| """ |
| E2E tests for falcon |
| """ |
|
|
| 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 TestFalcon(unittest.TestCase): |
| """ |
| Test case for falcon |
| """ |
|
|
| @with_temp_dir |
| def test_lora(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "illuin/tiny-random-FalconForCausalLM", |
| "flash_attention": True, |
| "sequence_len": 1024, |
| "load_in_8bit": True, |
| "adapter": "lora", |
| "lora_r": 32, |
| "lora_alpha": 64, |
| "lora_dropout": 0.05, |
| "lora_target_linear": True, |
| "lora_modules_to_save": [ |
| "word_embeddings", |
| "lm_head", |
| ], |
| "val_set_size": 0.1, |
| "special_tokens": { |
| "bos_token": "<|endoftext|>", |
| "pad_token": "<|endoftext|>", |
| }, |
| "datasets": [ |
| { |
| "path": "mhenrichsen/alpaca_2k_test", |
| "type": "alpaca", |
| }, |
| ], |
| "num_epochs": 2, |
| "micro_batch_size": 2, |
| "gradient_accumulation_steps": 1, |
| "output_dir": temp_dir, |
| "learning_rate": 0.00001, |
| "optimizer": "adamw_torch", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_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() |
|
|
| @with_temp_dir |
| def test_lora_added_vocab(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "illuin/tiny-random-FalconForCausalLM", |
| "flash_attention": True, |
| "sequence_len": 1024, |
| "load_in_8bit": True, |
| "adapter": "lora", |
| "lora_r": 32, |
| "lora_alpha": 64, |
| "lora_dropout": 0.05, |
| "lora_target_linear": True, |
| "lora_modules_to_save": [ |
| "word_embeddings", |
| "lm_head", |
| ], |
| "val_set_size": 0.1, |
| "special_tokens": { |
| "bos_token": "<|endoftext|>", |
| "pad_token": "<|endoftext|>", |
| }, |
| "tokens": [ |
| "<|im_start|>", |
| "<|im_end|>", |
| ], |
| "datasets": [ |
| { |
| "path": "mhenrichsen/alpaca_2k_test", |
| "type": "alpaca", |
| }, |
| ], |
| "num_epochs": 2, |
| "micro_batch_size": 2, |
| "gradient_accumulation_steps": 1, |
| "output_dir": temp_dir, |
| "learning_rate": 0.00001, |
| "optimizer": "adamw_torch", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_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() |
|
|
| @with_temp_dir |
| def test_ft(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "illuin/tiny-random-FalconForCausalLM", |
| "flash_attention": True, |
| "sequence_len": 1024, |
| "val_set_size": 0.1, |
| "special_tokens": { |
| "bos_token": "<|endoftext|>", |
| "pad_token": "<|endoftext|>", |
| }, |
| "datasets": [ |
| { |
| "path": "mhenrichsen/alpaca_2k_test", |
| "type": "alpaca", |
| }, |
| ], |
| "num_epochs": 2, |
| "micro_batch_size": 2, |
| "gradient_accumulation_steps": 1, |
| "output_dir": temp_dir, |
| "learning_rate": 0.00001, |
| "optimizer": "adamw_torch", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_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() |
|
|
