Use ungated models for unit tests (#196)

* Fix unit test

* Fix chat template tests

* Remove deprecated test

* up

Author: lewtun

src/alignment/data.py

@@ -32,7 +32,7 @@ def maybe_insert_system_message(messages, tokenizer):
     # chat template can be one of two attributes, we check in order
     chat_template = tokenizer.chat_template
     if chat_template is None:
-        chat_template = tokenizer.default_chat_template
+        chat_template = tokenizer.get_chat_template()
 
     # confirm the jinja template refers to a system message before inserting
     if "system" in chat_template or "<|im_start|>" in chat_template:

tests/test_data.py

@@ -122,21 +122,21 @@ def setUp(self):
         )
 
     def test_maybe_insert_system_message(self):
-        # does not accept system prompt
-        mistral_tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
-        # accepts system prompt. use codellama since it has no HF token requirement
-        llama_tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
+        # Chat template that does not accept system prompt. Use community checkpoint since it has no HF token requirement
+        tokenizer_sys_excl = AutoTokenizer.from_pretrained("mistral-community/Mistral-7B-Instruct-v0.3")
+        # Chat template that accepts system prompt
+        tokenizer_sys_incl = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
         messages_sys_excl = [{"role": "user", "content": "Tell me a joke."}]
         messages_sys_incl = [{"role": "system", "content": ""}, {"role": "user", "content": "Tell me a joke."}]
 
-        mistral_messages = deepcopy(messages_sys_excl)
-        llama_messages = deepcopy(messages_sys_excl)
-        maybe_insert_system_message(mistral_messages, mistral_tokenizer)
-        maybe_insert_system_message(llama_messages, llama_tokenizer)
+        messages_proc_excl = deepcopy(messages_sys_excl)
+        message_proc_incl = deepcopy(messages_sys_excl)
+        maybe_insert_system_message(messages_proc_excl, tokenizer_sys_excl)
+        maybe_insert_system_message(message_proc_incl, tokenizer_sys_incl)
 
         # output from mistral should not have a system message, output from llama should
-        self.assertEqual(mistral_messages, messages_sys_excl)
-        self.assertEqual(llama_messages, messages_sys_incl)
+        self.assertEqual(messages_proc_excl, messages_sys_excl)
+        self.assertEqual(message_proc_incl, messages_sys_incl)
 
     def test_sft(self):
         dataset = self.dataset.map(

tests/test_model_utils.py

@@ -15,7 +15,6 @@
 import unittest
 
 import torch
-from transformers import AutoTokenizer
 
 from alignment import (
     DataArguments,
@@ -64,19 +63,6 @@ def test_default_chat_template(self):
         tokenizer = get_tokenizer(self.model_args, DataArguments())
         self.assertEqual(tokenizer.chat_template, DEFAULT_CHAT_TEMPLATE)
 
-    def test_default_chat_template_no_overwrite(self):
-        """
-        If no chat template is passed explicitly in the config, then for models with a
-        `default_chat_template` but no `chat_template` we do not set a `chat_template`,
-        and that we do not change `default_chat_template`
-        """
-        model_args = ModelArguments(model_name_or_path="m-a-p/OpenCodeInterpreter-SC2-7B")
-        base_tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenCodeInterpreter-SC2-7B")
-        processed_tokenizer = get_tokenizer(model_args, DataArguments())
-
-        assert getattr(processed_tokenizer, "chat_template") is None
-        self.assertEqual(base_tokenizer.default_chat_template, processed_tokenizer.default_chat_template)
-
     def test_chatml_chat_template(self):
         chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
         tokenizer = get_tokenizer(self.model_args, DataArguments(chat_template=chat_template))