Attempt to run multigpu in PR CI for now to ensure it works (#1815) [skip ci]
* Attempt to run multigpu in PR CI for now to ensure it works * fix yaml file * forgot to include multigpu tests * fix call to cicd.multigpu * dump dictdefault to dict for yaml conversion * use to_dict instead of casting * 16bit-lora w flash attention, 8bit lora seems problematic * add llama fsdp test * more tests * Add test for qlora + fsdp with prequant * limit accelerate to 2 processes and disable broken qlora+fsdp+bnb test * move multigpu tests to biweekly
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Wing Lian
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tests/e2e/multigpu/__init__.py
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tests/e2e/multigpu/__init__.py
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tests/e2e/multigpu/test_llama.py
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tests/e2e/multigpu/test_llama.py
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"""
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E2E tests for multigpu lora tinyllama
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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 pytest
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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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.multigpu")
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os.environ["WANDB_DISABLED"] = "true"
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class TestMultiGPULlama(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_lora_ddp(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "TinyLlama/TinyLlama_v1.1",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 2048,
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.05,
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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": "tatsu-lab/alpaca",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 100,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 4,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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@with_temp_dir
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def test_lora_ddp_packed(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "TinyLlama/TinyLlama_v1.1",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 2048,
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"sample_packing": True,
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"eval_sample_packing": False,
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"pad_to_sequence_len": True,
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.05,
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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": "tatsu-lab/alpaca",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 50,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 4,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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@with_temp_dir
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def test_fsdp(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "TinyLlama/TinyLlama_v1.1",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 2048,
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"val_set_size": 0.05,
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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": "tatsu-lab/alpaca",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 100,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 4,
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"output_dir": temp_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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"flash_attention": True,
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"fsdp": [
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"full_shard",
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"auto_wrap",
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],
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"fsdp_config": {
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"fsdp_limit_all_gathers": True,
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"fsdp_offload_params": False,
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"fsdp_sync_module_states": True,
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"fsdp_use_orig_params": False,
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"fsdp_cpu_ram_efficient_loading": False,
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"fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
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"fsdp_state_dict_type": "SHARDED_STATE_DICT",
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"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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},
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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@with_temp_dir
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def test_fsdp_packed(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "TinyLlama/TinyLlama_v1.1",
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"tokenizer_type": "LlamaTokenizer",
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"sample_packing": True,
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"eval_sample_packing": False,
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"pad_to_sequence_len": True,
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"sequence_len": 2048,
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"val_set_size": 0.05,
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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": "tatsu-lab/alpaca",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 100,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 4,
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"output_dir": temp_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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"flash_attention": True,
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"fsdp": [
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"full_shard",
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"auto_wrap",
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],
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"fsdp_config": {
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"fsdp_limit_all_gathers": True,
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"fsdp_offload_params": False,
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"fsdp_sync_module_states": True,
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"fsdp_use_orig_params": False,
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"fsdp_cpu_ram_efficient_loading": False,
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"fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
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"fsdp_state_dict_type": "SHARDED_STATE_DICT",
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"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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},
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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@pytest.mark.skip("disabled due to upstream issue")
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@with_temp_dir
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def test_fsdp_qlora_prequant_packed(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "axolotl-ai-co/TinyLlama_v1.1-bnb-nf4-bf16",
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"tokenizer_type": "AutoTokenizer",
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"adapter": "qlora",
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"load_in_4bit": True,
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"lora_modules_to_save": [
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"embed_tokens",
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"lm_head",
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],
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"sample_packing": True,
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"eval_sample_packing": False,
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"pad_to_sequence_len": True,
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"sequence_len": 2048,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|end_of_text|>",
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},
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"datasets": [
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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"split": "train[:25%]",
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},
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],
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"num_epochs": 1,
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"max_steps": 100,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 4,
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"output_dir": temp_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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"flash_attention": True,
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"fsdp": [
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"full_shard",
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"auto_wrap",
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],
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"fsdp_config": {
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"fsdp_limit_all_gathers": True,
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"fsdp_offload_params": False,
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"fsdp_sync_module_states": True,
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"fsdp_use_orig_params": False,
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"fsdp_cpu_ram_efficient_loading": True,
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"fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
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"fsdp_state_dict_type": "SHARDED_STATE_DICT",
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"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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},
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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