| """ |
| E2E tests for mixtral |
| """ |
|
|
| import logging |
| import os |
| import unittest |
| from pathlib import Path |
|
|
| import torch |
| from transformers.utils import is_torch_bf16_gpu_available |
|
|
| 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 TestMixtral(unittest.TestCase): |
| """ |
| Test case for Llama models using LoRA |
| """ |
|
|
| @with_temp_dir |
| def test_qlora_w_fa2(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "hf-internal-testing/Mixtral-tiny", |
| "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
| "flash_attention": True, |
| "sequence_len": 1024, |
| "load_in_4bit": True, |
| "adapter": "qlora", |
| "lora_r": 4, |
| "lora_alpha": 8, |
| "lora_dropout": 0.1, |
| "lora_target_modules": [ |
| "o_proj", |
| "w3", |
| "k_proj", |
| "v_proj", |
| "w1", |
| "q_proj", |
| "w2", |
| ], |
| "val_set_size": 0.1, |
| "special_tokens": {}, |
| "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_bnb_8bit", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_steps": 10, |
| } |
| ) |
| normalize_config(cfg) |
| cli_args = TrainerCliArgs() |
| dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
| model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
| assert ( |
| model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
| == torch.float32 |
| ) |
| assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
| @with_temp_dir |
| def test_qlora_wo_fa2(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "hf-internal-testing/Mixtral-tiny", |
| "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
| "flash_attention": False, |
| "sequence_len": 1024, |
| "load_in_4bit": True, |
| "adapter": "qlora", |
| "lora_r": 4, |
| "lora_alpha": 8, |
| "lora_dropout": 0.1, |
| "lora_target_modules": [ |
| "o_proj", |
| "w3", |
| "k_proj", |
| "v_proj", |
| "w1", |
| "q_proj", |
| "w2", |
| ], |
| "val_set_size": 0.1, |
| "special_tokens": {}, |
| "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_bnb_8bit", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_steps": 10, |
| } |
| ) |
| normalize_config(cfg) |
| cli_args = TrainerCliArgs() |
| dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
| model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
| assert ( |
| model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
| == torch.float32 |
| ) |
| assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
| @with_temp_dir |
| def test_16bit_lora_w_fa2(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "hf-internal-testing/Mixtral-tiny", |
| "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
| "flash_attention": True, |
| "sequence_len": 1024, |
| "adapter": "lora", |
| "lora_r": 4, |
| "lora_alpha": 8, |
| "lora_dropout": 0.1, |
| "lora_target_modules": [ |
| "o_proj", |
| "w3", |
| "k_proj", |
| "v_proj", |
| "w1", |
| "q_proj", |
| "w2", |
| ], |
| "val_set_size": 0.1, |
| "special_tokens": {}, |
| "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_bnb_8bit", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_steps": 10, |
| } |
| ) |
| if is_torch_bf16_gpu_available(): |
| cfg.bf16 = True |
| else: |
| cfg.fp16 = True |
| normalize_config(cfg) |
| cli_args = TrainerCliArgs() |
| dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
| model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
| assert ( |
| model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
| == torch.float32 |
| ) |
| assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
| @with_temp_dir |
| def test_16bit_lora_wo_fa2(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "hf-internal-testing/Mixtral-tiny", |
| "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
| "flash_attention": False, |
| "sequence_len": 1024, |
| "adapter": "lora", |
| "lora_r": 4, |
| "lora_alpha": 8, |
| "lora_dropout": 0.1, |
| "lora_target_modules": [ |
| "o_proj", |
| "w3", |
| "k_proj", |
| "v_proj", |
| "w1", |
| "q_proj", |
| "w2", |
| ], |
| "val_set_size": 0.1, |
| "special_tokens": {}, |
| "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_bnb_8bit", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_steps": 10, |
| } |
| ) |
| normalize_config(cfg) |
| if is_torch_bf16_gpu_available(): |
| cfg.bf16 = True |
| else: |
| cfg.fp16 = True |
| cli_args = TrainerCliArgs() |
| dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
| model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
| assert ( |
| model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
| == torch.float32 |
| ) |
| assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
| @with_temp_dir |
| def test_ft(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "hf-internal-testing/Mixtral-tiny", |
| "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
| "flash_attention": True, |
| "sequence_len": 1024, |
| "val_set_size": 0.1, |
| "special_tokens": {}, |
| "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_bnb_8bit", |
| "lr_scheduler": "cosine", |
| "max_steps": 20, |
| "save_steps": 10, |
| "eval_steps": 10, |
| } |
| ) |
| if is_torch_bf16_gpu_available(): |
| cfg.bf16 = True |
| else: |
| cfg.fp16 = True |
| 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() |
|
|
