71d4030b79
* add more test cases for gradient accumulation and fix zero3 * swap out for smaller model * fix missing return * fix missing pad_token in config * support concurrency for multigpu testing * cast empty deepspeed to empty string for zero3 check * fix temp_dir as fixture so parametrize works properly * fix test file for multigpu evals * don't use default * don't use default for fsdp_state_dict_type * don't use llama tokenizer w smollm * also automatically cancel multigpu for concurrency
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
"""
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E2E tests for multigpu qwen2
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"""
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import logging
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import os
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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 transformers.testing_utils import get_torch_dist_unique_port
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from axolotl.utils.dict import DictDefault
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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 TestMultiGPUQwen2:
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"""
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Test case for Llama models using LoRA
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"""
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@pytest.mark.parametrize("base_model", ["Qwen/Qwen2-0.5B", "Qwen/Qwen2.5-0.5B"])
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def test_qlora_fsdp_dpo(self, base_model, 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": base_model,
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"load_in_4bit": True,
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"rl": "dpo",
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"chat_template": "chatml",
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"sequence_len": 2048,
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"adapter": "qlora",
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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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"datasets": [
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{
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"path": "Intel/orca_dpo_pairs",
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"split": "train",
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"type": "chatml.intel",
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},
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],
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"num_epochs": 1,
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"max_steps": 5,
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"warmup_steps": 20,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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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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"bf16": "auto",
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"tf32": True,
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"gradient_checkpointing": True,
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"gradient_checkpointing_kwargs": {
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"use_reentrant": False,
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},
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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": "Qwen2DecoderLayer",
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"fsdp_state_dict_type": "FULL_STATE_DICT",
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"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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"fsdp_sharding_strategy": "FULL_SHARD",
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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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"--main_process_port",
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f"{get_torch_dist_unique_port()}",
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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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