
+    )
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+    for name, param in model.named_parameters():
+        if name.startswith("model.layers.1.") or any(module in name for module in ["embed_tokens", "lm_head"]):
+            assert param.requires_grad is True
+            assert param.dtype == torch.float32
+        else:
+            assert param.requires_grad is False
+            assert param.dtype == torch.float16

tests/model/test_full.py

@@ -0,0 +1,33 @@
+import os
+
+import torch
+
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_model, 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,
+}
+
+
+def test_full():
+    model_args, _, _, finetuning_args, _ = get_train_args(TRAINING_ARGS)
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+    for param in model.parameters():
+        assert param.requires_grad is True
+        assert param.dtype == torch.float32