add mistral e2e tests (#649)
* mistral e2e tests * make sure to enable flash attention for the e2e tests * use latest transformers full sha * uninstall first
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Wing Lian
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1
.github/workflows/tests.yml
vendored
1
.github/workflows/tests.yml
vendored
@@ -69,6 +69,7 @@ jobs:
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- name: Install dependencies
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run: |
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pip3 uninstall -y transformers accelerate
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pip3 install -U -e .[flash-attn]
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pip3 install -r requirements-tests.txt
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@@ -4,7 +4,7 @@ torch==2.0.1
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auto-gptq
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packaging
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peft @ git+https://github.com/huggingface/peft.git
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transformers @ git+https://github.com/huggingface/transformers.git@78dd120
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transformers @ git+https://github.com/huggingface/transformers.git@5e11d72d4d0939138fbabfebe9a69d2061519547
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bitsandbytes>=0.41.1
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accelerate @ git+https://github.com/huggingface/accelerate@80da9cfb09bb3cc9f1b385cb55d6b90d025a5fd9
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deepspeed
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208
tests/e2e/test_mistral.py
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208
tests/e2e/test_mistral.py
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@@ -0,0 +1,208 @@
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"""
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E2E tests for lora llama
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"""
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import logging
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import os
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import tempfile
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import unittest
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from pathlib import Path
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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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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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class TestMistral(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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def test_lora(self):
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# pylint: disable=duplicate-code
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output_dir = tempfile.mkdtemp()
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cfg = DictDefault(
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{
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"base_model": "openaccess-ai-collective/tiny-mistral",
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"base_model_config": "openaccess-ai-collective/tiny-mistral",
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"flash_attention": True,
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 32,
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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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": output_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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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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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(output_dir) / "adapter_model.bin").exists()
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def test_lora_packing(self):
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# pylint: disable=duplicate-code
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output_dir = tempfile.mkdtemp()
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cfg = DictDefault(
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{
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"base_model": "openaccess-ai-collective/tiny-mistral",
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"base_model_config": "openaccess-ai-collective/tiny-mistral",
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"flash_attention": True,
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"sample_packing": True,
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 32,
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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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": output_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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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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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(output_dir) / "adapter_model.bin").exists()
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def test_ft(self):
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# pylint: disable=duplicate-code
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output_dir = tempfile.mkdtemp()
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cfg = DictDefault(
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{
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"base_model": "openaccess-ai-collective/tiny-mistral",
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"base_model_config": "openaccess-ai-collective/tiny-mistral",
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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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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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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": output_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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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(output_dir) / "pytorch_model.bin").exists()
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def test_ft_packing(self):
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# pylint: disable=duplicate-code
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output_dir = tempfile.mkdtemp()
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cfg = DictDefault(
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{
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"base_model": "openaccess-ai-collective/tiny-mistral",
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"base_model_config": "openaccess-ai-collective/tiny-mistral",
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"flash_attention": True,
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"sample_packing": 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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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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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": output_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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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(output_dir) / "pytorch_model.bin").exists()
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