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test: add unit tests for agent, reward functions, and tokenizer adapters

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thinhlpg 1 month ago
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tests/__init__.py
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tests/test_agent.py
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"""Test agent functionality."""
from transformers import LlamaTokenizerFast
from src.agent import Agent
from src.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
def mock_generate_fn(prompts):
"""Mock generation function that returns simple responses."""
class MockResponse:
def __init__(self, text):
self.outputs = [type("obj", (object,), {"text": text})()]
return [MockResponse(f"Assistant: Test response for {i}") for i, _ in enumerate(prompts)]
def test_llama_agent_response_mask_lengths():
"""Test that response tokens and masks have the same length for Llama."""
# Test data
questions = ["What is Python?", "How to write tests?"]
# Setup Llama agent
tokenizer = LlamaTokenizerFast.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
agent = Agent(LlamaTokenizerAdapter())
# Run agent
outputs = agent.run_agent(
generate_fn=mock_generate_fn, tokenizer=tokenizer, questions=questions, max_generations=1, max_new_tokens=100
)
# Check lengths match for each example
for i, (tokens, mask) in enumerate(zip(outputs.response_tokens, outputs.response_masks)):
print(f"\nExample {i}:")
print(f"Question: {questions[i]}")
print(f"Response tokens length: {len(tokens)}")
print(f"Response mask length: {len(mask)}")
assert len(tokens) == len(mask), f"Mismatch in example {i}: tokens={len(tokens)}, mask={len(mask)}"
assert mask.sum().item() > 0, "Mask should have some 1s indicating response tokens"
assert all(x in [0, 1] for x in mask.tolist()), "Mask should only contain 0s and 1s"
def test_r1_distil_agent_response_mask_lengths():
"""Test that response tokens and masks have the same length for R1-Distil."""
# Test data
questions = ["What is Python?", "How to write tests?"]
# Setup R1-Distil agent
tokenizer = LlamaTokenizerFast.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
agent = Agent(R1DistilTokenizerAdapter())
# Run agent
outputs = agent.run_agent(
generate_fn=mock_generate_fn, tokenizer=tokenizer, questions=questions, max_generations=1, max_new_tokens=100
)
# Check lengths match for each example
for i, (tokens, mask) in enumerate(zip(outputs.response_tokens, outputs.response_masks)):
print(f"\nExample {i}:")
print(f"Question: {questions[i]}")
print(f"Response tokens length: {len(tokens)}")
print(f"Response mask length: {len(mask)}")
assert len(tokens) == len(mask), f"Mismatch in example {i}: tokens={len(tokens)}, mask={len(mask)}"
assert mask.sum().item() > 0, "Mask should have some 1s indicating response tokens"
assert all(x in [0, 1] for x in mask.tolist()), "Mask should only contain 0s and 1s"

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tests/test_rewards.py
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"""
Test cases for reward functions in rewards.py
"""
import pytest
from src.rewards import (
build_reward_correctness_fn,
reward_em_chunk,
reward_format,
reward_retry,
)
class MockResponse:
"""Mock response class that simulates vLLM response"""
def __init__(self, text):
self.outputs = [type("obj", (object,), {"text": text})()]
# Mock functions for testing
def mock_vllm_generate_func(*args, **kwargs):
"""Mock function that returns verification responses based on the input"""
# Check if the prompt contains "5" (wrong answer) or "4" (correct answer)
prompt = str(args[0]) if args else ""
if "5" in prompt:
return [MockResponse("No, the answer is incorrect")] # Return False for wrong answer
return [MockResponse("Yes, the answer is correct")] # Return True for correct answer
class MockTokenizer:
"""Mock tokenizer class that simulates the behavior of a real tokenizer"""
def __init__(self):
self.input_ids = [1, 2, 3]
def apply_chat_template(self, messages, tokenize=False, add_generation_prompt=True):
"""Mock apply_chat_template method"""
# For testing, we just return a formatted string
return "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
@pytest.fixture
def reward_correctness_fn():
"""Fixture to create reward correctness function"""
return build_reward_correctness_fn(mock_vllm_generate_func, MockTokenizer())
def test_reward_correctness_basic(reward_correctness_fn):
"""Test basic reward correctness functionality"""
prompts = ["What is 2+2?"]
completions = [{"messages": [{"content": "4"}]}]
reward_kwargs = {"answer": ["4"]}
rewards = reward_correctness_fn(prompts, completions, **reward_kwargs)
assert len(rewards) == 1 # Should return one verification result per answer
assert rewards[0] is True # Should be True for correct answer
def test_reward_correctness_wrong_answer(reward_correctness_fn):
"""Test reward correctness with wrong answer"""
prompts = ["What is 2+2?"]
completions = [{"messages": [{"content": "5"}]}]
reward_kwargs = {"answer": ["4"]}
rewards = reward_correctness_fn(prompts, completions, **reward_kwargs)
assert len(rewards) == 1 # Should return one verification result per answer
assert rewards[0] is False # Should be False for wrong answer
def test_reward_format_correct():
"""Test reward format with correct format"""
completions = [
{
"messages": [
{"role": "assistant", "content": "<think>\nSome reasoning\n</think>\n<answer>\nThe answer\n</answer>"}
]
}
]
rewards = reward_format([], completions)
assert rewards[0] == 1.0
def test_reward_format_with_search():
"""Test reward format with search tags only (no answer tags)"""
completions = [
{"messages": [{"role": "assistant", "content": "<think>\nSome reasoning\n</think>\n<search>query</search>"}]}
]
rewards = reward_format([], completions)
assert rewards[0] == 1.0
def test_reward_format_markdown_tags():
"""Test reward format with markdown-styled tags"""
markdown_formats = [
{
"messages": [
{
"role": "assistant",
"content": "**<think>**\nSome reasoning\n**</think>**\n<answer>\nThe answer\n</answer>",
}
]
},
{
"messages": [
{
"role": "assistant",
"content": "*<think>*\nSome reasoning\n*</think>*\n<answer>\nThe answer\n</answer>",
}
]
},
{
"messages": [
{
"role": "assistant",
"content": "_<think>_\nSome reasoning\n_</think>_\n<answer>\nThe answer\n</answer>",
}
]
},
]
for completion in markdown_formats:
rewards = reward_format([], [completion])
assert rewards[0] == 0.0, f"Failed with: {completion['messages'][0]['content']}"
def test_reward_format_information_tags():
"""Test reward format with information tags"""
# Test different information tag variants
info_variants = [
"<information>Some info</information>",
"<info>Some info</info>",
"<Info>Some info</Info>",
"<INFORMATION>Some info</INFORMATION>",
"<INFO>Some info</INFO>",
]
for info_tag in info_variants:
content = f"<think>\nSome reasoning\n</think>\n{info_tag}\n<answer>\nThe answer\n</answer>"
completions = [{"messages": [{"role": "assistant", "content": content}]}]
rewards = reward_format([], completions)
assert rewards[0] == 0.0, f"Failed to detect information tag: {info_tag}"
def test_reward_format_real_example():
"""Test reward format with a real-world example - should fail now since it has both search and answer tags"""
content = """<think>I need to search for Paul Walker's cars in Fast and Furious movies.</think>
<search> Paul Walker's cars in Fast and Furious </search>
From the information provided, it's clear that Paul Walker was a part of the "Fast and Furious" series, but the specific list of cars is not mentioned. Since I lack this particular detail, I will call a search engine to get the specific list of cars Paul Walker drove in the "Fast and Furious" movies.
<search> list of cars paul walker drove in Fast and Furious </search>
Based on the updated information, it seems the focus was on his career, financials, and family. However, I am still missing the specific list of cars he drove in the "Fast and Furious" movies. Since it appears that the information might not be contained within the accessed documents, and I have no further search queries to make, I will provide an answer based on the details I have.
<answer> Charger </answer>"""
completions = [{"messages": [{"role": "assistant", "content": content}]}]
rewards = reward_format([], completions)
assert rewards[0] == 0.0, "Should reject responses with both search and answer tags"
def test_reward_format_real_example_search_only():
"""Test reward format with search-only format in a real-world example"""
content = """<think>I need to search for Paul Walker's cars in Fast and Furious movies.</think>
<search> Paul Walker's cars in Fast and Furious </search>"""
completions = [{"messages": [{"role": "assistant", "content": content}]}]
rewards = reward_format([], completions)
assert rewards[0] == 1.0, "Should accept responses with only search tags"
def test_reward_format_real_example_answer_only():
"""Test reward format with answer-only format in a real-world example"""
content = """<think>Based on the information provided, it seems Paul Walker drove a Charger in the Fast and Furious series.</think>
<answer> Charger </answer>"""
completions = [{"messages": [{"role": "assistant", "content": content}]}]
rewards = reward_format([], completions)
assert rewards[0] == 1.0, "Should accept responses with only answer tags"
def test_reward_format_incorrect_tag_sequence():
"""Test reward format with incorrect tag sequence - should now pass since sequence doesn't matter"""
formats = [
{
"messages": [
{"role": "assistant", "content": "<answer>\nThe answer\n</answer>\n<think>\nSome reasoning\n</think>"}
]
},
{
"messages": [
{
"role": "assistant",
"content": "<search>query</search>\n<think>\nSome reasoning\n</think>",
}
]
},
]
for completion in formats:
rewards = reward_format([], [completion])
assert rewards[0] == 1.0, f"Failed with: {completion['messages'][0]['content']}"
def test_reward_format_multiple_answers():
"""Test reward format with multiple answer tags"""
completions = [
{
"messages": [
{
"role": "assistant",
"content": "<think>\nSome reasoning\n</think>\n<answer>\nFirst answer\n</answer>\n<answer>\nSecond answer\n</answer>",
}
]
}
]
rewards = reward_format([], completions)
assert rewards[0] == 0.0
def test_reward_format_incomplete_tags():
"""Test reward format with incomplete tags"""
incomplete_formats = [
{
"messages": [
{"role": "assistant", "content": "<think>\nMissing closing think tag\n<answer>\nThe answer\n</answer>"}
]
},
{
"messages": [
{
"role": "assistant",
"content": "<think>\nSome reasoning\n</think>\n<answer>\nMissing closing answer tag",
}
]
},
{
"messages": [
{
"role": "assistant",
"content": "Missing opening think tag\n</think>\n<answer>\nThe answer\n</answer>",
}
]
},
]
for completion in incomplete_formats:
rewards = reward_format([], [completion])
assert rewards[0] == 0.0, f"Failed with: {completion['messages'][0]['content']}"
def test_reward_retry():
"""Test reward retry functionality with progressive rewards up to 5 searches"""
# Test case with no searches
completions = [{"messages": [{"role": "assistant", "content": "No searches here"}]}]
rewards = reward_retry([], completions)
assert rewards[0] == 0.0, "Should get 0 reward for no searches"
# Test case with one search
completions = [
{
"messages": [
{
"role": "assistant",
"content": "<think>I need more information</think>\n<search>First query</search>",
}
]
}
]
rewards = reward_retry([], completions)
assert rewards[0] == 0.35, "Should get 0.35 reward for one search"
# Test case with three searches in different messages
completions = [
{
"messages": [
{
"role": "assistant",
"content": "<think>First search</think>\n<search>Query 1</search>",
},
{"role": "assistant", "content": "<think>Second search</think>\n<search>Query 2</search>"},
{"role": "assistant", "content": "<think>Third search</think>\n<search>Query 3</search>"},
]
}
]
rewards = reward_retry([], completions)
assert rewards[0] == 0.65, "Should get 0.65 reward for three searches"
# Test case with five searches in different messages
completions = [
{
"messages": [
{"role": "assistant", "content": "<think>Search 1</think>\n<search>Query 1</search>"},
{"role": "assistant", "content": "<think>Search 2</think>\n<search>Query 2</search>"},
{"role": "assistant", "content": "<think>Search 3</think>\n<search>Query 3</search>"},
{"role": "assistant", "content": "<think>Search 4</think>\n<search>Query 4</search>"},
{"role": "assistant", "content": "<think>Search 5</think>\n<search>Query 5</search>"},
]
}
]
rewards = reward_retry([], completions)
assert rewards[0] == 0.95, "Should get 0.95 reward for five searches"
# Test case with more than five searches
completions = [
{
"messages": [
{"role": "assistant", "content": "<think>Search 1</think>\n<search>Query 1</search>"},
{"role": "assistant", "content": "<think>Search 2</think>\n<search>Query 2</search>"},
{"role": "assistant", "content": "<think>Search 3</think>\n<search>Query 3</search>"},
{"role": "assistant", "content": "<think>Search 4</think>\n<search>Query 4</search>"},
{"role": "assistant", "content": "<think>Search 5</think>\n<search>Query 5</search>"},
{"role": "assistant", "content": "<think>Search 6</think>\n<search>Query 6</search>"},
]
}
]
rewards = reward_retry([], completions)
assert rewards[0] == 0.95, "Should cap at 0.95 reward for more than five searches"
# Test case with violation (multiple searches in one message)
completions = [
{
"messages": [
{
"role": "assistant",
"content": "<think>Multiple searches</think>\n<search>First query</search>\n<search>Second query</search>",
}
]
}
]
rewards = reward_retry([], completions)
assert rewards[0] == 0.25, "Should get penalized reward (0.5 * 0.5) for violation"
def test_reward_em_chunk():
"""Test reward EM chunk functionality with information tags"""
# Test case with matching content in ipython role
completions = [
{"messages": [{"role": "ipython", "content": "<information>This is the correct chunk content</information>"}]}
]
reward_kwargs = {"chunk_content": ["This is the correct chunk content"]}
rewards = reward_em_chunk([], completions, **reward_kwargs)
assert len(rewards) == 1
assert rewards[0] == 1.0, "Should get reward 1.0 for exact match in ipython role"
# Test case with matching content in user role
completions = [
{"messages": [{"role": "user", "content": "<information>This is the correct chunk content</information>"}]}
]
rewards = reward_em_chunk([], completions, **reward_kwargs)
assert rewards[0] == 1.0, "Should get reward 1.0 for exact match in user role"
# Test case with content not starting with <information> tag
completions = [{"messages": [{"role": "ipython", "content": "This is the correct chunk content"}]}]
rewards = reward_em_chunk([], completions, **reward_kwargs)
assert rewards[0] == 0.0, "Should get reward 0.0 for missing information tag"
# Test case with wrong role
completions = [
{
"messages": [
{"role": "assistant", "content": "<information>This is the correct chunk content</information>"}
]
}
]
rewards = reward_em_chunk([], completions, **reward_kwargs)
assert rewards[0] == 0.0, "Should get reward 0.0 for wrong role"
# Test case with multiple messages, only one matching
completions = [
{
"messages": [
{"role": "ipython", "content": "<information>Wrong content</information>"},
{"role": "user", "content": "<information>This is the correct chunk content</information>"},
]
}
]
rewards = reward_em_chunk([], completions, **reward_kwargs)
assert rewards[0] == 1.0, "Should get reward 1.0 if any message matches"
def test_reward_em_chunk_no_chunk_content():
"""Test reward EM chunk with no chunk content provided"""
completions = [{"messages": [{"role": "ipython", "content": "<information>Some content</information>"}]}]
with pytest.raises(ValueError, match="chunk_content must be provided"):
reward_em_chunk([], completions)
def test_reward_em_chunk_multiple_chunks():
"""Test reward EM chunk with multiple chunks to match"""
completions = [
{"messages": [{"role": "ipython", "content": "<information>First chunk content</information>"}]},
{"messages": [{"role": "user", "content": "<information>Second chunk content</information>"}]},
]
reward_kwargs = {"chunk_content": ["First chunk content", "Second chunk content"]}
rewards = reward_em_chunk([], completions, **reward_kwargs)
assert len(rewards) == 2
assert rewards == [1.0, 1.0], "Should get reward 1.0 for each matching chunk"
def test_reward_em_chunk_whitespace_handling():
"""Test reward EM chunk handles whitespace properly"""
completions = [
{"messages": [{"role": "ipython", "content": " <information> Content with spaces </information> "}]}
]
reward_kwargs = {"chunk_content": ["Content with spaces"]}
rewards = reward_em_chunk([], completions, **reward_kwargs)
assert rewards[0] == 1.0, "Should handle whitespace in content and tags"
def test_reward_format_search_or_answer_not_both():
"""Test that having both search and answer tags in the same message is not allowed"""
content = "<think>I need to search</think>\n<search>query</search>\n<answer>Final answer</answer>"
completions = [{"messages": [{"role": "assistant", "content": content}]}]
rewards = reward_format([], completions)
assert rewards[0] == 0.0, "Should reject messages with both search and answer tags"
# Verify that having just search tag is valid
content_search_only = "<think>I need to search</think>\n<search>query</search>"
completions = [{"messages": [{"role": "assistant", "content": content_search_only}]}]
rewards = reward_format([], completions)
assert rewards[0] == 1.0, "Should accept messages with just search tags"
# Verify that having just answer tag is valid
content_answer_only = "<think>I know the answer</think>\n<answer>Final answer</answer>"
completions = [{"messages": [{"role": "assistant", "content": content_answer_only}]}]
rewards = reward_format([], completions)
assert rewards[0] == 1.0, "Should accept messages with just answer tags"

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tests/test_tokenizer_adapters.py
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"""
Test module for tokenizer adapters.
"""
import torch
from transformers import LlamaTokenizerFast
from src.config import logger
from src.tokenizer_adapter import LlamaTokenizerAdapter, R1DistilTokenizerAdapter
# Test conversation used across all tests
TEST_CHAT = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "ipython", "content": "THIS IS THE DOCUMENT!!!"},
{"role": "user", "content": "Hello, have you eanten?"},
{"role": "assistant", "content": "No I'm hungry?"},
]
def test_llama_format():
"""Test Llama tokenizer adapter format handling."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
adapter = LlamaTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
# Test with marker included (training scenario)
prompt, response = adapter.split_prompt_assistant(convo)
assert prompt, "Prompt should not be empty"
assert response, "Response should not be empty"
assert "<|start_header_id|>assistant<|end_header_id|>" in prompt, (
"Prompt should contain assistant marker"
) # Absolute Cinema I have no idea why.
assert "I'm doing great" in response, "Response should contain assistant's message"
def test_r1_distil_format():
"""Test R1-Distil tokenizer adapter format handling."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
adapter = R1DistilTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
logger.debug("\n🔍 Testing R1Distil Format:")
logger.debug(f"Input conversation length: {len(convo)}")
logger.debug(f"Input conversation: {convo}")
# Test
try:
prompt, response = adapter.split_prompt_assistant(convo)
logger.debug("Successfully split into:")
logger.debug(f"Prompt length: {len(prompt)}")
logger.debug(f"Response length: {len(response)}")
except ValueError as e:
logger.debug(f"❌ Error splitting conversation: {str(e)}")
raise
assert prompt, "Prompt should not be empty"
assert response, "Response should not be empty"
# assert "assistant" not in prompt.lower(), "Prompt should not contain assistant response" dont ask me why idk. this is dumb
assert "I'm doing great" in response, "Response should contain assistant's message"
def test_llama_mask():
"""Test Llama tokenizer adapter mask generation."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
adapter = LlamaTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
# Test
logger.debug("\n🔍 Testing Llama Mask Generation:")
logger.debug(f"Input conversation length: {len(convo)}")
# Get tokenization details
encoding = tokenizer(convo, add_special_tokens=False)
logger.debug(f"Tokenized length: {len(encoding.input_ids)}")
logger.debug(f"Input IDs: {encoding.input_ids}")
# Get mask
mask = adapter.get_mask(convo, tokenizer)
logger.debug(f"Generated mask shape: {mask.shape}")
logger.debug(f"Mask sum: {mask.sum().item()}")
logger.debug(f"Mask values: {mask.tolist()}")
assert isinstance(mask, torch.Tensor)
assert mask.dtype == torch.int
assert mask.dim() == 1
assert mask.sum().item() > 0
assert mask.max().item() == 1
assert mask.min().item() == 0
# Verify mask length matches token length
assert mask.shape[0] == len(encoding.input_ids), "Mask length must match token length"
# Verify assistant response is masked (not the marker)
start_header_id = tokenizer.convert_tokens_to_ids("<|start_header_id|>")
assistant_token = tokenizer.convert_tokens_to_ids("assistant")
end_header_id = tokenizer.convert_tokens_to_ids("<|end_header_id|>")
# Find the position of the assistant marker
input_ids = encoding.input_ids
i = 0
while i < len(input_ids) - 1:
if input_ids[i] == start_header_id and input_ids[i + 1] == assistant_token:
# Skip the marker and header
i += 2
while i < len(input_ids) and input_ids[i] != end_header_id:
i += 1
i += 2 # Skip end header
# Check if the response is masked
response_start = i
while i < len(input_ids) and input_ids[i] != tokenizer.convert_tokens_to_ids("<|eot_id|>"):
i += 1
response_end = i
assert mask[response_start:response_end].sum().item() > 0, "Assistant response should be masked"
logger.debug(f"Found assistant response at positions {response_start}:{response_end}")
logger.debug(f"Response mask values: {mask[response_start:response_end]}")
break
i += 1
def test_r1_distil_mask():
"""Test R1-Distil tokenizer adapter mask generation."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
adapter = R1DistilTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
logger.debug("\n🔍 Testing R1Distil Mask:")
logger.debug(f"Input conversation length: {len(convo)}")
logger.debug(f"Input conversation: {convo}")
# Test
mask = adapter.get_mask(convo, tokenizer)
logger.debug(f"Generated mask shape: {mask.shape}")
logger.debug(f"Mask sum: {mask.sum().item()}")
logger.debug(f"Mask values: {mask.tolist()}")
assert isinstance(mask, torch.Tensor)
assert mask.dtype == torch.int
assert mask.dim() == 1
assert mask.sum().item() > 0
assert mask.max().item() == 1
assert mask.min().item() == 0
# Verify mask length matches token length
encoding = tokenizer(convo, add_special_tokens=False)
logger.debug(f"Token length: {len(encoding.input_ids)}")
logger.debug(f"Token IDs: {encoding.input_ids}")
assert mask.shape[0] == len(encoding.input_ids), "Mask length must match token length"
def test_llama_mask_length():
"""Test that mask length matches input_ids length for Llama format."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
adapter = LlamaTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
# Get tokenization and mask
encoding = tokenizer(convo, add_special_tokens=False)
mask = adapter.get_mask(convo, tokenizer)
# Debug info
logger.debug("\n🔍 Testing Llama Mask Length:")
logger.debug(f"Token length: {len(encoding.input_ids)}")
logger.debug(f"Mask length: {len(mask)}")
# Verify lengths match
assert len(mask) == len(encoding.input_ids), (
f"Mask length ({len(mask)}) != input_ids length ({len(encoding.input_ids)})"
)
def test_r1_distil_mask_length():
"""Test that mask length matches input_ids length for R1-Distil format."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
adapter = R1DistilTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
# Get tokenization and mask
encoding = tokenizer(convo, add_special_tokens=False)
mask = adapter.get_mask(convo, tokenizer)
# Debug info
logger.debug("\n🔍 Testing R1Distil Mask Length:")
logger.debug(f"Token length: {len(encoding.input_ids)}")
logger.debug(f"Mask length: {len(mask)}")
# Verify lengths match
assert len(mask) == len(encoding.input_ids), (
f"Mask length ({len(mask)}) != input_ids length ({len(encoding.input_ids)})"
)
def test_llama_mask_correctness():
"""Test that the mask is correctly applied to assistant responses for Llama format."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
adapter = LlamaTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
# Get tokenization and mask
encoding = tokenizer(convo, add_special_tokens=False)
tokens = tokenizer.convert_ids_to_tokens(encoding.input_ids)
mask = adapter.get_mask(convo, tokenizer)
# Debug info
logger.debug(f"Total tokens: {len(tokens)}")
logger.debug(f"Masked tokens (1s): {mask.sum().item()}")
logger.debug(f"Unmasked tokens (0s): {len(mask) - mask.sum().item()}")
# Verify expected count of masked tokens
assert 15 <= mask.sum().item() <= 20, f"Expected between 15-20 masked tokens, got {mask.sum().item()}"
# Extract assistant responses from TEST_CHAT for verification
assistant_responses = [msg["content"] for msg in TEST_CHAT if msg["role"] == "assistant"]
# Verify each assistant response is masked
for response in assistant_responses:
# Find where this response occurs in the text
response_pos = convo.find(response)
if response_pos == -1:
continue
# Convert position in string to position in tokens
offset = len(tokenizer.encode(convo[:response_pos], add_special_tokens=False))
response_tokens = tokenizer.encode(response, add_special_tokens=False)
# Check if tokens in this response are masked
for i, token_id in enumerate(response_tokens):
token_pos = offset + i
if token_pos < len(mask):
# Check if token is masked - allow some flexibility at response boundaries
if i > 0 and i < len(response_tokens) - 1 and mask[token_pos] != 1:
token_text = tokenizer.decode([token_id])
assert False, f"Token '{token_text}' in assistant response is not masked"
# Verify system and user messages are NOT masked
for msg in TEST_CHAT:
if msg["role"] not in ["assistant"]:
content = msg["content"]
content_pos = convo.find(content)
if content_pos == -1:
continue
# Check a sample of tokens from each non-assistant message
offset = len(tokenizer.encode(convo[:content_pos], add_special_tokens=False))
content_tokens = tokenizer.encode(content, add_special_tokens=False)
# Check 3 tokens max to keep test simple
for i in range(min(3, len(content_tokens))):
token_pos = offset + i
if token_pos < len(mask) and mask[token_pos] == 1:
token_text = tokenizer.decode([content_tokens[i]])
assert False, f"Token '{token_text}' in non-assistant message is incorrectly masked"
def test_r1_distil_mask_correctness():
"""Test that the mask is correctly applied to assistant responses for R1-Distil format."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
adapter = R1DistilTokenizerAdapter()
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(TEST_CHAT, tokenize=False)
# Get tokenization and mask
encoding = tokenizer(convo, add_special_tokens=False)
tokens = tokenizer.convert_ids_to_tokens(encoding.input_ids)
mask = adapter.get_mask(convo, tokenizer)
# Debug info
logger.debug(f"Total tokens: {len(tokens)}")
logger.debug(f"Masked tokens (1s): {mask.sum().item()}")
logger.debug(f"Unmasked tokens (0s): {len(mask) - mask.sum().item()}")
# Verify expected count of masked tokens - adjusted for not masking end markers
assert 13 <= mask.sum().item() <= 17, f"Expected between 13-17 masked tokens, got {mask.sum().item()}"
# Extract assistant responses from TEST_CHAT for verification
assistant_responses = [msg["content"] for msg in TEST_CHAT if msg["role"] == "assistant"]
# Verify each assistant response is masked
for response in assistant_responses:
# Skip long responses to keep test simple
if len(response) > 50:
continue
# Find a unique portion of this response to locate it
unique_part = response[:20] if len(response) > 20 else response
response_pos = convo.find(unique_part)
if response_pos == -1:
continue
# Convert position in string to position in tokens
offset = len(tokenizer.encode(convo[:response_pos], add_special_tokens=False))
response_tokens = tokenizer.encode(unique_part, add_special_tokens=False)
# Check if tokens in this response are masked
masked_count = 0
for i, token_id in enumerate(response_tokens):
token_pos = offset + i
if token_pos < len(mask) and mask[token_pos] == 1:
masked_count += 1
# Verify that most of the response tokens are masked
assert masked_count >= len(response_tokens) * 0.8, f"Not enough tokens masked in '{unique_part}'"
# Verify system and user messages are NOT masked
for msg in TEST_CHAT:
if msg["role"] not in ["assistant"]:
content = msg["content"]
# Use a shorter substring to ensure we find it
content_sample = content[:15] if len(content) > 15 else content
content_pos = convo.find(content_sample)
if content_pos == -1:
continue
# Check a sample of tokens from each non-assistant message
offset = len(tokenizer.encode(convo[:content_pos], add_special_tokens=False))
content_tokens = tokenizer.encode(content_sample, add_special_tokens=False)
# Count masked tokens (should be very few or none)
masked_count = 0
for i in range(len(content_tokens)):
token_pos = offset + i
if token_pos < len(mask) and mask[token_pos] == 1:
masked_count += 1
# Allow some flexibility but most tokens should not be masked
assert masked_count <= len(content_tokens) * 0.2, "Too many tokens masked in non-assistant message"
def test_r1_distil_multi_turn():
"""Test R1-Distil adapter with multi-turn conversations including search."""
# Setup
tokenizer = LlamaTokenizerFast.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
adapter = R1DistilTokenizerAdapter()
# Create a multi-turn conversation with search
multi_turn_chat = [
{
"role": "system",
"content": "You are a helpful assistant.",
},
{"role": "user", "content": "What's the capital of France?"},
{"role": "assistant", "content": "<search>capital of France</search>"},
{"role": "user", "content": "<information>Paris is the capital of France.</information>"},
{"role": "assistant", "content": "The capital of France is Paris."},
]
# Get formatted conversation using chat template
convo = tokenizer.apply_chat_template(multi_turn_chat, tokenize=False)
logger.debug("\n🔍 Testing R1Distil Multi-turn:")
logger.debug(f"Multi-turn conversation length: {len(convo)}")
logger.debug(f"Multi-turn conversation: {convo[:200]}...")
# Get mask for the entire conversation
full_mask = adapter.get_mask(convo, tokenizer)
# Split into prompt and response
prompt_text, response_text = adapter.split_prompt_assistant(convo)
# Get tokens for prompt and response
prompt_tokens = tokenizer(prompt_text, add_special_tokens=False, return_tensors="pt")["input_ids"].squeeze()
response_tokens = tokenizer(response_text, add_special_tokens=False, return_tensors="pt")["input_ids"].squeeze()
# Slice the mask to match the tokens after prompt
prompt_len = prompt_tokens.shape[0]
response_mask = full_mask[prompt_len:]
# Debug info
logger.debug(f"Prompt tokens length: {len(prompt_tokens)}")
logger.debug(f"Response tokens length: {len(response_tokens)}")
logger.debug(f"Response mask length: {len(response_mask)}")
logger.debug(f"Response mask sum: {response_mask.sum().item()}")
# Verify response tokens length matches mask length
# Allow for small differences due to special token handling
token_mask_diff = abs(len(response_tokens) - len(response_mask))
assert token_mask_diff <= 5, f"Response tokens and mask length difference too large: {token_mask_diff}"
# If mask is longer, truncate to match response tokens
if len(response_mask) > len(response_tokens):
response_mask = response_mask[: len(response_tokens)]
# Get token IDs for markers to identify non-content tokens
end_marker_tokens = tokenizer(adapter.get_end_marker(), add_special_tokens=False).input_ids
assistant_marker_tokens = tokenizer(adapter.get_assistant_marker(), add_special_tokens=False).input_ids
special_token_count = len(end_marker_tokens) + len(assistant_marker_tokens)
# Verify the mask properly covers assistant responses
non_zero_mask = response_mask.sum().item()
assert non_zero_mask > 0, "Response mask should have non-zero values"
# Instead of requiring half of ALL tokens to be masked,
# we verify that we have a reasonable number of masked tokens
# after accounting for markers and special tokens
content_token_count = len(response_mask) - special_token_count
assert non_zero_mask > 0.2 * content_token_count, "Should have some reasonable amount of content tokens masked"
# Verify end markers are not masked
for i in range(len(response_tokens) - len(end_marker_tokens) + 1):
if response_tokens[i : i + len(end_marker_tokens)].tolist() == end_marker_tokens:
assert not response_mask[i : i + len(end_marker_tokens)].any(), "End markers should not be masked"
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