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test_data_collator_completion_only.py
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# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from transformers import AutoTokenizer
from trl import DataCollatorForCompletionOnlyLM
class DataCollatorForCompletionOnlyLMTester(unittest.TestCase):
def test_data_collator_finds_response_template_llama2_tokenizer(self):
# this should ideally be tested with meta-llama/Llama-2-7b-hf
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.instruction = """### System: You are a helpful assistant.
### User: How much is 2+2?
### Assistant: 2+2 equals 4"""
self.instruction_template = "\n### User:"
self.response_template = "\n### Assistant:"
# GPT2Tokenizer: [198, 21017, 11787, 25] -> [21017, 11787, 25]
# Llama2Tokenizer: [29871, 13, 2277, 29937, 4911, 29901] -> [2277, 29937, 4911, 29901]
# Note: If this test is ever switched to Llama2Tokenizer, this should be double checked,
# and possibly switched back to [2:] instead of [1:].
# With GPT2Tokenizer, [1:] is correct - we want the 21017 token included, which is ###.
self.tokenized_instruction_w_context = self.tokenizer.encode(
self.instruction_template, add_special_tokens=False
)[1:]
# GPT2Tokenizer: [198, 21017, 15286, 25] -> [15286, 25]
# Llama2Tokenizer: [29871, 13, 2277, 29937, 4007, 22137, 29901] -> [2277, 29937, 4007, 22137, 29901]
self.tokenized_response_w_context = self.tokenizer.encode(self.response_template, add_special_tokens=False)[2:]
# Plain check on string
self.assertIn(self.response_template, self.instruction)
self.tokenized_instruction = self.tokenizer.encode(self.instruction, add_special_tokens=False)
# Test the fix for #598
# Pass already tokenized (w context) and truncated response_template so token_ids are like in the instruction + response
self.collator = DataCollatorForCompletionOnlyLM(self.tokenized_response_w_context, tokenizer=self.tokenizer)
self.collator.torch_call([self.tokenized_instruction])
# Test for PR #749
# Pass already tokenized (w context) instruction and response both so token_ids are like in the instruction + response
self.collator = DataCollatorForCompletionOnlyLM(
self.tokenized_response_w_context, self.tokenized_instruction_w_context, tokenizer=self.tokenizer
)
self.collator.torch_call([self.tokenized_instruction])
# Test for PR #1185
# We pass in a string where the first user template is different than the rest.
# Usually this would happen due to context-sensitive tokenization, but here we
# explicitly change the template to test the fix.
self.instruction = """## User: First instruction
### Assistant: First response
### User: Second instruction
### Assistant: Second response"""
self.tokenized_instruction = self.tokenizer.encode(self.instruction, add_special_tokens=False)
self.collator = DataCollatorForCompletionOnlyLM(
self.tokenized_response_w_context, self.tokenized_instruction_w_context, tokenizer=self.tokenizer
)
collator_output = self.collator.torch_call([self.tokenized_instruction])
collator_text = self.tokenizer.decode(
collator_output["labels"][torch.where(collator_output["labels"] != -100)]
)
expected_text = " First response\n\n Second response" ""
self.assertEqual(collator_text, expected_text)
def test_data_collator_handling_of_long_sequences(self):
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.instruction = """### System: You are a helpful assistant.
### User: How much is 2+2? I'm asking because I'm not sure. And I'm not sure because I'm not good at math.
"""
self.response_template = "\n### Assistant:"
# check DataCollatorForCompletionOnlyLM using response template only
self.tokenized_instruction = self.tokenizer.encode(self.instruction, add_special_tokens=False)
self.collator = DataCollatorForCompletionOnlyLM(self.response_template, tokenizer=self.tokenizer)
encoded_instance = self.collator.torch_call([self.tokenized_instruction])
result = torch.all(encoded_instance["labels"] == -100)
self.assertTrue(result, "Not all values in the tensor are -100.")
# check DataCollatorForCompletionOnlyLM using response template and instruction template
self.instruction_template = "\n### User:"
self.collator = DataCollatorForCompletionOnlyLM(
self.response_template, self.instruction_template, tokenizer=self.tokenizer
)
encoded_instance = self.collator.torch_call([self.tokenized_instruction])
result = torch.all(encoded_instance["labels"] == -100)
self.assertTrue(result, "Not all values in the tensor are -100.")
def test_padding_free(self):
tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
inst1 = "### System: You are a helpful assistant.\n\n### User: How much is 2+2?\n\n### Assistant: 2+2 equals 4"
inst2 = "### System: You are a honest and helpful assistant.\n\n### User: What is the answer of 22x22?\n\n### Assistant: 22x22 equals 484"
response_template = "\n\n### Assistant:"
collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)
collator_paddingfree = DataCollatorForCompletionOnlyLM(
response_template, tokenizer=tokenizer, padding_free=True
)
tokenized_instruction = [tokenizer(x, add_special_tokens=False) for x in [inst1, inst2]]
batch = collator(tokenized_instruction)
batch_paddingfree = collator_paddingfree(tokenized_instruction)
self.assertNotIn("attention_mask", batch_paddingfree)
self.assertIn("input_ids", batch_paddingfree)
self.assertIn("labels", batch_paddingfree)
self.assertIn("position_ids", batch_paddingfree)
self.assertEqual(batch_paddingfree["input_ids"].size(), batch_paddingfree["labels"].size())
self.assertEqual(batch_paddingfree["labels"].size(), batch_paddingfree["position_ids"].size())
attn_mask = batch["attention_mask"]
input_ids_remove_pad = batch["input_ids"][attn_mask.bool()].unsqueeze(0)
expected_position_ids = attn_mask.cumsum(1)[attn_mask.bool()].unsqueeze(0) - 1
expected_labels = []
for idx in range(batch["input_ids"].size(0)):
expected_labels.append(batch["labels"][idx][attn_mask[idx].bool()])
expected_labels[-1][0] = collator.ignore_index
expected_labels = torch.cat(expected_labels).unsqueeze(0)
self.assertTrue((input_ids_remove_pad == batch_paddingfree["input_ids"]).all())
self.assertTrue((expected_position_ids == batch_paddingfree["position_ids"]).all())
self.assertTrue((expected_labels == batch_paddingfree["labels"]).all())
def test_data_collator_for_completion_only_lm(self):
# The tokenizer isn't use but the collator needs it to be provided.
tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
collator = DataCollatorForCompletionOnlyLM(tokenizer.decode(9999), tokenizer=tokenizer, padding_free=True)
tokenized_instruction = [
{"input_ids": [1, 2, 3, 9999, 4, 5], "attention_mask": [1, 1, 1, 1, 1, 1]},
{"input_ids": [6, 7, 8, 9, 9999, 10, 11], "attention_mask": [1, 1, 1, 1, 1, 1, 1]},
]
batch = collator(tokenized_instruction)
self.assertEqual(batch["position_ids"].tolist(), [[0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 6]]) # flat pos ids
self.assertEqual(batch["cu_seq_lens_q"].tolist(), [0, 6, 13]) # start idx of each seq + total number of tokens
self.assertEqual(batch["cu_seq_lens_k"].tolist(), [0, 6, 13]) # idem
self.assertEqual(batch["max_length_k"], 7) # max length in batch, here 7 (second sequence)
self.assertEqual(batch["max_length_q"], 7) # idem