Compare commits
9 Commits
vllm-0191
...
shared-pre
| Author | SHA1 | Date | |
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b79996bdc4 | ||
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68368de7ed | ||
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a94c4a014b | ||
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0102ca5943 | ||
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97e8c01a70 | ||
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5c4705b185 | ||
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47a88da330 | ||
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07ab737a55 | ||
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c40da3b5eb |
@@ -10,7 +10,7 @@ import shutil
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import sys
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import tempfile
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import time
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from pathlib import Path, PosixPath
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from pathlib import Path
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from typing import Generator
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import datasets
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@@ -423,9 +423,13 @@ def temp_dir() -> Generator[str, None, None]:
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shutil.rmtree(_temp_dir)
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@pytest.fixture(scope="function", autouse=True)
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def unique_triton_cache_dir(temp_dir: str | PosixPath) -> None:
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os.environ["TRITON_CACHE_DIR"] = str(temp_dir) + "/.triton/cache"
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@pytest.fixture(scope="module")
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def module_temp_dir() -> Generator[str, None, None]:
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# Create a temporary directory
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_temp_dir = tempfile.mkdtemp()
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yield _temp_dir
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# Clean up the directory after the test
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shutil.rmtree(_temp_dir)
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@pytest.fixture(scope="function", autouse=True)
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@@ -2,6 +2,8 @@
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E2E tests for multigpu lora tinyllama
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"""
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# pylint: disable=redefined-outer-name
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from pathlib import Path
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import pytest
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@@ -25,6 +27,60 @@ def download_model():
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snapshot_download("HuggingFaceTB/SmolLM2-135M")
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@pytest.fixture(scope="module")
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def sft_base_cfg():
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cfg = DictDefault(
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base_model="HuggingFaceTB/SmolLM2-135M",
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tokenizer_config="HuggingFaceTB/SmolLM2-135M", # this has to be manually set since we haven't done validation
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sequence_len=1024,
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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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val_set_size=0.1,
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sample_packing=True,
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flash_attention=True,
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learning_rate=0.00001,
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optimizer="adamw_8bit",
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seed=42,
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# these need to be set since we aren't running schema validation
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micro_batch_size=2,
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gradient_accumulation_steps=1,
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)
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return cfg
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@pytest.fixture(scope="module", name="sft_prepared_dataset_alpaca_cfg")
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def sft_prepared_dataset_alpaca_cfg(module_temp_dir, sft_base_cfg):
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dataset_prepared_path = module_temp_dir + "/last_run_prepared"
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cfg = sft_base_cfg | DictDefault(
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dataset_prepared_path=dataset_prepared_path,
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)
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Path(module_temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(module_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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"preprocess",
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str(Path(module_temp_dir) / "config.yaml"),
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]
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)
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# unset flash attention since we have some flex attention tests too
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cfg.flash_attention = None
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return cfg
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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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@@ -97,45 +153,36 @@ class TestMultiGPULlama:
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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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def test_lora_ddp_packed(
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self, temp_dir, sft_prepared_dataset_alpaca_cfg, gradient_accumulation_steps
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):
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# pylint: disable=duplicate-code
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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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}
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cfg = (
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DictDefault(
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{
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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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"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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"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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}
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)
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| sft_prepared_dataset_alpaca_cfg
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)
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# write cfg to yaml file
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@@ -385,59 +432,50 @@ class TestMultiGPULlama:
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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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temp_dir + "/runs", "train/train_loss", 2.5, "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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["FULL_STATE_DICT", "SHARDED_STATE_DICT"],
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)
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def test_fsdp_packed(self, temp_dir, fsdp_state_dict_type):
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def test_fsdp_packed(
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self, temp_dir, sft_prepared_dataset_alpaca_cfg, fsdp_state_dict_type
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):
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# pylint: disable=duplicate-code
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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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cfg = (
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DictDefault(
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{
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"pad_to_sequence_len": True,
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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": 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_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": fsdp_state_dict_type,
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"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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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": 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_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": 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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}
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"use_tensorboard": True,
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}
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)
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| sft_prepared_dataset_alpaca_cfg
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)
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# write cfg to yaml file
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@@ -458,7 +496,7 @@ class TestMultiGPULlama:
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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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temp_dir + "/runs", "train/train_loss", 2.4, "Train Loss (%s) is too high"
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)
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@require_torch_2_6_0
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@@ -471,51 +509,43 @@ class TestMultiGPULlama:
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[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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self,
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temp_dir,
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sft_prepared_dataset_alpaca_cfg,
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attention_backend,
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fsdp_reshard_after_forward,
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):
|
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# pylint: disable=duplicate-code
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cfg = DictDefault(
|
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{
|
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"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|>",
|
||||
},
|
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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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cfg = (
|
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DictDefault(
|
||||
{
|
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"pad_to_sequence_len": True,
|
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"num_epochs": 1,
|
||||
"max_steps": 2,
|
||||
"micro_batch_size": 4,
|
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"gradient_accumulation_steps": 2,
|
||||
"gradient_checkpointing": True,
|
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"output_dir": temp_dir,
|
||||
"learning_rate": 0.00001,
|
||||
"optimizer": "adamw_torch_8bit",
|
||||
"lr_scheduler": "cosine",
|
||||
"fsdp": [
|
||||
"auto_wrap",
|
||||
],
|
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"fsdp_config": {
|
||||
"fsdp_version": 2,
|
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# "fsdp_forward_prefetch": True, # not yet implemented in accelerate
|
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"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,
|
||||
},
|
||||
],
|
||||
"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,
|
||||
}
|
||||
"use_tensorboard": True,
|
||||
}
|
||||
)
|
||||
| sft_prepared_dataset_alpaca_cfg
|
||||
)
|
||||
if attention_backend == "flash":
|
||||
cfg.flash_attention = True
|
||||
@@ -543,64 +573,55 @@ class TestMultiGPULlama:
|
||||
temp_dir + "/runs", "train/train_loss", 2.1, "Train Loss (%s) is too high"
|
||||
)
|
||||
|
||||
def test_fsdp_qlora_prequant_packed(self, temp_dir):
|
||||
def test_fsdp_qlora_prequant_packed(
|
||||
self, temp_dir, sft_prepared_dataset_alpaca_cfg
|
||||
):
|
||||
# pylint: disable=duplicate-code
|
||||
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%]",
|
||||
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",
|
||||
# ],
|
||||
"eval_sample_packing": False,
|
||||
"pad_to_sequence_len": True,
|
||||
"num_epochs": 1,
|
||||
"max_steps": 2,
|
||||
"micro_batch_size": 2,
|
||||
"gradient_accumulation_steps": 2,
|
||||
# "gradient_checkpointing": True,
|
||||
"output_dir": temp_dir,
|
||||
"learning_rate": 0.00001,
|
||||
"optimizer": "adamw_torch_fused",
|
||||
"lr_scheduler": "cosine",
|
||||
"flash_attention": True,
|
||||
"fsdp": [
|
||||
"full_shard",
|
||||
"auto_wrap",
|
||||
],
|
||||
"fsdp_config": {
|
||||
"fsdp_limit_all_gathers": True,
|
||||
"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": "SHARDED_STATE_DICT",
|
||||
"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
|
||||
},
|
||||
],
|
||||
"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_limit_all_gathers": True,
|
||||
"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": "SHARDED_STATE_DICT",
|
||||
"fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
|
||||
},
|
||||
"use_tensorboard": True,
|
||||
}
|
||||
"use_tensorboard": True,
|
||||
}
|
||||
)
|
||||
| sft_prepared_dataset_alpaca_cfg
|
||||
)
|
||||
|
||||
# write cfg to yaml file
|
||||
@@ -641,7 +662,12 @@ class TestMultiGPULlama:
|
||||
[True, False],
|
||||
)
|
||||
def test_ds_zero3_packed(
|
||||
self, temp_dir, gradient_accumulation_steps, deepspeed, qlora
|
||||
self,
|
||||
temp_dir,
|
||||
sft_prepared_dataset_alpaca_cfg,
|
||||
gradient_accumulation_steps,
|
||||
deepspeed,
|
||||
qlora,
|
||||
):
|
||||
# pylint: disable=duplicate-code
|
||||
if qlora:
|
||||
@@ -655,37 +681,25 @@ class TestMultiGPULlama:
|
||||
}
|
||||
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,
|
||||
**adapter,
|
||||
}
|
||||
cfg = (
|
||||
DictDefault(
|
||||
{
|
||||
"pad_to_sequence_len": True,
|
||||
"num_epochs": 1,
|
||||
"max_steps": 2,
|
||||
"micro_batch_size": 1,
|
||||
"gradient_accumulation_steps": gradient_accumulation_steps,
|
||||
"output_dir": temp_dir,
|
||||
"learning_rate": 0.00001,
|
||||
"optimizer": "adamw_torch_fused",
|
||||
"lr_scheduler": "cosine",
|
||||
"flash_attention": True,
|
||||
"deepspeed": str(AXOLOTL_ROOT / deepspeed),
|
||||
"use_tensorboard": True,
|
||||
**adapter,
|
||||
}
|
||||
)
|
||||
| sft_prepared_dataset_alpaca_cfg
|
||||
)
|
||||
|
||||
# write cfg to yaml file
|
||||
@@ -706,7 +720,7 @@ class TestMultiGPULlama:
|
||||
)
|
||||
|
||||
check_tensorboard(
|
||||
temp_dir + "/runs", "train/train_loss", 2.4, "Train Loss (%s) is too high"
|
||||
temp_dir + "/runs", "train/train_loss", 2.5, "Train Loss (%s) is too high"
|
||||
)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
@@ -717,7 +731,13 @@ class TestMultiGPULlama:
|
||||
"qlora",
|
||||
[True, False],
|
||||
)
|
||||
def test_ds_zero2_packed(self, temp_dir, gradient_accumulation_steps, qlora):
|
||||
def test_ds_zero2_packed(
|
||||
self,
|
||||
temp_dir,
|
||||
sft_prepared_dataset_alpaca_cfg,
|
||||
gradient_accumulation_steps,
|
||||
qlora,
|
||||
):
|
||||
# pylint: disable=duplicate-code
|
||||
if qlora:
|
||||
adapter = {
|
||||
@@ -730,37 +750,25 @@ class TestMultiGPULlama:
|
||||
}
|
||||
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,
|
||||
**adapter,
|
||||
}
|
||||
cfg = (
|
||||
DictDefault(
|
||||
{
|
||||
"pad_to_sequence_len": True,
|
||||
"num_epochs": 1,
|
||||
"max_steps": 2,
|
||||
"micro_batch_size": 1,
|
||||
"gradient_accumulation_steps": gradient_accumulation_steps,
|
||||
"output_dir": temp_dir,
|
||||
"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,
|
||||
**adapter,
|
||||
}
|
||||
)
|
||||
| sft_prepared_dataset_alpaca_cfg
|
||||
)
|
||||
|
||||
# write cfg to yaml file
|
||||
@@ -781,7 +789,7 @@ class TestMultiGPULlama:
|
||||
)
|
||||
|
||||
check_tensorboard(
|
||||
temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
|
||||
temp_dir + "/runs", "train/train_loss", 2.5, "Train Loss (%s) is too high"
|
||||
)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
@@ -792,7 +800,13 @@ class TestMultiGPULlama:
|
||||
"qlora",
|
||||
[True, False],
|
||||
)
|
||||
def test_ds_zero1_packed(self, temp_dir, gradient_accumulation_steps, qlora):
|
||||
def test_ds_zero1_packed(
|
||||
self,
|
||||
temp_dir,
|
||||
sft_prepared_dataset_alpaca_cfg,
|
||||
gradient_accumulation_steps,
|
||||
qlora,
|
||||
):
|
||||
# pylint: disable=duplicate-code
|
||||
if qlora:
|
||||
adapter = {
|
||||
@@ -805,37 +819,25 @@ class TestMultiGPULlama:
|
||||
}
|
||||
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,
|
||||
**adapter,
|
||||
}
|
||||
cfg = (
|
||||
DictDefault(
|
||||
{
|
||||
"pad_to_sequence_len": True,
|
||||
"num_epochs": 1,
|
||||
"max_steps": 2,
|
||||
"micro_batch_size": 1,
|
||||
"gradient_accumulation_steps": gradient_accumulation_steps,
|
||||
"output_dir": temp_dir,
|
||||
"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,
|
||||
**adapter,
|
||||
}
|
||||
)
|
||||
| sft_prepared_dataset_alpaca_cfg
|
||||
)
|
||||
|
||||
# write cfg to yaml file
|
||||
@@ -856,7 +858,7 @@ class TestMultiGPULlama:
|
||||
)
|
||||
|
||||
check_tensorboard(
|
||||
temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
|
||||
temp_dir + "/runs", "train/train_loss", 2.5, "Train Loss (%s) is too high"
|
||||
)
|
||||
|
||||
@pytest.mark.skip(
|
||||
|
||||
Reference in New Issue
Block a user