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#
# 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 json
import os
from unittest.mock import call, patch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, Trainer, TrainingArguments
from trl import BEMACallback, LogCompletionsCallback
from .testing_utils import TrlTestCase, require_comet, require_wandb
class TestLogCompletionsCallback(TrlTestCase):
def setup_method(self):
self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer.pad_token = self.tokenizer.eos_token
dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only")
dataset["train"] = dataset["train"].select(range(8))
def tokenize_function(examples):
out = self.tokenizer(examples["prompt"], padding="max_length", max_length=16, truncation=True)
out["labels"] = out["input_ids"].copy()
return out
self.dataset = dataset.map(tokenize_function, batched=True)
self.generation_config = GenerationConfig(max_length=32)
@require_wandb
def test_basic_wandb(self):
import wandb
training_args = TrainingArguments(
output_dir=self.tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="wandb",
)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
completions_callback = LogCompletionsCallback(trainer, self.generation_config, num_prompts=2)
trainer.add_callback(completions_callback)
trainer.train()
# Get the current run
completions_path = wandb.run.summary.completions["path"]
json_path = os.path.join(wandb.run.dir, completions_path)
with open(json_path) as f:
completions = json.load(f)
# Check that the columns are correct
assert "step" in completions["columns"]
assert "prompt" in completions["columns"]
assert "completion" in completions["columns"]
# Check that the prompt is in the log
assert self.dataset["test"][0]["prompt"] in completions["data"][0]
@require_comet
def test_basic_comet(self):
import comet_ml
training_args = TrainingArguments(
output_dir=self.tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="comet_ml",
)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
completions_callback = LogCompletionsCallback(trainer, self.generation_config, num_prompts=2)
trainer.add_callback(completions_callback)
trainer.train()
# close experiment to make sure all pending data are flushed
experiment = comet_ml.get_running_experiment()
assert experiment is not None
experiment.end()
# get experiment assets and check that all required tables was logged
steps = len(self.dataset["train"]) + len(self.dataset["test"])
tables_logged = int(steps / 2) + 1 # +1 to include zero step
api_experiment = comet_ml.APIExperiment(previous_experiment=experiment.id)
tables = api_experiment.get_asset_list("dataframe")
assert tables is not None
assert len(tables) == tables_logged
assert all(table["fileName"] == "completions.csv" for table in tables)
class TestBEMACallback(TrlTestCase):
def setup_method(self):
self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer.pad_token = self.tokenizer.eos_token
dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling")
def tokenize_function(examples, tokenizer):
out = tokenizer(examples["text"], padding="max_length", max_length=17)
out["labels"] = out["input_ids"].copy()
return out
self.dataset = dataset.map(
tokenize_function, fn_kwargs={"tokenizer": self.tokenizer}, remove_columns=["text"], batched=True
)
def test_model_saved(self):
"""Test that BEMACallback saves the BEMA model."""
training_args = TrainingArguments(output_dir=self.tmp_dir, report_to="none")
bema_callback = BEMACallback(update_freq=2)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
processing_class=self.tokenizer,
callbacks=[bema_callback],
)
trainer.train()
# Check that the BEMA model was saved and can be loaded
bema_path = os.path.join(self.tmp_dir, "bema")
assert os.path.isdir(bema_path), "BEMA directory was not created"
AutoModelForCausalLM.from_pretrained(bema_path)
def test_update_frequency_0(self):
"""Test that BEMA callback respects the update frequency."""
training_args = TrainingArguments(output_dir=self.tmp_dir, report_to="none")
bema_callback = BEMACallback(update_freq=2)
with patch.object(bema_callback, "_update_bema_weights") as mock_update:
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
processing_class=self.tokenizer,
callbacks=[bema_callback],
)
trainer.train()
# Total 9 steps (17 samples, batch size 8, 3 epochs).
# BEMA starts after step 0 and updates every 2 steps → updates at 2, 4, 5, 8
assert mock_update.call_args_list == [call(2), call(4), call(6), call(8)]
def test_update_frequency_1(self):
"""Test that BEMA callback respects the update frequency."""
training_args = TrainingArguments(output_dir=self.tmp_dir, report_to="none")
bema_callback = BEMACallback(update_freq=3)
with patch.object(bema_callback, "_update_bema_weights") as mock_update:
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
processing_class=self.tokenizer,
callbacks=[bema_callback],
)
trainer.train()
# Total 9 steps (17 samples, batch size 8, 3 epochs).
# BEMA starts after step 0 and updates every 3 steps → updates at 3, 6, 9
assert mock_update.call_args_list == [call(3), call(6), call(9)]
def test_update_frequency_2(self):
"""Test that BEMA callback respects the update frequency."""
training_args = TrainingArguments(output_dir=self.tmp_dir, report_to="none")
bema_callback = BEMACallback(update_freq=2, update_after=3)
with patch.object(bema_callback, "_update_bema_weights") as mock_update:
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
processing_class=self.tokenizer,
callbacks=[bema_callback],
)
trainer.train()
# Total 9 steps (17 samples, batch size 8, 3 epochs).
# BEMA starts after step 3 and updates every 2 steps → updates at 5, 7, 9
assert mock_update.call_args_list == [call(5), call(7), call(9)]
def test_no_bema(self):
"""Test that BEMACallback works without BEMA updates."""
training_args = TrainingArguments(output_dir=self.tmp_dir, report_to="none")
bema_callback = BEMACallback(update_freq=2, bias_power=0.0)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
processing_class=self.tokenizer,
callbacks=[bema_callback],
)
trainer.train()
def test_no_ema(self):
"""Test that BEMACallback works without EMA updates."""
training_args = TrainingArguments(output_dir=self.tmp_dir, report_to="none")
bema_callback = BEMACallback(update_freq=2, ema_power=0.0)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
processing_class=self.tokenizer,
callbacks=[bema_callback],
)
trainer.train()
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