fc4e37920b
* Prepare for transformers v5 upgrade * fix hf cli * update for hf hub changes * fix tokenizer apply_chat_template args * remap include_tokens_per_second * fix tps * handle migration for warmup * use latest hf hub * Fix scan -> ls * fix import * fix for renaming of mistral common tokenizer -> backend * update for fixed tokenziation for llama * Skip phi35 tests for now * remove mistral patch fixed upstream in huggingface/transformers#41439 * use namespacing for patch * don't rely on sdist for e2e tests for now * run modal ci without waiting too * Fix dep for ci * fix imports * Fix fp8 check * fsdp2 fixes * fix version handling * update fsdp version tests for new v5 behavior * Fail multigpu tests after 3 failures * skip known v5 broken tests for now and cleanup * bump deps * unmark skipped test * re-enable test_fsdp_qlora_prequant_packed test * increase multigpu ci timeout * skip broken gemma3 test * reduce timout back to original 120min now that the hanging test is skipped * fix for un-necessary collator for pretraining with bsz=1 * fix: safe_serialization deprecated in transformers v5 rc01 (#3318) * torch_dtype deprecated * load model in float32 for consistency with tests * revert some test fixtures back * use hf cache ls instead of scan * don't strip fsdp_version more fdsp_Version fixes for v5 fix version in fsdp_config fix aliasing fix fsdp_version check check fsdp_version is 2 in both places * Transformers v5 rc2 (#3347) * bump dep * use latest fbgemm, grab model config as part of fixture, un-skip test * import AutoConfig * don't need more problematic autoconfig when specifying config.json manually * add fixtures for argilla ultrafeedback datasets * download phi4-reasoning * fix arg * update tests for phi fast tokenizer changes * use explicit model types for gemma3 --------- Co-authored-by: Wing Lian <wing@axolotl.ai> * fix: AutoModelForVision2Seq -> AutoModelForImageTextToText * chore: remove duplicate * fix: attempt fix gemma3 text mode * chore: lint * ga release of v5 * need property setter for name_or_path for mistral tokenizer * vllm not compatible with transformers v5 * setter for chat_template w mistral too --------- Co-authored-by: NanoCode012 <nano@axolotl.ai> Co-authored-by: salman <salman.mohammadi@outlook.com>
930 lines
32 KiB
Python
930 lines
32 KiB
Python
"""
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E2E tests for multigpu lora tinyllama
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"""
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from pathlib import Path
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import pytest
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import transformers
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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from huggingface_hub import snapshot_download
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from packaging import version
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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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from tests.e2e.utils import check_tensorboard, require_torch_2_6_0
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AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
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@pytest.fixture(scope="session", autouse=True)
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def download_model():
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# download the model
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snapshot_download("HuggingFaceTB/SmolLM2-135M")
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def transformers_version_eq(required_version):
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return version.parse(transformers.__version__) == version.parse(required_version)
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class TestMultiGPULlama:
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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_ddp(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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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": 0.01,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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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[:10%]",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 2,
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# "gradient_checkpointing": True,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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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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"use_tensorboard": True,
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"bf16": True,
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"save_first_step": False,
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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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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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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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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.8, "Train Loss (%s) is too high"
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)
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@pytest.mark.parametrize(
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"gradient_accumulation_steps",
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[1, 2],
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)
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def test_lora_ddp_packed(self, temp_dir, gradient_accumulation_steps):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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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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"pad_token": "<|endoftext|>",
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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[:20%]",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": gradient_accumulation_steps,
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# "gradient_checkpointing": True,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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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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"use_tensorboard": True,
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"bf16": True,
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"save_first_step": False,
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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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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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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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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
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)
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def test_dpo_lora_ddp(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 2048,
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"sample_packing": False,
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"eval_sample_packing": False,
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"pad_to_sequence_len": True,
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"load_in_8bit": 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": 0.01,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"rl": "dpo",
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"chat_template": "chatml",
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"datasets": [
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{
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"path": "fozziethebeat/alpaca_messages_2k_dpo_test",
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"type": "chat_template.default",
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"field_messages": "conversation",
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"field_chosen": "chosen",
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"field_rejected": "rejected",
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"message_field_role": "role",
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"message_field_content": "content",
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"roles": {
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"system": ["system"],
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"user": ["user"],
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"assistant": ["assistant"],
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},
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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"gradient_checkpointing": False,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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"warmup_steps": 0,
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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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"use_tensorboard": True,
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"bf16": True,
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"save_first_step": False,
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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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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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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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]
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)
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loss_threshold = 2.3
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check_tensorboard(
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temp_dir + "/runs",
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"train/train_loss",
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loss_threshold,
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"Train Loss (%s) is too high",
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)
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def test_dpo_qlora_ddp(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 2048,
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"sample_packing": False,
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"eval_sample_packing": False,
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"pad_to_sequence_len": True,
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"load_in_4bit": True,
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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": 0.01,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"rl": "dpo",
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"chat_template": "chatml",
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"datasets": [
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{
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"path": "fozziethebeat/alpaca_messages_2k_dpo_test",
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"type": "chat_template.default",
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"field_messages": "conversation",
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"field_chosen": "chosen",
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"field_rejected": "rejected",
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"message_field_role": "role",
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"message_field_content": "content",
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"roles": {
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"system": ["system"],
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"user": ["user"],
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"assistant": ["assistant"],
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},
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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"gradient_checkpointing": False,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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"warmup_steps": 0,
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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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"use_tensorboard": True,
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"bf16": True,
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"save_first_step": False,
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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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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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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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]
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)
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loss_threshold = 2.3
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check_tensorboard(
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temp_dir + "/runs",
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"train/train_loss",
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loss_threshold,
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"Train Loss (%s) is too high",
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)
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@pytest.mark.parametrize(
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"gradient_accumulation_steps",
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[1, 2],
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)
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def test_fsdp(self, temp_dir, gradient_accumulation_steps):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 2048,
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"val_set_size": 0.01,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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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[:10%]",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": gradient_accumulation_steps,
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# "gradient_checkpointing": True,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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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_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": "FULL_STATE_DICT",
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"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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},
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"use_tensorboard": True,
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"seed": 42,
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"save_first_step": False,
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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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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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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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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
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)
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@pytest.mark.parametrize(
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"fsdp_state_dict_type",
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[
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"FULL_STATE_DICT",
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# "SHARDED_STATE_DICT", # not supported since intermediate checkpoints fail with fsdp1
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],
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)
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def test_fsdp_packed(self, temp_dir, fsdp_state_dict_type):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sample_packing": True,
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"pad_to_sequence_len": True,
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"sequence_len": 1024,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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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[:10%]",
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},
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],
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"num_epochs": 1,
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"max_steps": 3,
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"save_steps": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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# "gradient_checkpointing": True,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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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_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": fsdp_state_dict_type,
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"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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},
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"use_tensorboard": True,
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"save_first_step": False,
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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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"axolotl",
|
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"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--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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]
|
|
)
|
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|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
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)
|
|
|
|
@require_torch_2_6_0
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|
@pytest.mark.parametrize(
|
|
"attention_backend",
|
|
["flash", "flex"],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"fsdp_reshard_after_forward",
|
|
[True, False],
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|
)
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|
def test_fsdp2_packed(
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self, temp_dir, attention_backend, fsdp_reshard_after_forward
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):
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"sample_packing": True,
|
|
"pad_to_sequence_len": True,
|
|
"sequence_len": 2048,
|
|
"val_set_size": 0.1,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
},
|
|
"datasets": [
|
|
{
|
|
"path": "tatsu-lab/alpaca",
|
|
"type": "alpaca",
|
|
"split": "train[:10%]",
|
|
},
|
|
],
|
|
"num_epochs": 1,
|
|
"max_steps": 2,
|
|
"micro_batch_size": 4,
|
|
"gradient_accumulation_steps": 2,
|
|
"gradient_checkpointing": True,
|
|
"output_dir": temp_dir,
|
|
"dataset_prepared_path": temp_dir + "/last_run_prepared",
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch_8bit",
|
|
"lr_scheduler": "cosine",
|
|
"fsdp": [
|
|
"auto_wrap",
|
|
],
|
|
"fsdp_config": {
|
|
"fsdp_version": 2,
|
|
# "fsdp_forward_prefetch": True, # not yet implemented in accelerate
|
|
"fsdp_offload_params": False,
|
|
"fsdp_cpu_ram_efficient_loading": False,
|
|
"fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
|
|
"fsdp_state_dict_type": "SHARDED_STATE_DICT",
|
|
"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
|
|
"fsdp_reshard_after_forward": fsdp_reshard_after_forward,
|
|
},
|
|
"use_tensorboard": True,
|
|
"save_first_step": False,
|
|
}
|
|
)
|
|
if attention_backend == "flash":
|
|
cfg.flash_attention = True
|
|
elif attention_backend == "flex":
|
|
cfg.flex_attention = True
|
|
|
|
# write cfg to yaml file
|
|
Path(temp_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
|
|
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
|
|
|
|
execute_subprocess_async(
|
|
[
|
|
"axolotl",
|
|
"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--num-processes",
|
|
"2",
|
|
"--main-process-port",
|
|
f"{get_torch_dist_unique_port()}",
|
|
]
|
|
)
|
|
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 2.1, "Train Loss (%s) is too high"
|
|
)
|
|
|
|
def test_fsdp_qlora_prequant_packed(self, temp_dir):
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "axolotl-ai-co/SmolLM2-135M-bnb-nf4-bf16",
|
|
"adapter": "qlora",
|
|
"mean_resizing_embeddings": True,
|
|
"load_in_4bit": True,
|
|
"lora_r": 8,
|
|
"lora_alpha": 16,
|
|
"lora_dropout": 0.05,
|
|
"lora_target_linear": True,
|
|
# "lora_modules_to_save": [
|
|
# "embed_tokens",
|
|
# "lm_head",
|
|
# ],
|
|
"sample_packing": True,
|
|
"eval_sample_packing": False,
|
|
"pad_to_sequence_len": True,
|
|
"sequence_len": 1024,
|
|
"val_set_size": 0.01,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
},
|
|
"datasets": [
|
|
{
|
|
"path": "tatsu-lab/alpaca",
|
|
"type": "alpaca",
|
|
"split": "train[:10%]",
|
|
},
|
|
],
|
|
"num_epochs": 1,
|
|
"max_steps": 2,
|
|
"micro_batch_size": 2,
|
|
"gradient_accumulation_steps": 2,
|
|
# "gradient_checkpointing": True,
|
|
"output_dir": temp_dir,
|
|
"dataset_prepared_path": temp_dir + "/last_run_prepared",
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch_fused",
|
|
"lr_scheduler": "cosine",
|
|
"flash_attention": True,
|
|
"fsdp": [
|
|
"full_shard",
|
|
"auto_wrap",
|
|
],
|
|
"fsdp_config": {
|
|
"fsdp_offload_params": False,
|
|
"fsdp_sync_module_states": True,
|
|
"fsdp_use_orig_params": False,
|
|
"fsdp_cpu_ram_efficient_loading": True,
|
|
"fsdp_transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
|
|
"fsdp_state_dict_type": "FULL_STATE_DICT",
|
|
"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
|
|
},
|
|
"use_tensorboard": True,
|
|
"save_first_step": False,
|
|
}
|
|
)
|
|
|
|
# write cfg to yaml file
|
|
Path(temp_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
|
|
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
|
|
|
|
execute_subprocess_async(
|
|
[
|
|
"axolotl",
|
|
"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--num-processes",
|
|
"2",
|
|
"--main-process-port",
|
|
f"{get_torch_dist_unique_port()}",
|
|
]
|
|
)
|
|
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
|
|
)
|
|
|
|
@pytest.mark.parametrize(
|
|
"gradient_accumulation_steps",
|
|
[1, 2],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"deepspeed",
|
|
[
|
|
"deepspeed_configs/zero3_bf16.json",
|
|
"deepspeed_configs/zero3_bf16_cpuoffload_all.json",
|
|
# "deepspeed_configs/zero3_bf16_cpuoffload_params.json",
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"qlora",
|
|
[True, False],
|
|
)
|
|
def test_ds_zero3_packed(
|
|
self, temp_dir, gradient_accumulation_steps, deepspeed, qlora
|
|
):
|
|
if qlora:
|
|
adapter = {
|
|
"adapter": "qlora",
|
|
"lora_r": 8,
|
|
"lora_alpha": 16,
|
|
"lora_dropout": 0.05,
|
|
"lora_target_linear": True,
|
|
"load_in_4bit": True,
|
|
}
|
|
else:
|
|
adapter = {}
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"sample_packing": True,
|
|
"pad_to_sequence_len": True,
|
|
"sequence_len": 1024,
|
|
"val_set_size": 0.05,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
},
|
|
"datasets": [
|
|
{
|
|
"path": "tatsu-lab/alpaca",
|
|
"type": "alpaca",
|
|
"split": "train[:10%]",
|
|
},
|
|
],
|
|
"num_epochs": 1,
|
|
"max_steps": 2,
|
|
"micro_batch_size": 1,
|
|
"gradient_accumulation_steps": gradient_accumulation_steps,
|
|
"output_dir": temp_dir,
|
|
"dataset_prepared_path": temp_dir + "/last_run_prepared",
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch_fused",
|
|
"lr_scheduler": "cosine",
|
|
"flash_attention": True,
|
|
"deepspeed": str(AXOLOTL_ROOT / deepspeed),
|
|
"use_tensorboard": True,
|
|
"save_first_step": False,
|
|
**adapter,
|
|
}
|
|
)
|
|
|
|
# write cfg to yaml file
|
|
Path(temp_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
|
|
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
|
|
|
|
execute_subprocess_async(
|
|
[
|
|
"axolotl",
|
|
"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--num-processes",
|
|
"2",
|
|
"--main-process-port",
|
|
f"{get_torch_dist_unique_port()}",
|
|
]
|
|
)
|
|
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 2.45, "Train Loss (%s) is too high"
|
|
)
|
|
|
|
@pytest.mark.parametrize(
|
|
"gradient_accumulation_steps",
|
|
[1, 2],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"qlora",
|
|
[True, False],
|
|
)
|
|
def test_ds_zero2_packed(self, temp_dir, gradient_accumulation_steps, qlora):
|
|
if qlora:
|
|
adapter = {
|
|
"adapter": "qlora",
|
|
"lora_r": 8,
|
|
"lora_alpha": 16,
|
|
"lora_dropout": 0.05,
|
|
"lora_target_linear": True,
|
|
"load_in_4bit": True,
|
|
}
|
|
else:
|
|
adapter = {}
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"sample_packing": True,
|
|
"pad_to_sequence_len": True,
|
|
"sequence_len": 1024,
|
|
"val_set_size": 0.01,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
},
|
|
"datasets": [
|
|
{
|
|
"path": "tatsu-lab/alpaca",
|
|
"type": "alpaca",
|
|
"split": "train[:10%]",
|
|
},
|
|
],
|
|
"num_epochs": 1,
|
|
"max_steps": 2,
|
|
"micro_batch_size": 1,
|
|
"gradient_accumulation_steps": gradient_accumulation_steps,
|
|
"output_dir": temp_dir,
|
|
"dataset_prepared_path": temp_dir + "/last_run_prepared",
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch_fused",
|
|
"lr_scheduler": "cosine",
|
|
"flash_attention": True,
|
|
"deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero2.json"),
|
|
"use_tensorboard": True,
|
|
"seed": 42,
|
|
"save_first_step": False,
|
|
**adapter,
|
|
}
|
|
)
|
|
|
|
# write cfg to yaml file
|
|
Path(temp_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
|
|
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
|
|
|
|
execute_subprocess_async(
|
|
[
|
|
"axolotl",
|
|
"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--num-processes",
|
|
"2",
|
|
"--main-process-port",
|
|
f"{get_torch_dist_unique_port()}",
|
|
]
|
|
)
|
|
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
|
|
)
|
|
|
|
@pytest.mark.parametrize(
|
|
"gradient_accumulation_steps",
|
|
[1, 2],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"qlora",
|
|
[True, False],
|
|
)
|
|
def test_ds_zero1_packed(self, temp_dir, gradient_accumulation_steps, qlora):
|
|
if qlora:
|
|
adapter = {
|
|
"adapter": "qlora",
|
|
"lora_r": 8,
|
|
"lora_alpha": 16,
|
|
"lora_dropout": 0.05,
|
|
"lora_target_linear": True,
|
|
"load_in_4bit": True,
|
|
}
|
|
else:
|
|
adapter = {}
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"sample_packing": True,
|
|
"pad_to_sequence_len": True,
|
|
"sequence_len": 1024,
|
|
"val_set_size": 0.01,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
},
|
|
"datasets": [
|
|
{
|
|
"path": "tatsu-lab/alpaca",
|
|
"type": "alpaca",
|
|
"split": "train[:10%]",
|
|
},
|
|
],
|
|
"num_epochs": 1,
|
|
"max_steps": 2,
|
|
"micro_batch_size": 1,
|
|
"gradient_accumulation_steps": gradient_accumulation_steps,
|
|
"output_dir": temp_dir,
|
|
"dataset_prepared_path": temp_dir + "/last_run_prepared",
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch_fused",
|
|
"lr_scheduler": "cosine",
|
|
"flash_attention": True,
|
|
"deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero1.json"),
|
|
"use_tensorboard": True,
|
|
"save_first_step": False,
|
|
**adapter,
|
|
}
|
|
)
|
|
|
|
# write cfg to yaml file
|
|
Path(temp_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
|
|
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
|
|
|
|
execute_subprocess_async(
|
|
[
|
|
"axolotl",
|
|
"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--num-processes",
|
|
"2",
|
|
"--main-process-port",
|
|
f"{get_torch_dist_unique_port()}",
|
|
]
|
|
)
|
|
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 2.5, "Train Loss (%s) is too high"
|
|
)
|
|
|
|
@pytest.mark.skip(
|
|
reason="fix untrained tokens brittle with lots of edge cases in latest transformers"
|
|
)
|
|
def test_fix_untrained_tokens(self, temp_dir):
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"fix_untrained_tokens": True,
|
|
"sequence_len": 512,
|
|
"val_set_size": 0.0,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
"bos_token": "<|custom_im_start|>",
|
|
"eos_token": "<|custom_im_end|>",
|
|
},
|
|
"datasets": [
|
|
{
|
|
"chat_template": "jinja",
|
|
"chat_template_jinja": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|custom_im_start|>' + message['role'] + '\n' + message['content'] + '<|custom_im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|custom_im_start|>assistant\n' }}{% endif %}",
|
|
"path": "mlabonne/FineTome-100k",
|
|
"type": "chat_template",
|
|
"split": "train[:10%]",
|
|
"field_messages": "conversations",
|
|
"message_field_role": "from",
|
|
"message_field_content": "value",
|
|
},
|
|
],
|
|
"num_epochs": 1,
|
|
"max_steps": 2,
|
|
"micro_batch_size": 1,
|
|
"gradient_accumulation_steps": 1,
|
|
# "gradient_checkpointing": True,
|
|
"output_dir": temp_dir,
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch_fused",
|
|
"lr_scheduler": "cosine",
|
|
"flash_attention": True,
|
|
"sample_packing": True,
|
|
"bf16": True,
|
|
# "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero1.json"),
|
|
"use_tensorboard": True,
|
|
"save_first_step": False,
|
|
}
|
|
)
|
|
|
|
# write cfg to yaml file
|
|
Path(temp_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
|
|
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
|
|
|
|
execute_subprocess_async(
|
|
[
|
|
"axolotl",
|
|
"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--num-processes",
|
|
"2",
|
|
"--main-process-port",
|
|
f"{get_torch_dist_unique_port()}",
|
|
]
|
|
)
|
|
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 4.0, "Train Loss (%s) is too high"
|
|
)
|