Phi2 multipack (#1173)

* phi2 multipack * update validation and examples for phi * more updates to phi examples * make sure to use the correct collator for phi multipack * phi needs attention mask now for multipack * if the special token already exists in the tokenizer, don't require in lora modules to save * fix qlora yml for phi, fix phi test validation * test qlora too * make sure flash attention is enabled for the test * don't use remote code for phi anymore * reduce sequence len for sample packing phi
2024-01-23 12:54:36 -05:00
parent b715cd549a
commit 814aee6603
18 changed files with 201 additions and 2269 deletions
--- a/tests/e2e/patched/test_phi_multipack.py
+++ b/tests/e2e/patched/test_phi_multipack.py
@@ -0,0 +1,123 @@
+"""
+E2E tests for lora llama
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from ..utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestPhiMultipack(unittest.TestCase):
+    """
+    Test case for Phi2 models
+    """
+
+    @with_temp_dir
+    def test_ft_packed(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "microsoft/phi-1_5",
+                "model_type": "PhiForCausalLM",
+                "tokenizer_type": "AutoTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "pad_to_sequence_len": True,
+                "load_in_8bit": False,
+                "adapter": None,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "dataset_shard_num": 10,
+                "dataset_shard_idx": 0,
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "eval_steps": 10,
+                "save_steps": 10,
+                "bf16": "auto",
+            }
+        )
+
+        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) / "pytorch_model.bin").exists()
+
+    @with_temp_dir
+    def test_qlora_packed(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "microsoft/phi-1_5",
+                "model_type": "PhiForCausalLM",
+                "tokenizer_type": "AutoTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "pad_to_sequence_len": True,
+                "load_in_8bit": False,
+                "adapter": "qlora",
+                "lora_r": 64,
+                "lora_alpha": 32,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "dataset_shard_num": 10,
+                "dataset_shard_idx": 0,
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "eval_steps": 10,
+                "save_steps": 10,
+                "bf16": "auto",
+            }
+        )
+
+        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) / "adapter_model.bin").exists()