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""" |
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E2E tests for mixtral |
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""" |
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import logging |
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import os |
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import unittest |
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from pathlib import Path |
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import torch |
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from transformers.utils import is_torch_bf16_gpu_available |
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from axolotl.cli import load_datasets |
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from axolotl.common.cli import TrainerCliArgs |
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from axolotl.train import train |
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from axolotl.utils.config import normalize_config |
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from axolotl.utils.dict import DictDefault |
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from .utils import with_temp_dir |
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LOG = logging.getLogger("axolotl.tests.e2e") |
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os.environ["WANDB_DISABLED"] = "true" |
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class TestMixtral(unittest.TestCase): |
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""" |
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Test case for Llama models using LoRA |
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""" |
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@with_temp_dir |
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def test_qlora_w_fa2(self, temp_dir): |
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cfg = DictDefault( |
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{ |
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"base_model": "hf-internal-testing/Mixtral-tiny", |
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"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1", |
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"flash_attention": True, |
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"sequence_len": 1024, |
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"load_in_4bit": True, |
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"adapter": "qlora", |
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"lora_r": 4, |
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"lora_alpha": 8, |
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"lora_dropout": 0.1, |
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"lora_target_modules": [ |
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"o_proj", |
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"w3", |
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"k_proj", |
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"v_proj", |
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"w1", |
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"q_proj", |
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"w2", |
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], |
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"val_set_size": 0.1, |
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"special_tokens": {}, |
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"datasets": [ |
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{ |
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"path": "mhenrichsen/alpaca_2k_test", |
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"type": "alpaca", |
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}, |
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], |
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"num_epochs": 2, |
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"micro_batch_size": 2, |
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"gradient_accumulation_steps": 1, |
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"output_dir": temp_dir, |
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"learning_rate": 0.00001, |
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"optimizer": "adamw_bnb_8bit", |
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"lr_scheduler": "cosine", |
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"max_steps": 20, |
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"save_steps": 10, |
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"eval_steps": 10, |
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} |
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) |
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normalize_config(cfg) |
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cli_args = TrainerCliArgs() |
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
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model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
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assert ( |
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model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
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== torch.uint8 |
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) |
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assert (Path(temp_dir) / "adapter_model.bin").exists() |
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@with_temp_dir |
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def test_qlora_wo_fa2(self, temp_dir): |
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cfg = DictDefault( |
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{ |
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"base_model": "hf-internal-testing/Mixtral-tiny", |
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"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1", |
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"flash_attention": False, |
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"sequence_len": 1024, |
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"load_in_4bit": True, |
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"adapter": "qlora", |
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"lora_r": 4, |
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"lora_alpha": 8, |
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"lora_dropout": 0.1, |
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"lora_target_modules": [ |
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"o_proj", |
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"w3", |
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"k_proj", |
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"v_proj", |
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"w1", |
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"q_proj", |
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"w2", |
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], |
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"val_set_size": 0.1, |
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"special_tokens": {}, |
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"datasets": [ |
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{ |
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"path": "mhenrichsen/alpaca_2k_test", |
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"type": "alpaca", |
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}, |
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], |
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"num_epochs": 2, |
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"micro_batch_size": 2, |
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"gradient_accumulation_steps": 1, |
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"output_dir": temp_dir, |
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"learning_rate": 0.00001, |
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"optimizer": "adamw_bnb_8bit", |
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"lr_scheduler": "cosine", |
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"max_steps": 20, |
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"save_steps": 10, |
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"eval_steps": 10, |
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} |
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) |
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normalize_config(cfg) |
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cli_args = TrainerCliArgs() |
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
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model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
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assert ( |
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model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
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== torch.uint8 |
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) |
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assert (Path(temp_dir) / "adapter_model.bin").exists() |
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@with_temp_dir |
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def test_16bit_lora_w_fa2(self, temp_dir): |
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cfg = DictDefault( |
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{ |
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"base_model": "hf-internal-testing/Mixtral-tiny", |
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"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1", |
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"flash_attention": True, |
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"sequence_len": 1024, |
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"adapter": "lora", |
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"lora_r": 4, |
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"lora_alpha": 8, |
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"lora_dropout": 0.1, |
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"lora_target_modules": [ |
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"o_proj", |
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"w3", |
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"k_proj", |
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"v_proj", |
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"w1", |
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"q_proj", |
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"w2", |
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], |
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"val_set_size": 0.1, |
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"special_tokens": {}, |
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"datasets": [ |
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{ |
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"path": "mhenrichsen/alpaca_2k_test", |
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"type": "alpaca", |
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}, |
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], |
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"num_epochs": 2, |
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"micro_batch_size": 2, |
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"gradient_accumulation_steps": 1, |
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"output_dir": temp_dir, |
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"learning_rate": 0.00001, |
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"optimizer": "adamw_bnb_8bit", |
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"lr_scheduler": "cosine", |
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"max_steps": 20, |
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"save_steps": 10, |
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"eval_steps": 10, |
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} |
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) |
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if is_torch_bf16_gpu_available(): |
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cfg.bf16 = True |
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else: |
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cfg.fp16 = True |
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normalize_config(cfg) |
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cli_args = TrainerCliArgs() |
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
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model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
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assert ( |
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model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
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== torch.float32 |
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) |
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assert (Path(temp_dir) / "adapter_model.bin").exists() |
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@with_temp_dir |
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def test_16bit_lora_wo_fa2(self, temp_dir): |
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cfg = DictDefault( |
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{ |
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"base_model": "hf-internal-testing/Mixtral-tiny", |
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"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1", |
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"flash_attention": False, |
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"sequence_len": 1024, |
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"adapter": "lora", |
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"lora_r": 4, |
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"lora_alpha": 8, |
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"lora_dropout": 0.1, |
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"lora_target_modules": [ |
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"o_proj", |
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"w3", |
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"k_proj", |
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"v_proj", |
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"w1", |
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"q_proj", |
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"w2", |
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], |
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"val_set_size": 0.1, |
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"special_tokens": {}, |
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"datasets": [ |
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{ |
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"path": "mhenrichsen/alpaca_2k_test", |
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"type": "alpaca", |
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}, |
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], |
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"num_epochs": 2, |
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"micro_batch_size": 2, |
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"gradient_accumulation_steps": 1, |
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"output_dir": temp_dir, |
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"learning_rate": 0.00001, |
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"optimizer": "adamw_bnb_8bit", |
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"lr_scheduler": "cosine", |
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"max_steps": 20, |
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"save_steps": 10, |
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"eval_steps": 10, |
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} |
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) |
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normalize_config(cfg) |
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if is_torch_bf16_gpu_available(): |
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cfg.bf16 = True |
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else: |
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cfg.fp16 = True |
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cli_args = TrainerCliArgs() |
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
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model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
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assert ( |
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model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
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== torch.float32 |
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) |
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assert (Path(temp_dir) / "adapter_model.bin").exists() |
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@with_temp_dir |
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def test_ft(self, temp_dir): |
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cfg = DictDefault( |
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{ |
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"base_model": "hf-internal-testing/Mixtral-tiny", |
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"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1", |
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"flash_attention": True, |
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"sequence_len": 1024, |
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"val_set_size": 0.1, |
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"special_tokens": {}, |
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"datasets": [ |
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{ |
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"path": "mhenrichsen/alpaca_2k_test", |
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"type": "alpaca", |
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}, |
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], |
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"num_epochs": 2, |
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"micro_batch_size": 2, |
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"gradient_accumulation_steps": 1, |
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"output_dir": temp_dir, |
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"learning_rate": 0.00001, |
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"optimizer": "adamw_bnb_8bit", |
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"lr_scheduler": "cosine", |
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"max_steps": 20, |
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"save_steps": 10, |
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"eval_steps": 10, |
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} |
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) |
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if is_torch_bf16_gpu_available(): |
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cfg.bf16 = True |
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else: |
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cfg.fp16 = True |
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normalize_config(cfg) |
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cli_args = TrainerCliArgs() |
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
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assert (Path(temp_dir) / "pytorch_model.bin").exists() |
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