fix #4402 #4617

Deprecate reserved_label_len arg


Former-commit-id: 1771251ce3

tests/data/test_formatter.py

@@ -28,7 +28,7 @@ def test_string_formatter():
 
 
 def test_function_formatter():
-    formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}\n"])
+    formatter = FunctionFormatter(slots=[], tool_format="default")
     tool_calls = json.dumps({"name": "tool_name", "arguments": {"foo": "bar", "size": 10}})
     assert formatter.apply(content=tool_calls) == [
         """Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n"""
@@ -36,7 +36,7 @@ def test_function_formatter():
 
 
 def test_multi_function_formatter():
-    formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}\n"])
+    formatter = FunctionFormatter(slots=[], tool_format="default")
     tool_calls = json.dumps([{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}] * 2)
     assert formatter.apply(content=tool_calls) == [
         """Action: tool_name\nAction Input: {\"foo\": \"bar\", \"size\": 10}\n""",

tests/data/test_processor.py

@@ -0,0 +1,32 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Tuple
+
+import pytest
+
+from llamafactory.data.processors.processor_utils import infer_seqlen
+
+
+@pytest.mark.parametrize(
+    "test_input,test_output",
+    [
+        ((3000, 2000, 1000), (600, 400)),
+        ((2000, 3000, 1000), (400, 600)),
+        ((1000, 100, 1000), (900, 100)),
+        ((100, 1000, 1000), (100, 900)),
+    ],
+)
+def test_infer_seqlen(test_input: Tuple[int, int, int], test_output: Tuple[int, int]):
+    assert test_output == infer_seqlen(*test_input)

tests/data/test_template.py

@@ -21,15 +21,60 @@ from llamafactory.data import get_template_and_fix_tokenizer
 
 TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
 
+MESSAGES = [
+    {"role": "user", "content": "How are you"},
+    {"role": "assistant", "content": "I am fine!"},
+    {"role": "user", "content": "你好"},
+    {"role": "assistant", "content": "很高兴认识你！"},
+]
+
+
+def test_encode_oneturn():
+    tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
+    template = get_template_and_fix_tokenizer(tokenizer, name="llama3")
+    prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
+    assert tokenizer.decode(prompt_ids) == (
+        "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
+        "<|start_header_id|>assistant<|end_header_id|>\n\nI am fine!<|eot_id|>"
+        "<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
+        "<|start_header_id|>assistant<|end_header_id|>\n\n"
+    )
+    assert tokenizer.decode(answer_ids) == "很高兴认识你！<|eot_id|>"
+
+
+def test_encode_multiturn():
+    tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
+    template = get_template_and_fix_tokenizer(tokenizer, name="llama3")
+    encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
+    assert tokenizer.decode(encoded_pairs[0][0]) == (
+        "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
+        "<|start_header_id|>assistant<|end_header_id|>\n\n"
+    )
+    assert tokenizer.decode(encoded_pairs[0][1]) == "I am fine!<|eot_id|>"
+    assert tokenizer.decode(encoded_pairs[1][0]) == (
+        "<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
+        "<|start_header_id|>assistant<|end_header_id|>\n\n"
+    )
+    assert tokenizer.decode(encoded_pairs[1][1]) == "很高兴认识你！<|eot_id|>"
+
 
 def test_jinja_template():
     tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     get_template_and_fix_tokenizer(tokenizer, name="llama3")
     assert tokenizer.chat_template != ref_tokenizer.chat_template
+    assert tokenizer.apply_chat_template(MESSAGES) == ref_tokenizer.apply_chat_template(MESSAGES)
 
-    messages = [
-        {"role": "user", "content": "hi!"},
-        {"role": "assistant", "content": "hello there"},
-    ]
-    assert tokenizer.apply_chat_template(messages) == ref_tokenizer.apply_chat_template(messages)
+
+def test_qwen_template():
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
+    template = get_template_and_fix_tokenizer(tokenizer, name="qwen")
+    prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
+    assert tokenizer.decode(prompt_ids) == (
+        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+        "<|im_start|>user\nHow are you<|im_end|>\n"
+        "<|im_start|>assistant\nI am fine!<|im_end|>\n"
+        "<|im_start|>user\n你好<|im_end|>\n"
+        "<|im_start|>assistant\n"
+    )
+    assert tokenizer.decode(answer_ids) == "很高兴认识你！<|im_end|>"