From 0dabde19621545342556828be436b5d4c0886a16 Mon Sep 17 00:00:00 2001
From: Wing Lian <wing.lian@gmail.com>
Date: Fri, 15 Nov 2024 19:10:14 -0500
Subject: [PATCH] support for schedule free and e2e ci smoke test (#2066) [skip
 ci]

* support for schedule free and e2e ci smoke test

* set default lr scheduler to constant in test

* ignore duplicate code

* fix quotes for config/dict
---
 requirements.txt             |  1 +
 tests/e2e/test_optimizers.py | 34 ++++++++++++++++++++++++++++++++++
 2 files changed, 35 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index aab4d0eade..f352fecda9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -53,3 +53,4 @@ immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
 torchao==0.5.0
+schedulefree==1.3.0
diff --git a/tests/e2e/test_optimizers.py b/tests/e2e/test_optimizers.py
index 3b68ec5ad5..b9fa368f6f 100644
--- a/tests/e2e/test_optimizers.py
+++ b/tests/e2e/test_optimizers.py
@@ -108,3 +108,37 @@ def test_adopt_adamw(self, temp_dir):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+    @with_temp_dir
+    def test_fft_schedule_free_adamw(self, temp_dir):
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM-135M",
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "schedule_free_adamw",
+                "lr_scheduler": "constant",
+                "save_safetensors": True,
+            }
+        )
+        # pylint: disable=duplicate-code
+        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()