add kd trainer e2e test

tests/e2e/integrations/kd.py

@@ -0,0 +1,84 @@
+"""
+e2e tests for kd trainer support in Axolotl
+"""
+from pathlib import Path
+
+import pytest
+from e2e.utils import check_tensorboard
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, prepare_plugins
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture(name="kd_min_cfg")
+def min_cfg(temp_dir):
+    return {
+        "base_model": "unsloth/Llama-3.2-1B",
+        "plugins": [
+            "axolotl.integrations.kd.KDPlugin",
+            "axolotl.integrations.liger.LigerPlugin",
+        ],
+        "liger_rms_norm": True,
+        "liger_glu_activation": True,
+        "torch_compile": False,
+        "chat_template": "llama3",
+        "kd_trainer": True,
+        "kd_ce_alpha": 0.1,
+        "kd_alpha": 0.9,
+        "kd_temperature": 2.0,
+        "dataloader_prefetch_factor": 8,
+        "dataloader_num_workers": 4,
+        "dataloader_pin_memory": True,
+        "datasets": [
+            {
+                "path": "axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample",
+                "type": "axolotl.integrations.kd.chat_template",
+                "field_messages": "messages_combined",
+                "split": "train",
+                "logprobs_field": "llm_text_generation_vllm_logprobs",
+                "temperature": 1.0,
+                "preprocess_shards": 2,
+            },
+        ],
+        "val_set_size": 0.0,
+        "sequence_len": 4096,
+        "sample_packing": True,
+        "pad_to_sequence_len": True,
+        "gradient_accumulation_steps": 2,
+        "micro_batch_size": 2,
+        "num_epochs": 1,
+        "optimizer": "adamw_8bit",
+        "lr_scheduler": "cosine",
+        "learning_rate": 0.0001,
+        "bf16": "auto",
+        "gradient_checkpointing": True,
+        "flash_attention": True,
+        "special_tokens": {
+            "pad_token": "<|end_of_text|>",
+            "eos_token": "<|eot_id|>",
+        },
+        "max_steps": 5,
+        "output_dir": temp_dir,
+    }
+
+
+class TestKnowledgeDistillation:
+    """
+    Test case for Knowledge Distillation
+    """
+
+    def test_llama_kd(self, temp_dir, kd_min_cfg):
+        cfg = DictDefault(kd_min_cfg)
+        prepare_plugins(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()
+        check_tensorboard(
+            temp_dir + "/runs", "train/loss", 1.0, "Train Loss is too high"
+        )