add tests

Signed-off-by: Kyle Sayers <[email protected]>

examples/quantization_w4a16/llama3_example.py

@@ -5,8 +5,7 @@
 from llmcompressor.transformers import oneshot
 
 # Select model and load it.
-# MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
-MODEL_ID = "meta-llama/Llama-3.2-1B-Instruct"
+MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
 
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,

src/llmcompressor/transformers/finetune/data/data_helpers.py

@@ -1,10 +1,9 @@
-import logging
 import os
-import warnings
 from typing import Any, Callable, Dict, List, Optional
 
 import torch
 from datasets import Dataset, load_dataset
+from loguru import logger
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.data.data_collator import (
     DataCollatorWithPadding,
@@ -13,7 +12,6 @@
 
 from llmcompressor.typing import Processor
 
-LOGGER = logging.getLogger(__name__)
 LABELS_MASK_VALUE = -100
 
 __all__ = [
@@ -56,7 +54,7 @@ def format_calibration_data(
     if num_calibration_samples is not None:
         safe_calibration_samples = min(len(tokenized_dataset), num_calibration_samples)
         if safe_calibration_samples != num_calibration_samples:
-            LOGGER.warn(
+            logger.warning(
                 f"Requested {num_calibration_samples} calibration samples but "
                 f"the provided dataset only has {safe_calibration_samples}. "
             )
@@ -68,7 +66,7 @@ def format_calibration_data(
         if hasattr(tokenizer, "pad"):
             collate_fn = DataCollatorWithPadding(tokenizer)
         else:
-            warnings.warn(
+            logger.warning(
                 "Could not find processor, attempting to collate with without padding "
                 "(may fail for batch_size > 1)"
             )

tests/llmcompressor/transformers/finetune/data/test_dataset_helpers.py

@@ -2,13 +2,15 @@
 import pytest
 import torch
 from datasets import Dataset
+from transformers import AutoTokenizer
 
 from llmcompressor.transformers.finetune.data.data_args import DataTrainingArguments
 from llmcompressor.transformers.finetune.data.data_helpers import (
     format_calibration_data,
     get_raw_dataset,
     make_dataset_splits,
 )
+from llmcompressor.transformers.finetune.text_generation import configure_processor
 
 
 @pytest.mark.unit
@@ -60,15 +62,42 @@ def test_separate_datasets():
 
 
 @pytest.mark.unit
-def test_format_calibration_data():
-    tokenized_dataset = Dataset.from_dict(
-        {"input_ids": torch.randint(0, 512, (8, 2048))}
+def test_format_calibration_data_padded_tokenized():
+    vocab_size = 512
+    seq_len = 2048
+    ds_size = 16
+    padded_tokenized_dataset = Dataset.from_dict(
+        {"input_ids": torch.randint(0, vocab_size, (ds_size, seq_len))}
     )
 
     calibration_dataloader = format_calibration_data(
-        tokenized_dataset, num_calibration_samples=4, batch_size=2
+        padded_tokenized_dataset, num_calibration_samples=8, batch_size=4
     )
 
     batch = next(iter(calibration_dataloader))
+    assert batch["input_ids"].size(0) == 4
 
+
+@pytest.mark.unit
+def test_format_calibration_data_unpaddded_tokenized():
+    vocab_size = 512
+    ds_size = 16
+    unpadded_tokenized_dataset = Dataset.from_dict(
+        {
+            "input_ids": [
+                torch.randint(0, vocab_size, (seq_len,)) for seq_len in range(ds_size)
+            ]
+        }
+    )
+    processor = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
+    configure_processor(processor)
+
+    calibration_dataloader = format_calibration_data(
+        unpadded_tokenized_dataset,
+        num_calibration_samples=8,
+        batch_size=4,
+        processor=processor,
+    )
+
+    batch = next(iter(calibration_dataloader))
     assert batch["input_ids"].size(0) == 2