| | """ |
| | 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() |
| |
|
