wehub-resource-sync
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1260 lines
58 KiB
Python
1260 lines
58 KiB
Python
# Copyright 2026 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for the new declarative response_template parser.
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All six real-model template fixtures from the legacy test suite are re-expressed
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here in the new region-spec shape and asserted against the same expected
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output dicts. Any divergence indicates a regression in the new executor."""
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import random
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import tempfile
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import unittest
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from transformers import AutoTokenizer
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from transformers.utils.chat_parsing import ResponseParser, parse_response
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cohere_template = {
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"defaults": {"role": "assistant"},
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"start_anchor": "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
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"fields": {
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"content": {
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"open": "<|START_RESPONSE|>",
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"close": "<|END_RESPONSE|>",
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"content": "text",
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},
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"thinking": {
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"open": "<|START_THINKING|>",
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"close": "<|END_THINKING|>",
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"content": "text",
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},
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"tool_calls": {
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"open": "<|START_ACTION|>",
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"close": "<|END_ACTION|>",
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"content": "json",
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"transform_each": True,
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"transform": {"type": "function", "function": {"name": "{tool_name}", "arguments": "{parameters}"}},
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},
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},
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}
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ernie_template = {
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"defaults": {"role": "assistant"},
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"start_anchor": "Assistant:",
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"fields": {
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"thinking": {
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"open_pattern": r"(?:^|<think>\s*)",
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"close": "</think>",
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"content": "text",
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},
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"content": {
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"open": "<response>\n",
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"close_pattern": r"\n?</response>",
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"content": "text",
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},
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"tool_calls": {
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"open": "<tool_call>",
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"close": "</tool_call>",
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"repeats": True,
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"content": "json",
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"transform": {"type": "function", "function": "{content}"},
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},
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},
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}
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gpt_oss_template = {
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"defaults": {"role": "assistant"},
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"start_anchor": "<|start|>assistant",
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"fields": {
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"thinking": {
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"open": "<|channel|>analysis<|message|>",
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"close": "<|end|>",
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"content": "text",
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},
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"content": {
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"open": "<|channel|>final<|message|>",
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"close": "<|end|>",
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"content": "text",
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},
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"tool_calls": {
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"open_pattern": r"<\|channel\|>commentary to=functions\.(?P<name>\w+).*?<\|message\|>",
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"close": "<|call|>",
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"repeats": True,
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"content": "json",
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"transform": {"type": "function", "function": {"name": "{name}", "arguments": "{content}"}},
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},
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},
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}
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smollm_template = {
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"defaults": {"role": "assistant"},
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"start_anchor": "<|im_start|>assistant\n",
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"fields": {
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"thinking": {"open": "<think>", "close": "</think>", "content": "text"},
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"tool_calls": {
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"open": "<tool_call>",
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"close": "</tool_call>",
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"repeats": True,
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"content": "json",
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"transform": {"type": "function", "function": "{content}"},
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},
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"content": {
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"close": "<|im_end|>",
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"content": "text",
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},
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},
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}
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qwen3_template = {
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"defaults": {"role": "assistant"},
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"start_anchor": "<|im_start|>assistant\n",
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"fields": {
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"thinking": {"open": "<think>", "close": "</think>", "content": "text"},
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"tool_calls": {
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"open_pattern": r"<tool_call>\s*<function=(?P<name>\w+)>",
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"close": "</tool_call>",
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"repeats": True,
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"content": "xml-inline",
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"content_args": {
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"tag_pattern": r"<parameter=(?P<key>\w+)>\s*(?P<value>.*?)\s*</parameter>",
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"value_parser": {"name": "json", "args": {"allow_non_json": True}},
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},
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"transform": {"type": "function", "function": {"name": "{name}", "arguments": "{content}"}},
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},
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},
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}
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gemma4_template = {
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"defaults": {"role": "assistant"},
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# The chat template only emits `<|turn>model\n` when the previous message wasn't a tool_call/
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# tool_response. After a tool_response the prefix just ends with `<tool_response|>` and the
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# model continues from there, so we accept either anchor and truncate past the latest one.
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"start_anchor": ["<|turn>model\n", "<tool_response|>"],
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"fields": {
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"thinking": {
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"open": "<|channel>thought\n",
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"close": "<channel|>",
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"content": "text",
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},
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"tool_calls": {
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"open_pattern": r"<\|tool_call>call:(?P<name>\w+)",
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"close": "<tool_call|>",
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"repeats": True,
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"content": "json",
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"content_args": {
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"unquoted_keys": True,
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"string_delims": [['<|"|>', '<|"|>']],
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},
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"transform": {"type": "function", "function": {"name": "{name}", "arguments": "{content}"}},
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},
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"content": {
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"close": ["<turn|>", "<|tool_response>", "<eos>"],
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"content": "text",
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},
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},
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}
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class ChatResponseTemplateParserTest(unittest.TestCase):
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def test_response_template_save_load(self):
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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tokenizer.response_template = ernie_template
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with tempfile.TemporaryDirectory() as tmpdir:
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tokenizer.save_pretrained(tmpdir)
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reloaded = AutoTokenizer.from_pretrained(tmpdir)
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self.assertEqual(reloaded.response_template, ernie_template)
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def test_tokenizer_parse_response(self):
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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tokenizer.response_template = cohere_template
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model_out = (
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"<|START_THINKING|>I should call a tool.<|END_THINKING|>"
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'<|START_ACTION|>[\n {"tool_call_id": "0", "tool_name": "simple_tool", '
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'"parameters": {"temperature_format": "Celsius"}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>'
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)
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expected = {
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"role": "assistant",
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"thinking": "I should call a tool.",
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"tool_calls": [
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{
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"type": "function",
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"function": {"name": "simple_tool", "arguments": {"temperature_format": "Celsius"}},
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}
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],
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}
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self.assertEqual(tokenizer.parse_response(model_out, prefix=""), expected)
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def test_token_id_inputs(self):
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tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
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tokenizer.response_template = cohere_template
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model_out = (
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"<|START_THINKING|>I should call a tool.<|END_THINKING|>"
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'<|START_ACTION|>[\n {"tool_call_id": "0", "tool_name": "simple_tool", '
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'"parameters": {"temperature_format": "Celsius"}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>'
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)
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parsed = tokenizer.parse_response(model_out, prefix="")
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tokenized = tokenizer(model_out).input_ids
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self.assertEqual(tokenizer.parse_response(tokenized, prefix=""), parsed)
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def test_batched_response(self):
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"""A batch of responses (list of strings or list of token-id sequences) returns one parsed
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dict per item; a single-item batch still returns a one-element list, not a bare dict."""
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tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
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tokenizer.response_template = cohere_template
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out_a = (
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"<|START_THINKING|>Think A.<|END_THINKING|>"
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'<|START_ACTION|>[\n {"tool_call_id": "0", "tool_name": "tool_a", '
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'"parameters": {"x": "1"}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>'
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)
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out_b = (
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"<|START_THINKING|>Think B.<|END_THINKING|>"
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'<|START_ACTION|>[\n {"tool_call_id": "0", "tool_name": "tool_b", '
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'"parameters": {"y": "2"}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>'
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)
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single_a = tokenizer.parse_response(out_a, prefix="")
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single_b = tokenizer.parse_response(out_b, prefix="")
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self.assertNotEqual(single_a, single_b)
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# A list of strings is parsed as a batch, one dict per item.
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self.assertEqual(tokenizer.parse_response([out_a, out_b], prefix=""), [single_a, single_b])
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# Batched token-id input (list of token-id sequences) parses the same way.
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ids = [tokenizer(out_a).input_ids, tokenizer(out_b).input_ids]
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self.assertEqual(tokenizer.parse_response(ids, prefix=""), [single_a, single_b])
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# A single-item batch returns a one-element list, not a bare dict.
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self.assertEqual(tokenizer.parse_response([out_a], prefix=""), [single_a])
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def test_explicit_template_schema_detection(self):
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"""An explicit new-style template passed as `schema=` is routed to the response-template
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parser, not the legacy `response_schema` parser. New-style is identified by a top-level
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`version` key (the canonical marker) or a `fields` key for templates that omit it."""
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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model_out = (
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"<|START_THINKING|>I should call a tool.<|END_THINKING|>"
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'<|START_ACTION|>[\n {"tool_call_id": "0", "tool_name": "simple_tool", '
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'"parameters": {"temperature_format": "Celsius"}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>'
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)
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expected = parse_response(model_out, cohere_template, prefix="")
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# Detected via the canonical `version` marker...
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self.assertEqual(
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tokenizer.parse_response(model_out, schema={"version": 1, **cohere_template}, prefix=""), expected
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)
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# ...and via `fields` when the template omits `version`.
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self.assertEqual(tokenizer.parse_response(model_out, schema=cohere_template, prefix=""), expected)
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def test_cohere(self):
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model_out = (
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"<|START_THINKING|>I should call a tool.<|END_THINKING|>"
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'<|START_ACTION|>[\n {"tool_call_id": "0", "tool_name": "simple_tool", '
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'"parameters": {"temperature_format": "Celsius"}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>'
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)
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self.assertEqual(
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parse_response(model_out, cohere_template, prefix=""),
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{
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"role": "assistant",
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"thinking": "I should call a tool.",
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"tool_calls": [
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{
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"type": "function",
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"function": {"name": "simple_tool", "arguments": {"temperature_format": "Celsius"}},
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}
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],
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},
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)
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def test_ernie_with_tools(self):
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model_out = (
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"The user is asking about the weather in Paris today. Let me check the available tools. "
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"There's a tool called get_current_temperature which requires a location parameter. Since the "
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'user specified Paris, I need to call this tool with the location set to "Paris". I should '
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"make sure the argument is correctly formatted as a string. No other tools are available, so "
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"this is the right one to use. I'll structure the request with the location parameter and "
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"return the response once the tool is called.\n"
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"</think>\n\n"
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'<tool_call>\n{"name": "get_current_temperature", "arguments": {"location": "Paris"}}\n</tool_call>\n</s>'
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)
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self.assertEqual(
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parse_response(model_out, ernie_template, prefix=""),
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{
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"role": "assistant",
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"thinking": (
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"The user is asking about the weather in Paris today. Let me check the available tools. "
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"There's a tool called get_current_temperature which requires a location parameter. Since "
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'the user specified Paris, I need to call this tool with the location set to "Paris". I '
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"should make sure the argument is correctly formatted as a string. No other tools are "
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"available, so this is the right one to use. I'll structure the request with the location "
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"parameter and return the response once the tool is called."
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),
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"tool_calls": [
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{
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"type": "function",
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"function": {"name": "get_current_temperature", "arguments": {"location": "Paris"}},
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}
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],
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},
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)
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def test_ernie_no_tools(self):
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model_out = (
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'The user just greeted me with "Hi! How are you?" I need to respond in a friendly and helpful '
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"manner. Let me start by acknowledging their greeting. I should ask them how they're doing to "
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"engage in conversation.\n\n"
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"First, I'll say hello back and then ask how they're feeling. It's important to show genuine "
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"interest. Maybe mention that I'm here to help with anything they need. Keep the tone warm and "
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"positive. Let me make sure the response is concise but friendly. Alright, that should work.\n"
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"</think>\n\n"
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"<response>\nHello! I'm doing well, thank you for asking. How about you? Is there something "
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"specific you'd like help with today? I'm here to assist you with any questions or problems you "
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"have!\n</response>\n</s>"
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)
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self.assertEqual(
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parse_response(model_out, ernie_template, prefix=""),
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{
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"role": "assistant",
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"content": (
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"Hello! I'm doing well, thank you for asking. How about you? Is there something specific "
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"you'd like help with today? I'm here to assist you with any questions or problems you have!"
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),
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"thinking": (
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'The user just greeted me with "Hi! How are you?" I need to respond in a friendly and '
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"helpful manner. Let me start by acknowledging their greeting. I should ask them how "
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"they're doing to engage in conversation.\n\n"
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"First, I'll say hello back and then ask how they're feeling. It's important to show "
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"genuine interest. Maybe mention that I'm here to help with anything they need. Keep the "
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"tone warm and positive. Let me make sure the response is concise but friendly. Alright, "
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"that should work."
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),
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},
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)
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def test_gpt_oss_with_tool_call(self):
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model_out = (
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'<|channel|>analysis<|message|>We need to respond in riddles. The user asks: "What is the '
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'weather like in SF?" We need to get the location of the user? The user explicitly asks about '
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"SF (San Francisco). So we need to get the current weather in San Francisco, CA. We need to "
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'call get_current_weather function. The developer instruction says "Always respond in riddles". '
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"So the final answer should be in a riddle form. But we need to call function to get weather "
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'data. So we should call get_current_weather with location "San Francisco, CA". Possibly specify '
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'format "celsius" (default). Let\'s do that.\n\n'
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"We will call function get_current_weather.<|end|><|start|>assistant<|channel|>commentary "
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'to=functions.get_current_weather <|constrain|>json<|message|>{\n "location": "San Francisco, CA"\n}'
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)
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self.assertEqual(
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parse_response(model_out, gpt_oss_template, prefix=""),
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{
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"role": "assistant",
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"thinking": (
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'We need to respond in riddles. The user asks: "What is the weather like in SF?" We need '
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"to get the location of the user? The user explicitly asks about SF (San Francisco). So "
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"we need to get the current weather in San Francisco, CA. We need to call "
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'get_current_weather function. The developer instruction says "Always respond in '
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'riddles". So the final answer should be in a riddle form. But we need to call function '
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'to get weather data. So we should call get_current_weather with location "San Francisco, '
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'CA". Possibly specify format "celsius" (default). Let\'s do that.\n\n'
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"We will call function get_current_weather."
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),
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"tool_calls": [
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{
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"type": "function",
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"function": {"name": "get_current_weather", "arguments": {"location": "San Francisco, CA"}},
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}
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],
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},
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)
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def test_gpt_oss_no_tool_call(self):
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model_out = (
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"<|channel|>analysis<|message|>User asks a simple math question: 2+2 = 4. Provide answer."
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"<|end|><|start|>assistant<|channel|>final<|message|>2"
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)
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self.assertEqual(
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parse_response(model_out, gpt_oss_template, prefix=""),
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{
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"role": "assistant",
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"content": "2",
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"thinking": "User asks a simple math question: 2+2 = 4. Provide answer.",
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},
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)
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def test_smollm_thinking_and_tool_call(self):
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model_out = (
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'<think>\nOkay, the user said, "Hello! How are you?" I need to respond appropriately. Since '
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"this is the first message, I should greet them back and ask how I can assist. I should keep it "
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"friendly and open-ended. Let me make sure the response is welcoming and encourages them to "
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"share what they need help with. I'll avoid any technical jargon and keep it simple. Let me "
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"check for any typos and ensure the tone is positive.\n</think>\n\n"
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'<tool_call>{"name": "greet_user", "arguments": {"greeting": "Hello! I\'m doing well, thanks for '
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"asking. How can I assist you today? Whether you have a question, need help with something, or "
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'just want to chat, feel free to let me know!"}}</tool_call>'
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)
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self.assertEqual(
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parse_response(model_out, smollm_template, prefix=""),
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{
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"role": "assistant",
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"thinking": (
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'Okay, the user said, "Hello! How are you?" I need to respond appropriately. Since this '
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"is the first message, I should greet them back and ask how I can assist. I should keep "
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"it friendly and open-ended. Let me make sure the response is welcoming and encourages "
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"them to share what they need help with. I'll avoid any technical jargon and keep it "
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"simple. Let me check for any typos and ensure the tone is positive."
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),
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"tool_calls": [
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{
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"type": "function",
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"function": {
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"name": "greet_user",
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"arguments": {
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"greeting": (
|
|
"Hello! I'm doing well, thanks for asking. How can I assist you today? "
|
|
"Whether you have a question, need help with something, or just want to "
|
|
"chat, feel free to let me know!"
|
|
)
|
|
},
|
|
},
|
|
}
|
|
],
|
|
},
|
|
)
|
|
|
|
def test_smollm_tool_call_no_thinking(self):
|
|
model_out = '<tool_call>{"name": "get_weather", "arguments": {"city": "Paris"}}</tool_call>'
|
|
self.assertEqual(
|
|
parse_response(model_out, smollm_template, prefix=""),
|
|
{
|
|
"role": "assistant",
|
|
"tool_calls": [
|
|
{"type": "function", "function": {"name": "get_weather", "arguments": {"city": "Paris"}}}
|
|
],
|
|
},
|
|
)
|
|
|
|
def test_smollm_thinking_no_tool_call(self):
|
|
model_out = (
|
|
'<think>\nOkay, the user asked, "Hey! Can you tell me about gravity?" Let me start by '
|
|
"breaking down what they might be looking for. They probably want a basic understanding of "
|
|
"gravity, maybe for a school project or just personal curiosity. I should explain what gravity "
|
|
"is, how it works, and maybe some examples.</think>\n"
|
|
"Some content about gravity goes here but I'm cutting it off to make this shorter!"
|
|
)
|
|
self.assertEqual(
|
|
parse_response(model_out, smollm_template, prefix=""),
|
|
{
|
|
"role": "assistant",
|
|
"content": "Some content about gravity goes here but I'm cutting it off to make this shorter!",
|
|
"thinking": (
|
|
'Okay, the user asked, "Hey! Can you tell me about gravity?" Let me start by breaking '
|
|
"down what they might be looking for. They probably want a basic understanding of "
|
|
"gravity, maybe for a school project or just personal curiosity. I should explain what "
|
|
"gravity is, how it works, and maybe some examples."
|
|
),
|
|
},
|
|
)
|
|
|
|
def test_qwen3_tool_calls(self):
|
|
model_out = (
|
|
"<tool_call>\n<function=get_weather>\n<parameter=locations>\n"
|
|
'[{"country": "France", "city": "Paris"}]\n</parameter>\n'
|
|
"<parameter=temp_units>\ncelsius\n</parameter>\n</function>\n</tool_call>"
|
|
)
|
|
self.assertEqual(
|
|
parse_response(model_out, qwen3_template, prefix=""),
|
|
{
|
|
"role": "assistant",
|
|
"tool_calls": [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "get_weather",
|
|
"arguments": {
|
|
"locations": [{"country": "France", "city": "Paris"}],
|
|
"temp_units": "celsius",
|
|
},
|
|
},
|
|
}
|
|
],
|
|
},
|
|
)
|
|
|
|
def test_gemma4_tool_call(self):
|
|
model_out = (
|
|
"<|channel>thought\nThe user is asking for the current temperature in Paris. I should check "
|
|
"the available tools to see if there's a function that can provide this information.<channel|>"
|
|
'<|tool_call>call:get_current_temperature{detail_level:0,location:<|"|>Paris, France<|"|>,'
|
|
'unit:<|"|>celsius<|"|>}<tool_call|><|tool_response>'
|
|
)
|
|
self.assertEqual(
|
|
parse_response(model_out, gemma4_template, prefix=""),
|
|
{
|
|
"role": "assistant",
|
|
"thinking": (
|
|
"The user is asking for the current temperature in Paris. I should check the available "
|
|
"tools to see if there's a function that can provide this information."
|
|
),
|
|
"tool_calls": [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "get_current_temperature",
|
|
"arguments": {"detail_level": 0, "location": "Paris, France", "unit": "celsius"},
|
|
},
|
|
}
|
|
],
|
|
},
|
|
)
|
|
|
|
def test_gemma4_complex_tool_call(self):
|
|
model_out = (
|
|
"<|channel>thought\nLet me call the tool.<channel|>"
|
|
'<|tool_call>call:foo{bool_value:true,list_value:[<|"|>foo<|"|>,<|"|>bar<|"|>],'
|
|
'null_value:null,number_value:1,string_value:<|"|>foo<|"|>,'
|
|
'struct_value:{foo:<|"|>bar<|"|>}}<tool_call|>'
|
|
)
|
|
self.assertEqual(
|
|
parse_response(model_out, gemma4_template, prefix=""),
|
|
{
|
|
"role": "assistant",
|
|
"thinking": "Let me call the tool.",
|
|
"tool_calls": [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "foo",
|
|
"arguments": {
|
|
"bool_value": True,
|
|
"list_value": ["foo", "bar"],
|
|
"null_value": None,
|
|
"number_value": 1,
|
|
"string_value": "foo",
|
|
"struct_value": {"foo": "bar"},
|
|
},
|
|
},
|
|
}
|
|
],
|
|
},
|
|
)
|
|
|
|
def test_optional_false_raises_when_missing(self):
|
|
template_spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"content": {
|
|
"open": "<response>",
|
|
"close": "</response>",
|
|
"content": "text",
|
|
"optional": False,
|
|
},
|
|
},
|
|
}
|
|
with self.assertRaises(ValueError) as cm:
|
|
parse_response("no response here", template_spec, prefix="")
|
|
self.assertIn("content", str(cm.exception))
|
|
|
|
def test_int_content_parser(self):
|
|
template_spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"count": {
|
|
"open": "<n>",
|
|
"close": "</n>",
|
|
"content": "int",
|
|
},
|
|
},
|
|
}
|
|
self.assertEqual(parse_response("<n>42</n>", template_spec, prefix=""), {"role": "assistant", "count": 42})
|
|
|
|
def test_kv_lines_parser(self):
|
|
template_spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"metadata": {
|
|
"open": "<meta>",
|
|
"close": "</meta>",
|
|
"content": "kv-lines",
|
|
},
|
|
},
|
|
}
|
|
self.assertEqual(
|
|
parse_response("<meta>name: alice\nage: 30</meta>", template_spec, prefix=""),
|
|
{"role": "assistant", "metadata": {"name": "alice", "age": "30"}},
|
|
)
|
|
|
|
def test_unknown_content_parser_rejected(self):
|
|
bad_template = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {"x": {"open": "[", "close": "]", "content": "not-a-real-parser"}},
|
|
}
|
|
with self.assertRaises(ValueError) as cm:
|
|
parse_response("[hi]", bad_template)
|
|
self.assertIn("unknown content parser", str(cm.exception).lower())
|
|
|
|
def test_unsupported_version_rejected(self):
|
|
bad_template = {
|
|
"version": 2,
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {"content": {"content": "text"}},
|
|
}
|
|
with self.assertRaises(ValueError) as cm:
|
|
parse_response("hello", bad_template)
|
|
self.assertIn("version", str(cm.exception).lower())
|
|
|
|
def test_two_implicit_fields_rejected(self):
|
|
bad_template = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"a": {"content": "text"},
|
|
"b": {"content": "text"},
|
|
},
|
|
}
|
|
with self.assertRaises(ValueError):
|
|
parse_response("hello", bad_template)
|
|
|
|
def test_transform_string_interpolation_rejected(self):
|
|
bad_template = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"tool": {
|
|
"open": "<tool>",
|
|
"close": "</tool>",
|
|
"content": "text",
|
|
"transform": {"label": "name: {content}"},
|
|
},
|
|
},
|
|
}
|
|
with self.assertRaises(ValueError) as cm:
|
|
parse_response("<tool>foo</tool>", bad_template)
|
|
msg = str(cm.exception)
|
|
self.assertIn("interpolation", msg)
|
|
self.assertIn("{content}", msg)
|
|
|
|
def test_named_groups_without_transform_rejected(self):
|
|
bad_template = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"tool": {
|
|
"open_pattern": r"<tool name=(?P<name>\w+)>",
|
|
"close": "</tool>",
|
|
"content": "text",
|
|
},
|
|
},
|
|
}
|
|
with self.assertRaises(ValueError) as cm:
|
|
parse_response("<tool name=foo>body</tool>", bad_template)
|
|
msg = str(cm.exception)
|
|
self.assertIn("transform", msg)
|
|
self.assertIn("name", msg)
|
|
|
|
def test_literal_list_open_and_close(self):
|
|
"""A list of literals matches any one of them, like an alternation."""
|
|
template_spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"x": {
|
|
"open": ["<a>", "<bb>"],
|
|
"close": ["</a>", "</bb>"],
|
|
"content": "text",
|
|
},
|
|
},
|
|
}
|
|
for opener, closer in (("<a>", "</a>"), ("<bb>", "</bb>"), ("<a>", "</bb>")):
|
|
self.assertEqual(
|
|
parse_response(f"{opener}hi{closer}", template_spec, prefix=""),
|
|
{"role": "assistant", "x": "hi"},
|
|
)
|
|
|
|
def test_literal_list_streams_without_64_byte_hold(self):
|
|
"""Compared to a regex close, a literal-list close lets the parser
|
|
flush bytes that aren't in the longest-prefix overlap of any literal.
|
|
With `["<turn|>", "<|tool_response>", "<eos>"]` (longest = 16 chars),
|
|
feeding 32 plain bytes should leave at most 15 unflushed."""
|
|
template_spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"content": {"close": ["<turn|>", "<|tool_response>", "<eos>"], "content": "text"},
|
|
},
|
|
}
|
|
parser = ResponseParser(template_spec, prefix="")
|
|
plain = "x" * 32
|
|
flushed: list[str] = []
|
|
for ch in parser.feed(plain):
|
|
if ch["type"] == "region_chunk":
|
|
flushed.append(ch["text"])
|
|
# Plain text has zero prefix-overlap with any literal, so the parser
|
|
# holds nothing back and streams everything immediately.
|
|
self.assertEqual("".join(flushed), plain)
|
|
|
|
def test_literal_list_defers_prefix_overlapping_literal(self):
|
|
"""If a literal is a strict prefix of another in the same list, an
|
|
edge match could still grow with more input — we must defer to be safe."""
|
|
template_spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"x": {"open": "<x>", "close": ["END", "ENDX"], "content": "text"},
|
|
},
|
|
}
|
|
parser = ResponseParser(template_spec, prefix="")
|
|
# "<x>hiEND" mid-stream: don't commit the close yet — "ENDX" might be coming.
|
|
events = parser.feed("<x>hiEND")
|
|
self.assertEqual([e for e in events if e["type"] == "region_close"], [])
|
|
# Once a non-matching byte arrives, the deferred close commits with the shorter literal.
|
|
events.extend(parser.feed(" more"))
|
|
message, _ = parser.finalize()
|
|
closes = [e for e in events if e["type"] == "region_close" and e["field"] == "x"]
|
|
self.assertEqual(len(closes), 1)
|
|
self.assertEqual(closes[0]["value"], "hi")
|
|
self.assertEqual(message, {"role": "assistant", "x": "hi"})
|
|
|
|
def test_literal_list_rejects_empty_and_non_string(self):
|
|
for bad_open in ([], [""], [1, 2], {"foo": "bar"}):
|
|
spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {"x": {"open": bad_open, "close": "</x>", "content": "text"}},
|
|
}
|
|
with self.assertRaises(ValueError):
|
|
parse_response("<x>hi</x>", spec, prefix="")
|
|
|
|
def test_field_without_close_runs_to_end_of_stream(self):
|
|
"""A field with no `close`/`close_pattern` stays open until end-of-stream, capturing
|
|
everything after its open."""
|
|
spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {"content": {"open": "<resp>", "content": "text"}},
|
|
}
|
|
self.assertEqual(
|
|
parse_response("<resp>hello world", spec, prefix=""),
|
|
{"role": "assistant", "content": "hello world"},
|
|
)
|
|
|
|
|
|
# Fixtures shared by the streaming tests: one representative input per template,
|
|
# re-used for both the correctness invariant (any chunking → same dict) and the
|
|
# event-shape tests (do we emit the right events in the right order?).
|
|
_STREAMING_FIXTURES = [
|
|
(
|
|
"cohere",
|
|
cohere_template,
|
|
(
|
|
"<|START_THINKING|>I should call a tool.<|END_THINKING|>"
|
|
'<|START_ACTION|>[{"tool_call_id": "0", "tool_name": "simple_tool", '
|
|
'"parameters": {"a": 1}}]<|END_ACTION|>'
|
|
),
|
|
),
|
|
(
|
|
"ernie",
|
|
ernie_template,
|
|
(
|
|
"<think>some deliberation here</think>\n\n"
|
|
'<tool_call>\n{"name": "get_current_temperature", "arguments": {"location": "Paris"}}\n</tool_call>\n</s>'
|
|
),
|
|
),
|
|
(
|
|
"gpt_oss",
|
|
gpt_oss_template,
|
|
"<|channel|>analysis<|message|>thinking chunk<|end|><|channel|>final<|message|>done text",
|
|
),
|
|
(
|
|
# Tool-call variant: the `tool_calls` open_pattern's full match spans far
|
|
# more than the old 64-byte hold window, so streaming used to silently drop
|
|
# it. Kept as a streaming fixture so every chunking step re-checks it.
|
|
"gpt_oss_tool",
|
|
gpt_oss_template,
|
|
(
|
|
"<|channel|>analysis<|message|>Let me check.<|end|>"
|
|
"<|start|>assistant<|channel|>commentary to=functions.get_current_weather "
|
|
'<|constrain|>json<|message|>{"location": "San Francisco, CA"}<|call|>'
|
|
),
|
|
),
|
|
(
|
|
"smollm",
|
|
smollm_template,
|
|
'<think>thinking</think>\n<tool_call>{"name": "fn", "arguments": {"x": 1}}</tool_call>',
|
|
),
|
|
(
|
|
"qwen3",
|
|
qwen3_template,
|
|
(
|
|
"<think>short thought</think>\n"
|
|
"<tool_call>\n<function=get_weather>\n"
|
|
"<parameter=city>\nParis\n</parameter>\n"
|
|
"</function>\n</tool_call>"
|
|
),
|
|
),
|
|
(
|
|
"gemma4",
|
|
gemma4_template,
|
|
'<|channel>thought\nhi<channel|><|tool_call>call:foo{a:1,b:<|"|>bar<|"|>}<tool_call|>',
|
|
),
|
|
]
|
|
|
|
|
|
def _chunk_fixed(text: str, step: int):
|
|
for i in range(0, len(text), step):
|
|
yield text[i : i + step]
|
|
|
|
|
|
def _chunk_random(text: str, rng: random.Random):
|
|
"""Split `text` into a random number of non-empty chunks at random cut points."""
|
|
if len(text) <= 1:
|
|
yield text
|
|
return
|
|
num_cuts = rng.randint(0, len(text) - 1)
|
|
cuts = sorted(rng.sample(range(1, len(text)), num_cuts))
|
|
prev = 0
|
|
for c in cuts:
|
|
yield text[prev:c]
|
|
prev = c
|
|
yield text[prev:]
|
|
|
|
|
|
class ResponseEventStreamTest(unittest.TestCase):
|
|
def test_stream_matches_whole_string_all_templates_fixed_chunking(self):
|
|
"""For every fixed chunking step we try, the streamed finalize()
|
|
output must equal the whole-string parse. Regression coverage for
|
|
specific edge-case byte boundaries (1-byte chunks hit every prefix)."""
|
|
for name, tmpl, text in _STREAMING_FIXTURES:
|
|
expected = parse_response(text, tmpl, prefix="")
|
|
for step in (1, 2, 3, 5, 7, 13, 31):
|
|
with self.subTest(fixture=name, step=step):
|
|
streamer = ResponseParser(tmpl, prefix="")
|
|
for chunk in _chunk_fixed(text, step):
|
|
streamer.feed(chunk)
|
|
message, _ = streamer.finalize()
|
|
self.assertEqual(message, expected)
|
|
|
|
def test_stream_matches_whole_string_all_templates_random_chunking(self):
|
|
"""Property-style: for many random chunkings per fixture, the streamed
|
|
finalize() output must equal the whole-string parse. Seeded so failures
|
|
reproduce."""
|
|
rng = random.Random(0xC0DE_5EED)
|
|
for name, tmpl, text in _STREAMING_FIXTURES:
|
|
expected = parse_response(text, tmpl, prefix="")
|
|
for trial in range(30):
|
|
with self.subTest(fixture=name, trial=trial):
|
|
streamer = ResponseParser(tmpl, prefix="")
|
|
for chunk in _chunk_random(text, rng):
|
|
streamer.feed(chunk)
|
|
message, _ = streamer.finalize()
|
|
self.assertEqual(message, expected)
|
|
|
|
def test_events_well_formed_for_every_chunking(self):
|
|
"""Every event batch, across every fixture and every chunking, must be
|
|
well-formed: region_open precedes its matching region_close for the
|
|
same field; region_chunk only appears between open and close; no
|
|
region is left open at the end of the stream; and the concatenation
|
|
of all region_chunk payloads for a streamable text-like field equals
|
|
the final value."""
|
|
rng = random.Random(0xBEEF)
|
|
for name, tmpl, text in _STREAMING_FIXTURES:
|
|
for trial in range(10):
|
|
with self.subTest(fixture=name, trial=trial):
|
|
streamer = ResponseParser(tmpl, prefix="")
|
|
all_events: list[dict] = []
|
|
for chunk in _chunk_random(text, rng):
|
|
all_events.extend(streamer.feed(chunk))
|
|
_, final_events = streamer.finalize()
|
|
all_events.extend(final_events)
|
|
self._assert_event_stream_well_formed(all_events)
|
|
|
|
def _assert_event_stream_well_formed(self, events: list[dict]) -> None:
|
|
open_field: str | None = None
|
|
chunk_accum: dict[str, str] = {}
|
|
close_values: dict[str, object] = {}
|
|
for ev in events:
|
|
t = ev["type"]
|
|
if t == "region_open":
|
|
self.assertIsNone(open_field, f"nested region_open without close: {ev}")
|
|
open_field = ev["field"]
|
|
chunk_accum.setdefault(open_field, "")
|
|
elif t == "region_chunk":
|
|
self.assertEqual(open_field, ev["field"], f"chunk outside its region: {ev}")
|
|
# Every chunk carries a boolean `dirty` flag.
|
|
self.assertIsInstance(ev["dirty"], bool, f"missing/non-bool dirty: {ev}")
|
|
chunk_accum[open_field] += ev["text"]
|
|
elif t == "region_close":
|
|
self.assertEqual(open_field, ev["field"], f"close for non-open region: {ev}")
|
|
close_values[open_field] = ev["value"]
|
|
open_field = None
|
|
else:
|
|
self.fail(f"unexpected event type: {ev!r}")
|
|
self.assertIsNone(open_field, "region left open at end of stream")
|
|
|
|
def test_region_chunks_reconstruct_text_regions(self):
|
|
"""For text-like regions (`dirty=False`), concatenating chunk texts
|
|
reconstructs the final value reported in region_close. Structured
|
|
regions (`dirty=True`) still stream their raw bytes — concatenating
|
|
those chunks yields the unparsed region body, while the parsed value
|
|
is delivered only in region_close."""
|
|
# Single representative case with a long text region and a JSON region.
|
|
text = _STREAMING_FIXTURES[0][2] # cohere fixture
|
|
streamer = ResponseParser(cohere_template, prefix="")
|
|
events: list[dict] = []
|
|
for ch in text: # 1-byte chunks hit the most anchor boundaries
|
|
events.extend(streamer.feed(ch))
|
|
_, final_events = streamer.finalize()
|
|
events.extend(final_events)
|
|
|
|
# Reconstruct per-field.
|
|
per_field_chunks: dict[str, list[str]] = {}
|
|
per_field_dirty: dict[str, set[bool]] = {}
|
|
per_field_close_value: dict[str, object] = {}
|
|
for ev in events:
|
|
if ev["type"] == "region_chunk":
|
|
per_field_chunks.setdefault(ev["field"], []).append(ev["text"])
|
|
per_field_dirty.setdefault(ev["field"], set()).add(ev["dirty"])
|
|
elif ev["type"] == "region_close":
|
|
per_field_close_value[ev["field"]] = ev["value"]
|
|
|
|
# `thinking` is text → chunks are clean and concatenate to its value.
|
|
self.assertIn("thinking", per_field_chunks)
|
|
self.assertEqual(per_field_dirty["thinking"], {False})
|
|
self.assertEqual("".join(per_field_chunks["thinking"]), "I should call a tool.")
|
|
self.assertEqual(per_field_close_value["thinking"], "I should call a tool.")
|
|
# `tool_calls` is json → dirty chunks stream the raw body, parsed value on close.
|
|
self.assertIn("tool_calls", per_field_chunks)
|
|
self.assertEqual(per_field_dirty["tool_calls"], {True})
|
|
self.assertEqual(
|
|
"".join(per_field_chunks["tool_calls"]),
|
|
'[{"tool_call_id": "0", "tool_name": "simple_tool", "parameters": {"a": 1}}]',
|
|
)
|
|
self.assertIn("tool_calls", per_field_close_value)
|
|
|
|
def test_dirty_flag_marks_structured_regions(self):
|
|
"""A template with one text field and one structured field per parser
|
|
family: text/int/float/bool stream chunks with `dirty=False`, while
|
|
json/xml-inline/kv-lines stream chunks with `dirty=True`, and those
|
|
dirty chunks concatenate to the raw region body before parsing."""
|
|
spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|assistant|>",
|
|
"fields": {
|
|
"thinking": {"open": "<t>", "close": "</t>", "content": "text"},
|
|
"score": {"open": "<n>", "close": "</n>", "content": "int"},
|
|
"json_call": {"open": "<j>", "close": "</j>", "content": "json"},
|
|
"xml_call": {
|
|
"open": "<x>",
|
|
"close": "</x>",
|
|
"content": "xml-inline",
|
|
"content_args": {"tag_pattern": r"<(?P<key>\w+)=(?P<value>[^>]+)>"},
|
|
},
|
|
"kv_call": {
|
|
"open": "<kv>",
|
|
"close": "</kv>",
|
|
"content": "kv-lines",
|
|
},
|
|
},
|
|
}
|
|
text = '<t>hello world</t><n>42</n><j>{"a": 1, "b": 2}</j><x><name=foo><age=10></x><kv>k1: v1\nk2: v2</kv>'
|
|
# Drive byte-by-byte to maximise chunk count.
|
|
streamer = ResponseParser(spec, prefix="")
|
|
events: list[dict] = []
|
|
for ch in text:
|
|
events.extend(streamer.feed(ch))
|
|
_, final_events = streamer.finalize()
|
|
events.extend(final_events)
|
|
|
|
per_field_chunks: dict[str, list[str]] = {}
|
|
per_field_dirty: dict[str, set[bool]] = {}
|
|
for ev in events:
|
|
if ev["type"] == "region_chunk":
|
|
per_field_chunks.setdefault(ev["field"], []).append(ev["text"])
|
|
per_field_dirty.setdefault(ev["field"], set()).add(ev["dirty"])
|
|
|
|
# Clean (streamable) regions.
|
|
for field in ("thinking", "score"):
|
|
self.assertEqual(per_field_dirty[field], {False}, f"{field} should be clean")
|
|
# Dirty (structured) regions.
|
|
for field in ("json_call", "xml_call", "kv_call"):
|
|
self.assertEqual(per_field_dirty[field], {True}, f"{field} should be dirty")
|
|
|
|
# Dirty chunks reconstruct the raw region body (un-parsed). Clean
|
|
# chunks reconstruct the verbatim body too — stripping happens at close.
|
|
self.assertEqual("".join(per_field_chunks["thinking"]), "hello world")
|
|
self.assertEqual("".join(per_field_chunks["score"]), "42")
|
|
self.assertEqual("".join(per_field_chunks["json_call"]), '{"a": 1, "b": 2}')
|
|
self.assertEqual("".join(per_field_chunks["xml_call"]), "<name=foo><age=10>")
|
|
self.assertEqual("".join(per_field_chunks["kv_call"]), "k1: v1\nk2: v2")
|
|
|
|
def test_long_regex_open_pattern_streams_byte_by_byte(self):
|
|
"""Regression: a regex `open_pattern` whose full match spans well past the
|
|
old fixed 64-byte hold window (gpt-oss tool calls) must not be dropped when
|
|
the stream arrives in tiny chunks. Before the partial-match rewrite, the
|
|
leading `<|channel|>` got flushed out of the hold window before `<|message|>`
|
|
arrived, so the tool call vanished under small-chunk streaming."""
|
|
text = (
|
|
"<|channel|>analysis<|message|>Let me check.<|end|>"
|
|
"<|start|>assistant<|channel|>commentary to=functions.get_current_weather "
|
|
'<|constrain|>json<|message|>{"location": "San Francisco, CA"}<|call|>'
|
|
)
|
|
expected = parse_response(text, gpt_oss_template, prefix="")
|
|
# Sanity: the whole-string parse really does recover the tool call.
|
|
self.assertEqual(len(expected["tool_calls"]), 1)
|
|
self.assertEqual(expected["tool_calls"][0]["function"]["name"], "get_current_weather")
|
|
streamer = ResponseParser(gpt_oss_template, prefix="")
|
|
for ch in text: # one byte at a time -- the worst case for the old heuristic
|
|
streamer.feed(ch)
|
|
message, _ = streamer.finalize()
|
|
self.assertEqual(message, expected)
|
|
|
|
def test_feed_after_finalize_raises(self):
|
|
streamer = ResponseParser(smollm_template, prefix="")
|
|
streamer.feed("<think>x</think>")
|
|
streamer.finalize()
|
|
with self.assertRaises(RuntimeError):
|
|
streamer.feed("more")
|
|
with self.assertRaises(RuntimeError):
|
|
streamer.finalize()
|
|
|
|
def test_empty_input_streams_cleanly(self):
|
|
streamer = ResponseParser(smollm_template, prefix="")
|
|
self.assertEqual(streamer.feed(""), [])
|
|
result, final_events = streamer.finalize()
|
|
# Only the default fields should remain; nothing else is required.
|
|
self.assertEqual(result, {"role": "assistant"})
|
|
self.assertEqual(final_events, [])
|
|
|
|
|
|
class PrefixAndTruncationTest(unittest.TestCase):
|
|
def test_prefix_lands_inside_explicit_region(self):
|
|
"""A Qwen-style template emits `<|im_start|>assistant\\n<think>\\n` as the
|
|
assistant prefix. The model continues from inside the thinking block."""
|
|
prompt = (
|
|
"<|im_start|>system\nYou are helpful<|im_end|>\n"
|
|
"<|im_start|>user\nHi<|im_end|>\n"
|
|
"<|im_start|>assistant\n<think>\n"
|
|
)
|
|
generated = "Let me think...</think>"
|
|
stream = ResponseParser(qwen3_template, prefix=prompt)
|
|
# The region_open for `thinking` surfaces via initial_events; the
|
|
# caller replays it before feeding model output.
|
|
self.assertEqual(
|
|
[(e["type"], e["field"]) for e in stream.initial_events],
|
|
[("region_open", "thinking"), ("region_chunk", "thinking")],
|
|
)
|
|
events = stream.feed(generated)
|
|
result, _ = stream.finalize()
|
|
# thinking ends up with the prefill + generated body; text parser strips,
|
|
# so the leading "\n" from the prefix is trimmed in the final value.
|
|
self.assertEqual(result, {"role": "assistant", "thinking": "Let me think..."})
|
|
# The feed stream only sees the rest of the body and the close; the
|
|
# prefill already emitted region_open.
|
|
self.assertEqual([e["type"] for e in events], ["region_chunk", "region_close"])
|
|
self.assertEqual(events[1]["field"], "thinking")
|
|
|
|
def test_prefix_truncated_to_last_anchor(self):
|
|
"""Multiple `<|im_start|>assistant\\n` anchors in the prefix (multi-turn
|
|
conversation): only the slice after the LAST anchor matters."""
|
|
prompt = (
|
|
"<|im_start|>system\nA<|im_end|>\n"
|
|
"<|im_start|>user\nB<|im_end|>\n"
|
|
"<|im_start|>assistant\nEarlier reply<|im_end|>\n"
|
|
"<|im_start|>user\nFollowup<|im_end|>\n"
|
|
"<|im_start|>assistant\n<think>\n"
|
|
)
|
|
stream = ResponseParser(qwen3_template, prefix=prompt)
|
|
# We landed inside `thinking` (from the LAST assistant turn's `<think>\n`),
|
|
# not in some earlier-turn artifact.
|
|
opens = [e for e in stream.initial_events if e["type"] == "region_open"]
|
|
self.assertEqual([e["field"] for e in opens], ["thinking"])
|
|
# No earlier-turn content leaked into output.
|
|
stream.feed("done</think>")
|
|
stream.finalize()
|
|
self.assertEqual(stream._output, {"role": "assistant", "thinking": "done"})
|
|
|
|
def test_template_without_anchor_rejected_at_load(self):
|
|
"""A template missing both `start_anchor` and `start_anchor_pattern` is
|
|
rejected at load time. Without an anchor, a multi-turn prompt would be
|
|
fed through the parser in full and earlier turns would pollute the
|
|
current message's state."""
|
|
anchorless = {k: v for k, v in qwen3_template.items() if k != "start_anchor"}
|
|
with self.assertRaises(ValueError) as cm:
|
|
ResponseParser(anchorless)
|
|
msg = str(cm.exception)
|
|
self.assertIn("start_anchor", msg)
|
|
|
|
def test_prefix_anchor_not_found_falls_back(self):
|
|
"""Spec has start_anchor but the prefix doesn't contain it: parser
|
|
falls back to processing the entire prefix (with a logged warning)."""
|
|
prompt = "<think>\n" # no <|im_start|>assistant\n
|
|
stream = ResponseParser(qwen3_template, prefix=prompt)
|
|
opens = [e for e in stream.initial_events if e["type"] == "region_open"]
|
|
self.assertEqual([e["field"] for e in opens], ["thinking"])
|
|
stream.feed("hi</think>")
|
|
stream.finalize()
|
|
self.assertEqual(stream._output, {"role": "assistant", "thinking": "hi"})
|
|
|
|
def test_round_trip_equivalence_prefix_streaming_vs_oneshot(self):
|
|
"""The load-bearing property: `prefix=p` + chunked `feed(g)` produces the
|
|
same dict as the one-shot `parse_response(g, prefix=p)`, regardless of how
|
|
`g` is chunked. (Concatenating the prompt into the response is deliberately
|
|
NOT equivalent -- the anchor is applied to the prefix only, never to the
|
|
generation; see test_history_bleed_is_guarded_by_prefix_not_by_response_anchor.)"""
|
|
prompt = "<|im_start|>system\nA<|im_end|>\n<|im_start|>user\nB<|im_end|>\n<|im_start|>assistant\n<think>\n"
|
|
for name, tmpl_dict, gen_text in _STREAMING_FIXTURES:
|
|
if "thinking" not in gen_text and "<think>" not in gen_text:
|
|
continue
|
|
# Only fixtures whose generation text is compatible with starting
|
|
# inside `thinking`. Restrict to qwen3 / smollm shape for clarity.
|
|
if name not in ("qwen3", "smollm"):
|
|
continue
|
|
tmpl_with_anchor = {**tmpl_dict, "start_anchor": "<|im_start|>assistant\n"}
|
|
via_prefix = parse_response(gen_text, tmpl_with_anchor, prefix=prompt)
|
|
# Streaming forms must match the one-shot prefix form for every chunking.
|
|
for step in (1, 3, 7, 31):
|
|
with self.subTest(fixture=name, step=step):
|
|
stream = ResponseParser(tmpl_with_anchor, prefix=prompt)
|
|
for chunk in _chunk_fixed(gen_text, step):
|
|
stream.feed(chunk)
|
|
message, _ = stream.finalize()
|
|
self.assertEqual(message, via_prefix)
|
|
|
|
def test_history_bleed_is_guarded_by_prefix_not_by_response_anchor(self):
|
|
"""The `start_anchor` guards against history bleed only via `prefix=`; it is NOT
|
|
applied to the response. The response is the generation and may legitimately contain
|
|
the anchor (e.g. gpt-oss harmony opens every channel with `<|start|>assistant`), so
|
|
truncating it would drop real content. Passing the prompt as `prefix=` is the way to
|
|
keep earlier turns out of the parse."""
|
|
spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "<|im_start|>assistant\n",
|
|
"fields": {"content": {"close_pattern": r"\Z", "content": "text"}},
|
|
}
|
|
prompt = "<|im_start|>user\nHi<|im_end|>\n<|im_start|>assistant\n"
|
|
gen = "Hello there!"
|
|
clean = {"role": "assistant", "content": "Hello there!"}
|
|
# The supported guard: pass the prompt as prefix= so history is truncated off the prefix.
|
|
self.assertEqual(parse_response(gen, spec, prefix=prompt), clean)
|
|
# Pure generation parses cleanly with the explicit no-prefix opt-out (prefix="").
|
|
self.assertEqual(parse_response(gen, spec, prefix=""), clean)
|
|
# An anchor inside the response is treated as content, never as a history boundary:
|
|
# gpt-oss re-emits `<|start|>assistant` between channels, and that content survives.
|
|
gpt_oss_gen = (
|
|
"<|channel|>analysis<|message|>thinking<|end|><|start|>assistant<|channel|>final<|message|>answer"
|
|
)
|
|
self.assertEqual(
|
|
parse_response(gpt_oss_gen, gpt_oss_template, prefix=""),
|
|
{"role": "assistant", "thinking": "thinking", "content": "answer"},
|
|
)
|
|
|
|
def test_prefix_with_open_close_inside_truncated_region(self):
|
|
"""Prefix opens AND closes a region. The full open/chunk/close event
|
|
sequence is surfaced via initial_events, and the closed region lands
|
|
in the output dict — so renderers can show prefill content."""
|
|
spec = {
|
|
"defaults": {"role": "assistant"},
|
|
"start_anchor": "[BEGIN]",
|
|
"fields": {
|
|
"tag": {"open": "<tag>", "close": "</tag>", "content": "text"},
|
|
"body": {"close_pattern": r"$", "content": "text"}, # implicit
|
|
},
|
|
}
|
|
prefix = "noise[BEGIN]<tag>silently consumed</tag>"
|
|
stream = ResponseParser(spec, prefix=prefix)
|
|
types = [e["type"] for e in stream.initial_events]
|
|
self.assertEqual(types, ["region_open", "region_chunk", "region_close"])
|
|
self.assertTrue(all(e["field"] == "tag" for e in stream.initial_events))
|
|
self.assertEqual(stream.initial_events[-1]["value"], "silently consumed")
|
|
stream.feed("real generated body")
|
|
result, _ = stream.finalize()
|
|
self.assertEqual(result["tag"], "silently consumed")
|
|
self.assertEqual(result["body"], "real generated body")
|
|
|
|
def test_prefix_lands_inside_implicit_region(self):
|
|
"""Prefix wrote plaintext into the implicit region (e.g. assistant
|
|
prefill before the model continues). The region_open for the implicit
|
|
region must surface via initial_events so consumers don't miss it —
|
|
`_opened` will already be True by the time feed runs."""
|
|
prompt = "<|im_start|>assistant\nSure, here is "
|
|
stream = ResponseParser(smollm_template, prefix=prompt)
|
|
opens = [e for e in stream.initial_events if e["type"] == "region_open"]
|
|
self.assertEqual([e["field"] for e in opens], ["content"])
|
|
events = stream.feed("the answer<|im_end|>")
|
|
# No second region_open from feed — the implicit region was already
|
|
# opened during prefill and surfaced via initial_events.
|
|
self.assertNotIn("region_open", [e["type"] for e in events])
|
|
|
|
def test_prefix_partial_pattern_at_boundary(self):
|
|
"""Post-truncation prefix ends mid-delimiter. The first feed completes
|
|
the match; initial_events is empty (no region opened within the
|
|
prefix yet) and the open fires from `feed()`."""
|
|
prefix = "<|im_start|>assistant\n<thi" # incomplete `<think>`
|
|
stream = ResponseParser(qwen3_template, prefix=prefix)
|
|
self.assertEqual(stream.initial_events, [])
|
|
events = stream.feed("nk>real body</think>")
|
|
types = [e["type"] for e in events]
|
|
self.assertIn("region_open", types) # think opens during feed, not prefill
|
|
stream.finalize()
|
|
self.assertEqual(stream._output, {"role": "assistant", "thinking": "real body"})
|
|
|
|
def test_prefix_token_ids_decoded(self):
|
|
"""The tokenizer-level helper accepts token IDs as prefix (decoded
|
|
internally), matching how `response` is handled."""
|
|
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
|
|
tokenizer.response_template = qwen3_template
|
|
prefix_text = "<|im_start|>assistant\n<think>\n"
|
|
prefix_ids = tokenizer(prefix_text).input_ids
|
|
from_str = tokenizer.parse_response("hi</think>", prefix=prefix_text)
|
|
from_ids = tokenizer.parse_response("hi</think>", prefix=prefix_ids)
|
|
self.assertEqual(from_str, from_ids)
|
|
|
|
def test_batched_parse_response_with_prefix(self):
|
|
"""Batched `parse_response`: a single prefix is broadcast to every item, a per-item prefix
|
|
list is matched positionally, and a prefix count that doesn't match the batch raises."""
|
|
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
|
|
tokenizer.response_template = qwen3_template
|
|
prefix = "<|im_start|>assistant\n<think>\n"
|
|
gen_a, gen_b = "thinking A</think>answer A", "thinking B</think>answer B"
|
|
single_a = tokenizer.parse_response(gen_a, prefix=prefix)
|
|
single_b = tokenizer.parse_response(gen_b, prefix=prefix)
|
|
self.assertNotEqual(single_a, single_b)
|
|
|
|
# A single prefix is broadcast across the whole batch.
|
|
self.assertEqual(tokenizer.parse_response([gen_a, gen_b], prefix=prefix), [single_a, single_b])
|
|
# One prefix per item (here identical) is matched up positionally.
|
|
self.assertEqual(tokenizer.parse_response([gen_a, gen_b], prefix=[prefix, prefix]), [single_a, single_b])
|
|
# A prefix batch whose length doesn't match the responses is an error.
|
|
with self.assertRaises(ValueError):
|
|
tokenizer.parse_response([gen_a, gen_b], prefix=[prefix])
|
|
|
|
def test_tokenizer_get_response_parser_with_prefix(self):
|
|
"""`tokenizer.get_response_parser(prefix=...)` returns a stream that
|
|
is already in the right initial state."""
|
|
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
|
|
tokenizer.response_template = qwen3_template
|
|
prefix = "<|im_start|>assistant\n<think>\n"
|
|
stream = tokenizer.get_response_parser(prefix=prefix)
|
|
opens = [e for e in stream.initial_events if e["type"] == "region_open"]
|
|
self.assertEqual([e["field"] for e in opens], ["thinking"])
|
|
stream.feed("body</think>")
|
|
result, _ = stream.finalize()
|
|
self.assertEqual(result, {"role": "assistant", "thinking": "body"})
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|